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WO2024238538A1 - Methods and systems for assessing circulating tumor dna fraction in liquid biopsy samples - Google Patents

Methods and systems for assessing circulating tumor dna fraction in liquid biopsy samples Download PDF

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WO2024238538A1
WO2024238538A1 PCT/US2024/029246 US2024029246W WO2024238538A1 WO 2024238538 A1 WO2024238538 A1 WO 2024238538A1 US 2024029246 W US2024029246 W US 2024029246W WO 2024238538 A1 WO2024238538 A1 WO 2024238538A1
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sample
tumor
cancer
fraction
somatic
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Yanmei HUANG
Jason D. HUGHES
Russell MADISON
Ryon P. GRAF
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Foundation Medicine Inc
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Foundation Medicine Inc
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/10—Ploidy or copy number detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H15/00—ICT specially adapted for medical reports, e.g. generation or transmission thereof
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/40—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G16B30/10—Sequence alignment; Homology search

Definitions

  • the present disclosure relates generally to methods and systems for analyzing genomic profiling data, and more specifically to methods and systems for assessing circulating tumor DNA (ctDNA) fraction in liquid biopsy samples based on genomic profiling data, and its use for detecting, monitoring, and making prognostic predictions for cancer.
  • ctDNA circulating tumor DNA
  • Liquid biopsy is a simple and non-invasive alternative to surgical biopsies that enables healthcare providers to determine a variety of important characteristics of a cancer patient’s tumor through the collection of a simple blood sample and subsequent genomic profiling of nucleic acid molecules extracted from the blood sample.
  • Analysis of trace cancer cell DNA in the blood can provide clues about which treatments are most likely to work for a given patient.
  • a cell-free DNA sample isolated from a liquid biopsy contains a mixture of DNA released from both cancer and normal cells.
  • ctDNA fraction is the fraction of DNA that is derived from cancer cells in the total cell-free DNA sample isolated from the liquid biopsy, however, existing methods for estimating ctDNA fraction lack the sensitivity and/or specificity required for accurate detection and determination of ctDNA fraction in cell-free DNA samples.
  • the disclosed methods are based on a combined approach that includes: (i) estimation of ctDNA fraction based on a copy number model that specifies a tumor fraction for the sample and exact copy number at probed genomic locations that explain the observed sequence coverage and single nucleotide polymorphism (SNP) allele frequency data, and/or (ii) estimation of ctDNA fraction from observed allele frequencies of tumor somatic variants identified in the sample based on a theoretical relationship between tumor fraction and tumor somatic variant allele frequency given a copy number profile, where the copy number profile is inferred from tumor somatic variant allele frequency based on historical observations.
  • SNP single nucleotide polymorphism
  • the more accurate determinations of ctDNA fraction enabled by using the disclosed methods can, in turn, be used to detect, monitor, and/or make prognostic predictions for treatment outcomes for cancer patients.
  • Disclosed herein are methods comprising: providing a plurality of nucleic acid molecules obtained from a sample from a subject; ligating one or more adapters onto one or more nucleic acid molecules from the plurality of nucleic acid molecules; amplifying the one or more ligated nucleic acid molecules from the plurality of nucleic acid molecules; capturing amplified nucleic acid molecules from the amplified nucleic acid molecules; sequencing, by a sequencer, the captured nucleic acid molecules to obtain a plurality of sequence reads that represent the captured nucleic acid molecules; receiving, at one or more processors, sequence read data for the plurality of sequence reads; determining, using the one or more processors, if the sequence read data is sufficient for performing copy number alteration (CNA) modeling; estimating, using the one or more processors, (1) the ctDNA fraction in the sample based on at least a tumor purity and a ploidy of the sample using a model if the sequence read data is determined to be sufficient for performing C
  • the method may further comprise comparing the estimated ctDNA fraction to at least one predetermined threshold, and outputting a status call of at least tumor fraction-high (TF-high) or tumor fraction-low (TF-low) for the sample based on the comparison.
  • determining if the sequence read data is sufficient for performing CNA modeling comprises determining sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof, for at least one genomic locus to which the plurality of sequence reads map.
  • the at least one genomic locus comprises at least one single nucleotide polymorphism (SNP) locus.
  • performing CNA modeling comprises: determining, using the one or more processors, a copy number model including a sample tumor purity, a sample ploidy, and a copy number of multiple genomic segments in the ctDNA that accounts for an observed sequence coverage ratio data, allele fraction data for at least one genomic locus within one or more subgenomic intervals to which the plurality of sequence reads map.
  • estimating the ctDNA fraction based on at least the sample tumor purity and sample ploidy using the CNA model comprises using a formula that describes a physical relationship between ctDNA fraction and sample tumor purity and sample ploidy.
  • estimating the ctDNA fraction based on at least one somatic short variant detected in the sequence read data comprises: obtaining a list of short variants detected in the sequence read data; applying a set of selection rules to the list of detected short variants to identify tumor somatic short variants; and estimating the ctDNA fraction based on the presence of at least one identified tumor somatic short variant.
  • the set of selection rules used to identify tumor somatic short variants in the list of detected short variants comprises: (i) removing short variants that appear on a blacklist of known germline variants, known clonal hematopoiesis of indeterminate potential (CHIP) variants, and known sequencing artifacts; (ii) retaining short variants that appear on a list of known tumor somatic short variants; (iii) retaining short variants that appear on a list of known genes that are prone to exhibit high amplification and that have an allele frequency that is higher than other somatic short variants in the sample; (iv) retaining short variants that appear on a list of known rearrangements; or any combination thereof.
  • CHIP indeterminate potential
  • estimating the ctDNA fraction based on the at least one tumor somatic short variant comprises: determining, using the one or more processors, a variant allele frequency (VAF) for one or more variants detected in the sample based on the sequence read data; generating, using the one or more processors, an empirical distribution of ctDNA fraction values corresponding to the determined VAF for the one or more variants based on historical data; fitting, using the one or more processors, a model to the empirical distribution of ctDNA fraction values; and determining a ctDNA fraction for the sample based on the model.
  • VAF variant allele frequency
  • the subject is suspected of having or is determined to have cancer.
  • the cancer is a B cell cancer (multiple myeloma), a melanoma, breast cancer, lung cancer, bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain cancer, central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine cancer, endometrial cancer, cancer of an oral cavity, cancer of a pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel cancer, appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, a cancer of hematological tissue, an adenocarcinoma, an inflammatory myofibroblastic tumor, a gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), acute
  • the cancer comprises acute lymphoblastic leukemia (Philadelphia chromosome positive), acute lymphoblastic leukemia (precursor B-cell), acute myeloid leukemia (FLT3+), acute myeloid leukemia (with an IDH2 mutation), anaplastic large cell lymphoma, basal cell carcinoma, B-cell chronic lymphocytic leukemia, bladder cancer, breast cancer (HER2 overexpressed/amplified), breast cancer (HER2+), breast cancer (HR+, HER2-), cervical cancer, cholangiocarcinoma, chronic lymphocytic leukemia, chronic lymphocytic leukemia (with 17p deletion), chronic myelogenous leukemia, chronic myelogenous leukemia (Philadelphia chromosome positive), classical Hodgkin lymphoma, colorectal cancer, colorectal cancer (dMMR/MSI-H), colorectal cancer (KRAS wild type), cryopyrin-associated periodic syndrome,
  • the method further comprises treating the subject with an anticancer therapy.
  • the anti-cancer therapy comprises a targeted anti-cancer therapy.
  • the targeted anti-cancer therapy comprises abemaciclib
  • the method further comprises obtaining the sample from the subject.
  • the sample comprises a tissue biopsy sample, a liquid biopsy sample, or a normal control.
  • the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
  • the sample is a liquid biopsy sample and comprises circulating tumor cells (CTCs).
  • the sample is a liquid biopsy sample and comprises cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.
  • the plurality of nucleic acid molecules comprises a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules.
  • the tumor nucleic acid molecules are derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules are derived from a normal portion of the heterogeneous tissue biopsy sample.
  • the sample comprises a liquid biopsy sample, and wherein the tumor nucleic acid molecules are derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample, and the non-tumor nucleic acid molecules are derived from a nontumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.
  • ctDNA circulating tumor DNA
  • cfDNA nontumor, cell-free DNA
  • the one or more adapters comprise amplification primers, flow cell adaptor sequences, substrate adapter sequences, or sample index sequences.
  • the captured nucleic acid molecules are captured from the amplified nucleic acid molecules by hybridization to one or more bait molecules.
  • the one or more bait molecules comprise one or more nucleic acid molecules, each comprising a region that is complementary to a region of a captured nucleic acid molecule.
  • amplifying nucleic acid molecules comprises performing a polymerase chain reaction (PCR) amplification technique, a non-PCR amplification technique, or an isothermal amplification technique.
  • PCR polymerase chain reaction
  • the sequencing comprises use of a massively parallel sequencing (MPS) technique, whole genome sequencing (WGS), whole exome sequencing, targeted sequencing, direct sequencing, or Sanger sequencing technique.
  • MPS massively parallel sequencing
  • WGS whole genome sequencing
  • GNS whole exome sequencing
  • targeted sequencing targeted sequencing
  • direct sequencing direct sequencing
  • Sanger sequencing technique e.g., a sequencing of a genome sequencing technique
  • the sequencing comprises massively parallel sequencing
  • the massively parallel sequencing technique comprises next generation sequencing (NGS).
  • the sequencer comprises a next generation sequencer.
  • one or more of the plurality of sequencing reads overlap one or more gene loci within one or more subgenomic intervals in the sample.
  • the one or more gene loci comprises between 10 and 20 loci, between 10 and 40 loci, between 10 and 60 loci, between 10 and 80 loci, between 10 and 100 loci, between 10 and 150 loci, between 10 and 200 loci, between 10 and 250 loci, between 10 and 300 loci, between 10 and 350 loci, between 10 and 400 loci, between 10 and 450 loci, between 10 and 500 loci, between 20 and 40 loci, between 20 and 60 loci, between 20 and 80 loci, between 20 and 100 loci, between 20 and 150 loci, between 20 and 200 loci, between 20 and 250 loci, between 20 and 300 loci, between 20 and 350 loci, between 20 and 400 loci, between 20 and 500 loci, between 40 and 60 loci, between 40 and 80 loci, between 40 and 100 loci, between 40 and 150 loci, between 40 and 200 and 200 loci, between 20 and 250 loci
  • the one or more gene loci comprise ABL1, ACVR1B, AKT1, AKT2, AKT3, ALK, ALOX12B, AMER1, APC, AR, ARAF, ARFRP1, ARID1A, ASXL1, ATM, ATR, ATRX, AURKA, AURKB, AXIN1, AXL, BAP1, BARD1, BCL2, BCL2L1, BCL2L2, BCL6, BCOR, BCORL1, BCR, BRAF, BRCA1, BRCA2, BRD4, BRIP1, BTG1, BTG2, BTK, CALR, CARD11, CASP8, CBFB, CBL, CCND1, CCND2, CCND3, CCNE1, CD22, CD274, CD70, CD74, CD79A, CD79B, CDC73, CDH1, CDK12, CDK4, CDK6, CDK8, CDKN1A, CDKN1B, CDKN2A, CDKN2B, CDKN2C, CEBPA,
  • the one or more gene loci comprise ABL, ALK, ALL, B4GALNT1, BAFF, BCL2, BRAF, BRCA, BTK, CD19, CD20, CD3, CD30, CD319, CD38, CD52, CDK4, CDK6, CML, CRACC, CS1, CTLA-4, dMMR, EGFR, ERBB1, ERBB2, FGFR1- 3, FLT3, GD2, HDAC, HER1, HER2, HR, IDH2, IL-ip, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSI-H, mTOR, PARP, PD-1, PDGFR, PDGFRa, PDGFRp, PD-L1, PI3K5, PIGF, PTCH, RAF, RANKL, RET, ROS1, SLAMF7, VEGF, VEGFA, VEGFB, or any combination thereof.
  • the method further comprises generating, by the one or more processors, a report indicating the estimated tumor fraction in the sample. In some embodiments, the method further comprises transmitting the report to a healthcare provider. In some embodiments, the report is transmitted via a computer network or a peer-to-peer connection.
  • CNA copy number alteration
  • the method further comprises comparing the estimated ctDNA fraction to at least one predetermined threshold, and outputting a status call of at least tumor fraction-high (TF-high) or tumor fraction-low (TF-low) for the sample based on the comparison.
  • determining if the sequence read data is sufficient for performing CNA modeling comprises determining sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof, for at least one genomic locus to which the plurality of sequence reads map.
  • the at least one genomic locus comprises at least one single nucleotide polymorphism (SNP) locus.
  • the determination of sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof, for the at least one genomic locus is based on pre-processing of the sequence read data.
  • performing CNA modeling comprises: determining, using the one or more processors, a copy number model including a sample tumor purity, a sample ploidy, and a copy number of multiple genomic segments that accounts for an observed sequence coverage ratio data, allele fraction data for at least one genomic locus within one or more subgenomic intervals to which the plurality of sequence reads map.
  • the sequence coverage ratio data is determined by aligning the plurality of sequence reads that overlap the at least one genomic locus within the one or more subgenomic intervals in the sample and in a control sample to a reference genome, and determining a number of sequence reads that overlap the at least one genomic locus within the one or more subgenomic intervals in the sample and in the control sample.
  • the control sample is a paired normal sample, a process-matched control sample, or a panel of normal control sample.
  • the allele fraction data is determined by aligning the plurality of sequence reads that overlap the at least one genomic locus within the one or more subgenomic intervals in the sample to a reference genome, detecting a number of alleles present at the at least one genomic locus, and determining an allele fraction for at least one of the alleles present at the at least one genomic locus.
  • performing CNA modeling further comprises generating segmentation data by: aligning a plurality of sequence reads that overlap the at least one genomic locus within the one or more subgenomic intervals in the sample to a reference genome, and processing the aligned sequence read data, coverage ratio data, and allele fraction data using a pruned exact linear time (PELT) method to determine a number of segments required to account for the aligned sequence read data, wherein each segment has a same copy number.
  • PELT pruned exact linear time
  • the copy number model also outputs the segmentation data.
  • the copy number model predicts a copy number for the at least one genomic locus based on the sequence coverage ratio data and allele fraction data.
  • the sequence coverage ratio data further comprises sequence coverage ratio data for single nucleotide polymorphisms (SNPs) and introns associated with the at least one genomic locus.
  • the copy number model also predicts a tumor purity and a ploidy for the sample.
  • the ploidy for the sample has a value ranging from 1 to 8.
  • amplification is detected when the copy number for the corresponding segment is greater than or equal to the ploidy of the sample. In some embodiments, an amplification is detected when the copy number for the corresponding segment is greater than or equal to the ploidy of the sample plus a first predetermined value. In some embodiments, the first predetermined value is a value ranging from 2 to 500. In some embodiments, the first predetermined value is a value ranging from 2 to 10.
  • an amplification is detected when the copy number for the corresponding segment is greater than or equal to the ploidy of the sample plus a second predetermined value and the genomic locus is a member of a first predefined set of genomic loci.
  • the second predetermined value is a value ranging from 0 to 500.
  • the second predetermined value is a value ranging from 2 to 10.
  • the first predefined set of genomic loci comprises one or more druggable gene target loci, prognostic gene loci, oncogene loci, or any combination thereof.
  • the first predefined set of genomic loci comprises the AR and ERBB2 gene loci.
  • detection of deletions comprises identifying homozygous deletions of the at least one genomic locus in a corresponding segment.
  • homozygous deletions are detected by determining a total copy number for a given genomic locus that is equal to the sum of the copy numbers for a first allele and a second allele at the genomic locus.
  • the first allele is a major allele and the second allele is a minor allele.
  • a homozygous deletion is called if the total copy number for a given genomic locus is equal to a third predetermined value. In some embodiments, the third predetermined value is about zero.
  • detection of deletions comprises identifying heterozygous deletions of the at least one genomic locus in a corresponding segment.
  • a heterozygous deletion is called if a copy number for a first allele at a given genomic locus is equal to a fourth predetermined value, and a copy number for a second allele at the given genomic locus in not equal to the fourth predetermined value.
  • the fourth predetermined value is about zero.
  • the first allele is a major allele and the second allele is a minor allele.
  • the detection of deletions comprises identifying partial deletions of the at least one genomic locus in a corresponding segment.
  • a partial deletion is called for a given genomic locus if log2 ratios (L2Rs) for neighboring genomic loci, single nucleotide polymorphisms (SNPs), and introns are significantly different than the log2 ratio for the genomic locus, and the log2 ratio for the given genomic locus is significantly different from a distribution of L2Rs for non-neighboring genomic loci, single nucleotide polymorphisms (SNPs), and introns.
  • L2Rs log2 ratios
  • SNPs single nucleotide polymorphisms
  • estimating the ctDNA fraction based on at least the sample tumor purity and sample ploidy using the CNA model comprises using a formula that describes a physical relationship between ctDNA fraction and sample tumor purity and sample ploidy.
  • the formula is given by: where p is sample tumor purity, and y is ploidy.
  • estimating the ctDNA fraction based on at least one somatic short variant detected in the sequence read data comprises: obtaining a list of short variants detected in the sequence read data; applying a set of selection rules to the list of detected short variants to identify tumor somatic short variants; and estimating the ctDNA fraction based on the presence of at least one identified tumor somatic short variant.
  • estimating the ctDNA fraction based on the at least one tumor somatic short variant comprises: determining, using the one or more processors, a variant allele frequency (VAF) for one or more variants detected in the sequence read data; generating, using the one or more processors, an empirical distribution of ctDNA fraction values corresponding to the determined VAF for the one or more variants based on historical data; fitting, using the one or more processors, a model to the empirical distribution of ctDNA fraction values; and determining a ctDNA fraction for the sample based on the model.
  • the method further comprises determining a confidence interval for the ctDNA fraction based on the model.
  • the one or more variants comprise one or more short variants. In some embodiments, the one or more short variants comprise one or more somatic short variants. In some embodiments, the one or more somatic short variants are known not to be associated with clonal hematopoiesis of indeterminate potential (CHIP).
  • CHIP indeterminate potential
  • generating the empirical distribution of ctDNA fraction values comprises calculating a ctDNA fraction value based on a known copy number for one or more tumor somatic short variants and a corresponding known sample ploidy for a plurality of historical subject samples having a known VAF for the one or more tumor somatic short variants that is substantially the same as the determined VAF for the one or more tumor somatic short variants.
  • generating the empirical distribution of ctDNA fraction values comprises pre-calculating a ctDNA fraction value based on a known copy number for the one or more tumor somatic short variants and a corresponding known sample ploidy for a plurality of historical subject samples having a range of VAF values for the one or more tumor somatic short variants, and selecting a subset of the pre-calculated ctDNA fraction values that corresponds to samples having a known VAF for the one or more tumor somatic short variants that is substantially the same as the determined VAF for the one or more tumor somatic short variants.
  • ctDNA fraction values are calculated or selected for a tumor somatic variant that exhibits the highest VAF in the sample from the subject. In some embodiments, ctDNA fraction values are calculated or selected for a rank-ordered set of two or more tumor somatic short variants that exhibit the highest rank-ordered VAFs in the sample from the subject. In some embodiments, the ctDNA fraction values are calculated or selected for a predetermined set of two or more tumor somatic short variants detected in the sample from the subject. In some embodiments, the ctDNA fraction values are calculated or selected for a predetermined set of two or more tumor somatic short variants detected in the sample from the subject that comprise known driver mutations. In some embodiments, the ctDNA fraction values are calculated or selected for all tumor somatic short variants detected in the sample from the subject.
  • ctDNA fraction values are calculated or pre-calculated based on the known VAF for the one or more tumor somatic short variants, the known copy number for the one or more tumor somatic short variants, and the corresponding known sample ploidy for the plurality of historical subject samples.
  • ctDNA fraction values are calculated or pre-calculated based on the known VAF for the one or more tumor somatic short variants, the known copy number for the one or more tumor somatic short variants, and the corresponding known sample ploidy for the plurality of historical subject samples by solving a set of equations that describe: (i) a relationship between ctDNA fraction, sample tumor purity, and sample ploidy, and (ii) a relationship between somatic VAF, sample tumor purity, copy number at the genomic location(s) of the one or more tumor somatic short variants, and variant allele number for each of the one or more tumor somatic short variants, to eliminate sample tumor purity and derive a relationship for ctDNA fraction as a function of somatic VAF, sample ploidy, copy number at a genomic location of the one or more tumor somatic short variants, and variant allele number for the one or more tumor somatic short variants.
  • ctDNA fraction values are calculated or pre-calculated based on the known VAF for the one or more tumor somatic short variants, the known copy number for the one or more tumor somatic short variants, and the corresponding known sample ploidy for the plurality of historical subject samples by solving a set of equations comprising: a first equation that equates ctDNA fraction to a product of sample tumor purity and sample ploidy, divided by a sum of the product of sample tumor purity and sample ploidy and a product of two times a quantity of one minus sample tumor purity; and a second equation that equates somatic VAF to a product of sample tumor purity and a variant allele number for each of the one or more tumor somatic short variants, divided by a sum of a product of sample tumor purity and copy number at a genomic location of the one or more tumor somatic short variants and a product of two times a quantity of one minus sample tumor purity; to eliminate sample tumor purity and derive a relationship that
  • the plurality of historical subject samples comprises solid biopsy samples, liquid biopsy samples, or any combination thereof. In some embodiments, the plurality of historical subject samples comprises cancer samples. In some embodiments, the plurality of historical subject samples comprises samples for a single type of cancer. In some embodiments, the plurality of historical subject samples comprises samples for multiple types of cancer.
  • the plurality of historical subject samples comprise bladder cancer samples, breast cancer samples, colorectal cancer samples, endometrial cancer samples, kidney cancer samples, leukemia samples, liver cancer samples, lung cancer samples, melanoma samples, non-Hodgkin lymphoma samples, pancreatic cancer samples, prostate cancer samples, thyroid cancer samples, or any combination thereof.
  • the plurality of historical subject samples comprise acute lymphoblastic leukemia (Philadelphia chromosome positive) samples, acute lymphoblastic leukemia (precursor B-cell) samples, acute myeloid leukemia (FLT3+) samples, acute myeloid leukemia (with an IDH2 mutation) samples, anaplastic large cell lymphoma samples, basal cell carcinoma samples, B-cell chronic lymphocytic leukemia samples, bladder cancer samples, breast cancer (HER2 overexpressed/amplified) samples, breast cancer (HER2+) samples, breast cancer (HR+, HER2-) samples, cervical cancer samples, cholangiocarcinoma samples, chronic lymphocytic leukemia samples, chronic lymphocytic leukemia (with 17p deletion) samples, chronic myelogenous leukemia samples, chronic myelogenous leukemia (Philadelphia chromosome positive) samples, classical Hodgkin lymphoma samples, colorectal cancer samples, colorec
  • the model is a non-parametric probability density model.
  • the determined ctDNA fraction for the sample is a most probable ctDNA fraction. In some embodiments, the determined ctDNA fraction for the sample is the mean, median, or mode of a dominant peak in the empirical distribution of ctDNA fraction values.
  • the sample comprises DNA extracted from a blood sample, a plasma sample, a cerebrospinal fluid sample, a pleural effusion fluid sample, a sputum sample, a stool sample, a urine sample, or a saliva sample.
  • estimating the ctDNA fraction is based on a determination of the maximum somatic allele frequency (MSAF) for the at least one tumor somatic short variant, the detection of one or more genomic rearrangements, a determination of micro satellite instability, or any combination thereof.
  • MSAF maximum somatic allele frequency
  • the set of selection rules used to identify tumor somatic short variants in the list of detected short variants comprises: (i) removing short variants that appear on a blacklist of known germline variants, known clonal hematopoiesis of indeterminate potential (CHIP) variants, and known sequencing artifacts; (ii) retaining short variants that appear on a list of known tumor somatic short variants; (iii) retaining short variants that appear on a list of known genes that are prone to exhibit high amplification and that have an allele frequency that is higher than other somatic short variants in the sample; (iv) retaining short variants that appear on a list of known rearrangements; or any combination thereof.
  • CHIP indeterminate potential
  • the blacklist of known germline variants, known clonal hematopoiesis of indeterminate potential (CHIP) variants, and known sequencing artifacts comprises a list of germline variants, CHIP variants, and sequencing artifacts, or any combination thereof, observed in historical sequencing data.
  • the list of known tumor somatic short variants comprises somatic short variants determined to have a high prevalence ratio or a high odds ratio between tumor and white blood cells.
  • the list of known genes that are prone to exhibit high amplification comprises KRAS, EGFR, CCND1, FGF19, FGF3, FGF4, MYC, AR, MDM2, CCNE1, ERBB2, WHSC1L1, FGFR1, ZNF703, or any combination thereof.
  • the list of known rearrangements comprises fusions between the following gene pairs: TMPRSS2-ERG, ALK-EML4, FGFR3-TACC3, RET-KIF5B, or any combination thereof.
  • the set of selection rules used to identify tumor somatic short variants in the list of detected short variants further comprises identifying short variants for which a fragment size shift between the reference allele and alternate allele is detected in sequence read data as being tumor somatic short variants.
  • the estimated ctDNA fraction of the sample is used to diagnose or confirm a diagnosis of disease in the subject.
  • the disease is cancer.
  • the method further comprises selecting an anti-cancer therapy to administer to the subject based on the estimated ctDNA fraction of the sample.
  • the method further comprises determining an effective amount of an anti-cancer therapy to administer to the subject based on the estimated ctDNA fraction of the sample.
  • the method further comprises administering the anti-cancer therapy to the subject based on the estimated ctDNA fraction of the sample.
  • the anti-cancer therapy comprises chemotherapy, radiation therapy, immunotherapy, a targeted therapy, or surgery.
  • the estimated ctDNA fraction is used as a prognostic biomarker for predicting a treatment outcome for a subject having cancer.
  • the cancer is prostate cancer.
  • the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
  • Disclosed herein are methods for predicting a treatment outcome for a subject having cancer comprising: receiving, at one or more processors, sequence read data for a plurality of sequence reads obtained for the sample from the subject; determining, using the one or more processors, if the sequence read data is sufficient for performing copy number alteration (CNA) modeling; estimating, using the one or more processors, a ctDNA fraction in the sample based on at least a sample tumor purity and a sample ploidy derived from a CNA model if the sequence read data is determined to be sufficient for performing CNA modeling; or estimating, using the one or more processors, the ctDNA fraction in the sample based on identification of at least one tumor somatic short variant detected in the sequence read data if the sequence read data is determined to be insufficient for performing CNA modeling; outputting, using the one or more processors, the estimated ctDNA fraction in the sample; and based on a comparison of the estimated ctDNA fraction to a predetermined threshold
  • the predetermined threshold is determined based on an analysis of ctDNA fraction and survival data for a cohort of patients having the cancer. In some embodiments, the predetermined threshold is determined by adjusting an empirical threshold to maximize a sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), or any combination thereof, for the ctDNA fraction data for the cohort of patients having the cancer.
  • the cancer is prostate cancer.
  • the anti-cancer therapy comprises an enzalutamide challenge following abiraterone treatment.
  • ctDNA fraction is determined according to any of the methods described herein.
  • Disclosed herein are methods of selecting an anti-cancer therapy comprising: responsive to determining a ctDNA fraction for a sample from a subject, selecting an anti-cancer therapy for the subject, wherein ctDNA fraction is determined according to any of the methods described herein.
  • ctDNA fraction is determined according to any of the methods described herein.
  • the second ctDNA fraction for the second sample is determined according to any of the methods described herein.
  • the method further comprises selecting an anti-cancer therapy for the subject in response to the cancer progression.
  • the method further comprises administering an anti-cancer therapy to the subject in response to the cancer progression. In some embodiments, the method further comprises adjusting an anti-cancer therapy for the subject in response to the cancer progression. In some embodiments, the method further comprises adjusting a dosage of the anti-cancer therapy or selecting a different anticancer therapy in response to the cancer progression. In some embodiments, the method further comprises administering the adjusted anti-cancer therapy to the subject. In some embodiments, the first time point is before the subject has been administered an anti-cancer therapy, and wherein the second time point is after the subject has been administered the anti-cancer therapy.
  • the subject has a cancer, is at risk of having a cancer, is being routine tested for cancer, or is suspected of having a cancer.
  • the cancer is a solid tumor.
  • the cancer is a hematological cancer.
  • the anti-cancer therapy comprises chemotherapy, radiation therapy, immunotherapy, a targeted therapy, or surgery.
  • the methods disclosed herein may further comprise determining, identifying, or applying the value of ctDNA fraction for the sample as a diagnostic value associated with the sample.
  • the methods may further comprise generating a genomic profile for the subject based on the determination of ctDNA fraction.
  • the genomic profile for the subject further comprises results from a comprehensive genomic profiling (CGP) test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof.
  • the genomic profile for the subject further comprises results from a nucleic acid sequencing-based test.
  • the disclosed methods may further comprise selecting an anticancer therapy, administering an anti-cancer therapy, or applying an anti-cancer therapy to the subject based on the generated genomic profile.
  • the determination of ctDNA fraction for the sample is used in making suggested treatment decisions for the subject. In some embodiments, the determination of ctDNA fraction for the sample is used in applying or administering a treatment to the subject.
  • systems comprising: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: receive sequence read data for a plurality of sequence reads obtained for the sample from the subject; determine if the sequence read data is sufficient for performing copy number alteration (CNA) modeling; estimate using the one or more processors, (1) the ctDNA fraction in the sample based on at least a sample tumor purity and a ploidy of the sample using a model if the sequence read data is determined to be sufficient for performing CNA modeling, or (2) the ctDNA fraction in the sample based on identification of at least one tumor somatic short variant in the sequence read data if the sequence read data is determined to be insufficient for performing CNA modeling; and output the estimated ctDNA fraction in the sample.
  • CNA copy number alteration
  • the system further comprises instructions that, when executed by the one or more processors, cause the system to compare the estimated ctDNA fraction to at least one predetermined threshold, and output a status call of at least tumor fraction-high (TF-high) or tumor fraction-low (TF-low) for the sample based on the comparison.
  • determining if the sequence read data is sufficient for performing CNA modeling comprises determining sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof, for at least one genomic locus to which the plurality of sequence reads map.
  • performing CNA modeling comprises: determining, using the one or more processors, a copy number model including a sample tumor purity, a sample ploidy, and a copy number of multiple genomic segments in the ctDNA that accounts for an observed sequence coverage ratio data, allele fraction data for at least one genomic locus within one or more subgenomic intervals to which the plurality of sequence reads map.
  • estimating the ctDNA fraction based on at least the sample tumor purity and sample ploidy using the CNA model comprises using a formula that describes a physical relationship between ctDNA fraction and sample tumor purity and sample ploidy.
  • estimating the ctDNA fraction based on at least one somatic short variant detected in the sequence read data comprises: obtaining a list of short variants detected in the sequence read data; applying a set of selection rules to the list of detected short variants to identify tumor somatic short variants; and estimating the ctDNA fraction based on the presence of at least one identified tumor somatic short variant.
  • the set of selection rules used to identify tumor somatic short variants in the list of detected short variants comprises: (i) removing short variants that appear on a blacklist of known germline variants, known clonal hematopoiesis of indeterminate potential (CHIP) variants, and known sequencing artifacts; (ii) retaining short variants that appear on a list of known tumor somatic short variants; (iii) retaining short variants that appear on a list of known genes that are prone to exhibit high amplification and that have an allele frequency that is higher than other somatic short variants in the sample; (iv) retaining short variants that appear on a list of known rearrangements; or any combination thereof.
  • CHIP indeterminate potential
  • estimating the ctDNA fraction based on the at least one tumor somatic short variant comprises: determining, using the one or more processors, a variant allele frequency (VAF) for one or more variants detected in the sample based on the sequence read data; generating, using the one or more processors, an empirical distribution of ctDNA fraction values corresponding to the determined VAF for the one or more variants based on historical data; fitting, using the one or more processors, a model to the empirical distribution of ctDNA fraction values; and determining a ctDNA fraction for the sample based on the model.
  • VAF variant allele frequency
  • non-transitory computer-readable storage media storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: receive sequence read data for a plurality of sequence reads obtained for the sample from the subject; determine if the sequence read data is sufficient for performing copy number alteration (CNA) modeling; estimate using the one or more processors, (1) the ctDNA fraction in the sample based on at least a tumor purity and a ploidy of the sample using a model if the sequence read data is determined to be sufficient for performing CNA modeling, or (2) the ctDNA fraction in the sample based on identification of at least one tumor somatic short variant in the sequence read data if the sequence read data is determined to be insufficient for performing CNA modeling; and output the estimated ctDNA fraction in the sample.
  • CNA copy number alteration
  • the non-transitory computer-readable storage medium further comprises instructions that, when executed by the one or more processors of the system, cause the system to compare the estimated ctDNA fraction to at least one predetermined threshold, and output a status call of at least tumor fraction-high (TF-high) or tumor fraction-low (TF-low) for the sample based on the comparison.
  • determining if the sequence read data is sufficient for performing CNA modeling comprises determining sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof, for at least one genomic locus to which the plurality of sequence reads map.
  • performing CNA modeling comprises: determining, using the one or more processors, a copy number model including a sample tumor purity, a sample ploidy, and a copy number of multiple genomic segments in the ctDNA that accounts for an observed sequence coverage ratio data, allele fraction data for at least one genomic locus within one or more subgenomic intervals to which the plurality of sequence reads map.
  • estimating the ctDNA fraction based on at least the sample tumor purity and sample ploidy using the CNA model comprises using a formula that describes a physical relationship between ctDNA fraction and sample tumor purity and sample ploidy.
  • estimating the ctDNA fraction based on at least one somatic short variant detected in the sequence read data comprises: obtaining a list of short variants detected in the sequence read data; applying a set of selection rules to the list of detected short variants to identify tumor somatic short variants; and estimating the ctDNA fraction based on the presence of at least one identified tumor somatic short variant.
  • the set of selection rules used to identify tumor somatic short variants in the list of detected short variants comprises: (i) removing short variants that appear on a blacklist of known germline variants, known clonal hematopoiesis of indeterminate potential (CHIP) variants, and known sequencing artifacts; (ii) retaining short variants that appear on a list of known tumor somatic short variants; (iii) retaining short variants that appear on a list of known genes that are prone to exhibit high amplification and that have an allele frequency that is higher than other somatic short variants in the sample; (iv) retaining short variants that appear on a list of known rearrangements; or any combination thereof.
  • CHIP indeterminate potential
  • estimating the ctDNA fraction based on the at least one tumor somatic short variant comprises: determining, using the one or more processors, a variant allele frequency (VAF) for one or more variants detected in the sample based on the sequence read data; generating, using the one or more processors, an empirical distribution of ctDNA fraction values corresponding to the determined VAF for the one or more variants based on historical data; fitting, using the one or more processors, a model to the empirical distribution of ctDNA fraction values; and determining a ctDNA fraction for the sample based on the model.
  • VAF variant allele frequency
  • FIG. 1 provides a non-limiting example of a process flowchart for estimating ctDNA fraction according to one embodiment of the methods disclosed herein.
  • FIG. 2 provides a non-limiting example of a process flowchart for estimating ctDNA fraction according to another embodiment of the methods disclosed herein.
  • FIG. 3 depicts an exemplary computing device or system in accordance with one embodiment of the present disclosure.
  • FIG. 4 depicts an exemplary computer system or computer network, in accordance with some instances of the systems described herein.
  • FIGS. 5A-B provide non-limiting examples of the application of the disclosed methods for determining tumor fraction in liquid biopsy samples to predict the probability of progression- free survival and the probability of survival for prostate cancer patients stratified according to tumor fraction (TF).
  • FIG. 5A plot of the probability of progression-free survival (PFS) as a function of time following initiation of enzalutamide treatment for prostate cancer patients previously treated with abiraterone.
  • FIG. 5B plot of the probability of overall survival (OS) as a function of time following initiation of enzalutamide treatment for prostate cancer patients previously treated with abiraterone.
  • FIGS. 5A plot of the probability of progression-free survival (PFS) as a function of time following initiation of enzalutamide treatment for prostate cancer patients previously treated with abiraterone.
  • OS overall survival
  • FIG. 6A-B provide non-limiting examples of the use of prostate specific antigen (PSA) as a prognostic biomarker for the probability of progression-free survival and the probability of survival for prostate cancer patients stratified according to prostate specific antigen (PSA) level.
  • FIG. 6A plot of the probability of progression-free survival (PFS) as a function of time following initiation of enzalutamide treatment for prostate cancer patients previously treated with abiraterone.
  • FIG. 6B plot of the probability of overall survival (OS) as a function of time following initiation of enzalutamide treatment of prostate cancer patients previously treated with abiraterone.
  • PFS progression-free survival
  • OS overall survival
  • Methods and systems are described that enable more accurate determination of ctDNA fraction based on sequence read data for cell-free DNA samples derived from liquid biopsy.
  • the disclosed methods are based on a combined approach that includes: (i) estimation of ctDNA fraction based on a copy number model that specifies a tumor fraction for the sample and exact copy number at probed genomic locations that explain the observed sequence coverage and single nucleotide polymorphism (SNP) allele frequency data, and/or (ii) estimation of ctDNA fraction based on a theoretical relationship between tumor fraction and somatic variant allele frequency given a copy number profile .
  • SNP single nucleotide polymorphism
  • the more accurate determinations of ctDNA fraction enabled by using the disclosed methods can, in turn, be used to detect, monitor, and/or make prognostic predictions for treatment outcomes for cancer patients.
  • methods for determining circulating tumor DNA (ctDNA) fraction in a sample from a subject comprise: receiving sequence read data for a plurality of sequence reads obtained for the sample from the subject; determining if the sequence read data is sufficient for performing copy number alteration (CNA) modeling; estimating (1) the ctDNA fraction in the sample based on at least a sample tumor purity and a ploidy of the sample if the sequence read data is determined to be sufficient for performing CNA modeling, or (2) the ctDNA fraction in the sample based on identification of at least one tumor somatic short variant in the sequence read data if the sequence read data is determined to be insufficient for performing CNA modeling; and outputting the estimated ctDNA fraction in the sample.
  • CNA copy number alteration
  • the method may further comprise comparing the estimated ctDNA fraction to at least one predetermined threshold, and outputting a status call of at least tumor fraction-high (TF-high) or tumor fraction-low (TF-low) for the sample based on the comparison.
  • ‘About” and “approximately” shall generally mean an acceptable degree of error for the quantity measured given the nature or precision of the measurements. Exemplary degrees of error are within 20 percent (%), typically, within 10%, and more typically, within 5% of a given value or range of values.
  • the terms “comprising” (and any form or variant of comprising, such as “comprise” and “comprises”), “having” (and any form or variant of having, such as “have” and “has”), “including” (and any form or variant of including, such as “includes” and “include”), or “containing” (and any form or variant of containing, such as “contains” and “contain”), are inclusive or open-ended and do not exclude additional, un-recited additives, components, integers, elements, or method steps.
  • the terms “individual,” “patient,” or “subject” are used interchangeably and refer to any single animal, e.g., a mammal (including such non-human animals as, for example, dogs, cats, horses, rabbits, zoo animals, cows, pigs, sheep, and non-human primates) for which treatment is desired.
  • a mammal including such non-human animals as, for example, dogs, cats, horses, rabbits, zoo animals, cows, pigs, sheep, and non-human primates
  • the individual, patient, or subject herein is a human.
  • cancer and “tumor” are used interchangeably herein. These terms refer to the presence of cells possessing characteristics typical of cancer-causing cells, such as uncontrolled proliferation, immortality, metastatic potential, rapid growth and proliferation rate, and certain characteristic morphological features. Cancer cells are often in the form of a tumor, but such cells can exist alone within an animal, or can be a non-tumorigenic cancer cell, such as a leukemia cell. These terms include a solid tumor, a soft tissue tumor, or a metastatic lesion. As used herein, the term “cancer” includes premalignant, as well as malignant cancers.
  • treatment refers to clinical intervention (e.g., administration of an anti-cancer agent or anticancer therapy) in an attempt to alter the natural course of the individual being treated, and can be performed either for prophylaxis or during the course of clinical pathology.
  • Desirable effects of treatment include, but are not limited to, preventing occurrence or recurrence of disease, alleviation of symptoms, diminishment of any direct or indirect pathological consequences of the disease, preventing metastasis, decreasing the rate of disease progression, amelioration or palliation of the disease state, and remission or improved prognosis.
  • genomic interval refers to a portion of a genomic sequence.
  • subject interval refers to a subgenomic interval or an expressed subgenomic interval (e.g., the transcribed sequence of a subgenomic interval).
  • variant sequence As used herein, the terms “variant sequence” or “variant” are used interchangeably and refer to a modified nucleic acid sequence relative to a corresponding “normal” or “wild-type” sequence. In some instances, a variant sequence may be a “short variant sequence” (or “short variant”), i.e., a variant sequence of less than about 50 base pairs in length.
  • allele frequency and “allele fraction” are used interchangeably herein and refer to the fraction of sequence reads corresponding to a particular allele relative to the total number of sequence reads for a genomic locus.
  • variant allele frequency and “variant allele fraction” are used interchangeably herein and refer to the fraction of sequence reads corresponding to a particular variant allele relative to the total number of sequence reads for a genomic locus.
  • tumor fraction generally refers to the proportion of tumor- derived DNA molecules in the total DNA extracted from a sample.
  • circulating tumor DNA fraction refers to the proportion of cancer cell-derived (or tumor-derived) DNA in the total DNA present in the cfDNA sample.
  • the methods described herein may be used to estimate the fraction of circulating tumor DNA contained in cell-free DNA samples isolated from liquid biopsy samples. These new methods overcome the problems of inadequate sensitivity and/or specificity often encountered with existing methods for detecting circulating tumor DNA and estimating ctDNA fraction in cell-free DNA samples.
  • ctDNA fraction e.g., the tumor fraction for a cell free DNA (cfDNA) sample
  • cfDNA cell free DNA
  • the more accurate determinations of ctDNA fraction allow determinations of ctDNA fraction to be used for detecting, monitoring, and/or making prognostic predictions of treatment outcomes for cancer patients.
  • the data presented in Example 3 below indicates that ctDNA fraction may provide a better prognostic biomarker for treatment outcomes in prostate cancer patients than prostate specific antigen (PSA) levels.
  • PSA prostate specific antigen
  • the disclosed methods leverage the unique features of a novel genomic profiling assay that utilizes a targeted sequencing approach (see, e.g., U.S. Patent No. 9,340,830, which is incorporated herein by reference in its entirety). Due to the constraints of sequencing cost, many commercial sequencing approaches have taken either a “wide and shallow” approach (z.e., a large region of the genome e.g., the entire genome, or just the exomes) is sequenced with low depth), or a “narrow and deep” approach (i.e., only small, selected portions of the genome are sequenced, but to a great depth).
  • the “wide and shallow” approach allows for detection of aneuploidy but cannot leverage short variant signal.
  • the ‘narrow and deep” approach can detect short variants that are present at very small allele fraction, but is inevitably limited to detection of only those signals that arise from the narrow region(s) sequenced.
  • the novel genomic profiling assay referenced above uses a method comprising contacting a sequencing library with at least two different bait sets designed to hybridize to and select specific genomic regions of interest, where the different bait sets exhibit differential efficiency for selecting their respective genomic regions of interest and result in different sequencing depth for the different genomic regions of interest.
  • Genomic regions that are expected to be hot spots for cancer-relevant somatic short variants are sequenced to great depth, thereby allowing for detection of somatic short variant signals at very low tumor fraction.
  • a set of carefully selected additional small genomic regions scattered throughout the genome are sequenced to a moderate depth, thereby allowing for construction of a copy number model to detect tumor fraction based on aneuploidy.
  • the methods described herein take advantage of this unique assay design to detect and estimate ctDNA fraction using both aneuploidy and the presence of other types of alterations that exist in a patient’s cancer genome, such as short variants, rearrangements, micro satellite instability, etc.
  • the new methods described herein also take advantage of a novel copy number modeling approach (see, e.g., PCT International Patent Publication No.
  • WO 2023/060236 entitled “Methods and Systems for Automated Calling of Copy Number Alterations”, which is incorporated herein by reference in its entirety) to leverage aneuploidy signal, and of a new set of heuristic rules for filtering variant data to identify tumor-derived variants, to detect circulating tumor DNA and estimate ctDNA fraction.
  • the method was verified against physical ground truth data for ctDNA fraction obtained from an analysis of sequence read data for paired plasma/buffy coat (PBMC) samples.
  • PBMC paired plasma/buffy coat
  • FIG. 1 provides a non-limiting example of a flowchart for a process 100 for estimating the ctDNA fraction of a liquid biopsy sample (or cell-free DNA sample derived therefrom).
  • Process 100 can be performed, for example, using one or more electronic devices implementing a software platform.
  • process 100 is performed using a client-server system, and the blocks of process 100 are divided up in any manner between the server and a client device.
  • the blocks of process 100 are divided up between the server and multiple client devices.
  • process 100 is performed using only a client device or only multiple client devices.
  • process 100 some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the process 100. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.
  • sequence read data for a plurality of sequence reads obtained for a sample from a subject is received (e.g., by one or more processors of a system configured to perform process 100).
  • the sequence read data is derived by sequencing the cell-free DNA (cfDNA) extracted from a liquid biopsy sample.
  • sequence read data may be received by a system as a Binary Alignment Map (BAM) file.
  • BAM Binary Alignment Map
  • the sequence read data for the plurality of sequence reads may be derived using a targeted sequencing technique, e.g. , a targeted exome sequencing method.
  • the sequence read data may be derived using, e.g., a whole genome or whole exome sequencing method, as opposed to a targeted exome sequencing method, to increase the number of genomic features (e.g., the number of short variants, rearrangements, etc.) detected.
  • the sequencing method may be based on a sequencing by synthesis (SBS) technology, e.g., a Next Generation Sequencing (NGS) technology.
  • SBS sequencing by synthesis
  • NGS Next Generation Sequencing
  • any of a variety of alternative sequencing methods e.g., sequencing by binding (SBS) or sequencing by avidity (SBA) methods
  • sequencing technologies e.g., nanopore-based or microarray-based sequencing technologies
  • liquid biopsy sample may comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
  • the sequence read data is evaluated to determine if it is of sufficient quality for performing copy number alteration (CNA) modeling.
  • the sequence read data may be evaluated, for example, by determining the level of noise in the data and determining if aneuploidy can be detected based on, e.g., a deflection of coverage log ratio (LR) and SNP frequency values from what would be expected if there is no aneuploidy present.
  • LR deflection of coverage log ratio
  • an estimate of circulating tumor DNA (ctDNA) fraction in the sample is determined based on a copy number alteration (CNA) model if the sequence read data has been determined to be sufficient for performing CNA modeling.
  • the ctDNA fraction for the sample is estimated based on a copy number profile (or aneuploidy signal) determined for the sequence read data based on the model, as will described in more detail below in reference to
  • an estimate of circulating tumor DNA (ctDNA) fraction in the sample is determined based on one or more somatic short variants (e.g., tumor-derived somatic short variants) detected in the sequence read data from the sample if the sequence read data has been determined to be insufficient for performing CNA modeling.
  • somatic short variants e.g., tumor-derived somatic short variants
  • the ctDNA fraction for the sample is estimated based on: (i) applying a set of heuristic rules to discriminate between tumor- derived somatic short variants (tumor somatic short variants) and non-tumor-derived (e.g., germline) somatic short variants, and (ii) the use of a model based on an empirical distribution of ctDNA fraction values corresponding to the determined VAF for one or more short variants in historical sequencing data, as will be described in more detail below in reference to FIG. 2.
  • the estimate of ctDNA fraction for the sample is output.
  • the method may further comprise comparing the estimated ctDNA fraction to at least one predetermined threshold, and outputting a status call of, e.g., tumor fraction-high (TF-high) or tumor fraction-low (TF-low), for the sample based on the comparison.
  • TF-high tumor fraction-high
  • TF-low tumor fraction-low
  • the estimated ctDNA fraction may be compared to at least two predetermined thresholds, and a status call of, e.g., tumor fraction-high (TF-high), tumor- fraction-medium (TF-medium), or tumor fraction-low (TF-low), may be output for the sample based on the comparison.
  • TF-high tumor fraction-high
  • TF-medium tumor- fraction-medium
  • TF-low tumor fraction-low
  • a predetermined threshold (or TF threshold) used to stratify samples may have a value of, e.g., 0.01, 0.02, 0.03, 0.04, 0.05, 0.1, 0.15, 0.2, or 0.25.
  • ctDNA fraction estimated determined using the methods disclosed herein may be used as a prognostic biomarker for predicting a treatment outcome for a subject having cancer.
  • the cancer may be prostate cancer.
  • FIG. 2 provides a non-limiting example of a flowchart for a process 200 for estimating ctDNA fraction of a liquid biopsy sample (or cell-free DNA sample derived therefrom).
  • Process 200 can be performed, for example, using one or more electronic devices implementing a software platform.
  • process 200 is performed using a client-server system, and the blocks of process 200 are divided up in any manner between the server and a client device.
  • the blocks of process 200 are divided up between the server and multiple client devices.
  • process 200 is performed using only a client device or only multiple client devices.
  • process 200 some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the process 200. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.
  • sequence read data for a plurality of sequence reads obtained for a sample from a subject is received (e.g., by one or more processors of a system configured to perform process 200).
  • the sequence read data is derived by sequencing the cell-free DNA (cfDNA) extracted from a liquid biopsy sample.
  • sequence read data may be received by a system as a Binary Alignment Map (BAM) file.
  • BAM Binary Alignment Map
  • the sequence read data for the plurality of sequence reads may be derived using a targeted sequencing technique, e.g. , a targeted exome sequencing method.
  • the sequence read data may be derived using, e.g., a whole genome or whole exome sequencing method, as opposed to a targeted exome sequencing method, to increase the number of genomic features (e.g., the number of short variants, rearrangements, etc.) detected.
  • the sequencing method may be based on a sequencing by synthesis (SBS) technology, e.g., a Next Generation Sequencing (NGS) technology.
  • SBS sequencing by synthesis
  • NGS Next Generation Sequencing
  • any of a variety of alternative sequencing methods e.g., sequencing by binding (SBS) or sequencing by avidity (SBA) methods
  • sequencing technologies e.g., nanopore-based or microarray-based sequencing technologies
  • liquid biopsy sample may comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
  • the sample may comprise DNA (e.g., cell-free DNA) extracted from a blood sample, a plasma sample, a cerebrospinal fluid sample, a pleural effusion fluid sample, a sputum sample, a stool sample, a urine sample, or a saliva sample.
  • the sequence read data is evaluated to determine if it is of sufficient quality for performing copy number alteration (CNA) modeling.
  • CNA copy number alteration
  • the sequence read data may be evaluated, for example, by determining the level of noise in the data and performing a quick check to determine if aneuploidy can be detected.
  • determining if the sequence read data is sufficient for performing CNA modeling may comprise determining sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof, for at least one genomic locus to which the plurality of sequence reads map.
  • the at least one genomic locus may comprise at least one single nucleotide polymorphism (SNP) locus.
  • the determination of sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof, for the at least one genomic locus may be based by pre-processing the sequence read data.
  • a copy number alteration (CNA) model is generated if the sequence read data has been determined to be sufficient for performing CNA modeling.
  • CNA copy number alteration
  • performing CNA modeling may comprises: generating a copy number model that determines and outputs a sample tumor purity, a sample ploidy (or “ploidy”), and a copy number for multiple genomic segments that account for an observed sequence coverage ratio data, allele fraction data for at least one genomic locus within one or more subgenomic intervals to which the plurality of sequence reads map.
  • the sequence coverage ratio data may be determined by aligning the plurality of sequence reads that overlap the at least one genomic locus e.g., at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or more than 100 genomic loci) within the one or more subgenomic intervals (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or more than 100 subgenomic intervals) in the sample and in a control sample to a reference genome (e.g., a human reference genome such as HG19 or HG38), and determining a number of sequence reads that overlap the at least one genomic locus within the one or more subgenomic intervals in the sample and in the control sample.
  • the control sample is a paired normal sample, a process-matched control sample, or a panel of normal control sample.
  • sequence coverage ratio data may further comprise sequence coverage ratio data for single nucleotide polymorphisms (SNPs) and introns associated with the at least one genomic locus.
  • SNPs single nucleotide polymorphisms
  • the allele fraction data may be determined by aligning the plurality of sequence reads that overlap the at least one genomic locus within the one or more subgenomic intervals in the sample to a reference genome (e.g., a human reference genome such as HG19 or HG38), detecting a number of alleles present at the at least one genomic locus, and determining an allele fraction for at least one of the alleles present at the at least one genomic locus.
  • a reference genome e.g., a human reference genome such as HG19 or HG38
  • performing CNA modeling may further comprise generating segmentation data by aligning a plurality of sequence reads that overlap the at least one genomic locus within the one or more subgenomic intervals in the sample to a reference genome, and processing the aligned sequence read data, coverage ratio data, and allele fraction data using a pruned exact linear time (PELT) method to determine a number of segments required to account for the aligned sequence read data, wherein each segment has a same copy number.
  • PELT pruned exact linear time
  • the copy number model may be used to predict and/or output a copy number for the at least one genomic locus based on the sequence coverage ratio data and allele fraction data.
  • the copy number model may also be used to predict and/or output a sample tumor purity (e.g., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.9, or 1.0, if expressed as a fraction rather than a percentage) and ploidy (e.g., an average copy number having a value ranging from 1 to 8) for the sample.
  • the copy number model also outputs the segmentation data.
  • an amplification may be detected when the copy number for the corresponding segment is greater than or equal to the ploidy of the sample.
  • an amplification is detected when the copy number for the corresponding segment is greater than or equal to the ploidy of the sample plus a first predetermined value (e.g., a first predetermined value ranging from 2 to 500; in some instances the first predetermined value may range from 2 to 10).
  • an amplification may be detected when the copy number for the corresponding segment is greater than or equal to the ploidy of the sample plus a second predetermined value (e.g., a second predetermined value ranging from 0 to 500 or, in some instances, ranging from 2 to 10) and the genomic locus is a member of a first predefined set of genomic loci (e.g., a first predefined set of genomic loci comprising one or more druggable gene target loci, prognostic gene loci, oncogene loci, or any combination thereof).
  • the first predefined set of genomic loci may comprise the AR and ERBB2 gene loci.
  • detection of deletions may comprise identifying homozygous deletions of the at least one genomic locus in a corresponding segment.
  • homozygous deletions may be detected by determining a total copy number for a given genomic locus that is equal to the sum of the copy numbers for a first allele and a second allele at the genomic locus.
  • the first allele is a major allele and the second allele is a minor allele.
  • a homozygous deletion is called if the total copy number for a given genomic locus is equal to a third predetermined value (e.g., a third predetermined value of about zero.
  • detection of deletions may comprise identifying heterozygous deletions of the at least one genomic locus in a corresponding segment.
  • a heterozygous deletion may be called if a copy number for a first allele at a given genomic locus is equal to a fourth predetermined value, and a copy number for a second allele at the given genomic locus in not equal to the fourth predetermined value (e.g., a fourth predetermined value of about zero).
  • the first allele is a major allele and the second allele is a minor allele.
  • detection of deletions may comprise identifying partial deletions of the at least one genomic locus in a corresponding segment.
  • a partial deletion may be called for a given genomic locus if log2 ratios (L2Rs) for neighboring genomic loci, single nucleotide polymorphisms (SNPs), and introns are significantly different than the log2 ratio for the genomic locus, and the log2 ratio for the given genomic locus is significantly different from a distribution of L2Rs for non-neighboring genomic loci, single nucleotide polymorphisms (SNPs), and introns.
  • L2Rs log2 ratios
  • SNPs single nucleotide polymorphisms
  • SNPs single nucleotide polymorphisms
  • the ctDNA fraction for the sample is calculated based on at least the sample tumor purity and ploidy predicted by the CNA model.
  • estimating the ctDNA fraction based on at least the sample tumor purity and sample ploidy predicted by the CAN model may comprise using a formula that describes a physical relationship between ctDNA fraction and sample tumor purity and sample ploidy.
  • the method may further comprise determining a confidence interval for the ctDNA fraction.
  • the estimate of ctDNA fraction for the sample is output.
  • the method may further comprise comparing the estimated ctDNA fraction to at least one predetermined threshold, and outputting a status call of, e.g., tumor fraction-high (TF-high) or tumor fraction-low (TF-low), for the sample based on the comparison.
  • TF-high tumor fraction-high
  • TF-low tumor fraction-low
  • a preliminary list of variants (e.g., short variants) detected in the sequence read data is obtained if the sequence read data has been determined to be insufficient for performing CNA modeling.
  • the list of variants may comprise one or more short variants.
  • the one or more short variants may comprise one or more somatic short variants.
  • the one or more somatic short variants may comprise one or more somatic short variants that are known not to be associated with clonal hematopoiesis of indeterminate potential (CHIP) (e.g., they may be tumor somatic short variants).
  • a set of selection rules is applied to the list of variants (e.g., somatic short variants) to distinguish between tumor-derived somatic short variants and non- tumor-derived somatic short variants (e.g., germline somatic short variants).
  • the set of selection rules used to identify tumor somatic short variants in the list of detected short variants may comprise: (i) removing short variants that appear on a blacklist of known germline variants, known clonal hematopoiesis of indeterminate potential (CHIP) variants, and known sequencing artifacts; (ii) retaining short variants that appear on a list of known tumor somatic short variants; (iii) retaining short variants that appear on a list of known genes that are prone to exhibit high amplification and that have an allele frequency that is higher than other somatic short variants in the sample; (iv) retaining short variants that appear on a list of known rearrangements; or any combination thereof.
  • CHIP indeterminate potential
  • the blacklist of known germline variants, known clonal hematopoiesis of indeterminate potential (CHIP) variants, and known sequencing artifacts may comprise, e.g., a list of germline variants, CHIP variants, and sequencing artifacts, or any combination thereof, observed in historical sequencing data.
  • the list of known tumor somatic short variants comprises somatic short variants determined to have a high prevalence odds ratio between tumor and white blood cells.
  • the list of known genes that are prone to exhibit high amplification may comprise, e.g., KRAS, EGFR, CCND1, FGF19, FGF3, FGF4, MYC, AR, MDM2, CCNE1, ERBB2, WHSC1L1, FGFR1, ZNF703, or any combination thereof.
  • the list of known rearrangements may comprise, e.g., fusions between the following gene pairs: TMPRSS2-ERG, ALK-EML4, FGFR3-TACC3, RET-KIF5B, or any combination thereof.
  • the set of selection rules used to identify tumor somatic short variants in the list of detected short variants may further comprise identifying short variants for which a fragment size shift between the reference allele and alternate allele is detected in sequence read data as being tumor somatic short variants.
  • a determination of “ctDNA not detected” is output if no tumor somatic short variant has been identified in the preliminary list of short variants.
  • the ctDNA fraction for the sample is determined based on the identified tumor somatic short variants if at least 1 tumor somatic short variant has been detected in the sequence read data for the sample.
  • estimating the ctDNA fraction for the sample based on at least one somatic short variant detected in the sequence read data may comprise obtaining a list of short variants detected in the sequence read data; applying a set of selection rules to the list of detected short variants to identify tumor somatic short variants; and estimating the ctDNA fraction based on the presence of at least one identified tumor somatic short variant.
  • estimating the ctDNA fraction for the sample based on at least one tumor somatic short variant detected in the sequence read data may comprise determining a variant allele frequency (VAF) for one or more variants detected in the sequence read data; generating an empirical distribution of ctDNA fraction values corresponding to the determined VAF for the one or more variants based on historical data; fitting a model to the empirical distribution of ctDNA fraction values; and determining a ctDNA fraction for the sample based on the model.
  • the model may be a non-parametric probability density model.
  • the method may further comprise determining a confidence interval for the ctDNA fraction based on the model.
  • generating the empirical distribution of ctDNA fraction values comprises calculating a ctDNA fraction value based on a known copy number for one or more tumor somatic short variants and a corresponding known sample ploidy for a plurality of historical subject samples having a known VAF for the one or more tumor somatic short variants that is substantially the same as the determined VAF for the one or more tumor somatic short variants.
  • generating the empirical distribution of ctDNA fraction values comprises pre-calculating a ctDNA fraction value based on a known copy number for the one or more tumor somatic short variants and a corresponding known sample ploidy for a plurality of historical subject samples having a range of VAF values for the one or more tumor somatic short variants, and selecting a subset of the pre-calculated ctDNA fraction values that corresponds to samples having a known VAF for the one or more tumor somatic short variants that is substantially the same as the determined VAF for the one or more tumor somatic short variants.
  • ctDNA fraction values may be calculated or selected for a tumor somatic variant that exhibits the highest VAF in the sample from the subject. In some instances, ctDNA fraction values may be calculated or selected for a rank-ordered set of two or more tumor somatic short variants that exhibit the highest rank-ordered VAFs. In some instances, ctDNA fraction values may be calculated or selected for a predetermined set of two or more tumor somatic short variants detected in the sample from the subject. In some instances, ctDNA fraction values may be calculated or selected for a predetermined set of two or more tumor somatic short variants detected in the sample from the subject that comprise known driver mutations. In some instances, ctDNA fraction values may be calculated or selected for all tumor somatic short variants detected in the sample from the subject.
  • ctDNA fraction values may be calculated or pre-calculated based on the known VAF for the one or more tumor somatic short variants, the known copy number for the one or more tumor somatic short variants, and the corresponding known sample ploidy for the plurality of historical subject samples.
  • ctDNA fraction values may be calculated or pre-calculated based on the known VAF for the one or more tumor somatic short variants, the known copy number for the one or more tumor somatic short variants, and the corresponding known sample ploidy for the plurality of historical subject samples by solving a set of equations that describe: (i) a relationship between ctDNA fraction, sample tumor purity, and sample ploidy, and (ii) a relationship between somatic VAF, sample tumor purity, copy number at the genomic location(s) of the one or more tumor somatic short variants, and variant allele number for each of the one or more tumor somatic short variants, to eliminate sample tumor purity and derive a relationship for ctDNA fraction as a function of somatic VAF, sample ploidy, copy number at a genomic location of the one or more tumor somatic short variants, and variant allele number for the one or more tumor somatic short variants.
  • ctDNA fraction values may be calculated or pre-calculated based on the known VAF for the one or more tumor somatic short variants, the known copy number for the one or more tumor somatic short variants, and the corresponding known sample ploidy for the plurality of historical subject samples by solving a set of equations comprising: i) a first equation that equates DNA fraction to a product of sample tumor purity and ploidy, divided by a sum of the product of sample tumor purity and ploidy and a product of two times a quantity of one minus sample tumor purity; and ii) a second equation that equates somatic VAF to a product of sample tumor purity and a variant allele number for each of the one or more tumor somatic short variants, divided by a sum of a product of sample tumor purity and copy number at a genomic location of the one or more tumor somatic short variants and a product of two times a quantity of one minus sample tumor purity; to eliminate sample tumor purity and derive a
  • ctDNA fraction values may be calculated or pre-calculated based on the known VAF for the one or more tumor somatic short variants, the known copy number for the one or more tumor somatic short variants, and the corresponding known sample ploidy for the plurality of historical subject samples by solving a set of equations: pv
  • Somatic VAF - - - r C + 2(l - ) to eliminate p and obtain a relationship between ctDNA fraction and somatic VAF described by: where p is sample tumor purity, y is ploidy, C is the copy number at a genomic location of the one or more tumor somatic short variants, and V is a variant allele number for each of the one or more tumor somatic short variants.
  • the plurality of historical subject samples may comprise solid biopsy samples, liquid biopsy samples, or any combination thereof. In some instances, the plurality of historical subject samples may comprise cancer samples. In some instances, the plurality of historical subject samples may comprise samples for a single type of cancer. In some instances, the plurality of historical subject samples may comprise samples for multiple types of cancer.
  • the plurality of historical subject samples may comprise bladder cancer samples, breast cancer samples, colorectal cancer samples, endometrial cancer samples, kidney cancer samples, leukemia samples, liver cancer samples, lung cancer samples, melanoma samples, non-Hodgkin lymphoma samples, pancreatic cancer samples, prostate cancer samples, thyroid cancer samples, or any combination thereof.
  • the plurality of historical subject samples may comprise acute lymphoblastic leukemia (Philadelphia chromosome positive) samples, acute lymphoblastic leukemia (precursor B-cell) samples, acute myeloid leukemia (FLT3+) samples, acute myeloid leukemia (with an IDH2 mutation) samples, anaplastic large cell lymphoma samples, basal cell carcinoma samples, B-cell chronic lymphocytic leukemia samples, bladder cancer samples, breast cancer (HER2 overexpressed/amplified) samples, breast cancer (HER2+) samples, breast cancer (HR+, HER2-) samples, cervical cancer samples, cholangiocarcinoma samples, chronic lymphocytic leukemia samples, chronic lymphocytic leukemia (with 17p deletion) samples, chronic myelogenous leukemia samples, chronic myelogenous leukemia (Philadelphia chromosome positive) samples, classical Hodgkin lymphoma samples, colorectal cancer samples, colorec
  • the determined ctDNA fraction for the sample may be the most probable ctDNA fraction. In some instances, the determined ctDNA fraction for the sample may be the mean, median, or mode of a dominant peak in the empirical distribution of ctDNA fraction values.
  • the ctDNA fraction determined for the sample may be based on a determination of the maximum somatic allele frequency (MSAF) for the at least one tumor somatic short variant, the detection of one or more genomic rearrangements, a determination of micro satellite instability, or any combination thereof.
  • MSAF maximum somatic allele frequency
  • the estimate of ctDNA fraction for the sample is then output at step 210 in FIG. 2.
  • the method may further comprise comparing the estimated ctDNA fraction to at least one predetermined threshold, and outputting a status call of, e.g., tumor fraction-high (TF-high) or tumor fraction-low (TF-low), for the sample based on the comparison.
  • the disclosed methods may further comprise one or more of the steps of: (i) obtaining the sample from the subject (e.g., a subject suspected of having or determined to have cancer), (ii) extracting nucleic acid molecules (e.g., a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules) from the sample, (iii) ligating one or more adapters to the nucleic acid molecules extracted from the sample (e.g., one or more amplification primers, flow cell adaptor sequences, substrate adapter sequences, or sample index sequences), (iv) performing a methylation conversion reaction to convert, e.g., non-methylated cytosine to uracil, (v) amplifying the nucleic acid molecules (e.g., using a polymerase chain reaction (PCR) amplification technique, a non-PCR amplification technique, or an isothermal amplification technique), (vi) capturing nucleic acid molecules from the amplified
  • PCR polymerase
  • the report comprises output from the methods described herein. In some instances, all or a portion of the report may be displayed in the graphical user interface of an online or web-based healthcare portal. In some instances, the report is transmitted via a computer network or peer-to-peer connection.
  • the sample may comprise a tissue biopsy sample, a liquid biopsy sample, or a normal control.
  • the sample may be a liquid biopsy sample and may comprise blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
  • the sample may be a liquid biopsy sample and may comprise circulating tumor cells (CTCs).
  • the sample may be a liquid biopsy sample and may comprise cell-free DNA (cfDNA).
  • the cell-free DNA (cfDNA), or a portion thereof, may comprise circulating tumor DNA (ctDNA).
  • the liquid biopsy sample may comprise a combination of cell-free DNA (cfDNA) and circulating tumor DNA (ctDNA).
  • the nucleic acid molecules extracted from a sample may comprise a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules.
  • the tumor nucleic acid molecules may be derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules may be derived from a normal portion of the heterogeneous tissue biopsy sample.
  • the sample may comprise a liquid biopsy sample, and the tumor nucleic acid molecules may be derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample while the non-tumor nucleic acid molecules may be derived from a non-tumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.
  • ctDNA circulating tumor DNA
  • the disclosed methods for determining ctDNA fraction may be used to diagnose (or as part of a diagnosis of) the presence of disease or other condition (e.g., cancer, genetic disorders (such as Down Syndrome and Fragile X), neurological disorders, or any other disease type where detection of variants, e.g., copy number alternations, are relevant to diagnosing, treating, or predicting said disease) in a subject (e.g., a patient).
  • disease or other condition e.g., cancer, genetic disorders (such as Down Syndrome and Fragile X), neurological disorders, or any other disease type where detection of variants, e.g., copy number alternations, are relevant to diagnosing, treating, or predicting said disease
  • a subject e.g., a patient
  • the disclosed methods may be applicable to diagnosis of any of a variety of cancers as described elsewhere herein.
  • the disclosed methods for determining ctDNA fraction may be used to select a subject (e.g., a patient) for a clinical trial based on the ctDNA fraction value determined for a sample from the subject.
  • patient selection for clinical trials based on, e.g., ctDNA fraction may accelerate the development of targeted therapies and improve the healthcare outcomes for treatment decisions.
  • the disclosed methods for determining ctDNA fraction may be used to select an appropriate therapy or treatment (e.g., an anti-cancer therapy or anti-cancer treatment) for a subject.
  • an appropriate therapy or treatment e.g., an anti-cancer therapy or anti-cancer treatment
  • the anti-cancer therapy or treatment may comprise use of a poly (ADP-ribose) polymerase inhibitor (PARPi), a platinum compound, chemotherapy, radiation therapy, a targeted therapy, an immunotherapy, a neoantigen-based therapy, surgery, or any combination thereof.
  • PARPi poly (ADP-ribose) polymerase inhibitor
  • the anti-cancer therapy or treatment may comprise a targeted anticancer therapy or treatment (e.g., a monoclonal antibody -based therapy, an enzyme inhibitorbased therapy, an antibody-drug conjugate therapy, a hormone therapy, and/or a targeted radiotherapy) that targets specific molecules required for cancer cell growth, division, and spreading.
  • a targeted anticancer therapy or treatment e.g., a monoclonal antibody -based therapy, an enzyme inhibitorbased therapy, an antibody-drug conjugate therapy, a hormone therapy, and/or a targeted radiotherapy
  • the targeted anti-cancer therapy or treatment may comprise abemaciclib (Verzenio), abiraterone acetate (Zytiga), acalabrutinib (Calquence), ado-trastuzumab emtansine (Kadcyla), afatinib dimaleate (Gilotrif), alectinib (Alecensa), alemtuzumab (Campath), alitretinoin (Panretin), alpelisib (Piqray), amivantamab-vmjw (Rybrevant), anastrozole (Arimidex), apalutamide (Erleada), asciminib hydrochloride (Scemblix), atezolizumab (Tecentriq), avapritinib (Ayvakit), avelumab (Bavencio), axicabtagene ciloleucel (Yescarta),
  • the anti-cancer therapy or treatment may comprise an immunotherapy (e.g., a cancer treatment that acts by stimulating the immune system to fight cancer).
  • the immunotherapy can be, for example, an immune system modulator (e.g., a cytokine, such as an interferon or interleukin), an immune checkpoint inhibitor (such as an anti- PD-1 or anti-PD-Ll antibody), a T-cell transfer therapy (e.g., a tumor infiltrating lymphocyte (TIL) therapy in lymphocytes extracted from a patient’ s tumor are selected for their ability to recognize tumor cells and propagated prior to reintroduction into the patient, or a CAR T-cell therapy in which a patient’s T-cells are modified to express the CAR protein prior to reintroduction into the patient), a monoclonal antibody -based therapy (e.g., a monoclonal antibody that binds to cell surface markers on cancer cells to facilitate recognition by the immune system), or
  • the anti-cancer therapy or treatment may comprise a neoantigen-based therapy.
  • neoantigen-based therapies include T-cell receptor (TCR) engineered T-cell (TCR-T) therapies, chimeric antigen receptor T-cell (CAR-T) therapies, TCR bispecific antibody therapies, and cancer vaccines.
  • TCR-T therapies are produced by genetically engineering a patient’s T-cells to express T-cell receptors that are specific to neoantigens of interest, and then infusing them back into the patient.
  • CAR-T therapies are produced by genetically engineering a patient’s T-cells to express chimeric antigen receptor molecules which contain an intracellular signaling and co-signaling domain as well as an extracellular antigenbinding domain; CAR-T therapies don’t always rely on neoantigen presentation, but can be designed to be directed towards neoantigens.
  • TCR bispecific antibody therapies are small, engineered antibody molecules that comprise a neoantigen- specific TCR on one end and a CD3- directed single-chain variable fragment on the other end.
  • Cancer vaccines can include RNA molecules, DNA molecules, peptides, or a combination thereof that are designed to boost the immune system’s ability to find and destroy neoantigen-presenting cells.
  • the disclosed methods for determining ctDNA fraction may be used in treating a disease (e.g., a cancer) in a subject.
  • a disease e.g., a cancer
  • an effective amount of an anti-cancer therapy or anti-cancer treatment may be administered to the subject.
  • the disclosed methods for determining ctDNA fraction may be used for monitoring disease progression or recurrence (e.g., cancer or tumor progression or recurrence) in a subject.
  • the methods may be used to determine ctDNA fraction in a first sample obtained from the subject at a first time point, and used to determine ctDNA fraction in a second sample obtained from the subject at a second time point, where comparison of the first determination of ctDNA fraction and the second determination of ctDNA fraction allows one to monitor disease progression or recurrence.
  • the first time point is chosen before the subject has been administered a therapy or treatment
  • the second time point is chosen after the subject has been administered the therapy or treatment.
  • the disclosed methods may be used for adjusting a therapy or treatment (e.g., an anti-cancer treatment or anti-cancer therapy) for a subject, e.g., by adjusting a treatment dose and/or selecting a different treatment in response to a change in the determination of ctDNA fraction.
  • a therapy or treatment e.g., an anti-cancer treatment or anti-cancer therapy
  • the value of ctDNA fraction determined using the disclosed methods may be used as a prognostic or diagnostic indicator associated with the sample.
  • the prognostic or diagnostic indicator may comprise an indicator of the presence of a disease (e.g., cancer) in the sample, an indicator of the probability that a disease (e.g., cancer) is present in the sample, an indicator of the probability that the subject from which the sample was derived will develop a disease (e.g., cancer) (z.e., a risk factor), or an indicator of the likelihood that the subject from which the sample was derived will respond to a particular therapy or treatment.
  • the disclosed methods for determining ctDNA fraction may be implemented as part of a genomic profiling process that comprises identification of the presence of variant sequences at one or more gene loci in a sample derived from a subject as part of detecting, monitoring, predicting a risk factor, or selecting a treatment for a particular disease, e.g., cancer.
  • the variant panel selected for genomic profiling may comprise the detection of variant sequences at a selected set of gene loci.
  • the variant panel selected for genomic profiling may comprise detection of variant sequences at a number of gene loci through comprehensive genomic profiling (CGP), which is a next- generation sequencing (NGS) approach used to assess hundreds of genes (including relevant cancer biomarkers) in a single assay.
  • CGP comprehensive genomic profiling
  • NGS next- generation sequencing
  • Inclusion of the disclosed methods for determining ctDNA fraction as part of a genomic profiling process can improve the validity of, e.g., disease detection calls and treatment decisions, made on the basis of the genomic profile by, for example, independently confirming the presence of cancer in a given patient sample.
  • a genomic profile may comprise information on the presence of genes (or variant sequences thereof), copy number variations, epigenetic traits, proteins (or modifications thereof), and/or other biomarkers in an individual’s genome and/or proteome, as well as information on the individual’s corresponding phenotypic traits and the interaction between genetic or genomic traits, phenotypic traits, and environmental factors.
  • a genomic profile for the subject may comprise results from a comprehensive genomic profiling (CGP) test, a nucleic acid sequencing-based test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof.
  • CGP genomic profiling
  • the method can further include administering or applying a treatment or therapy e.g., an anti-cancer agent, anti-cancer treatment, or anti-cancer therapy) to the subject based on the generated genomic profile.
  • a treatment or therapy e.g., an anti-cancer agent, anti-cancer treatment, or anti-cancer therapy
  • An anti-cancer agent or anti-cancer treatment may refer to a compound that is effective in the treatment of cancer cells.
  • anti-cancer agents or anti-cancer therapies include, but not limited to, alkylating agents, antimetabolites, natural products, hormones, chemotherapy, radiation therapy, immunotherapy, surgery, or a therapy configured to target a defect in a specific cell signaling pathway, e.g., a defect in a DNA mismatch repair (MMR) pathway.
  • MMR DNA mismatch repair
  • the disclosed methods and systems may be used with any of a variety of samples (also referred to herein as specimens) comprising nucleic acids (e.g., DNA or RNA) that are collected from a subject (e.g., a patient).
  • samples also referred to herein as specimens
  • nucleic acids e.g., DNA or RNA
  • a sample examples include, but are not limited to, a tumor sample, a tissue sample, a biopsy sample (e.g., a tissue biopsy, a liquid biopsy, or both), a blood sample (e.g., a peripheral whole blood sample), a blood plasma sample, a blood serum sample, a lymph sample, a saliva sample, a sputum sample, a urine sample, a gynecological fluid sample, a circulating tumor cell (CTC) sample, a cerebral spinal fluid (CSF) sample, a pericardial fluid sample, a pleural fluid sample, an ascites (peritoneal fluid) sample, a feces (or stool) sample, or other body fluid, secretion, and/or excretion sample (or cell sample derived therefrom).
  • the sample may be frozen sample or a formalin-fixed paraffin-embedded (FFPE) sample.
  • FFPE formalin-fixed paraffin-embedded
  • the sample may be collected by tissue resection (e.g., surgical resection), needle biopsy, bone marrow biopsy, bone marrow aspiration, skin biopsy, endoscopic biopsy, fine needle aspiration, oral swab, nasal swab, vaginal swab or a cytology smear, scrapings, washings or lavages (such as a ductal lavage or bronchoalveolar lavage), etc.
  • tissue resection e.g., surgical resection
  • needle biopsy e.g., bone marrow biopsy, bone marrow aspiration, skin biopsy, endoscopic biopsy, fine needle aspiration, oral swab, nasal swab, vaginal swab or a cytology smear
  • fine needle aspiration e.g., oral swab, nasal swab, vaginal swab or a cytology smear
  • scrapings e.
  • the sample is a liquid biopsy sample, and may comprise, e.g., whole blood, blood plasma, blood serum, urine, stool, sputum, saliva, or cerebrospinal fluid.
  • the sample may be a liquid biopsy sample and may comprise circulating tumor cells (CTCs).
  • the sample may be a liquid biopsy sample and may comprise cell- free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.
  • the sample may comprise one or more premalignant or malignant cells.
  • Premalignant refers to a cell or tissue that is not yet malignant but is poised to become malignant.
  • the sample may be acquired from a solid tumor, a soft tissue tumor, or a metastatic lesion.
  • the sample may be acquired from a hematologic malignancy or pre-malignancy.
  • the sample may comprise a tissue or cells from a surgical margin.
  • the sample may comprise tumor-infiltrating lymphocytes.
  • the sample may comprise one or more non- malignant cells.
  • the sample may be, or is part of, a primary tumor or a metastasis (e.g., a metastasis biopsy sample).
  • the sample may be obtained from a site (e.g., a tumor site) with the highest percentage of tumor (e.g., tumor cells) as compared to adjacent sites (e.g., sites adjacent to the tumor).
  • the sample may be obtained from a site (e.g., a tumor site) with the largest tumor focus (e.g., the largest number of tumor cells as visualized under a microscope) as compared to adjacent sites (e.g., sites adjacent to the tumor).
  • the disclosed methods may further comprise analyzing a primary control (e.g., a normal tissue sample). In some instances, the disclosed methods may further comprise determining if a primary control is available and, if so, isolating a control nucleic acid (e.g., DNA) from said primary control. In some instances, the sample may comprise any normal control (e.g., a normal adjacent tissue (NAT)) if no primary control is available. In some instances, the sample may be or may comprise histologically normal tissue. In some instances, the method includes evaluating a sample, e.g., a histologically normal sample (e.g., from a surgical tissue margin) using the methods described herein.
  • a primary control e.g., a normal tissue sample.
  • the disclosed methods may further comprise determining if a primary control is available and, if so, isolating a control nucleic acid (e.g., DNA) from said primary control.
  • the sample may comprise any normal control (e.g.,
  • the disclosed methods may further comprise acquiring a sub-sample enriched for non-tumor cells, e.g., by macro-dissecting non-tumor tissue from said NAT in a sample not accompanied by a primary control. In some instances, the disclosed methods may further comprise determining that no primary control and no NAT is available, and marking said sample for analysis without a matched control.
  • samples obtained from histologically normal tissues may still comprise a genetic alteration such as a variant sequence as described herein.
  • the methods may thus further comprise re-classifying a sample based on the presence of the detected genetic alteration.
  • multiple samples e.g., from different subjects are processed simultaneously.
  • tissue samples e.g., solid tissue samples, soft tissue samples, metastatic lesions, or liquid biopsy samples.
  • tissues include, but are not limited to, connective tissue, muscle tissue, nervous tissue, epithelial tissue, and blood.
  • Tissue samples may be collected from any of the organs within an animal or human body.
  • human organs include, but are not limited to, the brain, heart, lungs, liver, kidneys, pancreas, spleen, thyroid, mammary glands, uterus, prostate, large intestine, small intestine, bladder, bone, skin, etc.
  • the nucleic acids extracted from the sample may comprise deoxyribonucleic acid (DNA) molecules.
  • DNA DNA that may be suitable for analysis by the disclosed methods include, but are not limited to, genomic DNA or fragments thereof, mitochondrial DNA or fragments thereof, cell-free DNA (cfDNA), and circulating tumor DNA (ctDNA).
  • Cell-free DNA (cfDNA) is comprised of fragments of DNA that are released from normal and/or cancerous cells during apoptosis and necrosis, and circulate in the blood stream and/or accumulate in other bodily fluids.
  • Circulating tumor DNA ctDNA is comprised of fragments of DNA that are released from cancerous cells and tumors that circulate in the blood stream and/or accumulate in other bodily fluids.
  • DNA is extracted from nucleated cells from the sample.
  • a sample may have a low nucleated cellularity, e.g., when the sample is comprised mainly of erythrocytes, lesional cells that contain excessive cytoplasm, or tissue with fibrosis.
  • a sample with low nucleated cellularity may require more, e.g., greater, tissue volume for DNA extraction.
  • the nucleic acids extracted from the sample may comprise ribonucleic acid (RNA) molecules.
  • RNA ribonucleic acid
  • examples of RNA that may be suitable for analysis by the disclosed methods include, but are not limited to, total cellular RNA, total cellular RNA after depletion of certain abundant RNA sequences e.g., ribosomal RNAs), cell-free RNA (cfRNA), messenger RNA (mRNA) or fragments thereof, the poly(A)-tailed mRNA fraction of the total RNA, ribosomal RNA (rRNA) or fragments thereof, transfer RNA (tRNA) or fragments thereof, and mitochondrial RNA or fragments thereof.
  • RNA may be extracted from the sample and converted to complementary DNA (cDNA) using, e.g., a reverse transcription reaction.
  • cDNA complementary DNA
  • the cDNA is produced by random-primed cDNA synthesis methods.
  • the cDNA synthesis is initiated at the poly (A) tail of mature mRNAs by priming with oligo(dT)-containing oligonucleotides. Methods for depletion, poly(A) enrichment, and cDNA synthesis are well known to those of skill in the art.
  • the sample may comprise a tumor content (e.g., comprising tumor cells or tumor cell nuclei), or a non-tumor content (e.g., immune cells, fibroblasts, and other nontumor cells).
  • the tumor content of the sample may constitute a sample metric.
  • the sample may comprise a tumor content of at least 5-50%, 10-40%, 15-25%, or 20-30% tumor cell nuclei.
  • the sample may comprise a tumor content of at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, or at least 50% tumor cell nuclei.
  • the percent tumor cell nuclei (e.g., sample fraction) is determined (e.g., calculated) by dividing the number of tumor cells in the sample by the total number of all cells within the sample that have nuclei.
  • a different tumor content calculation may be required due to the presence of hepatocytes having nuclei with twice, or more than twice, the DNA content of other, e.g., non-hepatocyte, somatic cell nuclei.
  • the sensitivity of detection of a genetic alteration e.g., a variant sequence, or a determination of, e.g., micro satellite instability, may depend on the tumor content of the sample. For example, a sample having a lower tumor content can result in lower sensitivity of detection for a given size sample.
  • the sample comprises nucleic acid (e.g., DNA, RNA (or a cDNA derived from the RNA), or both), e.g., from a tumor or from normal tissue.
  • nucleic acid e.g., DNA, RNA (or a cDNA derived from the RNA), or both
  • the sample may further comprise a non-nucleic acid component, e.g., cells, protein, carbohydrate, or lipid, e.g., from the tumor or normal tissue.
  • the sample is obtained (e.g., collected) from a subject (e.g., patient) with a condition or disease (e.g., a hyperproliferative disease or a non-cancer indication) or suspected of having the condition or disease.
  • a condition or disease e.g., a hyperproliferative disease or a non-cancer indication
  • the hyperproliferative disease is a cancer.
  • the cancer is a solid tumor or a metastatic form thereof.
  • the cancer is a hematological cancer, e.g., a leukemia or lymphoma.
  • the subject has a cancer or is at risk of having a cancer.
  • the subject has a genetic predisposition to a cancer (e.g., having a genetic mutation that increases his or her baseline risk for developing a cancer).
  • the subject has been exposed to an environmental perturbation (e.g., radiation or a chemical) that increases his or her risk for developing a cancer.
  • the subject is in need of being monitored for development of a cancer.
  • the subject is in need of being monitored for cancer progression or regression, e.g., after being treated with an anti-cancer therapy (or anti-cancer treatment).
  • the subject is in need of being monitored for relapse of cancer.
  • the subject is in need of being monitored for minimum residual disease (MRD).
  • the subject has been, or is being treated, for cancer.
  • the subject has not been treated with an anti-cancer therapy (or anti-cancer treatment).
  • the subject e.g., a patient
  • a post-targeted therapy sample e.g., specimen
  • the post-targeted therapy sample is a sample obtained after the completion of the targeted therapy.
  • the patient has not been previously treated with a targeted therapy.
  • the sample comprises a resection, e.g., an original resection, or a resection following recurrence (e.g., following a disease recurrence post-therapy).
  • the sample is acquired from a subject having a cancer.
  • exemplary cancers include, but are not limited to, B cell cancer (e.g., multiple myeloma), melanomas, breast cancer, lung cancer (such as non-small cell lung carcinoma or NSCLC), bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain or central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine or endometrial cancer, cancer of the oral cavity or pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel or appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, cancer of hematological tissues, adenocarcinomas, inflammatory myofibroblastic tumors, gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM),
  • B cell cancer
  • the cancer comprises acute lymphoblastic leukemia (Philadelphia chromosome positive), acute lymphoblastic leukemia (precursor B-cell), acute myeloid leukemia (FLT3+), acute myeloid leukemia (with an IDH2 mutation), anaplastic large cell lymphoma, basal cell carcinoma, B-cell chronic lymphocytic leukemia, bladder cancer, breast cancer (HER2 overexpressed/amplified), breast cancer (HER2+), breast cancer (HR+, HER2-), cervical cancer, cholangiocarcinoma, chronic lymphocytic leukemia, chronic lymphocytic leukemia (with 17p deletion), chronic myelogenous leukemia, chronic myelogenous leukemia (Philadelphia chromosome positive), classical Hodgkin lymphoma, colorectal cancer, colorectal cancer (dMMR and MSLH), colorectal cancer (KRAS wild type), cryopyrin-associated periodic syndrome, a
  • the cancer is a hematologic malignancy (or premaligancy).
  • a hematologic malignancy refers to a tumor of the hematopoietic or lymphoid tissues, e.g., a tumor that affects blood, bone marrow, or lymph nodes.
  • Exemplary hematologic malignancies include, but are not limited to, leukemia (e.g., acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), chronic myelogenous leukemia (CML), hairy cell leukemia, acute monocytic leukemia (AMoL), chronic myelomonocytic leukemia (CMML), juvenile myelomonocytic leukemia (JMML), or large granular lymphocytic leukemia), lymphoma (e.g., AIDS-related lymphoma, cutaneous T-cell lymphoma, Hodgkin lymphoma (e.g., classical Hodgkin lymphoma or nodular lymphocyte- predominant Hodgkin lymphoma), mycosis fungoides, non-Hodgkin lymphoma (e.g., B-cell non-Hodgkin lymphoma (e.g.
  • DNA or RNA may be extracted from tissue samples, biopsy samples, blood samples, or other bodily fluid samples using any of a variety of techniques known to those of skill in the art (see, e.g., Example 1 of International Patent Application Publication No. WO 2012/092426; Tan, et al. (2009), “DNA, RNA, and Protein Extraction: The Past and The Present”, J. Biomed. Biotech. 2009:574398; the technical literature for the Maxwell® 16 LEV Blood DNA Kit (Promega Corporation, Madison, WI); and the Maxwell 16 Buccal Swab LEV DNA Purification Kit Technical Manual (Promega Literature #TM333, January 1, 2011, Promega Corporation, Madison, WI)). Protocols for RNA isolation are disclosed in, e.g., the Maxwell® 16 Total RNA Purification Kit Technical Bulletin (Promega Literature #TB351, August 2009, Promega Corporation, Madison, WI).
  • a typical DNA extraction procedure for example, comprises (i) collection of the fluid sample, cell sample, or tissue sample from which DNA is to be extracted, (ii) disruption of cell membranes (z.e., cell lysis), if necessary, to release DNA and other cytoplasmic components, (iii) treatment of the fluid sample or lysed sample with a concentrated salt solution to precipitate proteins, lipids, and RNA, followed by centrifugation to separate out the precipitated proteins, lipids, and RNA, and (iv) purification of DNA from the supernatant to remove detergents, proteins, salts, or other reagents used during the cell membrane lysis step.
  • Disruption of cell membranes may be performed using a variety of mechanical shear e.g., by passing through a French press or fine needle) or ultrasonic disruption techniques.
  • the cell lysis step often comprises the use of detergents and surfactants to solubilize lipids the cellular and nuclear membranes.
  • the lysis step may further comprise use of proteases to break down protein, and/or the use of an RNase for digestion of RNA in the sample.
  • Examples of suitable techniques for DNA purification include, but are not limited to, (i) precipitation in ice-cold ethanol or isopropanol, followed by centrifugation (precipitation of DNA may be enhanced by increasing ionic strength, e.g., by addition of sodium acetate), (ii) phenol-chloroform extraction, followed by centrifugation to separate the aqueous phase containing the nucleic acid from the organic phase containing denatured protein, and (iii) solid phase chromatography where the nucleic acids adsorb to the solid phase e.g., silica or other) depending on the pH and salt concentration of the buffer.
  • cellular and histone proteins bound to the DNA may be removed either by adding a protease or by having precipitated the proteins with sodium or ammonium acetate, or through extraction with a phenol-chloroform mixture prior to a DNA precipitation step.
  • DNA may be extracted using any of a variety of suitable commercial DNA extraction and purification kits. Examples include, but are not limited to, the QIAamp (for isolation of genomic DNA from human samples) and DNAeasy (for isolation of genomic DNA from animal or plant samples) kits from Qiagen (Germantown, MD) or the Maxwell® and ReliaPrepTM series of kits from Promega (Madison, WI).
  • the sample may comprise a formalin-fixed (also known as formaldehyde-fixed, or paraformaldehyde-fixed), paraffin-embedded (FFPE) tissue preparation.
  • FFPE formalin-fixed
  • the FFPE sample may be a tissue sample embedded in a matrix, e.g., an FFPE block.
  • Methods to isolate nucleic acids (e.g., DNA) from formaldehyde- or paraformaldehyde-fixed, paraffin-embedded (FFPE) tissues are disclosed in, e.g., Cronin, et al., (2004) Am J Pathol.
  • the Maxwell® 16 FFPE Plus EEV DNA Purification Kit is used with the Maxwell® 16 Instrument for purification of genomic DNA from 1 to 10 pm sections of FFPE tissue. DNA is purified using silica-clad paramagnetic particles (PMPs), and eluted in low elution volume.
  • the E.Z.N.A.® FFPE DNA Kit uses a spin column and buffer system for isolation of genomic DNA.
  • QIAamp® DNA FFPE Tissue Kit uses QIAamp® DNA Micro technology for purification of genomic and mitochondrial DNA.
  • the disclosed methods may further comprise determining or acquiring a yield value for the nucleic acid extracted from the sample and comparing the determined value to a reference value.
  • the disclosed methods may further comprise determining or acquiring a value for the size (or average size) of nucleic acid fragments in the sample, and comparing the determined or acquired value to a reference value, e.g., a size (or average size) of at least 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 base pairs (bps).
  • a reference value e.g., a size (or average size) of at least 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 base pairs (bps).
  • one or more parameters described herein may be adjusted or selected in response to this determination.
  • the nucleic acids are typically dissolved in a slightly alkaline buffer, e.g., Tris-EDTA (TE) buffer, or in ultra-pure water.
  • a slightly alkaline buffer e.g., Tris-EDTA (TE) buffer
  • the isolated nucleic acids may be fragmented or sheared by using any of a variety of techniques known to those of skill in the art.
  • genomic DNA can be fragmented by physical shearing methods, enzymatic cleavage methods, chemical cleavage methods, and other methods known to those of skill in the art. Methods for DNA shearing are described in Example 4 in International Patent Application Publication No. WO 2012/092426. In some instances, alternatives to DNA shearing methods can be used to avoid a ligation step during library preparation.
  • the nucleic acids isolated from the sample may be used to construct a library (e.g., a nucleic acid library as described herein).
  • the nucleic acids are fragmented using any of the methods described above, optionally subjected to repair of chain end damage, and optionally ligated to synthetic adapters, primers, and/or barcodes (e.g., amplification primers, sequencing adapters, flow cell adapters, substrate adapters, sample barcodes or indexes, and/or unique molecular identifier sequences), size-selected (e.g., by preparative gel electrophoresis), and/or amplified (e.g., using PCR, a non-PCR amplification technique, or an isothermal amplification technique).
  • synthetic adapters, primers, and/or barcodes e.g., amplification primers, sequencing adapters, flow cell adapters, substrate adapters, sample barcodes or indexes, and/or unique molecular identifier sequences
  • the fragmented and adapter-ligated group of nucleic acids is used without explicit size selection or amplification prior to hybridization-based selection of target sequences.
  • the nucleic acid is amplified by any of a variety of specific or nonspecific nucleic acid amplification methods known to those of skill in the art.
  • the nucleic acids are amplified, e.g., by a whole-genome amplification method such as random-primed strand-displacement amplification. Examples of nucleic acid library preparation techniques for next-generation sequencing are described in, e.g., van Dijk, et al. (2014), Exp. Cell Research 322:12 - 20, and Illumina’s genomic DNA sample preparation kit.
  • the resulting nucleic acid library may contain all or substantially all of the complexity of the genome.
  • the term “substantially all” in this context refers to the possibility that there can in practice be some unwanted loss of genome complexity during the initial steps of the procedure.
  • the methods described herein also are useful in cases where the nucleic acid library comprises a portion of the genome, e.g., where the complexity of the genome is reduced by design. In some instances, any selected portion of the genome can be used with a method described herein. For example, in certain embodiments, the entire exome or a subset thereof is isolated.
  • the library may include at least 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, or 5% of the genomic DNA.
  • the library may consist of cDNA copies of genomic DNA that includes copies of at least 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, or 5% of the genomic DNA.
  • the amount of nucleic acid used to generate the nucleic acid library may be less than 5 micrograms, less than 1 microgram, less than 500 ng, less than 200 ng, less than 100 ng, less than 50 ng, less than 10 ng, less than 5 ng, or less than 1 ng.
  • a library (e.g., a nucleic acid library) includes a collection of nucleic acid molecules.
  • the nucleic acid molecules of the library can include a target nucleic acid molecule (e.g., a tumor nucleic acid molecule, a reference nucleic acid molecule and/or a control nucleic acid molecule; also referred to herein as a first, second and/or third nucleic acid molecule, respectively).
  • the nucleic acid molecules of the library can be from a single subject or individual.
  • a library can comprise nucleic acid molecules derived from more than one subject (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30 or more subjects).
  • two or more libraries from different subjects can be combined to form a library having nucleic acid molecules from more than one subject (where the nucleic acid molecules derived from each subject are optionally ligated to a unique sample barcode corresponding to a specific subject).
  • the subject is a human having, or at risk of having, a cancer or tumor.
  • the library may comprise one or more subgenomic intervals.
  • a subgenomic interval can be a single nucleotide position, e.g., a nucleotide position for which a variant at the position is associated (positively or negatively) with a tumor phenotype.
  • a subgenomic interval comprises more than one nucleotide position. Such instances include sequences of at least 2, 5, 10, 50, 100, 150, 250, or more than 250 nucleotide positions in length.
  • Subgenomic intervals can comprise, e.g., one or more entire genes (or portions thereof), one or more exons or coding sequences (or portions thereof), one or more introns (or portion thereof), one or more microsatellite region (or portions thereof), or any combination thereof.
  • a subgenomic interval can comprise all or a part of a fragment of a naturally occurring nucleic acid molecule, e.g., a genomic DNA molecule.
  • a subgenomic interval can correspond to a fragment of genomic DNA which is subjected to a sequencing reaction.
  • a subgenomic interval is a continuous sequence from a genomic source.
  • a subgenomic interval includes sequences that are not contiguous in the genome, e.g., subgenomic intervals in cDNA can include exonexonjunctions formed as a result of splicing.
  • the subgenomic interval comprises a tumor nucleic acid molecule.
  • the subgenomic interval comprises a non-tumor nucleic acid molecule.
  • the methods described herein can be used in combination with, or as part of, a method for evaluating a plurality or set of subject intervals (e.g., target sequences), e.g., from a set of genomic loci (e.g., gene loci or fragments thereof), as described herein.
  • a plurality or set of subject intervals e.g., target sequences
  • genomic loci e.g., gene loci or fragments thereof
  • the set of genomic loci evaluated by the disclosed methods comprises a plurality of, e.g., genes, which in mutant form, are associated with an effect on cell division, growth or survival, or are associated with a cancer, e.g., a cancer described herein.
  • the set of gene loci evaluated by the disclosed methods comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, or more than 100 gene loci.
  • the selected gene loci also referred to herein as target gene loci or target sequences, or fragments thereof, may include subject intervals comprising non-coding sequences, coding sequences, intragenic regions, or intergenic regions of the subject genome.
  • the subject intervals can include a non-coding sequence or fragment thereof (e.g., a promoter sequence, enhancer sequence, 5’ untranslated region (5’ UTR), 3’ untranslated region (3’ UTR), or a fragment thereof), a coding sequence of fragment thereof, an exon sequence or fragment thereof, an intron sequence or a fragment thereof.
  • a non-coding sequence or fragment thereof e.g., a promoter sequence, enhancer sequence, 5’ untranslated region (5’ UTR), 3’ untranslated region (3’ UTR), or a fragment thereof
  • a coding sequence of fragment thereof e.g., an exon sequence or fragment thereof, an intron sequence or a fragment thereof.
  • the methods described herein may comprise contacting a nucleic acid library with a plurality of target capture reagents in order to select and capture a plurality of specific target sequences (e.g., gene sequences or fragments thereof) for analysis.
  • a target capture reagent z.e., a molecule which can bind to and thereby allow capture of a target molecule
  • a target capture reagent is used to select the subject intervals to be analyzed.
  • a target capture reagent can be a bait molecule, e.g., a nucleic acid molecule (e.g., a DNA molecule or RNA molecule) which can hybridize to (z.e., is complementary to) a target molecule, and thereby allows capture of the target nucleic acid.
  • the target capture reagent e.g., a bait molecule (or bait sequence)
  • the target nucleic acid is a genomic DNA molecule, an RNA molecule, a cDNA molecule derived from an RNA molecule, a microsatellite DNA sequence, and the like.
  • the target capture reagent is suitable for solution-phase hybridization to the target. In some instances, the target capture reagent is suitable for solid-phase hybridization to the target. In some instances, the target capture reagent is suitable for both solution-phase and solid-phase hybridization to the target.
  • the design and construction of target capture reagents is described in more detail in, e.g., International Patent Application Publication No. WO 2020/236941, the entire content of which is incorporated herein by reference.
  • the methods described herein provide for optimized sequencing of a large number of genomic loci (e.g., genes or gene products (e.g., mRNA), micro satellite loci, etc.) from samples (e.g., cancerous tissue specimens, liquid biopsy samples, and the like) from one or more subjects by the appropriate selection of target capture reagents to select the target nucleic acid molecules to be sequenced.
  • a target capture reagent may hybridize to a specific target locus, e.g., a specific target gene locus or fragment thereof.
  • a target capture reagent may hybridize to a specific group of target loci, e.g., a specific group of gene loci or fragments thereof.
  • a plurality of target capture reagents comprising a mix of target- specific and/or group- specific target capture reagents may be used.
  • the number of target capture reagents (e.g., bait molecules) in the plurality of target capture reagents (e.g., a bait set) contacted with a nucleic acid library to capture a plurality of target sequences for nucleic acid sequencing is greater than 10, greater than 50, greater than 100, greater than 200, greater than 300, greater than 400, greater than 500, greater than 600, greater than 700, greater than 800, greater than 900, greater than 1,000, greater than 1,250, greater than 1,500, greater than 1,750, greater than 2,000, greater than 3,000, greater than 4,000, greater than 5,000, greater than 10,000, greater than 25,000, or greater than 50,000.
  • the overall length of the target capture reagent sequence can be between about 70 nucleotides and 1000 nucleotides. In one instance, the target capture reagent length is between about 100 and 300 nucleotides, 110 and 200 nucleotides, or 120 and 170 nucleotides, in length. In addition to those mentioned above, intermediate oligonucleotide lengths of about 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 300, 400, 500, 600, 700, 800, and 900 nucleotides in length can be used in the methods described herein. In some embodiments, oligonucleotides of about 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, or 230 bases can be used.
  • each target capture reagent sequence can include: (i) a target-specific capture sequence (e.g., a gene locus or micro satellite locus- specific complementary sequence), (ii) an adapter, primer, barcode, and/or unique molecular identifier sequence, and (iii) universal tails on one or both ends.
  • a target-specific capture sequence e.g., a gene locus or micro satellite locus- specific complementary sequence
  • an adapter, primer, barcode, and/or unique molecular identifier sequence e.g., a target-specific capture sequence
  • universal tails e.g., a target-specific capture sequence
  • target capture reagent can refer to the targetspecific target capture sequence or to the entire target capture reagent oligonucleotide including the target- specific target capture sequence.
  • the target-specific capture sequences in the target capture reagents are between about 40 nucleotides and 1000 nucleotides in length. In some instances, the targetspecific capture sequence is between about 70 nucleotides and 300 nucleotides in length. In some instances, the target- specific sequence is between about 100 nucleotides and 200 nucleotides in length. In yet other instances, the target- specific sequence is between about 120 nucleotides and 170 nucleotides in length, typically 120 nucleotides in length.
  • target-specific sequences of about 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 300, 400, 500, 600, 700, 800, and 900 nucleotides in length, as well as target- specific sequences of lengths between the above-mentioned lengths.
  • the target capture reagent may be designed to select a subject interval containing one or more rearrangements, e.g., an intron containing a genomic rearrangement.
  • the target capture reagent is designed such that repetitive sequences are masked to increase the selection efficiency.
  • complementary target capture reagents can be designed to recognize the juncture sequence to increase the selection efficiency.
  • the disclosed methods may comprise the use of target capture reagents designed to capture two or more different target categories, each category having a different target capture reagent design strategy.
  • the hybridization-based capture methods and target capture reagent compositions disclosed herein may provide for the capture and homogeneous coverage of a set of target sequences, while minimizing coverage of genomic sequences outside of the targeted set of sequences.
  • the target sequences may include the entire exome of genomic DNA or a selected subset thereof.
  • the target sequences may include, e.g., a large chromosomal region (e.g., a whole chromosome arm).
  • the methods and compositions disclosed herein provide different target capture reagents for achieving different sequencing depths and patterns of coverage for complex sets of target nucleic acid sequences.
  • DNA molecules are used as target capture reagent sequences, although RNA molecules can also be used.
  • a DNA molecule target capture reagent can be single stranded DNA (ssDNA) or double- stranded DNA (dsDNA).
  • ssDNA single stranded DNA
  • dsDNA double- stranded DNA
  • an RNA- DNA duplex is more stable than a DNA-DNA duplex and therefore provides for potentially better capture of nucleic acids.
  • the disclosed methods comprise providing a selected set of nucleic acid molecules (e.g., a library catch) captured from one or more nucleic acid libraries.
  • the method may comprise: providing one or a plurality of nucleic acid libraries, each comprising a plurality of nucleic acid molecules (e.g., a plurality of target nucleic acid molecules and/or reference nucleic acid molecules) extracted from one or more samples from one or more subjects; contacting the one or a plurality of libraries (e.g., in a solution-based hybridization reaction) with one, two, three, four, five, or more than five pluralities of target capture reagents (e.g., oligonucleotide target capture reagents) to form a hybridization mixture comprising a plurality of target capture reagent/nucleic acid molecule hybrids; separating the plurality of target capture reagent/nucleic acid molecule hybrids from said hybridization mixture, e.g., by
  • the disclosed methods may further comprise amplifying the library catch (e.g., by performing PCR). In other instances, the library catch is not amplified.
  • the target capture reagents can be part of a kit which can optionally comprise instructions, standards, buffers or enzymes or other reagents.
  • the methods disclosed herein may include the step of contacting the library (e.g., the nucleic acid library) with a plurality of target capture reagents to provide a selected library target nucleic acid sequences (i.e., the library catch).
  • the contacting step can be effected in, e.g., solution-based hybridization.
  • the method includes repeating the hybridization step for one or more additional rounds of solution-based hybridization.
  • the method further includes subjecting the library catch to one or more additional rounds of solution-based hybridization with the same or a different collection of target capture reagents.
  • the contacting step is effected using a solid support, e.g., an array.
  • a solid support e.g., an array.
  • Suitable solid supports for hybridization are described in, e.g., Albert, T.J. et al. (2007) Nat. Methods 4(11):903-5; Hodges, E. et al. (2007) Nat. Genet. 39(12): 1522-7; and Okou, D.T. et al. (2007) Nat. Methods 4(11 ):907-9, the contents of which are incorporated herein by reference in their entireties.
  • Hybridization methods that can be adapted for use in the methods herein are described in the art, e.g., as described in International Patent Application Publication No.
  • the methods and systems disclosed herein can be used in combination with, or as part of, a method or system for sequencing nucleic acids (e.g., a next-generation sequencing system) to generate a plurality of sequence reads that overlap one or more gene loci within a subgenomic interval in the sample and thereby determine, e.g., gene allele sequences at a plurality of gene loci.
  • a method or system for sequencing nucleic acids e.g., a next-generation sequencing system
  • next-generation sequencing (or “NGS”) as used herein may also be referred to as “massively parallel sequencing” (or “MPS”), and refers to any sequencing method that determines the nucleotide sequence of either individual nucleic acid molecules (e.g., as in single molecule sequencing) or clonally expanded proxies for individual nucleic acid molecules in a high throughput fashion (e.g., wherein greater than 10 3 , 10 4 , 10 5 or more than 10 5 molecules are sequenced simultaneously).
  • next-generation sequencing methods are known in the art, and are described in, e.g., Metzker, M. (2010) Nature Biotechnology Reviews 11:31-46, which is incorporated herein by reference.
  • Other examples of sequencing methods suitable for use when implementing the methods and systems disclosed herein are described in, e.g., International Patent Application Publication No. WO 2012/092426.
  • the sequencing may comprise, for example, whole genome sequencing (WGS), whole exome sequencing, targeted sequencing, or direct sequencing.
  • GGS whole genome sequencing
  • sequencing may be performed using, e.g., Sanger sequencing.
  • the sequencing may comprise a paired-end sequencing technique that allows both ends of a fragment to be sequenced and generates high-quality, alignable sequence data for detection of, e.g., genomic rearrangements, repetitive sequence elements, gene fusions, and novel transcripts.
  • sequencing platforms such as the Roche/454 Genome Sequencer (GS) FLX System, Illumina/Solexa Genome Analyzer (GA), Illumina’s HiSeq® 2500, HiSeq® 3000, HiSeq® 4000 and NovaSeq® 6000 Sequencing Systems, Life/APG’s Support Oligonucleotide Ligation Detection (SOLiD) system, Polonator’s G.007 system, Helicos BioSciences’ HeliScope Gene Sequencing system, or Pacific Biosciences’ PacBio® RS platform.
  • sequencing may comprise Illumina MiSeqTM sequencing.
  • sequencing may comprise Illumina HiSeq® sequencing.
  • sequencing may comprise Illumina NovaSeq® sequencing. Optimized methods for sequencing a large number of target genomic loci in nucleic acids extracted from a sample are described in more detail in, e.g., International Patent Application Publication No. WO 2020/236941, the entire content of which is incorporated herein by reference.
  • the disclosed methods comprise one or more of the steps of: (a) acquiring a library comprising a plurality of normal and/or tumor nucleic acid molecules from a sample; (b) simultaneously or sequentially contacting the library with one, two, three, four, five, or more than five pluralities of target capture reagents under conditions that allow hybridization of the target capture reagents to the target nucleic acid molecules, thereby providing a selected set of captured normal and/or tumor nucleic acid molecules (z.e., a library catch); (c) separating the selected subset of the nucleic acid molecules (e.g., the library catch) from the hybridization mixture, e.g., by contacting the hybridization mixture with a binding entity that allows for separation of the target capture reagent/nucleic acid molecule hybrids from the hybridization mixture, (d) sequencing the library catch to acquiring a plurality of reads (e.g., sequence reads) that overlap one or more subject intervals (e.g.
  • acquiring sequence reads for one or more subject intervals may comprise sequencing at least 1, at least 5, at least 10, at least 20, at least 30, at least 40, at least 50, at least 100, at least 150, at least 200, at least 250, at least 300, at least 350, at least 400, at least 450, at least 500, at least 550, at least 600, at least 650, at least 700, at least 750, at least 800, at least 850, at least 900, at least 950, at least 1,000, at least 1,250, at least 1,500, at least 1,750, at least 2,000, at least 2,250, at least 2,500, at least 2,750, at least 3,000, at least 3,500, at least 4,000, at least 4,500, or at least 5,000 loci, e.g., genomic loci, gene loci, microsatellite loci, etc.
  • acquiring a sequence read for one or more subject intervals may comprise sequencing a subject interval for any number of loci within the range described in this paragraph,
  • acquiring a sequence read for one or more subject intervals comprises sequencing a subject interval with a sequencing method that provides a sequence read length (or average sequence read length) of at least 20 bases, at least 30 bases, at least 40 bases, at least 50 bases, at least 60 bases, at least 70 bases, at least 80 bases, at least 90 bases, at least 100 bases, at least 120 bases, at least 140 bases, at least 160 bases, at least 180 bases, at least 200 bases, at least 220 bases, at least 240 bases, at least 260 bases, at least 280 bases, at least 300 bases, at least 320 bases, at least 340 bases, at least 360 bases, at least 380 bases, or at least 400 bases.
  • a sequencing method that provides a sequence read length (or average sequence read length) of at least 20 bases, at least 30 bases, at least 40 bases, at least 50 bases, at least 60 bases, at least 70 bases, at least 80 bases, at least 90 bases, at least 100 bases, at least 120 bases, at least 140 bases, at least 160 bases, at least 180 bases, at
  • acquiring a sequence read for the one or more subject intervals may comprise sequencing a subject interval with a sequencing method that provides a sequence read length (or average sequence read length) of any number of bases within the range described in this paragraph, e.g., a sequence read length (or average sequence read length) of 56 bases.
  • acquiring a sequence read for one or more subject intervals may comprise sequencing with at least lOOx or more coverage (or depth) on average.
  • acquiring a sequence read for one or more subject intervals may comprise sequencing with at least lOOx, at least 150x, at least 200x, at least 250x, at least 500x, at least 750x, at least l,000x, at least 1,500 x, at least 2,000x, at least 2,500x, at least 3,000x, at least 3,500x, at least 4,000x, at least 4,500x, at least 5,000x, at least 5,500x, or at least 6,000x or more coverage (or depth) on average.
  • acquiring a sequence read for one or more subject intervals may comprise sequencing with an average coverage (or depth) having any value within the range of values described in this paragraph, e.g., at least 160x.
  • acquiring a read for the one or more subject intervals comprises sequencing with an average sequencing depth having any value ranging from at least lOOx to at least 6,000x for greater than about 90%, 92%, 94%, 95%, 96%, 97%, 98%, or 99% of the gene loci sequenced.
  • acquiring a read for the subject interval comprises sequencing with an average sequencing depth of at least 125x for at least 99% of the gene loci sequenced.
  • acquiring a read for the subject interval comprises sequencing with an average sequencing depth of at least 4,100x for at least 95% of the gene loci sequenced.
  • the relative abundance of a nucleic acid species in the library can be estimated by counting the relative number of occurrences of their cognate sequences (e.g., the number of sequence reads for a given cognate sequence) in the data generated by the sequencing experiment.
  • the disclosed methods and systems provide nucleotide sequences for a set of subject intervals (e.g., gene loci), as described herein.
  • the sequences are provided without using a method that includes a matched normal control (e.g., a wild-type control) and/or a matched tumor control (e.g., primary versus metastatic).
  • the level of sequencing depth as used herein refers to the number of reads (e.g., unique reads) obtained after detection and removal of duplicate reads (e.g., PCR duplicate reads).
  • duplicate reads are evaluated, e.g., to support detection of copy number alteration (CNAs).
  • Alignment is the process of matching a read with a location, e.g., a genomic location or locus.
  • NGS reads may be aligned to a known reference sequence (e.g., a wild-type sequence).
  • NGS reads may be assembled de novo. Methods of sequence alignment for NGS reads are described in, e.g., Trapnell, C. and Salzberg, S.L. Nature Biotech., 2009, 27:455-457. Examples of de novo sequence assemblies are described in, e.g., Warren R., et al., Bioinformatics, 2007, 23:500-501; Butler, J.
  • Misalignment e.g., the placement of base-pairs from a short read at incorrect locations in the genome
  • misalignment of reads due to sequence context can lead to reduction in sensitivity of mutation detection
  • sequence context e.g., the presence of repetitive sequence
  • Other examples of sequence context that may cause misalignment include short-tandem repeats, interspersed repeats, low complexity regions, insertions - deletions (indels), and paralogs.
  • misalignment may introduce artifactual reads of “mutated” alleles by placing reads of actual reference genome base sequences at the wrong location. Because mutation-calling algorithms for multigene analysis should be sensitive to even low-abundance mutations, sequence misalignments may increase false positive discovery rates and/or reduce specificity.
  • the methods and systems disclosed herein may integrate the use of multiple, individually-tuned, alignment methods or algorithms to optimize base-calling performance in sequencing methods, particularly in methods that rely on massively parallel sequencing (MPS) of a large number of diverse genetic events at a large number of diverse genomic loci.
  • the disclosed methods and systems may comprise the use of one or more global alignment algorithms.
  • the disclosed methods and systems may comprise the use of one or more local alignment algorithms. Examples of alignment algorithms that may be used include, but are not limited to, the Burrows-Wheeler Alignment (BWA) software bundle (see, e.g., Li, et al.
  • BWA Burrows-Wheeler Alignment
  • the methods and systems disclosed herein may also comprise the use of a sequence assembly algorithm, e.g., the Arachne sequence assembly algorithm (see, e.g., Batzoglou, et al. (2002), “ARACHNE: A Whole-Genome Shotgun Assembler”, Genome Res. 12:177-189).
  • a sequence assembly algorithm e.g., the Arachne sequence assembly algorithm (see, e.g., Batzoglou, et al. (2002), “ARACHNE: A Whole-Genome Shotgun Assembler”, Genome Res. 12:177-189).
  • the alignment method used to analyze sequence reads is not individually customized or tuned for detection of different variants (e.g., point mutations, insertions, deletions, and the like) at different genomic loci.
  • different alignment methods are used to analyze reads that are individually customized or tuned for detection of at least a subset of the different variants detected at different genomic loci.
  • different alignment methods are used to analyze reads that are individually customized or tuned to detect each different variant at different genomic loci.
  • tuning can be a function of one or more of: (i) the genetic locus (e.g., gene loci, micro satellite locus, or other subject interval) being sequenced, (ii) the tumor type associated with the sample, (iii) the variant being sequenced, or (iv) a characteristic of the sample or the subject.
  • the genetic locus e.g., gene loci, micro satellite locus, or other subject interval
  • the tumor type associated with the sample e.g., tumor type associated with the sample
  • the variant e.g., the variant being sequenced
  • a characteristic of the sample or the subject e.g., tuning can be a function of one or more of: (i) the genetic locus (e.g., gene loci, micro satellite locus, or other subject interval) being sequenced, (ii) the tumor type associated with the sample, (iii) the variant being sequenced, or (iv) a characteristic of the sample or the subject.
  • the method includes the use of an alignment method optimized for rearrangements in combination with other alignment methods optimized for subject intervals not associated with rearrangements.
  • the methods disclosed herein allow for the rapid and efficient alignment of troublesome reads, e.g., a read having a rearrangement.
  • a read for a subject interval comprises a nucleotide position with a rearrangement, e.g., a translocation
  • the method can comprise using an alignment method that is appropriately tuned and that includes: (i) selecting a rearrangement reference sequence for alignment with a read, wherein said rearrangement reference sequence aligns with a rearrangement (in some instances, the reference sequence is not identical to the genomic rearrangement); and (ii) comparing, e.g., aligning, a read with said rearrangement reference sequence.
  • a method of analyzing a sample can comprise: (i) performing a comparison (e.g., an alignment comparison) of a read using a first set of parameters (e.g., using a first mapping algorithm, or by comparison with a first reference sequence), and determining if said read meets a first alignment criterion (e.g., the read can be aligned with said first reference sequence, e.g., with less than a specific number of mismatches); (ii) if said read fails to meet the first alignment criterion, performing a second alignment comparison using a second set of parameters, (e.g., using a second mapping algorithm, or by comparison with a second reference sequence); and (iii) optionally, determining if said read meets said second criterion (e.g., the read can be
  • the alignment of sequence reads in the disclosed methods may be combined with a mutation calling method as described elsewhere herein.
  • reduced sensitivity for detecting actual mutations may be addressed by evaluating the quality of alignments (manually or in an automated fashion) around expected mutation sites in the genes or genomic loci (e.g., gene loci) being analyzed.
  • the sites to be evaluated can be obtained from databases of the human genome (e.g., the HG19 human reference genome) or cancer mutations (e.g., COSMIC).
  • Regions that are identified as problematic can be remedied with the use of an algorithm selected to give better performance in the relevant sequence context, e.g., by alignment optimization (or re-alignment) using slower, but more accurate alignment algorithms such as Smith-Waterman alignment.
  • customized alignment approaches may be created by, e.g., adjustment of maximum difference mismatch penalty parameters for genes with a high likelihood of containing substitutions; adjusting specific mismatch penalty parameters based on specific mutation types that are common in certain tumor types (e.g. C ⁇ T in melanoma); or adjusting specific mismatch penalty parameters based on specific mutation types that are common in certain sample types (e.g. substitutions that are common in FFPE).
  • Reduced specificity (increased false positive rate) in the evaluated subject intervals due to misalignment can be assessed by manual or automated examination of all mutation calls in the sequencing data. Those regions found to be prone to spurious mutation calls due to misalignment can be subjected to alignment remedies as discussed above. In cases where no algorithmic remedy is found possible, “mutations” from the problem regions can be classified or screened out from the panel of targeted loci.
  • Base calling refers to the raw output of a sequencing device, e.g., the determined sequence of nucleotides in an oligonucleotide molecule.
  • Mutation calling refers to the process of selecting a nucleotide value, e.g., A, G, T, or C, for a given nucleotide position being sequenced. Typically, the sequence reads (or base calling) for a position will provide more than one value, e.g., some reads will indicate a T and some will indicate a G.
  • Mutation calling is the process of assigning a correct nucleotide value, e.g., one of those values, to the sequence.
  • mutant calling it can be applied to assign a nucleotide value to any nucleotide position, e.g., positions corresponding to mutant alleles, wild-type alleles, alleles that have not been characterized as either mutant or wild-type, or to positions not characterized by variability.
  • the disclosed methods may comprise the use of customized or tuned mutation calling algorithms or parameters thereof to optimize performance when applied to sequencing data, particularly in methods that rely on massively parallel sequencing (MPS) of a large number of diverse genetic events at a large number of diverse genomic loci (e.g., gene loci, micro satellite regions, etc.) in samples, e.g., samples from a subject having cancer.
  • MPS massively parallel sequencing
  • optimization of mutation calling is described in the art, e.g., as set out in International Patent Application Publication No. WO 2012/092426.
  • Methods for mutation calling can include one or more of the following: making independent calls based on the information at each position in the reference sequence (e.g., examining the sequence reads; examining the base calls and quality scores; calculating the probability of observed bases and quality scores given a potential genotype; and assigning genotypes (e.g., using Bayes’ rule)); removing false positives (e.g., using depth thresholds to reject SNPs with read depth much lower or higher than expected; local realignment to remove false positives due to small indels); and performing linkage disequilibrium (LD)/imputation- based analysis to refine the calls.
  • making independent calls based on the information at each position in the reference sequence e.g., examining the sequence reads; examining the base calls and quality scores; calculating the probability of observed bases and quality scores given a potential genotype; and assigning genotypes (e.g., using Bayes’ rule)
  • removing false positives e.g., using depth thresholds to reject SNP
  • Equations used to calculate the genotype likelihood associated with a specific genotype and position are described in, e.g., Li, H. and Durbin, R. Bioinformatics, 2010; 26(5): 589-95.
  • the prior expectation for a particular mutation in a certain cancer type can be used when evaluating samples from that cancer type.
  • Such likelihood can be derived from public databases of cancer mutations, e.g., Catalogue of Somatic Mutation in Cancer (COSMIC), HGMD (Human Gene Mutation Database), The SNP Consortium, Breast Cancer Mutation Data Base (BIC), and Breast Cancer Gene Database (BCGD).
  • Examples of LD/imputation based analysis are described in, e.g., Browning, B.L. and Yu, Z. Am. J. Hum. Genet. 2009, 85(6):847-61.
  • Examples of low-coverage SNP calling methods are described in, e.g., Li, Y., et al., Annu. Rev. Genomics Hum. Genet. 2009, 10:387-406.
  • detection of substitutions can be performed using a mutation calling method (e.g., a Bayesian mutation calling method) which is applied to each base in each of the subject intervals, e.g., exons of a gene or other locus to be evaluated, where presence of alternate alleles is observed.
  • a mutation calling method e.g., a Bayesian mutation calling method
  • This method will compare the probability of observing the read data in the presence of a mutation with the probability of observing the read data in the presence of basecalling error alone. Mutations can be called if this comparison is sufficiently strongly supportive of the presence of a mutation.
  • An advantage of a Bayesian mutation detection approach is that the comparison of the probability of the presence of a mutation with the probability of base-calling error alone can be weighted by a prior expectation of the presence of a mutation at the site. If some reads of an alternate allele are observed at a frequently mutated site for the given cancer type, then presence of a mutation may be confidently called even if the amount of evidence of mutation does not meet the usual thresholds. This flexibility can then be used to increase detection sensitivity for even rarer mutations/lower purity samples, or to make the test more robust to decreases in read coverage.
  • the likelihood of a random base-pair in the genome being mutated in cancer is ⁇ le-6.
  • the likelihood of specific mutations occurring at many sites in, for example, a typical multigenic cancer genome panel can be orders of magnitude higher. These likelihoods can be derived from public databases of cancer mutations (e.g., COSMIC).
  • Indel calling is a process of finding bases in the sequencing data that differ from the reference sequence by insertion or deletion, typically including an associated confidence score or statistical evidence metric.
  • Methods of indel calling can include the steps of identifying candidate indels, calculating genotype likelihood through local re-alignment, and performing LD-based genotype inference and calling.
  • a Bayesian approach is used to obtain potential indel candidates, and then these candidates are tested together with the reference sequence in a Bayesian framework.
  • Methods for generating indel calls and individual-level genotype likelihoods include, e.g., the Dindel algorithm (Albers, C.A., et al., Genome Res. 2011 ;21 (6):961-73).
  • the Bayesian EM algorithm can be used to analyze the reads, make initial indel calls, and generate genotype likelihoods for each candidate indel, followed by imputation of genotypes using, e.g., QCALL (Le S.Q. and Durbin R. Genome Res. 2011 ;21(6):952-60).
  • Parameters, such as prior expectations of observing the indel can be adjusted e.g., increased or decreased), based on the size or location of the indels.
  • the mutation calling method used to analyze sequence reads is not individually customized or fine-tuned for detection of different mutations at different genomic loci.
  • different mutation calling methods are used that are individually customized or fine-tuned for at least a subset of the different mutations detected at different genomic loci.
  • different mutation calling methods are used that are individually customized or fine-tuned for each different mutant detected at each different genomic loci.
  • the customization or tuning can be based on one or more of the factors described herein, e.g., the type of cancer in a sample, the gene or locus in which the subject interval to be sequenced is located, or the variant to be sequenced. This selection or use of mutation calling methods individually customized or fine-tuned for a number of subject intervals to be sequenced allows for optimization of speed, sensitivity and specificity of mutation calling.
  • a nucleotide value is assigned for a nucleotide position in each of X unique subject intervals using a unique mutation calling method, and X is at least 2, at least 3, at least 4, at least 5, at least 10, at least 15, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 200, at least 300, at least 400, at least 500, at least 1000, at least 1500, at least 2000, at least 2500, at least 3000, at least 3500, at least 4000, at least 4500, at least 5000, or greater.
  • the calling methods can differ, and thereby be unique, e.g., by relying on different Bayesian prior values.
  • assigning said nucleotide value is a function of a value which is or represents the prior (e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type.
  • the method comprises assigning a nucleotide value (e.g., calling a mutation) for at least 10, 20, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1,000 nucleotide positions, wherein each assignment is a function of a unique value (as opposed to the value for the other assignments) which is or represents the prior (e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type.
  • a nucleotide value e.g., calling a mutation
  • assigning said nucleotide value is a function of a set of values which represent the probabilities of observing a read showing said variant at said nucleotide position if the variant is present in the sample at a specified frequency (e.g., 1%, 5%, 10%, etc.) and/or if the variant is absent (e.g., observed in the reads due to base-calling error alone).
  • the mutation calling methods described herein can include the following: (a) acquiring, for a nucleotide position in each of said X subject intervals: (i) a first value which is or represents the prior (e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type X; and (ii) a second set of values which represent the probabilities of observing a read showing said variant at said nucleotide position if the variant is present in the sample at a frequency (e.g., 1%, 5%, 10%, etc.) and/or if the variant is absent (e.g., observed in the reads due to base-calling error alone); and (b) responsive to said values, assigning a nucleotide value (e.g., calling a mutation) from said reads for each of said nucleotide positions by weighing, e.g., by a Bay
  • the systems may comprise, e.g., one or more processors, and a memory unit communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: receive sequence read data for a plurality of sequence reads obtained for the sample from the subject; determine if the sequence read data is sufficient for performing copy number alteration (CNA) modeling; estimate (1) the ctDNA fraction in the sample based on at least a sample tumor purity and a ploidy of the sample if the sequence read data is determined to be sufficient for performing CNA modeling, or (2) the ctDNA fraction in the sample based on identification of at least one tumor somatic short variant in the sequence read data if the sequence read data is determined to be insufficient for performing CNA modeling; and output the estimated ctDNA fraction in the sample.
  • CNA copy number alteration
  • the memory unit of the system may further comprise instructions that, when executed by the one or more processors, cause the system to compare the estimated ctDNA fraction to at least one predetermined threshold, and output a status call of at least tumor fraction-high (TF-high) or tumor fraction-low (TF-low) for the sample based on the comparison.
  • the disclosed systems may further comprise a sequencer, e.g., a next generation sequencer (also referred to as a massively parallel sequencer).
  • a sequencer e.g., a next generation sequencer (also referred to as a massively parallel sequencer).
  • next generation (or massively parallel) sequencing platforms include, but are not limited to, Roche/454’s Genome Sequencer (GS) FLX system, Illumina/Solexa’ s Genome Analyzer (GA), Illumina’s HiSeq® 2500, HiSeq® 3000, HiSeq® 4000 and NovaSeq® 6000 sequencing systems, Life/APG’s Support Oligonucleotide Ligation Detection (SOLiD) system, Polonator’s G.007 system, Helicos BioSciences’ HeliScope Gene Sequencing system, ThermoFisher Scientific’s Ion Torrent Genexus system, or Pacific Biosciences’ PacBio® RS system.
  • GS Geno
  • the nucleic acid sequence data is acquired using a next generation sequencing technique (also referred to as a massively parallel sequencing technique) having a read-length of less than 400 bases, less than 300 bases, less than 200 bases, less than 150 bases, less than 100 bases, less than 90 bases, less than 80 bases, less than 70 bases, less than 60 bases, less than 50 bases, less than 40 bases, or less than 30 bases.
  • a next generation sequencing technique also referred to as a massively parallel sequencing technique having a read-length of less than 400 bases, less than 300 bases, less than 200 bases, less than 150 bases, less than 100 bases, less than 90 bases, less than 80 bases, less than 70 bases, less than 60 bases, less than 50 bases, less than 40 bases, or less than 30 bases.
  • the determination of ctDNA fraction for a liquid biopsy sample may be used to select, initiate, adjust, or terminate a treatment for cancer in the subject (e.g., a patient) from which the sample was derived, as described elsewhere herein.
  • the disclosed systems may further comprise sample processing and library preparation workstations, microplate-handling robotics, fluid dispensing systems, temperature control modules, environmental control chambers, additional data storage modules, data communication modules (e.g., Bluetooth®, WiFi, intranet, or internet communication hardware and associated software), display modules, one or more local and/or cloud-based software packages (e.g., instrument / system control software packages, sequencing data analysis software packages), etc., or any combination thereof.
  • the systems may comprise, or be part of, a computer system or computer network as described elsewhere herein.
  • FIG. 3 illustrates an example of a computing device or system in accordance with one embodiment.
  • Device 300 can be a host computer connected to a network.
  • Device 300 can be a client computer or a server.
  • device 300 can be any suitable type of microprocessor-based device, such as a personal computer, workstation, server or handheld computing device (portable electronic device) such as a phone or tablet.
  • the device can include, for example, one or more processor(s) 310, input devices 320, output devices 930, memory or storage devices 340, communication devices 360, and nucleic acid sequencers 370.
  • Software 350 residing in memory or storage device 340 may comprise, e.g., an operating system as well as software for executing the methods described herein.
  • Input device 320 and output device 330 can generally correspond to those described herein, and can either be connectable or integrated with the computer.
  • Input device 320 can be any suitable device that provides input, such as a touch screen, keyboard or keypad, mouse, or voice-recognition device.
  • Output device 330 can be any suitable device that provides output, such as a touch screen, haptics device, or speaker.
  • Storage 340 can be any suitable device that provides storage (e.g., an electrical, magnetic or optical memory including a RAM (volatile and non-volatile), cache, hard drive, or removable storage disk).
  • Communication device 360 can include any suitable device capable of transmitting and receiving signals over a network, such as a network interface chip or device.
  • the components of the computer can be connected in any suitable manner, such as via a wired media (e.g., a physical system bus 380, Ethernet connection, or any other wire transfer technology) or wirelessly (e.g., Bluetooth®, Wi-Fi®, or any other wireless technology).
  • Software module 350 which can be stored as executable instructions in storage 340 and executed by processor(s) 310, can include, for example, an operating system and/or the processes that embody the functionality of the methods of the present disclosure (e.g., as embodied in the devices as described herein).
  • Software module 350 can also be stored and/or transported within any non-transitory computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described herein, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions.
  • a computer-readable storage medium can be any medium, such as storage 340, that can contain or store processes for use by or in connection with an instruction execution system, apparatus, or device.
  • Examples of computer- readable storage media may include memory units like hard drives, flash drives and distribute modules that operate as a single functional unit.
  • various processes described herein may be embodied as modules configured to operate in accordance with the embodiments and techniques described above. Further, while processes may be shown and/or described separately, those skilled in the art will appreciate that the above processes may be routines or modules within other processes.
  • Software module 350 can also be propagated within any transport medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described above, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions.
  • a transport medium can be any medium that can communicate, propagate or transport programming for use by or in connection with an instruction execution system, apparatus, or device.
  • the transport readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic or infrared wired or wireless propagation medium.
  • Device 300 may be connected to a network (e.g., network 404, as shown in FIG. 4 and/or described below), which can be any suitable type of interconnected communication system.
  • the network can implement any suitable communications protocol and can be secured by any suitable security protocol.
  • the network can comprise network links of any suitable arrangement that can implement the transmission and reception of network signals, such as wireless network connections, T1 or T3 lines, cable networks, DSL, or telephone lines.
  • Device 300 can be implemented using any operating system, e.g., an operating system suitable for operating on the network.
  • Software module 350 can be written in any suitable programming language, such as C, C++, Java or Python.
  • application software embodying the functionality of the present disclosure can be deployed in different configurations, such as in a client/server arrangement or through a Web browser as a Web-based application or Web service, for example.
  • the operating system is executed by one or more processors, e.g., processor(s) 310.
  • Device 300 can further include a sequencer 370, which can be any suitable nucleic acid sequencing instrument.
  • FIG. 4 illustrates an example of a computing system in accordance with one embodiment.
  • device 300 e.g., as described above and illustrated in FIG. 3
  • network 404 which is also connected to device 406.
  • device 406 is a sequencer.
  • Exemplary sequencers can include, without limitation, Roche/454’s Genome Sequencer (GS) FLX System, Illumina/Solexa’ s Genome Analyzer (GA), Illumina’s HiSeq® 2500, HiSeq® 3000, HiSeq® 4000 and NovaSeq® 6000 Sequencing Systems, Life/APG’s Support Oligonucleotide Ligation Detection (SOLiD) system, Polonator’s G.007 system, Helicos BioSciences’ HeliScope Gene Sequencing system, or Pacific Biosciences’ PacBio® RS system.
  • Devices 300 and 406 may communicate, e.g., using suitable communication interfaces via network 404, such as a Local Area Network (LAN), Virtual Private Network (VPN), or the Internet.
  • network 404 can be, for example, the Internet, an intranet, a virtual private network, a cloud network, a wired network, or a wireless network.
  • Devices 300 and 406 may communicate, in part or in whole, via wireless or hardwired communications, such as Ethernet, IEEE 802.1 lb wireless, or the like. Additionally, devices 300 and 406 may communicate, e.g., using suitable communication interfaces, via a second network, such as a mobile/cellular network.
  • Communication between devices 300 and 406 may further include or communicate with various servers such as a mail server, mobile server, media server, telephone server, and the like.
  • Devices 300 and 406 can communicate directly (instead of, or in addition to, communicating via network 404), e.g., via wireless or hardwired communications, such as Ethernet, IEEE 802.1 lb wireless, or the like.
  • devices 300 and 406 communicate via communications 408, which can be a direct connection or can occur via a network (e.g., network 404).
  • One or all of devices 300 and 406 generally include logic (e.g., http web server logic) or are programmed to format data, accessed from local or remote databases or other sources of data and content, for providing and/or receiving information via network 404 according to various examples described herein.
  • logic e.g., http web server logic
  • devices 300 and 406 are programmed to format data, accessed from local or remote databases or other sources of data and content, for providing and/or receiving information via network 404 according to various examples described herein.
  • Example 1 Verification of ctDN A tumor fraction determination (heuristic rule-based filtering)
  • the disclosed methods for determining ctDNA tumor fraction were verified using sequence read data for 530 matched plasma and huffy coat samples from various cancer types including Non-Small Cell Lung Cancer (NSCLC), Prostate, Breast, Colorectal Cancer (CRC), Pancreas, Ovary, Esophagus, Cholangio, and Cancer of Unknown Primary (CUP).
  • NSCLC Non-Small Cell Lung Cancer
  • CRC Colorectal Cancer
  • Pancreas Ovary
  • Esophagus Cholangio
  • CUP Cancer of Unknown Primary
  • Somatic short variants were identified in plasma samples based on extensive filtering of the list of short variants detected in the sequence read data derived from the samples according to a set of heuristic rules to remove known “blacklist” variants including, but not limited to, clonal hematopoiesis of indeterminate potential (CHIP) variants, germline variants, and artifacts of the sequencing and/or variant calling methods employed.
  • CHIP indeterminate potential
  • the determination of a tumor fraction (TF) positive status for a given sample or the determination of a TF value for the sample) required that multiple potential tumor-derived somatic short variants be identified to improve confidence that TF positive calls were correct.
  • the requirement for identification of multiple tumor-derived somatic short variants can be overridden if certain known non-CHIP somatic variants
  • whitelist variants e.g., KRAS variants, G12 variants, or EGFR exon 19 deletions
  • variants in commonly amplified genes e.g. KRAS, EGFR, CCND1, FGF19, FGF3, FGF4, MYC, AR, MDM2, CCNE1, ERBB2, WHSC1E1, FGFR1, ZNF703, etc.
  • Table 1 provides a summary of verification data for calling samples TF positive or TF negative based on the filtered variant data for the 530 matched plasma and buffy coat samples.
  • TF positive samples were defined as samples for which at least one true somatic variant was detected with a VAF > 0.01.
  • True somatic variants were defined as variants with coverage of greater than 200x in plasma samples and a VAF that was greater than that in buffy coat samples by a statistically significant difference.
  • Fragment size shift is a fairly consistent genome-wide parameter observed for cell-free DNA from a given sample. However, there is some variability in fragment size shift at some genomic loci, likely due to locale-specific biological differences. Therefore it is difficult to use a fragment size-based (fragmentomics) analysis to predict CHIP variants as some somatic variants will exhibit no fragment size shift even when the majority of DNA fragments for the overall sample are shifted. Furthermore, DNA fragments from a small fraction of samples show no shift, in which case all somatic variants fail to be identified as such.
  • fragment size shift for short variants is a strong indicator of somatic status. Based on an analysis of sequence read data for matched plasma-buffy coat samples, short variants that exhibit strong fragment size shifts are nearly always somatic (>99% for variants with significant fragment size shift defined as having a Kolmogorov-Smirnov p-value ⁇ 0.001 between a reference allele and an alternate allele).
  • a non-limiting example of fragment size shift data is provided in Table 2.
  • Table 3 provides a summary of verification data for calling a status of TF elevated, TF detected, or TF not detected for sample based on the variant data for the 530 matched plasma and peripheral blood mononuclear cell (PBMC) samples. Called plasma variants were assessed in sequence read data for matched PBMC samples using a production variant calling method. Variants with a PBMC VAF ⁇ (plasma VAF)/10 were assigned as somatic variants. Variants with significant coverage dropout in the PBMC sample were excluded from the analysis. Table 3. Verification data for TF elevated calls.
  • PBMC peripheral blood mononuclear cell
  • the overall TF call rate was 47% elevated and 16% detected.
  • the results indicate a very high predictivity for the method (> 99% of TF elevated calls were confirmed as elevated based on the matched buffy coat sample analysis. 52% of the samples had a Max sVAF of greater than 1% (88% were called as TF elevated, and 93% were called as TF elevated or TF detected), where Max sVAF was defined as the VAF of the called plasma variant that was present in the buffy coat sample at no more than one-tenth of the plasma VAF. Variants with very low coverage in the buffy coat sample (e.g., ⁇ lOOx or ⁇ 500x and relative buffy coat coverage ⁇ 0.2) were also excluded from the analysis.
  • FIGS. 5A-B provide non-limiting examples of the application of the disclosed methods for determining tumor fraction in liquid biopsy samples to predict the probability of progression- free survival and the probability of survival for prostate cancer patients.
  • FIGS. 6A-B provide non-limiting examples of the use of prostate specific antigen (PSA) as a prognostic biomarker for the probability of progression-free survival and the probability of survival for prostate cancer patients.
  • PSA prostate specific antigen
  • FIGS. 5A-B tumor fraction was determined based on an analysis of sequence read data for plasma samples using the methods disclosed herein.
  • a TF threshold of 2% was used to stratify prostate cancer patients treated with an enzalutamide (Enza) challenge after abiraterone (Abi) treatment. 26% of the patients in the cohort (494 patients in total) had a TF value of less than 2%.
  • FIG. 5A provides a plot of the probability of progression-free survival (PFS) as a function of time following initiation of Enza treatment for patients having a TF ⁇ 2% and patients having a TF > 2%.
  • PFS progression-free survival
  • 5B provides a similar plot of the probability of overall survival (OS) as a function of time following initiation of Enza treatment.
  • OS overall survival
  • the inset in each figure provided a summary of the observed median for the duration of progression-free survival or overall survival, respectively, along with the observed hazard ratio (HR), 95% confidence interval (CI) and p-values for each plot.
  • HR hazard ratio
  • CI 95% confidence interval
  • p-values for each plot.
  • the table below each plot provides the actual number of patients at risk as a function of time.
  • FIGS. 6A-B For the data plotted in FIGS. 6A-B, a ‘ ‘ ‘low” PSA level was defined as a PSA level less than equal to the PSA level for the 26 th percentile of patients in the cohort.
  • FIG. 6A provides a plot of the probability of progression-free survival (PFS) as a function of time following initiation of Enza treatment for patients having low PSA and patients having high PSA.
  • FIG. 6B provides a similar plot of the probability of overall survival (OS) as a function of time following initiation of Enza treatment.
  • the inset in each figure provided a summary of the observed median for the duration of progression-free survival or overall survival, respectively, along with the observed hazard ratio (HR), 95% confidence interval (CI) and p-values for each plot.
  • HR hazard ratio
  • CI 95% confidence interval
  • a method comprising: providing a plurality of nucleic acid molecules obtained from a sample from a subject; ligating one or more adapters onto one or more nucleic acid molecules from the plurality of nucleic acid molecules; amplifying the one or more ligated nucleic acid molecules from the plurality of nucleic acid molecules; capturing amplified nucleic acid molecules from the amplified nucleic acid molecules; sequencing, by a sequencer, the captured nucleic acid molecules to obtain a plurality of sequence reads that represent the captured nucleic acid molecules; receiving, at one or more processors, sequence read data for the plurality of sequence reads; determining, using the one or more processors, if the sequence read data is sufficient for performing copy number alteration (CNA) modeling; estimating, using the one or more processors,
  • CNA copy number alteration
  • the ctDNA fraction in the sample based on at least a tumor purity and a ploidy of the sample using a model if the sequence read data is determined to be sufficient for performing CNA modeling, or
  • determining if the sequence read data is sufficient for performing CNA modeling comprises determining sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof, for at least one genomic locus to which the plurality of sequence reads map.
  • the at least one genomic locus comprises at least one single nucleotide polymorphism (SNP) locus.
  • SNP single nucleotide polymorphism
  • performing CNA modeling comprises: determining, using the one or more processors, a copy number model including a sample tumor purity, a sample ploidy, and a copy number of multiple genomic segments in the ctDNA that accounts for an observed sequence coverage ratio data, allele fraction data for at least one genomic locus within one or more subgenomic intervals to which the plurality of sequence reads map.
  • estimating the ctDNA fraction based on at least one somatic short variant detected in the sequence read data comprises: obtaining a list of short variants detected in the sequence read data; applying a set of selection rules to the list of detected short variants to identify tumor somatic short variants; and estimating the ctDNA fraction based on the presence of at least one identified tumor somatic short variant.
  • the set of selection rules used to identify tumor somatic short variants in the list of detected short variants comprises: (i) removing short variants that appear on a blacklist of known germline variants, known clonal hematopoiesis of indeterminate potential (CHIP) variants, and known sequencing artifacts; (ii) retaining short variants that appear on a list of known tumor somatic short variants; (iii) retaining short variants that appear on a list of known genes that are prone to exhibit high amplification and that have an allele frequency that is higher than other somatic short variants in the sample; (iv) retaining short variants that appear on a list of known rearrangements; or any combination thereof.
  • CHIP indeterminate potential
  • estimating the ctDNA fraction based on the at least one tumor somatic short variant comprises: determining, using the one or more processors, a variant allele frequency (VAF) for one or more variants detected in the sample based on the sequence read data; generating, using the one or more processors, an empirical distribution of ctDNA fraction values corresponding to the determined VAF for the one or more variants based on historical data; fitting, using the one or more processors, a model to the empirical distribution of ctDNA fraction values; and determining a ctDNA fraction for the sample based on the model.
  • VAF variant allele frequency
  • the cancer is a B cell cancer (multiple myeloma), a melanoma, breast cancer, lung cancer, bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain cancer, central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine cancer, endometrial cancer, cancer of an oral cavity, cancer of a pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel cancer, appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, a cancer of hematological tissue, an adenocarcinoma, an inflammatory myofibroblastic tumor, a gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MP
  • the cancer comprises acute lymphoblastic leukemia (Philadelphia chromosome positive), acute lymphoblastic leukemia (precursor B-cell), acute myeloid leukemia (FLT3+), acute myeloid leukemia (with an IDH2 mutation), anaplastic large cell lymphoma, basal cell carcinoma, B-cell chronic lymphocytic leukemia, bladder cancer, breast cancer (HER2 overexpressed/amplified), breast cancer (HER2+), breast cancer (HR+, HER2-), cervical cancer, cholangiocarcinoma, chronic lymphocytic leukemia, chronic lymphocytic leukemia (with 17p deletion), chronic myelogenous leukemia, chronic myelogenous leukemia (Philadelphia chromosome positive), classical Hodgkin lymphoma, colorectal cancer, colorectal cancer (dMMR/MSI-H), colorectal cancer (KRAS wild type), cryopyrin-associated periodic syndrome,
  • the targeted anti-cancer therapy comprises abemaciclib (Verzenio), abiraterone acetate (Zytiga), acalabrutinib (Calquence), ado-trastuzumab emtansine (Kadcyla), afatinib dimaleate (Gilotrif), aldesleukin (Proleukin), alectinib (Alecensa), alemtuzumab (Campath), alitretinoin (Panretin), alpelisib (Piqray), amivantamab-vmjw (Rybrevant), anastrozole (Arimidex), apalutamide (Erleada), asciminib hydrochloride (Scemblix), atezolizumab (Tecentriq), avapritinib (Ayvakit), avelumab (Bavencio), axicabtagene
  • the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
  • the sample is a liquid biopsy sample and comprises circulating tumor cells (CTCs).
  • CTCs circulating tumor cells
  • the sample is a liquid biopsy sample and comprises cell- free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.
  • cfDNA cell- free DNA
  • ctDNA circulating tumor DNA
  • tumor nucleic acid molecules are derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules are derived from a normal portion of the heterogeneous tissue biopsy sample.
  • the sample comprises a liquid biopsy sample
  • the tumor nucleic acid molecules are derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample
  • the non-tumor nucleic acid molecules are derived from a non- tumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.
  • the one or more bait molecules comprise one or more nucleic acid molecules, each comprising a region that is complementary to a region of a captured nucleic acid molecule.
  • amplifying nucleic acid molecules comprises performing a polymerase chain reaction (PCR) amplification technique, a non-PCR amplification technique, or an isothermal amplification technique.
  • PCR polymerase chain reaction
  • the one or more gene loci comprises between 10 and 20 loci, between 10 and 40 loci, between 10 and 60 loci, between 10 and 80 loci, between 10 and 100 loci, between 10 and 150 loci, between 10 and 200 loci, between 10 and 250 loci, between 10 and 300 loci, between 10 and 350 loci, between 10 and 400 loci, between 10 and 450 loci, between 10 and 500 loci, between 20 and 40 loci, between 20 and 60 loci, between 20 and 80 loci, between 20 and 100 loci, between 20 and 150 loci, between 20 and 200 loci, between 20 and 250 loci, between 20 and 300 loci, between 20 and 350 loci, between 20 and 400 loci, between 20 and 500 loci, between 40 and 60 loci, between 40 and 80 loci, between 40 and 100 loci, between 40 and 150 loci, between 40 and 200 loci, between 40 and 250 loci, between 40 and 300 loci, between 40 and 350 loci, between 40 and 400 loci, between 40 and 500 loci, between 40 and 60 loci,
  • the one or more gene loci comprise ABL, ALK, ALL, B4GALNT1, BAFF, BCL2, BRAF, BRCA, BTK, CD19, CD20, CD3, CD30, CD319, CD38, CD52, CDK4, CDK6, CML, CRACC, CS1, CTLA-4, dMMR, EGFR, ERBB1, ERBB2, FGFR1-3, FLT3, GD2, HDAC, HER1, HER2, HR, IDH2, IL-ip, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSI-H, mTOR, PARP, PD-1, PDGFR, PDGFRa, PDGFRP, PD-L1, PI3K5, PIGF, PTCH, RAF, RANKL, RET, ROS1, SLAMF7, VEGF, VEGFA, VEGFB, or any combination thereof.
  • a method for determining circulating tumor DNA (ctDNA) fraction in a sample from a subject comprising: receiving, at one or more processors, sequence read data for a plurality of sequence reads obtained for the sample from the subject; determining, using the one or more processors, if the sequence read data is sufficient for performing copy number alteration (CNA) modeling; estimating, using the one or more processors,
  • the ctDNA fraction in the sample based on at least a tumor purity and a ploidy of the sample using a model if the sequence read data is determined to be sufficient for performing CNA modeling, or (2) the ctDNA fraction in the sample based on identification of at least one tumor somatic short variant in the sequence read data if the sequence read data is determined to be insufficient for performing CNA modeling; and outputting, using the one or more processors, the estimated ctDNA fraction in the sample.
  • determining if the sequence read data is sufficient for performing CNA modeling comprises determining sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof, for at least one genomic locus to which the plurality of sequence reads map.
  • performing CNA modeling comprises: determining, using the one or more processors, a copy number model including a sample tumor purity, a sample ploidy, and a copy number of multiple genomic segments that accounts for an observed sequence coverage ratio data, allele fraction data for at least one genomic locus within one or more subgenomic intervals to which the plurality of sequence reads map.
  • sequence coverage ratio data is determined by aligning the plurality of sequence reads that overlap the at least one genomic locus within the one or more subgenomic intervals in the sample and in a control sample to a reference genome, and determining a number of sequence reads that overlap the at least one genomic locus within the one or more subgenomic intervals in the sample and in the control sample.
  • control sample is a paired normal sample, a process- matched control sample, or a panel of normal control sample.
  • the allele fraction data is determined by aligning the plurality of sequence reads that overlap the at least one genomic locus within the one or more subgenomic intervals in the sample to a reference genome, detecting a number of alleles present at the at least one genomic locus, and determining an allele fraction for at least one of the alleles present at the at least one genomic locus.
  • performing CNA modeling further comprises generating segmentation data by: aligning a plurality of sequence reads that overlap the at least one genomic locus within the one or more subgenomic intervals in the sample to a reference genome, and processing the aligned sequence read data, coverage ratio data, and allele fraction data using a pruned exact linear time (PELT) method to determine a number of segments required to account for the aligned sequence read data, wherein each segment has a same copy number.
  • PELT pruned exact linear time
  • sequence coverage ratio data further comprises sequence coverage ratio data for single nucleotide polymorphisms (SNPs) and introns associated with the at least one genomic locus.
  • SNPs single nucleotide polymorphisms
  • estimating the ctDNA fraction based on at least one somatic short variant detected in the sequence read data comprises: obtaining a list of short variants detected in the sequence read data; applying a set of selection rules to the list of detected short variants to identify tumor somatic short variants; and estimating the ctDNA fraction based on the presence of at least one identified tumor somatic short variant.
  • estimating the ctDNA fraction based on the at least one tumor somatic short variant comprises: determining, using the one or more processors, a variant allele frequency (VAF) for one or more variants detected in the sequence read data; generating, using the one or more processors, an empirical distribution of ctDNA fraction values corresponding to the determined VAF for the one or more variants based on historical data; fitting, using the one or more processors, a model to the empirical distribution of ctDNA fraction values; and determining a ctDNA fraction for the sample based on the model.
  • VAF variant allele frequency
  • generating the empirical distribution of ctDNA fraction values comprises pre-calculating a ctDNA fraction value based on a known copy number for the one or more tumor somatic short variants and a corresponding known sample ploidy for a plurality of historical subject samples having a range of VAF values for the one or more tumor somatic short variants, and selecting a subset of the pre-calculated ctDNA fraction values that corresponds to samples having a known VAF for the one or more tumor somatic short variants that is substantially the same as the determined VAF for the one or more tumor somatic short variants.
  • Somatic VAF - - - r C + 2(l - ) to eliminate p and obtain a relationship between ctDNA fraction and somatic VAF described by: where p is sample tumor purity, is sample ploidy, C is the copy number at a genomic location of the one or more tumor somatic short variants, and V is a variant allele number for each of the one or more tumor somatic short variants.
  • the plurality of historical subject samples comprise bladder cancer samples, breast cancer samples, colorectal cancer samples, endometrial cancer samples, kidney cancer samples, leukemia samples, liver cancer samples, lung cancer samples, melanoma samples, non-Hodgkin lymphoma samples, pancreatic cancer samples, prostate cancer samples, thyroid cancer samples, or any combination thereof.
  • the plurality of historical subject samples comprise acute lymphoblastic leukemia (Philadelphia chromosome positive) samples, acute lymphoblastic leukemia (precursor B-cell) samples, acute myeloid leukemia (FLT3+) samples, acute myeloid leukemia (with an IDH2 mutation) samples, anaplastic large cell lymphoma samples, basal cell carcinoma samples, B-cell chronic lymphocytic leukemia samples, bladder cancer samples, breast cancer (HER2 overexpressed/amplified) samples, breast cancer (HER2+) samples, breast cancer (HR+, HER2-) samples, cervical cancer samples, cholangiocarcinoma samples, chronic lymphocytic leukemia samples, chronic lymphocytic leukemia (with 17p deletion) samples, chronic myelogenous leukemia samples, chronic myelogenous leukemia (Philadelphia chromosome positive) samples, classical Hodgkin lymphoma samples,
  • the sample comprises DNA extracted from a blood sample, a plasma sample, a cerebrospinal fluid sample, a pleural effusion fluid sample, a sputum sample, a stool sample, a urine sample, or a saliva sample.
  • the set of selection rules used to identify tumor somatic short variants in the list of detected short variants comprises: (i) removing short variants that appear on a blacklist of known germline variants, known clonal hematopoiesis of indeterminate potential (CHIP) variants, and known sequencing artifacts; (ii) retaining short variants that appear on a list of known tumor somatic short variants; (iii) retaining short variants that appear on a list of known genes that are prone to exhibit high amplification and that have an allele frequency that is higher than other somatic short variants in the sample; (iv) retaining short variants that appear on a list of known rearrangements; or any combination thereof.
  • CHIP indeterminate potential
  • a method for predicting a treatment outcome for a subject having cancer comprising: receiving, at one or more processors, sequence read data for a plurality of sequence reads obtained for the sample from the subject; determining, using the one or more processors, if the sequence read data is sufficient for performing copy number alteration (CNA) modeling; estimating, using the one or more processors, a ctDNA fraction in the sample based on at least a sample tumor purity and a sample ploidy derived from a CNA model if the sequence read data is determined to be sufficient for performing CNA modeling; or estimating, using the one or more processors, the ctDNA fraction in the sample based on identification of at least one tumor somatic short variant detected in the sequence read data if the sequence read data is determined to be insufficient for performing CNA modeling; outputting, using the one or more processors, the estimated ctDNA fraction in the sample; and based on a comparison of the estimated ctDNA fraction to a predetermined threshold, predicting the outcome of
  • the predetermined threshold is determined by adjusting an empirical threshold to maximize a sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), or any combination thereof, for the ctDNA fraction data for the cohort of patients having the cancer.
  • a method for diagnosing a disease comprising: diagnosing that a subject has the disease based on a determination of ctDNA fraction for a sample from the subject, wherein ctDNA fraction is determined according to the method of any one of clauses 1 to 117.
  • a method of selecting an anti-cancer therapy comprising: responsive to determining a ctDNA fraction for a sample from a subject, selecting an anti-cancer therapy for the subject, wherein ctDNA fraction is determined according to the method of any one of clauses 1 to 117.
  • a method of treating a cancer in a subject comprising: responsive to determining a ctDNA fraction for a sample from the subject, administering an effective amount of an anti-cancer therapy to the subject, wherein ctDNA fraction is determined according to the method of any one of clauses 1 to 117.
  • a method for monitoring cancer progression or recurrence in a subject comprising: determining a first ctDNA fraction in a first sample obtained from the subject at a first time point according to the method of any one of clauses 1 to 117; determining a second ctDNA fraction in a second sample obtained from the subject at a second time point; and comparing the first ctDNA fraction to the second ctDNA fraction, thereby monitoring the cancer progression or recurrence.
  • genomic profile for the subject further comprises results from a comprehensive genomic profiling (CGP) test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof.
  • CGP genomic profiling
  • the genomic profile for the subject further comprises results from a nucleic acid sequencing-based test.
  • a system comprising: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: receive sequence read data for a plurality of sequence reads obtained for the sample from the subject; determine if the sequence read data is sufficient for performing copy number alteration (CNA) modeling; estimate using the one or more processors,
  • CNA copy number alteration
  • the ctDNA fraction in the sample based on at least a sample tumor purity and a ploidy of the sample using a model if the sequence read data is determined to be sufficient for performing CNA modeling, or
  • determining if the sequence read data is sufficient for performing CNA modeling comprises determining sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof, for at least one genomic locus to which the plurality of sequence reads map.
  • performing CNA modeling comprises: determining, using the one or more processors, a copy number model including a sample tumor purity, a sample ploidy, and a copy number of multiple genomic segments in the ctDNA that accounts for an observed sequence coverage ratio data, allele fraction data for at least one genomic locus within one or more subgenomic intervals to which the plurality of sequence reads map.
  • estimating the ctDNA fraction based on at least one somatic short variant detected in the sequence read data comprises: obtaining a list of short variants detected in the sequence read data; applying a set of selection rules to the list of detected short variants to identify tumor somatic short variants; and estimating the ctDNA fraction based on the presence of at least one identified tumor somatic short variant.
  • the set of selection rules used to identify tumor somatic short variants in the list of detected short variants comprises: (i) removing short variants that appear on a blacklist of known germline variants, known clonal hematopoiesis of indeterminate potential (CHIP) variants, and known sequencing artifacts; (ii) retaining short variants that appear on a list of known tumor somatic short variants; (iii) retaining short variants that appear on a list of known genes that are prone to exhibit high amplification and that have an allele frequency that is higher than other somatic short variants in the sample; (iv) retaining short variants that appear on a list of known rearrangements; or any combination thereof.
  • CHIP indeterminate potential
  • estimating the ctDNA fraction based on the at least one tumor somatic short variant comprises: determining, using the one or more processors, a variant allele frequency (VAF) for one or more variants detected in the sample based on the sequence read data; generating, using the one or more processors, an empirical distribution of ctDNA fraction values corresponding to the determined VAF for the one or more variants based on historical data; fitting, using the one or more processors, a model to the empirical distribution of ctDNA fraction values; and determining a ctDNA fraction for the sample based on the model.
  • VAF variant allele frequency
  • a non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: receive sequence read data for a plurality of sequence reads obtained for the sample from the subject; determine if the sequence read data is sufficient for performing copy number alteration (CNA) modeling; estimate using the one or more processors,
  • CNA copy number alteration
  • the ctDNA fraction in the sample based on at least a tumor purity and a ploidy of the sample using a model if the sequence read data is determined to be sufficient for performing CNA modeling, or
  • non-transitory computer-readable storage medium of clause 153 further comprising instructions that, when executed by the one or more processors of the system, cause the system to compare the estimated ctDNA fraction to at least one predetermined threshold, and output a status call of at least tumor fraction-high (TF-high) or tumor fraction-low (TF-low) for the sample based on the comparison.
  • determining if the sequence read data is sufficient for performing CNA modeling comprises determining sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof, for at least one genomic locus to which the plurality of sequence reads map.
  • performing CNA modeling comprises: determining, using the one or more processors, a copy number model including a sample tumor purity, a sample ploidy, and a copy number of multiple genomic segments in the ctDNA that accounts for an observed sequence coverage ratio data, allele fraction data for at least one genomic locus within one or more subgenomic intervals to which the plurality of sequence reads map.
  • estimating the ctDNA fraction based on at least one somatic short variant detected in the sequence read data comprises: obtaining a list of short variants detected in the sequence read data; applying a set of selection rules to the list of detected short variants to identify tumor somatic short variants; and estimating the ctDNA fraction based on the presence of at least one identified tumor somatic short variant.
  • the set of selection rules used to identify tumor somatic short variants in the list of detected short variants comprises: (i) removing short variants that appear on a blacklist of known germline variants, known clonal hematopoiesis of indeterminate potential (CHIP) variants, and known sequencing artifacts; (ii) retaining short variants that appear on a list of known tumor somatic short variants; (iii) retaining short variants that appear on a list of known genes that are prone to exhibit high amplification and that have an allele frequency that is higher than other somatic short variants in the sample; (iv) retaining short variants that appear on a list of known rearrangements; or any combination thereof.
  • the set of selection rules used to identify tumor somatic short variants in the list of detected short variants comprises: (i) removing short variants that appear on a blacklist of known germline variants, known clonal hematopoiesis of indeterminate potential (CHIP) variants, and known sequencing art
  • estimating the ctDNA fraction based on the at least one tumor somatic short variant comprises: determining, using the one or more processors, a variant allele frequency (VAF) for one or more variants detected in the sample based on the sequence read data; generating, using the one or more processors, an empirical distribution of ctDNA fraction values corresponding to the determined VAF for the one or more variants based on historical data; fitting, using the one or more processors, a model to the empirical distribution of ctDNA fraction values; and determining a ctDNA fraction for the sample based on the model.
  • VAF variant allele frequency

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Abstract

Methods for determining circulating tumor DNA fraction in liquid biopsy samples are described. The methods may comprise, for example, receiving sequence read data for a plurality of sequence reads obtained for the sample from the subject; determining if the sequence read data is sufficient for performing copy number alteration (CNA) modeling; estimating the ctDNA fraction in the sample based on a copy number determined for at least one CNA using a CNA model if the sequence read data is determined to be sufficient for performing CNA modeling; or estimating the ctDNA fraction in the sample based on identification of at least one somatic short variant in the sequence read data if the sequence read data is determined to be insufficient for performing CNA modeling; and outputting the estimated ctDNA fraction in the sample.

Description

METHODS AND SYSTEMS FOR ASSESSING CIRCULATING TUMOR DNA FRACTION IN LIQUID BIOPSY SAMPLES
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application claims the priority benefit of United States Provisional Patent Application Serial No. 63/466,541, filed May 15, 2023, the contents of which are incorporated herein by reference in their entirety.
FIELD OF THE INVENTION
[0002] The present disclosure relates generally to methods and systems for analyzing genomic profiling data, and more specifically to methods and systems for assessing circulating tumor DNA (ctDNA) fraction in liquid biopsy samples based on genomic profiling data, and its use for detecting, monitoring, and making prognostic predictions for cancer.
BACKGROUND
[0003] Liquid biopsy is a simple and non-invasive alternative to surgical biopsies that enables healthcare providers to determine a variety of important characteristics of a cancer patient’s tumor through the collection of a simple blood sample and subsequent genomic profiling of nucleic acid molecules extracted from the blood sample. Analysis of trace cancer cell DNA in the blood (commonly referred to as circulating tumor DNA (ctDNA)) can provide clues about which treatments are most likely to work for a given patient.
[0004] Typically, a cell-free DNA sample isolated from a liquid biopsy contains a mixture of DNA released from both cancer and normal cells. ctDNA fraction is the fraction of DNA that is derived from cancer cells in the total cell-free DNA sample isolated from the liquid biopsy, however, existing methods for estimating ctDNA fraction lack the sensitivity and/or specificity required for accurate detection and determination of ctDNA fraction in cell-free DNA samples.
BRIEF SUMMARY OF THE INVENTION
[0005] Disclosed herein are methods and systems that enable more accurate determination of ctDNA fraction based on sequence read data for cell-free DNA samples derived from liquid biopsy. The disclosed methods are based on a combined approach that includes: (i) estimation of ctDNA fraction based on a copy number model that specifies a tumor fraction for the sample and exact copy number at probed genomic locations that explain the observed sequence coverage and single nucleotide polymorphism (SNP) allele frequency data, and/or (ii) estimation of ctDNA fraction from observed allele frequencies of tumor somatic variants identified in the sample based on a theoretical relationship between tumor fraction and tumor somatic variant allele frequency given a copy number profile, where the copy number profile is inferred from tumor somatic variant allele frequency based on historical observations.
[0006] The more accurate determinations of ctDNA fraction enabled by using the disclosed methods can, in turn, be used to detect, monitor, and/or make prognostic predictions for treatment outcomes for cancer patients.
[0007] Disclosed herein are methods comprising: providing a plurality of nucleic acid molecules obtained from a sample from a subject; ligating one or more adapters onto one or more nucleic acid molecules from the plurality of nucleic acid molecules; amplifying the one or more ligated nucleic acid molecules from the plurality of nucleic acid molecules; capturing amplified nucleic acid molecules from the amplified nucleic acid molecules; sequencing, by a sequencer, the captured nucleic acid molecules to obtain a plurality of sequence reads that represent the captured nucleic acid molecules; receiving, at one or more processors, sequence read data for the plurality of sequence reads; determining, using the one or more processors, if the sequence read data is sufficient for performing copy number alteration (CNA) modeling; estimating, using the one or more processors, (1) the ctDNA fraction in the sample based on at least a tumor purity and a ploidy of the sample using a model if the sequence read data is determined to be sufficient for performing CNA modeling, or (2) the ctDNA fraction in the sample based on identification of at least one tumor somatic short variant in the sequence read data if the sequence read data is determined to be insufficient for performing CNA modeling; and outputting, using the one or more processors, the estimated ctDNA fraction in the sample.
[0008] In some embodiments, the method may further comprise comparing the estimated ctDNA fraction to at least one predetermined threshold, and outputting a status call of at least tumor fraction-high (TF-high) or tumor fraction-low (TF-low) for the sample based on the comparison.
[0009] In some embodiments, determining if the sequence read data is sufficient for performing CNA modeling comprises determining sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof, for at least one genomic locus to which the plurality of sequence reads map. In some embodiments, the at least one genomic locus comprises at least one single nucleotide polymorphism (SNP) locus.
[0010] In some embodiments, performing CNA modeling comprises: determining, using the one or more processors, a copy number model including a sample tumor purity, a sample ploidy, and a copy number of multiple genomic segments in the ctDNA that accounts for an observed sequence coverage ratio data, allele fraction data for at least one genomic locus within one or more subgenomic intervals to which the plurality of sequence reads map.
[0011] In some embodiments, estimating the ctDNA fraction based on at least the sample tumor purity and sample ploidy using the CNA model comprises using a formula that describes a physical relationship between ctDNA fraction and sample tumor purity and sample ploidy.
[0012] In some embodiments, estimating the ctDNA fraction based on at least one somatic short variant detected in the sequence read data comprises: obtaining a list of short variants detected in the sequence read data; applying a set of selection rules to the list of detected short variants to identify tumor somatic short variants; and estimating the ctDNA fraction based on the presence of at least one identified tumor somatic short variant. In some embodiments, the set of selection rules used to identify tumor somatic short variants in the list of detected short variants comprises: (i) removing short variants that appear on a blacklist of known germline variants, known clonal hematopoiesis of indeterminate potential (CHIP) variants, and known sequencing artifacts; (ii) retaining short variants that appear on a list of known tumor somatic short variants; (iii) retaining short variants that appear on a list of known genes that are prone to exhibit high amplification and that have an allele frequency that is higher than other somatic short variants in the sample; (iv) retaining short variants that appear on a list of known rearrangements; or any combination thereof.
[0013] In some embodiments, estimating the ctDNA fraction based on the at least one tumor somatic short variant comprises: determining, using the one or more processors, a variant allele frequency (VAF) for one or more variants detected in the sample based on the sequence read data; generating, using the one or more processors, an empirical distribution of ctDNA fraction values corresponding to the determined VAF for the one or more variants based on historical data; fitting, using the one or more processors, a model to the empirical distribution of ctDNA fraction values; and determining a ctDNA fraction for the sample based on the model. [0014] In some embodiments, the subject is suspected of having or is determined to have cancer. In some embodiments, the cancer is a B cell cancer (multiple myeloma), a melanoma, breast cancer, lung cancer, bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain cancer, central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine cancer, endometrial cancer, cancer of an oral cavity, cancer of a pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel cancer, appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, a cancer of hematological tissue, an adenocarcinoma, an inflammatory myofibroblastic tumor, a gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), acute lymphocytic leukemia (ALL), acute myelocytic leukemia (AML), chronic myelocytic leukemia (CML), chronic lymphocytic leukemia (CLL), polycythemia Vera, Hodgkin lymphoma, non-Hodgkin lymphoma (NHL), soft- tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endotheliosarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, bladder carcinoma, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, neuroblastoma, retinoblastoma, follicular lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, hepatocellular carcinoma, thyroid cancer, gastric cancer, head and neck cancer, small cell cancer, essential thrombocythemia, agnogenic myeloid metaplasia, hypereosinophilic syndrome, systemic mastocytosis, familiar hypereosinophilia, chronic eosinophilic leukemia, neuroendocrine cancers, or a carcinoid tumor.
[0015] In some embodiments, the cancer comprises acute lymphoblastic leukemia (Philadelphia chromosome positive), acute lymphoblastic leukemia (precursor B-cell), acute myeloid leukemia (FLT3+), acute myeloid leukemia (with an IDH2 mutation), anaplastic large cell lymphoma, basal cell carcinoma, B-cell chronic lymphocytic leukemia, bladder cancer, breast cancer (HER2 overexpressed/amplified), breast cancer (HER2+), breast cancer (HR+, HER2-), cervical cancer, cholangiocarcinoma, chronic lymphocytic leukemia, chronic lymphocytic leukemia (with 17p deletion), chronic myelogenous leukemia, chronic myelogenous leukemia (Philadelphia chromosome positive), classical Hodgkin lymphoma, colorectal cancer, colorectal cancer (dMMR/MSI-H), colorectal cancer (KRAS wild type), cryopyrin-associated periodic syndrome, a cutaneous T-cell lymphoma, dermatofibrosarcoma protuberans, a diffuse large B-cell lymphoma, fallopian tube cancer, a follicular B-cell non-Hodgkin lymphoma, a follicular lymphoma, gastric cancer, gastric cancer (HER2+), gastroesophageal junction (GEJ) adenocarcinoma, a gastrointestinal stromal tumor, a gastrointestinal stromal tumor (KIT+), a giant cell tumor of the bone, a glioblastoma, granulomatosis with polyangiitis, a head and neck squamous cell carcinoma, a hepatocellular carcinoma, Hodgkin lymphoma, juvenile idiopathic arthritis, lupus erythematosus, a mantle cell lymphoma, medullary thyroid cancer, melanoma, a melanoma with a BRAF V600 mutation, a melanoma with a BRAF V600E or V600K mutation, Merkel cell carcinoma, multicentric Castleman's disease, multiple hematologic malignancies including Philadelphia chromosome-positive ALL and CML, multiple myeloma, myelofibrosis, a non-Hodgkin’ s lymphoma, a nonresectable subependymal giant cell astrocytoma associated with tuberous sclerosis, a non-small cell lung cancer, a non-small cell lung cancer (ALK+), a non-small cell lung cancer (PD-L1+), a non-small cell lung cancer (with ALK fusion or ROS1 gene alteration), a non-small cell lung cancer (with BRAF V600E mutation), a non-small cell lung cancer (with an EGFR exon 19 deletion or exon 21 substitution (L858R) mutations), a non- small cell lung cancer (with an EGFR T790M mutation), ovarian cancer, ovarian cancer (with a BRCA mutation), pancreatic cancer, a pancreatic, gastrointestinal, or lung origin neuroendocrine tumor, a pediatric neuroblastoma, a peripheral T-cell lymphoma, peritoneal cancer, prostate cancer, a renal cell carcinoma, rheumatoid arthritis, a small lymphocytic lymphoma, a soft tissue sarcoma, a solid tumor (MSI-H/dMMR), a squamous cell cancer of the head and neck, a squamous non-small cell lung cancer, thyroid cancer, a thyroid carcinoma, urothelial cancer, a urothelial carcinoma, or Waldenstrom's macroglobulinemia.
[0016] In some embodiments, the method further comprises treating the subject with an anticancer therapy. In some embodiments, the anti-cancer therapy comprises a targeted anti-cancer therapy. In some embodiments, the targeted anti-cancer therapy comprises abemaciclib
(Verzenio), abiraterone acetate (Zytiga), acalabrutinib (Calquence), ado-trastuzumab emtansine (Kadcyla), afatinib dimaleate (Gilotrif), aldesleukin (Proleukin), alectinib (Alecensa), alemtuzumab (Campath), alitretinoin (Panretin), alpelisib (Piqray), amivantamab-vmjw (Rybrevant), anastrozole (Arimidex), apalutamide (Erleada), asciminib hydrochloride (Scemblix), atezolizumab (Tecentriq), avapritinib (Ayvakit), avelumab (Bavencio), axicabtagene ciloleucel (Yescarta), axitinib (Inlyta), belantamab mafodotin-blmf (Blenrep), belimumab (Benlysta), belinostat (Beleodaq), belzutifan (Welireg), bevacizumab (Avastin), bexarotene (Targretin), binimetinib (Mektovi), blinatumomab (Blincyto), bortezomib (Velcade), bosutinib (Bosulif), brentuximab vedotin (Adcetris), brexucabtagene autoleucel (Tecartus), brigatinib (Alunbrig), cabazitaxel (Jevtana), cabozantinib (Cabometyx), cabozantinib (Cabometyx, Cometriq), canakinumab (Haris), capmatinib hydrochloride (Tabrecta), carfilzomib (Kyprolis), cemiplimab-rwlc (Libtayo), ceritinib (LDK378/Zykadia), cetuximab (Erbitux), cobimetinib (Cotellic), copanlisib hydrochloride (Aliqopa), crizotinib (Xalkori), dabrafenib (Tafinlar), dacomitinib (Vizimpro), daratumumab (Darzalex), daratumumab and hyaluronidase-fihj (Darzalex Faspro), darolutamide (Nubeqa), dasatinib (Sprycel), denileukin diftitox (Ontak), denosumab (Xgeva), dinutuximab (Unituxin), dostarlimab-gxly (Jemperli), durvalumab (Imfinzi), duvelisib (Copiktra), elotuzumab (Empliciti), enasidenib mesylate (Idhifa), encorafenib (Braftovi), enfortumab vedotin-ejfv (Padcev), entrectinib (Rozlytrek), enzalutamide (Xtandi), erdafitinib (Balversa), erlotinib (Tarceva), everolimus (Afinitor), exemestane (Aromasin), fam-trastuzumab deruxtecan-nxki (Enhertu), fedratinib hydrochloride (Inrebic), fulvestrant (Faslodex), gefitinib (Iressa), gemtuzumab ozogamicin (Mylotarg), gilteritinib (Xospata), glasdegib maleate (Daurismo), hyaluronidase-zzxf (Phesgo), ibrutinib (Imbruvica), ibritumomab tiuxetan (Zevalin), idecabtagene vicleucel (Abecma), idelalisib (Zydelig), imatinib mesylate (Gleevec), infigratinib phosphate (Truseltiq), inotuzumab ozogamicin (Besponsa), iobenguane 1131 (Azedra), ipilimumab (Yervoy), isatuximab-irfc (Sarclisa), ivosidenib (Tibsovo), ixazomib citrate (Ninlaro), lanreotide acetate (Somatuline Depot), lapatinib (Tykerb), larotrectinib sulfate (Vitrakvi), lenvatinib mesylate (Lenvima), letrozole (Femara), lisocabtagene maraleucel (Breyanzi), loncastuximab tesirine-lpyl (Zynlonta), lorlatinib (Lorbrena), lutetium Lu 177-dotatate (Lutathera), margetuximab-cmkb (Margenza), midostaurin (Rydapt), mobocertinib succinate (Exkivity), mogamulizumab-kpkc (Poteligeo), moxetumomab pasudotox-tdfk (Lumoxiti), naxitamab-gqgk (Danyelza), necitumumab (Portrazza), neratinib maleate (Nerlynx), nilotinib (Tasigna), niraparib tosylate monohydrate (Zejula), nivolumab (Opdivo), obinutuzumab (Gazyva), ofatumumab (Arzerra), olaparib (Lynparza), olaratumab (Lartruvo), osimertinib (Tagrisso), palbociclib (Ibrance), panitumumab (Vectibix), panobinostat (Farydak), pazopanib (Votrient), pembrolizumab (Keytruda), pemigatinib (Pemazyre), pertuzumab (Perjeta), pexidartinib hydrochloride (Turalio), polatuzumab vedotin-piiq (Polivy), ponatinib hydrochloride (Iclusig), pralatrexate (Folotyn), pralsetinib (Gavreto), radium 223 dichloride (Xofigo), ramucirumab (Cyramza), regorafenib (Stivarga), ribociclib (Kisqali), ripretinib (Qinlock), rituximab (Rituxan), rituximab and hyaluronidase human (Rituxan Hycela), romidepsin (Istodax), rucaparib camsylate (Rubraca), ruxolitinib phosphate (Jakafi), sacituzumab govitecan-hziy (Trodelvy), seliciclib, selinexor (Xpovio), selpercatinib (Retevmo), selumetinib sulfate (Koselugo), siltuximab (Sylvant), sipuleucel-T (Provenge), sirolimus protein-bound particles (Fyarro), sonidegib (Odomzo), sorafenib (Nexavar), sotorasib (Lumakras), sunitinib (Sutent), tafasitamab-cxix (Monjuvi), tagraxofusp-erzs (Elzonris), talazoparib tosylate (Talzenna), tamoxifen (Nolvadex), tazemetostat hydrobromide (Tazverik), tebentafusp-tebn (Kimmtrak), temsirolimus (Torisel), tepotinib hydrochloride (Tepmetko), tisagenlecleucel (Kymriah), tisotumab vedotin-tftv (Tivdak), tocilizumab (Actemra), tofacitinib (Xeljanz), tositumomab (Bexxar), trametinib (Mekinist), trastuzumab (Herceptin), tretinoin (Vesanoid), tivozanib hydrochloride (Fotivda), toremifene (Fareston), tucatinib (Tukysa), umbralisib tosylate (Ukoniq), vandetanib (Caprelsa), vemurafenib (Zelboraf), venetoclax (Venclexta), vismodegib (Erivedge), vorinostat (Zolinza), zanubrutinib (Brukinsa), ziv-aflibercept (Zaltrap), or any combination thereof.
[0017] In some embodiments, the method further comprises obtaining the sample from the subject. In some embodiments, the sample comprises a tissue biopsy sample, a liquid biopsy sample, or a normal control. In some embodiments, the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva. In some embodiments, the sample is a liquid biopsy sample and comprises circulating tumor cells (CTCs). In some embodiments, the sample is a liquid biopsy sample and comprises cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof. In some embodiments, the plurality of nucleic acid molecules comprises a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules. In some embodiments, the tumor nucleic acid molecules are derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules are derived from a normal portion of the heterogeneous tissue biopsy sample. In some embodiments, the sample comprises a liquid biopsy sample, and wherein the tumor nucleic acid molecules are derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample, and the non-tumor nucleic acid molecules are derived from a nontumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.
[0018] In some embodiments, the one or more adapters comprise amplification primers, flow cell adaptor sequences, substrate adapter sequences, or sample index sequences. In some embodiments, the captured nucleic acid molecules are captured from the amplified nucleic acid molecules by hybridization to one or more bait molecules. In some embodiments, the one or more bait molecules comprise one or more nucleic acid molecules, each comprising a region that is complementary to a region of a captured nucleic acid molecule. In some embodiments, amplifying nucleic acid molecules comprises performing a polymerase chain reaction (PCR) amplification technique, a non-PCR amplification technique, or an isothermal amplification technique.
[0019] In some embodiments, the sequencing comprises use of a massively parallel sequencing (MPS) technique, whole genome sequencing (WGS), whole exome sequencing, targeted sequencing, direct sequencing, or Sanger sequencing technique. In some embodiments, the sequencing comprises massively parallel sequencing, and the massively parallel sequencing technique comprises next generation sequencing (NGS). In some embodiments, the sequencer comprises a next generation sequencer.
[0020] In some embodiments, one or more of the plurality of sequencing reads overlap one or more gene loci within one or more subgenomic intervals in the sample. In some embodiments, the one or more gene loci comprises between 10 and 20 loci, between 10 and 40 loci, between 10 and 60 loci, between 10 and 80 loci, between 10 and 100 loci, between 10 and 150 loci, between 10 and 200 loci, between 10 and 250 loci, between 10 and 300 loci, between 10 and 350 loci, between 10 and 400 loci, between 10 and 450 loci, between 10 and 500 loci, between 20 and 40 loci, between 20 and 60 loci, between 20 and 80 loci, between 20 and 100 loci, between 20 and 150 loci, between 20 and 200 loci, between 20 and 250 loci, between 20 and 300 loci, between 20 and 350 loci, between 20 and 400 loci, between 20 and 500 loci, between 40 and 60 loci, between 40 and 80 loci, between 40 and 100 loci, between 40 and 150 loci, between 40 and 200 loci, between 40 and 250 loci, between 40 and 300 loci, between 40 and 350 loci, between 40 and 400 loci, between 40 and 500 loci, between 60 and 80 loci, between 60 and 100 loci, between 60 and 150 loci, between 60 and 200 loci, between 60 and 250 loci, between 60 and 300 loci, between 60 and 350 loci, between 60 and 400 loci, between 60 and 500 loci, between 80 and 100 loci, between 80 and 150 loci, between 80 and 200 loci, between 80 and 250 loci, between 80 and 300 loci, between 80 and 350 loci, between 80 and 400 loci, between 80 and 500 loci, between 100 and 150 loci, between 100 and 200 loci, between 100 and 250 loci, between 100 and 300 loci, between 100 and 350 loci, between 100 and 400 loci, between 100 and 500 loci, between 150 and 200 loci, between 150 and 250 loci, between 150 and 300 loci, between 150 and 350 loci, between 150 and 400 loci, between 150 and 500 loci, between 200 and 250 loci, between 200 and 300 loci, between 200 and 350 loci, between 200 and 400 loci, between 200 and 500 loci, between 250 and 300 loci, between 250 and 350 loci, between 250 and 400 loci, between 250 and 500 loci, between 300 and 350 loci, between 300 and 400 loci, between 300 and 500 loci, between 350 and 400 loci, between 350 and 500 loci, or between 400 and 500 loci.
[0021] In some embodiments, the one or more gene loci comprise ABL1, ACVR1B, AKT1, AKT2, AKT3, ALK, ALOX12B, AMER1, APC, AR, ARAF, ARFRP1, ARID1A, ASXL1, ATM, ATR, ATRX, AURKA, AURKB, AXIN1, AXL, BAP1, BARD1, BCL2, BCL2L1, BCL2L2, BCL6, BCOR, BCORL1, BCR, BRAF, BRCA1, BRCA2, BRD4, BRIP1, BTG1, BTG2, BTK, CALR, CARD11, CASP8, CBFB, CBL, CCND1, CCND2, CCND3, CCNE1, CD22, CD274, CD70, CD74, CD79A, CD79B, CDC73, CDH1, CDK12, CDK4, CDK6, CDK8, CDKN1A, CDKN1B, CDKN2A, CDKN2B, CDKN2C, CEBPA, CHEK1, CHEK2, CIC, CREBBP, CRKL, CSF1R, CSF3R, CTCF, CTNNA1, CTNNB1, CUL3, CUL4A, CXCR4, CYP17A1, DAXX, DDR1, DDR2, DIS3, DNMT3A, DOT1L, EED, EGFR, EMSY (Cl lorf30), EP300, EPHA3, EPHB1, EPHB4, ERBB2, ERBB3, ERBB4, ERCC4, ERG, ERRFI1, ESRI, ETV4, ETV5, ETV6, EWSR1, EZH2, EZR, FAM46C, FANCA, FANCC, FANCG, FANCL, FAS, FBXW7, FGF10, FGF12, FGF14, FGF19, FGF23, FGF3, FGF4, FGF6, FGFR1, FGFR2, FGFR3, FGFR4, FH, FLCN, FLT1, FLT3, FOXL2, FUBP1, GABRA6, GATA3, GATA4, GATA6, GID4 (C17orf39), GNA11, GNA13, GNAQ, GNAS, GRM3, GSK3B, H3F3A, HDAC1, HGF, HNF1A, HRAS, HSD3B1, ID3, IDH1, IDH2, IGF1R, IKBKE, IKZF1, INPP4B, IRF2, IRF4, IRS2, JAK1, JAK2, JAK3, JUN, KDM5A, KDM5C, KDM6A, KDR, KEAP1, KEL, KIT, KLHL6, KMT2A (MLL), KMT2D (MLL2), KRAS, LTK, LYN, MAF, MAP2K1, MAP2K2, MAP2K4, MAP3K1, MAP3K13, MAPK1, MCL1, MDM2, MDM4, MED12, MEF2B, MEN1, MERTK, MET, MITF, MKNK1, MLH1, MPL, MRE11A, MSH2, MSH3, MSH6, MST1R, MTAP, MTOR, MUTYH, MYB, MYC, MYCL, MYCN, MYD88, NBN, NF1, NF2, NFE2L2, NFKBIA, NKX2-1, NOTCH1, NOTCH2, NOTCH3, NPM1, NRAS, NT5C2, NTRK1, NTRK2, NTRK3, NUTM1, P2RY8, PALB2, PARK2, PARP1, PARP2, PARP3, PAX5, PBRM1, PDCD1, PDCD1LG2, PDGFRA, PDGFRB, PDK1, PIK3C2B, PIK3C2G, PIK3CA, PIK3CB, PIK3R1, PIM1, PMS2, POLDI, POLE, PPARG, PPP2R1A, PPP2R2A, PRDM1, PRKAR1A, PRKCI, PTCHI, PTEN, PTPN11, PTPRO, QKI, RAC1, RAD21, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54L, RAFI, RARA, RBI, RBM10, REL, RET, RICTOR, RNF43, ROS1, RPTOR, RSPO2, SDC4, SDHA, SDHB, SDHC, SDHD, SETD2, SF3B1, SGK1, SLC34A2, SMAD2, SMAD4, SMARCA4, SMARCB1, SMO, SNCAIP, S0CS1, SOX2, SOX9, SPEN, SPOP, SRC, STAG2, STAT3, STK11, SUFU, SYK, TBX3, TEK, TERC, TERT, TET2, TGFBR2, TIPARP, TMPRSS2, TNFAIP3, TNFRSF14, TP53, TSC1, TSC2, TYRO3, U2AF1, VEGFA, VHL, WHSCI, WHSC1L1, WT1, XPO1, XRCC2, ZNF217, ZNF703, or any combination thereof.
[0022] In some embodiments, the one or more gene loci comprise ABL, ALK, ALL, B4GALNT1, BAFF, BCL2, BRAF, BRCA, BTK, CD19, CD20, CD3, CD30, CD319, CD38, CD52, CDK4, CDK6, CML, CRACC, CS1, CTLA-4, dMMR, EGFR, ERBB1, ERBB2, FGFR1- 3, FLT3, GD2, HDAC, HER1, HER2, HR, IDH2, IL-ip, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSI-H, mTOR, PARP, PD-1, PDGFR, PDGFRa, PDGFRp, PD-L1, PI3K5, PIGF, PTCH, RAF, RANKL, RET, ROS1, SLAMF7, VEGF, VEGFA, VEGFB, or any combination thereof.
[0023] In some embodiments, the method further comprises generating, by the one or more processors, a report indicating the estimated tumor fraction in the sample. In some embodiments, the method further comprises transmitting the report to a healthcare provider. In some embodiments, the report is transmitted via a computer network or a peer-to-peer connection.
[0024] Also disclosed herein are methods for determining circulating tumor DNA (ctDNA) fraction in a sample from a subject, the methods comprising: receiving, at one or more processors, sequence read data for a plurality of sequence reads obtained for the sample from the subject; determining, using the one or more processors, if the sequence read data is sufficient for performing copy number alteration (CNA) modeling; estimating, using the one or more processors, (1) the ctDNA fraction in the sample based on at least a tumor purity and a ploidy of the sample using a model if the sequence read data is determined to be sufficient for performing CNA modeling, or (2) the ctDNA fraction in the sample based on identification of at least one tumor somatic short variant in the sequence read data if the sequence read data is determined to be insufficient for performing CNA modeling; and outputting, using the one or more processors, the estimated ctDNA fraction in the sample.
[0025] In some embodiments, the method further comprises comparing the estimated ctDNA fraction to at least one predetermined threshold, and outputting a status call of at least tumor fraction-high (TF-high) or tumor fraction-low (TF-low) for the sample based on the comparison.
[0026] In some embodiments, determining if the sequence read data is sufficient for performing CNA modeling comprises determining sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof, for at least one genomic locus to which the plurality of sequence reads map. In some embodiments, the at least one genomic locus comprises at least one single nucleotide polymorphism (SNP) locus. In some embodiments, the determination of sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof, for the at least one genomic locus is based on pre-processing of the sequence read data.
[0027] In some embodiments, performing CNA modeling comprises: determining, using the one or more processors, a copy number model including a sample tumor purity, a sample ploidy, and a copy number of multiple genomic segments that accounts for an observed sequence coverage ratio data, allele fraction data for at least one genomic locus within one or more subgenomic intervals to which the plurality of sequence reads map. In some embodiments, the sequence coverage ratio data is determined by aligning the plurality of sequence reads that overlap the at least one genomic locus within the one or more subgenomic intervals in the sample and in a control sample to a reference genome, and determining a number of sequence reads that overlap the at least one genomic locus within the one or more subgenomic intervals in the sample and in the control sample. In some embodiments, the control sample is a paired normal sample, a process-matched control sample, or a panel of normal control sample. In some embodiments, the allele fraction data is determined by aligning the plurality of sequence reads that overlap the at least one genomic locus within the one or more subgenomic intervals in the sample to a reference genome, detecting a number of alleles present at the at least one genomic locus, and determining an allele fraction for at least one of the alleles present at the at least one genomic locus.
[0028] In some embodiments, performing CNA modeling further comprises generating segmentation data by: aligning a plurality of sequence reads that overlap the at least one genomic locus within the one or more subgenomic intervals in the sample to a reference genome, and processing the aligned sequence read data, coverage ratio data, and allele fraction data using a pruned exact linear time (PELT) method to determine a number of segments required to account for the aligned sequence read data, wherein each segment has a same copy number. In some embodiments, the copy number model also outputs the segmentation data.
[0029] In some embodiments, the copy number model predicts a copy number for the at least one genomic locus based on the sequence coverage ratio data and allele fraction data. In some embodiments, the sequence coverage ratio data further comprises sequence coverage ratio data for single nucleotide polymorphisms (SNPs) and introns associated with the at least one genomic locus.
[0030] In some embodiments, the copy number model also predicts a tumor purity and a ploidy for the sample. In some embodiments, the ploidy for the sample has a value ranging from 1 to 8.
[0031] In some embodiments, amplification is detected when the copy number for the corresponding segment is greater than or equal to the ploidy of the sample. In some embodiments, an amplification is detected when the copy number for the corresponding segment is greater than or equal to the ploidy of the sample plus a first predetermined value. In some embodiments, the first predetermined value is a value ranging from 2 to 500. In some embodiments, the first predetermined value is a value ranging from 2 to 10.
[0032] In some embodiments, an amplification is detected when the copy number for the corresponding segment is greater than or equal to the ploidy of the sample plus a second predetermined value and the genomic locus is a member of a first predefined set of genomic loci. In some embodiments, the second predetermined value is a value ranging from 0 to 500. In some embodiments, the second predetermined value is a value ranging from 2 to 10. In some embodiments, the first predefined set of genomic loci comprises one or more druggable gene target loci, prognostic gene loci, oncogene loci, or any combination thereof. In some embodiments, the first predefined set of genomic loci comprises the AR and ERBB2 gene loci.
[0033] In some embodiments, detection of deletions comprises identifying homozygous deletions of the at least one genomic locus in a corresponding segment. In some embodiments, homozygous deletions are detected by determining a total copy number for a given genomic locus that is equal to the sum of the copy numbers for a first allele and a second allele at the genomic locus. In some embodiments, the first allele is a major allele and the second allele is a minor allele. In some embodiments, a homozygous deletion is called if the total copy number for a given genomic locus is equal to a third predetermined value. In some embodiments, the third predetermined value is about zero.
[0034] In some embodiments, detection of deletions comprises identifying heterozygous deletions of the at least one genomic locus in a corresponding segment. In some embodiments, a heterozygous deletion is called if a copy number for a first allele at a given genomic locus is equal to a fourth predetermined value, and a copy number for a second allele at the given genomic locus in not equal to the fourth predetermined value. In some embodiments, the fourth predetermined value is about zero. In some embodiments, the first allele is a major allele and the second allele is a minor allele.
[0035] In some embodiments, the detection of deletions comprises identifying partial deletions of the at least one genomic locus in a corresponding segment. In some embodiments, a partial deletion is called for a given genomic locus if log2 ratios (L2Rs) for neighboring genomic loci, single nucleotide polymorphisms (SNPs), and introns are significantly different than the log2 ratio for the genomic locus, and the log2 ratio for the given genomic locus is significantly different from a distribution of L2Rs for non-neighboring genomic loci, single nucleotide polymorphisms (SNPs), and introns.
[0036] In some embodiments, estimating the ctDNA fraction based on at least the sample tumor purity and sample ploidy using the CNA model comprises using a formula that describes a physical relationship between ctDNA fraction and sample tumor purity and sample ploidy. In some embodiments, the formula is given by:
Figure imgf000015_0001
where p is sample tumor purity, and y is ploidy.
[0037] In some embodiments, estimating the ctDNA fraction based on at least one somatic short variant detected in the sequence read data comprises: obtaining a list of short variants detected in the sequence read data; applying a set of selection rules to the list of detected short variants to identify tumor somatic short variants; and estimating the ctDNA fraction based on the presence of at least one identified tumor somatic short variant.
[0038] In some embodiments, estimating the ctDNA fraction based on the at least one tumor somatic short variant comprises: determining, using the one or more processors, a variant allele frequency (VAF) for one or more variants detected in the sequence read data; generating, using the one or more processors, an empirical distribution of ctDNA fraction values corresponding to the determined VAF for the one or more variants based on historical data; fitting, using the one or more processors, a model to the empirical distribution of ctDNA fraction values; and determining a ctDNA fraction for the sample based on the model. In some embodiments, the method further comprises determining a confidence interval for the ctDNA fraction based on the model. In some embodiments, the one or more variants comprise one or more short variants. In some embodiments, the one or more short variants comprise one or more somatic short variants. In some embodiments, the one or more somatic short variants are known not to be associated with clonal hematopoiesis of indeterminate potential (CHIP).
[0039] In some embodiments, generating the empirical distribution of ctDNA fraction values comprises calculating a ctDNA fraction value based on a known copy number for one or more tumor somatic short variants and a corresponding known sample ploidy for a plurality of historical subject samples having a known VAF for the one or more tumor somatic short variants that is substantially the same as the determined VAF for the one or more tumor somatic short variants.
[0040] In some embodiments, generating the empirical distribution of ctDNA fraction values comprises pre-calculating a ctDNA fraction value based on a known copy number for the one or more tumor somatic short variants and a corresponding known sample ploidy for a plurality of historical subject samples having a range of VAF values for the one or more tumor somatic short variants, and selecting a subset of the pre-calculated ctDNA fraction values that corresponds to samples having a known VAF for the one or more tumor somatic short variants that is substantially the same as the determined VAF for the one or more tumor somatic short variants.
[0041] In some embodiments, ctDNA fraction values are calculated or selected for a tumor somatic variant that exhibits the highest VAF in the sample from the subject. In some embodiments, ctDNA fraction values are calculated or selected for a rank-ordered set of two or more tumor somatic short variants that exhibit the highest rank-ordered VAFs in the sample from the subject. In some embodiments, the ctDNA fraction values are calculated or selected for a predetermined set of two or more tumor somatic short variants detected in the sample from the subject. In some embodiments, the ctDNA fraction values are calculated or selected for a predetermined set of two or more tumor somatic short variants detected in the sample from the subject that comprise known driver mutations. In some embodiments, the ctDNA fraction values are calculated or selected for all tumor somatic short variants detected in the sample from the subject.
[0042] In some embodiments, ctDNA fraction values are calculated or pre-calculated based on the known VAF for the one or more tumor somatic short variants, the known copy number for the one or more tumor somatic short variants, and the corresponding known sample ploidy for the plurality of historical subject samples. In some embodiments, ctDNA fraction values are calculated or pre-calculated based on the known VAF for the one or more tumor somatic short variants, the known copy number for the one or more tumor somatic short variants, and the corresponding known sample ploidy for the plurality of historical subject samples by solving a set of equations that describe: (i) a relationship between ctDNA fraction, sample tumor purity, and sample ploidy, and (ii) a relationship between somatic VAF, sample tumor purity, copy number at the genomic location(s) of the one or more tumor somatic short variants, and variant allele number for each of the one or more tumor somatic short variants, to eliminate sample tumor purity and derive a relationship for ctDNA fraction as a function of somatic VAF, sample ploidy, copy number at a genomic location of the one or more tumor somatic short variants, and variant allele number for the one or more tumor somatic short variants.
[0043] In some embodiments, ctDNA fraction values are calculated or pre-calculated based on the known VAF for the one or more tumor somatic short variants, the known copy number for the one or more tumor somatic short variants, and the corresponding known sample ploidy for the plurality of historical subject samples by solving a set of equations comprising: a first equation that equates ctDNA fraction to a product of sample tumor purity and sample ploidy, divided by a sum of the product of sample tumor purity and sample ploidy and a product of two times a quantity of one minus sample tumor purity; and a second equation that equates somatic VAF to a product of sample tumor purity and a variant allele number for each of the one or more tumor somatic short variants, divided by a sum of a product of sample tumor purity and copy number at a genomic location of the one or more tumor somatic short variants and a product of two times a quantity of one minus sample tumor purity; to eliminate sample tumor purity and derive a relationship that equates ctDNA fraction to sample ploidy divided by a quantity equal to sample ploidy minus the copy number at a genomic location of the one or more tumor somatic short variants plus a ratio of variant allele number for the one or more tumor somatic short variants to somatic VAF for each of the one or more tumor somatic short variants.
[0044] In some embodiments ctDNA fraction values are calculated or pre-calculated based on the known VAF for the one or more tumor somatic short variants, the known copy number for the one or more tumor somatic short variants, and the corresponding known sample ploidy for the plurality of historical subject samples by solving a set of equations:
Figure imgf000018_0001
pV Somatic VAF = — — — — - C + 2(l - ) to eliminate p and obtain a relationship between ctDNA fraction and somatic VAF described by:
Figure imgf000018_0002
where p is sample tumor purity, y is sample ploidy, C is the copy number at a genomic location of the one or more tumor somatic short variants, and V is a variant allele number for each of the one or more tumor somatic short variants.
[0045] In some embodiments, the plurality of historical subject samples comprises solid biopsy samples, liquid biopsy samples, or any combination thereof. In some embodiments, the plurality of historical subject samples comprises cancer samples. In some embodiments, the plurality of historical subject samples comprises samples for a single type of cancer. In some embodiments, the plurality of historical subject samples comprises samples for multiple types of cancer.
[0046] In some embodiments, the plurality of historical subject samples comprise bladder cancer samples, breast cancer samples, colorectal cancer samples, endometrial cancer samples, kidney cancer samples, leukemia samples, liver cancer samples, lung cancer samples, melanoma samples, non-Hodgkin lymphoma samples, pancreatic cancer samples, prostate cancer samples, thyroid cancer samples, or any combination thereof.
[0047] In some embodiments, the plurality of historical subject samples comprise acute lymphoblastic leukemia (Philadelphia chromosome positive) samples, acute lymphoblastic leukemia (precursor B-cell) samples, acute myeloid leukemia (FLT3+) samples, acute myeloid leukemia (with an IDH2 mutation) samples, anaplastic large cell lymphoma samples, basal cell carcinoma samples, B-cell chronic lymphocytic leukemia samples, bladder cancer samples, breast cancer (HER2 overexpressed/amplified) samples, breast cancer (HER2+) samples, breast cancer (HR+, HER2-) samples, cervical cancer samples, cholangiocarcinoma samples, chronic lymphocytic leukemia samples, chronic lymphocytic leukemia (with 17p deletion) samples, chronic myelogenous leukemia samples, chronic myelogenous leukemia (Philadelphia chromosome positive) samples, classical Hodgkin lymphoma samples, colorectal cancer samples, colorectal cancer (dMMR and MSI-H) samples, colorectal cancer (KRAS wild type) samples, cryopyrin-associated periodic syndrome samples, cutaneous T-cell lymphoma samples, dermatofibrosarcoma protuberans samples, diffuse large B-cell lymphoma samples, fallopian tube cancer samples, follicular B-cell non-Hodgkin lymphoma samples, follicular lymphoma samples, gastric cancer samples, gastric cancer (HER2+) samples, gastroesophageal junction (GEJ) adenocarcinoma samples, gastrointestinal stromal tumor samples, gastrointestinal stromal tumor (KIT+) samples, giant cell tumor of the bone samples, glioblastoma samples, granulomatosis with polyangiitis samples, head and neck squamous cell carcinoma samples, hepatocellular carcinoma samples, Hodgkin lymphoma samples, juvenile idiopathic arthritis samples, lupus erythematosus samples, mantle cell lymphoma samples, medullary thyroid cancer samples, melanoma samples, melanoma samples with a BRAF V600 mutation, melanoma samples with a BRAF V600E or V600K mutation, Merkel cell carcinoma samples, multicentric Castleman's disease samples, multiple hematologic malignancy samples including Philadelphia chromosome-positive ALL and CML, multiple myeloma samples, myelofibrosis samples, nonHodgkin’s lymphoma samples, nonresectable subependymal giant cell astrocytoma samples associated with tuberous sclerosis, non-small cell lung cancer samples, non-small cell lung cancer (ALK+) samples, non-small cell lung cancer (PD-L1+) samples, non-small cell lung cancer (with ALK fusion or ROS1 gene alteration) samples, non-small cell lung cancer (with BRAF V600E mutation) samples, non-small cell lung cancer samples (with an EGFR exon 19 deletion or exon 21 substitution (L858R) mutation), non-small cell lung cancer samples (with an EGFR T790M mutation), ovarian cancer samples, ovarian cancer samples (with a BRCA mutation), pancreatic cancer samples, pancreatic cancer samples, gastrointestinal cancer samples, lung origin neuroendocrine tumor samples, pediatric neuroblastoma samples, peripheral T-cell lymphoma samples, peritoneal cancer samples, prostate cancer samples, renal cell carcinoma samples, rheumatoid arthritis samples, small lymphocytic lymphoma samples, soft tissue sarcoma samples, solid tumor (MSLH/dMMR) samples, squamous cell cancer samples of the head and neck, squamous non-small cell lung cancer samples, thyroid cancer samples, thyroid carcinoma samples, urothelial cancer samples, urothelial carcinoma samples, Waldenstrom's macroglobulinemia samples, or any combination thereof.
[0048] In some embodiments, the model is a non-parametric probability density model.
[0049] In some embodiments, the determined ctDNA fraction for the sample is a most probable ctDNA fraction. In some embodiments, the determined ctDNA fraction for the sample is the mean, median, or mode of a dominant peak in the empirical distribution of ctDNA fraction values.
[0050] In some embodiments, the sample comprises DNA extracted from a blood sample, a plasma sample, a cerebrospinal fluid sample, a pleural effusion fluid sample, a sputum sample, a stool sample, a urine sample, or a saliva sample.
[0051] In some embodiments, estimating the ctDNA fraction is based on a determination of the maximum somatic allele frequency (MSAF) for the at least one tumor somatic short variant, the detection of one or more genomic rearrangements, a determination of micro satellite instability, or any combination thereof.
[0052] In some embodiments, the set of selection rules used to identify tumor somatic short variants in the list of detected short variants comprises: (i) removing short variants that appear on a blacklist of known germline variants, known clonal hematopoiesis of indeterminate potential (CHIP) variants, and known sequencing artifacts; (ii) retaining short variants that appear on a list of known tumor somatic short variants; (iii) retaining short variants that appear on a list of known genes that are prone to exhibit high amplification and that have an allele frequency that is higher than other somatic short variants in the sample; (iv) retaining short variants that appear on a list of known rearrangements; or any combination thereof.
[0053] In some embodiments, the blacklist of known germline variants, known clonal hematopoiesis of indeterminate potential (CHIP) variants, and known sequencing artifacts comprises a list of germline variants, CHIP variants, and sequencing artifacts, or any combination thereof, observed in historical sequencing data.
[0054] In some embodiments, the list of known tumor somatic short variants comprises somatic short variants determined to have a high prevalence ratio or a high odds ratio between tumor and white blood cells.
[0055] In some embodiments, the list of known genes that are prone to exhibit high amplification comprises KRAS, EGFR, CCND1, FGF19, FGF3, FGF4, MYC, AR, MDM2, CCNE1, ERBB2, WHSC1L1, FGFR1, ZNF703, or any combination thereof.
[0056] In some embodiments, the list of known rearrangements comprises fusions between the following gene pairs: TMPRSS2-ERG, ALK-EML4, FGFR3-TACC3, RET-KIF5B, or any combination thereof.
[0057] In some embodiments, the set of selection rules used to identify tumor somatic short variants in the list of detected short variants further comprises identifying short variants for which a fragment size shift between the reference allele and alternate allele is detected in sequence read data as being tumor somatic short variants.
[0058] In some embodiments, the estimated ctDNA fraction of the sample is used to diagnose or confirm a diagnosis of disease in the subject. In some embodiments, the disease is cancer. In some embodiments, the method further comprises selecting an anti-cancer therapy to administer to the subject based on the estimated ctDNA fraction of the sample. In some embodiments, the method further comprises determining an effective amount of an anti-cancer therapy to administer to the subject based on the estimated ctDNA fraction of the sample. In some embodiments, the method further comprises administering the anti-cancer therapy to the subject based on the estimated ctDNA fraction of the sample. In some embodiments, the anti-cancer therapy comprises chemotherapy, radiation therapy, immunotherapy, a targeted therapy, or surgery.
[0059] In some embodiments, the estimated ctDNA fraction is used as a prognostic biomarker for predicting a treatment outcome for a subject having cancer. In some embodiments, the cancer is prostate cancer.
[0060] In some embodiments, the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
[0061] Disclosed herein are methods for predicting a treatment outcome for a subject having cancer, the method comprising: receiving, at one or more processors, sequence read data for a plurality of sequence reads obtained for the sample from the subject; determining, using the one or more processors, if the sequence read data is sufficient for performing copy number alteration (CNA) modeling; estimating, using the one or more processors, a ctDNA fraction in the sample based on at least a sample tumor purity and a sample ploidy derived from a CNA model if the sequence read data is determined to be sufficient for performing CNA modeling; or estimating, using the one or more processors, the ctDNA fraction in the sample based on identification of at least one tumor somatic short variant detected in the sequence read data if the sequence read data is determined to be insufficient for performing CNA modeling; outputting, using the one or more processors, the estimated ctDNA fraction in the sample; and based on a comparison of the estimated ctDNA fraction to a predetermined threshold, predicting the outcome of treating the subject with a specified anti-cancer therapy. In some embodiments, the predetermined threshold is determined based on an analysis of ctDNA fraction and survival data for a cohort of patients having the cancer. In some embodiments, the predetermined threshold is determined by adjusting an empirical threshold to maximize a sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), or any combination thereof, for the ctDNA fraction data for the cohort of patients having the cancer. In some embodiments, the cancer is prostate cancer. In some embodiments, the anti-cancer therapy comprises an enzalutamide challenge following abiraterone treatment. [0062] Disclosed herein are methods for diagnosing a disease, the methods comprising: diagnosing that a subject has the disease based on a determination of ctDNA fraction for a sample from the subject, wherein ctDNA fraction is determined according to any of the methods described herein.
[0063] Disclosed herein are methods of selecting an anti-cancer therapy, the methods comprising: responsive to determining a ctDNA fraction for a sample from a subject, selecting an anti-cancer therapy for the subject, wherein ctDNA fraction is determined according to any of the methods described herein.
[0064] Disclosed herein are methods of treating a cancer in a subject, comprising: responsive to determining a ctDNA fraction for a sample from the subject, administering an effective amount of an anti-cancer therapy to the subject, wherein ctDNA fraction is determined according to any of the methods described herein.
[0065] Disclosed herein are methods for monitoring cancer progression or recurrence in a subject, the method comprising: determining a first ctDNA fraction in a first sample obtained from the subject at a first time point according to any of the methods described herein; determining a second ctDNA fraction in a second sample obtained from the subject at a second time point; and comparing the first ctDNA fraction to the second ctDNA fraction, thereby monitoring the cancer progression or recurrence. In some embodiments, the second ctDNA fraction for the second sample is determined according to any of the methods described herein. In some embodiments, the method further comprises selecting an anti-cancer therapy for the subject in response to the cancer progression. In some embodiments, the method further comprises administering an anti-cancer therapy to the subject in response to the cancer progression. In some embodiments, the method further comprises adjusting an anti-cancer therapy for the subject in response to the cancer progression. In some embodiments, the method further comprises adjusting a dosage of the anti-cancer therapy or selecting a different anticancer therapy in response to the cancer progression. In some embodiments, the method further comprises administering the adjusted anti-cancer therapy to the subject. In some embodiments, the first time point is before the subject has been administered an anti-cancer therapy, and wherein the second time point is after the subject has been administered the anti-cancer therapy. In some embodiments, the subject has a cancer, is at risk of having a cancer, is being routine tested for cancer, or is suspected of having a cancer. In some embodiments, the cancer is a solid tumor. In some embodiments, the cancer is a hematological cancer. In some embodiments, the anti-cancer therapy comprises chemotherapy, radiation therapy, immunotherapy, a targeted therapy, or surgery.
[0066] In some embodiments, the methods disclosed herein may further comprise determining, identifying, or applying the value of ctDNA fraction for the sample as a diagnostic value associated with the sample. In some embodiments, the methods may further comprise generating a genomic profile for the subject based on the determination of ctDNA fraction. In some embodiments, the genomic profile for the subject further comprises results from a comprehensive genomic profiling (CGP) test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof. In some embodiments, the genomic profile for the subject further comprises results from a nucleic acid sequencing-based test.
[0067] In some embodiments, the disclosed methods may further comprise selecting an anticancer therapy, administering an anti-cancer therapy, or applying an anti-cancer therapy to the subject based on the generated genomic profile.
[0068] In some embodiments, the determination of ctDNA fraction for the sample is used in making suggested treatment decisions for the subject. In some embodiments, the determination of ctDNA fraction for the sample is used in applying or administering a treatment to the subject.
[0069] Disclosed herein are systems comprising: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: receive sequence read data for a plurality of sequence reads obtained for the sample from the subject; determine if the sequence read data is sufficient for performing copy number alteration (CNA) modeling; estimate using the one or more processors, (1) the ctDNA fraction in the sample based on at least a sample tumor purity and a ploidy of the sample using a model if the sequence read data is determined to be sufficient for performing CNA modeling, or (2) the ctDNA fraction in the sample based on identification of at least one tumor somatic short variant in the sequence read data if the sequence read data is determined to be insufficient for performing CNA modeling; and output the estimated ctDNA fraction in the sample. [0070] In some embodiments, the system further comprises instructions that, when executed by the one or more processors, cause the system to compare the estimated ctDNA fraction to at least one predetermined threshold, and output a status call of at least tumor fraction-high (TF-high) or tumor fraction-low (TF-low) for the sample based on the comparison.
[0071] In some embodiments, determining if the sequence read data is sufficient for performing CNA modeling comprises determining sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof, for at least one genomic locus to which the plurality of sequence reads map.
[0072] In some embodiments, performing CNA modeling comprises: determining, using the one or more processors, a copy number model including a sample tumor purity, a sample ploidy, and a copy number of multiple genomic segments in the ctDNA that accounts for an observed sequence coverage ratio data, allele fraction data for at least one genomic locus within one or more subgenomic intervals to which the plurality of sequence reads map.
[0073] In some embodiments, estimating the ctDNA fraction based on at least the sample tumor purity and sample ploidy using the CNA model comprises using a formula that describes a physical relationship between ctDNA fraction and sample tumor purity and sample ploidy.
[0074] In some embodiments, estimating the ctDNA fraction based on at least one somatic short variant detected in the sequence read data comprises: obtaining a list of short variants detected in the sequence read data; applying a set of selection rules to the list of detected short variants to identify tumor somatic short variants; and estimating the ctDNA fraction based on the presence of at least one identified tumor somatic short variant.
[0075] In some embodiments, the set of selection rules used to identify tumor somatic short variants in the list of detected short variants comprises: (i) removing short variants that appear on a blacklist of known germline variants, known clonal hematopoiesis of indeterminate potential (CHIP) variants, and known sequencing artifacts; (ii) retaining short variants that appear on a list of known tumor somatic short variants; (iii) retaining short variants that appear on a list of known genes that are prone to exhibit high amplification and that have an allele frequency that is higher than other somatic short variants in the sample; (iv) retaining short variants that appear on a list of known rearrangements; or any combination thereof. [0076] In some embodiments, estimating the ctDNA fraction based on the at least one tumor somatic short variant comprises: determining, using the one or more processors, a variant allele frequency (VAF) for one or more variants detected in the sample based on the sequence read data; generating, using the one or more processors, an empirical distribution of ctDNA fraction values corresponding to the determined VAF for the one or more variants based on historical data; fitting, using the one or more processors, a model to the empirical distribution of ctDNA fraction values; and determining a ctDNA fraction for the sample based on the model.
[0077] Also disclosed herein are non-transitory computer-readable storage media storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: receive sequence read data for a plurality of sequence reads obtained for the sample from the subject; determine if the sequence read data is sufficient for performing copy number alteration (CNA) modeling; estimate using the one or more processors, (1) the ctDNA fraction in the sample based on at least a tumor purity and a ploidy of the sample using a model if the sequence read data is determined to be sufficient for performing CNA modeling, or (2) the ctDNA fraction in the sample based on identification of at least one tumor somatic short variant in the sequence read data if the sequence read data is determined to be insufficient for performing CNA modeling; and output the estimated ctDNA fraction in the sample.
[0078] In some embodiments, the non-transitory computer-readable storage medium further comprises instructions that, when executed by the one or more processors of the system, cause the system to compare the estimated ctDNA fraction to at least one predetermined threshold, and output a status call of at least tumor fraction-high (TF-high) or tumor fraction-low (TF-low) for the sample based on the comparison.
[0079] In some embodiments, determining if the sequence read data is sufficient for performing CNA modeling comprises determining sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof, for at least one genomic locus to which the plurality of sequence reads map.
[0080] In some embodiments, performing CNA modeling comprises: determining, using the one or more processors, a copy number model including a sample tumor purity, a sample ploidy, and a copy number of multiple genomic segments in the ctDNA that accounts for an observed sequence coverage ratio data, allele fraction data for at least one genomic locus within one or more subgenomic intervals to which the plurality of sequence reads map.
[0081] In some embodiments, estimating the ctDNA fraction based on at least the sample tumor purity and sample ploidy using the CNA model comprises using a formula that describes a physical relationship between ctDNA fraction and sample tumor purity and sample ploidy.
[0082] In some embodiments, estimating the ctDNA fraction based on at least one somatic short variant detected in the sequence read data comprises: obtaining a list of short variants detected in the sequence read data; applying a set of selection rules to the list of detected short variants to identify tumor somatic short variants; and estimating the ctDNA fraction based on the presence of at least one identified tumor somatic short variant.
[0083] In some embodiments, the set of selection rules used to identify tumor somatic short variants in the list of detected short variants comprises: (i) removing short variants that appear on a blacklist of known germline variants, known clonal hematopoiesis of indeterminate potential (CHIP) variants, and known sequencing artifacts; (ii) retaining short variants that appear on a list of known tumor somatic short variants; (iii) retaining short variants that appear on a list of known genes that are prone to exhibit high amplification and that have an allele frequency that is higher than other somatic short variants in the sample; (iv) retaining short variants that appear on a list of known rearrangements; or any combination thereof.
[0084] In some embodiments, estimating the ctDNA fraction based on the at least one tumor somatic short variant comprises: determining, using the one or more processors, a variant allele frequency (VAF) for one or more variants detected in the sample based on the sequence read data; generating, using the one or more processors, an empirical distribution of ctDNA fraction values corresponding to the determined VAF for the one or more variants based on historical data; fitting, using the one or more processors, a model to the empirical distribution of ctDNA fraction values; and determining a ctDNA fraction for the sample based on the model.
[0085] It should be appreciated that all combinations of the foregoing concepts and additional concepts discussed in greater detail below (provided such concepts are not mutually inconsistent) are contemplated as being part of the inventive subject matter disclosed herein. In particular, all combinations of claimed subject matter appearing at the end of this disclosure are contemplated as being part of the inventive subject matter disclosed herein. INCORPORATION BY REFERENCE
[0086] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference in their entirety to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference in its entirety. In the event of a conflict between a term herein and a term in an incorporated reference, the term herein controls.
BRIEF DESCRIPTION OF THE DRAWINGS
[0087] Various aspects of the disclosed methods, devices, and systems are set forth with particularity in the appended claims. A better understanding of the features and advantages of the disclosed methods, devices, and systems will be obtained by reference to the following detailed description of illustrative embodiments and the accompanying drawings, of which:
[0088] FIG. 1 provides a non-limiting example of a process flowchart for estimating ctDNA fraction according to one embodiment of the methods disclosed herein.
[0089] FIG. 2 provides a non-limiting example of a process flowchart for estimating ctDNA fraction according to another embodiment of the methods disclosed herein.
[0090] FIG. 3 depicts an exemplary computing device or system in accordance with one embodiment of the present disclosure.
[0091] FIG. 4 depicts an exemplary computer system or computer network, in accordance with some instances of the systems described herein.
[0092] FIGS. 5A-B provide non-limiting examples of the application of the disclosed methods for determining tumor fraction in liquid biopsy samples to predict the probability of progression- free survival and the probability of survival for prostate cancer patients stratified according to tumor fraction (TF). FIG. 5A: plot of the probability of progression-free survival (PFS) as a function of time following initiation of enzalutamide treatment for prostate cancer patients previously treated with abiraterone. FIG. 5B: plot of the probability of overall survival (OS) as a function of time following initiation of enzalutamide treatment for prostate cancer patients previously treated with abiraterone. [0093] FIGS. 6A-B provide non-limiting examples of the use of prostate specific antigen (PSA) as a prognostic biomarker for the probability of progression-free survival and the probability of survival for prostate cancer patients stratified according to prostate specific antigen (PSA) level. FIG. 6A: plot of the probability of progression-free survival (PFS) as a function of time following initiation of enzalutamide treatment for prostate cancer patients previously treated with abiraterone. FIG. 6B: plot of the probability of overall survival (OS) as a function of time following initiation of enzalutamide treatment of prostate cancer patients previously treated with abiraterone.
DETAILED DESCRIPTION
[0094] Methods and systems are described that enable more accurate determination of ctDNA fraction based on sequence read data for cell-free DNA samples derived from liquid biopsy. The disclosed methods are based on a combined approach that includes: (i) estimation of ctDNA fraction based on a copy number model that specifies a tumor fraction for the sample and exact copy number at probed genomic locations that explain the observed sequence coverage and single nucleotide polymorphism (SNP) allele frequency data, and/or (ii) estimation of ctDNA fraction based on a theoretical relationship between tumor fraction and somatic variant allele frequency given a copy number profile .
[0095] The more accurate determinations of ctDNA fraction enabled by using the disclosed methods can, in turn, be used to detect, monitor, and/or make prognostic predictions for treatment outcomes for cancer patients.
[0096] In some instances, for example, methods for determining circulating tumor DNA (ctDNA) fraction in a sample from a subject are described that comprise: receiving sequence read data for a plurality of sequence reads obtained for the sample from the subject; determining if the sequence read data is sufficient for performing copy number alteration (CNA) modeling; estimating (1) the ctDNA fraction in the sample based on at least a sample tumor purity and a ploidy of the sample if the sequence read data is determined to be sufficient for performing CNA modeling, or (2) the ctDNA fraction in the sample based on identification of at least one tumor somatic short variant in the sequence read data if the sequence read data is determined to be insufficient for performing CNA modeling; and outputting the estimated ctDNA fraction in the sample. 1 [0097] In some instances, the method may further comprise comparing the estimated ctDNA fraction to at least one predetermined threshold, and outputting a status call of at least tumor fraction-high (TF-high) or tumor fraction-low (TF-low) for the sample based on the comparison.
Definitions
[0098] Unless otherwise defined, all of the technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art in the field to which this disclosure belongs.
[0099] As used in this specification and the appended claims, the singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise. Any reference to “or” herein is intended to encompass “and/or” unless otherwise stated.
[0100] ‘ ‘About” and “approximately” shall generally mean an acceptable degree of error for the quantity measured given the nature or precision of the measurements. Exemplary degrees of error are within 20 percent (%), typically, within 10%, and more typically, within 5% of a given value or range of values.
[0101] As used herein, the terms "comprising" (and any form or variant of comprising, such as "comprise" and "comprises"), "having" (and any form or variant of having, such as "have" and "has"), "including" (and any form or variant of including, such as "includes" and "include"), or "containing" (and any form or variant of containing, such as "contains" and "contain"), are inclusive or open-ended and do not exclude additional, un-recited additives, components, integers, elements, or method steps.
[0102] As used herein, the terms “individual,” “patient,” or “subject” are used interchangeably and refer to any single animal, e.g., a mammal (including such non-human animals as, for example, dogs, cats, horses, rabbits, zoo animals, cows, pigs, sheep, and non-human primates) for which treatment is desired. In particular embodiments, the individual, patient, or subject herein is a human.
[0103] The terms “cancer” and “tumor” are used interchangeably herein. These terms refer to the presence of cells possessing characteristics typical of cancer-causing cells, such as uncontrolled proliferation, immortality, metastatic potential, rapid growth and proliferation rate, and certain characteristic morphological features. Cancer cells are often in the form of a tumor, but such cells can exist alone within an animal, or can be a non-tumorigenic cancer cell, such as a leukemia cell. These terms include a solid tumor, a soft tissue tumor, or a metastatic lesion. As used herein, the term “cancer” includes premalignant, as well as malignant cancers.
[0104] As used herein, “treatment” (and grammatical variations thereof such as “treat” or “treating”) refers to clinical intervention (e.g., administration of an anti-cancer agent or anticancer therapy) in an attempt to alter the natural course of the individual being treated, and can be performed either for prophylaxis or during the course of clinical pathology. Desirable effects of treatment include, but are not limited to, preventing occurrence or recurrence of disease, alleviation of symptoms, diminishment of any direct or indirect pathological consequences of the disease, preventing metastasis, decreasing the rate of disease progression, amelioration or palliation of the disease state, and remission or improved prognosis.
[0105] As used herein, the term “subgenomic interval” (or “subgenomic sequence interval”) refers to a portion of a genomic sequence.
[0106] As used herein, the term "subject interval" refers to a subgenomic interval or an expressed subgenomic interval (e.g., the transcribed sequence of a subgenomic interval).
[0107] As used herein, the terms “variant sequence” or “variant” are used interchangeably and refer to a modified nucleic acid sequence relative to a corresponding “normal” or “wild-type” sequence. In some instances, a variant sequence may be a “short variant sequence” (or “short variant”), i.e., a variant sequence of less than about 50 base pairs in length.
[0108] The terms “allele frequency” and “allele fraction” are used interchangeably herein and refer to the fraction of sequence reads corresponding to a particular allele relative to the total number of sequence reads for a genomic locus.
[0109] The terms “variant allele frequency” and “variant allele fraction” are used interchangeably herein and refer to the fraction of sequence reads corresponding to a particular variant allele relative to the total number of sequence reads for a genomic locus.
[0110] As used herein, the term “tumor fraction” generally refers to the proportion of tumor- derived DNA molecules in the total DNA extracted from a sample. In the case of liquid biopsy samples, or cell-free DNA (cfDNA) samples derived therefrom, “circulating tumor DNA fraction” (or “ctDNA fraction”) refers to the proportion of cancer cell-derived (or tumor-derived) DNA in the total DNA present in the cfDNA sample.
[0111] The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.
Methods for determining circulating tumor DNA fraction in liquid biopsy samples
[0112] The methods described herein may be used to estimate the fraction of circulating tumor DNA contained in cell-free DNA samples isolated from liquid biopsy samples. These new methods overcome the problems of inadequate sensitivity and/or specificity often encountered with existing methods for detecting circulating tumor DNA and estimating ctDNA fraction in cell-free DNA samples.
[0113] The more accurate determinations of ctDNA fraction (e.g., the tumor fraction for a cell free DNA (cfDNA) sample) enabled by the disclosed methods allow determinations of ctDNA fraction to be used for detecting, monitoring, and/or making prognostic predictions of treatment outcomes for cancer patients. For example, the data presented in Example 3 below indicates that ctDNA fraction may provide a better prognostic biomarker for treatment outcomes in prostate cancer patients than prostate specific antigen (PSA) levels.
[0114] The disclosed methods leverage the unique features of a novel genomic profiling assay that utilizes a targeted sequencing approach (see, e.g., U.S. Patent No. 9,340,830, which is incorporated herein by reference in its entirety). Due to the constraints of sequencing cost, many commercial sequencing approaches have taken either a “wide and shallow” approach (z.e., a large region of the genome e.g., the entire genome, or just the exomes) is sequenced with low depth), or a “narrow and deep” approach (i.e., only small, selected portions of the genome are sequenced, but to a great depth). The “wide and shallow” approach allows for detection of aneuploidy but cannot leverage short variant signal. The ‘narrow and deep” approach can detect short variants that are present at very small allele fraction, but is inevitably limited to detection of only those signals that arise from the narrow region(s) sequenced.
[0115] The novel genomic profiling assay referenced above uses a method comprising contacting a sequencing library with at least two different bait sets designed to hybridize to and select specific genomic regions of interest, where the different bait sets exhibit differential efficiency for selecting their respective genomic regions of interest and result in different sequencing depth for the different genomic regions of interest. Genomic regions that are expected to be hot spots for cancer-relevant somatic short variants are sequenced to great depth, thereby allowing for detection of somatic short variant signals at very low tumor fraction. In addition, a set of carefully selected additional small genomic regions scattered throughout the genome are sequenced to a moderate depth, thereby allowing for construction of a copy number model to detect tumor fraction based on aneuploidy.
[0116] As noted above, the methods described herein take advantage of this unique assay design to detect and estimate ctDNA fraction using both aneuploidy and the presence of other types of alterations that exist in a patient’s cancer genome, such as short variants, rearrangements, micro satellite instability, etc. The new methods described herein also take advantage of a novel copy number modeling approach (see, e.g., PCT International Patent Publication No. WO 2023/060236, entitled “Methods and Systems for Automated Calling of Copy Number Alterations”, which is incorporated herein by reference in its entirety) to leverage aneuploidy signal, and of a new set of heuristic rules for filtering variant data to identify tumor-derived variants, to detect circulating tumor DNA and estimate ctDNA fraction. The method was verified against physical ground truth data for ctDNA fraction obtained from an analysis of sequence read data for paired plasma/buffy coat (PBMC) samples.
[0117] FIG. 1 provides a non-limiting example of a flowchart for a process 100 for estimating the ctDNA fraction of a liquid biopsy sample (or cell-free DNA sample derived therefrom). Process 100 can be performed, for example, using one or more electronic devices implementing a software platform. In some examples, process 100 is performed using a client-server system, and the blocks of process 100 are divided up in any manner between the server and a client device. In other examples, the blocks of process 100 are divided up between the server and multiple client devices. Thus, while portions of process 100 are described herein as being performed by particular devices of a client-server system, it will be appreciated that process 100 is not so limited. In other examples, process 100 is performed using only a client device or only multiple client devices. In process 100, some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the process 100. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.
[0118] At step 102 in FIG. 1, sequence read data for a plurality of sequence reads obtained for a sample from a subject (e.g., a patient) is received (e.g., by one or more processors of a system configured to perform process 100). In some instances, the sequence read data is derived by sequencing the cell-free DNA (cfDNA) extracted from a liquid biopsy sample. In some instances, the sequence read data may be received by a system as a Binary Alignment Map (BAM) file.
[0119] In some instances, the sequence read data for the plurality of sequence reads may be derived using a targeted sequencing technique, e.g. , a targeted exome sequencing method. In some instances, the sequence read data may be derived using, e.g., a whole genome or whole exome sequencing method, as opposed to a targeted exome sequencing method, to increase the number of genomic features (e.g., the number of short variants, rearrangements, etc.) detected. In some instances, the sequencing method may be based on a sequencing by synthesis (SBS) technology, e.g., a Next Generation Sequencing (NGS) technology. In some instances, any of a variety of alternative sequencing methods (e.g., sequencing by binding (SBS) or sequencing by avidity (SBA) methods) and sequencing technologies (e.g., nanopore-based or microarray-based sequencing technologies) may be used to generate the sequence read data.
[0120] In some instances, liquid biopsy sample may comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
[0121] At step 104 in FIG. 1, the sequence read data is evaluated to determine if it is of sufficient quality for performing copy number alteration (CNA) modeling. The sequence read data may be evaluated, for example, by determining the level of noise in the data and determining if aneuploidy can be detected based on, e.g., a deflection of coverage log ratio (LR) and SNP frequency values from what would be expected if there is no aneuploidy present.
[0122] At step 106 in FIG. 1, an estimate of circulating tumor DNA (ctDNA) fraction in the sample is determined based on a copy number alteration (CNA) model if the sequence read data has been determined to be sufficient for performing CNA modeling. The ctDNA fraction for the sample is estimated based on a copy number profile (or aneuploidy signal) determined for the sequence read data based on the model, as will described in more detail below in reference to
FIG. 2.
[0123] At step 108 in FIG. 1, an estimate of circulating tumor DNA (ctDNA) fraction in the sample is determined based on one or more somatic short variants (e.g., tumor-derived somatic short variants) detected in the sequence read data from the sample if the sequence read data has been determined to be insufficient for performing CNA modeling. The ctDNA fraction for the sample is estimated based on: (i) applying a set of heuristic rules to discriminate between tumor- derived somatic short variants (tumor somatic short variants) and non-tumor-derived (e.g., germline) somatic short variants, and (ii) the use of a model based on an empirical distribution of ctDNA fraction values corresponding to the determined VAF for one or more short variants in historical sequencing data, as will be described in more detail below in reference to FIG. 2.
[0124] At step 110 in FIG. 1, the estimate of ctDNA fraction for the sample is output. In some instances, the method may further comprise comparing the estimated ctDNA fraction to at least one predetermined threshold, and outputting a status call of, e.g., tumor fraction-high (TF-high) or tumor fraction-low (TF-low), for the sample based on the comparison.
[0125] In some instances, the estimated ctDNA fraction may be compared to at least two predetermined thresholds, and a status call of, e.g., tumor fraction-high (TF-high), tumor- fraction-medium (TF-medium), or tumor fraction-low (TF-low), may be output for the sample based on the comparison.
[0126] In some instances, a predetermined threshold (or TF threshold) used to stratify samples (e.g., a first, second, or third predetermined threshold) may have a value of, e.g., 0.01, 0.02, 0.03, 0.04, 0.05, 0.1, 0.15, 0.2, or 0.25.
[0127] In some instances, ctDNA fraction estimated determined using the methods disclosed herein may be used as a prognostic biomarker for predicting a treatment outcome for a subject having cancer. In some instances, for example, the cancer may be prostate cancer.
[0128] FIG. 2 provides a non-limiting example of a flowchart for a process 200 for estimating ctDNA fraction of a liquid biopsy sample (or cell-free DNA sample derived therefrom). Process 200 can be performed, for example, using one or more electronic devices implementing a software platform. In some examples, process 200 is performed using a client-server system, and the blocks of process 200 are divided up in any manner between the server and a client device. In other examples, the blocks of process 200 are divided up between the server and multiple client devices. Thus, while portions of process 200 are described herein as being performed by particular devices of a client-server system, it will be appreciated that process 200 is not so limited. In other examples, process 200 is performed using only a client device or only multiple client devices. In process 200, some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the process 200. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.
[0129] At step 202 in FIG. 2, sequence read data for a plurality of sequence reads obtained for a sample from a subject (e.g., a patient) is received (e.g., by one or more processors of a system configured to perform process 200). In some instances, the sequence read data is derived by sequencing the cell-free DNA (cfDNA) extracted from a liquid biopsy sample. In some instances, the sequence read data may be received by a system as a Binary Alignment Map (BAM) file.
[0130] In some instances, the sequence read data for the plurality of sequence reads may be derived using a targeted sequencing technique, e.g. , a targeted exome sequencing method. In some instances, the sequence read data may be derived using, e.g., a whole genome or whole exome sequencing method, as opposed to a targeted exome sequencing method, to increase the number of genomic features (e.g., the number of short variants, rearrangements, etc.) detected. In some instances, the sequencing method may be based on a sequencing by synthesis (SBS) technology, e.g., a Next Generation Sequencing (NGS) technology. In some instances, any of a variety of alternative sequencing methods (e.g., sequencing by binding (SBS) or sequencing by avidity (SBA) methods) and sequencing technologies (e.g., nanopore-based or microarray-based sequencing technologies) may be used to generate the sequence read data.
[0131] In some instances, liquid biopsy sample may comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva. [0132] In some instances, the sample may comprise DNA (e.g., cell-free DNA) extracted from a blood sample, a plasma sample, a cerebrospinal fluid sample, a pleural effusion fluid sample, a sputum sample, a stool sample, a urine sample, or a saliva sample.
[0133] At step 204 in FIG. 2, the sequence read data is evaluated to determine if it is of sufficient quality for performing copy number alteration (CNA) modeling. As noted above with reference to step 104 in FIG. 1, the sequence read data may be evaluated, for example, by determining the level of noise in the data and performing a quick check to determine if aneuploidy can be detected.
[0134] In some instances, determining if the sequence read data is sufficient for performing CNA modeling may comprise determining sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof, for at least one genomic locus to which the plurality of sequence reads map. In some instances, for example, the at least one genomic locus may comprise at least one single nucleotide polymorphism (SNP) locus. In some instances, the determination of sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof, for the at least one genomic locus may be based by pre-processing the sequence read data.
[0135] At step 206 in FIG. 2, a copy number alteration (CNA) model is generated if the sequence read data has been determined to be sufficient for performing CNA modeling. Methods for modeling copy number and calling copy number alterations have been described in, for example, PCT International Patent Publication No. WO 2023/060236, entitled “Methods and Systems for Automated Calling of Copy Number Alterations”, which is incorporated herein by reference in its entirety.
[0136] In some instances, for example, performing CNA modeling may comprises: generating a copy number model that determines and outputs a sample tumor purity, a sample ploidy (or “ploidy”), and a copy number for multiple genomic segments that account for an observed sequence coverage ratio data, allele fraction data for at least one genomic locus within one or more subgenomic intervals to which the plurality of sequence reads map.
[0137] In some instances, the sequence coverage ratio data may be determined by aligning the plurality of sequence reads that overlap the at least one genomic locus e.g., at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or more than 100 genomic loci) within the one or more subgenomic intervals (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or more than 100 subgenomic intervals) in the sample and in a control sample to a reference genome (e.g., a human reference genome such as HG19 or HG38), and determining a number of sequence reads that overlap the at least one genomic locus within the one or more subgenomic intervals in the sample and in the control sample. In some instances, the control sample is a paired normal sample, a process-matched control sample, or a panel of normal control sample.
[0138] In some instances, the sequence coverage ratio data may further comprise sequence coverage ratio data for single nucleotide polymorphisms (SNPs) and introns associated with the at least one genomic locus.
[0139] In some instances, the allele fraction data may be determined by aligning the plurality of sequence reads that overlap the at least one genomic locus within the one or more subgenomic intervals in the sample to a reference genome (e.g., a human reference genome such as HG19 or HG38), detecting a number of alleles present at the at least one genomic locus, and determining an allele fraction for at least one of the alleles present at the at least one genomic locus.
[0140] In some instances, performing CNA modeling may further comprise generating segmentation data by aligning a plurality of sequence reads that overlap the at least one genomic locus within the one or more subgenomic intervals in the sample to a reference genome, and processing the aligned sequence read data, coverage ratio data, and allele fraction data using a pruned exact linear time (PELT) method to determine a number of segments required to account for the aligned sequence read data, wherein each segment has a same copy number.
[0141] The copy number model may be used to predict and/or output a copy number for the at least one genomic locus based on the sequence coverage ratio data and allele fraction data. The copy number model may also be used to predict and/or output a sample tumor purity (e.g., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.9, or 1.0, if expressed as a fraction rather than a percentage) and ploidy (e.g., an average copy number having a value ranging from 1 to 8) for the sample. In some instances, the copy number model also outputs the segmentation data.
[0142] As noted above, the details of an exemplary copy number modeling approach are described in PCT International Patent Publication No. WO 2023/060236, entitled “Methods and Systems for Automated Calling of Copy Number Alterations”, which is incorporated herein by reference in its entirety. In some instances, for example, an amplification may be detected when the copy number for the corresponding segment is greater than or equal to the ploidy of the sample. In some instances, an amplification is detected when the copy number for the corresponding segment is greater than or equal to the ploidy of the sample plus a first predetermined value (e.g., a first predetermined value ranging from 2 to 500; in some instances the first predetermined value may range from 2 to 10). In some instances, an amplification may be detected when the copy number for the corresponding segment is greater than or equal to the ploidy of the sample plus a second predetermined value (e.g., a second predetermined value ranging from 0 to 500 or, in some instances, ranging from 2 to 10) and the genomic locus is a member of a first predefined set of genomic loci (e.g., a first predefined set of genomic loci comprising one or more druggable gene target loci, prognostic gene loci, oncogene loci, or any combination thereof). In some instances, the first predefined set of genomic loci may comprise the AR and ERBB2 gene loci.
[0143] In some instances, detection of deletions may comprise identifying homozygous deletions of the at least one genomic locus in a corresponding segment. For example, in some instances, homozygous deletions may be detected by determining a total copy number for a given genomic locus that is equal to the sum of the copy numbers for a first allele and a second allele at the genomic locus. In some instances, the first allele is a major allele and the second allele is a minor allele. In some instances, a homozygous deletion is called if the total copy number for a given genomic locus is equal to a third predetermined value (e.g., a third predetermined value of about zero.
[0144] In some instances, detection of deletions may comprise identifying heterozygous deletions of the at least one genomic locus in a corresponding segment. In some instances, a heterozygous deletion may be called if a copy number for a first allele at a given genomic locus is equal to a fourth predetermined value, and a copy number for a second allele at the given genomic locus in not equal to the fourth predetermined value (e.g., a fourth predetermined value of about zero). In some instances, the first allele is a major allele and the second allele is a minor allele.
[0145] In some instances, detection of deletions may comprise identifying partial deletions of the at least one genomic locus in a corresponding segment. In some instances, for example, a partial deletion may be called for a given genomic locus if log2 ratios (L2Rs) for neighboring genomic loci, single nucleotide polymorphisms (SNPs), and introns are significantly different than the log2 ratio for the genomic locus, and the log2 ratio for the given genomic locus is significantly different from a distribution of L2Rs for non-neighboring genomic loci, single nucleotide polymorphisms (SNPs), and introns.
[0146] At step 208 in FIG. 2, the ctDNA fraction for the sample is calculated based on at least the sample tumor purity and ploidy predicted by the CNA model. In some instances, estimating the ctDNA fraction based on at least the sample tumor purity and sample ploidy predicted by the CAN model may comprise using a formula that describes a physical relationship between ctDNA fraction and sample tumor purity and sample ploidy. For example, in some instances, the formula may be given by: pib ctDNA fraction = — - - — - -
7 pif + 2(1 — p) where p is sample tumor purity, and »// is ploidy.
[0147] In some instances, the method may further comprise determining a confidence interval for the ctDNA fraction.
[0148] At step 210 in FIG. 2, the estimate of ctDNA fraction for the sample is output. In some instances, as noted above in reference to FIG. 1, the method may further comprise comparing the estimated ctDNA fraction to at least one predetermined threshold, and outputting a status call of, e.g., tumor fraction-high (TF-high) or tumor fraction-low (TF-low), for the sample based on the comparison.
[0149] At step 212 in FIG. 2, a preliminary list of variants (e.g., short variants) detected in the sequence read data is obtained if the sequence read data has been determined to be insufficient for performing CNA modeling. In some instances, the list of variants may comprise one or more short variants. In some instances, the one or more short variants may comprise one or more somatic short variants. In some instances, the one or more somatic short variants may comprise one or more somatic short variants that are known not to be associated with clonal hematopoiesis of indeterminate potential (CHIP) (e.g., they may be tumor somatic short variants). [0150] At step 214 in FIG. 2, a set of selection rules is applied to the list of variants (e.g., somatic short variants) to distinguish between tumor-derived somatic short variants and non- tumor-derived somatic short variants (e.g., germline somatic short variants).
[0151] In some instances, the set of selection rules used to identify tumor somatic short variants in the list of detected short variants may comprise: (i) removing short variants that appear on a blacklist of known germline variants, known clonal hematopoiesis of indeterminate potential (CHIP) variants, and known sequencing artifacts; (ii) retaining short variants that appear on a list of known tumor somatic short variants; (iii) retaining short variants that appear on a list of known genes that are prone to exhibit high amplification and that have an allele frequency that is higher than other somatic short variants in the sample; (iv) retaining short variants that appear on a list of known rearrangements; or any combination thereof.
[0152] In some instances, the blacklist of known germline variants, known clonal hematopoiesis of indeterminate potential (CHIP) variants, and known sequencing artifacts may comprise, e.g., a list of germline variants, CHIP variants, and sequencing artifacts, or any combination thereof, observed in historical sequencing data.
[0153] In some instances, the list of known tumor somatic short variants comprises somatic short variants determined to have a high prevalence odds ratio between tumor and white blood cells.
[0154] In some instances, the list of known genes that are prone to exhibit high amplification may comprise, e.g., KRAS, EGFR, CCND1, FGF19, FGF3, FGF4, MYC, AR, MDM2, CCNE1, ERBB2, WHSC1L1, FGFR1, ZNF703, or any combination thereof.
[0155] In some instances, the list of known rearrangements may comprise, e.g., fusions between the following gene pairs: TMPRSS2-ERG, ALK-EML4, FGFR3-TACC3, RET-KIF5B, or any combination thereof.
[0156] In some instances, the set of selection rules used to identify tumor somatic short variants in the list of detected short variants may further comprise identifying short variants for which a fragment size shift between the reference allele and alternate allele is detected in sequence read data as being tumor somatic short variants.
[0157] At step 216 in FIG. 2, a determination of whether or not at least one tumor somatic short variant has been identified/selected from the short variants in the preliminary list. [0158] At step 218 in FIG. 2, a determination of “ctDNA not detected” is output if no tumor somatic short variant has been identified in the preliminary list of short variants.
[0159] At step 220 in FIG. 2, the ctDNA fraction for the sample is determined based on the identified tumor somatic short variants if at least 1 tumor somatic short variant has been detected in the sequence read data for the sample.
[0160] In some instances, estimating the ctDNA fraction for the sample based on at least one somatic short variant detected in the sequence read data may comprise obtaining a list of short variants detected in the sequence read data; applying a set of selection rules to the list of detected short variants to identify tumor somatic short variants; and estimating the ctDNA fraction based on the presence of at least one identified tumor somatic short variant.
[0161] In some instances, for example, estimating the ctDNA fraction for the sample based on at least one tumor somatic short variant detected in the sequence read data may comprise determining a variant allele frequency (VAF) for one or more variants detected in the sequence read data; generating an empirical distribution of ctDNA fraction values corresponding to the determined VAF for the one or more variants based on historical data; fitting a model to the empirical distribution of ctDNA fraction values; and determining a ctDNA fraction for the sample based on the model. In some instances, the model may be a non-parametric probability density model.
[0162] In some instances, the method may further comprise determining a confidence interval for the ctDNA fraction based on the model.
[0163] In some instances, generating the empirical distribution of ctDNA fraction values comprises calculating a ctDNA fraction value based on a known copy number for one or more tumor somatic short variants and a corresponding known sample ploidy for a plurality of historical subject samples having a known VAF for the one or more tumor somatic short variants that is substantially the same as the determined VAF for the one or more tumor somatic short variants.
[0164] In some instances, generating the empirical distribution of ctDNA fraction values comprises pre-calculating a ctDNA fraction value based on a known copy number for the one or more tumor somatic short variants and a corresponding known sample ploidy for a plurality of historical subject samples having a range of VAF values for the one or more tumor somatic short variants, and selecting a subset of the pre-calculated ctDNA fraction values that corresponds to samples having a known VAF for the one or more tumor somatic short variants that is substantially the same as the determined VAF for the one or more tumor somatic short variants.
[0165] In some instances, ctDNA fraction values may be calculated or selected for a tumor somatic variant that exhibits the highest VAF in the sample from the subject. In some instances, ctDNA fraction values may be calculated or selected for a rank-ordered set of two or more tumor somatic short variants that exhibit the highest rank-ordered VAFs. In some instances, ctDNA fraction values may be calculated or selected for a predetermined set of two or more tumor somatic short variants detected in the sample from the subject. In some instances, ctDNA fraction values may be calculated or selected for a predetermined set of two or more tumor somatic short variants detected in the sample from the subject that comprise known driver mutations. In some instances, ctDNA fraction values may be calculated or selected for all tumor somatic short variants detected in the sample from the subject.
[0166] In some instances, ctDNA fraction values may be calculated or pre-calculated based on the known VAF for the one or more tumor somatic short variants, the known copy number for the one or more tumor somatic short variants, and the corresponding known sample ploidy for the plurality of historical subject samples.
[0167] In some instances, ctDNA fraction values may be calculated or pre-calculated based on the known VAF for the one or more tumor somatic short variants, the known copy number for the one or more tumor somatic short variants, and the corresponding known sample ploidy for the plurality of historical subject samples by solving a set of equations that describe: (i) a relationship between ctDNA fraction, sample tumor purity, and sample ploidy, and (ii) a relationship between somatic VAF, sample tumor purity, copy number at the genomic location(s) of the one or more tumor somatic short variants, and variant allele number for each of the one or more tumor somatic short variants, to eliminate sample tumor purity and derive a relationship for ctDNA fraction as a function of somatic VAF, sample ploidy, copy number at a genomic location of the one or more tumor somatic short variants, and variant allele number for the one or more tumor somatic short variants. [0168] In some instances, ctDNA fraction values may be calculated or pre-calculated based on the known VAF for the one or more tumor somatic short variants, the known copy number for the one or more tumor somatic short variants, and the corresponding known sample ploidy for the plurality of historical subject samples by solving a set of equations comprising: i) a first equation that equates DNA fraction to a product of sample tumor purity and ploidy, divided by a sum of the product of sample tumor purity and ploidy and a product of two times a quantity of one minus sample tumor purity; and ii) a second equation that equates somatic VAF to a product of sample tumor purity and a variant allele number for each of the one or more tumor somatic short variants, divided by a sum of a product of sample tumor purity and copy number at a genomic location of the one or more tumor somatic short variants and a product of two times a quantity of one minus sample tumor purity; to eliminate sample tumor purity and derive a relationship that equates ctDNA fraction to sample ploidy divided by a quantity equal to sample ploidy minus the copy number at a genomic location of the one or more tumor somatic short variants plus a ratio of variant allele number for the one or more tumor somatic short variants to somatic VAF for each of the one or more tumor somatic short variants.
[0169] In some instances, ctDNA fraction values may be calculated or pre-calculated based on the known VAF for the one or more tumor somatic short variants, the known copy number for the one or more tumor somatic short variants, and the corresponding known sample ploidy for the plurality of historical subject samples by solving a set of equations:
Figure imgf000044_0001
pv
Somatic VAF = - - - r C + 2(l - ) to eliminate p and obtain a relationship between ctDNA fraction and somatic VAF described by:
Figure imgf000044_0002
where p is sample tumor purity, y is ploidy, C is the copy number at a genomic location of the one or more tumor somatic short variants, and V is a variant allele number for each of the one or more tumor somatic short variants.
[0170] In some instances, the plurality of historical subject samples may comprise solid biopsy samples, liquid biopsy samples, or any combination thereof. In some instances, the plurality of historical subject samples may comprise cancer samples. In some instances, the plurality of historical subject samples may comprise samples for a single type of cancer. In some instances, the plurality of historical subject samples may comprise samples for multiple types of cancer.
[0171] In some instances, the plurality of historical subject samples may comprise bladder cancer samples, breast cancer samples, colorectal cancer samples, endometrial cancer samples, kidney cancer samples, leukemia samples, liver cancer samples, lung cancer samples, melanoma samples, non-Hodgkin lymphoma samples, pancreatic cancer samples, prostate cancer samples, thyroid cancer samples, or any combination thereof.
[0172] In some instances, the plurality of historical subject samples may comprise acute lymphoblastic leukemia (Philadelphia chromosome positive) samples, acute lymphoblastic leukemia (precursor B-cell) samples, acute myeloid leukemia (FLT3+) samples, acute myeloid leukemia (with an IDH2 mutation) samples, anaplastic large cell lymphoma samples, basal cell carcinoma samples, B-cell chronic lymphocytic leukemia samples, bladder cancer samples, breast cancer (HER2 overexpressed/amplified) samples, breast cancer (HER2+) samples, breast cancer (HR+, HER2-) samples, cervical cancer samples, cholangiocarcinoma samples, chronic lymphocytic leukemia samples, chronic lymphocytic leukemia (with 17p deletion) samples, chronic myelogenous leukemia samples, chronic myelogenous leukemia (Philadelphia chromosome positive) samples, classical Hodgkin lymphoma samples, colorectal cancer samples, colorectal cancer (dMMR and MSI-H) samples, colorectal cancer (KRAS wild type) samples, cryopyrin-associated periodic syndrome samples, cutaneous T-cell lymphoma samples, dermatofibrosarcoma protuberans samples, diffuse large B-cell lymphoma samples, fallopian tube cancer samples, follicular B-cell non-Hodgkin lymphoma samples, follicular lymphoma samples, gastric cancer samples, gastric cancer (HER2+) samples, gastroesophageal junction (GEJ) adenocarcinoma samples, gastrointestinal stromal tumor samples, gastrointestinal stromal tumor (KIT+) samples, giant cell tumor of the bone samples, glioblastoma samples, granulomatosis with polyangiitis samples, head and neck squamous cell carcinoma samples, hepatocellular carcinoma samples, Hodgkin lymphoma samples, juvenile idiopathic arthritis samples, lupus erythematosus samples, mantle cell lymphoma samples, medullary thyroid cancer samples, melanoma samples, melanoma samples with a BRAF V600 mutation, melanoma samples with a BRAF V600E or V600K mutation, Merkel cell carcinoma samples, multicentric Castleman's disease samples, multiple hematologic malignancy samples including Philadelphia chromosome-positive ALL and CML, multiple myeloma samples, myelofibrosis samples, nonHodgkin’s lymphoma samples, nonresectable subependymal giant cell astrocytoma samples associated with tuberous sclerosis, non-small cell lung cancer samples, non-small cell lung cancer (ALK+) samples, non-small cell lung cancer (PD-L1+) samples, non-small cell lung cancer (with ALK fusion or ROS1 gene alteration) samples, non-small cell lung cancer (with BRAF V600E mutation) samples, non-small cell lung cancer samples (with an EGFR exon 19 deletion or exon 21 substitution (L858R) mutation), non-small cell lung cancer samples (with an EGFR T790M mutation), ovarian cancer samples, ovarian cancer samples (with a BRCA mutation), pancreatic cancer samples, pancreatic cancer samples, gastrointestinal cancer samples, lung origin neuroendocrine tumor samples, pediatric neuroblastoma samples, peripheral T-cell lymphoma samples, peritoneal cancer samples, prostate cancer samples, renal cell carcinoma samples, rheumatoid arthritis samples, small lymphocytic lymphoma samples, soft tissue sarcoma samples, solid tumor (MSLH/dMMR) samples, squamous cell cancer samples of the head and neck, squamous non-small cell lung cancer samples, thyroid cancer samples, thyroid carcinoma samples, urothelial cancer samples, urothelial carcinoma samples, Waldenstrom's macroglobulinemia samples, or any combination thereof.
[0173] In some instances, the determined ctDNA fraction for the sample may be the most probable ctDNA fraction. In some instances, the determined ctDNA fraction for the sample may be the mean, median, or mode of a dominant peak in the empirical distribution of ctDNA fraction values.
[0174] In some instances, the ctDNA fraction determined for the sample may be based on a determination of the maximum somatic allele frequency (MSAF) for the at least one tumor somatic short variant, the detection of one or more genomic rearrangements, a determination of micro satellite instability, or any combination thereof. [0175] The estimate of ctDNA fraction for the sample is then output at step 210 in FIG. 2. Again, as noted above in reference to FIG. 1, the method may further comprise comparing the estimated ctDNA fraction to at least one predetermined threshold, and outputting a status call of, e.g., tumor fraction-high (TF-high) or tumor fraction-low (TF-low), for the sample based on the comparison.
Methods of use
[0176] In some instances, the disclosed methods may further comprise one or more of the steps of: (i) obtaining the sample from the subject (e.g., a subject suspected of having or determined to have cancer), (ii) extracting nucleic acid molecules (e.g., a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules) from the sample, (iii) ligating one or more adapters to the nucleic acid molecules extracted from the sample (e.g., one or more amplification primers, flow cell adaptor sequences, substrate adapter sequences, or sample index sequences), (iv) performing a methylation conversion reaction to convert, e.g., non-methylated cytosine to uracil, (v) amplifying the nucleic acid molecules (e.g., using a polymerase chain reaction (PCR) amplification technique, a non-PCR amplification technique, or an isothermal amplification technique), (vi) capturing nucleic acid molecules from the amplified nucleic acid molecules (e.g., by hybridization to one or more bait molecules, where the bait molecules each comprise one or more nucleic acid molecules that each comprising a region that is complementary to a region of a captured nucleic acid molecule), (vii) sequencing the nucleic acid molecules extracted from the sample (or library proxies derived therefrom) using, e.g., a next-generation (massively parallel) sequencing technique, a whole genome sequencing (WGS) technique, a whole exome sequencing technique, a targeted sequencing technique, a direct sequencing technique, or a Sanger sequencing technique) using, e.g., a next-generation (massively parallel) sequencer, (viii) combining the nucleic acid sequence data (including, e.g., variant data, copy number data, methylation status data, etc., of the sequenced nucleic acid molecules) with other biomarker data modalities including, but not limited to, proteomics-based biomarker data (e.g., the detection of specific polypeptides, such as proteins) or fragmentomics-based biomarker data (e.g., the detection of certain attributes related to nucleic acid fragments, such as fragment size or the sequences of fragment ends), to determine, for example, the presence of ctDNA in the sample and/or to determine a diagnostic, prognostic, and/or treatment response prediction for the subject, and (ix) generating, displaying, transmitting, and/or delivering a report (e.g., an electronic, webbased, or paper report) to the subject (or patient), a caregiver, a healthcare provider, a physician, an oncologist, an electronic medical record system, a hospital, a clinic, a third-party payer, an insurance company, or a government office. In some instances, the report comprises output from the methods described herein. In some instances, all or a portion of the report may be displayed in the graphical user interface of an online or web-based healthcare portal. In some instances, the report is transmitted via a computer network or peer-to-peer connection.
[0177] The disclosed methods may be used with any of a variety of samples. For example, in some instances, the sample may comprise a tissue biopsy sample, a liquid biopsy sample, or a normal control. In some instances, the sample may be a liquid biopsy sample and may comprise blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva. In some instances, the sample may be a liquid biopsy sample and may comprise circulating tumor cells (CTCs). In some instances, the sample may be a liquid biopsy sample and may comprise cell-free DNA (cfDNA). In some instances, the cell-free DNA (cfDNA), or a portion thereof, may comprise circulating tumor DNA (ctDNA). In some instances, the liquid biopsy sample may comprise a combination of cell-free DNA (cfDNA) and circulating tumor DNA (ctDNA).
[0178] In some instances, the nucleic acid molecules extracted from a sample may comprise a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules. In some instances, the tumor nucleic acid molecules may be derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules may be derived from a normal portion of the heterogeneous tissue biopsy sample. In some instances, the sample may comprise a liquid biopsy sample, and the tumor nucleic acid molecules may be derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample while the non-tumor nucleic acid molecules may be derived from a non-tumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.
[0179] In some instances, the disclosed methods for determining ctDNA fraction may be used to diagnose (or as part of a diagnosis of) the presence of disease or other condition (e.g., cancer, genetic disorders (such as Down Syndrome and Fragile X), neurological disorders, or any other disease type where detection of variants, e.g., copy number alternations, are relevant to diagnosing, treating, or predicting said disease) in a subject (e.g., a patient). In some instances, the disclosed methods may be applicable to diagnosis of any of a variety of cancers as described elsewhere herein.
[0180] In some instances, the disclosed methods for determining ctDNA fraction may be used to select a subject (e.g., a patient) for a clinical trial based on the ctDNA fraction value determined for a sample from the subject. In some instances, patient selection for clinical trials based on, e.g., ctDNA fraction, may accelerate the development of targeted therapies and improve the healthcare outcomes for treatment decisions.
[0181] In some instances, the disclosed methods for determining ctDNA fraction may be used to select an appropriate therapy or treatment (e.g., an anti-cancer therapy or anti-cancer treatment) for a subject. In some instances, for example, the anti-cancer therapy or treatment may comprise use of a poly (ADP-ribose) polymerase inhibitor (PARPi), a platinum compound, chemotherapy, radiation therapy, a targeted therapy, an immunotherapy, a neoantigen-based therapy, surgery, or any combination thereof.
[0182] In some instances, the anti-cancer therapy or treatment may comprise a targeted anticancer therapy or treatment (e.g., a monoclonal antibody -based therapy, an enzyme inhibitorbased therapy, an antibody-drug conjugate therapy, a hormone therapy, and/or a targeted radiotherapy) that targets specific molecules required for cancer cell growth, division, and spreading. In some instances, the targeted anti-cancer therapy or treatment may comprise abemaciclib (Verzenio), abiraterone acetate (Zytiga), acalabrutinib (Calquence), ado-trastuzumab emtansine (Kadcyla), afatinib dimaleate (Gilotrif), alectinib (Alecensa), alemtuzumab (Campath), alitretinoin (Panretin), alpelisib (Piqray), amivantamab-vmjw (Rybrevant), anastrozole (Arimidex), apalutamide (Erleada), asciminib hydrochloride (Scemblix), atezolizumab (Tecentriq), avapritinib (Ayvakit), avelumab (Bavencio), axicabtagene ciloleucel (Yescarta), axitinib (Inlyta), belantamab mafodotin-blmf (Blenrep), belimumab (Benlysta), belinostat (Beleodaq), belzutifan (Welireg), bevacizumab (Avastin), bexarotene (Targretin), binimetinib (Mektovi), blinatumomab (Blincyto), bortezomib (Velcade), bosutinib (Bosulif), brentuximab vedotin (Adcetris), brexucabtagene autoleucel (Tecartus), brigatinib (Alunbrig), cabazitaxel (Jevtana), cabozantinib (Cabometyx), cabozantinib (Cabometyx, Cometriq), canakinumab (Haris), capmatinib hydrochloride (Tabrecta), carfilzomib (Kyprolis), cemiplimab- rwlc (Libtayo), ceritinib (LDK378/Zykadia), cetuximab (Erbitux), cobimetinib (Cotellic), crizotinib (Xalkori), dabrafenib (Tafinlar), dacomitinib (Vizimpro), daratumumab (Darzalex), daratumumab and hyaluronidase-fihj (Darzalex Faspro), darolutamide (Nubeqa), dasatinib (Sprycel), denileukin diftitox (Ontak), denosumab (Xgeva), dinutuximab (Unituxin), dostarlimab-gxly (Jemperli), durvalumab (Imfinzi), duvelisib (Copiktra), elotuzumab (Empliciti), enasidenib mesylate (Idhifa), encorafenib (Braftovi), enfortumab vedotin-ejfv (Padcev), entrectinib (Rozlytrek), enzalutamide (Xtandi), erdafitinib (Balversa), erlotinib (Tarceva), everolimus (Afinitor), exemestane (Aromasin), fam-trastuzumab deruxtecan-nxki (Enhertu), fedratinib hydrochloride (Inrebic), fulvestrant (Faslodex), gefitinib (Iressa), gemtuzumab ozogamicin (Mylotarg), gilteritinib (Xospata), glasdegib maleate (Daurismo), hyaluronidase-zzxf (Phesgo), ibrutinib (Imbruvica), ibritumomab tiuxetan (Zevalin), idecabtagene vicleucel (Abecma), idelalisib (Zydelig), imatinib mesylate (Gleevec), infigratinib phosphate (Truseltiq), inotuzumab ozogamicin (Besponsa), ipilimumab (Yervoy), isatuximab-irfc (Sarclisa), ivosidenib (Tibsovo), ixazomib citrate (Ninlaro), lanreotide acetate (Somatuline Depot), lapatinib (Tykerb), larotrectinib sulfate (Vitrakvi), lenvatinib mesylate (Lenvima), letrozole (Femara), lisocabtagene maraleucel (Breyanzi), loncastuximab tesirine-lpyl (Zynlonta), lorlatinib (Lorbrena), lutetium Lu 177-dotatate (Lutathera), margetuximab-cmkb (Margenza), midostaurin (Rydapt), mobocertinib succinate (Exkivity), mogamulizumab-kpkc (Poteligeo), moxetumomab pasudotox-tdfk (Lumoxiti), naxitamab-gqgk (Danyelza), necitumumab (Portrazza), neratinib maleate (Nerlynx), nilotinib (Tasigna), niraparib tosylate monohydrate (Zejula), nivolumab (Opdivo), obinutuzumab (Gazyva), ofatumumab (Arzerra), olaparib (Lynparza), olaratumab (Lartruvo), osimertinib (Tagrisso), palbociclib (Ibrance), panitumumab (Vectibix), pazopanib (Votrient), pembrolizumab (Keytruda), pemigatinib (Pemazyre), pertuzumab (Perjeta), pexidartinib hydrochloride (Turalio), polatuzumab vedotin-piiq (Polivy), ponatinib hydrochloride (Iclusig), pralatrexate (Folotyn), pralsetinib (Gavreto), radium 223 dichloride (Xofigo), ramucirumab (Cyramza), regorafenib (Stivarga), ribociclib (Kisqali), ripretinib (Qinlock), rituximab (Rituxan), rituximab and hyaluronidase human (Rituxan Hycela), romidepsin (Istodax), rucaparib camsylate (Rubraca), ruxolitinib phosphate (Jakafi), sacituzumab govitecan-hziy (Trodelvy), seliciclib, selinexor (Xpovio), selpercatinib (Retevmo), selumetinib sulfate (Koselugo), siltuximab (Sylvant), sirolimus protein-bound particles (Fyarro), sonidegib (Odomzo), sorafenib (Nexavar), sotorasib (Lumakras), sunitinib (Sutent), tafasitamab-cxix (Monjuvi), tagraxofusp-erzs (Elzonris), talazoparib tosylate (Talzenna), tamoxifen (Nolvadex), tazemetostat hydrobromide (Tazverik), tebentafusp-tebn (Kimmtrak), temsirolimus (Torisel), tepotinib hydrochloride (Tepmetko), tisagenlecleucel (Kymriah), tisotumab vedotin-tftv (Tivdak), tocilizumab (Actemra), tofacitinib (Xeljanz), tositumomab (Bexxar), trametinib (Mekinist), trastuzumab (Herceptin), tretinoin (Vesanoid), tivozanib hydrochloride (Fotivda), toremifene (Fareston), tucatinib (Tukysa), umbralisib tosylate (Ukoniq), vandetanib (Caprelsa), vemurafenib (Zelboraf), venetoclax (Venclexta), vismodegib (Erivedge), vorinostat (Zolinza), zanubrutinib (Brukinsa), ziv- aflibercept (Zaltrap), or any combination thereof.
[0183] In some instances, the anti-cancer therapy or treatment may comprise an immunotherapy (e.g., a cancer treatment that acts by stimulating the immune system to fight cancer). In some instances, the immunotherapy can be, for example, an immune system modulator (e.g., a cytokine, such as an interferon or interleukin), an immune checkpoint inhibitor (such as an anti- PD-1 or anti-PD-Ll antibody), a T-cell transfer therapy (e.g., a tumor infiltrating lymphocyte (TIL) therapy in lymphocytes extracted from a patient’ s tumor are selected for their ability to recognize tumor cells and propagated prior to reintroduction into the patient, or a CAR T-cell therapy in which a patient’s T-cells are modified to express the CAR protein prior to reintroduction into the patient), a monoclonal antibody -based therapy (e.g., a monoclonal antibody that binds to cell surface markers on cancer cells to facilitate recognition by the immune system), or a cancer treatment vaccine (e.g., a vaccine based on tumor cells, tumor- associated neoantigens, or dendritic cells, etc., that stimulates the immune system to fight cancer).
[0184] In some instances, the anti-cancer therapy or treatment may comprise a neoantigen-based therapy. Non-limiting examples of neoantigen-based therapies include T-cell receptor (TCR) engineered T-cell (TCR-T) therapies, chimeric antigen receptor T-cell (CAR-T) therapies, TCR bispecific antibody therapies, and cancer vaccines. TCR-T therapies are produced by genetically engineering a patient’s T-cells to express T-cell receptors that are specific to neoantigens of interest, and then infusing them back into the patient. CAR-T therapies are produced by genetically engineering a patient’s T-cells to express chimeric antigen receptor molecules which contain an intracellular signaling and co-signaling domain as well as an extracellular antigenbinding domain; CAR-T therapies don’t always rely on neoantigen presentation, but can be designed to be directed towards neoantigens. TCR bispecific antibody therapies are small, engineered antibody molecules that comprise a neoantigen- specific TCR on one end and a CD3- directed single-chain variable fragment on the other end. Cancer vaccines can include RNA molecules, DNA molecules, peptides, or a combination thereof that are designed to boost the immune system’s ability to find and destroy neoantigen-presenting cells.
[0185] In some instances, the disclosed methods for determining ctDNA fraction may be used in treating a disease (e.g., a cancer) in a subject. For example, in response to determining ctDNA fraction in a sample from the subject using any of the methods disclosed herein, an effective amount of an anti-cancer therapy or anti-cancer treatment may be administered to the subject.
[0186] In some instances, the disclosed methods for determining ctDNA fraction may be used for monitoring disease progression or recurrence (e.g., cancer or tumor progression or recurrence) in a subject. For example, in some instances, the methods may be used to determine ctDNA fraction in a first sample obtained from the subject at a first time point, and used to determine ctDNA fraction in a second sample obtained from the subject at a second time point, where comparison of the first determination of ctDNA fraction and the second determination of ctDNA fraction allows one to monitor disease progression or recurrence. In some instances, the first time point is chosen before the subject has been administered a therapy or treatment, and the second time point is chosen after the subject has been administered the therapy or treatment.
[0187] In some instances, the disclosed methods may be used for adjusting a therapy or treatment (e.g., an anti-cancer treatment or anti-cancer therapy) for a subject, e.g., by adjusting a treatment dose and/or selecting a different treatment in response to a change in the determination of ctDNA fraction.
[0188] In some instances, the value of ctDNA fraction determined using the disclosed methods may be used as a prognostic or diagnostic indicator associated with the sample. For example, in some instances, the prognostic or diagnostic indicator may comprise an indicator of the presence of a disease (e.g., cancer) in the sample, an indicator of the probability that a disease (e.g., cancer) is present in the sample, an indicator of the probability that the subject from which the sample was derived will develop a disease (e.g., cancer) (z.e., a risk factor), or an indicator of the likelihood that the subject from which the sample was derived will respond to a particular therapy or treatment. [0189] In some instances, the disclosed methods for determining ctDNA fraction may be implemented as part of a genomic profiling process that comprises identification of the presence of variant sequences at one or more gene loci in a sample derived from a subject as part of detecting, monitoring, predicting a risk factor, or selecting a treatment for a particular disease, e.g., cancer. In some instances, the variant panel selected for genomic profiling may comprise the detection of variant sequences at a selected set of gene loci. In some instances, the variant panel selected for genomic profiling may comprise detection of variant sequences at a number of gene loci through comprehensive genomic profiling (CGP), which is a next- generation sequencing (NGS) approach used to assess hundreds of genes (including relevant cancer biomarkers) in a single assay. Inclusion of the disclosed methods for determining ctDNA fraction as part of a genomic profiling process (or inclusion of the output from the disclosed methods for determining ctDNA fraction as part of the genomic profile of the subject) can improve the validity of, e.g., disease detection calls and treatment decisions, made on the basis of the genomic profile by, for example, independently confirming the presence of cancer in a given patient sample.
[0190] In some instances, a genomic profile may comprise information on the presence of genes (or variant sequences thereof), copy number variations, epigenetic traits, proteins (or modifications thereof), and/or other biomarkers in an individual’s genome and/or proteome, as well as information on the individual’s corresponding phenotypic traits and the interaction between genetic or genomic traits, phenotypic traits, and environmental factors.
[0191] In some instances, a genomic profile for the subject may comprise results from a comprehensive genomic profiling (CGP) test, a nucleic acid sequencing-based test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof.
[0192] In some instances, the method can further include administering or applying a treatment or therapy e.g., an anti-cancer agent, anti-cancer treatment, or anti-cancer therapy) to the subject based on the generated genomic profile. An anti-cancer agent or anti-cancer treatment may refer to a compound that is effective in the treatment of cancer cells. Examples of anti-cancer agents or anti-cancer therapies include, but not limited to, alkylating agents, antimetabolites, natural products, hormones, chemotherapy, radiation therapy, immunotherapy, surgery, or a therapy configured to target a defect in a specific cell signaling pathway, e.g., a defect in a DNA mismatch repair (MMR) pathway.
Samples
[0193] The disclosed methods and systems may be used with any of a variety of samples (also referred to herein as specimens) comprising nucleic acids (e.g., DNA or RNA) that are collected from a subject (e.g., a patient). Examples of a sample include, but are not limited to, a tumor sample, a tissue sample, a biopsy sample (e.g., a tissue biopsy, a liquid biopsy, or both), a blood sample (e.g., a peripheral whole blood sample), a blood plasma sample, a blood serum sample, a lymph sample, a saliva sample, a sputum sample, a urine sample, a gynecological fluid sample, a circulating tumor cell (CTC) sample, a cerebral spinal fluid (CSF) sample, a pericardial fluid sample, a pleural fluid sample, an ascites (peritoneal fluid) sample, a feces (or stool) sample, or other body fluid, secretion, and/or excretion sample (or cell sample derived therefrom). In certain instances, the sample may be frozen sample or a formalin-fixed paraffin-embedded (FFPE) sample.
[0194] In some instances, the sample may be collected by tissue resection (e.g., surgical resection), needle biopsy, bone marrow biopsy, bone marrow aspiration, skin biopsy, endoscopic biopsy, fine needle aspiration, oral swab, nasal swab, vaginal swab or a cytology smear, scrapings, washings or lavages (such as a ductal lavage or bronchoalveolar lavage), etc.
[0195] In some instances, the sample is a liquid biopsy sample, and may comprise, e.g., whole blood, blood plasma, blood serum, urine, stool, sputum, saliva, or cerebrospinal fluid. In some instances, the sample may be a liquid biopsy sample and may comprise circulating tumor cells (CTCs). In some instances, the sample may be a liquid biopsy sample and may comprise cell- free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.
[0196] In some instances, the sample may comprise one or more premalignant or malignant cells. Premalignant, as used herein, refers to a cell or tissue that is not yet malignant but is poised to become malignant. In certain instances, the sample may be acquired from a solid tumor, a soft tissue tumor, or a metastatic lesion. In certain instances, the sample may be acquired from a hematologic malignancy or pre-malignancy. In other instances, the sample may comprise a tissue or cells from a surgical margin. In certain instances, the sample may comprise tumor-infiltrating lymphocytes. In some instances, the sample may comprise one or more non- malignant cells. In some instances, the sample may be, or is part of, a primary tumor or a metastasis (e.g., a metastasis biopsy sample). In some instances, the sample may be obtained from a site (e.g., a tumor site) with the highest percentage of tumor (e.g., tumor cells) as compared to adjacent sites (e.g., sites adjacent to the tumor). In some instances, the sample may be obtained from a site (e.g., a tumor site) with the largest tumor focus (e.g., the largest number of tumor cells as visualized under a microscope) as compared to adjacent sites (e.g., sites adjacent to the tumor).
[0197] In some instances, the disclosed methods may further comprise analyzing a primary control (e.g., a normal tissue sample). In some instances, the disclosed methods may further comprise determining if a primary control is available and, if so, isolating a control nucleic acid (e.g., DNA) from said primary control. In some instances, the sample may comprise any normal control (e.g., a normal adjacent tissue (NAT)) if no primary control is available. In some instances, the sample may be or may comprise histologically normal tissue. In some instances, the method includes evaluating a sample, e.g., a histologically normal sample (e.g., from a surgical tissue margin) using the methods described herein. In some instances, the disclosed methods may further comprise acquiring a sub-sample enriched for non-tumor cells, e.g., by macro-dissecting non-tumor tissue from said NAT in a sample not accompanied by a primary control. In some instances, the disclosed methods may further comprise determining that no primary control and no NAT is available, and marking said sample for analysis without a matched control.
[0198] In some instances, samples obtained from histologically normal tissues (e.g., otherwise histologically normal surgical tissue margins) may still comprise a genetic alteration such as a variant sequence as described herein. The methods may thus further comprise re-classifying a sample based on the presence of the detected genetic alteration. In some instances, multiple samples (e.g., from different subjects) are processed simultaneously.
[0199] The disclosed methods and systems may be applied to the analysis of nucleic acids extracted from any of variety of tissue samples (or disease states thereof), e.g., solid tissue samples, soft tissue samples, metastatic lesions, or liquid biopsy samples. Examples of tissues include, but are not limited to, connective tissue, muscle tissue, nervous tissue, epithelial tissue, and blood. Tissue samples may be collected from any of the organs within an animal or human body. Examples of human organs include, but are not limited to, the brain, heart, lungs, liver, kidneys, pancreas, spleen, thyroid, mammary glands, uterus, prostate, large intestine, small intestine, bladder, bone, skin, etc.
[0200] In some instances, the nucleic acids extracted from the sample may comprise deoxyribonucleic acid (DNA) molecules. Examples of DNA that may be suitable for analysis by the disclosed methods include, but are not limited to, genomic DNA or fragments thereof, mitochondrial DNA or fragments thereof, cell-free DNA (cfDNA), and circulating tumor DNA (ctDNA). Cell-free DNA (cfDNA) is comprised of fragments of DNA that are released from normal and/or cancerous cells during apoptosis and necrosis, and circulate in the blood stream and/or accumulate in other bodily fluids. Circulating tumor DNA (ctDNA) is comprised of fragments of DNA that are released from cancerous cells and tumors that circulate in the blood stream and/or accumulate in other bodily fluids.
[0201] In some instances, DNA is extracted from nucleated cells from the sample. In some instances, a sample may have a low nucleated cellularity, e.g., when the sample is comprised mainly of erythrocytes, lesional cells that contain excessive cytoplasm, or tissue with fibrosis. In some instances, a sample with low nucleated cellularity may require more, e.g., greater, tissue volume for DNA extraction.
[0202] In some instances, the nucleic acids extracted from the sample may comprise ribonucleic acid (RNA) molecules. Examples of RNA that may be suitable for analysis by the disclosed methods include, but are not limited to, total cellular RNA, total cellular RNA after depletion of certain abundant RNA sequences e.g., ribosomal RNAs), cell-free RNA (cfRNA), messenger RNA (mRNA) or fragments thereof, the poly(A)-tailed mRNA fraction of the total RNA, ribosomal RNA (rRNA) or fragments thereof, transfer RNA (tRNA) or fragments thereof, and mitochondrial RNA or fragments thereof. In some instances, RNA may be extracted from the sample and converted to complementary DNA (cDNA) using, e.g., a reverse transcription reaction. In some instances, the cDNA is produced by random-primed cDNA synthesis methods. In other instances, the cDNA synthesis is initiated at the poly (A) tail of mature mRNAs by priming with oligo(dT)-containing oligonucleotides. Methods for depletion, poly(A) enrichment, and cDNA synthesis are well known to those of skill in the art. [0203] In some instances, the sample may comprise a tumor content (e.g., comprising tumor cells or tumor cell nuclei), or a non-tumor content (e.g., immune cells, fibroblasts, and other nontumor cells). In some instances, the tumor content of the sample may constitute a sample metric. In some instances, the sample may comprise a tumor content of at least 5-50%, 10-40%, 15-25%, or 20-30% tumor cell nuclei. In some instances, the sample may comprise a tumor content of at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, or at least 50% tumor cell nuclei. In some instances, the percent tumor cell nuclei (e.g., sample fraction) is determined (e.g., calculated) by dividing the number of tumor cells in the sample by the total number of all cells within the sample that have nuclei. In some instances, for example when the sample is a liver sample comprising hepatocytes, a different tumor content calculation may be required due to the presence of hepatocytes having nuclei with twice, or more than twice, the DNA content of other, e.g., non-hepatocyte, somatic cell nuclei. In some instances, the sensitivity of detection of a genetic alteration, e.g., a variant sequence, or a determination of, e.g., micro satellite instability, may depend on the tumor content of the sample. For example, a sample having a lower tumor content can result in lower sensitivity of detection for a given size sample.
[0204] In some instances, as noted above, the sample comprises nucleic acid (e.g., DNA, RNA (or a cDNA derived from the RNA), or both), e.g., from a tumor or from normal tissue. In certain instances, the sample may further comprise a non-nucleic acid component, e.g., cells, protein, carbohydrate, or lipid, e.g., from the tumor or normal tissue.
Subjects
[0205] In some instances, the sample is obtained (e.g., collected) from a subject (e.g., patient) with a condition or disease (e.g., a hyperproliferative disease or a non-cancer indication) or suspected of having the condition or disease. In some instances, the hyperproliferative disease is a cancer. In some instances, the cancer is a solid tumor or a metastatic form thereof. In some instances, the cancer is a hematological cancer, e.g., a leukemia or lymphoma.
[0206] In some instances, the subject has a cancer or is at risk of having a cancer. For example, in some instances, the subject has a genetic predisposition to a cancer (e.g., having a genetic mutation that increases his or her baseline risk for developing a cancer). In some instances, the subject has been exposed to an environmental perturbation (e.g., radiation or a chemical) that increases his or her risk for developing a cancer. In some instances, the subject is in need of being monitored for development of a cancer. In some instances, the subject is in need of being monitored for cancer progression or regression, e.g., after being treated with an anti-cancer therapy (or anti-cancer treatment). In some instances, the subject is in need of being monitored for relapse of cancer. In some instances, the subject is in need of being monitored for minimum residual disease (MRD). In some instances, the subject has been, or is being treated, for cancer. In some instances, the subject has not been treated with an anti-cancer therapy (or anti-cancer treatment).
[0207] In some instances, the subject (e.g., a patient) is being treated, or has been previously treated, with one or more targeted therapies. In some instances, e.g., for a patient who has been previously treated with a targeted therapy, a post-targeted therapy sample (e.g., specimen) is obtained (e.g., collected). In some instances, the post-targeted therapy sample is a sample obtained after the completion of the targeted therapy.
[0208] In some instances, the patient has not been previously treated with a targeted therapy. In some instances, e.g., for a patient who has not been previously treated with a targeted therapy, the sample comprises a resection, e.g., an original resection, or a resection following recurrence (e.g., following a disease recurrence post-therapy).
Cancers
[0209] In some instances, the sample is acquired from a subject having a cancer. Exemplary cancers include, but are not limited to, B cell cancer (e.g., multiple myeloma), melanomas, breast cancer, lung cancer (such as non-small cell lung carcinoma or NSCLC), bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain or central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine or endometrial cancer, cancer of the oral cavity or pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel or appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, cancer of hematological tissues, adenocarcinomas, inflammatory myofibroblastic tumors, gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), acute lymphocytic leukemia (ALL), acute myelocytic leukemia (AML), chronic myelocytic leukemia (CML), chronic lymphocytic leukemia (CLL), polycythemia Vera, Hodgkin lymphoma, non- Hodgkin lymphoma (NHL), soft-tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endotheliosarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, bladder carcinoma, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, neuroblastoma, retinoblastoma, follicular lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, hepatocellular carcinoma, thyroid cancer, gastric cancer, head and neck cancer, small cell cancers, essential thrombocythemia, agnogenic myeloid metaplasia, hypereosinophilic syndrome, systemic mastocytosis, familiar hypereosinophilia, chronic eosinophilic leukemia, neuroendocrine cancers, carcinoid tumors, and the like.
[0210] In some instances, the cancer comprises acute lymphoblastic leukemia (Philadelphia chromosome positive), acute lymphoblastic leukemia (precursor B-cell), acute myeloid leukemia (FLT3+), acute myeloid leukemia (with an IDH2 mutation), anaplastic large cell lymphoma, basal cell carcinoma, B-cell chronic lymphocytic leukemia, bladder cancer, breast cancer (HER2 overexpressed/amplified), breast cancer (HER2+), breast cancer (HR+, HER2-), cervical cancer, cholangiocarcinoma, chronic lymphocytic leukemia, chronic lymphocytic leukemia (with 17p deletion), chronic myelogenous leukemia, chronic myelogenous leukemia (Philadelphia chromosome positive), classical Hodgkin lymphoma, colorectal cancer, colorectal cancer (dMMR and MSLH), colorectal cancer (KRAS wild type), cryopyrin-associated periodic syndrome, a cutaneous T-cell lymphoma, dermatofibrosarcoma protuberans, a diffuse large B- cell lymphoma, fallopian tube cancer, a follicular B-cell non-Hodgkin lymphoma, a follicular lymphoma, gastric cancer, gastric cancer (HER2+), a gastroesophageal junction (GEJ) adenocarcinoma, a gastrointestinal stromal tumor, a gastrointestinal stromal tumor (KIT+), a giant cell tumor of the bone, a glioblastoma, granulomatosis with polyangiitis, a head and neck squamous cell carcinoma, a hepatocellular carcinoma, Hodgkin lymphoma, juvenile idiopathic arthritis, lupus erythematosus, a mantle cell lymphoma, medullary thyroid cancer, melanoma, a melanoma with a BRAF V600 mutation, a melanoma with a BRAF V600E or V600K mutation, Merkel cell carcinoma, multicentric Castleman's disease, multiple hematologic malignancies including Philadelphia chromosome-positive ALL and CML, multiple myeloma, myelofibrosis, a non-Hodgkin’s lymphoma, a nonresectable subependymal giant cell astrocytoma associated with tuberous sclerosis, a non-small cell lung cancer, a non-small cell lung cancer (ALK+), a non-small cell lung cancer (PD-L1+), a non-small cell lung cancer (with ALK fusion or ROS1 gene alteration), a non-small cell lung cancer (with BRAF V600E mutation), a non-small cell lung cancer (with an EGFR exon 19 deletion or exon 21 substitution (L858R) mutations), a non- small cell lung cancer (with an EGFR T790M mutation), ovarian cancer, ovarian cancer (with a BRCA mutation), pancreatic cancer, a pancreatic, gastrointestinal, or lung origin neuroendocrine tumor, a pediatric neuroblastoma, a peripheral T-cell lymphoma, peritoneal cancer, prostate cancer, a renal cell carcinoma, rheumatoid arthritis, a small lymphocytic lymphoma, a soft tissue sarcoma, a solid tumor (MSLH/dMMR), a squamous cell cancer of the head and neck, a squamous non-small cell lung cancer, thyroid cancer, a thyroid carcinoma, urothelial cancer, a urothelial carcinoma, or Waldenstrom's macroglobulinemia.
[0211] In some instances, the cancer is a hematologic malignancy (or premaligancy). As used herein, a hematologic malignancy refers to a tumor of the hematopoietic or lymphoid tissues, e.g., a tumor that affects blood, bone marrow, or lymph nodes. Exemplary hematologic malignancies include, but are not limited to, leukemia (e.g., acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), chronic myelogenous leukemia (CML), hairy cell leukemia, acute monocytic leukemia (AMoL), chronic myelomonocytic leukemia (CMML), juvenile myelomonocytic leukemia (JMML), or large granular lymphocytic leukemia), lymphoma (e.g., AIDS-related lymphoma, cutaneous T-cell lymphoma, Hodgkin lymphoma (e.g., classical Hodgkin lymphoma or nodular lymphocyte- predominant Hodgkin lymphoma), mycosis fungoides, non-Hodgkin lymphoma (e.g., B-cell non-Hodgkin lymphoma (e.g., Burkitt lymphoma, small lymphocytic lymphoma (CLL/SLL), diffuse large B-cell lymphoma, follicular lymphoma, immunoblastic large cell lymphoma, precursor B-lymphoblastic lymphoma, or mantle cell lymphoma) or T-cell non-Hodgkin lymphoma (mycosis fungoides, anaplastic large cell lymphoma, or precursor T-lymphoblastic lymphoma)), primary central nervous system lymphoma, Sezary syndrome, Waldenstrom macroglobulinemia), chronic myeloproliferative neoplasm, Langerhans cell histiocytosis, multiple myeloma/plasma cell neoplasm, myelodysplastic syndrome, or myelodysplastic/myeloproliferative neoplasm.
Nucleic acid extraction and processing
[0212] DNA or RNA may be extracted from tissue samples, biopsy samples, blood samples, or other bodily fluid samples using any of a variety of techniques known to those of skill in the art (see, e.g., Example 1 of International Patent Application Publication No. WO 2012/092426; Tan, et al. (2009), “DNA, RNA, and Protein Extraction: The Past and The Present”, J. Biomed. Biotech. 2009:574398; the technical literature for the Maxwell® 16 LEV Blood DNA Kit (Promega Corporation, Madison, WI); and the Maxwell 16 Buccal Swab LEV DNA Purification Kit Technical Manual (Promega Literature #TM333, January 1, 2011, Promega Corporation, Madison, WI)). Protocols for RNA isolation are disclosed in, e.g., the Maxwell® 16 Total RNA Purification Kit Technical Bulletin (Promega Literature #TB351, August 2009, Promega Corporation, Madison, WI).
[0213] A typical DNA extraction procedure, for example, comprises (i) collection of the fluid sample, cell sample, or tissue sample from which DNA is to be extracted, (ii) disruption of cell membranes (z.e., cell lysis), if necessary, to release DNA and other cytoplasmic components, (iii) treatment of the fluid sample or lysed sample with a concentrated salt solution to precipitate proteins, lipids, and RNA, followed by centrifugation to separate out the precipitated proteins, lipids, and RNA, and (iv) purification of DNA from the supernatant to remove detergents, proteins, salts, or other reagents used during the cell membrane lysis step.
[0214] Disruption of cell membranes may be performed using a variety of mechanical shear e.g., by passing through a French press or fine needle) or ultrasonic disruption techniques. The cell lysis step often comprises the use of detergents and surfactants to solubilize lipids the cellular and nuclear membranes. In some instances, the lysis step may further comprise use of proteases to break down protein, and/or the use of an RNase for digestion of RNA in the sample.
[0215] Examples of suitable techniques for DNA purification include, but are not limited to, (i) precipitation in ice-cold ethanol or isopropanol, followed by centrifugation (precipitation of DNA may be enhanced by increasing ionic strength, e.g., by addition of sodium acetate), (ii) phenol-chloroform extraction, followed by centrifugation to separate the aqueous phase containing the nucleic acid from the organic phase containing denatured protein, and (iii) solid phase chromatography where the nucleic acids adsorb to the solid phase e.g., silica or other) depending on the pH and salt concentration of the buffer.
[0216] In some instances, cellular and histone proteins bound to the DNA may be removed either by adding a protease or by having precipitated the proteins with sodium or ammonium acetate, or through extraction with a phenol-chloroform mixture prior to a DNA precipitation step.
[0217] In some instances, DNA may be extracted using any of a variety of suitable commercial DNA extraction and purification kits. Examples include, but are not limited to, the QIAamp (for isolation of genomic DNA from human samples) and DNAeasy (for isolation of genomic DNA from animal or plant samples) kits from Qiagen (Germantown, MD) or the Maxwell® and ReliaPrep™ series of kits from Promega (Madison, WI).
[0218] As noted above, in some instances the sample may comprise a formalin-fixed (also known as formaldehyde-fixed, or paraformaldehyde-fixed), paraffin-embedded (FFPE) tissue preparation. For example, the FFPE sample may be a tissue sample embedded in a matrix, e.g., an FFPE block. Methods to isolate nucleic acids (e.g., DNA) from formaldehyde- or paraformaldehyde-fixed, paraffin-embedded (FFPE) tissues are disclosed in, e.g., Cronin, et al., (2004) Am J Pathol. 164(l):35-42; Masuda, et al., (1999) Nucleic Acids Res. 27 (22): 4436-4443; Specht, et al., (2001) Am J Pathol. 158(2):419-429; the Ambion RecoverAll™ Total Nucleic Acid Isolation Protocol (Ambion, Cat. No. AM1975, September 2008); the Maxwell® 16 FFPE Plus EEV DNA Purification Kit Technical Manual (Promega Eiterature #TM349, February 2011); the E.Z.N.A.® FFPE DNA Kit Handbook (OMEGA bio-tek, Norcross, GA, product numbers D3399-00, D3399-01, and D3399-02, June 2009); and the QIAamp® DNA FFPE Tissue Handbook (Qiagen, Cat. No. 37625, October 2007). For example, the RecoverAll™ Total Nucleic Acid Isolation Kit uses xylene at elevated temperatures to solubilize paraffin- embedded samples and a glass-fiber filter to capture nucleic acids. The Maxwell® 16 FFPE Plus EEV DNA Purification Kit is used with the Maxwell® 16 Instrument for purification of genomic DNA from 1 to 10 pm sections of FFPE tissue. DNA is purified using silica-clad paramagnetic particles (PMPs), and eluted in low elution volume. The E.Z.N.A.® FFPE DNA Kit uses a spin column and buffer system for isolation of genomic DNA. QIAamp® DNA FFPE Tissue Kit uses QIAamp® DNA Micro technology for purification of genomic and mitochondrial DNA. [0219] In some instances, the disclosed methods may further comprise determining or acquiring a yield value for the nucleic acid extracted from the sample and comparing the determined value to a reference value. For example, if the determined or acquired value is less than the reference value, the nucleic acids may be amplified prior to proceeding with library construction. In some instances, the disclosed methods may further comprise determining or acquiring a value for the size (or average size) of nucleic acid fragments in the sample, and comparing the determined or acquired value to a reference value, e.g., a size (or average size) of at least 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 base pairs (bps). In some instances, one or more parameters described herein may be adjusted or selected in response to this determination.
[0220] After isolation, the nucleic acids are typically dissolved in a slightly alkaline buffer, e.g., Tris-EDTA (TE) buffer, or in ultra-pure water. In some instances, the isolated nucleic acids (e.g., genomic DNA) may be fragmented or sheared by using any of a variety of techniques known to those of skill in the art. For example, genomic DNA can be fragmented by physical shearing methods, enzymatic cleavage methods, chemical cleavage methods, and other methods known to those of skill in the art. Methods for DNA shearing are described in Example 4 in International Patent Application Publication No. WO 2012/092426. In some instances, alternatives to DNA shearing methods can be used to avoid a ligation step during library preparation.
Library preparation
[0221] In some instances, the nucleic acids isolated from the sample may be used to construct a library (e.g., a nucleic acid library as described herein). In some instances, the nucleic acids are fragmented using any of the methods described above, optionally subjected to repair of chain end damage, and optionally ligated to synthetic adapters, primers, and/or barcodes (e.g., amplification primers, sequencing adapters, flow cell adapters, substrate adapters, sample barcodes or indexes, and/or unique molecular identifier sequences), size-selected (e.g., by preparative gel electrophoresis), and/or amplified (e.g., using PCR, a non-PCR amplification technique, or an isothermal amplification technique). In some instances, the fragmented and adapter-ligated group of nucleic acids is used without explicit size selection or amplification prior to hybridization-based selection of target sequences. In some instances, the nucleic acid is amplified by any of a variety of specific or nonspecific nucleic acid amplification methods known to those of skill in the art. In some instances, the nucleic acids are amplified, e.g., by a whole-genome amplification method such as random-primed strand-displacement amplification. Examples of nucleic acid library preparation techniques for next-generation sequencing are described in, e.g., van Dijk, et al. (2014), Exp. Cell Research 322:12 - 20, and Illumina’s genomic DNA sample preparation kit.
[0222] In some instances, the resulting nucleic acid library may contain all or substantially all of the complexity of the genome. The term “substantially all” in this context refers to the possibility that there can in practice be some unwanted loss of genome complexity during the initial steps of the procedure. The methods described herein also are useful in cases where the nucleic acid library comprises a portion of the genome, e.g., where the complexity of the genome is reduced by design. In some instances, any selected portion of the genome can be used with a method described herein. For example, in certain embodiments, the entire exome or a subset thereof is isolated. In some instances, the library may include at least 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, or 5% of the genomic DNA. In some instances, the library may consist of cDNA copies of genomic DNA that includes copies of at least 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, or 5% of the genomic DNA. In certain instances, the amount of nucleic acid used to generate the nucleic acid library may be less than 5 micrograms, less than 1 microgram, less than 500 ng, less than 200 ng, less than 100 ng, less than 50 ng, less than 10 ng, less than 5 ng, or less than 1 ng.
[0223] In some instances, a library (e.g., a nucleic acid library) includes a collection of nucleic acid molecules. As described herein, the nucleic acid molecules of the library can include a target nucleic acid molecule (e.g., a tumor nucleic acid molecule, a reference nucleic acid molecule and/or a control nucleic acid molecule; also referred to herein as a first, second and/or third nucleic acid molecule, respectively). The nucleic acid molecules of the library can be from a single subject or individual. In some instances, a library can comprise nucleic acid molecules derived from more than one subject (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30 or more subjects). For example, two or more libraries from different subjects can be combined to form a library having nucleic acid molecules from more than one subject (where the nucleic acid molecules derived from each subject are optionally ligated to a unique sample barcode corresponding to a specific subject). In some instances, the subject is a human having, or at risk of having, a cancer or tumor.
[0224] In some instances, the library (or a portion thereof) may comprise one or more subgenomic intervals. In some instances, a subgenomic interval can be a single nucleotide position, e.g., a nucleotide position for which a variant at the position is associated (positively or negatively) with a tumor phenotype. In some instances, a subgenomic interval comprises more than one nucleotide position. Such instances include sequences of at least 2, 5, 10, 50, 100, 150, 250, or more than 250 nucleotide positions in length. Subgenomic intervals can comprise, e.g., one or more entire genes (or portions thereof), one or more exons or coding sequences (or portions thereof), one or more introns (or portion thereof), one or more microsatellite region (or portions thereof), or any combination thereof. A subgenomic interval can comprise all or a part of a fragment of a naturally occurring nucleic acid molecule, e.g., a genomic DNA molecule. For example, a subgenomic interval can correspond to a fragment of genomic DNA which is subjected to a sequencing reaction. In some instances, a subgenomic interval is a continuous sequence from a genomic source. In some instances, a subgenomic interval includes sequences that are not contiguous in the genome, e.g., subgenomic intervals in cDNA can include exonexonjunctions formed as a result of splicing. In some instances, the subgenomic interval comprises a tumor nucleic acid molecule. In some instances, the subgenomic interval comprises a non-tumor nucleic acid molecule.
Targeting gene loci for analysis
[0225] The methods described herein can be used in combination with, or as part of, a method for evaluating a plurality or set of subject intervals (e.g., target sequences), e.g., from a set of genomic loci (e.g., gene loci or fragments thereof), as described herein.
[0226] In some instances, the set of genomic loci evaluated by the disclosed methods comprises a plurality of, e.g., genes, which in mutant form, are associated with an effect on cell division, growth or survival, or are associated with a cancer, e.g., a cancer described herein.
[0227] In some instances, the set of gene loci evaluated by the disclosed methods comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, or more than 100 gene loci. [0228] In some instances, the selected gene loci (also referred to herein as target gene loci or target sequences), or fragments thereof, may include subject intervals comprising non-coding sequences, coding sequences, intragenic regions, or intergenic regions of the subject genome. For example, the subject intervals can include a non-coding sequence or fragment thereof (e.g., a promoter sequence, enhancer sequence, 5’ untranslated region (5’ UTR), 3’ untranslated region (3’ UTR), or a fragment thereof), a coding sequence of fragment thereof, an exon sequence or fragment thereof, an intron sequence or a fragment thereof.
Target capture reagents
[0229] The methods described herein may comprise contacting a nucleic acid library with a plurality of target capture reagents in order to select and capture a plurality of specific target sequences (e.g., gene sequences or fragments thereof) for analysis. In some instances, a target capture reagent (z.e., a molecule which can bind to and thereby allow capture of a target molecule) is used to select the subject intervals to be analyzed. For example, a target capture reagent can be a bait molecule, e.g., a nucleic acid molecule (e.g., a DNA molecule or RNA molecule) which can hybridize to (z.e., is complementary to) a target molecule, and thereby allows capture of the target nucleic acid. In some instances, the target capture reagent, e.g., a bait molecule (or bait sequence), is a capture oligonucleotide (or capture probe). In some instances, the target nucleic acid is a genomic DNA molecule, an RNA molecule, a cDNA molecule derived from an RNA molecule, a microsatellite DNA sequence, and the like. In some instances, the target capture reagent is suitable for solution-phase hybridization to the target. In some instances, the target capture reagent is suitable for solid-phase hybridization to the target. In some instances, the target capture reagent is suitable for both solution-phase and solid-phase hybridization to the target. The design and construction of target capture reagents is described in more detail in, e.g., International Patent Application Publication No. WO 2020/236941, the entire content of which is incorporated herein by reference.
[0230] The methods described herein provide for optimized sequencing of a large number of genomic loci (e.g., genes or gene products (e.g., mRNA), micro satellite loci, etc.) from samples (e.g., cancerous tissue specimens, liquid biopsy samples, and the like) from one or more subjects by the appropriate selection of target capture reagents to select the target nucleic acid molecules to be sequenced. In some instances, a target capture reagent may hybridize to a specific target locus, e.g., a specific target gene locus or fragment thereof. In some instances, a target capture reagent may hybridize to a specific group of target loci, e.g., a specific group of gene loci or fragments thereof. In some instances, a plurality of target capture reagents comprising a mix of target- specific and/or group- specific target capture reagents may be used.
[0231] In some instances, the number of target capture reagents (e.g., bait molecules) in the plurality of target capture reagents (e.g., a bait set) contacted with a nucleic acid library to capture a plurality of target sequences for nucleic acid sequencing is greater than 10, greater than 50, greater than 100, greater than 200, greater than 300, greater than 400, greater than 500, greater than 600, greater than 700, greater than 800, greater than 900, greater than 1,000, greater than 1,250, greater than 1,500, greater than 1,750, greater than 2,000, greater than 3,000, greater than 4,000, greater than 5,000, greater than 10,000, greater than 25,000, or greater than 50,000.
[0232] In some instances, the overall length of the target capture reagent sequence can be between about 70 nucleotides and 1000 nucleotides. In one instance, the target capture reagent length is between about 100 and 300 nucleotides, 110 and 200 nucleotides, or 120 and 170 nucleotides, in length. In addition to those mentioned above, intermediate oligonucleotide lengths of about 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 300, 400, 500, 600, 700, 800, and 900 nucleotides in length can be used in the methods described herein. In some embodiments, oligonucleotides of about 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, or 230 bases can be used.
[0233] In some instances, each target capture reagent sequence can include: (i) a target- specific capture sequence (e.g., a gene locus or micro satellite locus- specific complementary sequence), (ii) an adapter, primer, barcode, and/or unique molecular identifier sequence, and (iii) universal tails on one or both ends. As used herein, the term "target capture reagent" can refer to the targetspecific target capture sequence or to the entire target capture reagent oligonucleotide including the target- specific target capture sequence.
[0234] In some instances, the target- specific capture sequences in the target capture reagents are between about 40 nucleotides and 1000 nucleotides in length. In some instances, the targetspecific capture sequence is between about 70 nucleotides and 300 nucleotides in length. In some instances, the target- specific sequence is between about 100 nucleotides and 200 nucleotides in length. In yet other instances, the target- specific sequence is between about 120 nucleotides and 170 nucleotides in length, typically 120 nucleotides in length. Intermediate lengths in addition to those mentioned above also can be used in the methods described herein, such as target- specific sequences of about 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 300, 400, 500, 600, 700, 800, and 900 nucleotides in length, as well as target- specific sequences of lengths between the above-mentioned lengths.
[0235] In some instances, the target capture reagent may be designed to select a subject interval containing one or more rearrangements, e.g., an intron containing a genomic rearrangement. In such instances, the target capture reagent is designed such that repetitive sequences are masked to increase the selection efficiency. In those instances where the rearrangement has a known juncture sequence, complementary target capture reagents can be designed to recognize the juncture sequence to increase the selection efficiency.
[0236] In some instances, the disclosed methods may comprise the use of target capture reagents designed to capture two or more different target categories, each category having a different target capture reagent design strategy. In some instances, the hybridization-based capture methods and target capture reagent compositions disclosed herein may provide for the capture and homogeneous coverage of a set of target sequences, while minimizing coverage of genomic sequences outside of the targeted set of sequences. In some instances, the target sequences may include the entire exome of genomic DNA or a selected subset thereof. In some instances, the target sequences may include, e.g., a large chromosomal region (e.g., a whole chromosome arm). The methods and compositions disclosed herein provide different target capture reagents for achieving different sequencing depths and patterns of coverage for complex sets of target nucleic acid sequences.
[0237] Typically, DNA molecules are used as target capture reagent sequences, although RNA molecules can also be used. In some instances, a DNA molecule target capture reagent can be single stranded DNA (ssDNA) or double- stranded DNA (dsDNA). In some instances, an RNA- DNA duplex is more stable than a DNA-DNA duplex and therefore provides for potentially better capture of nucleic acids.
[0238] In some instances, the disclosed methods comprise providing a selected set of nucleic acid molecules (e.g., a library catch) captured from one or more nucleic acid libraries. For example, the method may comprise: providing one or a plurality of nucleic acid libraries, each comprising a plurality of nucleic acid molecules (e.g., a plurality of target nucleic acid molecules and/or reference nucleic acid molecules) extracted from one or more samples from one or more subjects; contacting the one or a plurality of libraries (e.g., in a solution-based hybridization reaction) with one, two, three, four, five, or more than five pluralities of target capture reagents (e.g., oligonucleotide target capture reagents) to form a hybridization mixture comprising a plurality of target capture reagent/nucleic acid molecule hybrids; separating the plurality of target capture reagent/nucleic acid molecule hybrids from said hybridization mixture, e.g., by contacting said hybridization mixture with a binding entity that allows for separation of said plurality of target capture reagent/nucleic acid molecule hybrids from the hybridization mixture, thereby providing a library catch (e.g., a selected or enriched subgroup of nucleic acid molecules from the one or a plurality of libraries).
[0239] In some instances, the disclosed methods may further comprise amplifying the library catch (e.g., by performing PCR). In other instances, the library catch is not amplified.
[0240] In some instances, the target capture reagents can be part of a kit which can optionally comprise instructions, standards, buffers or enzymes or other reagents.
Hybridization conditions
[0241] As noted above, the methods disclosed herein may include the step of contacting the library (e.g., the nucleic acid library) with a plurality of target capture reagents to provide a selected library target nucleic acid sequences (i.e., the library catch). The contacting step can be effected in, e.g., solution-based hybridization. In some instances, the method includes repeating the hybridization step for one or more additional rounds of solution-based hybridization. In some instances, the method further includes subjecting the library catch to one or more additional rounds of solution-based hybridization with the same or a different collection of target capture reagents.
[0242] In some instances, the contacting step is effected using a solid support, e.g., an array. Suitable solid supports for hybridization are described in, e.g., Albert, T.J. et al. (2007) Nat. Methods 4(11):903-5; Hodges, E. et al. (2007) Nat. Genet. 39(12): 1522-7; and Okou, D.T. et al. (2007) Nat. Methods 4(11 ):907-9, the contents of which are incorporated herein by reference in their entireties. [0243] Hybridization methods that can be adapted for use in the methods herein are described in the art, e.g., as described in International Patent Application Publication No. WO 2012/092426. Methods for hybridizing target capture reagents to a plurality of target nucleic acids are described in more detail in, e.g., International Patent Application Publication No. WO 2020/236941, the entire content of which is incorporated herein by reference.
Sequencing methods
[0244] The methods and systems disclosed herein can be used in combination with, or as part of, a method or system for sequencing nucleic acids (e.g., a next-generation sequencing system) to generate a plurality of sequence reads that overlap one or more gene loci within a subgenomic interval in the sample and thereby determine, e.g., gene allele sequences at a plurality of gene loci. “Next-generation sequencing” (or “NGS”) as used herein may also be referred to as “massively parallel sequencing” (or “MPS”), and refers to any sequencing method that determines the nucleotide sequence of either individual nucleic acid molecules (e.g., as in single molecule sequencing) or clonally expanded proxies for individual nucleic acid molecules in a high throughput fashion (e.g., wherein greater than 103, 104, 105 or more than 105 molecules are sequenced simultaneously).
[0245] Next-generation sequencing methods are known in the art, and are described in, e.g., Metzker, M. (2010) Nature Biotechnology Reviews 11:31-46, which is incorporated herein by reference. Other examples of sequencing methods suitable for use when implementing the methods and systems disclosed herein are described in, e.g., International Patent Application Publication No. WO 2012/092426. In some instances, the sequencing may comprise, for example, whole genome sequencing (WGS), whole exome sequencing, targeted sequencing, or direct sequencing. In some instances, sequencing may be performed using, e.g., Sanger sequencing. In some instances, the sequencing may comprise a paired-end sequencing technique that allows both ends of a fragment to be sequenced and generates high-quality, alignable sequence data for detection of, e.g., genomic rearrangements, repetitive sequence elements, gene fusions, and novel transcripts.
[0246] The disclosed methods and systems may be implemented using sequencing platforms such as the Roche/454 Genome Sequencer (GS) FLX System, Illumina/Solexa Genome Analyzer (GA), Illumina’s HiSeq® 2500, HiSeq® 3000, HiSeq® 4000 and NovaSeq® 6000 Sequencing Systems, Life/APG’s Support Oligonucleotide Ligation Detection (SOLiD) system, Polonator’s G.007 system, Helicos BioSciences’ HeliScope Gene Sequencing system, or Pacific Biosciences’ PacBio® RS platform. In some instances, sequencing may comprise Illumina MiSeq™ sequencing. In some instances, sequencing may comprise Illumina HiSeq® sequencing. In some instances, sequencing may comprise Illumina NovaSeq® sequencing. Optimized methods for sequencing a large number of target genomic loci in nucleic acids extracted from a sample are described in more detail in, e.g., International Patent Application Publication No. WO 2020/236941, the entire content of which is incorporated herein by reference.
[0247] In certain instances, the disclosed methods comprise one or more of the steps of: (a) acquiring a library comprising a plurality of normal and/or tumor nucleic acid molecules from a sample; (b) simultaneously or sequentially contacting the library with one, two, three, four, five, or more than five pluralities of target capture reagents under conditions that allow hybridization of the target capture reagents to the target nucleic acid molecules, thereby providing a selected set of captured normal and/or tumor nucleic acid molecules (z.e., a library catch); (c) separating the selected subset of the nucleic acid molecules (e.g., the library catch) from the hybridization mixture, e.g., by contacting the hybridization mixture with a binding entity that allows for separation of the target capture reagent/nucleic acid molecule hybrids from the hybridization mixture, (d) sequencing the library catch to acquiring a plurality of reads (e.g., sequence reads) that overlap one or more subject intervals (e.g., one or more target sequences) from said library catch that may comprise a mutation (or alteration), e.g., a variant sequence comprising a somatic mutation or germline mutation; (e) aligning said sequence reads using an alignment method as described elsewhere herein; and/or (f) assigning a nucleotide value for a nucleotide position in the subject interval (e.g., calling a mutation using, e.g., a Bayesian method or other method described herein) from one or more sequence reads of the plurality.
[0248] In some instances, acquiring sequence reads for one or more subject intervals may comprise sequencing at least 1, at least 5, at least 10, at least 20, at least 30, at least 40, at least 50, at least 100, at least 150, at least 200, at least 250, at least 300, at least 350, at least 400, at least 450, at least 500, at least 550, at least 600, at least 650, at least 700, at least 750, at least 800, at least 850, at least 900, at least 950, at least 1,000, at least 1,250, at least 1,500, at least 1,750, at least 2,000, at least 2,250, at least 2,500, at least 2,750, at least 3,000, at least 3,500, at least 4,000, at least 4,500, or at least 5,000 loci, e.g., genomic loci, gene loci, microsatellite loci, etc. In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing a subject interval for any number of loci within the range described in this paragraph, e.g., for at least 2,850 gene loci.
[0249] In some instances, acquiring a sequence read for one or more subject intervals comprises sequencing a subject interval with a sequencing method that provides a sequence read length (or average sequence read length) of at least 20 bases, at least 30 bases, at least 40 bases, at least 50 bases, at least 60 bases, at least 70 bases, at least 80 bases, at least 90 bases, at least 100 bases, at least 120 bases, at least 140 bases, at least 160 bases, at least 180 bases, at least 200 bases, at least 220 bases, at least 240 bases, at least 260 bases, at least 280 bases, at least 300 bases, at least 320 bases, at least 340 bases, at least 360 bases, at least 380 bases, or at least 400 bases. In some instances, acquiring a sequence read for the one or more subject intervals may comprise sequencing a subject interval with a sequencing method that provides a sequence read length (or average sequence read length) of any number of bases within the range described in this paragraph, e.g., a sequence read length (or average sequence read length) of 56 bases.
[0250] In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing with at least lOOx or more coverage (or depth) on average. In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing with at least lOOx, at least 150x, at least 200x, at least 250x, at least 500x, at least 750x, at least l,000x, at least 1,500 x, at least 2,000x, at least 2,500x, at least 3,000x, at least 3,500x, at least 4,000x, at least 4,500x, at least 5,000x, at least 5,500x, or at least 6,000x or more coverage (or depth) on average. In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing with an average coverage (or depth) having any value within the range of values described in this paragraph, e.g., at least 160x.
[0251] In some instances, acquiring a read for the one or more subject intervals comprises sequencing with an average sequencing depth having any value ranging from at least lOOx to at least 6,000x for greater than about 90%, 92%, 94%, 95%, 96%, 97%, 98%, or 99% of the gene loci sequenced. For example, in some instances acquiring a read for the subject interval comprises sequencing with an average sequencing depth of at least 125x for at least 99% of the gene loci sequenced. As another example, in some instances acquiring a read for the subject interval comprises sequencing with an average sequencing depth of at least 4,100x for at least 95% of the gene loci sequenced.
[0252] In some instances, the relative abundance of a nucleic acid species in the library can be estimated by counting the relative number of occurrences of their cognate sequences (e.g., the number of sequence reads for a given cognate sequence) in the data generated by the sequencing experiment.
[0253] In some instances, the disclosed methods and systems provide nucleotide sequences for a set of subject intervals (e.g., gene loci), as described herein. In certain instances, the sequences are provided without using a method that includes a matched normal control (e.g., a wild-type control) and/or a matched tumor control (e.g., primary versus metastatic).
[0254] In some instances, the level of sequencing depth as used herein (e.g., an X-fold level of sequencing depth) refers to the number of reads (e.g., unique reads) obtained after detection and removal of duplicate reads (e.g., PCR duplicate reads). In other instances, duplicate reads are evaluated, e.g., to support detection of copy number alteration (CNAs).
Alignment
[0255] Alignment is the process of matching a read with a location, e.g., a genomic location or locus. In some instances, NGS reads may be aligned to a known reference sequence (e.g., a wild-type sequence). In some instances, NGS reads may be assembled de novo. Methods of sequence alignment for NGS reads are described in, e.g., Trapnell, C. and Salzberg, S.L. Nature Biotech., 2009, 27:455-457. Examples of de novo sequence assemblies are described in, e.g., Warren R., et al., Bioinformatics, 2007, 23:500-501; Butler, J. et al., Genome Res., 2008, 18:810-820; and Zerbino, D.R. and Birney, E., Genome Res., 2008, 18:821-829. Optimization of sequence alignment is described in the art, e.g., as set out in International Patent Application Publication No. WO 2012/092426. Additional description of sequence alignment methods is provided in, e.g., International Patent Application Publication No. WO 2020/236941, the entire content of which is incorporated herein by reference.
[0256] Misalignment (e.g., the placement of base-pairs from a short read at incorrect locations in the genome), e.g., misalignment of reads due to sequence context (e.g., the presence of repetitive sequence) around an actual cancer mutation can lead to reduction in sensitivity of mutation detection, can lead to a reduction in sensitivity of mutation detection, as reads for the alternate allele may be shifted off the histogram peak of alternate allele reads. Other examples of sequence context that may cause misalignment include short-tandem repeats, interspersed repeats, low complexity regions, insertions - deletions (indels), and paralogs. If the problematic sequence context occurs where no actual mutation is present, misalignment may introduce artifactual reads of “mutated” alleles by placing reads of actual reference genome base sequences at the wrong location. Because mutation-calling algorithms for multigene analysis should be sensitive to even low-abundance mutations, sequence misalignments may increase false positive discovery rates and/or reduce specificity.
[0257] In some instances, the methods and systems disclosed herein may integrate the use of multiple, individually-tuned, alignment methods or algorithms to optimize base-calling performance in sequencing methods, particularly in methods that rely on massively parallel sequencing (MPS) of a large number of diverse genetic events at a large number of diverse genomic loci. In some instances, the disclosed methods and systems may comprise the use of one or more global alignment algorithms. In some instances, the disclosed methods and systems may comprise the use of one or more local alignment algorithms. Examples of alignment algorithms that may be used include, but are not limited to, the Burrows-Wheeler Alignment (BWA) software bundle (see, e.g., Li, et al. (2009), “Fast and Accurate Short Read Alignment with Burrows-Wheeler Transform”, Bioinformatics 25: 1754-60; Li, et al. (2010), Fast and Accurate Long-Read Alignment with Burrows-Wheeler Transform”, Bioinformatics epub.
PMID: 20080505), the Smith- Waterman algorithm (see, e.g., Smith, et al. (1981), "Identification of Common Molecular Subsequences", J. Molecular Biology 147(1): 195-197), the Striped Smith-Waterman algorithm (see, e.g., Farrar (2007), “Striped Smith-Waterman Speeds Database Searches Six Times Over Other SIMD Implementations”, Bioinformatics 23(2): 156-161), the Needleman-Wunsch algorithm (Needleman, et al. (1970) "A General Method Applicable to the Search for Similarities in the Amino Acid Sequence of Two Proteins", J. Molecular Biology 48(3):443-53), or any combination thereof.
[0258] In some instances, the methods and systems disclosed herein may also comprise the use of a sequence assembly algorithm, e.g., the Arachne sequence assembly algorithm (see, e.g., Batzoglou, et al. (2002), “ARACHNE: A Whole-Genome Shotgun Assembler”, Genome Res. 12:177-189).
[0259] In some instances, the alignment method used to analyze sequence reads is not individually customized or tuned for detection of different variants (e.g., point mutations, insertions, deletions, and the like) at different genomic loci. In some instances, different alignment methods are used to analyze reads that are individually customized or tuned for detection of at least a subset of the different variants detected at different genomic loci. In some instances, different alignment methods are used to analyze reads that are individually customized or tuned to detect each different variant at different genomic loci. In some instances, tuning can be a function of one or more of: (i) the genetic locus (e.g., gene loci, micro satellite locus, or other subject interval) being sequenced, (ii) the tumor type associated with the sample, (iii) the variant being sequenced, or (iv) a characteristic of the sample or the subject. The selection or use of alignment conditions that are individually tuned to a number of specific subject intervals to be sequenced allows optimization of speed, sensitivity, and specificity. The method is particularly effective when the alignment of reads for a relatively large number of diverse subject intervals are optimized.
[0260] In some instances, the method includes the use of an alignment method optimized for rearrangements in combination with other alignment methods optimized for subject intervals not associated with rearrangements.
[0261] In some instances, the methods disclosed herein further comprise selecting or using an alignment method for analyzing, e.g., aligning, a sequence read, wherein said alignment method is a function of, is selected responsive to, or is optimized for, one or more of: (i) tumor type, e.g., the tumor type in the sample; (ii) the location (e.g., a gene locus) of the subject interval being sequenced; (iii) the type of variant (e.g., a point mutation, insertion, deletion, substitution, copy number variation (CNV), rearrangement, or fusion) in the subject interval being sequenced; (iv) the site (e.g., nucleotide position) being analyzed; (v) the type of sample (e.g., a sample described herein); and/or (vi) adjacent sequence(s) in or near the subject interval being evaluated (e.g., according to the expected propensity thereof for misalignment of the subject interval due to, e.g., the presence of repeated sequences in or near the subject interval). [0262] In some instances, the methods disclosed herein allow for the rapid and efficient alignment of troublesome reads, e.g., a read having a rearrangement. Thus, in some instances where a read for a subject interval comprises a nucleotide position with a rearrangement, e.g., a translocation, the method can comprise using an alignment method that is appropriately tuned and that includes: (i) selecting a rearrangement reference sequence for alignment with a read, wherein said rearrangement reference sequence aligns with a rearrangement (in some instances, the reference sequence is not identical to the genomic rearrangement); and (ii) comparing, e.g., aligning, a read with said rearrangement reference sequence.
[0263] In some instances, alternative methods may be used to align troublesome reads. These methods are particularly effective when the alignment of reads for a relatively large number of diverse subject intervals is optimized. By way of example, a method of analyzing a sample can comprise: (i) performing a comparison (e.g., an alignment comparison) of a read using a first set of parameters (e.g., using a first mapping algorithm, or by comparison with a first reference sequence), and determining if said read meets a first alignment criterion (e.g., the read can be aligned with said first reference sequence, e.g., with less than a specific number of mismatches); (ii) if said read fails to meet the first alignment criterion, performing a second alignment comparison using a second set of parameters, (e.g., using a second mapping algorithm, or by comparison with a second reference sequence); and (iii) optionally, determining if said read meets said second criterion (e.g., the read can be aligned with said second reference sequence, e.g., with less than a specific number of mismatches), wherein said second set of parameters comprises use of, e.g., said second reference sequence, which, compared with said first set of parameters, is more likely to result in an alignment with a read for a variant (e.g., a rearrangement, insertion, deletion, or translocation).
[0264] In some instances, the alignment of sequence reads in the disclosed methods may be combined with a mutation calling method as described elsewhere herein. As discussed herein, reduced sensitivity for detecting actual mutations may be addressed by evaluating the quality of alignments (manually or in an automated fashion) around expected mutation sites in the genes or genomic loci (e.g., gene loci) being analyzed. In some instances, the sites to be evaluated can be obtained from databases of the human genome (e.g., the HG19 human reference genome) or cancer mutations (e.g., COSMIC). Regions that are identified as problematic can be remedied with the use of an algorithm selected to give better performance in the relevant sequence context, e.g., by alignment optimization (or re-alignment) using slower, but more accurate alignment algorithms such as Smith-Waterman alignment. In cases where general alignment algorithms cannot remedy the problem, customized alignment approaches may be created by, e.g., adjustment of maximum difference mismatch penalty parameters for genes with a high likelihood of containing substitutions; adjusting specific mismatch penalty parameters based on specific mutation types that are common in certain tumor types (e.g. C~^T in melanoma); or adjusting specific mismatch penalty parameters based on specific mutation types that are common in certain sample types (e.g. substitutions that are common in FFPE).
[0265] Reduced specificity (increased false positive rate) in the evaluated subject intervals due to misalignment can be assessed by manual or automated examination of all mutation calls in the sequencing data. Those regions found to be prone to spurious mutation calls due to misalignment can be subjected to alignment remedies as discussed above. In cases where no algorithmic remedy is found possible, “mutations” from the problem regions can be classified or screened out from the panel of targeted loci.
Mutation calling
[0266] Base calling refers to the raw output of a sequencing device, e.g., the determined sequence of nucleotides in an oligonucleotide molecule. Mutation calling refers to the process of selecting a nucleotide value, e.g., A, G, T, or C, for a given nucleotide position being sequenced. Typically, the sequence reads (or base calling) for a position will provide more than one value, e.g., some reads will indicate a T and some will indicate a G. Mutation calling is the process of assigning a correct nucleotide value, e.g., one of those values, to the sequence. Although it is referred to as “mutation” calling, it can be applied to assign a nucleotide value to any nucleotide position, e.g., positions corresponding to mutant alleles, wild-type alleles, alleles that have not been characterized as either mutant or wild-type, or to positions not characterized by variability.
[0267] In some instances, the disclosed methods may comprise the use of customized or tuned mutation calling algorithms or parameters thereof to optimize performance when applied to sequencing data, particularly in methods that rely on massively parallel sequencing (MPS) of a large number of diverse genetic events at a large number of diverse genomic loci (e.g., gene loci, micro satellite regions, etc.) in samples, e.g., samples from a subject having cancer. Optimization of mutation calling is described in the art, e.g., as set out in International Patent Application Publication No. WO 2012/092426.
[0268] Methods for mutation calling can include one or more of the following: making independent calls based on the information at each position in the reference sequence (e.g., examining the sequence reads; examining the base calls and quality scores; calculating the probability of observed bases and quality scores given a potential genotype; and assigning genotypes (e.g., using Bayes’ rule)); removing false positives (e.g., using depth thresholds to reject SNPs with read depth much lower or higher than expected; local realignment to remove false positives due to small indels); and performing linkage disequilibrium (LD)/imputation- based analysis to refine the calls.
[0269] Equations used to calculate the genotype likelihood associated with a specific genotype and position are described in, e.g., Li, H. and Durbin, R. Bioinformatics, 2010; 26(5): 589-95. The prior expectation for a particular mutation in a certain cancer type can be used when evaluating samples from that cancer type. Such likelihood can be derived from public databases of cancer mutations, e.g., Catalogue of Somatic Mutation in Cancer (COSMIC), HGMD (Human Gene Mutation Database), The SNP Consortium, Breast Cancer Mutation Data Base (BIC), and Breast Cancer Gene Database (BCGD).
[0270] Examples of LD/imputation based analysis are described in, e.g., Browning, B.L. and Yu, Z. Am. J. Hum. Genet. 2009, 85(6):847-61. Examples of low-coverage SNP calling methods are described in, e.g., Li, Y., et al., Annu. Rev. Genomics Hum. Genet. 2009, 10:387-406.
[0271] After alignment, detection of substitutions can be performed using a mutation calling method (e.g., a Bayesian mutation calling method) which is applied to each base in each of the subject intervals, e.g., exons of a gene or other locus to be evaluated, where presence of alternate alleles is observed. This method will compare the probability of observing the read data in the presence of a mutation with the probability of observing the read data in the presence of basecalling error alone. Mutations can be called if this comparison is sufficiently strongly supportive of the presence of a mutation.
[0272] An advantage of a Bayesian mutation detection approach is that the comparison of the probability of the presence of a mutation with the probability of base-calling error alone can be weighted by a prior expectation of the presence of a mutation at the site. If some reads of an alternate allele are observed at a frequently mutated site for the given cancer type, then presence of a mutation may be confidently called even if the amount of evidence of mutation does not meet the usual thresholds. This flexibility can then be used to increase detection sensitivity for even rarer mutations/lower purity samples, or to make the test more robust to decreases in read coverage. The likelihood of a random base-pair in the genome being mutated in cancer is ~le-6. The likelihood of specific mutations occurring at many sites in, for example, a typical multigenic cancer genome panel can be orders of magnitude higher. These likelihoods can be derived from public databases of cancer mutations (e.g., COSMIC).
[0273] Indel calling is a process of finding bases in the sequencing data that differ from the reference sequence by insertion or deletion, typically including an associated confidence score or statistical evidence metric. Methods of indel calling can include the steps of identifying candidate indels, calculating genotype likelihood through local re-alignment, and performing LD-based genotype inference and calling. Typically, a Bayesian approach is used to obtain potential indel candidates, and then these candidates are tested together with the reference sequence in a Bayesian framework.
[0274] Algorithms to generate candidate indels are described in, e.g., McKenna, A., et al., Genome Res. 2010; 20(9): 1297-303; Ye, K., et al., Bioinformatics, 2009; 25(21):2865-71; Lunter, G., and Goodson, M., Genome Res. 2011; 21(6):936-9; and Li, H., et al. (2009), Bioinformatics 25(16):2078-9.
[0275] Methods for generating indel calls and individual-level genotype likelihoods include, e.g., the Dindel algorithm (Albers, C.A., et al., Genome Res. 2011 ;21 (6):961-73). For example, the Bayesian EM algorithm can be used to analyze the reads, make initial indel calls, and generate genotype likelihoods for each candidate indel, followed by imputation of genotypes using, e.g., QCALL (Le S.Q. and Durbin R. Genome Res. 2011 ;21(6):952-60). Parameters, such as prior expectations of observing the indel can be adjusted e.g., increased or decreased), based on the size or location of the indels.
[0276] Methods have been developed that address limited deviations from allele frequencies of 50% or 100% for the analysis of cancer DNA. (see, e.g., SNVMix -Bioinformatics. 2010 March 15; 26(6): 730-736.) Methods disclosed herein, however, allow consideration of the possibility of the presence of a mutant allele at frequencies (or allele fractions) ranging from 1% to 100% (z.e., allele fractions ranging from 0.01 to 1.0), and especially at levels lower than 50%. This approach is particularly important for the detection of mutations in, for example, low-purity FFPE samples of natural (multi-clonal) tumor DNA.
[0277] In some instances, the mutation calling method used to analyze sequence reads is not individually customized or fine-tuned for detection of different mutations at different genomic loci. In some instances, different mutation calling methods are used that are individually customized or fine-tuned for at least a subset of the different mutations detected at different genomic loci. In some instances, different mutation calling methods are used that are individually customized or fine-tuned for each different mutant detected at each different genomic loci. The customization or tuning can be based on one or more of the factors described herein, e.g., the type of cancer in a sample, the gene or locus in which the subject interval to be sequenced is located, or the variant to be sequenced. This selection or use of mutation calling methods individually customized or fine-tuned for a number of subject intervals to be sequenced allows for optimization of speed, sensitivity and specificity of mutation calling.
[0278] In some instances, a nucleotide value is assigned for a nucleotide position in each of X unique subject intervals using a unique mutation calling method, and X is at least 2, at least 3, at least 4, at least 5, at least 10, at least 15, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 200, at least 300, at least 400, at least 500, at least 1000, at least 1500, at least 2000, at least 2500, at least 3000, at least 3500, at least 4000, at least 4500, at least 5000, or greater. The calling methods can differ, and thereby be unique, e.g., by relying on different Bayesian prior values.
[0279] In some instances, assigning said nucleotide value is a function of a value which is or represents the prior (e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type.
[0280] In some instances, the method comprises assigning a nucleotide value (e.g., calling a mutation) for at least 10, 20, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1,000 nucleotide positions, wherein each assignment is a function of a unique value (as opposed to the value for the other assignments) which is or represents the prior (e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type. [0281] In some instances, assigning said nucleotide value is a function of a set of values which represent the probabilities of observing a read showing said variant at said nucleotide position if the variant is present in the sample at a specified frequency (e.g., 1%, 5%, 10%, etc.) and/or if the variant is absent (e.g., observed in the reads due to base-calling error alone).
[0282] In some instances, the mutation calling methods described herein can include the following: (a) acquiring, for a nucleotide position in each of said X subject intervals: (i) a first value which is or represents the prior (e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type X; and (ii) a second set of values which represent the probabilities of observing a read showing said variant at said nucleotide position if the variant is present in the sample at a frequency (e.g., 1%, 5%, 10%, etc.) and/or if the variant is absent (e.g., observed in the reads due to base-calling error alone); and (b) responsive to said values, assigning a nucleotide value (e.g., calling a mutation) from said reads for each of said nucleotide positions by weighing, e.g., by a Bayesian method described herein, the comparison among the values in the second set using the first value (e.g., computing the posterior probability of the presence of a mutation), thereby analyzing said sample.
[0283] Additional description of exemplary nucleic acid sequencing methods, mutation calling methods, and methods for analysis of genetic variants is provided in, e.g., U.S. Patent No. 9,340,830, U.S. Patent No. 9,792,403, U.S. Patent No. 11,136,619, U.S. Patent No. 11,118,213, and International Patent Application Publication No. WO 2020/236941, the entire contents of each of which is incorporated herein by reference.
Systems
[0284] Also disclosed herein are systems designed to implement any of the disclosed methods for determining ctDNA fraction in liquid biopsy samples from a subject. The systems may comprise, e.g., one or more processors, and a memory unit communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: receive sequence read data for a plurality of sequence reads obtained for the sample from the subject; determine if the sequence read data is sufficient for performing copy number alteration (CNA) modeling; estimate (1) the ctDNA fraction in the sample based on at least a sample tumor purity and a ploidy of the sample if the sequence read data is determined to be sufficient for performing CNA modeling, or (2) the ctDNA fraction in the sample based on identification of at least one tumor somatic short variant in the sequence read data if the sequence read data is determined to be insufficient for performing CNA modeling; and output the estimated ctDNA fraction in the sample.
[0285] In some instances, the memory unit of the system may further comprise instructions that, when executed by the one or more processors, cause the system to compare the estimated ctDNA fraction to at least one predetermined threshold, and output a status call of at least tumor fraction-high (TF-high) or tumor fraction-low (TF-low) for the sample based on the comparison.
[0286] In some instances, the disclosed systems may further comprise a sequencer, e.g., a next generation sequencer (also referred to as a massively parallel sequencer). Examples of next generation (or massively parallel) sequencing platforms include, but are not limited to, Roche/454’s Genome Sequencer (GS) FLX system, Illumina/Solexa’ s Genome Analyzer (GA), Illumina’s HiSeq® 2500, HiSeq® 3000, HiSeq® 4000 and NovaSeq® 6000 sequencing systems, Life/APG’s Support Oligonucleotide Ligation Detection (SOLiD) system, Polonator’s G.007 system, Helicos BioSciences’ HeliScope Gene Sequencing system, ThermoFisher Scientific’s Ion Torrent Genexus system, or Pacific Biosciences’ PacBio® RS system.
[0287] In some instance, the nucleic acid sequence data is acquired using a next generation sequencing technique (also referred to as a massively parallel sequencing technique) having a read-length of less than 400 bases, less than 300 bases, less than 200 bases, less than 150 bases, less than 100 bases, less than 90 bases, less than 80 bases, less than 70 bases, less than 60 bases, less than 50 bases, less than 40 bases, or less than 30 bases.
[0288] In some instances, the determination of ctDNA fraction for a liquid biopsy sample may be used to select, initiate, adjust, or terminate a treatment for cancer in the subject (e.g., a patient) from which the sample was derived, as described elsewhere herein.
[0289] In some instances, the disclosed systems may further comprise sample processing and library preparation workstations, microplate-handling robotics, fluid dispensing systems, temperature control modules, environmental control chambers, additional data storage modules, data communication modules (e.g., Bluetooth®, WiFi, intranet, or internet communication hardware and associated software), display modules, one or more local and/or cloud-based software packages (e.g., instrument / system control software packages, sequencing data analysis software packages), etc., or any combination thereof. In some instances, the systems may comprise, or be part of, a computer system or computer network as described elsewhere herein.
Computer systems and networks
[0290] FIG. 3 illustrates an example of a computing device or system in accordance with one embodiment. Device 300 can be a host computer connected to a network. Device 300 can be a client computer or a server. As shown in FIG. 3, device 300 can be any suitable type of microprocessor-based device, such as a personal computer, workstation, server or handheld computing device (portable electronic device) such as a phone or tablet. The device can include, for example, one or more processor(s) 310, input devices 320, output devices 930, memory or storage devices 340, communication devices 360, and nucleic acid sequencers 370. Software 350 residing in memory or storage device 340 may comprise, e.g., an operating system as well as software for executing the methods described herein. Input device 320 and output device 330 can generally correspond to those described herein, and can either be connectable or integrated with the computer.
[0291] Input device 320 can be any suitable device that provides input, such as a touch screen, keyboard or keypad, mouse, or voice-recognition device. Output device 330 can be any suitable device that provides output, such as a touch screen, haptics device, or speaker.
[0292] Storage 340 can be any suitable device that provides storage (e.g., an electrical, magnetic or optical memory including a RAM (volatile and non-volatile), cache, hard drive, or removable storage disk). Communication device 360 can include any suitable device capable of transmitting and receiving signals over a network, such as a network interface chip or device. The components of the computer can be connected in any suitable manner, such as via a wired media (e.g., a physical system bus 380, Ethernet connection, or any other wire transfer technology) or wirelessly (e.g., Bluetooth®, Wi-Fi®, or any other wireless technology).
[0293] Software module 350, which can be stored as executable instructions in storage 340 and executed by processor(s) 310, can include, for example, an operating system and/or the processes that embody the functionality of the methods of the present disclosure (e.g., as embodied in the devices as described herein). [0294] Software module 350 can also be stored and/or transported within any non-transitory computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described herein, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions. In the context of this disclosure, a computer-readable storage medium can be any medium, such as storage 340, that can contain or store processes for use by or in connection with an instruction execution system, apparatus, or device. Examples of computer- readable storage media may include memory units like hard drives, flash drives and distribute modules that operate as a single functional unit. Also, various processes described herein may be embodied as modules configured to operate in accordance with the embodiments and techniques described above. Further, while processes may be shown and/or described separately, those skilled in the art will appreciate that the above processes may be routines or modules within other processes.
[0295] Software module 350 can also be propagated within any transport medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described above, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions. In the context of this disclosure, a transport medium can be any medium that can communicate, propagate or transport programming for use by or in connection with an instruction execution system, apparatus, or device. The transport readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic or infrared wired or wireless propagation medium.
[0296] Device 300 may be connected to a network (e.g., network 404, as shown in FIG. 4 and/or described below), which can be any suitable type of interconnected communication system. The network can implement any suitable communications protocol and can be secured by any suitable security protocol. The network can comprise network links of any suitable arrangement that can implement the transmission and reception of network signals, such as wireless network connections, T1 or T3 lines, cable networks, DSL, or telephone lines.
[0297] Device 300 can be implemented using any operating system, e.g., an operating system suitable for operating on the network. Software module 350 can be written in any suitable programming language, such as C, C++, Java or Python. In various embodiments, application software embodying the functionality of the present disclosure can be deployed in different configurations, such as in a client/server arrangement or through a Web browser as a Web-based application or Web service, for example. In some embodiments, the operating system is executed by one or more processors, e.g., processor(s) 310.
[0298] Device 300 can further include a sequencer 370, which can be any suitable nucleic acid sequencing instrument.
[0299] FIG. 4 illustrates an example of a computing system in accordance with one embodiment. In system 400, device 300 (e.g., as described above and illustrated in FIG. 3) is connected to network 404, which is also connected to device 406. In some embodiments, device 406 is a sequencer. Exemplary sequencers can include, without limitation, Roche/454’s Genome Sequencer (GS) FLX System, Illumina/Solexa’ s Genome Analyzer (GA), Illumina’s HiSeq® 2500, HiSeq® 3000, HiSeq® 4000 and NovaSeq® 6000 Sequencing Systems, Life/APG’s Support Oligonucleotide Ligation Detection (SOLiD) system, Polonator’s G.007 system, Helicos BioSciences’ HeliScope Gene Sequencing system, or Pacific Biosciences’ PacBio® RS system.
[0300] Devices 300 and 406 may communicate, e.g., using suitable communication interfaces via network 404, such as a Local Area Network (LAN), Virtual Private Network (VPN), or the Internet. In some embodiments, network 404 can be, for example, the Internet, an intranet, a virtual private network, a cloud network, a wired network, or a wireless network. Devices 300 and 406 may communicate, in part or in whole, via wireless or hardwired communications, such as Ethernet, IEEE 802.1 lb wireless, or the like. Additionally, devices 300 and 406 may communicate, e.g., using suitable communication interfaces, via a second network, such as a mobile/cellular network. Communication between devices 300 and 406 may further include or communicate with various servers such as a mail server, mobile server, media server, telephone server, and the like. In some embodiments, Devices 300 and 406 can communicate directly (instead of, or in addition to, communicating via network 404), e.g., via wireless or hardwired communications, such as Ethernet, IEEE 802.1 lb wireless, or the like. In some embodiments, devices 300 and 406 communicate via communications 408, which can be a direct connection or can occur via a network (e.g., network 404).
[0301] One or all of devices 300 and 406 generally include logic (e.g., http web server logic) or are programmed to format data, accessed from local or remote databases or other sources of data and content, for providing and/or receiving information via network 404 according to various examples described herein.
EXAMPLES
[0302] The following examples are included for illustrative purposes only and are not intended to limit the scope of the present disclosure.
Example 1 - Verification of ctDN A tumor fraction determination (heuristic rule-based filtering)
[0303] The disclosed methods for determining ctDNA tumor fraction were verified using sequence read data for 530 matched plasma and huffy coat samples from various cancer types including Non-Small Cell Lung Cancer (NSCLC), Prostate, Breast, Colorectal Cancer (CRC), Pancreas, Ovary, Esophagus, Cholangio, and Cancer of Unknown Primary (CUP). The sequence read data for the huffy coat samples was used to confirm the identity of non-tumor somatic variants so that they could be removed from further analysis.
[0304] Somatic short variants were identified in plasma samples based on extensive filtering of the list of short variants detected in the sequence read data derived from the samples according to a set of heuristic rules to remove known “blacklist” variants including, but not limited to, clonal hematopoiesis of indeterminate potential (CHIP) variants, germline variants, and artifacts of the sequencing and/or variant calling methods employed. The determination of a tumor fraction (TF) positive status for a given sample (or the determination of a TF value for the sample) required that multiple potential tumor-derived somatic short variants be identified to improve confidence that TF positive calls were correct. The requirement for identification of multiple tumor-derived somatic short variants can be overridden if certain known non-CHIP somatic variants
(“whitelist” variants (e.g., KRAS variants, G12 variants, or EGFR exon 19 deletions)) or variants in commonly amplified genes (e.g. KRAS, EGFR, CCND1, FGF19, FGF3, FGF4, MYC, AR, MDM2, CCNE1, ERBB2, WHSC1E1, FGFR1, ZNF703, etc.) are detected and found to have higher VAF compared to other variants detected in the sample. Other potential filters that may be used to discriminate between CHIP and germline variants and somatic variants include certain rearrangements that are known to be somatic (e.g., TMPRSS2-ERG, AEK-EME4, FGFR3- TACC3, RET-KIF5B) and/or removal of CHIP variants identified using a fragmentomics-based (fragment size-based) approach in combination with detected somatic short variants. [0305] Table 1 provides a summary of verification data for calling samples TF positive or TF negative based on the filtered variant data for the 530 matched plasma and buffy coat samples. For this example, TF positive samples were defined as samples for which at least one true somatic variant was detected with a VAF > 0.01. True somatic variants were defined as variants with coverage of greater than 200x in plasma samples and a VAF that was greater than that in buffy coat samples by a statistically significant difference.
Table 1. Verification data for TF positive calls.
Figure imgf000087_0001
[0306] The sensitivity for calling TF positive status was 262/283 = 92.6%. The specificity for calling TF positive status was 234/247 = 94.7%. The positive predictive value (PPV) was 262/275 = 95.3%. The negative predictive value (NPV) was 234/255 = 91.8%.
Example 2 - Verification of ctDN A tumor fraction determination (fragmentomics & short variants)
[0307] In this example, an alternative approach - based on a combination of fragmentomics and short variant analysis - was used for discriminating between CHIP and germline variants and tumor-derived somatic variants. Again, the verification data was based on sequence read data for the 530 matched plasma and buffy coat samples. The sequence read data for the buffy coat samples was used to confirm the identity of non-tumor somatic variants so that they could be removed from further analysis.
[0308] Fragment size shift is a fairly consistent genome-wide parameter observed for cell-free DNA from a given sample. However, there is some variability in fragment size shift at some genomic loci, likely due to locale-specific biological differences. Therefore it is difficult to use a fragment size-based (fragmentomics) analysis to predict CHIP variants as some somatic variants will exhibit no fragment size shift even when the majority of DNA fragments for the overall sample are shifted. Furthermore, DNA fragments from a small fraction of samples show no shift, in which case all somatic variants fail to be identified as such.
[0309] In contrast, fragment size shift for short variants is a strong indicator of somatic status. Based on an analysis of sequence read data for matched plasma-buffy coat samples, short variants that exhibit strong fragment size shifts are nearly always somatic (>99% for variants with significant fragment size shift defined as having a Kolmogorov-Smirnov p-value < 0.001 between a reference allele and an alternate allele). A non-limiting example of fragment size shift data is provided in Table 2.
Table 2. Fragment size shift data for somatic and CHIP variants.
Figure imgf000088_0001
[0310] Table 3 provides a summary of verification data for calling a status of TF elevated, TF detected, or TF not detected for sample based on the variant data for the 530 matched plasma and peripheral blood mononuclear cell (PBMC) samples. Called plasma variants were assessed in sequence read data for matched PBMC samples using a production variant calling method. Variants with a PBMC VAF < (plasma VAF)/10 were assigned as somatic variants. Variants with significant coverage dropout in the PBMC sample were excluded from the analysis. Table 3. Verification data for TF elevated calls.
Figure imgf000089_0001
[0311] The overall TF call rate was 47% elevated and 16% detected. The results indicate a very high predictivity for the method (> 99% of TF elevated calls were confirmed as elevated based on the matched buffy coat sample analysis. 52% of the samples had a Max sVAF of greater than 1% (88% were called as TF elevated, and 93% were called as TF elevated or TF detected), where Max sVAF was defined as the VAF of the called plasma variant that was present in the buffy coat sample at no more than one-tenth of the plasma VAF. Variants with very low coverage in the buffy coat sample (e.g., < lOOx or < 500x and relative buffy coat coverage < 0.2) were also excluded from the analysis.
[0312] The specificity was > 95% (no false positives were detected in TF elevated samples). Three white-listed somatic short variants and two fragment size-shifted false positives were identified (with VAFS of 0.14%, 0.16%, 0.19%, 0.21%, and 0.27%, respectively).
Example 3 - Application to prognostic predictions for prostate cancer
[0313] FIGS. 5A-B provide non-limiting examples of the application of the disclosed methods for determining tumor fraction in liquid biopsy samples to predict the probability of progression- free survival and the probability of survival for prostate cancer patients. FIGS. 6A-B provide non-limiting examples of the use of prostate specific antigen (PSA) as a prognostic biomarker for the probability of progression-free survival and the probability of survival for prostate cancer patients.
[0314] For the data plotted in FIGS. 5A-B, tumor fraction was determined based on an analysis of sequence read data for plasma samples using the methods disclosed herein. A TF threshold of 2% was used to stratify prostate cancer patients treated with an enzalutamide (Enza) challenge after abiraterone (Abi) treatment. 26% of the patients in the cohort (494 patients in total) had a TF value of less than 2%. FIG. 5A provides a plot of the probability of progression-free survival (PFS) as a function of time following initiation of Enza treatment for patients having a TF < 2% and patients having a TF > 2%. FIG. 5B provides a similar plot of the probability of overall survival (OS) as a function of time following initiation of Enza treatment. The inset in each figure provided a summary of the observed median for the duration of progression-free survival or overall survival, respectively, along with the observed hazard ratio (HR), 95% confidence interval (CI) and p-values for each plot. The table below each plot provides the actual number of patients at risk as a function of time.
[0315] For the data plotted in FIGS. 6A-B, a ‘ ‘low” PSA level was defined as a PSA level less than equal to the PSA level for the 26th percentile of patients in the cohort. FIG. 6A provides a plot of the probability of progression-free survival (PFS) as a function of time following initiation of Enza treatment for patients having low PSA and patients having high PSA. FIG. 6B provides a similar plot of the probability of overall survival (OS) as a function of time following initiation of Enza treatment. The inset in each figure provided a summary of the observed median for the duration of progression-free survival or overall survival, respectively, along with the observed hazard ratio (HR), 95% confidence interval (CI) and p-values for each plot. The table below each plot provides the actual number of patients at risk as a function of time.
[0316] Comparison of the data in FIGS. 5A-B and FIGS. 6A-B indicates that TF is a much better prognostic biomarker for identifying men who are likely to have clinical benefit from treatment with an enzalutamide (Enza) challenge following abiraterone (Abi) treatment
EXEMPLARY IMPLEMENTATIONS
[0317] Exemplary implementations of the methods and systems described herein include:
1. A method comprising: providing a plurality of nucleic acid molecules obtained from a sample from a subject; ligating one or more adapters onto one or more nucleic acid molecules from the plurality of nucleic acid molecules; amplifying the one or more ligated nucleic acid molecules from the plurality of nucleic acid molecules; capturing amplified nucleic acid molecules from the amplified nucleic acid molecules; sequencing, by a sequencer, the captured nucleic acid molecules to obtain a plurality of sequence reads that represent the captured nucleic acid molecules; receiving, at one or more processors, sequence read data for the plurality of sequence reads; determining, using the one or more processors, if the sequence read data is sufficient for performing copy number alteration (CNA) modeling; estimating, using the one or more processors,
(1) the ctDNA fraction in the sample based on at least a tumor purity and a ploidy of the sample using a model if the sequence read data is determined to be sufficient for performing CNA modeling, or
(2) the ctDNA fraction in the sample based on identification of at least one tumor somatic short variant in the sequence read data if the sequence read data is determined to be insufficient for performing CNA modeling; and outputting, using the one or more processors, the estimated ctDNA fraction in the sample.
2. The method of clause 1, further comprising comparing the estimated ctDNA fraction to at least one predetermined threshold, and outputting a status call of at least tumor fraction-high (TF-high) or tumor fraction-low (TF-low) for the sample based on the comparison.
3. The method of clause 1 or clause 2, wherein determining if the sequence read data is sufficient for performing CNA modeling comprises determining sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof, for at least one genomic locus to which the plurality of sequence reads map.
4. The method of clause 3, wherein the at least one genomic locus comprises at least one single nucleotide polymorphism (SNP) locus.
5. The method of any one of clauses 1 to 4, wherein performing CNA modeling comprises: determining, using the one or more processors, a copy number model including a sample tumor purity, a sample ploidy, and a copy number of multiple genomic segments in the ctDNA that accounts for an observed sequence coverage ratio data, allele fraction data for at least one genomic locus within one or more subgenomic intervals to which the plurality of sequence reads map.
6. The method of any one of clauses 1 to 5, wherein estimating the ctDNA fraction based on at least the sample tumor purity and sample ploidy using the CNA model comprises using a formula that describes a physical relationship between ctDNA fraction and sample tumor purity and sample ploidy.
7. The method of any one of clauses 1 to 6, wherein estimating the ctDNA fraction based on at least one somatic short variant detected in the sequence read data comprises: obtaining a list of short variants detected in the sequence read data; applying a set of selection rules to the list of detected short variants to identify tumor somatic short variants; and estimating the ctDNA fraction based on the presence of at least one identified tumor somatic short variant.
8. The method of clause 7, wherein the set of selection rules used to identify tumor somatic short variants in the list of detected short variants comprises: (i) removing short variants that appear on a blacklist of known germline variants, known clonal hematopoiesis of indeterminate potential (CHIP) variants, and known sequencing artifacts; (ii) retaining short variants that appear on a list of known tumor somatic short variants; (iii) retaining short variants that appear on a list of known genes that are prone to exhibit high amplification and that have an allele frequency that is higher than other somatic short variants in the sample; (iv) retaining short variants that appear on a list of known rearrangements; or any combination thereof.
9. The method of any one of clauses 1 to 8, wherein estimating the ctDNA fraction based on the at least one tumor somatic short variant comprises: determining, using the one or more processors, a variant allele frequency (VAF) for one or more variants detected in the sample based on the sequence read data; generating, using the one or more processors, an empirical distribution of ctDNA fraction values corresponding to the determined VAF for the one or more variants based on historical data; fitting, using the one or more processors, a model to the empirical distribution of ctDNA fraction values; and determining a ctDNA fraction for the sample based on the model.
10. The method of any one of clauses 1 to 9, wherein the subject is suspected of having or is determined to have cancer.
11. The method of clause 10, wherein the cancer is a B cell cancer (multiple myeloma), a melanoma, breast cancer, lung cancer, bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain cancer, central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine cancer, endometrial cancer, cancer of an oral cavity, cancer of a pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel cancer, appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, a cancer of hematological tissue, an adenocarcinoma, an inflammatory myofibroblastic tumor, a gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), acute lymphocytic leukemia (ALL), acute myelocytic leukemia (AML), chronic myelocytic leukemia (CML), chronic lymphocytic leukemia (CLL), polycythemia Vera, Hodgkin lymphoma, nonHodgkin lymphoma (NHL), soft-tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endotheliosarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, bladder carcinoma, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, neuroblastoma, retinoblastoma, follicular lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, hepatocellular carcinoma, thyroid cancer, gastric cancer, head and neck cancer, small cell cancer, essential thrombocythemia, agnogenic myeloid metaplasia, hypereosinophilic syndrome, systemic mastocytosis, familiar hypereosinophilia, chronic eosinophilic leukemia, neuroendocrine cancers, or a carcinoid tumor.
12. The method of clause 10, wherein the cancer comprises acute lymphoblastic leukemia (Philadelphia chromosome positive), acute lymphoblastic leukemia (precursor B-cell), acute myeloid leukemia (FLT3+), acute myeloid leukemia (with an IDH2 mutation), anaplastic large cell lymphoma, basal cell carcinoma, B-cell chronic lymphocytic leukemia, bladder cancer, breast cancer (HER2 overexpressed/amplified), breast cancer (HER2+), breast cancer (HR+, HER2-), cervical cancer, cholangiocarcinoma, chronic lymphocytic leukemia, chronic lymphocytic leukemia (with 17p deletion), chronic myelogenous leukemia, chronic myelogenous leukemia (Philadelphia chromosome positive), classical Hodgkin lymphoma, colorectal cancer, colorectal cancer (dMMR/MSI-H), colorectal cancer (KRAS wild type), cryopyrin-associated periodic syndrome, a cutaneous T-cell lymphoma, dermatofibrosarcoma protuberans, a diffuse large B-cell lymphoma, fallopian tube cancer, a follicular B-cell non-Hodgkin lymphoma, a follicular lymphoma, gastric cancer, gastric cancer (HER2+), gastroesophageal junction (GEJ) adenocarcinoma, a gastrointestinal stromal tumor, a gastrointestinal stromal tumor (KIT+), a giant cell tumor of the bone, a glioblastoma, granulomatosis with polyangiitis, a head and neck squamous cell carcinoma, a hepatocellular carcinoma, Hodgkin lymphoma, juvenile idiopathic arthritis, lupus erythematosus, a mantle cell lymphoma, medullary thyroid cancer, melanoma, a melanoma with a BRAF V600 mutation, a melanoma with a BRAF V600E or V600K mutation, Merkel cell carcinoma, multicentric Castleman's disease, multiple hematologic malignancies including Philadelphia chromosome-positive ALL and CML, multiple myeloma, myelofibrosis, a non-Hodgkin’ s lymphoma, a nonresectable subependymal giant cell astrocytoma associated with tuberous sclerosis, a non-small cell lung cancer, a non-small cell lung cancer (ALK+), a non-small cell lung cancer (PD-L1+), a non-small cell lung cancer (with ALK fusion or ROS1 gene alteration), a non-small cell lung cancer (with BRAF V600E mutation), a non-small cell lung cancer (with an EGFR exon 19 deletion or exon 21 substitution (L858R) mutations), a non- small cell lung cancer (with an EGFR T790M mutation), ovarian cancer, ovarian cancer (with a BRCA mutation), pancreatic cancer, a pancreatic, gastrointestinal, or lung origin neuroendocrine tumor, a pediatric neuroblastoma, a peripheral T-cell lymphoma, peritoneal cancer, prostate cancer, a renal cell carcinoma, rheumatoid arthritis, a small lymphocytic lymphoma, a soft tissue sarcoma, a solid tumor (MSI-H/dMMR), a squamous cell cancer of the head and neck, a squamous non- small cell lung cancer, thyroid cancer, a thyroid carcinoma, urothelial cancer, a urothelial carcinoma, or Waldenstrom's macroglobulinemia.
13. The method of any one of clauses 10 to 12, further comprising treating the subject with an anti-cancer therapy.
14. The method of clause 13, wherein the anti-cancer therapy comprises a targeted anti-cancer therapy.
15. The method of clause 14, wherein the targeted anti-cancer therapy comprises abemaciclib (Verzenio), abiraterone acetate (Zytiga), acalabrutinib (Calquence), ado-trastuzumab emtansine (Kadcyla), afatinib dimaleate (Gilotrif), aldesleukin (Proleukin), alectinib (Alecensa), alemtuzumab (Campath), alitretinoin (Panretin), alpelisib (Piqray), amivantamab-vmjw (Rybrevant), anastrozole (Arimidex), apalutamide (Erleada), asciminib hydrochloride (Scemblix), atezolizumab (Tecentriq), avapritinib (Ayvakit), avelumab (Bavencio), axicabtagene ciloleucel (Yescarta), axitinib (Inlyta), belantamab mafodotin-blmf (Blenrep), belimumab (Benlysta), belinostat (Beleodaq), belzutifan (Welireg), bevacizumab (Avastin), bexarotene (Targretin), binimetinib (Mektovi), blinatumomab (Blincyto), bortezomib (Velcade), bosutinib (Bosulif), brentuximab vedotin (Adcetris), brexucabtagene autoleucel (Tecartus), brigatinib (Alunbrig), cabazitaxel (Jevtana), cabozantinib (Cabometyx), cabozantinib (Cabometyx, Cometriq), canakinumab (Haris), capmatinib hydrochloride (Tabrecta), carfilzomib (Kyprolis), cemiplimab-rwlc (Libtayo), ceritinib (LDK378/Zykadia), cetuximab (Erbitux), cobimetinib (Cotellic), copanlisib hydrochloride (Aliqopa), crizotinib (Xalkori), dabrafenib (Tafinlar), dacomitinib (Vizimpro), daratumumab (Darzalex), daratumumab and hyaluronidase-fihj (Darzalex Faspro), darolutamide (Nubeqa), dasatinib (Sprycel), denileukin diftitox (Ontak), denosumab (Xgeva), dinutuximab (Unituxin), dostarlimab-gxly (Jemperli), durvalumab (Imfinzi), duvelisib (Copiktra), elotuzumab (Empliciti), enasidenib mesylate (Idhifa), encorafenib (Braftovi), enfortumab vedotin-ejfv (Padcev), entrectinib (Rozlytrek), enzalutamide (Xtandi), erdafitinib (Balversa), erlotinib (Tarceva), everolimus (Afinitor), exemestane (Aromasin), fam-trastuzumab deruxtecan-nxki (Enhertu), fedratinib hydrochloride (Inrebic), fulvestrant (Faslodex), gefitinib (Iressa), gemtuzumab ozogamicin (Mylotarg), gilteritinib (Xospata), glasdegib maleate (Daurismo), hyaluronidase-zzxf (Phesgo), ibrutinib (Imbruvica), ibritumomab tiuxetan (Zevalin), idecabtagene vicleucel (Abecma), idelalisib (Zydelig), imatinib mesylate (Gleevec), infigratinib phosphate (Truseltiq), inotuzumab ozogamicin (Besponsa), iobenguane 1131 (Azedra), ipilimumab (Yervoy), isatuximab-irfc (Sarclisa), ivosidenib (Tibsovo), ixazomib citrate (Ninlaro), lanreotide acetate (Somatuline Depot), lapatinib (Tykerb), larotrectinib sulfate (Vitrakvi), lenvatinib mesylate (Lenvima), letrozole (Femara), lisocabtagene maraleucel (Breyanzi), loncastuximab tesirine-lpyl (Zynlonta), lorlatinib (Lorbrena), lutetium Lu 177-dotatate (Lutathera), margetuximab-cmkb (Margenza), midostaurin (Rydapt), mobocertinib succinate (Exkivity), mogamulizumab-kpkc (Poteligeo), moxetumomab pasudotox-tdfk (Lumoxiti), naxitamab-gqgk (Danyelza), necitumumab (Portrazza), neratinib maleate (Nerlynx), nilotinib (Tasigna), niraparib tosylate monohydrate (Zejula), nivolumab (Opdivo), obinutuzumab (Gazyva), ofatumumab (Arzerra), olaparib (Lynparza), olaratumab (Lartruvo), osimertinib (Tagrisso), palbociclib (Ibrance), panitumumab (Vectibix), panobinostat (Farydak), pazopanib (Votrient), pembrolizumab (Keytruda), pemigatinib (Pemazyre), pertuzumab (Perjeta), pexidartinib hydrochloride (Turalio), polatuzumab vedotin-piiq (Polivy), ponatinib hydrochloride (Iclusig), pralatrexate (Folotyn), pralsetinib (Gavreto), radium 223 dichloride (Xofigo), ramucirumab (Cyramza), regorafenib (Stivarga), ribociclib (Kisqali), ripretinib (Qinlock), rituximab (Rituxan), rituximab and hyaluronidase human (Rituxan Hycela), romidepsin (Istodax), rucaparib camsylate (Rubraca), ruxolitinib phosphate (Jakafi), sacituzumab govitecan-hziy (Trodelvy), seliciclib, selinexor (Xpovio), selpercatinib (Retevmo), selumetinib sulfate (Koselugo), siltuximab (Sylvant), sipuleucel-T (Provenge), sirolimus protein-bound particles (Fyarro), sonidegib (Odomzo), sorafenib (Nexavar), sotorasib (Lumakras), sunitinib (Sutent), tafasitamab-cxix (Monjuvi), tagraxofusp-erzs (Elzonris), talazoparib tosylate (Talzenna), tamoxifen (Nolvadex), tazemetostat hydrobromide (Tazverik), tebentafusp-tebn (Kimmtrak), temsirolimus (Torisel), tepotinib hydrochloride (Tepmetko), tisagenlecleucel (Kymriah), tisotumab vedotin-tftv (Tivdak), tocilizumab (Actemra), tofacitinib (Xeljanz), tositumomab (Bexxar), trametinib (Mekinist), trastuzumab (Herceptin), tretinoin (Vesanoid), tivozanib hydrochloride (Fotivda), toremifene (Fareston), tucatinib (Tukysa), umbralisib tosylate (Ukoniq), vandetanib (Caprelsa), vemurafenib (Zelboraf), venetoclax (Venclexta), vismodegib (Erivedge), vorinostat (Zolinza), zanubrutinib (Brukinsa), ziv-aflibercept (Zaltrap), or any combination thereof. 16. The method of any one of clauses 1 to 15, further comprising obtaining the sample from the subject.
17. The method of any one of clauses 1 to 16, wherein the sample comprises a tissue biopsy sample, a liquid biopsy sample, or a normal control.
18. The method of clause 17, wherein the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
19. The method of clause 17, wherein the sample is a liquid biopsy sample and comprises circulating tumor cells (CTCs).
20. The method of clause 17, wherein the sample is a liquid biopsy sample and comprises cell- free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.
21. The method of any one of clauses 1 to 20, wherein the plurality of nucleic acid molecules comprises a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules.
22. The method of clause 21, wherein the tumor nucleic acid molecules are derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules are derived from a normal portion of the heterogeneous tissue biopsy sample.
23. The method of clause 21, wherein the sample comprises a liquid biopsy sample, and wherein the tumor nucleic acid molecules are derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample, and the non-tumor nucleic acid molecules are derived from a non- tumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.
24. The method of any one of clauses 1 to 23, wherein the one or more adapters comprise amplification primers, flow cell adaptor sequences, substrate adapter sequences, or sample index sequences.
25. The method of any one of clauses 1 to 24, wherein the captured nucleic acid molecules are captured from the amplified nucleic acid molecules by hybridization to one or more bait molecules.
26. The method of clause 25, wherein the one or more bait molecules comprise one or more nucleic acid molecules, each comprising a region that is complementary to a region of a captured nucleic acid molecule. 27. The method of any one of clauses 1 to 26, wherein amplifying nucleic acid molecules comprises performing a polymerase chain reaction (PCR) amplification technique, a non-PCR amplification technique, or an isothermal amplification technique.
28. The method of any one of clauses 1 to 27, wherein the sequencing comprises use of a massively parallel sequencing (MPS) technique, whole genome sequencing (WGS), whole exome sequencing, targeted sequencing, direct sequencing, or Sanger sequencing technique.
29. The method of clause 28, wherein the sequencing comprises massively parallel sequencing, and the massively parallel sequencing technique comprises next generation sequencing (NGS).
30. The method of any one of clauses 1 to 29, wherein the sequencer comprises a next generation sequencer.
31. The method of any one of clauses 1 to 30, wherein one or more of the plurality of sequencing reads overlap one or more gene loci within one or more subgenomic intervals in the sample.
32. The method of clause 31, wherein the one or more gene loci comprises between 10 and 20 loci, between 10 and 40 loci, between 10 and 60 loci, between 10 and 80 loci, between 10 and 100 loci, between 10 and 150 loci, between 10 and 200 loci, between 10 and 250 loci, between 10 and 300 loci, between 10 and 350 loci, between 10 and 400 loci, between 10 and 450 loci, between 10 and 500 loci, between 20 and 40 loci, between 20 and 60 loci, between 20 and 80 loci, between 20 and 100 loci, between 20 and 150 loci, between 20 and 200 loci, between 20 and 250 loci, between 20 and 300 loci, between 20 and 350 loci, between 20 and 400 loci, between 20 and 500 loci, between 40 and 60 loci, between 40 and 80 loci, between 40 and 100 loci, between 40 and 150 loci, between 40 and 200 loci, between 40 and 250 loci, between 40 and 300 loci, between 40 and 350 loci, between 40 and 400 loci, between 40 and 500 loci, between 60 and 80 loci, between 60 and 100 loci, between 60 and 150 loci, between 60 and 200 loci, between 60 and 250 loci, between 60 and 300 loci, between 60 and 350 loci, between 60 and 400 loci, between 60 and 500 loci, between 80 and 100 loci, between 80 and 150 loci, between 80 and 200 loci, between 80 and 250 loci, between 80 and 300 loci, between 80 and 350 loci, between 80 and 400 loci, between 80 and 500 loci, between 100 and 150 loci, between 100 and 200 loci, between 100 and 250 loci, between 100 and 300 loci, between 100 and 350 loci, between 100 and 400 loci, between 100 and 500 loci, between 150 and 200 loci, between 150 and 250 loci, between 150 and 300 loci, between 150 and 350 loci, between 150 and 400 loci, between 150 and 500 loci, between 200 and 250 loci, between 200 and 300 loci, between 200 and 350 loci, between 200 and 400 loci, between 200 and 500 loci, between 250 and 300 loci, between 250 and 350 loci, between 250 and 400 loci, between 250 and 500 loci, between 300 and 350 loci, between 300 and 400 loci, between 300 and 500 loci, between 350 and 400 loci, between 350 and 500 loci, or between 400 and 500 loci.
33. The method of clause 31 or clause 32, wherein the one or more gene loci comprise ABL1, ACVR1B, AKT1, AKT2, AKT3, ALK, ALOX12B, AMER1, APC, AR, ARAF, ARFRP1, ARID1A, ASXL1, ATM, ATR, ATRX, AURKA, AURKB, AXIN1, AXL, BAP1, BARD1, BCL2, BCL2L1, BCL2L2, BCL6, BCOR, BCORL1, BCR, BRAF, BRCA1, BRCA2, BRD4, BRIP1, BTG1, BTG2, BTK, CALR, CARD11, CASP8, CBFB, CBL, CCND1, CCND2, CCND3, CCNE1, CD22, CD274, CD70, CD74, CD79A, CD79B, CDC73, CDH1, CDK12, CDK4, CDK6, CDK8, CDKN1A, CDKN1B, CDKN2A, CDKN2B, CDKN2C, CEBPA, CHEK1, CHEK2, CIC, CREBBP, CRKL, CSF1R, CSF3R, CTCF, CTNNA1, CTNNB1, CUL3, CUL4A, CXCR4, CYP17A1, DAXX, DDR1, DDR2, DIS3, DNMT3A, DOT1L, EED, EGFR, EMSY (Cllorf30), EP300, EPHA3, EPHB1, EPHB4, ERBB2, ERBB3, ERBB4, ERCC4, ERG, ERRFI1, ESRI, ETV4, ETV5, ETV6, EWSR1, EZH2, EZR, FAM46C, FANCA, FANCC, FANCG, FANCL, FAS, FBXW7, FGF10, FGF12, FGF14, FGF19, FGF23, FGF3, FGF4, FGF6, FGFR1, FGFR2, FGFR3, FGFR4, FH, FLCN, FLT1, FLT3, FOXL2, FUBP1, GABRA6, GATA3, GATA4, GATA6, GID4 (C17orf39), GNA11, GNA13, GNAQ, GNAS, GRM3, GSK3B, H3F3A, HDAC1, HGF, HNF1A, HRAS, HSD3B1, ID3, IDH1, IDH2, IGF1R, IKBKE, IKZF1, INPP4B, IRF2, IRF4, IRS2, JAK1, JAK2, JAK3, JUN, KDM5A, KDM5C, KDM6A, KDR, KEAP1, KEL, KIT, KLHL6, KMT2A (MLL), KMT2D (MLL2), KRAS, LTK, LYN, MAF, MAP2K1, MAP2K2, MAP2K4, MAP3K1, MAP3K13, MAPK1, MCL1, MDM2, MDM4, MED12, MEF2B, MEN1, MERTK, MET, MITF, MKNK1, MLH1, MPL, MRE11A, MSH2, MSH3, MSH6, MST1R, MTAP, MTOR, MUTYH, MYB, MYC, MYCL, MYCN, MYD88, NBN, NF1, NF2, NFE2L2, NFKBIA, NKX2-1, NOTCH1, NOTCH2, NOTCH3, NPM1, NRAS, NT5C2, NTRK1, NTRK2, NTRK3, NUTM1, P2RY8, PALB2, PARK2, PARP1, PARP2, PARP3, PAX5, PBRM1, PDCD1, PDCD1LG2, PDGFRA, PDGFRB, PDK1, PIK3C2B, PIK3C2G, PIK3CA, PIK3CB, PIK3R1, PIM1, PMS2, POLDI, POLE, PPARG, PPP2R1A, PPP2R2A, PRDM1, PRKAR1A, PRKCI, PTCHI, PTEN, PTPN11, PTPRO, QKI, RAC1, RAD21, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54L, RAFI, RARA, RBI, RBM10, REL, RET, RICTOR, RNF43, ROS1, RPTOR, RSPO2, SDC4, SDHA, SDHB, SDHC, SDHD, SETD2, SF3B1, SGK1, SLC34A2, SMAD2, SMAD4, SMARCA4, SMARCB1, SMO, SNCAIP, SOCS1, SOX2, SOX9, SPEN, SPOP, SRC, STAG2, STAT3, STK11, SUFU, SYK, TBX3, TEK, TERC, TERT, TET2, TGFBR2, TIPARP, TMPRSS2, TNFAIP3, TNFRSF14, TP53, TSC1, TSC2, TYRO3, U2AF1, VEGFA, VHL, WHSCI, WHSC1L1, WT1, XPO1, XRCC2, ZNF217, ZNF703, or any combination thereof.
34. The method of clause 31 or clause 32, wherein the one or more gene loci comprise ABL, ALK, ALL, B4GALNT1, BAFF, BCL2, BRAF, BRCA, BTK, CD19, CD20, CD3, CD30, CD319, CD38, CD52, CDK4, CDK6, CML, CRACC, CS1, CTLA-4, dMMR, EGFR, ERBB1, ERBB2, FGFR1-3, FLT3, GD2, HDAC, HER1, HER2, HR, IDH2, IL-ip, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSI-H, mTOR, PARP, PD-1, PDGFR, PDGFRa, PDGFRP, PD-L1, PI3K5, PIGF, PTCH, RAF, RANKL, RET, ROS1, SLAMF7, VEGF, VEGFA, VEGFB, or any combination thereof.
35. The method of any one of clauses 1 to 34, further comprising generating, by the one or more processors, a report indicating the estimated tumor fraction in the sample.
36. The method of clause 35, further comprising transmitting the report to a healthcare provider.
37. The method of clause 36, wherein the report is transmitted via a computer network or a peer- to-peer connection.
38. A method for determining circulating tumor DNA (ctDNA) fraction in a sample from a subject, the method comprising: receiving, at one or more processors, sequence read data for a plurality of sequence reads obtained for the sample from the subject; determining, using the one or more processors, if the sequence read data is sufficient for performing copy number alteration (CNA) modeling; estimating, using the one or more processors,
(1) the ctDNA fraction in the sample based on at least a tumor purity and a ploidy of the sample using a model if the sequence read data is determined to be sufficient for performing CNA modeling, or (2) the ctDNA fraction in the sample based on identification of at least one tumor somatic short variant in the sequence read data if the sequence read data is determined to be insufficient for performing CNA modeling; and outputting, using the one or more processors, the estimated ctDNA fraction in the sample.
39. The method of clause 38, further comprising comparing the estimated ctDNA fraction to at least one predetermined threshold, and outputting a status call of at least tumor fraction-high (TF-high) or tumor fraction-low (TF-low) for the sample based on the comparison.
40. The method of clause 38 or clause 39, wherein determining if the sequence read data is sufficient for performing CNA modeling comprises determining sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof, for at least one genomic locus to which the plurality of sequence reads map.
41. The method of clause 40, wherein the at least one genomic locus comprises at least one single nucleotide polymorphism (SNP) locus.
42. The method of clause 40 or clause 41, wherein the determination of sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof, for the at least one genomic locus is based on pre-processing of the sequence read data.
43. The method of any one of clauses 38 to 42, wherein performing CNA modeling comprises: determining, using the one or more processors, a copy number model including a sample tumor purity, a sample ploidy, and a copy number of multiple genomic segments that accounts for an observed sequence coverage ratio data, allele fraction data for at least one genomic locus within one or more subgenomic intervals to which the plurality of sequence reads map.
44. The method of clause 42 or clause 43 wherein the sequence coverage ratio data is determined by aligning the plurality of sequence reads that overlap the at least one genomic locus within the one or more subgenomic intervals in the sample and in a control sample to a reference genome, and determining a number of sequence reads that overlap the at least one genomic locus within the one or more subgenomic intervals in the sample and in the control sample.
45. The method of clause 44, wherein the control sample is a paired normal sample, a process- matched control sample, or a panel of normal control sample. 46. The method of any one of clauses 43 to 45, wherein the allele fraction data is determined by aligning the plurality of sequence reads that overlap the at least one genomic locus within the one or more subgenomic intervals in the sample to a reference genome, detecting a number of alleles present at the at least one genomic locus, and determining an allele fraction for at least one of the alleles present at the at least one genomic locus.
47. The method of any one of clauses 43 to 46, wherein performing CNA modeling further comprises generating segmentation data by: aligning a plurality of sequence reads that overlap the at least one genomic locus within the one or more subgenomic intervals in the sample to a reference genome, and processing the aligned sequence read data, coverage ratio data, and allele fraction data using a pruned exact linear time (PELT) method to determine a number of segments required to account for the aligned sequence read data, wherein each segment has a same copy number.
48. The method of any one of clauses 43 to 47, wherein the copy number model predicts a copy number for the at least one genomic locus based on the sequence coverage ratio data and allele fraction data.
49. The method of 48, wherein the sequence coverage ratio data further comprises sequence coverage ratio data for single nucleotide polymorphisms (SNPs) and introns associated with the at least one genomic locus.
50. The method of clause 48 or clause 49, wherein the copy number model also predicts a tumor purity and a ploidy for the sample.
51. The method of any one of clauses 47 to 50, wherein the copy number model also outputs the segmentation data.
52. The method of any one of clauses 43 to 51, wherein the ploidy for the sample has a value ranging from 1 to 8.
53. The method of any one of clauses 43 to 52, wherein an amplification is detected when the copy number for the corresponding segment is greater than or equal to the ploidy of the sample. 54. The method of clause 53, wherein an amplification is detected when the copy number for the corresponding segment is greater than or equal to the ploidy of the sample plus a first predetermined value.
55. The method of clause 54, wherein the first predetermined value is a value ranging from 2 to 500.
56. The method of clause 54 or clause 55, wherein the first predetermined value is a value ranging from 2 to 10.
57. The method of any one of clauses 54 to 56, wherein an amplification is detected when the copy number for the corresponding segment is greater than or equal to the ploidy of the sample plus a second predetermined value and the genomic locus is a member of a first predefined set of genomic loci.
58. The method of clause 57, wherein the second predetermined value is a value ranging from 0 to 500.
59. The method of clause 57 or clause 58, wherein the second predetermined value is a value ranging from 2 to 10.
60. The method of any one of clauses 57 to 59, wherein the first predefined set of genomic loci comprises one or more druggable gene target loci, prognostic gene loci, oncogene loci, or any combination thereof.
61. The method of clause 60, wherein the first predefined set of genomic loci comprises the AR and ERBB2 gene loci.
62. The method of any one of clauses 43 to 61, wherein detection of deletions comprises identifying homozygous deletions of the at least one genomic locus in a corresponding segment.
63. The method of clause 62, wherein homozygous deletions are detected by determining a total copy number for a given genomic locus that is equal to the sum of the copy numbers for a first allele and a second allele at the genomic locus.
64. The method of clause 63, wherein the first allele is a major allele and the second allele is a minor allele. 65. The method of clause 63 or clause 64, wherein a homozygous deletion is called if the total copy number for a given genomic locus is equal to a third predetermined value.
66. The method of clause 65, wherein the third predetermined value is about zero.
67. The method of any one of clauses 43 to 66, wherein detection of deletions comprises identifying heterozygous deletions of the at least one genomic locus in a corresponding segment.
68. The method of clause 67, wherein a heterozygous deletion is called if a copy number for a first allele at a given genomic locus is equal to a fourth predetermined value, and a copy number for a second allele at the given genomic locus in not equal to the fourth predetermined value.
69. The method of clause 68, wherein the fourth predetermined value is about zero.
70. The method of clause 68 or clause 69, wherein the first allele is a major allele and the second allele is a minor allele.
71. The method of any one of clauses 43 to 70, wherein the detection of deletions comprises identifying partial deletions of the at least one genomic locus in a corresponding segment.
72. The method of clause 71, wherein a partial deletion is called for a given genomic locus if log2 ratios (L2Rs) for neighboring genomic loci, single nucleotide polymorphisms (SNPs), and introns are significantly different than the log2 ratio for the genomic locus, and the log2 ratio for the given genomic locus is significantly different from a distribution of L2Rs for nonneighboring genomic loci, single nucleotide polymorphisms (SNPs), and introns.
73. The method of any one of clauses 38 to 72, wherein estimating the ctDNA fraction based on at least the sample tumor purity and sample ploidy using the CNA model comprises using a formula that describes a physical relationship between ctDNA fraction and sample tumor purity and sample ploidy.
74. The method of clause 73, wherein the formula is given by:
Figure imgf000104_0001
where p is sample tumor purity, and is ploidy.
75. The method of any one of clauses 38 to 74, wherein estimating the ctDNA fraction based on at least one somatic short variant detected in the sequence read data comprises: obtaining a list of short variants detected in the sequence read data; applying a set of selection rules to the list of detected short variants to identify tumor somatic short variants; and estimating the ctDNA fraction based on the presence of at least one identified tumor somatic short variant.
76. The method of any one of clauses 38 to 75, wherein estimating the ctDNA fraction based on the at least one tumor somatic short variant comprises: determining, using the one or more processors, a variant allele frequency (VAF) for one or more variants detected in the sequence read data; generating, using the one or more processors, an empirical distribution of ctDNA fraction values corresponding to the determined VAF for the one or more variants based on historical data; fitting, using the one or more processors, a model to the empirical distribution of ctDNA fraction values; and determining a ctDNA fraction for the sample based on the model.
77. The method of clause 76, further comprising determining a confidence interval for the ctDNA fraction based on the model.
78. The method of clause 76 or clause 77, wherein the one or more variants comprise one or more short variants.
79. The method of clause 78, wherein the one or more short variants comprise one or more somatic short variants.
80. The method of clause 79, wherein the one or more somatic short variants are known not to be associated with clonal hematopoiesis of indeterminate potential (CHIP).
81. The method of any one of clauses 76 to 80, wherein generating the empirical distribution of ctDNA fraction values comprises calculating a ctDNA fraction value based on a known copy number for one or more tumor somatic short variants and a corresponding known sample ploidy for a plurality of historical subject samples having a known VAF for the one or more tumor somatic short variants that is substantially the same as the determined VAF for the one or more tumor somatic short variants.
82. The method of any one of clauses 76 to 81, wherein generating the empirical distribution of ctDNA fraction values comprises pre-calculating a ctDNA fraction value based on a known copy number for the one or more tumor somatic short variants and a corresponding known sample ploidy for a plurality of historical subject samples having a range of VAF values for the one or more tumor somatic short variants, and selecting a subset of the pre-calculated ctDNA fraction values that corresponds to samples having a known VAF for the one or more tumor somatic short variants that is substantially the same as the determined VAF for the one or more tumor somatic short variants.
83. The method of clause 81 or clause 82, wherein ctDNA fraction values are calculated or selected for a tumor somatic variant that exhibits the highest VAF in the sample from the subject.
84. The method of clause 81 or clause 82, wherein ctDNA fraction values are calculated or selected for a rank-ordered set of two or more tumor somatic short variants that exhibit the highest rank-ordered VAFs in the sample from the subject.
85. The method of clause 81 or clause 82, wherein the ctDNA fraction values are calculated or selected for a predetermined set of two or more tumor somatic short variants detected in the sample from the subject.
86. The method of clause 81 or clause 82, wherein the ctDNA fraction values are calculated or selected for a predetermined set of two or more tumor somatic short variants detected in the sample from the subject that comprise known driver mutations.
87. The method of clause 81 or clause 82, wherein the ctDNA fraction values are calculated or selected for all tumor somatic short variants detected in the sample from the subject.
88. The method of any one of clauses 81 to 87, wherein ctDNA fraction values are calculated or pre-calculated based on the known VAF for the one or more tumor somatic short variants, the known copy number for the one or more tumor somatic short variants, and the corresponding known sample ploidy for the plurality of historical subject samples.
89. The method of any one of clauses 81 to 88, wherein ctDNA fraction values are calculated or pre-calculated based on the known VAF for the one or more tumor somatic short variants, the known copy number for the one or more tumor somatic short variants, and the corresponding known sample ploidy for the plurality of historical subject samples by solving a set of equations that describe: (i) a relationship between ctDNA fraction, sample tumor purity, and sample ploidy, and (ii) a relationship between somatic VAF, sample tumor purity, copy number at the genomic location(s) of the one or more tumor somatic short variants, and variant allele number for each of the one or more tumor somatic short variants, to eliminate sample tumor purity and derive a relationship for ctDNA fraction as a function of somatic VAF, sample ploidy, copy number at a genomic location of the one or more tumor somatic short variants, and variant allele number for the one or more tumor somatic short variants.
90. The method of any one of clauses 81 to 89, wherein ctDNA fraction values are calculated or pre-calculated based on the known VAF for the one or more tumor somatic short variants, the known copy number for the one or more tumor somatic short variants, and the corresponding known sample ploidy for the plurality of historical subject samples by solving a set of equations comprising: a first equation that equates ctDNA fraction to a product of sample tumor purity and sample ploidy, divided by a sum of the product of sample tumor purity and sample ploidy and a product of two times a quantity of one minus sample tumor purity; and a second equation that equates somatic VAF to a product of sample tumor purity and a variant allele number for each of the one or more tumor somatic short variants, divided by a sum of a product of sample tumor purity and copy number at a genomic location of the one or more tumor somatic short variants and a product of two times a quantity of one minus sample tumor purity; to eliminate sample tumor purity and derive a relationship that equates ctDNA fraction to sample ploidy divided by a quantity equal to sample ploidy minus the copy number at a genomic location of the one or more tumor somatic short variants plus a ratio of variant allele number for the one or more tumor somatic short variants to somatic VAF for each of the one or more tumor somatic short variants.
91. The method of any one of clauses 81 to 90, wherein ctDNA fraction values are calculated or pre-calculated based on the known VAF for the one or more tumor somatic short variants, the known copy number for the one or more tumor somatic short variants, and the corresponding known sample ploidy for the plurality of historical subject samples by solving a set of equations:
Figure imgf000108_0001
pV
Somatic VAF = - - - r C + 2(l - ) to eliminate p and obtain a relationship between ctDNA fraction and somatic VAF described by:
Figure imgf000108_0002
where p is sample tumor purity, is sample ploidy, C is the copy number at a genomic location of the one or more tumor somatic short variants, and V is a variant allele number for each of the one or more tumor somatic short variants.
92. The method of any one of clauses 81 to 91, wherein the plurality of historical subject samples comprises solid biopsy samples, liquid biopsy samples, or any combination thereof.
93. The method of any one of clauses 81 to 92, wherein the plurality of historical subject samples comprises cancer samples.
94. The method of clause 93, wherein the plurality of historical subject samples comprises samples for a single type of cancer.
95. The method of clause 93, wherein the plurality of historical subject samples comprises samples for multiple types of cancer.
96. The method of any one of clauses 81 to 95, wherein the plurality of historical subject samples comprise bladder cancer samples, breast cancer samples, colorectal cancer samples, endometrial cancer samples, kidney cancer samples, leukemia samples, liver cancer samples, lung cancer samples, melanoma samples, non-Hodgkin lymphoma samples, pancreatic cancer samples, prostate cancer samples, thyroid cancer samples, or any combination thereof.
97. The method of any one of clauses 81 to 96, wherein the plurality of historical subject samples comprise acute lymphoblastic leukemia (Philadelphia chromosome positive) samples, acute lymphoblastic leukemia (precursor B-cell) samples, acute myeloid leukemia (FLT3+) samples, acute myeloid leukemia (with an IDH2 mutation) samples, anaplastic large cell lymphoma samples, basal cell carcinoma samples, B-cell chronic lymphocytic leukemia samples, bladder cancer samples, breast cancer (HER2 overexpressed/amplified) samples, breast cancer (HER2+) samples, breast cancer (HR+, HER2-) samples, cervical cancer samples, cholangiocarcinoma samples, chronic lymphocytic leukemia samples, chronic lymphocytic leukemia (with 17p deletion) samples, chronic myelogenous leukemia samples, chronic myelogenous leukemia (Philadelphia chromosome positive) samples, classical Hodgkin lymphoma samples, colorectal cancer samples, colorectal cancer (dMMR and MSI-H) samples, colorectal cancer (KRAS wild type) samples, cryopyrin-associated periodic syndrome samples, cutaneous T-cell lymphoma samples, dermatofibrosarcoma protuberans samples, diffuse large B- cell lymphoma samples, fallopian tube cancer samples, follicular B-cell non-Hodgkin lymphoma samples, follicular lymphoma samples, gastric cancer samples, gastric cancer (HER2+) samples, gastroesophageal junction (GEJ) adenocarcinoma samples, gastrointestinal stromal tumor samples, gastrointestinal stromal tumor (KIT+) samples, giant cell tumor of the bone samples, glioblastoma samples, granulomatosis with polyangiitis samples, head and neck squamous cell carcinoma samples, hepatocellular carcinoma samples, Hodgkin lymphoma samples, juvenile idiopathic arthritis samples, lupus erythematosus samples, mantle cell lymphoma samples, medullary thyroid cancer samples, melanoma samples, melanoma samples with a BRAF V600 mutation, melanoma samples with a BRAF V600E or V600K mutation, Merkel cell carcinoma samples, multicentric Castleman's disease samples, multiple hematologic malignancy samples including Philadelphia chromosome-positive ALL and CML, multiple myeloma samples, myelofibrosis samples, non-Hodgkin’ s lymphoma samples, nonresectable subependymal giant cell astrocytoma samples associated with tuberous sclerosis, non-small cell lung cancer samples, non-small cell lung cancer (ALK+) samples, non-small cell lung cancer (PD-L1+) samples, non- small cell lung cancer (with ALK fusion or ROS1 gene alteration) samples, non-small cell lung cancer (with BRAF V600E mutation) samples, non-small cell lung cancer samples (with an EGFR exon 19 deletion or exon 21 substitution (L858R) mutation), non-small cell lung cancer samples (with an EGFR T790M mutation), ovarian cancer samples, ovarian cancer samples (with a BRCA mutation), pancreatic cancer samples, pancreatic cancer samples, gastrointestinal cancer samples, lung origin neuroendocrine tumor samples, pediatric neuroblastoma samples, peripheral T-cell lymphoma samples, peritoneal cancer samples, prostate cancer samples, renal cell carcinoma samples, rheumatoid arthritis samples, small lymphocytic lymphoma samples, soft tissue sarcoma samples, solid tumor (MSI-H/dMMR) samples, squamous cell cancer samples of the head and neck, squamous non-small cell lung cancer samples, thyroid cancer samples, thyroid carcinoma samples, urothelial cancer samples, urothelial carcinoma samples, Waldenstrom's macroglobulinemia samples, or any combination thereof.
98. The method of any one of clauses 76 to 97, wherein the model is a non-parametric probability density model.
99. The method of any one of clauses 76 to 98, wherein the determined ctDNA fraction for the sample is a most probable ctDNA fraction.
100. The method of any one of clauses 76 to 99, wherein the determined ctDNA fraction for the sample is the mean, median, or mode of a dominant peak in the empirical distribution of ctDNA fraction values.
101. The method of any one of clauses 76 to 100, wherein the sample comprises DNA extracted from a blood sample, a plasma sample, a cerebrospinal fluid sample, a pleural effusion fluid sample, a sputum sample, a stool sample, a urine sample, or a saliva sample.
102. The method of any one of clauses 38 to 101, wherein estimating the ctDNA fraction is based on a determination of the maximum somatic allele frequency (MSAF) for the at least one tumor somatic short variant, the detection of one or more genomic rearrangements, a determination of micro satellite instability, or any combination thereof.
103. The method of any one of clauses 75 to 102, wherein the set of selection rules used to identify tumor somatic short variants in the list of detected short variants comprises: (i) removing short variants that appear on a blacklist of known germline variants, known clonal hematopoiesis of indeterminate potential (CHIP) variants, and known sequencing artifacts; (ii) retaining short variants that appear on a list of known tumor somatic short variants; (iii) retaining short variants that appear on a list of known genes that are prone to exhibit high amplification and that have an allele frequency that is higher than other somatic short variants in the sample; (iv) retaining short variants that appear on a list of known rearrangements; or any combination thereof.
104. The method of clause 103, wherein the blacklist of known germline variants, known clonal hematopoiesis of indeterminate potential (CHIP) variants, and known sequencing artifacts comprises a list of germline variants, CHIP variants, and sequencing artifacts, or any combination thereof, observed in historical sequencing data.
105. The method of clause 103 or clause 104, wherein the list of known tumor somatic short variants comprises somatic short variants determined to have a high prevalence ratio or a high odds ratio between tumor and white blood cells.
106. The method of any one of clauses 103 to 105, wherein the list of known genes that are prone to exhibit high amplification comprises KRAS, EGFR, CCND1, FGF19, FGF3, FGF4, MYC, AR, MDM2, CCNE1, ERBB2, WHSC1L1, FGFR1, ZNF703, or any combination thereof.
107. The method of any one of clauses 103 to 106, wherein the list of known rearrangements comprises fusions between the following gene pairs: TMPRSS2-ERG, ALK-EML4, FGFR3- TACC3, RET-KIF5B, or any combination thereof.
108. The method of any one of clauses 75 to 102, wherein the set of selection rules used to identify tumor somatic short variants in the list of detected short variants further comprises identifying short variants for which a fragment size shift between the reference allele and alternate allele is detected in sequence read data as being tumor somatic short variants.
109. The method of any one of clauses 38 to 108, wherein the estimated ctDNA fraction of the sample is used to diagnose or confirm a diagnosis of disease in the subject.
110. The method of clause 109, wherein the disease is cancer.
111. The method of clause 110, further comprising selecting an anti-cancer therapy to administer to the subject based on the estimated ctDNA fraction of the sample.
112. The method of clause 110 or clause 111, further comprising determining an effective amount of an anti-cancer therapy to administer to the subject based on the estimated ctDNA fraction of the sample.
113. The method of any one of clauses 110 to 112, further comprising administering the anticancer therapy to the subject based on the estimated ctDNA fraction of the sample.
114. The method of clause 113, wherein the anti-cancer therapy comprises chemotherapy, radiation therapy, immunotherapy, a targeted therapy, or surgery. 115. The method of any one of clauses 38 to 114, wherein the estimated ctDNA fraction is used as a prognostic biomarker for predicting a treatment outcome for a subject having cancer.
116. The method of clause 115, wherein the cancer is prostate cancer.
117. The method of any one of clauses 38 to 116, wherein the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
118. A method for predicting a treatment outcome for a subject having cancer, the method comprising: receiving, at one or more processors, sequence read data for a plurality of sequence reads obtained for the sample from the subject; determining, using the one or more processors, if the sequence read data is sufficient for performing copy number alteration (CNA) modeling; estimating, using the one or more processors, a ctDNA fraction in the sample based on at least a sample tumor purity and a sample ploidy derived from a CNA model if the sequence read data is determined to be sufficient for performing CNA modeling; or estimating, using the one or more processors, the ctDNA fraction in the sample based on identification of at least one tumor somatic short variant detected in the sequence read data if the sequence read data is determined to be insufficient for performing CNA modeling; outputting, using the one or more processors, the estimated ctDNA fraction in the sample; and based on a comparison of the estimated ctDNA fraction to a predetermined threshold, predicting the outcome of treating the subject with a specified anti-cancer therapy.
119. The method of clause 118, wherein the predetermined threshold is determined based on an analysis of ctDNA fraction and survival data for a cohort of patients having the cancer.
120. The method of clause 119, wherein the predetermined threshold is determined by adjusting an empirical threshold to maximize a sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), or any combination thereof, for the ctDNA fraction data for the cohort of patients having the cancer.
121. The method of any one of clauses 118 to 120, wherein the cancer is prostate cancer. 122. The method of clause 121, wherein the anti-cancer therapy comprises an enzalutamide challenge following abiraterone treatment.
123. A method for diagnosing a disease, the method comprising: diagnosing that a subject has the disease based on a determination of ctDNA fraction for a sample from the subject, wherein ctDNA fraction is determined according to the method of any one of clauses 1 to 117.
124. A method of selecting an anti-cancer therapy, the method comprising: responsive to determining a ctDNA fraction for a sample from a subject, selecting an anti-cancer therapy for the subject, wherein ctDNA fraction is determined according to the method of any one of clauses 1 to 117.
125. A method of treating a cancer in a subject, comprising: responsive to determining a ctDNA fraction for a sample from the subject, administering an effective amount of an anti-cancer therapy to the subject, wherein ctDNA fraction is determined according to the method of any one of clauses 1 to 117.
126. A method for monitoring cancer progression or recurrence in a subject, the method comprising: determining a first ctDNA fraction in a first sample obtained from the subject at a first time point according to the method of any one of clauses 1 to 117; determining a second ctDNA fraction in a second sample obtained from the subject at a second time point; and comparing the first ctDNA fraction to the second ctDNA fraction, thereby monitoring the cancer progression or recurrence.
127. The method of clause 126, wherein the second ctDNA fraction for the second sample is determined according to the method of any one of clauses 1 to 117.
128. The method of clause 126 or clause 127, further comprising selecting an anti-cancer therapy for the subject in response to the cancer progression.
I l l 129. The method of any one of clauses 126 to 128, further comprising administering an anticancer therapy to the subject in response to the cancer progression.
130. The method of any one of clauses 126 to 129, further comprising adjusting an anti-cancer therapy for the subject in response to the cancer progression.
131. The method of any one of clauses 128 to 130, further comprising adjusting a dosage of the anti-cancer therapy or selecting a different anti-cancer therapy in response to the cancer progression.
132. The method of clause 131, further comprising administering the adjusted anti-cancer therapy to the subject.
133. The method of any one of clauses 126 to 132, wherein the first time point is before the subject has been administered an anti-cancer therapy, and wherein the second time point is after the subject has been administered the anti-cancer therapy.
134. The method of any one of clauses 126 to 133, wherein the subject has a cancer, is at risk of having a cancer, is being routine tested for cancer, or is suspected of having a cancer.
135. The method of any one of clauses 126 to 134, wherein the cancer is a solid tumor.
136. The method of any one of clauses 126 to 134, wherein the cancer is a hematological cancer.
137. The method of any one of clauses 128 to 136, wherein the anti-cancer therapy comprises chemotherapy, radiation therapy, immunotherapy, a targeted therapy, or surgery.
138. The method of any one of clauses 1 to 117, further comprising determining, identifying, or applying the value of ctDNA fraction for the sample as a diagnostic value associated with the sample.
139. The method of any one of clauses 1 to 117, further comprising generating a genomic profile for the subject based on the determination of ctDNA fraction.
140. The method of clause 139, wherein the genomic profile for the subject further comprises results from a comprehensive genomic profiling (CGP) test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof. 141. The method of clause 139 or clause 140, wherein the genomic profile for the subject further comprises results from a nucleic acid sequencing-based test.
142. The method of any one of clauses 139 to 141, further comprising selecting an anti-cancer therapy, administering an anti-cancer therapy, or applying an anti-cancer therapy to the subject based on the generated genomic profile.
143. The method of any one of clauses 1 to 117, wherein the determination of ctDNA fraction for the sample is used in making suggested treatment decisions for the subject.
144. The method of any one of clauses 1 to 117, wherein the determination of ctDNA fraction for the sample is used in applying or administering a treatment to the subject.
145. A system comprising: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: receive sequence read data for a plurality of sequence reads obtained for the sample from the subject; determine if the sequence read data is sufficient for performing copy number alteration (CNA) modeling; estimate using the one or more processors,
(1) the ctDNA fraction in the sample based on at least a sample tumor purity and a ploidy of the sample using a model if the sequence read data is determined to be sufficient for performing CNA modeling, or
(2) the ctDNA fraction in the sample based on identification of at least one tumor somatic short variant in the sequence read data if the sequence read data is determined to be insufficient for performing CNA modeling; and output the estimated ctDNA fraction in the sample.
146. The system of clause 145, further comprising instructions that, when executed by the one or more processors, cause the system to compare the estimated ctDNA fraction to at least one predetermined threshold, and output a status call of at least tumor fraction-high (TF-high) or tumor fraction-low (TF-low) for the sample based on the comparison.
147. The system of clause 145 or clause 146, wherein determining if the sequence read data is sufficient for performing CNA modeling comprises determining sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof, for at least one genomic locus to which the plurality of sequence reads map.
148. The system of any one of clauses 145 to 147, wherein performing CNA modeling comprises: determining, using the one or more processors, a copy number model including a sample tumor purity, a sample ploidy, and a copy number of multiple genomic segments in the ctDNA that accounts for an observed sequence coverage ratio data, allele fraction data for at least one genomic locus within one or more subgenomic intervals to which the plurality of sequence reads map.
149. The system of any one of clauses 145 to 148, wherein estimating the ctDNA fraction based on at least the sample tumor purity and sample ploidy using the CNA model comprises using a formula that describes a physical relationship between ctDNA fraction and sample tumor purity and sample ploidy.
150. The system of any one of clauses 145 to 149, wherein estimating the ctDNA fraction based on at least one somatic short variant detected in the sequence read data comprises: obtaining a list of short variants detected in the sequence read data; applying a set of selection rules to the list of detected short variants to identify tumor somatic short variants; and estimating the ctDNA fraction based on the presence of at least one identified tumor somatic short variant.
151. The system of clause 150, wherein the set of selection rules used to identify tumor somatic short variants in the list of detected short variants comprises: (i) removing short variants that appear on a blacklist of known germline variants, known clonal hematopoiesis of indeterminate potential (CHIP) variants, and known sequencing artifacts; (ii) retaining short variants that appear on a list of known tumor somatic short variants; (iii) retaining short variants that appear on a list of known genes that are prone to exhibit high amplification and that have an allele frequency that is higher than other somatic short variants in the sample; (iv) retaining short variants that appear on a list of known rearrangements; or any combination thereof.
152. The system of any one of clauses 145 to 151, wherein estimating the ctDNA fraction based on the at least one tumor somatic short variant comprises: determining, using the one or more processors, a variant allele frequency (VAF) for one or more variants detected in the sample based on the sequence read data; generating, using the one or more processors, an empirical distribution of ctDNA fraction values corresponding to the determined VAF for the one or more variants based on historical data; fitting, using the one or more processors, a model to the empirical distribution of ctDNA fraction values; and determining a ctDNA fraction for the sample based on the model.
153. A non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: receive sequence read data for a plurality of sequence reads obtained for the sample from the subject; determine if the sequence read data is sufficient for performing copy number alteration (CNA) modeling; estimate using the one or more processors,
(1) the ctDNA fraction in the sample based on at least a tumor purity and a ploidy of the sample using a model if the sequence read data is determined to be sufficient for performing CNA modeling, or
(2) the ctDNA fraction in the sample based on identification of at least one tumor somatic short variant in the sequence read data if the sequence read data is determined to be insufficient for performing CNA modeling; and output the estimated ctDNA fraction in the sample.
154. The non-transitory computer-readable storage medium of clause 153, further comprising instructions that, when executed by the one or more processors of the system, cause the system to compare the estimated ctDNA fraction to at least one predetermined threshold, and output a status call of at least tumor fraction-high (TF-high) or tumor fraction-low (TF-low) for the sample based on the comparison.
155. The non-transitory computer-readable storage medium of clause 153 or clause 154, wherein determining if the sequence read data is sufficient for performing CNA modeling comprises determining sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof, for at least one genomic locus to which the plurality of sequence reads map.
156. The non-transitory computer-readable storage medium of any one of clauses 153 to 155, wherein performing CNA modeling comprises: determining, using the one or more processors, a copy number model including a sample tumor purity, a sample ploidy, and a copy number of multiple genomic segments in the ctDNA that accounts for an observed sequence coverage ratio data, allele fraction data for at least one genomic locus within one or more subgenomic intervals to which the plurality of sequence reads map.
157. The non-transitory computer-readable storage medium of any one of clauses 153 to 156, wherein estimating the ctDNA fraction based on at least the sample tumor purity and sample ploidy using the CNA model comprises using a formula that describes a physical relationship between ctDNA fraction and sample tumor purity and sample ploidy.
158. The non-transitory computer-readable storage medium of any one of clauses 153 to 157, wherein estimating the ctDNA fraction based on at least one somatic short variant detected in the sequence read data comprises: obtaining a list of short variants detected in the sequence read data; applying a set of selection rules to the list of detected short variants to identify tumor somatic short variants; and estimating the ctDNA fraction based on the presence of at least one identified tumor somatic short variant.
159. The non-transitory computer-readable storage medium of clause 158, wherein the set of selection rules used to identify tumor somatic short variants in the list of detected short variants comprises: (i) removing short variants that appear on a blacklist of known germline variants, known clonal hematopoiesis of indeterminate potential (CHIP) variants, and known sequencing artifacts; (ii) retaining short variants that appear on a list of known tumor somatic short variants; (iii) retaining short variants that appear on a list of known genes that are prone to exhibit high amplification and that have an allele frequency that is higher than other somatic short variants in the sample; (iv) retaining short variants that appear on a list of known rearrangements; or any combination thereof.
160. The non-transitory computer-readable storage medium of any one of clauses 153 to 159, wherein estimating the ctDNA fraction based on the at least one tumor somatic short variant comprises: determining, using the one or more processors, a variant allele frequency (VAF) for one or more variants detected in the sample based on the sequence read data; generating, using the one or more processors, an empirical distribution of ctDNA fraction values corresponding to the determined VAF for the one or more variants based on historical data; fitting, using the one or more processors, a model to the empirical distribution of ctDNA fraction values; and determining a ctDNA fraction for the sample based on the model.
[0318] It should be understood from the foregoing that, while particular implementations of the disclosed methods and systems have been illustrated and described, various modifications can be made thereto and are contemplated herein. It is also not intended that the invention be limited by the specific examples provided within the specification. While the invention has been described with reference to the aforementioned specification, the descriptions and illustrations of the preferable embodiments herein are not meant to be construed in a limiting sense. Furthermore, it shall be understood that all aspects of the invention are not limited to the specific depictions, configurations or relative proportions set forth herein which depend upon a variety of conditions and variables. Various modifications in form and detail of the embodiments of the invention will be apparent to a person skilled in the art. It is therefore contemplated that the invention shall also cover any such modifications, variations and equivalents.

Claims

CLAIMS What is claimed is:
1. A method for determining circulating tumor DNA (ctDNA) fraction in a sample from a subject, the method comprising: receiving, at one or more processors, sequence read data for a plurality of sequence reads obtained for the sample from the subject; determining, using the one or more processors, if the sequence read data is sufficient for performing copy number alteration (CNA) modeling; estimating, using the one or more processors,
(1) the ctDNA fraction in the sample based on at least a tumor purity and a ploidy of the sample using a model if the sequence read data is determined to be sufficient for performing CNA modeling, or
(2) the ctDNA fraction in the sample based on identification of at least one tumor somatic short variant in the sequence read data if the sequence read data is determined to be insufficient for performing CNA modeling; and outputting, using the one or more processors, the estimated ctDNA fraction in the sample.
2. The method of claim 1, further comprising comparing the estimated ctDNA fraction to at least one predetermined threshold, and outputting a status call of at least tumor fraction-high (TF- high) or tumor fraction-low (TF-low) for the sample based on the comparison.
3. The method of claim 1, wherein determining if the sequence read data is sufficient for performing CNA modeling comprises determining sequence coverage data, sequence coverage ratio data, allele fraction data, or any combination thereof, for at least one genomic locus to which the plurality of sequence reads map.
4. The method of claim 1, wherein performing CNA modeling comprises: determining, using the one or more processors, a copy number model including a sample tumor purity, a sample ploidy, and a copy number of multiple genomic segments that accounts for an observed sequence coverage ratio data, allele fraction data for at least one genomic locus within one or more subgenomic intervals to which the plurality of sequence reads map.
5. The method of claim 4, wherein the allele fraction data is determined by aligning the plurality of sequence reads that overlap the at least one genomic locus within the one or more subgenomic intervals in the sample to a reference genome, detecting a number of alleles present at the at least one genomic locus, and determining an allele fraction for at least one of the alleles present at the at least one genomic locus.
6. The method of claim 4, wherein performing CNA modeling further comprises generating segmentation data by: aligning a plurality of sequence reads that overlap the at least one genomic locus within the one or more subgenomic intervals in the sample to a reference genome, and processing the aligned sequence read data, coverage ratio data, and allele fraction data using a pruned exact linear time (PELT) method to determine a number of segments required to account for the aligned sequence read data, wherein each segment has a same copy number.
7. The method of claim 4, wherein an amplification is detected when the copy number for the corresponding segment is greater than or equal to the ploidy of the sample.
8. The method of claim 4, wherein detection of deletions comprises identifying homozygous deletions of the at least one genomic locus in a corresponding segment.
9. The method of claim 4, wherein detection of deletions comprises identifying heterozygous deletions of the at least one genomic locus in a corresponding segment.
10. The method of claim 4, wherein the detection of deletions comprises identifying partial deletions of the at least one genomic locus in a corresponding segment.
11. The method of claim 1, wherein estimating the ctDNA fraction based on at least the sample tumor purity and sample ploidy using the CNA model comprises using a formula that describes a physical relationship between ctDNA fraction and sample tumor purity and sample ploidy.
12. The method of claim 11, wherein the formula is given by:
Figure imgf000123_0001
where p is sample tumor purity, and i/ is ploidy.
13. The method of claim 1, wherein estimating the ctDNA fraction based on at least one somatic short variant detected in the sequence read data comprises: obtaining a list of short variants detected in the sequence read data; applying a set of selection rules to the list of detected short variants to identify tumor somatic short variants; and estimating the ctDNA fraction based on the presence of at least one identified tumor somatic short variant.
14. The method of claim 1, wherein estimating the ctDNA fraction based on the at least one tumor somatic short variant comprises: determining, using the one or more processors, a variant allele frequency (VAF) for one or more variants detected in the sequence read data; generating, using the one or more processors, an empirical distribution of ctDNA fraction values corresponding to the determined VAF for the one or more variants based on historical data; fitting, using the one or more processors, a model to the empirical distribution of ctDNA fraction values; and determining a ctDNA fraction for the sample based on the model.
15. The method of claim 14, further comprising determining a confidence interval for the ctDNA fraction based on the model.
16. The method of claim 14, wherein generating the empirical distribution of ctDNA fraction values comprises calculating a ctDNA fraction value based on a known copy number for one or more tumor somatic short variants and a corresponding known sample ploidy for a plurality of historical subject samples having a known VAF for the one or more tumor somatic short variants that is substantially the same as the determined VAF for the one or more tumor somatic short variants.
17. The method of claim 14, wherein generating the empirical distribution of ctDNA fraction values comprises pre-calculating a ctDNA fraction value based on a known copy number for the one or more tumor somatic short variants and a corresponding known sample ploidy for a plurality of historical subject samples having a range of VAF values for the one or more tumor somatic short variants, and selecting a subset of the pre-calculated ctDNA fraction values that corresponds to samples having a known VAF for the one or more tumor somatic short variants that is substantially the same as the determined VAF for the one or more tumor somatic short variants.
18. The method of claim 16, wherein ctDNA fraction values are calculated or pre-calculated based on the known VAF for the one or more tumor somatic short variants, the known copy number for the one or more tumor somatic short variants, and the corresponding known sample ploidy for the plurality of historical subject samples.
19. The method of claim 16, wherein ctDNA fraction values are calculated or pre-calculated based on the known VAF for the one or more tumor somatic short variants, the known copy number for the one or more tumor somatic short variants, and the corresponding known sample ploidy for the plurality of historical subject samples by solving a set of equations that describe: (i) a relationship between ctDNA fraction, sample tumor purity, and sample ploidy, and (ii) a relationship between somatic VAF, sample tumor purity, copy number at the genomic location(s) of the one or more tumor somatic short variants, and variant allele number for each of the one or more tumor somatic short variants, to eliminate sample tumor purity and derive a relationship for ctDNA fraction as a function of somatic VAF, sample ploidy, copy number at a genomic location of the one or more tumor somatic short variants, and variant allele number for the one or more tumor somatic short variants.
20. The method of claim 16, wherein ctDNA fraction values are calculated or pre-calculated based on the known VAF for the one or more tumor somatic short variants, the known copy number for the one or more tumor somatic short variants, and the corresponding known sample ploidy for the plurality of historical subject samples by solving a set of equations:
Figure imgf000125_0001
pV Somatic VAF = — — — — - C + 2(l - ) to eliminate p and obtain a relationship between ctDNA fraction and somatic VAF described by:
Figure imgf000125_0002
where p is sample tumor purity, is sample ploidy, C is the copy number at a genomic location of the one or more tumor somatic short variants, and V is a variant allele number for each of the one or more tumor somatic short variants.
21. The method of claim 14, wherein the model is a non-parametric probability density model.
22. The method of claim 1, wherein estimating the ctDNA fraction is based on a determination of the maximum somatic allele frequency (MSAF) for the at least one tumor somatic short variant, the detection of one or more genomic rearrangements, a determination of micro satellite instability, or any combination thereof.
23. The method of claim 13, wherein the set of selection rules used to identify tumor somatic short variants in the list of detected short variants comprises: (i) removing short variants that appear on a blacklist of known germline variants, known clonal hematopoiesis of indeterminate potential (CHIP) variants, and known sequencing artifacts; (ii) retaining short variants that appear on a list of known tumor somatic short variants; (iii) retaining short variants that appear on a list of known genes that are prone to exhibit high amplification and that have an allele frequency that is higher than other somatic short variants in the sample; (iv) retaining short variants that appear on a list of known rearrangements; or any combination thereof.
24. The method of claim 13, wherein the set of selection rules used to identify tumor somatic short variants in the list of detected short variants further comprises identifying short variants for which a fragment size shift between the reference allele and alternate allele is detected in sequence read data as being tumor somatic short variants.
25. The method of claim 1, wherein the estimated ctDNA fraction of the sample is used to diagnose or confirm a diagnosis of cancer in the subject.
26. The method of claim 25, further comprising selecting an anti-cancer therapy to administer to the subject, determining an effective amount of an anti-cancer therapy to administer to the subject, or administering the anti-cancer therapy to the subject based on the estimated ctDNA fraction of the sample.
27. The method of claim 26, wherein the anti-cancer therapy comprises chemotherapy, radiation therapy, immunotherapy, a targeted therapy, or surgery.
28. The method of claim 1, wherein the estimated ctDNA fraction is used as a prognostic biomarker for predicting a treatment outcome for a subject having cancer.
29. The method of claim 28, wherein the cancer is prostate cancer.
30. The method of claim 1, wherein the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
31. A method for predicting a treatment outcome for a subject having cancer, the method comprising: receiving, at one or more processors, sequence read data for a plurality of sequence reads obtained for the sample from the subject; determining, using the one or more processors, if the sequence read data is sufficient for performing copy number alteration (CNA) modeling; estimating, using the one or more processors, a ctDNA fraction in the sample based on at least a sample tumor purity and a sample ploidy derived from a CNA model if the sequence read data is determined to be sufficient for performing CNA modeling; or estimating, using the one or more processors, the ctDNA fraction in the sample based on identification of at least one tumor somatic short variant detected in the sequence read data if the sequence read data is determined to be insufficient for performing CNA modeling; outputting, using the one or more processors, the estimated ctDNA fraction in the sample; and based on a comparison of the estimated ctDNA fraction to a predetermined threshold, predicting the outcome of treating the subject with a specified anti-cancer therapy.
32. The method of claim 31, wherein the predetermined threshold is determined based on an analysis of ctDNA fraction and survival data for a cohort of patients having the cancer.
33. The method of claim 32, wherein the predetermined threshold is determined by adjusting an empirical threshold to maximize a sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), or any combination thereof, for the ctDNA fraction data for the cohort of patients having the cancer.
34. The method of claim 31, wherein the cancer is prostate cancer.
35. The method of claim 31, wherein the anti-cancer therapy comprises an enzalutamide challenge following abiraterone treatment.
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