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WO2025024307A2 - Procédés et systèmes de détection de contaminants d'arn dans un échantillon - Google Patents

Procédés et systèmes de détection de contaminants d'arn dans un échantillon Download PDF

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Publication number
WO2025024307A2
WO2025024307A2 PCT/US2024/038831 US2024038831W WO2025024307A2 WO 2025024307 A2 WO2025024307 A2 WO 2025024307A2 US 2024038831 W US2024038831 W US 2024038831W WO 2025024307 A2 WO2025024307 A2 WO 2025024307A2
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rna
sample
sequencing data
positions
dna
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WO2025024307A3 (fr
Inventor
Eddie GRINMAN
Andrej SAVOL
Jason D. HUGHES
Daniela Beatriz MUNAFO
Sarah RUDAWSKY
Yanmei HUANG
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Foundation Medicine Inc
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Foundation Medicine Inc
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6809—Methods for determination or identification of nucleic acids involving differential 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
    • 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
    • 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
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6869—Methods for sequencing

Definitions

  • Nucleic acid sequencing is a significant tool in the clinic. Sequencing methods are used to diagnose, monitor, and/or determine the appropriate treatment(s) for disease, such as cancer.
  • the sequencing of cancers and other samples can be subject to contamination.
  • Some of the contaminants can comprise nucleic acid molecules, albeit from sources distinct from the sample under study. Given that the physical compositions of nucleic acid molecule contaminants are oftentimes indistinct from the nucleic acid molecules targeted for study, improved methods are needed for detecting contamination. Such methods would improve the overall quality control of nucleic acid sequencing and analysis of biological samples.
  • the probability that the difference between the RNA sequencing data and the DNA sequencing data can be due to the sequencing error is less than the probability that the difference between the RNA sequencing data and the DNA sequencing data.
  • the sample can have an RNA contaminate if at least a predetermined threshold number of positions in the subset of the plurality of positions is indicative of contamination.
  • labeling a portion of the RNA sequencing data as having an RNA contaminate if at least a predetermined threshold number of positions in the subset of the plurality of positions can be indicative of contamination.
  • a position can be indicative of contamination when the probability, for said position, that the difference between the RNA sequencing data and the DNA sequencing data is due to sequencing error is below a predetermined probability threshold.
  • the predetermined threshold number of positions is 1.
  • the determined level of RNA contamination can be based on the number of positions in the subset of the plurality of positions for which the probability that the difference between the RNA sequencing data and the DNA sequencing data is due to sequencing error is below a predetermined probability threshold.
  • the determined level of RNA contamination can be based on a calibration curve wherein known amounts of contaminant are added to a known amount of RNA molecules.
  • the predetermined probability threshold can be about 0.05.
  • the embodiments disclosed herein can further comprise sequencing DNA from the DNA sample to obtain the DNA sequencing data.
  • the embodiments disclosed herein can further comprise sequencing RNA from the RNA sample to obtain the RNA sequencing data.
  • the sequencing can comprise 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
  • NGS next generation sequencing
  • the differential information can comprise a number of RNA sequence reads in the RNA sequencing data having the type of nucleotide substitution. In any of the embodiments herein, the differential information can comprise a total number of RNA sequence reads in the RNA sequencing data at the plurality of positions associated with the type of nucleotide substitution. In any of the embodiments herein, the differential information can comprise a frequency of RNA sequence reads in the RNA sequencing data having the type of nucleotide substitution relative to a total number of RNA sequence reads in the RNA sequencing data at the plurality of positions associated with the type of nucleotide substitution.
  • the predicted sequencing error rate can be a frequency of RNA sequence reads in the RNA sequencing data having the type of nucleotide substitution relative to a total number of RNA sequence reads in the RNA sequencing data at the plurality of positions associated with the type of nucleotide substitution.
  • the plurality of homozygous positions in the DNA sequencing data can have a SNP frequency below a predetermined SNP frequency threshold.
  • the predetermined SNP frequency threshold can be about 0.01.
  • the methods disclosed herein can comprise processing a sample from a subject to make the DNA sample and the RNA sample.
  • the sample from the subject can be a tissue biopsy sample or liquid biopsy sample obtained from the subject.
  • the sample can be a liquid biopsy sample and can comprise blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
  • the sample can be a liquid biopsy sample and can comprise circulating tumor cells (CTCs).
  • the sample can be a liquid biopsy sample and can comprise cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.
  • the DNA sample or the RNA sample can comprise a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules.
  • the tumor nucleic acid molecules can be derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules can be derived from a normal portion of the heterogeneous tissue biopsy sample.
  • the sample from the subject can comprise a liquid biopsy sample, and the tumor nucleic acid molecules can be derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample, and the non-tumor nucleic acid molecules can be derived from a non- tumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.
  • ctDNA circulating tumor DNA
  • cfDNA non- tumor, cell-free DNA
  • the RNA sample can be processed for sequencing in a multi-sample container.
  • processing the subject sample can comprise enriching the DNA sample or the RNA sample for selected subgenomic intervals.
  • processing the subject sample to make the DNA sample can comprise: providing a plurality of DNA molecules obtained from the subject sample; ligating one or more adapters onto one or more DNA molecules from the plurality of DNA molecules; amplifying the one or more ligated DNA molecules from the plurality of DNA molecules; and capturing amplified DNA molecules from the amplified DNA molecules.
  • processing the subject sample to make the RNA sample can comprise: providing a plurality of cDNA molecules obtained from RNA molecules obtained from the subject sample; ligating one or more adapters onto one or more cDNA molecules from the plurality of cDNA molecules; amplifying the one or more ligated cDNA molecules from the plurality of cDNA molecules; and capturing amplified cDNA molecules from the amplified cDNA molecules.
  • the one or more adapters can comprise amplification primers, flow cell adaptor sequences, substrate adapter sequences, or sample index sequences.
  • the captured nucleic acid molecules can be captured from the amplified nucleic acid molecules by hybridization to one or more bait molecules.
  • the one or more bait molecules can comprise one or more nucleic acid molecules, each comprising a region that can be complementary to a region of a captured nucleic acid molecule.
  • amplifying nucleic acid molecules can comprise performing a polymerase chain reaction (PCR) amplification technique, a non-PCR amplification technique, or an isothermal amplification technique.
  • the DNA sequencing data and the RNA sequencing data each can comprise one or more sequence reads that overlap one or more gene loci within one or more subgenomic intervals in the sample.
  • the one or more gene loci can comprise 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 can 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
  • the one or more gene loci can comprise ABL, ALK, ALL, B4GALNT1, BALE, BCL2, BRAE, 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 disclosed methods can further comprise generating, by the one or more processors, a report indicating the presence or absence of a genetic variant, or an indication that the genetic variant cannot be called because of contamination in the RNA sample.
  • the disclosed methods can further comprise transmitting the report to a healthcare provider.
  • the report can be transmitted via a computer network or a peer-to-peer connection.
  • the embodiments disclosed herein can comprise 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 perform the method of any one of the embodiments disclosed herein.
  • FIG. 1 depicts a non-limiting exemplary method for detecting RNA contaminants in a sample.
  • FIG. 2 depicts an exemplary computing device or system, in accordance with some embodiments of the present disclosure.
  • FIG. 3 depicts an exemplary computer system or computer network, in accordance with some instances of the systems described herein.
  • FIG. 4 provides a non-limiting example of data indicating the number of observed loci for which an anomalous single nucleotide polymorphism was identified across at lease one contaminated sample.
  • FIG. 5 provides a non-limiting example of data indicating a distribution of read frequencies for observed anomalous single nucleotide polymorphisms (SNPs).
  • SNPs single nucleotide polymorphisms
  • FIG. 6 provides a non-limiting example of data comparing two exemplary strategies for detecting contamination in biological samples.
  • RNA contamination from RNA sequence reads is challenging but critically important to ensure accurate sample characterization and medical diagnosis. This problem is especially difficult given that many contaminants are nucleic acid molecules, as are the target molecules for sequencing. As a result, RNA contaminants are oftentimes erroneously attributed to a patient sample when they should be excluded from analyses. Disclosed herein are methods and systems for identifying nucleic acid contaminants in an RNA sample. The disclosed methods use both DNA sequencing data and RNA sequencing data from the same subject. The DNA sequencing data may be analyzed to identify homozygous bases in the subject’s genome.
  • the DNA and RNA sequencing data are then compared at the homozygous DNA bases such that for every base where the DNA and RNA sequence differs, an error model is used to determine whether the observed RNA sequencing data is unexpected given a relevant sequencing error rate or probability.
  • the error models assume binomial distributions regarding the sequencing errors. If the RNA sequencing data at a particular position is unexpected, given the relevant error model, the observed RNA sequencing data is categorized as indicating contamination.
  • RNA sequence reads often require the use of a matched control.
  • the use of a matched control allows for the identification of nucleic acid contaminants by comparing the sample under study against the control sample, such differences in sample content can be classified as nucleic acid contaminants.
  • Such methods use two different samples from a subject.
  • matched control analyses assume the absence of common contaminants between the control sample and the sample under study. If the source of contamination is systemic, however, both the control sample and the sample under study would harbor largely the same contaminants, and a comparison between the two samples would fail to identify the common contaminations.
  • Improved methods and systems are needed for identifying nucleic acid contaminants from samples processed for RNA sequencing.
  • the methods and systems disclosed herein can include the use of statistical processes to estimate nucleic acid contamination from RNA sequence reads.
  • the methods disclosed herein may employ an error model that is based on sequence read error rates being binomially distributed.
  • the binomial probability of observing the number of read counts or more for anomalous RNA sequencing data, at a given position is then compared against a probability threshold, i.e., p-value, to determine whether the null hypothesis (i.e., that the different RNA sequence data is due to sequencing error rather than contamination) should be rejected. If the null hypothesis is rejected, the observed number of reads for the anomalous RNA sequencing data is interpreted to not be the result of sequencing error, given that the error model is informed by the sequencing error rate.
  • the observed number of reads for the anomalous RNA sequencing data is also inferred to not be a true but rare biological variant, because the disclosed methods analyze only bases that are homozygous according to DNA sequencing data for the sample but are different bases when compared to the sample’s RNA sequencing data. By process of elimination, the observed number of reads for the anomaly is interpreted to be RNA contamination.
  • RNA sequencing data for a DNA sample and RNA sequencing data for the RNA sample, wherein the DNA sample and the RNA sample are obtained from the same subject; selecting a plurality of homozygous positions in the DNA sequencing data; selecting a plurality of positions in the RNA sequencing data corresponding to the plurality of homozygous positions in the DNA sequencing data; determining sequencing differential information for each of a plurality of different types of nucleotide substitutions indicative of a difference between the DNA sequencing data and the RNA sequencing data at the plurality of positions; generating an error model for each type of nucleotide substitution based on the differential information for each type of nucleotide substitution; selecting a subset of the plurality of positions having a difference between the RNA sequencing data and the DNA sequencing data; and determining for positions in the subset of the plurality of positions, using the error model, a probability that the difference between the RNA sequencing data and the DNA sequencing
  • ‘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.
  • the term “subject interval” refers to a subgenomic interval or an expressed subgenomic interval (e.g., the transcribed sequence of a subgenomic interval).
  • 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.
  • 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.
  • FIG. 1 The figures illustrate processes according to various embodiments.
  • some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted.
  • additional steps may be performed in combination with the exemplary processes. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.
  • the disclosed methods for detecting RNA contamination in a sample employs statistical models for assessing whether anomalous RNA sequencing data at a particular position are the result of routine sequencing error, or RNA contamination.
  • the disclosed methods use both DNA and RNA sequencing data from a subject, such that only nucleotide bases that are homozygous according to the DNA sequencing data are analyzed. Of the homozygous variants, only the bases that differ in sequence between the DNA and RNA sequence reads are analyzed further. By filtering the variants to be analyzed, the disclosed methods minimize the possibility that the reads being analyzed are derived from a true albeit rare biological variant. Each observed variant is then assessed according to a statistical model that is informed by an estimated sequencing error rate.
  • the statistical model is based on the observed number of reads being binomially distributed. More specifically, the observed number of reads is subject to binomial hypothesis testing, such that the binomial test assesses whether observing the number of reads or more is likely to come from routine sequencing error. In the case that observing the number of reads or more is not likely to come from routine sequencing error, the null hypothesis is rejected. In the case that the observed number of anomalous RNA sequence reads neither derives from routine sequencing error, nor represents a true biological variant, the method disclosed herein interprets the observed reads to be from an RNA contaminant. The method discussed herein provide for the detection of RNA contamination in a sample without the use of a process-matched control. In doing so, clinical and biotechnological applications based on RNA sequencing data are bettered due to improved quality control.
  • FIG. 1 shows an exemplary schematic showing a general process 100 for detecting RNA contamination in a sample.
  • the method of detecting RNA contamination in a sample can include: receiving DNA sequencing data for a DNA sample and RNA sequencing data for an RNA sample, wherein the DNA sample and the RNA sample are obtained from a same subject (102); selecting a plurality of homozygous positions in the DNA sequencing data (104); selecting a plurality of positions in the RNA sequencing data corresponding to the plurality of homozygous positions in the DNA sequencing data (106); determining sequencing differential information for each of a plurality of different types of nucleotide substitutions indicative of a difference between the DNA sequencing data and the RNA sequencing data at the plurality of positions (108); generating an error model for each type of nucleotide substitution based on the differential information for each type of nucleotide substitution (110); selecting a subset of the plurality of positions having a difference between the RNA sequencing data and the DNA sequencing data (112); and determining for positions in the sub
  • Process 100 can be performed, for example, using one or more electronic devices implementing a software platform.
  • process 100 is performed using a clientserver 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.
  • some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted.
  • 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.
  • DNA sequencing data for a DNA sample and RNA sequencing data for an RNA sample are received, wherein the DNA sample and the RNA sample are obtained from the same subject.
  • a sample can be processed from a subject to make the DNA sample and the RNA sample.
  • the sample from the subject can be a tissue biopsy sample or a liquid biopsy sample.
  • the sample can comprise blood, plasma, cerebrospinal fluid, sputum, stool, urine, saliva, or a combination thereof.
  • the liquid biopsy sample can also comprise circulating tumor cells (CTCs), cell-free DNA (cfDNA), circulating tumor cells (ctDNA), or any combination thereof.
  • the DNA sample or the RNA sample can comprise a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules.
  • the tumor nucleic acid molecules can be derived from a tumor portion of a heterogenous tissue biopsy sample, and the non-tumor nucleic acid molecules can be derived from a normal portion of the heterogenous tissue biopsy sample.
  • the sample from the subject can comprise a liquid biopsy sample, wherein the tumor nucleic acid molecules are derived from ctDNA fraction of the liquid biopsy sample, and the non-tumor nucleic acid molecules are derived from a non-tumor cfDNA fraction of the liquid biopsy sample.
  • the RNA sample can be processed for sequencing in a multi-sample container. Further, processing the subject sample can comprise enriching the DNA sample or the RNA sample for selected subgenomic intervals.
  • Processing the subject sample to make the DNA sample can comprise: providing a plurality of DNA molecules obtained from the subject sample; ligating one or more adapters onto one or more DNA molecules from the plurality of DNA molecules; amplifying the one or more ligated DNA molecules from the plurality of DNA molecules; and capturing amplified DNA molecules from the amplified DNA molecules.
  • processing the subject sample to make the RNA sample can comprise: providing a plurality of cDNA molecules obtained from RNA molecules obtained from the subject sample; ligating one or more adapters onto one or more cDNA molecules from the plurality of cDNA molecules, amplifying the one or more ligated cDNA molecules from the plurality of cDNA molecules; and capturing amplified cDNA molecules from the amplified cDNA molecules.
  • the one or more adapters can comprise amplification primers, flow cell adapter sequences, substrate adapter sequences, or sample index sequences.
  • the captured nucleic acid molecules can be captured from the amplified nucleic acid molecules by hybridization to one or more bait molecules.
  • the one or more bait molecules can comprise one or more nucleic acid molecules, each comprising a region that is complementary to a region of a captured nucleic acid molecule.
  • Amplifying the nucleic acid molecules can comprise performing a polymerase chain reaction (PCR) amplification technique, a non-PCR amplification technique, or an isothermal amplification technique.
  • PCR polymerase chain reaction
  • the DNA sequencing data and the RNA sequencing data can each comprise one or more sequence reads that 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 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 can 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 can 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.
  • sequencing techniques can be used to obtain the sequencing data. Namely, sequencing DNA from the DNA sample can be used to obtain the DNA sequencing data, and sequencing RNA from the RNA sample can be used to obtain the RNA sequencing data.
  • the sequencing can comprise massively parallel sequencing, and the massively parallel sequencing technique can comprise nest generation sequencing (NGS).
  • NGS nest generation sequencing
  • the plurality of homozygous positions in the DNA sequencing data can have a SNP frequency below a predetermined SNP frequency threshold.
  • the predetermined SNP frequency threshold can be at or below about 0.05, 0.04, 0.03, 0.02, 0.01, or 0.005. In some embodiments, the predetermined SNP frequency threshold can be about 0.01. In some implementations, the SNP frequency of the homozygous positions can be less than 1% homozygous.
  • RNA sequencing data corresponding to the plurality of homozygous positions in the DNA sequencing data are selected.
  • the plurality of positions in the RNA sequencing data can refer to any nucleotide base observed in the RNA sequence reads.
  • the RNA sequencing data’s correspondence to the homozygous positions in the DNA sequencing data refers to the nucleotide positions observed in the RNA sequence reads that when mapped to a reference genome, map to the same nucleotide positions as the homozygous nucleotides observed in the DNA sequencing data.
  • the selection of the positions in the RNA sequencing data that correspond to the homozygous positions in the DNA sequencing data refers to the exclusion of all other positions observed in the RNA sequence reads from downstream analyses.
  • the selection of the RNA sequence reads harboring nucleotide positions corresponding to only homozygous positions in the DNA sequencing data comprises excluding analyses of reads harboring nucleotide positions that are heterozygous, according to the DNA sequencing data.
  • sequencing differential information for each of a plurality of different types of nucleotide substitutions indicative of a difference between the DNA sequencing data and the RNA sequencing data at the plurality of positions are determined.
  • the sequencing differential information can refer to base sequences observed in the RNA sequence reads that differ from the base sequences observed in the DNA sequence reads at the corresponding positions.
  • the sequencing differential information can, but is not limited to, the homozygous positions observed in the DNA sequence reads from 106.
  • the differential information can comprise a number of RNA sequence reads in the RNA sequencing data having the type of nucleotide substitution.
  • the differential information can also comprise a total number of RNA sequence reads in the RNA sequencing data at the plurality of positions associated with the type of nucleotide substitution.
  • the total number of RNA sequence reads in the RNA sequencing data need not be normalized and can be expressed as raw counts of the RNA sequence reads.
  • the differential information can also comprise a frequency of RNA sequence reads in the RNA sequencing data having the type of nucleotide substitution relative to a total number of RNA sequence reads in the RNA sequencing data at the plurality of positions associated with the type of nucleotide substitution.
  • the frequency of RNA sequence reads can be expressed as a normalized value, such as a value normalized to a total number of reads and can range in value between zero and one.
  • an error model for each type of nucleotide substitution based on the differential information for each type of nucleotide substitution is generated.
  • the error models can refer to statistical models, where given a probability estimate, such as an estimated or predicted sequencing rate, the likelihood of an observed event, such as an observed number of anomalous RNA sequence reads, is estimated.
  • the error model can be used for each type of nucleotide substitution, such that given the type of nucleotide substitution, a corresponding probability is provided, such as a corresponding predicted sequencing rate, and a likelihood of observing the type of nucleotide substitution from the observed reads can be determined.
  • Each error model can be based on a binomial distribution.
  • the error model can assess the likelihood that the observed event, such as the observed number of anomalous RNA sequence reads, is a certain type of error by testing the observed event under binomial testing conditions. Binomial testing can be the error model that is based on the binomial distribution.
  • the predicted sequencing error rate can be a frequency of RNA sequence reads in the RNA sequencing data having the type of nucleotide substitution relative to a total number of RNA sequence reads in the RNA sequencing data at the plurality of positions with the type of nucleotide substitution.
  • the predicted sequencing error rate can be informed by empirical measurements, such as, but not limited to, a calibration curve comprising of known and varying amounts of contaminants across RNA sequence reads, or synthetic datasets consisting of in silica simulations and contaminants.
  • a subset of the plurality of positions having a difference between the RNA sequencing data and the DNA sequencing data is selected.
  • the plurality of positions having a difference between the RNA and DNA sequencing data can feature positions where the DNA sequencing data is homozygous. Selecting positions from the RNA sequencing data that correspond to homozygous DNA sequencing data can severely reduce the possibility that the observed anomalous RNA sequence reads indicate true albeit rare biological variants.
  • the term “anomalous RNA sequence read” or “anomalous RNA sequence” refers to an RNA sequence read or RNA sequence that differs from a corresponding DNA sequence (i.e., mappable to the same coordinates of a reference sequence) determined for the same sample.
  • RNA sequence data refers to a set of RNA sequence reads that differ from a corresponding DNA sequence (i.e., mappable to the same coordinates of a reference sequence) determined for the same sample.
  • Selecting the subset of the plurality of positions having a difference between the RNA sequencing data and the DNA sequencing data can comprise selecting positions associated with a SNP population frequency above or greater than a lower threshold and below or lesser than an upper threshold.
  • the lower threshold can be 5% and the upper threshold can be 95%.
  • nucleotide positions associated with appropriate values of variance from the population can be selected. The selection of positions associated with SNP frequencies of an acceptable range can help generate more reliable error models for assessing whether observed anomalous RNA sequence reads derive from routine sequencing error.
  • a probability that the difference between RNA sequencing data and the DNA sequencing data is due to a sequencing error or a probability that the difference between the RNA sequencing data and the DNA sequencing data is not due to a sequencing error is determined for positions in the subset of the plurality of positions.
  • the probability can be denoted as P(x), wherein P(x) can be determined by: wherein: i is the index of the summation; x is the number of RNA sequence reads in the RNA sequencing data at a given position within the plurality of the positions having the type of nucleotide substitution, the error rate of which is captured by the error model; n is a total number of RNA sequence reads in the RNA sequencing data at a given position within the plurality of positions; p is a predicted sequencing error rate for the type of nucleotide substitution captured in the error model; and q is 1 — p.
  • the probability, P(x), or more specifically, the binomial probability is used to estimate the likelihood that an observed number of reads derives from routine sequencing error. That is, the binomial probability can be used to estimate the probability that the difference between the RNA sequencing data and the DNA sequencing data is not due to a sequencing error.
  • the probability P(x) can be used in hypothesis testing, namely in binomial testing, such that P(x) is compared against a probability threshold or p-value, which can be approximately 0.05.
  • the null hypothesis is that the observed number of reads or more (i.e., the observed number of reads or rarer) is due to routine sequencing error, given that p refers to the predicted sequencing error rate for the observed reads for which its abundance is quantified as x.
  • computing the probability, P(x) that the difference between the RNA sequencing data and the DNA sequencing data is due to sequencing error involves summing the probability of observing exactly the observed number of reads, x, with the probabilities of observing even more than the observed number of reads.
  • the methods disclosed herein involve single-tailed binomial testing. In the case that P(x) is less than a probability threshold or p-value, which can be approximately 0.05, the null hypothesis can be rejected.
  • the methods disclosed herein can be used to conclude that the observed reads are not due to sequencing error.
  • previous filtering is considered. For example, given that the observed reads correspond to homozygous sites on the genome, according to the DNA sequencing data, the observed reads can be inferred to not be true albeit rare biological variants. Provided that the observed reads can reject the null hypothesis from binomial testing, the reads also do not derive from routine sequencing error. By elimination, the methods disclosed herein can categorize the observed reads as being from RNA contamination.
  • the sample can be labeled as having an RNA contaminate if at least a predetermined threshold number of positions in the subset of the plurality of positions has the probability that the difference between the RNA sequencing data and the DNA sequencing data is due to sequencing error below a predetermined probability threshold.
  • a portion of the RNA sequencing data can be labeled as having an RNA contaminate if a predetermined threshold number of positions in the subset of the plurality of positions has the probability that the difference between the RNA sequencing data and the DNA sequencing data is due to sequencing error below a predetermined probability threshold.
  • the predetermined threshold number of positions can be 1. In some implementations, if a single RNA sequence position in the sample is considered to represent a contaminant, as indicated by the number of observed anomalous RNA sequence reads, the entire sample can be labelled as contaminated.
  • the predetermined threshold number of positions can also be greater than 1, such as, but not limited to, 2, 3, or 4.
  • the predetermined threshold number of positions can be as high as the total number of positions across which anomalous RNA sequence reads are observed. In such an implementation, the sample is labelled contaminated, only if all the observed reads are considered contaminated, after significance testing.
  • the methods disclosed herein can be used to make binary calls about the sample’s contamination status.
  • the determined level of RNA contamination can also be based on whether the number of positions in the subset of the plurality of positions for which the probability that the difference between the RNA sequencing data and the DNA sequencing data is due to sequencing error is below a predetermined probability threshold.
  • the methods disclosed herein can be used to make not binary calls about the sample’s contamination status, but quantitative calls regarding the extent to which the sample is contaminated.
  • Various techniques can be used to estimate the extent to which the sample is contaminated. For example, the number of anomalous RNA sequence reads or the number of positions across anomalous RNA sequence reads that indicate contamination may be used to generate a proportion, relative to the total number of reads, and the proportion can reflect the extent to which the sample as a whole is contaminated.
  • the determined level of RNA contamination can also be based on a calibration curve wherein known amounts of contaminant are added to a known amount of RNA molecules.
  • increasing amounts of known contaminant can be added to a fixed amount of total RNA molecules.
  • the proportion or number of relative contaminant reads can then be measured, and a regression can be performed, such that contaminant amounts can be interpolated or extrapolated from the regression.
  • the methods disclosed herein can further comprise filtering the subset of the plurality of positions to remove one or more positions based on a read directionality bias at the plurality of positions.
  • the one or more removed positions can have a forward read frequency compared to a total read frequency of below a predetermined lower threshold or above a predetermined upper threshold.
  • the predetermined lower threshold can be 0.05 and the predetermined upper threshold can be 0.95.
  • Filtering the subset of the plurality of positions based on a read directionality bias can be performed following the binomial testing, or before the binomial testing, such as immediately after determining the anomalous reads from the RNA sequencing data that differ in sequence from the DNA sequencing data.
  • the methods disclosed herein can comprise generating a report indicating the presence or absence of a genetic variant, or an indication that the genetic variant cannot be called because of contamination in the RNA sample.
  • the report can be transmitted to a healthcare provider, and/or the report can be transmitted via a computer network or a peer-to- peer connection.
  • the disclosed methods may be used to detect contaminants when identifying variants in the 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, CDKN
  • the disclosed methods may be used to detect contaminants when identifying variants in the 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, or VEGFB gene locus
  • 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 disclosed methods may be used with any of a variety of samples.
  • 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), circulating tumor DNA (ctDNA), or any combination thereof.
  • 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 detecting RNA contaminants 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 detecting RNA contaminants may be used to predict genetic disorders in fetal DNA. e.g., for invasive or non-invasive prenatal testing). For example, sequence read data obtained by sequencing fetal DNA extracted from samples obtained using invasive amniocentesis, chorionic villus sampling (cVS), or fetal umbilical cord sampling techniques, or obtained using non-invasive sampling of cell-free DNA (cfDNA) samples (which comprises a mix of maternal cfDNA and fetal cfDNA), may be processed according to the disclosed methods to identify variants, e.g., copy number alterations, associated with, e.g., Down Syndrome (trisomy 21), trisomy 18, trisomy 13, and extra or missing copies of the X and Y chromosomes.
  • the disclosed methods for detecting RNA contaminants in a sample may be used to help select a subject (e.g., a patient) for a clinical trial based on the probability of contamination determined for one or more gene loci.
  • patient selection for clinical trials based on, e.g., identification of contamination at one or more gene loci may 1 accelerate the development of targeted therapies and improve the healthcare outcomes for treatment decisions.
  • the disclosed methods for detecting RNA contaminants 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 (e.g., immunotherapy), surgery, or any combination thereof.
  • PARPi poly (ADP-ribose) polymerase inhibitor
  • the targeted therapy may comprise 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),
  • the disclosed methods for detecting RNA contaminants may be used to help treat 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 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 detection of RNA contamination in a sample.
  • a therapy or treatment e.g., an anti-cancer treatment or anti-cancer therapy
  • the disclosed methods for detecting RNA contaminants in a sample 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.
  • 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.
  • 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 non-tumor 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;
  • 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 (i.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.
  • the solid phase e.g., silica or other
  • 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 LEV 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.
  • PMPs silica-clad paramagnetic particles
  • 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. 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.
  • 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 non-specific 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 exonexon junctions 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 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.
  • 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 i.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 (i.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 gene locus or micro satellite locus- specific complementary sequence
  • universal tails e.g., a target-specific capture sequence
  • target capture reagent can refer to the target- specific 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).
  • RNA 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(l l):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. 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.
  • 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 may comprise Illumina MiSeq 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 (i.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
  • 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.
  • 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, microsatellite locus, or other subject interval) being sequenced, (ii) the tumor type associated with the sample,
  • 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.
  • 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 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;
  • an alignment method for analyzing e.g., aligning, a sequence read
  • 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 (
  • 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
  • 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.
  • LD linkage disequilibrium
  • 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.
  • 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% (i.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.
  • 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 perform the methods of any one of the embodiments disclosed herein.
  • 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.
  • the disclosed systems may be used for detecting RNA contaminants in any of a variety of samples as described herein (e.g., a tissue sample, biopsy sample, hematological sample, or liquid biopsy sample derived from the subject).
  • samples e.g., a tissue sample, biopsy sample, hematological sample, or liquid biopsy sample derived from the subject.
  • the plurality of gene loci for which sequencing data is processed to detect RNA contaminants from a sample may comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10 gene loci.
  • 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 detection of RNA contaminants from a sample is 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. 2 illustrates an example of a computing device or system in accordance with one embodiment.
  • Device 200 can be a host computer connected to a network.
  • Device 200 can be a client computer or a server.
  • device 200 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) 210, input devices 220, output devices 230, memory or storage devices 240, communication devices 260, and nucleic acid sequencers 270.
  • Software 250 residing in memory or storage device 240 may comprise, e.g., an operating system as well as software for executing the methods described herein.
  • Input device 220 and output device 230 can generally correspond to those described herein, and can either be connectable or integrated with the computer.
  • Input device 220 can be any suitable device that provides input, such as a touch screen, keyboard or keypad, mouse, or voice-recognition device.
  • Output device 230 can be any suitable device that provides output, such as a touch screen, haptics device, or speaker.
  • Storage 240 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 260 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 280, Ethernet connection, or any other wire transfer technology) or wirelessly (e.g., Bluetooth®, Wi-Fi®, or any other wireless technology).
  • Software module 250 which can be stored as executable instructions in storage 240 and executed by processor(s) 210, 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 250 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 940, 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 250 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 200 may be connected to a network (e.g., network 304, as shown in FIG. 3 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 200 can be implemented using any operating system, e.g., an operating system suitable for operating on the network.
  • Software module 250 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) 210.
  • Device 200 can further include a sequencer 270, which can be any suitable nucleic acid sequencing instrument.
  • FIG. 3 illustrates an example of a computing system in accordance with one embodiment.
  • device 200 e.g., as described above and illustrated in FIG. 2
  • network 304 which is also connected to device 306.
  • device 306 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 200 and 306 may communicate, e.g., using suitable communication interfaces via network 304, such as a Local Area Network (LAN), Virtual Private Network (VPN), or the Internet.
  • network 304 can be, for example, the Internet, an intranet, a virtual private network, a cloud network, a wired network, or a wireless network.
  • Devices 200 and 306 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 200 and 306 may communicate, e.g., using suitable communication interfaces, via a second network, such as a mobile/cellular network.
  • Communication between devices 200 and 306 may further include or communicate with various servers such as a mail server, mobile server, media server, telephone server, and the like.
  • Devices 200 and 306 can communicate directly (instead of, or in addition to, communicating via network 304), e.g., via wireless or hardwired communications, such as Ethernet, IEEE 802.11b wireless, or the like.
  • devices 200 and 306 communicate via communications 308, which can be a direct connection or can occur via a network (e.g., network 304).
  • One or all of devices 200 and 306 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 304 according to various examples described herein.
  • logic e.g., http web server logic
  • devices 200 and 306 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 304 according to various examples described herein.
  • Embodiment 1 A method for detecting RNA contamination in an RNA sample, comprising: receiving, at one or more processors, DNA sequencing data for a DNA sample and RNA sequencing data for the RNA sample, wherein the DNA sample and the RNA sample are obtained from a same subject; selecting, using the one or more processors, a plurality of homozygous positions in the DNA sequencing data; selecting, using the one or more processors, a plurality of positions in the RNA sequencing data corresponding to the plurality of homozygous positions in the DNA sequencing data; determining, using the one or more processors, sequencing differential information for each of a plurality of different types of nucleotide substitutions indicative of a difference between the DNA sequencing data and the RNA sequencing data at the plurality of positions; generating, using the one or more processors, an error model for each type of nucleotide substitution based on the differential information for each type of nucleotide substitution; selecting, using the one or more processors, a subset of the plurality of positions having
  • Embodiment 2 The method of embodiment 1, comprising determining, for the positions in the subset of the plurality of positions, using the one or more processors and the error model, the probability that the difference between the RNA sequencing data and the DNA sequencing data is due to the sequencing error.
  • Embodiment 3 The method of embodiment 1, comprising determining, for the positions in the subset of the plurality of positions, using the one or more processors and the error model, the probability that the difference between the RNA sequencing data and the DNA sequencing data is not due to the sequencing error.
  • Embodiment 4 The method of embodiment 1, further comprising filtering, using the one or more processors, the subset of the plurality of positions to remove one or more positions based on a read directionality bias at the plurality of positions.
  • Embodiment 5 The method of embodiment 4, wherein one or more removed positions has a forward read frequency compared to total read frequency of below a predetermined lower threshold or above a predetermined upper threshold.
  • Embodiment 6 The method of embodiment 5, wherein the predetermined lower threshold is 0.05 and the predetermined upper threshold is 0.95.
  • Embodiment 7 The method of any one of embodiments 1-6, wherein selecting the subset of the plurality of positions having a difference between the RNA sequencing data and the DNA sequencing data comprises selecting positions associated with a SNP population frequency above or greater than a lower threshold and below or lesser than an upper threshold.
  • Embodiment 8 The method of embodiment 7, wherein the lower threshold is 5% and the upper threshold is 95%.
  • Embodiment 9 The method of any one of embodiments 1-8, further comprising labeling, using the one or more processors, the sample as having an RNA contaminate if at least a predetermined threshold number of positions in the subset of the plurality of positions is indicative of contamination.
  • Embodiment 10 The method of any one of embodiments 1-9, further comprising labeling, using the one or more processors, a portion of the RNA sequencing data as having an RNA contaminate if at least a predetermined threshold number of positions in the subset of the plurality of positions is indicative of contamination.
  • Embodiment 11 The method of embodiment 9 or 10, wherein a position is indicative of contamination when the probability, for said position, that the difference between the RNA sequencing data and the DNA sequencing data is due to sequencing error is below a predetermined probability threshold.
  • Embodiment 12 The method of embodiment 11, wherein the predetermined threshold number of positions is 1.
  • Embodiment 13 The method of any one of embodiments 1-12, further comprising labeling, using the one or more processors, the sample with a determined level of RNA contamination.
  • Embodiment 14 The method of embodiment 13, wherein the determined level of RNA contamination is based on the number of positions in the subset of the plurality of positions for which the probability that the difference between the RNA sequencing data and the DNA sequencing data is due to sequencing error is below a predetermined probability threshold.
  • Embodiment 15 The method of embodiment 14, wherein the determined level of RNA contamination is based on a calibration curve wherein known amounts of contaminant are added to a known amount of RNA molecules.
  • Embodiment 16 The method of any one of embodiments 11-15, wherein the predetermined probability threshold is about 0.05.
  • Embodiment 17 The method of any one of embodiments 1-16, further comprising sequencing DNA from the DNA sample to obtain the DNA sequencing data.
  • Embodiment 18 The method of any one of embodiments 1-17, further comprising sequencing RNA from the RNA sample to obtain the RNA sequencing data.
  • Embodiment 19 The method of embodiment 17 or 18, 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.
  • MPS massively parallel sequencing
  • WGS whole genome sequencing
  • S whole exome sequencing
  • Embodiment 20 The method of embodiment 19, wherein the sequencing comprises massively parallel sequencing, and the massively parallel sequencing technique comprises next generation sequencing (NGS).
  • NGS next generation sequencing
  • Embodiment 21 The method of any one of embodiments 1-20, wherein the differential information comprises a number of RNA sequence reads in the RNA sequencing data having the type of nucleotide substitution.
  • Embodiment 22 The method of any one of embodiments 1-21, wherein the differential information comprises a total number of RNA sequence reads in the RNA sequencing data at the plurality of positions associated with the type of nucleotide substitution.
  • Embodiment 23 The method of any one of embodiments 1-22, wherein the differential information comprises a frequency of RNA sequence reads in the RNA sequencing data having the type of nucleotide substitution relative to a total number of RNA sequence reads in the RNA sequencing data at the plurality of positions associated with the type of nucleotide substitution.
  • Embodiment 24 The method of any one of embodiments 1-23, wherein each error model is based a binomial distribution.
  • Embodiment 25 The method of any one of embodiments 1-24, wherein the probability P(x) is determined by: wherein: z is the index of the summation; x is number of RNA sequence reads in the RNA sequencing data at a given position within the plurality of the positions having the type of nucleotide substitution, the error rate of which is captured by the error model; n is a total number of RNA sequence reads in the RNA sequencing data at a given position within the plurality of positions; p is a predicted sequencing error rate for the type of nucleotide substitution captured in the error model; and q is 1 - p.
  • Embodiment 26 The method of embodiment 25, wherein the predicted sequencing error rate is a frequency of RNA sequence reads in the RNA sequencing data having the type of nucleotide substitution relative to a total number of RNA sequence reads in the RNA sequencing data at the plurality of positions associated with the type of nucleotide substitution.
  • Embodiment 27 The method of any one of embodiments 1-26, wherein the plurality of homozygous positions in the DNA sequencing data have a SNP frequency below a predetermined SNP frequency threshold.
  • Embodiment 28 The method of embodiment 27, wherein the predetermined SNP frequency threshold is about 0.01.
  • Embodiment 29 The method of any one of embodiments 1-28, comprising processing a sample from a subject to make the DNA sample and the RNA sample.
  • Embodiment 30 The method of embodiment 29, wherein the sample from the subject is a tissue biopsy sample or liquid biopsy sample obtained from the subject.
  • Embodiment 31 The method of embodiment 30, wherein the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
  • Embodiment 32 The method of embodiment 31, wherein the sample is a liquid biopsy sample and comprises circulating tumor cells (CTCs).
  • CTCs circulating tumor cells
  • Embodiment 33 The method of embodiment 32, wherein the sample is a liquid biopsy sample and comprises cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.
  • Embodiment 34 The method of any one of embodiments 1-33, wherein the DNA sample or the RNA sample comprises a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules.
  • Embodiment 35 The method of embodiment 34, 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.
  • Embodiment 36 The method of embodiment 35, wherein the sample from the subject 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.
  • ctDNA circulating tumor DNA
  • cfDNA cell-free DNA
  • Embodiment 37 The method of any one of embodiments 1-36, wherein the RNA sample is processed for sequencing in a multi-sample container.
  • Embodiment 38 The method of any one of embodiments 29-37, wherein processing the subject sample comprises enriching the DNA sample or the RNA sample for selected subgenomic intervals.
  • Embodiment 39 The method of any one of embodiments 29-38, wherein processing the subject sample to make the DNA sample comprises: providing a plurality of DNA molecules obtained from the subject sample; ligating one or more adapters onto one or more DNA molecules from the plurality of DNA molecules; amplifying the one or more ligated DNA molecules from the plurality of DNA molecules; and capturing amplified DNA molecules from the amplified DNA molecules.
  • Embodiment 40 The method of any one of embodiments 29-39, wherein processing the subject sample to make the RNA sample comprises: providing a plurality of cDNA molecules obtained from RNA molecules obtained from the subject sample; ligating one or more adapters onto one or more cDNA molecules from the plurality of cDNA molecules; amplifying the one or more ligated cDNA molecules from the plurality of cDNA molecules; and capturing amplified cDNA molecules from the amplified cDNA molecules.
  • Embodiment 41 The method of embodiment 39 or 40, wherein the one or more adapters comprise amplification primers, flow cell adaptor sequences, substrate adapter sequences, or sample index sequences.
  • Embodiment 42 The method of any one of embodiments 39-41, wherein the captured nucleic acid molecules are captured from the amplified nucleic acid molecules by hybridization to one or more bait molecules.
  • Embodiment 43 The method of embodiment 42, 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.
  • Embodiment 44 The method of any one of embodiments 39-43, wherein 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
  • Embodiment 45 The method of any one of embodiments 1-44, wherein the DNA sequencing data and the RNA sequencing data each comprise one or more sequence reads that overlap one or more gene loci within one or more subgenomic intervals in the sample.
  • Embodiment 46 The method of embodiment 45, 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
  • Embodiment 47 The method of embodiment 45 or 46, 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, CAER, CARD11, CASP8, CBFB, CBE, CCND1, CCND2, CCND3, CCNE1, CD22, CD274, CD70, CD74, CD79A, CD79B, CDC73, CDH1, CDK12, CDK4, CDK6, CDK8, CDKN1A, CDKN1B, CDKN2A, CDKN2B
  • Embodiment 48 The method of embodiment 46 or 47, wherein the one or more gene loci comprise ABL, ALK, ALL, B4GALNT1, BAFF, BCL2, BRAF, BRCA, BTK, CD 19, 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,
  • Embodiment 49 The method of any one of embodiments 1-48, further comprising generating, by the one or more processors, a report indicating the presence or absence of a genetic variant, or an indication that the genetic variant cannot be called because of contamination in the RNA sample.
  • Embodiment 50 The method of embodiment 49, further comprising transmitting the report to a healthcare provider.
  • Embodiment 51 The method of embodiment 49 or 50, wherein the report is transmitted via a computer network or a peer-to-peer connection.
  • Embodiment 52 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 perform the method of any one of embodiments 1-51.
  • Embodiment 53 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 perform the method of any one of embodiments 1-52.
  • the samples were derived from a tissue sample from a subject. Six samples with existing DNA matched pairs were selected, and two replicates were used per condition. The six samples were lung tissue samples that were empirically spiked with a known amount of a different sample — in this example, the different sample was either a lung tissue sample from a different subject, or a brain tissue sample — and the different sample represents a contaminant.
  • FIG. 4 depicts non-limiting examples of data that illustrate the number of anomalous SNPs across loci for which the population SNP frequency ranges between 5% to 95%, inclusive. Each data point represents the number of different anomalous SNP types for a given contamination level, i.e., each data point represents a unique locus for which the anomalous polymorphism was identified.
  • the y-axis shows the count values of the anomalous polymorphisms
  • the x-axis shows different percentages of contamination for the sample, ranging from 0% to 100%.
  • the sample with 0% contamination derives from a tissue type distinct from the tissues that were contaminated at non-zero levels.
  • the number of observed anomalous RNA sequence reads increases.
  • the increase in observed anomalous RNA sequence reads appears linear, with respect to the increased amount of contamination.
  • Example 2 The data from FIG. 5 derives from a lung tissue sample that is empirically spiked with a known amount of a different sample that represents a contaminant.
  • FIG. 5 shows a non-limiting example of how an error model is derived from a single sample. The method for which the results are depicted in FIG. 5 is based on generating a series of distributions for each substitution type, defined by the allele frequency of the anomalous SNP.
  • FIG. 5 depicts nonlimiting examples of data that illustrate the distributions of SNPs across loci for which the population SNP frequency ranges between 5% to 95%, inclusive.
  • the y-axis shows the frequencies of the SNPs, whereas the x-axis shows the different SNP mutations at the locus, and the number of SNPs that were used to generate the distributions.
  • the distributions are represented as a box plot for each anomalous SNP type, such that the median and interquartile ranges are shown.
  • Outliers are illustrated as square glyph data points. Each data point represents the anomalous SNP frequency for a given loci at which the anomalous SNP was observed.
  • the “N” or sample size for each categorical variable on the X-axis represents the total number of loci across the genome that possess the anomalous base transition.
  • N has a value of 208, which refers to the 208 loci for which the anomalous C>T transition or polymorphism was observed.
  • Example 3 The data depicted in FIG. 6 derive from the same samples from which the data depicted in FIG. 4 were derived.
  • the samples were derived from a tissue sample from a subject.
  • Six samples with existing DNA matched pairs were selected, and two replicates were used per condition.
  • the six samples were lung tissue samples that are empirically spiked with a known amount of a different sample — in this example, the different sample was either a lung tissue sample from a different subject, or a brain tissue sample — and the different sample represents a contaminant.
  • FIG. 6 depicts non-limiting examples of data that illustrate the sensitivities of two strategies for detecting RNA contamination, one of which conform to the methods disclosed herein.
  • the y-axis shows the sensitivity of the employed strategy, normalized between 0 and 1, whereas the x-axis shows the contamination level in log percentage.
  • the contamination detection strategy exhibits higher levels of sensitivity at all contamination levels, even at lower levels of contamination levels, when compared to the SNP concordance method.

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Abstract

L'invention concerne des procédés et des systèmes de détection de contaminants d'ARN à partir d'un échantillon. Les procédés peuvent consister à, par exemple, recevoir des données de séquençage d'ADN et des données de séquençage d'ARN ; sélectionner une pluralité de positions homozygotes dans les données d'ADN ; sélectionner une pluralité de positions dans les données d'ARN correspondant à la pluralité de positions homozygotes dans les données d'ADN ; déterminer des informations différentielles pour chaque type d'une pluralité de types différents de substitutions nucléotidiques indiquant une différence entre les données d'ADN et d'ARN ; générer un modèle d'erreur pour chaque type de substitution nucléotidique sur la base des informations différentielles ; sélectionner un sous-ensemble de la pluralité de positions ayant une différence entre les données d'ARN et d'ADN ; et déterminer une probabilité que la différence entre les données d'ARN et d'ADN soit due à une erreur de séquençage ou une probabilité que la différence entre les données d'ARN et d'ADN ne soit pas due à une erreur de séquençage.
PCT/US2024/038831 2023-07-21 2024-07-19 Procédés et systèmes de détection de contaminants d'arn dans un échantillon Pending WO2025024307A2 (fr)

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US9394567B2 (en) * 2008-11-07 2016-07-19 Adaptive Biotechnologies Corporation Detection and quantification of sample contamination in immune repertoire analysis
US20140127688A1 (en) * 2012-11-07 2014-05-08 Good Start Genetics, Inc. Methods and systems for identifying contamination in samples
EP2971087B1 (fr) * 2013-03-14 2017-11-01 Qiagen Sciences, LLC Evaluation de la qualité d'adn en utilisant la pcr en temps réel et les valeurs ct
WO2014160243A1 (fr) * 2013-03-14 2014-10-02 The Trustees Of The University Of Pennsylvania Purification et évaluation de la pureté de molécules d'arn synthétisées comprenant des nucléosides modifiés
WO2014150910A1 (fr) * 2013-03-15 2014-09-25 Ibis Biosciences, Inc. Séquences d'adn pour estimer la contamination dans le séquençage d'adn
AU2017290237B2 (en) * 2016-06-30 2020-10-22 GRAIL, Inc Differential tagging of RNA for preparation of a cell-free DNA/RNA sequencing library
US20180080021A1 (en) * 2016-09-17 2018-03-22 The Board Of Trustees Of The Leland Stanford Junior University Simultaneous sequencing of rna and dna from the same sample
US12006533B2 (en) * 2017-02-17 2024-06-11 Grail, Llc Detecting cross-contamination in sequencing data using regression techniques

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