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WO2025178926A1 - Methods and systems for intra-tumor heterogeneity classification - Google Patents

Methods and systems for intra-tumor heterogeneity classification

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Publication number
WO2025178926A1
WO2025178926A1 PCT/US2025/016431 US2025016431W WO2025178926A1 WO 2025178926 A1 WO2025178926 A1 WO 2025178926A1 US 2025016431 W US2025016431 W US 2025016431W WO 2025178926 A1 WO2025178926 A1 WO 2025178926A1
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Prior art keywords
cancer
sample
ith
score
genomic
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French (fr)
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Karthikeyan Murugesan
Ericka EBOT
Meagan Kathleen MONTESION
Lucas Dennis
Jonathan Keith Killian
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Foundation Medicine Inc
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Foundation Medicine Inc
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/10—Ploidy or copy number detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • 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
    • G16B40/20—Supervised data analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H15/00—ICT specially adapted for medical reports, e.g. generation or transmission thereof
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Definitions

  • Cancer is a complex, dynamic disease.
  • the stochastic nature of the mutational processes underlying tumor initiation reinforces the idea that, at the genome level, tumor initiation and development does not follow a uniform pathway.
  • genomic instability the increased tendency of cancer cells to undergo genomic alterations during cell division - leads to genetic diversity and gives rise to a heterogenous mix of tumor cells, each with its own distinct molecular signature.
  • Genomic instability can be driven not only by UV exposure, tobacco smoke, DNA mismatch repair, and/or prior chemotherapy treatment, but also by large scale aneuploidy events (chromosomal instability) and genomic rearrangements.
  • Intra-tumor heterogeneity driven by the underlying genomic instability of cancer cells, is manifested through a variety of genomic features, such as the clonality of short variant genomic alterations, chromosomal instability, genomic rearrangements, whole genome doubling, etc.
  • genomics data-derived assessments of intra-tumor heterogeneity have primarily been based on determining the clonality of short variant alterations.
  • Accurate determination of ITH has important implications for diagnosis, prognosis, and treatment of cancer patients. For example, accurate predictions of the duration of therapeutic benefit at baseline could have significant clinical implications for combinatorial treatment strategies, nextline therapy decisions and tailoring novel monitoring capabilities based on the likelihood of relapse.
  • the machine learning model is trained on training data comprising both genomic data (e.g., short variant feature data, copy number feature data, and/or phylogeny feature data) and non-genomic data (e.g., corresponding intra-tumor heterogeneity scores derived from digital pathology images) for a cohort of subjects (e.g., a cohort of cancer patients).
  • genomic data e.g., short variant feature data, copy number feature data, and/or phylogeny feature data
  • non-genomic data e.g., corresponding intra-tumor heterogeneity scores derived from digital pathology images
  • the disclosed methods and systems are compatible with genomic profiling / targeted sequencing assay (e.g., targeted exome sequencing assay) pipelines used during routine clinical care, and can provide a more accurate determinations of ITH that serve as an improved biomarker for clinical decision making and prediction of clinical treatment outcomes. More accurate determinations of ITH can, for example, lead to more accurate predictions of the duration of a given cancer patient’
  • Disclosed herein are methods comprising: providing a plurality of nucleic acid molecules obtained from a sample from a subject diagnosed with a cancer; ligating one or more adapters onto one or more nucleic acid molecules from the plurality of nucleic acid molecules; amplifying the one or more ligated nucleic acid molecules from the plurality of nucleic acid molecules; capturing amplified nucleic acid molecules from the amplified nucleic acid molecules; sequencing, by a sequencer, the captured nucleic acid molecules to obtain a plurality of sequence reads that represent the captured nucleic acid molecules; receiving, at one or more processors, sequence read data for the plurality of sequence reads; processing, using the one or more processors, the sequence read data to identify one or more genomic features; providing, using the one or more processors, the one or more genomic features as input to a trained machine learning model configured to predict an intra-tumor heterogeneity (ITH) score for the sample based on the one or more genomic features, wherein the trained machine learning
  • the method further comprises comparing the predicted intra-tumor heterogeneity (ITH) score for the sample to at least one predetermined ITH score threshold to classify the sample.
  • the predicted intra-tumor heterogeneity (ITH) score for the sample is compared to a single predetermined ITH score threshold, and the sample is classified as ITH-High if the predicted ITH score is greater than or equal to the single predetermined ITH score threshold; or the sample is classified as ITH-Low if the predicted ITH score is less than the single predetermined ITH score threshold.
  • a sample classification of ITH-High is indicative of a shorter duration of the subject’s therapeutic response to an anti-cancer therapy.
  • the cancer comprises acute lymphoblastic leukemia (Philadelphia chromosome positive), acute lymphoblastic leukemia (precursor B-cell), acute myeloid leukemia (FLT3+), acute myeloid leukemia (with an IDH2 mutation), anaplastic large cell lymphoma, basal cell carcinoma, B-cell chronic lymphocytic leukemia, bladder cancer, breast cancer (HER2 overexpressed/amplified), breast cancer (HER2+), breast cancer (HR+, HER2-), cervical cancer, cholangiocarcinoma, chronic lymphocytic leukemia, chronic lymphocytic leukemia (with 17p deletion), chronic myelogenous leukemia, chronic myelogenous leukemia (Philadelphia chromosome positive), classical Hodgkin lymphoma, colorectal cancer, colorectal cancer (dMMR/MSI-H), colorectal cancer (KRAS wild type), cryopyrin-associated periodic syndrome,
  • the method further comprises treating the subject with an anticancer therapy.
  • the anti-cancer therapy comprises a targeted anti-cancer therapy.
  • the targeted anti-cancer therapy comprises abemaciclib (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
  • the method further comprises obtaining the sample from the subject.
  • the sample comprises a tissue biopsy sample, a liquid biopsy sample, or a normal control.
  • the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
  • the sample is a liquid biopsy sample and comprises circulating tumor cells (CTCs).
  • the sample is a liquid biopsy sample and comprises cell-free DNA (cfDNA). In some embodiments, all or a portion of the cell-free DNA (cfDNA) comprises circulating tumor DNA (ctDNA).
  • the plurality of nucleic acid molecules comprises a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules.
  • the tumor nucleic acid molecules are derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules are derived from a normal portion of the heterogeneous tissue biopsy sample.
  • the sample comprises a liquid biopsy sample, 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.
  • the one or more gene loci comprise ABL1, ACVR1B, AKT1, AKT2, AKT3, ALK, ALOX12B, AMER1, APC, AR, ARAF, ARFRP1, ARID1A, ASXL1, ATM, ATR, ATRX, AURKA, AURKB, AXIN1, AXL, BAP1, BARD1, BCL2, BCL2L1, BCL2L2, BCL6, BCOR, BCORL1, BCR, BRAF, BRCA1, BRCA2, BRD4, BRIP1, BTG1, BTG2, BTK, CALR, CARD11, CASP8, CBFB, CBL, CCND1, CCND2, CCND3, CCNE1, CD22, CD274, CD70, CD74, CD79A, CD79B, CDC73, CDH1, CDK12, CDK4, CDK6, CDK8, CDKN1A, CDKN1B, CDKN2A, CDKN2B, CDKN2C, CEBPA,
  • the one or more gene loci comprise ABL, ALK, ALL, B4GALNT1, BAFF, BCL2, BRAF, BRCA, BTK, CD19, CD20, CD3, CD30, CD319, CD38, CD52, CDK4, CDK6, CML, CRACC, CS1, CTLA-4, dMMR, EGFR, ERBB1, ERBB2, FGFR1- 3, FLT3, GD2, HDAC, HER1, HER2, HR, IDH2, IL-ip, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSI-H, mTOR, PARP, PD-1, PDGFR, PDGFRa, PDGFRp, PD-L1, PI3K5, PIGF, PTCH, RAF, RANKL, RET, ROS1, SLAMF7, VEGF, VEGFA, VEGFB, or any combination thereof.
  • the method further comprises generating, by the one or more processors, a report indicating the ITH score determined for the sample. In some embodiments, the method further comprises transmitting the report to a healthcare provider. In some embodiments, the report is transmitted via a computer network or a peer-to-peer connection.
  • the method further comprises comparing the predicted intra-tumor heterogeneity (ITH) score for the sample to at least one predetermined ITH score threshold to classify the sample.
  • the predicted intra-tumor heterogeneity (ITH) score for the sample is compared to a single predetermined ITH score threshold, and the sample is classified as ITH-High if the predicted ITH score is greater than or equal to the single predetermined ITH score threshold; or the sample is classified as ITH-Low if the predicted ITH score is less than the single predetermined ITH score threshold.
  • the predicted intra-tumor heterogeneity (ITH) score for the sample is compared to a first predetermined ITH score threshold and a second predetermined ITH score threshold, and the sample is classified as ITH-High if the predicted ITH score is greater than or equal to the first predetermined ITH score threshold; the sample is classified as ITH-Low if the predicted ITH score is less than or equal to the second predetermined ITH score threshold; or the sample is classified as ITH-indeterminate if the predicted ITH score is less than the first predetermined ITH score threshold and greater than the second ITH score threshold.
  • a sample classification of ITH-High is indicative of a shorter duration of the subject’s therapeutic response to an anti-cancer therapy.
  • a sample classification of ITH-High is indicative of a shorter duration of the subject’s therapeutic response to an anti-cancer therapy comprising a targeted anti-cancer therapy selected, at least in part, based on knowledge of a driver mutation identified in the sample.
  • a sample classification of ITH-Low is indicative of a longer duration of the subject’s therapeutic response to an anti-cancer therapy.
  • a sample classification of ITH-Low is indicative of a longer duration of the subject’s therapeutic response to an anti-cancer therapy comprising a targeted anti-cancer therapy selected, at least in part, based on knowledge of a driver mutation identified in the sample.
  • the at least one predetermined ITH score threshold is determined based on a statistical analysis of the cohort of individuals diagnosed with the cancer and their associated survival time data.
  • the associated survival time data comprises mean overall survival data, median overall survival data, one-year survival data, hazard ratio data, progression free survival data, or any combination thereof.
  • the at least one predetermined ITH score threshold is different for different anti-cancer therapies. In some embodiments, the at least one predetermined ITH score threshold is different for different cancers.
  • the determination of an ITH score for the sample is used in making suggested treatment decisions for the subject. In some embodiments, the determination of an ITH score for the sample is used in applying or administering a treatment to the subject.
  • FIG. 3 provides a schematic illustration of an exemplary node within a layer of an artificial neural network or deep learning model architecture.
  • FIG. 8 provides a non-limiting example of a pathology image of a tumor tissue section that illustrates a precision punching process for sampling tumor tissue sections as part of generating a data set to validate algorithms for determining short variant clonality and intratumor heterogeneity.
  • FIG. 9 provides a non-limiting example of a pathology image of the tissue section shown in FIG. 8 after performing precision punching to collect intra-tumor samples.
  • Machine learning-based methods and systems for determining an ITH score for a sample from a subject are described.
  • the ITH score is inferred by a trained machine learning model based on input comprising a plurality of genomic features identified in genomic profiling / targeted sequencing data, including short variant features, copy number features, phylogeny features, or a combination thereof.
  • the machine learning model is trained on training data comprising both genomic data (e.g., short variant feature data, copy number feature data, and/or phylogeny feature data) and non-genomic data (e.g., corresponding intra-tumor heterogeneity scores derived from digital pathology images) for a cohort of subjects (e.g., a cohort of cancer patients).
  • genomic data e.g., short variant feature data, copy number feature data, and/or phylogeny feature data
  • non-genomic data e.g., corresponding intra-tumor heterogeneity scores derived from digital pathology images
  • the disclosed methods and systems are compatible with genomic profiling / targeted sequencing assay (e.g., targeted exome sequencing assay) pipelines used during routine clinical care, and can provide a more accurate determinations of ITH that serve as an improved biomarker for clinical decision making and prediction of clinical treatment outcomes. More accurate determinations of ITH can, for example, lead to more accurate predictions of the duration of a given cancer patient’
  • methods for determining intra-tumor heterogeneity (ITH) scores are described that comprise: receiving sequence read data derived from a sample from a subject diagnosed with a cancer; processing the sequence read data to identify one or more genomic features; providing the one or more genomic features as input to a trained machine learning model configured to predict an intra-tumor heterogeneity (ITH) score for the sample based on the one or more genomic features, where the trained machine learning model is trained using a training data set comprising both genomic and non-genomic data for samples from a cohort of individuals diagnosed with the cancer; and outputting the predicted intra-tumor heterogeneity (ITH) score for the sample.
  • ITH intra-tumor heterogeneity
  • the method can further comprise comparing the predicted intra-tumor heterogeneity (ITH) score for the sample to at least one predetermined ITH score threshold to classify the sample.
  • the predicted intra-tumor heterogeneity (ITH) score for the sample may be compared to a single predetermined ITH score threshold, and the sample is classified as ITH-High if the predicted ITH score is greater than or equal to the single predetermined ITH score threshold; or the sample is classified as ITH-Low if the predicted ITH score is less than the single predetermined ITH score threshold.
  • a sample classification of ITH-High can be indicative of a shorter duration of the subject’s therapeutic response to an anti-cancer therapy.
  • a sample classification of ITH-High can be indicative of a shorter duration of the subject’s therapeutic response to an anti-cancer therapy comprising a targeted anti-cancer therapy selected, at least in part, based on knowledge of a driver mutation identified in the sample.
  • ‘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 “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.
  • subgenomic interval refers to a portion of a genomic sequence.
  • subject interval refers to a subgenomic interval or an expressed subgenomic interval (e.g., the transcribed sequence of a subgenomic interval).
  • variant sequence As used herein, the terms “variant sequence” or “variant” are used interchangeably and refer to a modified nucleic acid sequence relative to a corresponding “normal” or “wild-type” sequence. In some instances, a variant sequence may be a “short variant sequence” (or “short variant”), i.e., a variant sequence of less than about 50 base pairs in length.
  • allele frequency and “allele fraction” are used interchangeably herein and refer to the fraction of sequence reads corresponding to a particular allele relative to the total number of sequence reads for a genomic locus.
  • variant allele frequency and “variant allele fraction” are used interchangeably herein and refer to the fraction of sequence reads corresponding to a particular variant allele relative to the total number of sequence reads for a genomic locus.
  • breakpoint refers to a genomic region or locus where a sample genomic sequence may have a different copy number level than an adjacent segment. These may include sites of breakage where a chromosome breaks (and recombines).
  • copy number oscillation refers to copy number patterns in the DNA, such as repeating copy number patterns, that may arise through various processes including, but not limited to, chromothripsis.
  • the number of segments with oscillating copy number represents a traversal of the genome, or a portion thereof, while counting the number of repeated alternating segments between two copy numbers.
  • chromothripsis refers to a mutational process in which a large number of clustered chromosomal rearrangements occur in a single event in localized and confined genomic regions in one or a few chromosomes. Chromothripsis is known to be involved in both cancer and congenital diseases.
  • the section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.
  • ITH intra-tumor heterogeneity
  • cancer is a complex, dynamic disease.
  • Various models - including linear, branched, neutral and punctuated evolution models - have been proposed to facilitate our understanding of how intra-tumor heterogeneity influences somatic tumor evolution.
  • Norwell proposed the linear clonal expansion model (see, e.g., Nowell (1976), “The clonal evolution of tumor cell populations”, Science 194:23-28), where a clone refers to a group of cells that share a common ancestor and are genetically identical; every new mutation creates a new clone. Every time a cell divides, errors may occur during DNA replication, often in the absence of any internal or external influence.
  • Branched evolution is common in tumors driven by short variants including, but not limited to, non-small cell lung cancer (NSCLC), colon cancer, and melanoma.
  • NSCLC non-small cell lung cancer
  • melanoma Branched evolution is common in tumors driven by short variants including, but not limited to, non-small cell lung cancer (NSCLC), colon cancer, and melanoma.
  • NSCLC non-small cell lung cancer
  • melanoma the number of genomic alterations present in the tumor gradually increases over time.
  • An emerging model of somatic tumor evolution is that of punctured evolution / macroevolution.
  • tumors acquire many aberrations in a short intense burst of genomic change (due, for example, to changes in tumor micro-environment and the associated selection pressure on the tumor) at the very early stages of tumor development.
  • Extensive ITH is generated at the earliest stages of tumor evolution as strong driver events, if generated, drive tumor progression.
  • Punctured evolution is prevalent in tumors driven by large scale aneuploidy changes and chromosomal rearrangements including but not limited to, ovarian and prostate cancer.
  • Punctured evolution a plot of the number of genomic alterations over time resembles the shape of stairs with unequal lengths and heights. There are periods of no increase in genomic alterations followed by short bursts of large gains.
  • the oncology field has usually estimated intra-tumor heterogeneity by quantifying a single genomic phenomenon, e.g., the clonality of short variant genomic alterations, as opposed to attempting to quantify a plurality of genomic phenomena as part of generating a composite metric.
  • the approach disclosed herein is based on characterizing a genomics feature space derived from complete genomic profiling (CGP) / targeted sequencing date to estimate ITH- associated metrics for a plurality of genomic phenomena, including short variant-derived, copy number-derived and evolutionary phylogeny-derived features.
  • genomic features are then input into a machine learning-based, decision-making model which is trained and tested on training data that includes digital pathology (DP)-derived intra-tumor heterogeneity estimates, where the trained model is configured to output a prediction of the degree of intra-tumor heterogeneity for a given sample.
  • the trained machine learning model is trained using a training data set comprising CGP and DP data derived from routine clinical care CGP and DP pipelines, as opposed to research use only whole-genome or whole-exome sequencing datasets.
  • FIG. 1 provides a non-limiting example of a flowchart for a process 100 for determining an intra-tumor heterogeneity (ITH) score.
  • Process 100 can be performed, for example, using one or more electronic devices implementing a software platform.
  • process 100 is performed using a client-server system, and the blocks of process 100 are divided up in any manner between the server and a client device.
  • the blocks of process 100 are divided up between the server and multiple client devices.
  • portions of process 100 are described herein as being performed by particular devices of a client-server system, it will be appreciated that process 100 is not so limited.
  • process 100 is performed using only a client device or only multiple client devices.
  • process 100 some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the process 100. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.
  • sequence read data for a sample from a subject diagnosed with a cancer is received (e.g., by one or more processors of a system configured to perform process 100).
  • the sequence read data may be derived from a plurality of sequence reads that each comprise a nucleic acid sequence describing the order of nucleotides in a DNA molecule or fragment thereof.
  • the sequence read data may be derived using, for example, a whole genome sequencing (WGS) method, a whole exome sequencing (WES) method, and/or a targeted sequencing method.
  • sequence read data may be stored as, e.g., a BAM file.
  • the sample may comprise, for example, a tissue biopsy sample, or a liquid biopsy sample.
  • the sample is a liquid biopsy sample and may comprise blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
  • the sample is a liquid biopsy sample and may comprise circulating tumor cells (CTCs).
  • the sample is a liquid biopsy sample and may comprise cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.
  • the cancer may be any of a variety of cancers known to those of skill in the art. Examples 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 myelo
  • B cell cancer e
  • 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 may comprise, for example, ovarian cancer, prostate cancer, pancreatic cancer, non-small cell lung cancer, colorectal cancer, or melanoma.
  • the sequence read data is processed to identify one or more genomic features.
  • the one or more genomic features may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 18, 20, 40, 60, 80, 100, or more than 100 genomic features.
  • the one of more genomic features can comprise, for example, one or more short variant features, one or more copy number features, one or more phylogeny features, or any combination thereof.
  • identifying the one or more genomic features can comprise determining the clonality for each of a plurality of short variants, where the clonality for each short variant is treated as a different genomic feature.
  • the individual clonality estimates determined for all of the detected short variants can be used to calculate a single score (e.g., by summation, averaging, weighted averaging, etc.) and used as a genomic feature in conjunction with other described short variant, copy number, and/or phylogeny features.
  • the one or more genomic features can comprise one or more short variant features (e.g., a variant sequences (e.g., base substitutions, insertions, deletions, etc.) of less than about 50 base pairs, 100 base pairs, 150 base pairs, 200 base pairs, 250 base pairs, or 300 base pairs in length), and the one or more short variant features can comprise, for example, a total number of short variants detected, a number of clonal variants (e.g., the number short variants that are present in all of the cancer cells in a sample) detected, a number of sub-clonal variants detected (e.g., the short variants that are present in a subset of the cancer cells in a sample), a cancer cell fraction (CCF)-derived tumor heterogeneity score, a presence or absence of a genome doubling event (e.g., a recurrent event in human cancers that promotes chromosomal instability and acquisition of aneuploidies; the timing of genomic doubling can
  • estimating the clonality of a variant can be decoupled from determining the timing of the mutation relative to a genome doubling event, and the two determinations can be performed independently.
  • a mutation that has occurred prior to a genome doubling event will be present in multiple copies of the genome, and a mutation that has occurred after a genome doubling event will typically be present in only one copy of the genome.
  • the trained machine learning model is trained using a training data set comprising both genomic data (e.g., genomic feature data derived from sequencing) and non- genomic data (e.g., digital pathology-based data for estimated ITH scores) for samples from a cohort of individuals diagnosed with a variety of different cancers.
  • genomic data e.g., genomic feature data derived from sequencing
  • non- genomic data e.g., digital pathology-based data for estimated ITH scores
  • the machine learning model may comprise a supervised learning model (z.e., a model trained using labeled sets of training data), an unsupervised learning model (z.e., a model trained using unlabeled sets of training data), a semisupervised learning model (z.e., a model trained using a combination of labeled and unlabeled training data), a deep learning model (z.e., a model inspired by the structure and function of the human brain comprising many layers of coupled "nodes" that may be trained in a supervised, unsupervised, or semi-supervised manner), or any combination thereof.
  • one or more machine learning models e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10 machine learning models
  • one or more machine learning models may be utilized to implement the disclosed methods.
  • the trained machine learning model can comprise, for example, a trained regression-based machine learning model, regularization-based machine learning model, instance-based machine learning model, Bayesian -based machine learning model, clusteringbased machine learning model, ensemble-based machine learning model, neural network-based machine learning model, graph neural network model, generative adversarial network model, or deep learning-based machine learning model.
  • the machine learning model/algorithm used for implementing the disclosed methods and systems may be an artificial neural network (ANN) or deep learning model/algorithm that comprises any type of neural network model known to those of skill in the art, such as a feedforward neural network, radial basis function network, recurrent neural network, or convolutional neural network, and the like.
  • ANN artificial neural network
  • the disclosed methods and systems may employ a pre-trained ANN or deep learning model.
  • the disclosed methods and systems may employ a continuous learning ANN or deep learning model, where the model is periodically or continuously updated based on new training data provided by, e.g., a single local operational system, a plurality of local operational systems, or a plurality of geographically-distributed operational systems.
  • Artificial neural networks generally comprise an interconnected group of nodes organized into multiple layers of nodes.
  • the ANN architecture may comprise at least an input layer, one or more hidden layers, and an output layer.
  • FIG. 2 provides a non-limiting schematic illustration of an artificial neural network with one hidden layer.
  • the ANN may comprise any total number of layers e.g., 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, or more than 20), and any number of hidden layers (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, or more than 20), where the hidden layers function as trainable feature extractors that allow mapping of a set of input data to a preferred output value or set of output values.
  • Each layer of the neural network comprises a number of nodes (e.g., at least 10, 25, 50, 75 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10,000, or more than 10,000 nodes).
  • a node receives input data (e.g., genomic feature data, non-genomic feature data, or other types of input data) that comes either directly from one or more input data nodes or from the output of one or more nodes in previous layers, and performs a specific operation, e.g., a summation operation.
  • a connection from an input to a node is associated with a weight (or weighting factor).
  • the node may, for example, sum up the products of all pairs of inputs, Xi, and their associated weights, Wi, as illustrated in FIG. 3.
  • the weighted sum is offset with a bias, b, as illustrated in FIG. 3.
  • the output of a neuron may be gated using a threshold or activation function,/, as illustrated in FIG. 3, where /may be a linear or non-linear function.
  • the activation function may be, for example, a rectified linear unit (ReLU) activation function or other function such as a saturating hyperbolic tangent, identity, binary step, logistic, arcTan, softsign, parameteric rectified linear unit, exponential linear unit, softPlus, bent identity, softExponential, Sinusoid, Sine, Gaussian, or sigmoid function, or any combination thereof.
  • ReLU rectified linear unit
  • the genomic data included in the training data set comprises data for the one or more genomic features identified by processing sequence read data for a cohort of individuals diagnosed with the cancer (e.g., where the cancer is the same as that with which the subject has been diagnosed). In some instances, the genomic data included in the training data set comprises data for the one or more genomic features identified by processing sequence read data for a cohort of individuals diagnosed with a variety of different cancers, as described elsewhere herein.
  • the non-genomic data included in the training data set comprises digital pathology image data for the cohort of individuals diagnosed with the same cancer or a variety of different cancers.
  • the ground truth data labels used for training the machine learning model comprise intra-tumor heterogeneity scores derived from the digital pathology image data.
  • the trained machine learning model is trained using a training data set that further comprises single cell sequencing data, long-read sequencing data, spatial omics data, cyclic immunofluorescence data, stimulated Raman spectroscopy data, optical coherence tomography data, cytometry by time-of-flight (CyTOF) data, multiplexed ion beam imaging data, or any combination thereof, for the cohort of individuals diagnosed with the same cancer or a variety of different cancers.
  • a training data set that further comprises single cell sequencing data, long-read sequencing data, spatial omics data, cyclic immunofluorescence data, stimulated Raman spectroscopy data, optical coherence tomography data, cytometry by time-of-flight (CyTOF) data, multiplexed ion beam imaging data, or any combination thereof, for the cohort of individuals diagnosed with the same cancer or a variety of different cancers.
  • the predicted intra-tumor heterogeneity (ITH) score for the sample is output (e.g., by one or more processors of a system configured to perform process 100).
  • the method can further comprise comparing the predicted intra-tumor heterogeneity (ITH) score for the sample to at least one predetermined ITH score threshold (e.g., at least 1, 2, 3, 4, or 5 predetermined ITH score thresholds) to classify the sample.
  • ITH score threshold e.g., at least 1, 2, 3, 4, or 5 predetermined ITH score thresholds
  • the predicted intra-tumor heterogeneity (ITH) score for the sample may be compared to a single predetermined ITH score threshold, and the sample can be classified as ITH-High if the predicted ITH score is greater than or equal to the single predetermined ITH score threshold; or the sample can be classified as ITH-Low if the predicted ITH score is less than the single predetermined ITH score threshold.
  • ITH intra-tumor heterogeneity
  • the predicted intra-tumor heterogeneity (ITH) score for the sample may be compared to a first predetermined ITH score threshold and a second predetermined ITH score threshold, and the sample can be classified as ITH-High if the predicted ITH score is greater than or equal to the first predetermined ITH score threshold; the sample can be classified as ITH-Low if the predicted ITH score is less than or equal to the second predetermined ITH score threshold; or the sample can be classified as UH-indeterminate if the predicted ITH score is less than the first predetermined ITH score threshold and greater than the second ITH score threshold.
  • a sample classification of ITH-High can be indicative of a shorter duration of the subject’s therapeutic response to an anti-cancer therapy.
  • a sample classification of ITH-High can be indicative of a shorter duration of the subject’s therapeutic response to an anti-cancer therapy comprising a targeted anti-cancer therapy selected, at least in part, based on knowledge of a driver mutation identified in the sample.
  • a sample classification of ITH-Low can be indicative of a longer duration of the subject’s therapeutic response to an anti-cancer therapy.
  • a sample classification of ITH-Low can be indicative of a longer duration of the subject’s therapeutic response to an anti-cancer therapy comprising a targeted anti-cancer therapy selected, at least in part, based on knowledge of a driver mutation identified in the sample.
  • the at least one predetermined ITH score threshold can be determined based on a statistical analysis of the cohort of individuals diagnosed with the cancer and their associated survival time data (or other clinical outcome measures, e.g., time to next treatment, time to treatment discontinuation, objective response rate, disease control rate, etc.). In some instances, the at least one predetermined ITH score threshold can be determined based on a statistical analysis of the cohort of individuals diagnosed with a variety of different cancers and their associated survival time data (or other clinical outcome data). In some instances, the associated survival time data can comprise, for example, mean overall survival data, median overall survival data, one-year survival data, hazard ratio data, progression free survival data, or any combination thereof.
  • the at least one predetermined ITH score threshold may be determined based on, for example, an analysis of receiver operator characteristic (ROC) curves (or additional model performance/evaluation metrics, such as accuracy, precision, recall, Fl score, Matthews correlation coefficient, etc., that can be derived from a 2x2 confusion matrix) or an analysis of hazard ratios.
  • ROC receiver operator characteristic
  • additional model performance/evaluation metrics such as accuracy, precision, recall, Fl score, Matthews correlation coefficient, etc., that can be derived from a 2x2 confusion matrix
  • the at least one predetermined ITH score threshold may be different for different anti-cancer therapies.
  • the at least one predetermined ITH score threshold may be different for different cancers.
  • the predicted ITH score for the sample can be a pan-cancer ITH score.
  • the clonality estimate for each short variant comprises a cancer cell fraction (CCF) value calculated according to the relationship (CCF vl.0): i(p)V r + 2(l - p)),
  • the clonality threshold may comprise a CCF value greater than 0.50, 0.75, 0.80, 0.85, 0.90, or 0.95 (i.e., 50%, 75%, 80%, 85%, 90%, or 95%). In some instances, the clonality threshold may be set to 1.0 (i.e., 100%). In some instances, the clonality threshold may be set such that the probability that the short variant has a CCF greater than or equal to 0.5, 0.75, 0.80, 0.85, 0.90, or 0.95 is greater than or equal to, e.g., 0.75, 0.80, 0.85, 0.90, or 0.95.
  • the disclosed methods may comprise determining the clonality of short variants in at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, 160, 180, 200, 220, 240, 260, 280, 300, 320, 340, 360, 380, 400, 420, 440, 460, 480, 500, or more than gene loci.
  • the disclosed methods may comprise determining the clonality of short 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, CD
  • the disclosed methods may comprise determining the clonalit of short variants identified 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, HD AC, 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 V
  • the report comprises output from the methods described herein. In some instances, all or a portion of the report may be displayed in the graphical user interface of an online or web-based healthcare portal. In some instances, the report is transmitted via a computer network or peer-to-peer connection.
  • the sample may comprise a tissue biopsy sample, a liquid biopsy sample, or a normal control.
  • the sample may be a liquid biopsy sample and may comprise blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
  • the sample may be a liquid biopsy sample and may comprise circulating tumor cells (CTCs).
  • the sample may be a liquid biopsy sample and may comprise cell-free DNA (cfDNA).
  • the cell-free DNA (cfDNA) or a portion thereof may comprise circulating tumor DNA (ctDNA).
  • the liquid biopsy sample may comprise a combination of cell-free DNA (cfDNA) and circulating tumor DNA (ctDNA).
  • the disclosed methods for determining ITH scores may be used to select an appropriate therapy or treatment (e.g., an anti-cancer therapy or anti-cancer treatment) for a subject.
  • an appropriate therapy or treatment e.g., an anti-cancer therapy or anti-cancer treatment
  • the anti-cancer therapy or treatment may comprise use of a poly (ADP-ribose) polymerase inhibitor (PARPi), a platinum compound, chemotherapy, radiation therapy, a targeted therapy, an immunotherapy, surgery, or any combination thereof.
  • PARPi poly (ADP-ribose) polymerase inhibitor
  • the anti-cancer therapy or treatment may comprise an immunotherapy (e.g., a cancer treatment that acts by stimulating the immune system to fight cancer).
  • the immunotherapy can be, for example, an immune system modulator (e.g., a cytokine, such as an interferon or interleukin), an immune checkpoint inhibitor (such as an anti- PD-1 or anti-PD-Ll antibody), a T-cell transfer therapy (e.g., a tumor infiltrating lymphocyte (TIL) therapy in lymphocytes extracted from a patient’ s tumor are selected for their ability to recognize tumor cells and propagated prior to reintroduction into the patient, or a CAR T-cell therapy in which a patient’s T-cells are modified to express the CAR protein prior to reintroduction into the patient), a monoclonal antibody -based therapy (e.g., a monoclonal antibody that binds to cell surface markers on cancer cells to facilitate recognition by the immune system), or
  • the 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
  • 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 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 nucleic acids extracted from the sample may comprise ribonucleic acid (RNA) molecules.
  • RNA ribonucleic acid
  • examples of RNA that may be suitable for analysis by the disclosed methods include, but are not limited to, total cellular RNA, total cellular RNA after depletion of certain abundant RNA sequences e.g., ribosomal RNAs), cell-free RNA (cfRNA), messenger RNA (mRNA) or fragments thereof, the poly(A)-tailed mRNA fraction of the total RNA, ribosomal RNA (rRNA) or fragments thereof, transfer RNA (tRNA) or fragments thereof, and mitochondrial RNA or fragments thereof.
  • RNA may be extracted from the sample and converted to complementary DNA (cDNA) using, e.g., a reverse transcription reaction.
  • cDNA complementary DNA
  • the cDNA is produced by random-primed cDNA synthesis methods.
  • the cDNA synthesis is initiated at the poly (A) tail of mature mRNAs by priming with oligo(dT)-containing oligonucleotides. Methods for depletion, poly(A) enrichment, and cDNA synthesis are well known to those of skill in the art.
  • the sample may comprise a tumor content (e.g., comprising tumor cells or tumor cell nuclei), or a non-tumor content (e.g., immune cells, fibroblasts, and other nontumor cells).
  • the tumor content of the sample may constitute a sample metric.
  • the sample may comprise a tumor content of at least 5-50%, 10-40%, 15-25%, or 20-30% tumor cell nuclei.
  • the sample may comprise a tumor content of at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, or at least 50% tumor cell nuclei.
  • the 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 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 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.
  • DNA or RNA may be extracted from tissue samples, biopsy samples, blood samples, or other bodily fluid samples using any of a variety of techniques known to those of skill in the art (see, e.g., Example 1 of International Patent Application Publication No. WO 2012/092426; Tan, et al. (2009), “DNA, RNA, and Protein Extraction: The Past and The Present”, J. Biomed. Biotech. 2009:574398; the technical literature for the Maxwell® 16 LEV Blood DNA Kit (Promega Corporation, Madison, WI); and the Maxwell 16 Buccal Swab LEV DNA Purification Kit Technical Manual (Promega Literature #TM333, January 1, 2011, Promega Corporation, Madison, WI)). Protocols for RNA isolation are disclosed in, e.g., the Maxwell® 16 Total RNA Purification Kit Technical Bulletin (Promega Literature #TB351, August 2009, Promega Corporation, Madison, WI).
  • a typical DNA extraction procedure for example, comprises (i) collection of the fluid sample, cell sample, or tissue sample from which DNA is to be extracted, (ii) disruption of cell membranes (z.e., cell lysis), if necessary, to release DNA and other cytoplasmic components, (iii) treatment of the fluid sample or lysed sample with a concentrated salt solution to precipitate proteins, lipids, and RNA, followed by centrifugation to separate out the precipitated proteins, lipids, and RNA, and (iv) purification of DNA from the supernatant to remove detergents, proteins, salts, or other reagents used during the cell membrane lysis step.
  • Disruption of cell membranes may be performed using a variety of mechanical shear e.g., by passing through a French press or fine needle) or ultrasonic disruption techniques.
  • the cell lysis step often comprises the use of detergents and surfactants to solubilize lipids the cellular and nuclear membranes.
  • the lysis step may further comprise use of proteases to break down protein, and/or the use of an RNase for digestion of RNA in the sample.
  • Examples of suitable techniques for DNA purification include, but are not limited to, (i) precipitation in ice-cold ethanol or isopropanol, followed by centrifugation (precipitation of DNA may be enhanced by increasing ionic strength, e.g., by addition of sodium acetate), (ii) phenol-chloroform extraction, followed by centrifugation to separate the aqueous phase containing the nucleic acid from the organic phase containing denatured protein, and (iii) solid phase chromatography where the nucleic acids adsorb to the solid phase (e.g., silica or other) depending on the pH and salt concentration of the buffer.
  • 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.
  • 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 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.
  • a subgenomic interval includes sequences that are not contiguous in the genome, e.g., subgenomic intervals in cDNA can include exonexonjunctions formed as a result of splicing.
  • the subgenomic interval comprises a tumor nucleic acid molecule.
  • the subgenomic interval comprises a non-tumor nucleic acid molecule.
  • the methods described herein 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 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 (z.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.
  • 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.
  • 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 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
  • Alignment is the process of matching a read with a location, e.g., a genomic location or locus.
  • NGS reads may be aligned to a known reference sequence (e.g., a wild-type sequence).
  • NGS reads may be assembled de novo. Methods of sequence alignment for NGS reads are described in, e.g., Trapnell, C. and Salzberg, S.L. Nature Biotech., 2009, 27:455-457. Examples of de novo sequence assemblies are described in, e.g., Warren R., et al., Bioinformatics, 2007, 23:500-501; Butler, J.
  • Misalignment e.g., the placement of base-pairs from a short read at incorrect locations in the genome
  • misalignment of reads due to sequence context can lead to reduction in sensitivity of mutation detection
  • sequence context e.g., the presence of repetitive sequence
  • Other examples of sequence context that may cause misalignment include short-tandem repeats, interspersed repeats, low complexity regions, insertions - deletions (indels), and paralogs.
  • misalignment may introduce artifactual reads of “mutated” alleles by placing reads of actual reference genome base sequences at the wrong location. Because mutation-calling algorithms for multigene analysis should be sensitive to even low-abundance mutations, sequence misalignments may increase false positive discovery rates and/or reduce specificity.
  • the methods and systems disclosed herein may integrate the use of multiple, individually-tuned, alignment methods or algorithms to optimize base-calling performance in sequencing methods, particularly in methods that rely on massively parallel sequencing (MPS) of a large number of diverse genetic events at a large number of diverse genomic loci.
  • the disclosed methods and systems may comprise the use of one or more global alignment algorithms.
  • the disclosed methods and systems may comprise the use of one or more local alignment algorithms. Examples of alignment algorithms that may be used include, but are not limited to, the Burrows-Wheeler Alignment (BWA) software bundle (see, e.g., Li, et al.
  • BWA Burrows-Wheeler Alignment
  • tuning can be a function of one or more of: (i) the genetic locus (e.g., gene loci, micro satellite locus, or other subject interval) being sequenced, (ii) the tumor type associated with the sample, (iii) the variant being sequenced, or (iv) a characteristic of the sample or the subject.
  • the selection or use of alignment conditions that are individually tuned to a number of specific subject intervals to be sequenced allows optimization of speed, sensitivity, and specificity.
  • the method is particularly effective when the alignment of reads for a relatively large number of diverse subject intervals are optimized.
  • the method includes the use of an alignment method optimized for rearrangements in combination with other alignment methods optimized for subject intervals not associated with rearrangements.
  • the methods disclosed herein allow for the rapid and efficient alignment of troublesome reads, e.g., a read having a rearrangement.
  • a read for a subject interval comprises a nucleotide position with a rearrangement, e.g., a translocation
  • the method can comprise using an alignment method that is appropriately tuned and that includes: (i) selecting a rearrangement reference sequence for alignment with a read, wherein said rearrangement reference sequence aligns with a rearrangement (in some instances, the reference sequence is not identical to the genomic rearrangement); and (ii) comparing, e.g., aligning, a read with said rearrangement reference sequence.
  • a method of analyzing a sample can comprise: (i) performing a comparison (e.g., an alignment comparison) of a read using a first set of parameters (e.g., using a first mapping algorithm, or by comparison with a first reference sequence), and determining if said read meets a first alignment criterion (e.g., the read can be aligned with said first reference sequence, e.g., with less than a specific number of mismatches); (ii) if said read fails to meet the first alignment criterion, performing a second alignment comparison using a second set of parameters, (e.g., using a second mapping algorithm, or by comparison with a second reference sequence); and (iii) optionally, determining if said read meets said second criterion (e.g., the read can be
  • 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).
  • Base calling refers to the raw output of a sequencing device, e.g., the determined sequence of nucleotides in an oligonucleotide molecule.
  • Mutation calling refers to the process of selecting a nucleotide value, e.g., A, G, T, or C, for a given nucleotide position being sequenced. Typically, the sequence reads (or base calling) for a position will provide more than one value, e.g., some reads will indicate a T and some will indicate a G.
  • Mutation calling is the process of assigning a correct nucleotide value, e.g., one of those values, to the sequence.
  • mutant calling it can be applied to assign a nucleotide value to any nucleotide position, e.g., positions corresponding to mutant alleles, wild-type alleles, alleles that have not been characterized as either mutant or wild-type, or to positions not characterized by variability.
  • the disclosed methods may comprise the use of customized or tuned mutation calling algorithms or parameters thereof to optimize performance when applied to sequencing data, particularly in methods that rely on massively parallel sequencing (MPS) of a large number of diverse genetic events at a large number of diverse genomic loci (e.g., gene loci, micro satellite regions, etc.) in samples, e.g., samples from a subject having cancer.
  • MPS massively parallel sequencing
  • optimization of mutation calling is described in the art, e.g., as set out in International Patent Application Publication No. WO 2012/092426.
  • Methods for mutation calling can include one or more of the following: making independent calls based on the information at each position in the reference sequence (e.g., examining the sequence reads; examining the base calls and quality scores; calculating the probability of observed bases and quality scores given a potential genotype; and assigning genotypes (e.g., using Bayes’ rule)); removing false positives (e.g., using depth thresholds to reject SNPs with read depth much lower or higher than expected; local realignment to remove false positives due to small indels); and performing linkage disequilibrium (LD)/imputation- based analysis to refine the calls.
  • making independent calls based on the information at each position in the reference sequence e.g., examining the sequence reads; examining the base calls and quality scores; calculating the probability of observed bases and quality scores given a potential genotype; and assigning genotypes (e.g., using Bayes’ rule)
  • removing false positives e.g., using depth thresholds to reject SNP
  • Equations used to calculate the genotype likelihood associated with a specific genotype and position are described in, e.g., Li, H. and Durbin, R. Bioinformatics, 2010; 26(5): 589-95.
  • the prior expectation for a particular mutation in a certain cancer type can be used when evaluating samples from that cancer type.
  • Such likelihood can be derived from public databases of cancer mutations, e.g., Catalogue of Somatic Mutation in Cancer (COSMIC), HGMD (Human Gene Mutation Database), The SNP Consortium, Breast Cancer Mutation Data Base (BIC), and Breast Cancer Gene Database (BCGD).
  • Examples of LD/imputation based analysis are described in, e.g., Browning, B.L. and Yu, Z. Am. J. Hum. Genet. 2009, 85(6):847-61.
  • Examples of low-coverage SNP calling methods are described in, e.g., Li, Y., et al., Annu. Rev. Genomics Hum. Genet. 2009, 10:387-406.
  • detection of substitutions can be performed using a mutation calling method (e.g., a Bayesian mutation calling method) which is applied to each base in each of the subject intervals, e.g., exons of a gene or other locus to be evaluated, where presence of alternate alleles is observed.
  • a mutation calling method e.g., a Bayesian mutation calling method
  • This method will compare the probability of observing the read data in the presence of a mutation with the probability of observing the read data in the presence of basecalling error alone. Mutations can be called if this comparison is sufficiently strongly supportive of the presence of a mutation.
  • An advantage of a Bayesian mutation detection approach is that the comparison of the probability of the presence of a mutation with the probability of base-calling error alone can be weighted by a prior expectation of the presence of a mutation at the site. If some reads of an alternate allele are observed at a frequently mutated site for the given cancer type, then presence of a mutation may be confidently called even if the amount of evidence of mutation does not meet the usual thresholds. This flexibility can then be used to increase detection sensitivity for even rarer mutations/lower purity samples, or to make the test more robust to decreases in read coverage.
  • the likelihood of a random base-pair in the genome being mutated in cancer is ⁇ le-6.
  • the likelihood of specific mutations occurring at many sites in, for example, a typical multigenic cancer genome panel can be orders of magnitude higher. These likelihoods can be derived from public databases of cancer mutations (e.g., COSMIC).
  • Indel calling is a process of finding bases in the sequencing data that differ from the reference sequence by insertion or deletion, typically including an associated confidence score or statistical evidence metric.
  • Methods of indel calling can include the steps of identifying candidate indels, calculating genotype likelihood through local re-alignment, and performing LD-based genotype inference and calling.
  • a Bayesian approach is used to obtain potential indel candidates, and then these candidates are tested together with the reference sequence in a Bayesian framework.
  • 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
  • a method for determining an intra-tumor heterogeneity (ITH) score comprising: receiving, at one or more processors, sequence read data derived from a sample from a subject diagnosed with a cancer; processing, using the one or more processors, the sequence read data to identify one or more genomic features; providing, using the one or more processors, the one or more genomic features as input to a trained machine learning model configured to predict an intra-tumor heterogeneity (ITH) score for the sample based on the one or more genomic features, wherein the trained machine learning model is trained using a training data set comprising both genomic and digital pathology image data for samples from a cohort of individuals diagnosed with the cancer; and outputting, using the one or more processors, the predicted intra-tumor heterogeneity (ITH) score for the sample.
  • ITH intra-tumor heterogeneity
  • the one or more genomic features comprise one or more short variant features
  • the one or more short variant features comprise a total number of short variants detected, a number of clonal variants detected, a number of sub-clonal variants detected, a cancer cell fraction (CCF)-derived tumor heterogeneity score, a presence or absence of a genome doubling event, a number of pre-genome doubling event clonal variants detected, a number of post-genome doubling event clonal variants detected, an estimate of elapsed time between initiation of the disease and a genome doubling event, a COSMIC insertion-deletion feature, a ratio of non- synonymous short variants detected to synonymous short variants detected in one or more protein coding genes, or any combination thereof.
  • CCF cancer cell fraction
  • ground truth data labels used for training the machine learning model comprise intra-tumor heterogeneity scores derived from the digital pathology image data.
  • the trained machine learning model is trained using a training data set that further comprises single cell sequencing data, long-read sequencing data, spatial omics data, cyclic immunofluorescence data, stimulated Raman spectroscopy data, optical coherence tomography data, cytometry by time-of-flight (CyTOF) data, multiplexed ion beam imaging data, or any combination thereof.
  • a training data set that further comprises single cell sequencing data, long-read sequencing data, spatial omics data, cyclic immunofluorescence data, stimulated Raman spectroscopy data, optical coherence tomography data, cytometry by time-of-flight (CyTOF) data, multiplexed ion beam imaging data, or any combination thereof.
  • the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
  • a method for diagnosing a disease comprising: diagnosing that a subject has the disease based on a determination of ITH score for a sample from the subject, wherein ITH score is determined according to the method of any one of clauses 1 to 65.
  • a method of selecting an anti-cancer therapy comprising: responsive to determining an ITH score for a sample from a subject, selecting an anticancer therapy for the subject, wherein the ITH score is determined according to the method of any one of clauses 1 to 65.
  • a method of treating a cancer in a subject comprising: responsive to determining an ITH score for a sample from the subject, administering an effective amount of an anti-cancer therapy to the subject, wherein the ITH score is determined according to the method of any one of clauses 1 to 65.
  • a method for monitoring cancer progression or recurrence in a subject comprising: determining a first ITH score in a first sample obtained from the subject at a first time point according to the method of any one of clauses 1 to 65; determining a second ITH score in a second sample obtained from the subject at a second time point; and comparing the first ITH score to the second ITH score, thereby monitoring the cancer progression or recurrence.
  • genomic profile for the subject further comprises results from a comprehensive genomic profiling (CGP) test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof.
  • CGP genomic profiling
  • a system comprising: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: receive sequence read data derived from a sample from a subject diagnosed with a cancer; process the sequence read data to identify one or more genomic features; provide the one or more genomic features as input to a trained machine learning model configured to predict an intra-tumor heterogeneity (ITH) score for the sample based on the one or more genomic features, wherein the trained machine learning model is trained using a training data set comprising both genomic and digital pathology image data for samples from a cohort of individuals diagnosed with the cancer; and output the predicted intra-tumor heterogeneity (ITH) score for the sample.
  • ITH intra-tumor heterogeneity
  • a non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: receive sequence read data derived from a sample from a subject diagnosed with a cancer; process the sequence read data to identify one or more genomic features; provide the one or more genomic features as input to a trained machine learning model configured to predict an intra-tumor heterogeneity (ITH) score for the sample based on the one or more genomic features, wherein the trained machine learning model is trained using a training data set comprising both genomic and digital pathology image data for samples from a cohort of individuals diagnosed with the cancer; and output the predicted intra-tumor heterogeneity (ITH) score for the sample.
  • ITH intra-tumor heterogeneity

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Abstract

Methods for determining an intra-tumor heterogeneity (ITH) score for a tumor specimen and the use thereof as a biomarker for clinical decision making are described. The methods may comprise, for example, receiving sequence read data derived from a sample from a subject diagnosed with a cancer; processing the sequence read data to identify one or more genomic features; providing the one or more genomic features as input to a trained machine learning model configured to predict an intra-tumor heterogeneity (ITH) score for the sample based on the one or more genomic features, wherein the trained machine learning model is trained using a training data set comprising both genomic and non-genomic data (e.g., digital pathology image data) for samples from a cohort of individuals diagnosed with the cancer; and outputting the predicted intra-tumor heterogeneity (ITH) score for the sample.

Description

METHODS AND SYSTEMS FOR INTRA-TUMOR HETEROGENEITY CLASSIFICATION
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the priority benefit of United States Provisional Patent Application Serial No. 63/556,536, filed February 22, 2024, the contents of which are incorporated herein by reference in their entirety.
FIELD OF THE INVENTION
[0002] The present disclosure relates generally to methods and systems for analyzing genomic profiling data, and more specifically to machine learning-based methods and systems for determining an inter-tumor heterogeneity score for classification of tumor samples.
BACKGROUND
[0003] Cancer is a complex, dynamic disease. The stochastic nature of the mutational processes underlying tumor initiation reinforces the idea that, at the genome level, tumor initiation and development does not follow a uniform pathway. During tumor evolution, genomic instability - the increased tendency of cancer cells to undergo genomic alterations during cell division - leads to genetic diversity and gives rise to a heterogenous mix of tumor cells, each with its own distinct molecular signature. Genomic instability can be driven not only by UV exposure, tobacco smoke, DNA mismatch repair, and/or prior chemotherapy treatment, but also by large scale aneuploidy events (chromosomal instability) and genomic rearrangements. This in turn can lead to activation of oncogenes, loss of function of tumor suppressor genes, and/or accumulation of passenger alterations with no immediate selective fitness advantage. This genetic diversity, or tumor heterogeneity, provides a readily available substrate for fueling tumor progression and the development of therapeutic resistance to targeted therapy/immuno therapy.
[0004] Intra-tumor heterogeneity (ITH), driven by the underlying genomic instability of cancer cells, is manifested through a variety of genomic features, such as the clonality of short variant genomic alterations, chromosomal instability, genomic rearrangements, whole genome doubling, etc. Traditionally, genomics data-derived assessments of intra-tumor heterogeneity have primarily been based on determining the clonality of short variant alterations. Accurate determination of ITH has important implications for diagnosis, prognosis, and treatment of cancer patients. For example, accurate predictions of the duration of therapeutic benefit at baseline could have significant clinical implications for combinatorial treatment strategies, nextline therapy decisions and tailoring novel monitoring capabilities based on the likelihood of relapse. However, most currently-available commercial cancer diagnostic tests do not evaluate ITH, and for those that do, the reliance on determining ITH based solely on the clonality of short variant alterations ignores the enormous potential for making more accurate clinical predictions if ITH was inferred by also taking other genomic events into account.
BRIEF SUMMARY OF THE INVENTION
[0005] Disclosed herein are machine learning-based methods and systems for determining an ITH score for a sample from a subject (e.g., a tumor sample from a cancer patient), where the ITH score is inferred by a trained machine learning model based on input comprising a plurality of genomic features identified in genomic profiling / targeted sequencing data, including short variant features, copy number features, phylogeny features, or a combination thereof. The machine learning model is trained on training data comprising both genomic data (e.g., short variant feature data, copy number feature data, and/or phylogeny feature data) and non-genomic data (e.g., corresponding intra-tumor heterogeneity scores derived from digital pathology images) for a cohort of subjects (e.g., a cohort of cancer patients). The disclosed methods and systems are compatible with genomic profiling / targeted sequencing assay (e.g., targeted exome sequencing assay) pipelines used during routine clinical care, and can provide a more accurate determinations of ITH that serve as an improved biomarker for clinical decision making and prediction of clinical treatment outcomes. More accurate determinations of ITH can, for example, lead to more accurate predictions of the duration of a given cancer patient’ s therapeutic response to treatment.
[0006] Disclosed herein are methods comprising: providing a plurality of nucleic acid molecules obtained from a sample from a subject diagnosed with a cancer; ligating one or more adapters onto one or more nucleic acid molecules from the plurality of nucleic acid molecules; amplifying the one or more ligated nucleic acid molecules from the plurality of nucleic acid molecules; capturing amplified nucleic acid molecules from the amplified nucleic acid molecules; sequencing, by a sequencer, the captured nucleic acid molecules to obtain a plurality of sequence reads that represent the captured nucleic acid molecules; receiving, at one or more processors, sequence read data for the plurality of sequence reads; processing, using the one or more processors, the sequence read data to identify one or more genomic features; providing, using the one or more processors, the one or more genomic features as input to a trained machine learning model configured to predict an intra-tumor heterogeneity (ITH) score for the sample based on the one or more genomic features, wherein the trained machine learning model is trained using a training data set comprising both genomic and digital pathology image data for samples from a cohort of individuals diagnosed with the cancer; and outputting, using the one or more processors, the predicted intra-tumor heterogeneity (ITH) score for the sample.
[0007] In some embodiments, the method further comprises comparing the predicted intra-tumor heterogeneity (ITH) score for the sample to at least one predetermined ITH score threshold to classify the sample. In some embodiments, the predicted intra-tumor heterogeneity (ITH) score for the sample is compared to a single predetermined ITH score threshold, and the sample is classified as ITH-High if the predicted ITH score is greater than or equal to the single predetermined ITH score threshold; or the sample is classified as ITH-Low if the predicted ITH score is less than the single predetermined ITH score threshold. In some embodiments, a sample classification of ITH-High is indicative of a shorter duration of the subject’s therapeutic response to an anti-cancer therapy. In some embodiments, a sample classification of ITH-High is indicative of a shorter duration of the subject’s therapeutic response to an anti-cancer therapy comprising a targeted anti-cancer therapy selected, at least in part, based on knowledge of a driver mutation identified in the sample. In some embodiments, a sample classification of ITH- Low is indicative of a longer duration of the subject’s therapeutic response to an anti-cancer therapy. In some embodiments, a sample classification of ITH-Low is indicative of a longer duration of the subject’s therapeutic response to an anti-cancer therapy comprising a targeted anti-cancer therapy selected, at least in part, based on knowledge of a driver mutation identified in the sample.
[0008] In some embodiments, the cancer is a B cell cancer (multiple myeloma), a melanoma, breast cancer, lung cancer, bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain cancer, central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine cancer, endometrial cancer, cancer of an oral cavity, cancer of a pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel cancer, appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, a cancer of hematological tissue, an adenocarcinoma, an inflammatory myofibroblastic tumor, a gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), acute lymphocytic leukemia (ALL), acute myelocytic leukemia (AML), chronic myelocytic leukemia (CML), chronic lymphocytic leukemia (CLL), polycythemia Vera, Hodgkin lymphoma, non-Hodgkin lymphoma (NHL), soft- tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endotheliosarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, bladder carcinoma, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, neuroblastoma, retinoblastoma, follicular lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, hepatocellular carcinoma, thyroid cancer, gastric cancer, head and neck cancer, small cell cancer, essential thrombocythemia, agnogenic myeloid metaplasia, hypereosinophilic syndrome, systemic mastocytosis, familiar hypereosinophilia, chronic eosinophilic leukemia, neuroendocrine cancers, or a carcinoid tumor.
[0009] In some embodiments, the cancer comprises acute lymphoblastic leukemia (Philadelphia chromosome positive), acute lymphoblastic leukemia (precursor B-cell), acute myeloid leukemia (FLT3+), acute myeloid leukemia (with an IDH2 mutation), anaplastic large cell lymphoma, basal cell carcinoma, B-cell chronic lymphocytic leukemia, bladder cancer, breast cancer (HER2 overexpressed/amplified), breast cancer (HER2+), breast cancer (HR+, HER2-), cervical cancer, cholangiocarcinoma, chronic lymphocytic leukemia, chronic lymphocytic leukemia (with 17p deletion), chronic myelogenous leukemia, chronic myelogenous leukemia (Philadelphia chromosome positive), classical Hodgkin lymphoma, colorectal cancer, colorectal cancer (dMMR/MSI-H), colorectal cancer (KRAS wild type), cryopyrin-associated periodic syndrome, a cutaneous T-cell lymphoma, dermatofibrosarcoma protuberans, a diffuse large B-cell lymphoma, fallopian tube cancer, a follicular B-cell non-Hodgkin lymphoma, a follicular lymphoma, gastric cancer, gastric cancer (HER2+), gastroesophageal junction (GEJ) adenocarcinoma, a gastrointestinal stromal tumor, a gastrointestinal stromal tumor (KIT+), a giant cell tumor of the bone, a glioblastoma, granulomatosis with polyangiitis, a head and neck squamous cell carcinoma, a hepatocellular carcinoma, Hodgkin lymphoma, juvenile idiopathic arthritis, lupus erythematosus, a mantle cell lymphoma, medullary thyroid cancer, melanoma, a melanoma with a BRAF V600 mutation, a melanoma with a BRAF V600E or V600K mutation, Merkel cell carcinoma, multicentric Castleman's disease, multiple hematologic malignancies including Philadelphia chromosome-positive ALL and CML, multiple myeloma, myelofibrosis, a non-Hodgkin’ s lymphoma, a nonresectable subependymal giant cell astrocytoma associated with tuberous sclerosis, a non-small cell lung cancer, a non-small cell lung cancer (ALK+), a non-small cell lung cancer (PD-L1+), a non-small cell lung cancer (with ALK fusion or ROS1 gene alteration), a non-small cell lung cancer (with BRAF V600E mutation), a non-small cell lung cancer (with an EGFR exon 19 deletion or exon 21 substitution (L858R) mutations), a non- small cell lung cancer (with an EGFR T790M mutation), ovarian cancer, ovarian cancer (with a BRCA mutation), pancreatic cancer, a pancreatic, gastrointestinal, or lung origin neuroendocrine tumor, a pediatric neuroblastoma, a peripheral T-cell lymphoma, peritoneal cancer, prostate cancer, a renal cell carcinoma, rheumatoid arthritis, a small lymphocytic lymphoma, a soft tissue sarcoma, a solid tumor (MSI-H/dMMR), a squamous cell cancer of the head and neck, a squamous non-small cell lung cancer, thyroid cancer, a thyroid carcinoma, urothelial cancer, a urothelial carcinoma, or Waldenstrom's macroglobulinemia.
[0010] In some embodiments, the method further comprises treating the subject with an anticancer therapy. In some embodiments, the anti-cancer therapy comprises a targeted anti-cancer therapy. In some embodiments, the targeted anti-cancer therapy comprises abemaciclib (Verzenio), abiraterone acetate (Zytiga), acalabrutinib (Calquence), ado-trastuzumab emtansine (Kadcyla), afatinib dimaleate (Gilotrif), aldesleukin (Proleukin), alectinib (Alecensa), alemtuzumab (Campath), alitretinoin (Panretin), alpelisib (Piqray), amivantamab-vmjw (Rybrevant), anastrozole (Arimidex), apalutamide (Erleada), asciminib hydrochloride
(Scemblix), atezolizumab (Tecentriq), avapritinib (Ayvakit), avelumab (Bavencio), axicabtagene ciloleucel (Yescarta), axitinib (Inlyta), belantamab mafodotin-blmf (Blenrep), belimumab (Benlysta), belinostat (Beleodaq), belzutifan (Welireg), bevacizumab (Avastin), bexarotene (Targretin), binimetinib (Mektovi), blinatumomab (Blincyto), bortezomib (Velcade), bosutinib (Bosulif), brentuximab vedotin (Adcetris), brexucabtagene autoleucel (Tecartus), brigatinib (Alunbrig), cabazitaxel (Jevtana), cabozantinib (Cabometyx), cabozantinib (Cabometyx, Cometriq), canakinumab (Haris), capmatinib hydrochloride (Tabrecta), carfilzomib (Kyprolis), cemiplimab-rwlc (Libtayo), ceritinib (LDK378/Zykadia), cetuximab (Erbitux), cobimetinib (Cotellic), copanlisib hydrochloride (Aliqopa), crizotinib (Xalkori), dabrafenib (Tafinlar), dacomitinib (Vizimpro), daratumumab (Darzalex), daratumumab and hyaluronidase-fihj (Darzalex Faspro), darolutamide (Nubeqa), dasatinib (Sprycel), denileukin diftitox (Ontak), denosumab (Xgeva), dinutuximab (Unituxin), dostarlimab-gxly (Jemperli), durvalumab (Imfinzi), duvelisib (Copiktra), elotuzumab (Empliciti), enasidenib mesylate (Idhifa), encorafenib (Braftovi), enfortumab vedotin-ejfv (Padcev), entrectinib (Rozlytrek), enzalutamide (Xtandi), erdafitinib (Balversa), erlotinib (Tarceva), everolimus (Afinitor), exemestane (Aromasin), fam-trastuzumab deruxtecan-nxki (Enhertu), fedratinib hydrochloride (Inrebic), fulvestrant (Faslodex), gefitinib (Iressa), gemtuzumab ozogamicin (Mylotarg), gilteritinib (Xospata), glasdegib maleate (Daurismo), hyaluronidase-zzxf (Phesgo), ibrutinib (Imbruvica), ibritumomab tiuxetan (Zevalin), idecabtagene vicleucel (Abecma), idelalisib (Zydelig), imatinib mesylate (Gleevec), infigratinib phosphate (Truseltiq), inotuzumab ozogamicin (Besponsa), iobenguane 1131 (Azedra), ipilimumab (Yervoy), isatuximab-irfc (Sarclisa), ivosidenib (Tibsovo), ixazomib citrate (Ninlaro), lanreotide acetate (Somatuline Depot), lapatinib (Tykerb), larotrectinib sulfate (Vitrakvi), lenvatinib mesylate (Lenvima), letrozole (Femara), lisocabtagene maraleucel (Breyanzi), loncastuximab tesirine-lpyl (Zynlonta), lorlatinib (Lorbrena), lutetium Lu 177-dotatate (Lutathera), margetuximab-cmkb (Margenza), midostaurin (Rydapt), mobocertinib succinate (Exkivity), mogamulizumab-kpkc (Poteligeo), moxetumomab pasudotox-tdfk (Lumoxiti), naxitamab-gqgk (Danyelza), necitumumab (Portrazza), neratinib maleate (Nerlynx), nilotinib (Tasigna), niraparib tosylate monohydrate (Zejula), nivolumab (Opdivo), obinutuzumab (Gazyva), ofatumumab (Arzerra), olaparib (Lynparza), olaratumab (Lartruvo), osimertinib (Tagrisso), palbociclib (Ibrance), panitumumab (Vectibix), panobinostat (Farydak), pazopanib (Votrient), pembrolizumab (Keytruda), pemigatinib (Pemazyre), pertuzumab (Perjeta), pexidartinib hydrochloride (Turalio), polatuzumab vedotin-piiq (Polivy), ponatinib hydrochloride (Iclusig), pralatrexate (Folotyn), pralsetinib (Gavreto), radium 223 dichloride (Xofigo), ramucirumab (Cyramza), regorafenib (Stivarga), ribociclib (Kisqali), ripretinib (Qinlock), rituximab (Rituxan), rituximab and hyaluronidase human (Rituxan Hycela), romidepsin (Istodax), rucaparib camsylate (Rubraca), ruxolitinib phosphate (Jakafi), sacituzumab govitecan-hziy (Trodelvy), seliciclib, selinexor (Xpovio), selpercatinib (Retevmo), selumetinib sulfate (Koselugo), siltuximab (Sylvant), sipuleucel-T (Provenge), sirolimus protein-bound particles (Fyarro), sonidegib (Odomzo), sorafenib (Nexavar), sotorasib (Lumakras), sunitinib (Sutent), tafasitamab-cxix (Monjuvi), tagraxofusp-erzs (Elzonris), talazoparib tosylate (Talzenna), tamoxifen (Nolvadex), tazemetostat hydrobromide (Tazverik), tebentafusp-tebn (Kimmtrak), temsirolimus (Torisel), tepotinib hydrochloride (Tepmetko), tisagenlecleucel (Kymriah), tisotumab vedotin-tftv (Tivdak), tocilizumab (Actemra), tofacitinib (Xeljanz), tositumomab (Bexxar), trametinib (Mekinist), trastuzumab (Herceptin), tretinoin (Vesanoid), tivozanib hydrochloride (Fotivda), toremifene (Fareston), tucatinib (Tukysa), umbralisib tosylate (Ukoniq), vandetanib (Caprelsa), vemurafenib (Zelboraf), venetoclax (Venclexta), vismodegib (Erivedge), vorinostat (Zolinza), zanubrutinib (Brukinsa), ziv-aflibercept (Zaltrap), or any combination thereof.
[0011] In some embodiments, the method further comprises obtaining the sample from the subject. In some embodiments, the sample comprises a tissue biopsy sample, a liquid biopsy sample, or a normal control. In some embodiments, the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva. In some embodiments, the sample is a liquid biopsy sample and comprises circulating tumor cells (CTCs). In some embodiments, the sample is a liquid biopsy sample and comprises cell-free DNA (cfDNA). In some embodiments, all or a portion of the cell-free DNA (cfDNA) comprises circulating tumor DNA (ctDNA).
[0012] In some embodiments, the plurality of nucleic acid molecules comprises a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules. In some embodiments, the tumor nucleic acid molecules are derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules are derived from a normal portion of the heterogeneous tissue biopsy sample. [0013] In some embodiments, the sample comprises a liquid biopsy sample, the tumor nucleic acid molecules are derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample, and the non-tumor nucleic acid molecules are derived from a non-tumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.
[0014] In some embodiments, the one or more adapters comprise amplification primers, flow cell adaptor sequences, substrate adapter sequences, or sample index sequences. In some embodiments, the captured nucleic acid molecules are captured from the amplified nucleic acid molecules by hybridization to one or more bait molecules. In some embodiments, the one or more bait molecules comprise one or more nucleic acid molecules, each comprising a region that is complementary to a region of a captured nucleic acid molecule. In some embodiments, amplifying nucleic acid molecules comprises performing a polymerase chain reaction (PCR) amplification technique, a non-PCR amplification technique, or an isothermal amplification technique. In some embodiments, the sequencing comprises use of a massively parallel sequencing (MPS) technique, whole genome sequencing (WGS), whole exome sequencing, targeted sequencing, direct sequencing, or Sanger sequencing technique. In some embodiments, the sequencing comprises massively parallel sequencing, and the massively parallel sequencing technique comprises next generation sequencing (NGS). In some embodiments, the sequencer comprises a next generation sequencer.
[0015] In some embodiments, one or more of the plurality of sequencing reads overlap one or more gene loci within one or more subgenomic intervals in the sample. In some embodiments, the one or more gene loci comprises between 10 and 20 loci, between 10 and 40 loci, between 10 and 60 loci, between 10 and 80 loci, between 10 and 100 loci, between 10 and 150 loci, between 10 and 200 loci, between 10 and 250 loci, between 10 and 300 loci, between 10 and 350 loci, between 10 and 400 loci, between 10 and 450 loci, between 10 and 500 loci, between 20 and 40 loci, between 20 and 60 loci, between 20 and 80 loci, between 20 and 100 loci, between 20 and 150 loci, between 20 and 200 loci, between 20 and 250 loci, between 20 and 300 loci, between 20 and 350 loci, between 20 and 400 loci, between 20 and 500 loci, between 40 and 60 loci, between 40 and 80 loci, between 40 and 100 loci, between 40 and 150 loci, between 40 and 200 loci, between 40 and 250 loci, between 40 and 300 loci, between 40 and 350 loci, between 40 and 400 loci, between 40 and 500 loci, between 60 and 80 loci, between 60 and 100 loci, between 60 and 150 loci, between 60 and 200 loci, between 60 and 250 loci, between 60 and 300 loci, between 60 and 350 loci, between 60 and 400 loci, between 60 and 500 loci, between 80 and 100 loci, between 80 and 150 loci, between 80 and 200 loci, between 80 and 250 loci, between 80 and 300 loci, between 80 and 350 loci, between 80 and 400 loci, between 80 and 500 loci, between 100 and 150 loci, between 100 and 200 loci, between 100 and 250 loci, between 100 and 300 loci, between 100 and 350 loci, between 100 and 400 loci, between 100 and 500 loci, between 150 and 200 loci, between 150 and 250 loci, between 150 and 300 loci, between 150 and 350 loci, between 150 and 400 loci, between 150 and 500 loci, between 200 and 250 loci, between 200 and 300 loci, between 200 and 350 loci, between 200 and 400 loci, between 200 and 500 loci, between 250 and 300 loci, between 250 and 350 loci, between 250 and 400 loci, between 250 and 500 loci, between 300 and 350 loci, between 300 and 400 loci, between 300 and 500 loci, between 350 and 400 loci, between 350 and 500 loci, or between 400 and 500 loci.
[0016] In some embodiments, the one or more gene loci comprise ABL1, ACVR1B, AKT1, AKT2, AKT3, ALK, ALOX12B, AMER1, APC, AR, ARAF, ARFRP1, ARID1A, ASXL1, ATM, ATR, ATRX, AURKA, AURKB, AXIN1, AXL, BAP1, BARD1, BCL2, BCL2L1, BCL2L2, BCL6, BCOR, BCORL1, BCR, BRAF, BRCA1, BRCA2, BRD4, BRIP1, BTG1, BTG2, BTK, CALR, CARD11, CASP8, CBFB, CBL, CCND1, CCND2, CCND3, CCNE1, CD22, CD274, CD70, CD74, CD79A, CD79B, CDC73, CDH1, CDK12, CDK4, CDK6, CDK8, CDKN1A, CDKN1B, CDKN2A, CDKN2B, CDKN2C, CEBPA, CHEK1, CHEK2, CIC, CREBBP, CRKL, CSF1R, CSF3R, CTCF, CTNNA1, CTNNB1, CUL3, CUL4A, CXCR4, CYP17A1, DAXX, DDR1, DDR2, DIS3, DNMT3A, DOT1L, EED, EGFR, EMSY (Cl lorf30), EP300, EPHA3, EPHB1, EPHB4, ERBB2, ERBB3, ERBB4, ERCC4, ERG, ERRFI1, ESRI, ETV4, ETV5, ETV6, EWSR1, EZH2, EZR, FAM46C, FANCA, FANCC, FANCG, FANCL, FAS, FBXW7, FGF10, FGF12, FGF14, FGF19, FGF23, FGF3, FGF4, FGF6, FGFR1, FGFR2, FGFR3, FGFR4, FH, FLCN, FLT1, FLT3, FOXL2, FUBP1, GABRA6, GATA3, GATA4, GATA6, GID4 (C17orf39), GNA11, GNA13, GNAQ, GNAS, GRM3, GSK3B, H3F3A, HDAC1, HGF, HNF1A, HRAS, HSD3B1, ID3, IDH1, IDH2, IGF1R, IKBKE, IKZF1, INPP4B, IRF2, IRF4, IRS2, JAK1, JAK2, JAK3, JUN, KDM5A, KDM5C, KDM6A, KDR, KEAP1, KEL, KIT, KLHL6, KMT2A (MLL), KMT2D (MLL2), KRAS, LTK, LYN, MAF, MAP2K1, MAP2K2, MAP2K4, MAP3K1, MAP3K13, MAPK1, MCL1, MDM2, MDM4, MED12, MEF2B, MEN1, MERTK, MET, MITF, MKNK1, MLH1, MPL, MRE11A, MSH2, MSH3, MSH6, MST1R, MTAP, MTOR, MUTYH, MYB, MYC, MYCL, MYCN, MYD88, NBN, NF1, NF2, NFE2L2, NFKBIA, NKX2-1, NOTCH1, NOTCH2, NOTCH3, NPM1, NRAS, NT5C2, NTRK1, NTRK2, NTRK3, NUTM1, P2RY8, PALB2, PARK2, PARP1, PARP2, PARP3, PAX5, PBRM1, PDCD1, PDCD1LG2, PDGFRA, PDGFRB, PDK1, PIK3C2B, PIK3C2G, PIK3CA, PIK3CB, PIK3R1, PIM1, PMS2, POLDI, POLE, PPARG, PPP2R1A, PPP2R2A, PRDM1, PRKAR1A, PRKCI, PTCHI, PTEN, PTPN11, PTPRO, QKI, RAC1, RAD21, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54L, RAFI, RARA, RBI, RBM10, REL, RET, RICTOR, RNF43, ROS1, RPTOR, RSPO2, SDC4, SDHA, SDHB, SDHC, SDHD, SETD2, SF3B1, SGK1, SLC34A2, SMAD2, SMAD4, SMARCA4, SMARCB1, SMO, SNCAIP, SOCS1, SOX2, SOX9, SPEN, SPOP, SRC, STAG2, STAT3, STK11, SUFU, SYK, TBX3, TEK, TERC, TERT, TET2, TGFBR2, TIPARP, TMPRSS2, TNFAIP3, TNFRSF14, TP53, TSC1, TSC2, TYRO3, U2AF1, VEGFA, VHL, WHSCI, WHSC1L1, WT1, XPO1, XRCC2, ZNF217, ZNF703, or any combination thereof.
[0017] In some embodiments, the one or more gene loci comprise ABL, ALK, ALL, B4GALNT1, BAFF, BCL2, BRAF, BRCA, BTK, CD19, CD20, CD3, CD30, CD319, CD38, CD52, CDK4, CDK6, CML, CRACC, CS1, CTLA-4, dMMR, EGFR, ERBB1, ERBB2, FGFR1- 3, FLT3, GD2, HDAC, HER1, HER2, HR, IDH2, IL-ip, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSI-H, mTOR, PARP, PD-1, PDGFR, PDGFRa, PDGFRp, PD-L1, PI3K5, PIGF, PTCH, RAF, RANKL, RET, ROS1, SLAMF7, VEGF, VEGFA, VEGFB, or any combination thereof.
[0018] In some embodiments, the method further comprises generating, by the one or more processors, a report indicating the ITH score determined for the sample. In some embodiments, the method further comprises transmitting the report to a healthcare provider. In some embodiments, the report is transmitted via a computer network or a peer-to-peer connection.
[0019] Disclosed herein are methods for determining an intra-tumor heterogeneity (ITH) score comprising: receiving, at one or more processors, sequence read data derived from a sample from a subject diagnosed with a cancer; processing, using the one or more processors, the sequence read data to identify one or more genomic features; providing, using the one or more processors, the one or more genomic features as input to a trained machine learning model configured to predict an intra-tumor heterogeneity (ITH) score for the sample based on the one or more genomic features, wherein the trained machine learning model is trained using a training data set comprising both genomic and digital pathology image data for samples from a cohort of individuals diagnosed with the cancer; and outputting, using the one or more processors, the predicted intra-tumor heterogeneity (ITH) score for the sample.
[0020] In some embodiments, the method further comprises comparing the predicted intra-tumor heterogeneity (ITH) score for the sample to at least one predetermined ITH score threshold to classify the sample. In some embodiments, the predicted intra-tumor heterogeneity (ITH) score for the sample is compared to a single predetermined ITH score threshold, and the sample is classified as ITH-High if the predicted ITH score is greater than or equal to the single predetermined ITH score threshold; or the sample is classified as ITH-Low if the predicted ITH score is less than the single predetermined ITH score threshold. In some embodiments, the predicted intra-tumor heterogeneity (ITH) score for the sample is compared to a first predetermined ITH score threshold and a second predetermined ITH score threshold, and the sample is classified as ITH-High if the predicted ITH score is greater than or equal to the first predetermined ITH score threshold; the sample is classified as ITH-Low if the predicted ITH score is less than or equal to the second predetermined ITH score threshold; or the sample is classified as ITH-indeterminate if the predicted ITH score is less than the first predetermined ITH score threshold and greater than the second ITH score threshold.
[0021] In some embodiments, a sample classification of ITH-High is indicative of a shorter duration of the subject’s therapeutic response to an anti-cancer therapy. In some embodiments, a sample classification of ITH-High is indicative of a shorter duration of the subject’s therapeutic response to an anti-cancer therapy comprising a targeted anti-cancer therapy selected, at least in part, based on knowledge of a driver mutation identified in the sample. In some embodiments, a sample classification of ITH-Low is indicative of a longer duration of the subject’s therapeutic response to an anti-cancer therapy. In some embodiments, a sample classification of ITH-Low is indicative of a longer duration of the subject’s therapeutic response to an anti-cancer therapy comprising a targeted anti-cancer therapy selected, at least in part, based on knowledge of a driver mutation identified in the sample. [0022] In some embodiments, the at least one predetermined ITH score threshold is determined based on a statistical analysis of the cohort of individuals diagnosed with the cancer and their associated survival time data. In some embodiments, the associated survival time data comprises mean overall survival data, median overall survival data, one-year survival data, hazard ratio data, progression free survival data, or any combination thereof.
[0023] In some embodiments, the at least one predetermined ITH score threshold is different for different anti-cancer therapies. In some embodiments, the at least one predetermined ITH score threshold is different for different cancers.
[0024] In some embodiments, the one of more genomic features comprise one or more short variant features, one or more copy number features, one or more phylogeny features, or any combination thereof.
[0025] In some embodiments, the one or more genomic features comprise one or more short variant features, and the one or more short variant features comprise a total number of short variants detected, a number of clonal variants detected, a number of sub-clonal variants detected, a cancer cell fraction (CCF)-derived tumor heterogeneity score, a presence or absence of a genome doubling event, a number of pre-genome doubling event clonal variants detected, a number of post-genome doubling event clonal variants detected, an estimate of elapsed time between initiation of the disease and a genome doubling event, a COSMIC insertion-deletion feature, a ratio of non- synonymous short variants detected to synonymous short variants detected in one or more protein coding genes, or any combination thereof.
[0026] In some embodiments, the one or more genomic features comprise one or more copy number features, and the one or more copy number features comprise a number of breakpoints per 10 Mb of genomic sequence, a number of breakpoints per 25 Mb of genomic sequence, a number of breakpoints per 50 Mb of genomic sequence, a number of breakpoints per 100 Mb of genomic sequence, a number of breakpoints per chromosome arm for each chromosome, a chromosome arm-level copy number change, a magnitude of a copy number change between any two adjacent copy number segments; a length of each copy number segment; the copy number of segments; a count of contiguous oscillating copy number chains, a fraction of genomic sequence with a copy number that is different from a sample ploidy, a fraction of genomic sequence with a copy number that is different from an integer value, or any combination thereof.
[0027] In some embodiments, the one or more genomic features comprise one or more phylogeny features, and the one or more phylogeny features comprise a total number of branching events, a total number of genomic clusters, a number of unique phylogenetic trees that correctly account for the sequence read data from the sample, or any combination thereof.
[0028] In some embodiments, the trained machine learning model comprises a trained regression-based machine learning model, regularization-based machine learning model, instance-based machine learning model, Bayesian -based machine learning model, clusteringbased machine learning model, ensemble-based machine learning model, neural network-based machine learning model, graph neural network model, generative adversarial network model, or deep learning-based machine learning model.
[0029] In some embodiments, the genomic data included in the training data set comprises data for the one or more genomic features identified by processing sequence read data for the cohort of individuals diagnosed with the cancer. In some embodiments, ground truth data labels used for training the machine learning model comprise intra-tumor heterogeneity scores derived from the digital pathology image data. In some embodiments, the trained machine learning model is trained using a training data set that further comprises single cell sequencing data, long-read sequencing data, spatial omics data, cyclic immunofluorescence data, stimulated Raman spectroscopy data, optical coherence tomography data, cytometry by time-of-flight (CyTOF) data, multiplexed ion beam imaging data, or any combination thereof.
[0030] In some embodiments, the sample comprises a tissue biopsy sample, or a liquid biopsy sample. In some embodiments, the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva. In some embodiments, the sample is a liquid biopsy sample and comprises circulating tumor cells (CTCs). In some embodiments, the sample is a liquid biopsy sample and comprises cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.
[0031] In some embodiments, the cancer comprises ovarian cancer, prostate cancer, pancreatic cancer, non-small cell lung cancer, colorectal cancer, or melanoma. [0032] In some embodiments, the predicted ITH score for the sample is a pan-cancer ITH score.
[0033] In some embodiments, the determination of ITH score is used to diagnose or confirm a diagnosis of cancer in the subject. In some embodiments, the method further comprises selecting an anti-cancer therapy to administer to the subject based on the determination of ITH score. In some embodiments, the method further comprises determining an effective amount of an anticancer therapy to administer to the subject based on the determination of ITH score. In some embodiments, the method further comprises administering an anti-cancer therapy to the subject based on the determination of ITH score. In some embodiments, the anti-cancer therapy comprises chemotherapy, radiation therapy, immunotherapy, a targeted therapy, or surgery.
[0034] Disclosed herein are methods for diagnosing a disease, the methods comprising: diagnosing that a subject has the disease based on a determination of ITH score for a sample from the subject, wherein ITH score is determined according to any of the methods described herein.
[0035] Disclosed herein are methods of selecting an anti-cancer therapy, the methods comprising: responsive to determining an ITH score for a sample from a subject, selecting an anti-cancer therapy for the subject, wherein the ITH score is determined according to any of the methods described herein.
[0036] Disclosed herein are methods of treating a cancer in a subject, comprising: responsive to determining an ITH score for a sample from the subject, administering an effective amount of an anti-cancer therapy to the subject, wherein the ITH score is determined according to any of the methods described herein.
[0037] Disclosed herein are methods for monitoring cancer progression or recurrence in a subject, the methods comprising: determining a first ITH score in a first sample obtained from the subject at a first time point according to any of the methods described herein; determining a second ITH score in a second sample obtained from the subject at a second time point; and comparing the first ITH score to the second ITH score, thereby monitoring the cancer progression or recurrence. In some embodiments, the second ITH score for the second sample is determined according to any of the methods described herein. In some embodiments, the method further comprises selecting an anti-cancer therapy for the subject in response to the cancer progression. In some embodiments, the method further comprises administering an anti-cancer therapy to the subject in response to the cancer progression. In some embodiments, the method further comprises adjusting an anti-cancer therapy for the subject in response to the cancer progression. In some embodiments, the method further comprises adjusting a dosage of the anticancer therapy or selecting a different anti-cancer therapy in response to the cancer progression. In some embodiments, the method further comprises administering the adjusted anti-cancer therapy to the subject. In some embodiments, the first time point is before the subject has been administered an anti-cancer therapy, and the second time point is after the subject has been administered the anti-cancer therapy. In some embodiments, the subject has a cancer, is at risk of having a cancer, is being routine tested for cancer, or is suspected of having a cancer. In some embodiments, the cancer is a solid tumor. In some embodiments, the cancer is a hematological cancer. In some embodiments, the anti-cancer therapy comprises chemotherapy, radiation therapy, immunotherapy, a targeted therapy, or surgery.
[0038] In any of the embodiments described herein, the method can further comprise determining, identifying, or applying the ITH score for the sample as a diagnostic value associated with the sample. In any of the embodiments described herein, the method can further comprise generating a genomic profile for the subject based on the determination of ITH score. In some embodiments, the genomic profile for the subject further comprises results from a comprehensive genomic profiling (CGP) test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof. In some embodiments, the genomic profile for the subject further comprises results from a nucleic acid sequencing-based test. In some embodiments, the method further comprises selecting an anti-cancer therapy, administering an anti-cancer therapy, or applying an anti-cancer therapy to the subject based on the generated genomic profile.
[0039] In some embodiments, the determination of an ITH score for the sample is used in making suggested treatment decisions for the subject. In some embodiments, the determination of an ITH score for the sample is used in applying or administering a treatment to the subject.
[0040] Also disclose herein are systems comprising: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: receive sequence read data derived from a sample from a subject diagnosed with a cancer; process the sequence read data to identify one or more genomic features; provide the one or more genomic features as input to a trained machine learning model configured to predict an intra-tumor heterogeneity (ITH) score for the sample based on the one or more genomic features, wherein the trained machine learning model is trained using a training data set comprising both genomic and digital pathology image data for samples from a cohort of individuals diagnosed with the cancer; and output the predicted intra-tumor heterogeneity (ITH) score for the sample.
[0041] Disclosed herein are non-transitory computer-readable storage media storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: receive sequence read data derived from a sample from a subject diagnosed with a cancer; process the sequence read data to identify one or more genomic features; provide the one or more genomic features as input to a trained machine learning model configured to predict an intra-tumor heterogeneity (ITH) score for the sample based on the one or more genomic features, wherein the trained machine learning model is trained using a training data set comprising both genomic and digital pathology image data for samples from a cohort of individuals diagnosed with the cancer; and output the predicted intra- tumor heterogeneity (ITH) score for the sample.
[0042] It should be appreciated that all combinations of the foregoing concepts and additional concepts discussed in greater detail below (provided such concepts are not mutually inconsistent) are contemplated as being part of the inventive subject matter disclosed herein. In particular, all combinations of claimed subject matter appearing at the end of this disclosure are contemplated as being part of the inventive subject matter disclosed herein.
INCORPORATION BY REFERENCE
[0043] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference in their entirety to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference in its entirety. In the event of a conflict between a term herein and a term in an incorporated reference, the term herein controls. BRIEF DESCRIPTION OF THE DRAWINGS
[0044] Various aspects of the disclosed methods, devices, and systems are set forth with particularity in the appended claims. A better understanding of the features and advantages of the disclosed methods, devices, and systems will be obtained by reference to the following detailed description of illustrative embodiments and the accompanying drawings, of which:
[0045] FIG. 1 provides a non-limiting example of a process flowchart for determining an intratumor heterogeneity (ITH) score, according to one implementation of the methods and systems disclosed herein.
[0046] FIG. 2 provides a schematic illustration of an exemplary machine learning architecture comprising an artificial neural network with one hidden layer.
[0047] FIG. 3 provides a schematic illustration of an exemplary node within a layer of an artificial neural network or deep learning model architecture.
[0048] FIG. 4 provides a non-limiting example of a plot of posterior probability as a function of cancer cell fraction (CCF) according to one method of estimating the clonality of short variant mutations described herein.
[0049] FIG. 5 depicts an exemplary computing device or system in accordance with one embodiment of the present disclosure.
[0050] FIG. 6 depicts an exemplary computer system or computer network, in accordance with some instances of the systems described herein.
[0051] FIG. 7 provides a non-limiting schematic illustration of a process for generating a data set to validate algorithms for determining short variant clonality and intra- tumor heterogeneity.
[0052] FIG. 8 provides a non-limiting example of a pathology image of a tumor tissue section that illustrates a precision punching process for sampling tumor tissue sections as part of generating a data set to validate algorithms for determining short variant clonality and intratumor heterogeneity. [0053] FIG. 9 provides a non-limiting example of a pathology image of the tissue section shown in FIG. 8 after performing precision punching to collect intra-tumor samples.
[0054] FIGS. 10A-10B provide non-limiting schematic illustrations of training and deploying a machine learning model to predict ITH scores. FIG. 10A: training a machine learning model to predict ITH scores based on a training data set comprising both genomic feature data and non- genomic data (e.g., digital pathology-derived ITH score data). FIG. 10B: deploying the trained machine learning model to predict ITH scores based on input genomic feature data.
DETAILED DESCRIPTION
[0055] Machine learning-based methods and systems for determining an ITH score for a sample from a subject (e.g., a tumor sample from a cancer patient) are described. In the disclosed methods the ITH score is inferred by a trained machine learning model based on input comprising a plurality of genomic features identified in genomic profiling / targeted sequencing data, including short variant features, copy number features, phylogeny features, or a combination thereof. The machine learning model is trained on training data comprising both genomic data (e.g., short variant feature data, copy number feature data, and/or phylogeny feature data) and non-genomic data (e.g., corresponding intra-tumor heterogeneity scores derived from digital pathology images) for a cohort of subjects (e.g., a cohort of cancer patients). The disclosed methods and systems are compatible with genomic profiling / targeted sequencing assay (e.g., targeted exome sequencing assay) pipelines used during routine clinical care, and can provide a more accurate determinations of ITH that serve as an improved biomarker for clinical decision making and prediction of clinical treatment outcomes. More accurate determinations of ITH can, for example, lead to more accurate predictions of the duration of a given cancer patient’s therapeutic response to treatment.
[0056] In some instances, for example, methods (e.g., computer-implemented methods) for determining intra-tumor heterogeneity (ITH) scores are described that comprise: receiving sequence read data derived from a sample from a subject diagnosed with a cancer; processing the sequence read data to identify one or more genomic features; providing the one or more genomic features as input to a trained machine learning model configured to predict an intra-tumor heterogeneity (ITH) score for the sample based on the one or more genomic features, where the trained machine learning model is trained using a training data set comprising both genomic and non-genomic data for samples from a cohort of individuals diagnosed with the cancer; and outputting the predicted intra-tumor heterogeneity (ITH) score for the sample.
[0057] In some instances, the method can further comprise comparing the predicted intra-tumor heterogeneity (ITH) score for the sample to at least one predetermined ITH score threshold to classify the sample. For example, in some instances, the predicted intra-tumor heterogeneity (ITH) score for the sample may be compared to a single predetermined ITH score threshold, and the sample is classified as ITH-High if the predicted ITH score is greater than or equal to the single predetermined ITH score threshold; or the sample is classified as ITH-Low if the predicted ITH score is less than the single predetermined ITH score threshold.
[0058] In some instances, a sample classification of ITH-High can be indicative of a shorter duration of the subject’s therapeutic response to an anti-cancer therapy. For example, a sample classification of ITH-High can be indicative of a shorter duration of the subject’s therapeutic response to an anti-cancer therapy comprising a targeted anti-cancer therapy selected, at least in part, based on knowledge of a driver mutation identified in the sample.
[0059] The method of claim 3, or claim 4, wherein a sample classification of ITH-Low is indicative of a longer duration of the subject’s therapeutic response to an anti-cancer therapy selected at least in part based on knowledge of a driver mutation identified in the sample.
Definitions
[0060] Unless otherwise defined, all of the technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art in the field to which this disclosure belongs.
[0061] As used in this specification and the appended claims, the singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise. Any reference to “or” herein is intended to encompass “and/or” unless otherwise stated.
[0062] ‘ ‘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.
[0063] As used herein, the terms "comprising" (and any form or variant of comprising, such as "comprise" and "comprises"), "having" (and any form or variant of having, such as "have" and "has"), "including" (and any form or variant of including, such as "includes" and "include"), or "containing" (and any form or variant of containing, such as "contains" and "contain"), are inclusive or open-ended and do not exclude additional, un-recited additives, components, integers, elements, or method steps.
[0064] As used herein, the terms “individual,” “patient,” or “subject” are used interchangeably and refer to any single animal, e.g., a mammal (including such non-human animals as, for example, dogs, cats, horses, rabbits, zoo animals, cows, pigs, sheep, and non-human primates) for which treatment is desired. In particular embodiments, the individual, patient, or subject herein is a human.
[0065] The terms “cancer” and “tumor” are used interchangeably herein. These terms refer to the presence of cells possessing characteristics typical of cancer-causing cells, such as uncontrolled proliferation, immortality, metastatic potential, rapid growth and proliferation rate, and certain characteristic morphological features. Cancer cells are often in the form of a tumor, but such cells can exist alone within an animal, or can be a non-tumorigenic cancer cell, such as a leukemia cell. These terms include a solid tumor, a soft tissue tumor, or a metastatic lesion. As used herein, the term “cancer” includes premalignant, as well as malignant cancers.
[0066] As used herein, “treatment” (and grammatical variations thereof such as “treat” or “treating”) refers to clinical intervention e.g., administration of an anti-cancer agent or anticancer therapy) in an attempt to alter the natural course of the individual being treated, and can be performed either for prophylaxis or during the course of clinical pathology. Desirable effects of treatment include, but are not limited to, preventing occurrence or recurrence of disease, alleviation of symptoms, diminishment of any direct or indirect pathological consequences of the disease, preventing metastasis, decreasing the rate of disease progression, amelioration or palliation of the disease state, and remission or improved prognosis. [0067] As used herein, the term “subgenomic interval” (or “subgenomic sequence interval”) refers to a portion of a genomic sequence.
[0068] As used herein, the term "subject interval" refers to a subgenomic interval or an expressed subgenomic interval (e.g., the transcribed sequence of a subgenomic interval).
[0069] As used herein, the terms “variant sequence” or “variant” are used interchangeably and refer to a modified nucleic acid sequence relative to a corresponding “normal” or “wild-type” sequence. In some instances, a variant sequence may be a “short variant sequence” (or “short variant”), i.e., a variant sequence of less than about 50 base pairs in length.
[0070] The terms “allele frequency” and “allele fraction” are used interchangeably herein and refer to the fraction of sequence reads corresponding to a particular allele relative to the total number of sequence reads for a genomic locus.
[0071] The terms “variant allele frequency” and “variant allele fraction” are used interchangeably herein and refer to the fraction of sequence reads corresponding to a particular variant allele relative to the total number of sequence reads for a genomic locus.
[0072] As used herein, the term “breakpoint” refers to a genomic region or locus where a sample genomic sequence may have a different copy number level than an adjacent segment. These may include sites of breakage where a chromosome breaks (and recombines).
[0073] As used herein, the term “copy number oscillation” refers to copy number patterns in the DNA, such as repeating copy number patterns, that may arise through various processes including, but not limited to, chromothripsis. The number of segments with oscillating copy number represents a traversal of the genome, or a portion thereof, while counting the number of repeated alternating segments between two copy numbers.
[0074] As used herein, the term “chromothripsis” refers to a mutational process in which a large number of clustered chromosomal rearrangements occur in a single event in localized and confined genomic regions in one or a few chromosomes. Chromothripsis is known to be involved in both cancer and congenital diseases. [0075] The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.
Methods for determining an intra-tumor heterogeneity (ITH) score
[0076] As noted above, cancer is a complex, dynamic disease. Various models - including linear, branched, neutral and punctuated evolution models - have been proposed to facilitate our understanding of how intra-tumor heterogeneity influences somatic tumor evolution. Norwell proposed the linear clonal expansion model (see, e.g., Nowell (1976), “The clonal evolution of tumor cell populations”, Science 194:23-28), where a clone refers to a group of cells that share a common ancestor and are genetically identical; every new mutation creates a new clone. Every time a cell divides, errors may occur during DNA replication, often in the absence of any internal or external influence. Some of these errors occur in oncogenes/tumor suppressor genes as a result of random chance, thereby providing a selective fitness advantage to some cells that may lead to uncontrolled cell division and tumor formation. Each sequential clonal expansion is initiated by the acquisition of one or many co-occurring mutations that confer a large fitness gain (natural selection leading to selective sweeps), thereby allowing the mutant clone to outcompete and outgrow the cells that lack these mutations e.g., the parent clone). The expansion of the mutant clone tends to homogenize the tumor up until the acquisition of a next set of mutations, which starts the cycle again. This is a well-known yet out-of-date model of somatic tumor evolution.
[0077] The publication by Gerlinger et al. (2012), “Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing”, N Engl J Med. 366(10):883-892, was influential in driving the shift in thinking about tumor evolution from linear to branched models. In branched evolution, multiple mutations are gained by different cells independently and in parallel, thereby leading to multiple independent clonal lineages that evolve in parallel. There is no need for a selective sweep as seen in linear evolution, as each of the mutant clones has increased fitness as compared to its parent clone. In the branched evolution paradigm, cooperation and/or competition between multiple subclones defines the clonal architecture of individual cancers. Branched evolution is common in tumors driven by short variants including, but not limited to, non-small cell lung cancer (NSCLC), colon cancer, and melanoma. In both linear and branched evolution, the number of genomic alterations present in the tumor gradually increases over time. [0078] An emerging model of somatic tumor evolution is that of punctured evolution / macroevolution. In this model (also termed the ‘big bang’ model of tumor evolution), tumors acquire many aberrations in a short intense burst of genomic change (due, for example, to changes in tumor micro-environment and the associated selection pressure on the tumor) at the very early stages of tumor development. Extensive ITH is generated at the earliest stages of tumor evolution as strong driver events, if generated, drive tumor progression. There is no further change in ITH until the next cycle of intense genomic mutational bursts. Punctured evolution is prevalent in tumors driven by large scale aneuploidy changes and chromosomal rearrangements including but not limited to, ovarian and prostate cancer. In punctuated evolution, a plot of the number of genomic alterations over time resembles the shape of stairs with unequal lengths and heights. There are periods of no increase in genomic alterations followed by short bursts of large gains.
[0079] The oncology field has usually estimated intra-tumor heterogeneity by quantifying a single genomic phenomenon, e.g., the clonality of short variant genomic alterations, as opposed to attempting to quantify a plurality of genomic phenomena as part of generating a composite metric. The approach disclosed herein is based on characterizing a genomics feature space derived from complete genomic profiling (CGP) / targeted sequencing date to estimate ITH- associated metrics for a plurality of genomic phenomena, including short variant-derived, copy number-derived and evolutionary phylogeny-derived features. These genomic features are then input into a machine learning-based, decision-making model which is trained and tested on training data that includes digital pathology (DP)-derived intra-tumor heterogeneity estimates, where the trained model is configured to output a prediction of the degree of intra-tumor heterogeneity for a given sample. The trained machine learning model is trained using a training data set comprising CGP and DP data derived from routine clinical care CGP and DP pipelines, as opposed to research use only whole-genome or whole-exome sequencing datasets.
[0080] FIG. 1 provides a non-limiting example of a flowchart for a process 100 for determining an intra-tumor heterogeneity (ITH) score. Process 100 can be performed, for example, using one or more electronic devices implementing a software platform. In some examples, process 100 is performed using a client-server system, and the blocks of process 100 are divided up in any manner between the server and a client device. In other examples, the blocks of process 100 are divided up between the server and multiple client devices. Thus, while portions of process 100 are described herein as being performed by particular devices of a client-server system, it will be appreciated that process 100 is not so limited. In other examples, process 100 is performed using only a client device or only multiple client devices. In process 100, some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the process 100. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.
[0081] At step 102 in FIG. 1, sequence read data for a sample from a subject diagnosed with a cancer is received (e.g., by one or more processors of a system configured to perform process 100).
[0082] The sequence read data may be derived from a plurality of sequence reads that each comprise a nucleic acid sequence describing the order of nucleotides in a DNA molecule or fragment thereof. In some instances, the sequence read data may be derived using, for example, a whole genome sequencing (WGS) method, a whole exome sequencing (WES) method, and/or a targeted sequencing method. In some instances, sequence read data may be stored as, e.g., a BAM file.
[0083] In some instances, the sample may comprise, for example, a tissue biopsy sample, or a liquid biopsy sample. In some instances, the sample is a liquid biopsy sample and may comprise blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva. In some instances, the sample is a liquid biopsy sample and may comprise circulating tumor cells (CTCs). In some instances, the sample is a liquid biopsy sample and may comprise cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.
[0084] In some instances, the cancer may be any of a variety of cancers known to those of skill in the art. Examples include, but are not limited to, B cell cancer e.g., multiple myeloma), melanomas, breast cancer, lung cancer (such as non-small cell lung carcinoma or NSCLC), bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain or central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine or endometrial cancer, cancer of the oral cavity or pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel or appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, cancer of hematological tissues, adenocarcinomas, inflammatory myofibroblastic tumors, gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), acute lymphocytic leukemia (ALL), acute myelocytic leukemia (AML), chronic myelocytic leukemia (CML), chronic lymphocytic leukemia (CLL), polycythemia Vera, Hodgkin lymphoma, nonHodgkin lymphoma (NHL), soft-tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endotheliosarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, bladder carcinoma, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, neuroblastoma, retinoblastoma, follicular lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, hepatocellular carcinoma, thyroid cancer, gastric cancer, head and neck cancer, small cell cancers, essential thrombocythemia, agnogenic myeloid metaplasia, hypereosinophilic syndrome, systemic mastocytosis, familiar hypereosinophilia, chronic eosinophilic leukemia, neuroendocrine cancers, carcinoid tumors, and the like.
[0085] In some instances, the cancer comprises acute lymphoblastic leukemia (Philadelphia chromosome positive), acute lymphoblastic leukemia (precursor B-cell), acute myeloid leukemia (FLT3+), acute myeloid leukemia (with an IDH2 mutation), anaplastic large cell lymphoma, basal cell carcinoma, B-cell chronic lymphocytic leukemia, bladder cancer, breast cancer (HER2 overexpressed/amplified), breast cancer (HER2+), breast cancer (HR+, HER2-), cervical cancer, cholangiocarcinoma, chronic lymphocytic leukemia, chronic lymphocytic leukemia (with 17p deletion), chronic myelogenous leukemia, chronic myelogenous leukemia (Philadelphia chromosome positive), classical Hodgkin lymphoma, colorectal cancer, colorectal cancer (dMMR and MSLH), colorectal cancer (KRAS wild type), cryopyrin-associated periodic syndrome, a cutaneous T-cell lymphoma, dermatofibrosarcoma protuberans, a diffuse large B- cell lymphoma, fallopian tube cancer, a follicular B-cell non-Hodgkin lymphoma, a follicular lymphoma, gastric cancer, gastric cancer (HER2+), a gastroesophageal junction (GEJ) adenocarcinoma, a gastrointestinal stromal tumor, a gastrointestinal stromal tumor (KIT+), a giant cell tumor of the bone, a glioblastoma, granulomatosis with polyangiitis, a head and neck squamous cell carcinoma, a hepatocellular carcinoma, Hodgkin lymphoma, juvenile idiopathic arthritis, lupus erythematosus, a mantle cell lymphoma, medullary thyroid cancer, melanoma, a melanoma with a BRAF V600 mutation, a melanoma with a BRAF V600E or V600K mutation, Merkel cell carcinoma, multicentric Castleman's disease, multiple hematologic malignancies including Philadelphia chromosome-positive ALL and CML, multiple myeloma, myelofibrosis, a non-Hodgkin’ s lymphoma, a nonresectable subependymal giant cell astrocytoma associated with tuberous sclerosis, a non-small cell lung cancer, a non-small cell lung cancer (ALK+), a non-small cell lung cancer (PD-L1+), a non-small cell lung cancer (with ALK fusion or ROS1 gene alteration), a non-small cell lung cancer (with BRAF V600E mutation), a non-small cell lung cancer (with an EGFR exon 19 deletion or exon 21 substitution (L858R) mutations), a non- small cell lung cancer (with an EGFR T790M mutation), ovarian cancer, ovarian cancer (with a BRCA mutation), pancreatic cancer, a pancreatic, gastrointestinal, or lung origin neuroendocrine tumor, a pediatric neuroblastoma, a peripheral T-cell lymphoma, peritoneal cancer, prostate cancer, a renal cell carcinoma, rheumatoid arthritis, a small lymphocytic lymphoma, a soft tissue sarcoma, a solid tumor (MSI-H/dMMR), a squamous cell cancer of the head and neck, a squamous non-small cell lung cancer, thyroid cancer, a thyroid carcinoma, urothelial cancer, a urothelial carcinoma, or Waldenstrom's macroglobulinemia.
[0086] In some instances, the cancer may comprise, for example, ovarian cancer, prostate cancer, pancreatic cancer, non-small cell lung cancer, colorectal cancer, or melanoma.
[0087] At step 104 in FIG. 1, the sequence read data is processed to identify one or more genomic features. In some instances, the one or more genomic features may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 18, 20, 40, 60, 80, 100, or more than 100 genomic features.
[0088] In some instances, the one of more genomic features can comprise, for example, one or more short variant features, one or more copy number features, one or more phylogeny features, or any combination thereof. In some instances, for example, identifying the one or more genomic features can comprise determining the clonality for each of a plurality of short variants, where the clonality for each short variant is treated as a different genomic feature. In some instances, the individual clonality estimates determined for all of the detected short variants can be used to calculate a single score (e.g., by summation, averaging, weighted averaging, etc.) and used as a genomic feature in conjunction with other described short variant, copy number, and/or phylogeny features.
[0089] In some instances, the one or more genomic features can comprise one or more short variant features (e.g., a variant sequences (e.g., base substitutions, insertions, deletions, etc.) of less than about 50 base pairs, 100 base pairs, 150 base pairs, 200 base pairs, 250 base pairs, or 300 base pairs in length), and the one or more short variant features can comprise, for example, a total number of short variants detected, a number of clonal variants (e.g., the number short variants that are present in all of the cancer cells in a sample) detected, a number of sub-clonal variants detected (e.g., the short variants that are present in a subset of the cancer cells in a sample), a cancer cell fraction (CCF)-derived tumor heterogeneity score, a presence or absence of a genome doubling event (e.g., a recurrent event in human cancers that promotes chromosomal instability and acquisition of aneuploidies; the timing of genomic doubling can be determined, for example, from the ratio of the number of clonal variants identified to the number of sub-clonal variants identified), a number of pre-genome doubling event clonal variants detected, a number of post-genome doubling event clonal variants detected, an estimate of elapsed time between initiation of the disease and a genome doubling event, a COSMIC insertion-deletion (indel) feature, a ratio of non- synonymous short variants detected to synonymous short variants detected in one or more protein coding genes, or any combination thereof. The determination of clonality and cancer cell fraction (CCF) is described in more detail below. In some instances, estimating the clonality of a variant can be decoupled from determining the timing of the mutation relative to a genome doubling event, and the two determinations can be performed independently. A mutation that has occurred prior to a genome doubling event will be present in multiple copies of the genome, and a mutation that has occurred after a genome doubling event will typically be present in only one copy of the genome.
[0090] In some instances, the one or more genomic features can comprise one or more copy number features, and the one or more copy number features can comprise, for example, a number of breakpoints per 10 Mb of genomic sequence, a number of breakpoints per 25 Mb of genomic 1 sequence, a number of breakpoints per 50 Mb of genomic sequence, a number of breakpoints per 100 Mb of genomic sequence, a number of breakpoints per chromosome arm for each chromosome, a chromosome arm-level copy number change, a magnitude of a copy number change between any two adjacent copy number segments; a length of each copy number segment; the copy number of segments; a count of contiguous oscillating copy number chains, a fraction of genomic sequence with a copy number that is different from a sample ploidy, a fraction of genomic sequence with a copy number that is different from an integer value, or any combination thereof.
[0091] In some instances, the one or more genomic features can comprise one or more phylogeny features, and the one or more phylogeny features can comprise, for example, a total number of branching events (e.g., the number of branch points in a phylogenetic tree generated based on the heterogeneous genetic make-up of a tumor sample), a total number of genomic clusters (e.g., the number of groups of cells (clones) that have the same genomic content), a number of unique phylogenetic trees that correctly account for the sequence read data from the sample (the larger the number of subclones present, the larger the uncertainty in the structure of the phylogenetic tree -especially near the “leaves”; the larger the uncertainty in the structure of the phylogenetic tree, the more likely it is that multiple phylogenetic trees can adequately describe the genomic data), or any combination thereof.
[0092] At step 106 in FIG. 1, the one or more genomic features are provided as input to a trained machine learning model configured to predict an intra-tumor heterogeneity (ITH) score for the sample based on the one or more genomic features, where the trained machine learning model is trained using a training data set comprising both genomic data (e.g., genomic feature data derived from sequencing) and non-genomic data (e.g., digital pathology -based data for estimated ITH scores) for samples from a cohort of individuals (e.g., patients) diagnosed with the cancer. In some instances, the trained machine learning model is trained using a training data set comprising both genomic data (e.g., genomic feature data derived from sequencing) and non- genomic data (e.g., digital pathology-based data for estimated ITH scores) for samples from a cohort of individuals diagnosed with a variety of different cancers.
[0093] Any of a variety of machine learning approaches & algorithms (where a machine learning model, as referred to herein, comprises a trained machine learning algorithm) may be used in implementing the disclosed methods. For example, the machine learning model may comprise a supervised learning model (z.e., a model trained using labeled sets of training data), an unsupervised learning model (z.e., a model trained using unlabeled sets of training data), a semisupervised learning model (z.e., a model trained using a combination of labeled and unlabeled training data), a deep learning model (z.e., a model inspired by the structure and function of the human brain comprising many layers of coupled "nodes" that may be trained in a supervised, unsupervised, or semi-supervised manner), or any combination thereof. In some instances, one or more machine learning models (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10 machine learning models) may be utilized to implement the disclosed methods.
[0094] In some instances, the trained machine learning model can comprise, for example, a trained regression-based machine learning model, regularization-based machine learning model, instance-based machine learning model, Bayesian -based machine learning model, clusteringbased machine learning model, ensemble-based machine learning model, neural network-based machine learning model, graph neural network model, generative adversarial network model, or deep learning-based machine learning model.
[0095] In some instances, the machine learning model/algorithm used for implementing the disclosed methods and systems may be an artificial neural network (ANN) or deep learning model/algorithm that comprises any type of neural network model known to those of skill in the art, such as a feedforward neural network, radial basis function network, recurrent neural network, or convolutional neural network, and the like. In some instances, the disclosed methods and systems may employ a pre-trained ANN or deep learning model. In some embodiment, the disclosed methods and systems may employ a continuous learning ANN or deep learning model, where the model is periodically or continuously updated based on new training data provided by, e.g., a single local operational system, a plurality of local operational systems, or a plurality of geographically-distributed operational systems.
[0096] Artificial neural networks generally comprise an interconnected group of nodes organized into multiple layers of nodes. For example, the ANN architecture may comprise at least an input layer, one or more hidden layers, and an output layer. FIG. 2 provides a non-limiting schematic illustration of an artificial neural network with one hidden layer. The ANN may comprise any total number of layers e.g., 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, or more than 20), and any number of hidden layers (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, or more than 20), where the hidden layers function as trainable feature extractors that allow mapping of a set of input data to a preferred output value or set of output values. Each layer of the neural network comprises a number of nodes (e.g., at least 10, 25, 50, 75 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10,000, or more than 10,000 nodes). A node receives input data (e.g., genomic feature data, non-genomic feature data, or other types of input data) that comes either directly from one or more input data nodes or from the output of one or more nodes in previous layers, and performs a specific operation, e.g., a summation operation. In some cases, a connection from an input to a node is associated with a weight (or weighting factor). In some cases, the node may, for example, sum up the products of all pairs of inputs, Xi, and their associated weights, Wi, as illustrated in FIG. 3. In some cases, the weighted sum is offset with a bias, b, as illustrated in FIG. 3. In some cases, the output of a neuron may be gated using a threshold or activation function,/, as illustrated in FIG. 3, where /may be a linear or non-linear function. The activation function may be, for example, a rectified linear unit (ReLU) activation function or other function such as a saturating hyperbolic tangent, identity, binary step, logistic, arcTan, softsign, parameteric rectified linear unit, exponential linear unit, softPlus, bent identity, softExponential, Sinusoid, Sine, Gaussian, or sigmoid function, or any combination thereof.
[0097] The weighting factors, bias values, and threshold values, or other computational parameters of the neural network, can be "taught" or "learned" in a training phase using one or more sets of training data (e.g., 1, 2, 3, 4, 5, or more than 5 sets of training data). For example, the parameters may be trained using the input data from a training data set and a gradient descent or backward propagation method so that the output value(s) (e.g., an ITH score for a sample) that the ANN computes are consistent with the examples included in the training data set. The adjustable parameters of the model may be obtained from a back propagation neural network training process that may or may not be performed using the same hardware as that used for processing genomic feature data and predicting ITH scores.
[0098] In some instances, the genomic data included in the training data set comprises data for the one or more genomic features identified by processing sequence read data for a cohort of individuals diagnosed with the cancer (e.g., where the cancer is the same as that with which the subject has been diagnosed). In some instances, the genomic data included in the training data set comprises data for the one or more genomic features identified by processing sequence read data for a cohort of individuals diagnosed with a variety of different cancers, as described elsewhere herein.
[0099] In some instances, the non-genomic data included in the training data set comprises digital pathology image data for the cohort of individuals diagnosed with the same cancer or a variety of different cancers. In some instances, for example, the ground truth data labels used for training the machine learning model comprise intra-tumor heterogeneity scores derived from the digital pathology image data.
[0100] In some instances, the trained machine learning model is trained using a training data set that further comprises single cell sequencing data, long-read sequencing data, spatial omics data, cyclic immunofluorescence data, stimulated Raman spectroscopy data, optical coherence tomography data, cytometry by time-of-flight (CyTOF) data, multiplexed ion beam imaging data, or any combination thereof, for the cohort of individuals diagnosed with the same cancer or a variety of different cancers.
[0101] At step 108 in FIG. 1, the predicted intra-tumor heterogeneity (ITH) score for the sample is output (e.g., by one or more processors of a system configured to perform process 100).
[0102] In some instances, the method can further comprise comparing the predicted intra-tumor heterogeneity (ITH) score for the sample to at least one predetermined ITH score threshold (e.g., at least 1, 2, 3, 4, or 5 predetermined ITH score thresholds) to classify the sample.
[0103] In some instances, for example, the predicted intra-tumor heterogeneity (ITH) score for the sample may be compared to a single predetermined ITH score threshold, and the sample can be classified as ITH-High if the predicted ITH score is greater than or equal to the single predetermined ITH score threshold; or the sample can be classified as ITH-Low if the predicted ITH score is less than the single predetermined ITH score threshold.
[0104] In some instances, the predicted intra-tumor heterogeneity (ITH) score for the sample may be compared to a first predetermined ITH score threshold and a second predetermined ITH score threshold, and the sample can be classified as ITH-High if the predicted ITH score is greater than or equal to the first predetermined ITH score threshold; the sample can be classified as ITH-Low if the predicted ITH score is less than or equal to the second predetermined ITH score threshold; or the sample can be classified as UH-indeterminate if the predicted ITH score is less than the first predetermined ITH score threshold and greater than the second ITH score threshold.
[0105] In some instances, a sample classification of ITH-High can be indicative of a shorter duration of the subject’s therapeutic response to an anti-cancer therapy. For example, a sample classification of ITH-High can be indicative of a shorter duration of the subject’s therapeutic response to an anti-cancer therapy comprising a targeted anti-cancer therapy selected, at least in part, based on knowledge of a driver mutation identified in the sample.
[0106] In some instances, a sample classification of ITH-Low can be indicative of a longer duration of the subject’s therapeutic response to an anti-cancer therapy. For example, a sample classification of ITH-Low can be indicative of a longer duration of the subject’s therapeutic response to an anti-cancer therapy comprising a targeted anti-cancer therapy selected, at least in part, based on knowledge of a driver mutation identified in the sample.
[0107] In some instances, the at least one predetermined ITH score threshold can be determined based on a statistical analysis of the cohort of individuals diagnosed with the cancer and their associated survival time data (or other clinical outcome measures, e.g., time to next treatment, time to treatment discontinuation, objective response rate, disease control rate, etc.). In some instances, the at least one predetermined ITH score threshold can be determined based on a statistical analysis of the cohort of individuals diagnosed with a variety of different cancers and their associated survival time data (or other clinical outcome data). In some instances, the associated survival time data can comprise, for example, mean overall survival data, median overall survival data, one-year survival data, hazard ratio data, progression free survival data, or any combination thereof. In some instances, the at least one predetermined ITH score threshold may be determined based on, for example, an analysis of receiver operator characteristic (ROC) curves (or additional model performance/evaluation metrics, such as accuracy, precision, recall, Fl score, Matthews correlation coefficient, etc., that can be derived from a 2x2 confusion matrix) or an analysis of hazard ratios. [0108] In some instances, the at least one predetermined ITH score threshold may be different for different anti-cancer therapies. In some instances, the at least one predetermined ITH score threshold may be different for different cancers.
[0109] In some instances, the predicted ITH score for the sample can be a pan-cancer ITH score.
Determination of clonality, sub-clonality, and cancer cell fraction (CCF)
[0110] The clonality of short variants e.g., clonal, sub-clonal, or ambiguous) can be estimated based on, e.g., confidence intervals for cancer cell fraction (CCF) - the fraction of cancer cells in a sample that contain a given short variant.
[0111] In some instances, the clonality estimate for each short variant comprises a cancer cell fraction (CCF) value calculated according to the relationship (CCF vl.0): i(p)Vr + 2(l - p)),
(see Tarabichi, el al. (2021), “A Practical Guide to Cancer Subclonal Reconstruction from DNA Sequencing”, Nature Methods 18:144-155) where/is indicative of an allele frequency of the variant sequence, m is indicative of a number of mutant copies of a gene, p is indicative of tumor purity, and NT is indicative of total copies of the gene. In some instances, copy number and tumor purity are derived from a copy number model such as that described in PCT International Patent Application Publication No. WO 2023/060236, which is incorporated herein by reference in its entirety.
[0112] In some instances, the CCF value may be calculated based on a probabilistic model of allele frequency. For example, the observed allele frequency (AF) is given by a/N, where a is the number of altered sequence reads detected and N is the sequencing coverage at the short variant locus. The expected allele frequency, expressed as a function of CCF, may be given by (CCF vl.l): (see McGranahan, et al. (2015), “Clonal Status of Actionable Driver Events and the Timing of Mutational Processes in Cancer Evolution”, Science Translational Medicine 7(283):283ra54; Tarabichi, et al. (2021), ibid.).
[0113] By setting the relationships for observed AF and expected AF equal to each other and solving for CCF, one may obtain a probabilistic relationship for CCF given by:
P(CCF) = btnomtal( i\N,AF(CCF )
[0114] Table 1 provides a non-limiting example of calculated values of CCF probability and posterior probability for different CCF values calculated for the TP53 C238S variant (with p = 0.85, N = 1049, AF = 0.1459). The expected AF value as determined from the distribution is CCF = 0.347 (95% confidence interval = 0.345 - 0.349).
Table 1. Calculated values of CCF probability and posterior probability.
[0115] FIG. 4 provides a non-limiting example of posterior probability plotted as a function of cancer cell fraction (CCF) based on the parameters used to generate the data summarized in Table 1. The CCF value is determined based on the maximum value of the distribution of posterior probability plotted versus CCF.
[0116] The clonality of short variants (e.g., clonal, subclonal, or ambiguous clonality) can be estimated leveraging the confidence interval (CI) for the CCF determination (e.g., the 95% CI of CCF as calculated using the CCF vl.l relationship). A short variant can be classified as clonal if the lower confidence interval of its CCF value is greater than, e.g., 0.5, subclonal if the upper confidence interval of its CCF value is less than, e.g., 0.5, of classified as ambiguous if the 95% CI overlaps 0.5.
[0117] In some instances, the clonality threshold may comprise a CCF value greater than 0.50, 0.75, 0.80, 0.85, 0.90, or 0.95 (i.e., 50%, 75%, 80%, 85%, 90%, or 95%). In some instances, the clonality threshold may be set to 1.0 (i.e., 100%). In some instances, the clonality threshold may be set such that the probability that the short variant has a CCF greater than or equal to 0.5, 0.75, 0.80, 0.85, 0.90, or 0.95 is greater than or equal to, e.g., 0.75, 0.80, 0.85, 0.90, or 0.95.
[0118] In some instances, the disclosed methods may comprise determining the clonality of short variants in at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, 160, 180, 200, 220, 240, 260, 280, 300, 320, 340, 360, 380, 400, 420, 440, 460, 480, 500, or more than gene loci.
[0119] In some instances, the disclosed methods may comprise determining the clonality of short 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, CDKN2C, CEBPA, CHEK1, CHEK2, CIC, CREBBP, CRKL, CSF1R, CSF3R, CTCF, CTNNA1, CTNNB1, CUL3, CUL4A, CXCR4, CYP17A1, DAXX, DDR1, DDR2, DIS3, DNMT3A, DOT1L, EED, EGFR, EMSY (Cl lorf30), EP300, EPHA3, EPHB1, EPHB4, ERBB2, ERBB3, ERBB4, ERCC4, ERG, ERRFI1, ESRI, ETV4, ETV5, ETV6, EWSR1, EZH2, EZR, FAM46C, FANCA, FANCC, FANCG, FANCL, FAS, FBXW7, FGF10, FGF12, FGF14, FGF19, FGF23, FGF3, FGF4, FGF6, FGFR1, FGFR2, FGFR3, FGFR4, FH, FLCN, FLT1, FLT3, FOXL2, FUBP1, GABRA6, GATA3, GATA4, GATA6, GID4 (C17orf39), GNA11, GNA13, GNAQ, GNAS, GRM3, GSK3B, H3F3A, HDAC1, HGF, HNF1A, HRAS, HSD3B1, ID3, IDH1, IDH2, IGF1R, IKBKE, IKZF1, INPP4B, IRF2, IRF4, IRS2, JAK1, JAK2, JAK3, JUN, KDM5A, KDM5C, KDM6A, KDR, KEAP1, KEL, KIT, KLHL6, KMT2A (MLL), KMT2D (MLL2), KRAS, LTK, LYN, MAF, MAP2K1, MAP2K2, MAP2K4, MAP3K1, MAP3K13, MAPK1, MCL1, MDM2, MDM4, MED12, MEF2B, MEN1, MERTK, MET, MITF, MKNK1, MLH1, MPL, MRE11A, MSH2, MSH3, MSH6, MST1R, MTAP, MTOR, MUTYH, MYB, MYC, MYCL, MYCN, MYD88, NBN, NF1, NF2, NFE2L2, NFKBIA, NKX2-1, NOTCH1, NOTCH2, NOTCH3, NPM1, NRAS, NT5C2, NTRK1, NTRK2, NTRK3, NUTM1, P2RY8, PALB2, PARK2, PARP1, PARP2, PARP3, PAX5, PBRM1, PDCD1, PDCD1LG2, PDGFRA, PDGFRB, PDK1, PIK3C2B, PIK3C2G, PIK3CA, PIK3CB, PIK3R1, PIM1, PMS2, POLDI, POLE, PPARG, PPP2R1A, PPP2R2A, PRDM1, PRKAR1A, PRKCI, PTCHI, PTEN, PTPN11, PTPRO, QKI, RAC1, RAD21, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54L, RAFI, RARA, RBI, RBM10, REL, RET, RICTOR, RNF43, ROS1, RPTOR, RSPO2, SDC4, SDHA, SDHB, SDHC, SDHD, SETD2, SF3B1, SGK1, SLC34A2, SMAD2, SMAD4, SMARCA4, SMARCB1, SMO, SNCAIP, SOCS1, SOX2, SOX9, SPEN, SPOP, SRC, STAG2, STAT3, STK11, SUFU, SYK, TBX3, TEK, TERC, TERT, TET2, TGFBR2, TIPARP, TMPRSS2, TNFAIP3, TNFRSF14, TP53, TSC1, TSC2, TYRO3, U2AF1, VEGFA, VHL, WHSCI, WHSC1L1, WT1, XPO1, XRCC2, ZNF217, or ZNF703 gene locus, or any combination thereof.
[0120] In some instances, the disclosed methods may comprise determining the clonalit of short variants identified 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, HD AC, 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, or any combination thereof.
Methods of use
[0121] In some instances, the disclosed methods may further comprise one or more of the steps of: (i) obtaining the sample from the subject (e.g., a subject suspected of having or determined to have cancer), (ii) extracting nucleic acid molecules (e.g., a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules) from the sample, (iii) ligating one or more adapters to the nucleic acid molecules extracted from the sample (e.g., one or more amplification primers, flow cell adaptor sequences, substrate adapter sequences, or sample index sequences), (iv) performing a methylation conversion reaction to convert, e.g., non-methylated cytosine to uracil, (v) amplifying the nucleic acid molecules (e.g., using a polymerase chain reaction (PCR) amplification technique, a non-PCR amplification technique, or an isothermal amplification technique), (vi) capturing nucleic acid molecules from the amplified nucleic acid molecules (e.g., by hybridization to one or more bait molecules, where the bait molecules each comprise one or more nucleic acid molecules that each comprising a region that is complementary to a region of a captured nucleic acid molecule), (vii) sequencing the nucleic acid molecules extracted from the sample (or library proxies derived therefrom) using, e.g., a next-generation (massively parallel) sequencing technique, a whole genome sequencing (WGS) technique, a whole exome sequencing technique, a targeted sequencing technique, a direct sequencing technique, or a Sanger sequencing technique) using, e.g., a next-generation (massively parallel) sequencer, (viii) combining the nucleic acid sequence data (including, e.g., variant data, copy number data, methylation status data, etc., of the sequenced nucleic acid molecules) with other biomarker data modalities including, but not limited to, proteomics-based biomarker data (e.g., the detection of specific polypeptides, such as proteins) or fragmentomics-based biomarker data (e.g., the detection of certain attributes related to nucleic acid fragments, such as fragment size or the sequences of fragment ends), to determine, for example, the presence of ctDNA in the sample and/or to determine a diagnostic, prognostic, and/or treatment response prediction for the subject, and (ix) generating, displaying, transmitting, and/or delivering a report (e.g., an electronic, webbased, or paper report) to the subject (or patient), a caregiver, a healthcare provider, a physician, an oncologist, an electronic medical record system, a hospital, a clinic, a third-party payer, an insurance company, or a government office. In some instances, the report comprises output from the methods described herein. In some instances, all or a portion of the report may be displayed in the graphical user interface of an online or web-based healthcare portal. In some instances, the report is transmitted via a computer network or peer-to-peer connection.
[0122] The disclosed methods may be used with any of a variety of samples. For example, in some instances, the sample may comprise a tissue biopsy sample, a liquid biopsy sample, or a normal control. In some instances, the sample may be a liquid biopsy sample and may comprise blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva. In some instances, the sample may be a liquid biopsy sample and may comprise circulating tumor cells (CTCs). In some instances, the sample may be a liquid biopsy sample and may comprise cell-free DNA (cfDNA). In some instances, the cell-free DNA (cfDNA) or a portion thereof may comprise circulating tumor DNA (ctDNA). In some instances, the liquid biopsy sample may comprise a combination of cell-free DNA (cfDNA) and circulating tumor DNA (ctDNA).
[0123] In some instances, the nucleic acid molecules extracted from a sample may comprise a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules. In some instances, the tumor nucleic acid molecules may be derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules may be derived from a normal portion of the heterogeneous tissue biopsy sample. In some instances, the sample may comprise a liquid biopsy sample, and the tumor nucleic acid molecules may be derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample while the non-tumor nucleic acid molecules may be derived from a non-tumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.
[0124] In some instances, the disclosed methods for determining ITH scores may be used to diagnose (or as part of a diagnosis of) the presence of disease or other condition (e.g., cancer, genetic disorders (such as Down Syndrome and Fragile X), neurological disorders, or any other disease type where detection of variants, e.g., copy number alternations, are relevant to diagnosing, treating, or predicting said disease) in a subject (e.g., a patient). In some instances, the disclosed methods may be applicable to diagnosis of any of a variety of cancers as described elsewhere herein.
[0125] In some instances, the disclosed methods for determining ITH scores may be used to select a subject (e.g., a patient) for a clinical trial based on the ITH score determined for a sample from the subject. In some instances, patient selection for clinical trials based on, e.g., ITH score, may accelerate the development of targeted therapies and improve the healthcare outcomes for treatment decisions.
[0126] In some instances, the disclosed methods for determining ITH scores may be used to select an appropriate therapy or treatment (e.g., an anti-cancer therapy or anti-cancer treatment) for a subject. In some instances, for example, the anti-cancer therapy or treatment may comprise use of a poly (ADP-ribose) polymerase inhibitor (PARPi), a platinum compound, chemotherapy, radiation therapy, a targeted therapy, an immunotherapy, surgery, or any combination thereof. [0127] [0103] In some instances, the anti-cancer therapy or treatment may comprise a targeted anti-cancer therapy or treatment (e.g., a monoclonal antibody-based therapy, an enzyme inhibitor-based therapy, an antibody-drug conjugate therapy, a hormone therapy, and/or a targeted radiotherapy) that targets specific molecules required for cancer cell growth, division, and spreading. In some instances, the targeted anti-cancer target therapy or treatment may comprise abemaciclib (Verzenio), abiraterone acetate (Zytiga), acalabrutinib (Calquence), ado- trastuzumab emtansine (Kadcyla), afatinib dimaleate (Gilotrif), alectinib (Alecensa), alemtuzumab (Campath), alitretinoin (Panretin), alpelisib (Piqray), amivantamab-vmjw (Rybrevant), anastrozole (Arimidex), apalutamide (Erleada), asciminib hydrochloride (Scemblix), atezolizumab (Tecentriq), avapritinib (Ayvakit), avelumab (Bavencio), axicabtagene ciloleucel (Yescarta), axitinib (Inlyta), belantamab mafodotin-blmf (Blenrep), belimumab (Benlysta), belinostat (Beleodaq), belzutifan (Welireg), bevacizumab (Avastin), bexarotene (Targretin), binimetinib (Mektovi), blinatumomab (Blincyto), bortezomib (Velcade), bosutinib (Bosulif), brentuximab vedotin (Adcetris), brexucabtagene autoleucel (Tecartus), brigatinib (Alunbrig), cabazitaxel (Jevtana), cabozantinib (Cabometyx), cabozantinib (Cabometyx, Cometriq), canakinumab (Haris), capmatinib hydrochloride (Tabrecta), carfilzomib (Kyprolis), cemiplimab-rwlc (Libtayo), ceritinib (LDK378/Zykadia), cetuximab (Erbitux), cobimetinib (Cotellic), crizotinib (Xalkori), dabrafenib (Tafinlar), dacomitinib (Vizimpro), daratumumab (Darzalex), daratumumab and hyaluronidase-fihj (Darzalex Faspro), darolutamide (Nubeqa), dasatinib (Sprycel), denileukin diftitox (Ontak), denosumab (Xgeva), dinutuximab (Unituxin), dostarlimab-gxly (Jemperli), durvalumab (Imfinzi), duvelisib (Copiktra), elotuzumab (Empliciti), enasidenib mesylate (Idhifa), encorafenib (Braftovi), enfortumab vedotin-ejfv (Padcev), entrectinib (Rozlytrek), enzalutamide (Xtandi), erdafitinib (Balversa), erlotinib (Tarceva), everolimus (Afinitor), exemestane (Aromasin), fam-trastuzumab deruxtecan-nxki (Enhertu), fedratinib hydrochloride (Inrebic), fulvestrant (Faslodex), gefitinib (Iressa), gemtuzumab ozogamicin (Mylotarg), gilteritinib (Xospata), glasdegib maleate (Daurismo), hyaluronidase-zzxf (Phesgo), ibrutinib (Imbruvica), ibritumomab tiuxetan (Zevalin), idecabtagene vicleucel (Abecma), idelalisib (Zydelig), imatinib mesylate (Gleevec), infigratinib phosphate (Truseltiq), inotuzumab ozogamicin (Besponsa), ipilimumab (Yervoy), isatuximab-irfc (Sarclisa), ivosidenib (Tibsovo), ixazomib citrate (Ninlaro), lanreotide acetate (Somatuline Depot), lapatinib (Tykerb), larotrectinib sulfate (Vitrakvi), lenvatinib mesylate (Lenvima), letrozole (Femara), lisocabtagene maraleucel (Breyanzi), loncastuximab tesirine-lpyl (Zynlonta), lorlatinib (Lorbrena), lutetium Lu 177-dotatate (Lutathera), margetuximab-cmkb (Margenza), midostaurin (Rydapt), mobocertinib succinate (Exkivity), mogamulizumab-kpkc (Poteligeo), moxetumomab pasudotox-tdfk (Lumoxiti), naxitamab-gqgk (Danyelza), necitumumab (Portrazza), neratinib maleate (Nerlynx), nilotinib (Tasigna), niraparib tosylate monohydrate (Zejula), nivolumab (Opdivo), obinutuzumab (Gazyva), ofatumumab (Arzerra), olaparib (Lynparza), olaratumab (Lartruvo), osimertinib (Tagrisso), palbociclib (Ibrance), panitumumab (Vectibix), pazopanib (Votrient), pembrolizumab (Keytruda), pemigatinib (Pemazyre), pertuzumab (Perjeta), pexidartinib hydrochloride (Turalio), polatuzumab vedotin-piiq (Polivy), ponatinib hydrochloride (Iclusig), pralatrexate (Folotyn), pralsetinib (Gavreto), radium 223 dichloride (Xofigo), ramucirumab (Cyramza), regorafenib (Stivarga), ribociclib (Kisqali), ripretinib (Qinlock), rituximab (Rituxan), rituximab and hyaluronidase human (Rituxan Hycela), romidepsin (Istodax), rucaparib camsylate (Rubraca), ruxolitinib phosphate (Jakafi), sacituzumab govitecan-hziy (Trodelvy), seliciclib, selinexor (Xpovio), selpercatinib (Retevmo), selumetinib sulfate (Koselugo), siltuximab (Sylvant), sirolimus protein-bound particles (Fyarro), sonidegib (Odomzo), sorafenib (Nexavar), sotorasib (Lumakras), sunitinib (Sutent), tafasitamab-cxix (Monjuvi), tagraxofusp-erzs (Elzonris), talazoparib tosylate (Talzenna), tamoxifen (Nolvadex), tazemetostat hydrobromide (Tazverik), tebentafusp-tebn (Kimmtrak), temsirolimus (Torisel), tepotinib hydrochloride (Tepmetko), tisagenlecleucel (Kymriah), tisotumab vedotin-tftv (Tivdak), tocilizumab (Actemra), tofacitinib (Xeljanz), tositumomab (Bexxar), trametinib (Mekinist), trastuzumab (Herceptin), tretinoin (Vesanoid), tivozanib hydrochloride (Fotivda), toremifene (Fareston), tucatinib (Tukysa), umbralisib tosylate (Ukoniq), vandetanib (Caprelsa), vemurafenib (Zelboraf), venetoclax (Venclexta), vismodegib (Erivedge), vorinostat (Zolinza), zanubrutinib (Brukinsa), ziv- aflibercept (Zaltrap), or any combination thereof.
[0128] In some instances, the anti-cancer therapy or treatment may comprise an immunotherapy (e.g., a cancer treatment that acts by stimulating the immune system to fight cancer). In some instances, the immunotherapy can be, for example, an immune system modulator (e.g., a cytokine, such as an interferon or interleukin), an immune checkpoint inhibitor (such as an anti- PD-1 or anti-PD-Ll antibody), a T-cell transfer therapy (e.g., a tumor infiltrating lymphocyte (TIL) therapy in lymphocytes extracted from a patient’ s tumor are selected for their ability to recognize tumor cells and propagated prior to reintroduction into the patient, or a CAR T-cell therapy in which a patient’s T-cells are modified to express the CAR protein prior to reintroduction into the patient), a monoclonal antibody -based therapy (e.g., a monoclonal antibody that binds to cell surface markers on cancer cells to facilitate recognition by the immune system), or a cancer treatment vaccine (e.g., a vaccine based on tumor cells, tumor- associated neoantigens, or dendritic cells, etc., that stimulates the immune system to fight cancer).
[0129] In some instances, the disclosed methods for determining ITH scores may be used in treating a disease (e.g., a cancer) in a subject. For example, in response to determining an ITH score using any of the methods disclosed herein, an effective amount of an anti-cancer therapy or anti-cancer treatment may be administered to the subject.
[0130] In some instances, the disclosed methods for determining ITH scores may be used for monitoring disease progression or recurrence (e.g., cancer or tumor progression or recurrence) in a subject. For example, in some instances, the methods may be used to determine an ITH score for a first sample obtained from the subject at a first time point, and used to determine an ITH score for a second sample obtained from the subject at a second time point, where comparison of the first determination of ITH score and the second determination of ITH score allows one to monitor disease progression or recurrence. In some instances, the first time point is chosen before the subject has been administered a therapy or treatment, and the second time point is chosen after the subject has been administered the therapy or treatment.
[0131] In some instances, the disclosed methods may be used for adjusting a therapy or treatment (e.g., an anti-cancer treatment or anti-cancer therapy) for a subject, e.g., by adjusting a treatment dose and/or selecting a different treatment in response to a change in the determined ITH score for a sample from the subject.
[0132] In some instances, the value of the ITH score determined using the disclosed methods may be used as a prognostic or diagnostic indicator associated with the sample. For example, in some instances, the prognostic or diagnostic indicator may comprise an indicator of the presence of a disease (e.g., cancer) in the sample, an indicator of the probability that a disease (e.g., cancer) is present in the sample, an indicator of the probability that the subject from which the sample was derived will develop a disease (e.g., cancer) (z.e., a risk factor), or an indicator of the likelihood that the subject from which the sample was derived will respond to a particular therapy or treatment.
[0133] In some instances, the disclosed methods for determining ITH scores may be implemented as part of a genomic profiling process that comprises identification of the presence of variant sequences at one or more gene loci in a sample derived from a subject as part of detecting, monitoring, predicting a risk factor, or selecting a treatment for a particular disease, e.g., cancer. In some instances, the variant panel selected for genomic profiling may comprise the detection of variant sequences at a selected set of gene loci. In some instances, the variant panel selected for genomic profiling may comprise detection of variant sequences at a number of gene loci through comprehensive genomic profiling (CGP), which is a next- generation sequencing (NGS) approach used to assess hundreds of genes (including relevant cancer biomarkers) in a single assay. Inclusion of the disclosed methods for determining ITH scores as part of a genomic profiling process (or inclusion of the output from the disclosed methods for determining ITH scores as part of the genomic profile of the subject) can improve the validity of, e.g., disease detection calls and treatment decisions, made on the basis of the genomic profile in a given patient sample.
[0134] In some instances, a genomic profile may comprise information on the presence of genes (or variant sequences thereof), copy number variations, epigenetic traits, proteins (or modifications thereof), and/or other biomarkers in an individual’s genome and/or proteome, as well as information on the individual’s corresponding phenotypic traits and the interaction between genetic or genomic traits, phenotypic traits, and environmental factors.
[0135] In some instances, a genomic profile for the subject may comprise results from a comprehensive genomic profiling (CGP) test, a nucleic acid sequencing-based test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof.
[0136] In some instances, the method can further include administering or applying a treatment or therapy e.g., an anti-cancer agent, anti-cancer treatment, or anti-cancer therapy) to the subject based on the generated genomic profile. An anti-cancer agent or anti-cancer treatment may refer to a compound that is effective in the treatment of cancer cells. Examples of anti-cancer agents or anti-cancer therapies include, but not limited to, alkylating agents, antimetabolites, natural products, hormones, chemotherapy, radiation therapy, immunotherapy, surgery, or a therapy configured to target a defect in a specific cell signaling pathway, e.g., a defect in a DNA mismatch repair (MMR) pathway.
Samples
[0137] The disclosed methods and systems may be used with any of a variety of samples (also referred to herein as specimens) comprising nucleic acids (e.g., DNA or RNA) that are collected from a subject (e.g., a patient). Examples of a sample include, but are not limited to, a tumor sample, a tissue sample, a biopsy sample (e.g., a tissue biopsy, a liquid biopsy, or both), a blood sample (e.g., a peripheral whole blood sample), a blood plasma sample, a blood serum sample, a lymph sample, a saliva sample, a sputum sample, a urine sample, a gynecological fluid sample, a circulating tumor cell (CTC) sample, a cerebral spinal fluid (CSF) sample, a pericardial fluid sample, a pleural fluid sample, an ascites (peritoneal fluid) sample, a feces (or stool) sample, or other body fluid, secretion, and/or excretion sample (or cell sample derived therefrom). In certain instances, the sample may be frozen sample or a formalin-fixed paraffin-embedded (FFPE) sample.
[0138] In some instances, the sample may be collected by tissue resection (e.g., surgical resection), needle biopsy, bone marrow biopsy, bone marrow aspiration, skin biopsy, endoscopic biopsy, fine needle aspiration, oral swab, nasal swab, vaginal swab or a cytology smear, scrapings, washings or lavages (such as a ductal lavage or bronchoalveolar lavage), etc.
[0139] In some instances, the sample is a liquid biopsy sample, and may comprise, e.g., whole blood, blood plasma, blood serum, urine, stool, sputum, saliva, or cerebrospinal fluid. In some instances, the sample may be a liquid biopsy sample and may comprise circulating tumor cells (CTCs). In some instances, the sample may be a liquid biopsy sample and may comprise cell- free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.
[0140] In some instances, the sample may comprise one or more premalignant or malignant cells. Premalignant, as used herein, refers to a cell or tissue that is not yet malignant but is poised to become malignant. In certain instances, the sample may be acquired from a solid tumor, a soft tissue tumor, or a metastatic lesion. In certain instances, the sample may be acquired from a hematologic malignancy or pre-malignancy. In other instances, the sample may comprise a tissue or cells from a surgical margin. In certain instances, the sample may comprise tumor-infiltrating lymphocytes. In some instances, the sample may comprise one or more non- malignant cells. In some instances, the sample may be, or is part of, a primary tumor or a metastasis (e.g., a metastasis biopsy sample). In some instances, the sample may be obtained from a site (e.g., a tumor site) with the highest percentage of tumor (e.g., tumor cells) as compared to adjacent sites (e.g., sites adjacent to the tumor). In some instances, the sample may be obtained from a site (e.g., a tumor site) with the largest tumor focus (e.g., the largest number of tumor cells as visualized under a microscope) as compared to adjacent sites (e.g., sites adjacent to the tumor).
[0141] In some instances, the disclosed methods may further comprise analyzing a primary control (e.g., a normal tissue sample). In some instances, the disclosed methods may further comprise determining if a primary control is available and, if so, isolating a control nucleic acid (e.g., DNA) from said primary control. In some instances, the sample may comprise any normal control (e.g., a normal adjacent tissue (NAT)) if no primary control is available. In some instances, the sample may be or may comprise histologically normal tissue. In some instances, the method includes evaluating a sample, e.g., a histologically normal sample (e.g., from a surgical tissue margin) using the methods described herein. In some instances, the disclosed methods may further comprise acquiring a sub-sample enriched for non-tumor cells, e.g., by macro-dissecting non-tumor tissue from said NAT in a sample not accompanied by a primary control. In some instances, the disclosed methods may further comprise determining that no primary control and no NAT is available, and marking said sample for analysis without a matched control.
[0142] In some instances, samples obtained from histologically normal tissues (e.g., otherwise histologically normal surgical tissue margins) may still comprise a genetic alteration such as a variant sequence as described herein. The methods may thus further comprise re-classifying a sample based on the presence of the detected genetic alteration. In some instances, multiple samples (e.g., from different subjects) are processed simultaneously.
[0143] The disclosed methods and systems may be applied to the analysis of nucleic acids extracted from any of variety of tissue samples (or disease states thereof), e.g., solid tissue samples, soft tissue samples, metastatic lesions, or liquid biopsy samples. Examples of tissues include, but are not limited to, connective tissue, muscle tissue, nervous tissue, epithelial tissue, and blood. Tissue samples may be collected from any of the organs within an animal or human body. Examples of human organs include, but are not limited to, the brain, heart, lungs, liver, kidneys, pancreas, spleen, thyroid, mammary glands, uterus, prostate, large intestine, small intestine, bladder, bone, skin, etc.
[0144] In some instances, the nucleic acids extracted from the sample may comprise deoxyribonucleic acid (DNA) molecules. Examples of DNA that may be suitable for analysis by the disclosed methods include, but are not limited to, genomic DNA or fragments thereof, mitochondrial DNA or fragments thereof, cell-free DNA (cfDNA), and circulating tumor DNA (ctDNA). Cell-free DNA (cfDNA) is comprised of fragments of DNA that are released from normal and/or cancerous cells during apoptosis and necrosis, and circulate in the blood stream and/or accumulate in other bodily fluids. Circulating tumor DNA (ctDNA) is comprised of fragments of DNA that are released from cancerous cells and tumors that circulate in the blood stream and/or accumulate in other bodily fluids.
[0145] In some instances, DNA is extracted from nucleated cells from the sample. In some instances, a sample may have a low nucleated cellularity, e.g., when the sample is comprised mainly of erythrocytes, lesional cells that contain excessive cytoplasm, or tissue with fibrosis. In some instances, a sample with low nucleated cellularity may require more, e.g., greater, tissue volume for DNA extraction.
[0146] In some instances, the nucleic acids extracted from the sample may comprise ribonucleic acid (RNA) molecules. Examples of RNA that may be suitable for analysis by the disclosed methods include, but are not limited to, total cellular RNA, total cellular RNA after depletion of certain abundant RNA sequences e.g., ribosomal RNAs), cell-free RNA (cfRNA), messenger RNA (mRNA) or fragments thereof, the poly(A)-tailed mRNA fraction of the total RNA, ribosomal RNA (rRNA) or fragments thereof, transfer RNA (tRNA) or fragments thereof, and mitochondrial RNA or fragments thereof. In some instances, RNA may be extracted from the sample and converted to complementary DNA (cDNA) using, e.g., a reverse transcription reaction. In some instances, the cDNA is produced by random-primed cDNA synthesis methods. In other instances, the cDNA synthesis is initiated at the poly (A) tail of mature mRNAs by priming with oligo(dT)-containing oligonucleotides. Methods for depletion, poly(A) enrichment, and cDNA synthesis are well known to those of skill in the art.
[0147] In some instances, the sample may comprise a tumor content (e.g., comprising tumor cells or tumor cell nuclei), or a non-tumor content (e.g., immune cells, fibroblasts, and other nontumor cells). In some instances, the tumor content of the sample may constitute a sample metric. In some instances, the sample may comprise a tumor content of at least 5-50%, 10-40%, 15-25%, or 20-30% tumor cell nuclei. In some instances, the sample may comprise a tumor content of at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, or at least 50% tumor cell nuclei. In some instances, the percent tumor cell nuclei (e.g., sample fraction) is determined (e.g., calculated) by dividing the number of tumor cells in the sample by the total number of all cells within the sample that have nuclei. In some instances, for example when the sample is a liver sample comprising hepatocytes, a different tumor content calculation may be required due to the presence of hepatocytes having nuclei with twice, or more than twice, the DNA content of other, e.g., non-hepatocyte, somatic cell nuclei. In some instances, the sensitivity of detection of a genetic alteration, e.g., a variant sequence, or a determination of, e.g., micro satellite instability, may depend on the tumor content of the sample. For example, a sample having a lower tumor content can result in lower sensitivity of detection for a given size sample.
[0148] In some instances, as noted above, the sample comprises nucleic acid (e.g., DNA, RNA (or a cDNA derived from the RNA), or both), e.g., from a tumor or from normal tissue. In certain instances, the sample may further comprise a non-nucleic acid component, e.g., cells, protein, carbohydrate, or lipid, e.g., from the tumor or normal tissue.
Subjects
[0149] In some instances, the sample is obtained (e.g., collected) from a subject (e.g., patient) with a condition or disease (e.g., a hyperproliferative disease or a non-cancer indication) or suspected of having the condition or disease. In some instances, the hyperproliferative disease is a cancer. In some instances, the cancer is a solid tumor or a metastatic form thereof. In some instances, the cancer is a hematological cancer, e.g., a leukemia or lymphoma.
[0150] In some instances, the subject has a cancer or is at risk of having a cancer. For example, in some instances, the subject has a genetic predisposition to a cancer (e.g., having a genetic mutation that increases his or her baseline risk for developing a cancer). In some instances, the subject has been exposed to an environmental perturbation (e.g., radiation or a chemical) that increases his or her risk for developing a cancer. In some instances, the subject is in need of being monitored for development of a cancer. In some instances, the subject is in need of being monitored for cancer progression or regression, e.g., after being treated with an anti-cancer therapy (or anti-cancer treatment). In some instances, the subject is in need of being monitored for relapse of cancer. In some instances, the subject is in need of being monitored for minimum residual disease (MRD). In some instances, the subject has been, or is being treated, for cancer. In some instances, the subject has not been treated with an anti-cancer therapy (or anti-cancer treatment).
[0151] In some instances, the subject (e.g., a patient) is being treated, or has been previously treated, with one or more targeted therapies. In some instances, e.g., for a patient who has been previously treated with a targeted therapy, a post-targeted therapy sample (e.g., specimen) is obtained (e.g., collected). In some instances, the post-targeted therapy sample is a sample obtained after the completion of the targeted therapy.
[0152] In some instances, the patient has not been previously treated with a targeted therapy. In some instances, e.g., for a patient who has not been previously treated with a targeted therapy, the sample comprises a resection, e.g., an original resection, or a resection following recurrence (e.g., following a disease recurrence post-therapy).
Cancers
[0153] In some instances, the sample is acquired from a subject having a cancer. Exemplary cancers include, but are not limited to, B cell cancer (e.g., multiple myeloma), melanomas, breast cancer, lung cancer (such as non-small cell lung carcinoma or NSCLC), bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain or central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine or endometrial cancer, cancer of the oral cavity or pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel or appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, cancer of hematological tissues, adenocarcinomas, inflammatory myofibroblastic tumors, gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), acute lymphocytic leukemia (ALL), acute myelocytic leukemia (AML), chronic myelocytic leukemia (CML), chronic lymphocytic leukemia (CLL), polycythemia Vera, Hodgkin lymphoma, nonHodgkin lymphoma (NHL), soft-tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endotheliosarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, bladder carcinoma, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, neuroblastoma, retinoblastoma, follicular lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, hepatocellular carcinoma, thyroid cancer, gastric cancer, head and neck cancer, small cell cancers, essential thrombocythemia, agnogenic myeloid metaplasia, hypereosinophilic syndrome, systemic mastocytosis, familiar hypereosinophilia, chronic eosinophilic leukemia, neuroendocrine cancers, carcinoid tumors, and the like.
[0154] In some instances, the cancer comprises acute lymphoblastic leukemia (Philadelphia chromosome positive), acute lymphoblastic leukemia (precursor B-cell), acute myeloid leukemia (FLT3+), acute myeloid leukemia (with an IDH2 mutation), anaplastic large cell lymphoma, basal cell carcinoma, B-cell chronic lymphocytic leukemia, bladder cancer, breast cancer (HER2 overexpressed/amplified), breast cancer (HER2+), breast cancer (HR+, HER2-), cervical cancer, cholangiocarcinoma, chronic lymphocytic leukemia, chronic lymphocytic leukemia (with 17p deletion), chronic myelogenous leukemia, chronic myelogenous leukemia (Philadelphia chromosome positive), classical Hodgkin lymphoma, colorectal cancer, colorectal cancer (dMMR and MSLH), colorectal cancer (KRAS wild type), cryopyrin-associated periodic syndrome, a cutaneous T-cell lymphoma, dermatofibrosarcoma protuberans, a diffuse large B- cell lymphoma, fallopian tube cancer, a follicular B-cell non-Hodgkin lymphoma, a follicular lymphoma, gastric cancer, gastric cancer (HER2+), a gastroesophageal junction (GEJ) adenocarcinoma, a gastrointestinal stromal tumor, a gastrointestinal stromal tumor (KIT+), a giant cell tumor of the bone, a glioblastoma, granulomatosis with polyangiitis, a head and neck squamous cell carcinoma, a hepatocellular carcinoma, Hodgkin lymphoma, juvenile idiopathic arthritis, lupus erythematosus, a mantle cell lymphoma, medullary thyroid cancer, melanoma, a melanoma with a BRAF V600 mutation, a melanoma with a BRAF V600E or V600K mutation, Merkel cell carcinoma, multicentric Castleman's disease, multiple hematologic malignancies including Philadelphia chromosome-positive ALL and CML, multiple myeloma, myelofibrosis, a non-Hodgkin’s lymphoma, a nonresectable subependymal giant cell astrocytoma associated with tuberous sclerosis, a non-small cell lung cancer, a non-small cell lung cancer (ALK+), a non-small cell lung cancer (PD-L1+), a non-small cell lung cancer (with ALK fusion or ROS1 gene alteration), a non-small cell lung cancer (with BRAF V600E mutation), a non-small cell lung cancer (with an EGFR exon 19 deletion or exon 21 substitution (L858R) mutations), a non- small cell lung cancer (with an EGFR T790M mutation), ovarian cancer, ovarian cancer (with a BRCA mutation), pancreatic cancer, a pancreatic, gastrointestinal, or lung origin neuroendocrine tumor, a pediatric neuroblastoma, a peripheral T-cell lymphoma, peritoneal cancer, prostate cancer, a renal cell carcinoma, rheumatoid arthritis, a small lymphocytic lymphoma, a soft tissue sarcoma, a solid tumor (MSLH/dMMR), a squamous cell cancer of the head and neck, a squamous non-small cell lung cancer, thyroid cancer, a thyroid carcinoma, urothelial cancer, a urothelial carcinoma, or Waldenstrom's macroglobulinemia.
[0155] In some instances, the cancer is a hematologic malignancy (or premaligancy). As used herein, a hematologic malignancy refers to a tumor of the hematopoietic or lymphoid tissues, e.g., a tumor that affects blood, bone marrow, or lymph nodes. Exemplary hematologic malignancies include, but are not limited to, leukemia (e.g., acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), chronic myelogenous leukemia (CML), hairy cell leukemia, acute monocytic leukemia (AMoL), chronic myelomonocytic leukemia (CMML), juvenile myelomonocytic leukemia (JMML), or large granular lymphocytic leukemia), lymphoma (e.g., AIDS-related lymphoma, cutaneous T-cell lymphoma, Hodgkin lymphoma (e.g., classical Hodgkin lymphoma or nodular lymphocyte- predominant Hodgkin lymphoma), mycosis fungoides, non-Hodgkin lymphoma (e.g., B-cell non-Hodgkin lymphoma (e.g., Burkitt lymphoma, small lymphocytic lymphoma (CLL/SLL), diffuse large B-cell lymphoma, follicular lymphoma, immunoblastic large cell lymphoma, precursor B-lymphoblastic lymphoma, or mantle cell lymphoma) or T-cell non-Hodgkin lymphoma (mycosis fungoides, anaplastic large cell lymphoma, or precursor T-lymphoblastic lymphoma)), primary central nervous system lymphoma, Sezary syndrome, Waldenstrom macroglobulinemia), chronic myeloproliferative neoplasm, Langerhans cell histiocytosis, multiple myeloma/plasma cell neoplasm, myelodysplastic syndrome, or myelodysplastic/myeloproliferative neoplasm.
Nucleic acid extraction and processing
[0156] DNA or RNA may be extracted from tissue samples, biopsy samples, blood samples, or other bodily fluid samples using any of a variety of techniques known to those of skill in the art (see, e.g., Example 1 of International Patent Application Publication No. WO 2012/092426; Tan, et al. (2009), “DNA, RNA, and Protein Extraction: The Past and The Present”, J. Biomed. Biotech. 2009:574398; the technical literature for the Maxwell® 16 LEV Blood DNA Kit (Promega Corporation, Madison, WI); and the Maxwell 16 Buccal Swab LEV DNA Purification Kit Technical Manual (Promega Literature #TM333, January 1, 2011, Promega Corporation, Madison, WI)). Protocols for RNA isolation are disclosed in, e.g., the Maxwell® 16 Total RNA Purification Kit Technical Bulletin (Promega Literature #TB351, August 2009, Promega Corporation, Madison, WI).
[0157] A typical DNA extraction procedure, for example, comprises (i) collection of the fluid sample, cell sample, or tissue sample from which DNA is to be extracted, (ii) disruption of cell membranes (z.e., cell lysis), if necessary, to release DNA and other cytoplasmic components, (iii) treatment of the fluid sample or lysed sample with a concentrated salt solution to precipitate proteins, lipids, and RNA, followed by centrifugation to separate out the precipitated proteins, lipids, and RNA, and (iv) purification of DNA from the supernatant to remove detergents, proteins, salts, or other reagents used during the cell membrane lysis step.
[0158] Disruption of cell membranes may be performed using a variety of mechanical shear e.g., by passing through a French press or fine needle) or ultrasonic disruption techniques. The cell lysis step often comprises the use of detergents and surfactants to solubilize lipids the cellular and nuclear membranes. In some instances, the lysis step may further comprise use of proteases to break down protein, and/or the use of an RNase for digestion of RNA in the sample. [0159] 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.
[0160] In some instances, cellular and histone proteins bound to the DNA may be removed either by adding a protease or by having precipitated the proteins with sodium or ammonium acetate, or through extraction with a phenol-chloroform mixture prior to a DNA precipitation step.
[0161] In some instances, DNA may be extracted using any of a variety of suitable commercial DNA extraction and purification kits. Examples include, but are not limited to, the QIAamp (for isolation of genomic DNA from human samples) and DNAeasy (for isolation of genomic DNA from animal or plant samples) kits from Qiagen (Germantown, MD) or the Maxwell® and ReliaPrep™ series of kits from Promega (Madison, WI).
[0162] As noted above, in some instances the sample may comprise a formalin-fixed (also known as formaldehyde-fixed, or paraformaldehyde-fixed), paraffin-embedded (FFPE) tissue preparation. For example, the FFPE sample may be a tissue sample embedded in a matrix, e.g., an FFPE block. Methods to isolate nucleic acids (e.g., DNA) from formaldehyde- or paraformaldehyde-fixed, paraffin-embedded (FFPE) tissues are disclosed in, e.g., Cronin, et al., (2004) Am J Pathol. 164(l):35-42; Masuda, et al., (1999) Nucleic Acids Res. 27 (22): 4436-4443; Specht, et al., (2001) Am J Pathol. 158(2):419-429; the Ambion RecoverAll™ Total Nucleic Acid Isolation Protocol (Ambion, Cat. No. AM1975, September 2008); the Maxwell® 16 FFPE Plus LEV DNA Purification Kit Technical Manual (Promega Literature #TM349, February 2011); the E.Z.N.A.® FFPE DNA Kit Handbook (OMEGA bio-tek, Norcross, GA, product numbers D3399-00, D3399-01, and D3399-02, June 2009); and the QIAamp® DNA FFPE Tissue Handbook (Qiagen, Cat. No. 37625, October 2007). For example, the RecoverAll™ Total Nucleic Acid Isolation Kit uses xylene at elevated temperatures to solubilize paraffin- embedded samples and a glass-fiber filter to capture nucleic acids. The Maxwell® 16 FFPE Plus 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. 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.
[0163] In some instances, the disclosed methods may further comprise determining or acquiring a yield value for the nucleic acid extracted from the sample and comparing the determined value to a reference value. For example, if the determined or acquired value is less than the reference value, the nucleic acids may be amplified prior to proceeding with library construction. In some instances, the disclosed methods may further comprise determining or acquiring a value for the size (or average size) of nucleic acid fragments in the sample, and comparing the determined or acquired value to a reference value, e.g., a size (or average size) of at least 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 base pairs (bps). In some instances, one or more parameters described herein may be adjusted or selected in response to this determination.
[0164] After isolation, the nucleic acids are typically dissolved in a slightly alkaline buffer, e.g., Tris-EDTA (TE) buffer, or in ultra-pure water. In some instances, the isolated nucleic acids (e.g., genomic DNA) may be fragmented or sheared by using any of a variety of techniques known to those of skill in the art. For example, genomic DNA can be fragmented by physical shearing methods, enzymatic cleavage methods, chemical cleavage methods, and other methods known to those of skill in the art. Methods for DNA shearing are described in Example 4 in International Patent Application Publication No. WO 2012/092426. In some instances, alternatives to DNA shearing methods can be used to avoid a ligation step during library preparation.
Library preparation
[0165] In some instances, the nucleic acids isolated from the sample may be used to construct a library (e.g., a nucleic acid library as described herein). In some instances, the nucleic acids are fragmented using any of the methods described above, optionally subjected to repair of chain end damage, and optionally ligated to synthetic adapters, primers, and/or barcodes (e.g., amplification primers, sequencing adapters, flow cell adapters, substrate adapters, sample barcodes or indexes, and/or unique molecular identifier sequences), size-selected (e.g., by preparative gel electrophoresis), and/or amplified (e.g., using PCR, a non-PCR amplification technique, or an isothermal amplification technique). In some instances, the fragmented and adapter-ligated group of nucleic acids is used without explicit size selection or amplification prior to hybridization-based selection of target sequences. In some instances, the nucleic acid is amplified by any of a variety of specific or non-specific nucleic acid amplification methods known to those of skill in the art. In some instances, the nucleic acids are amplified, e.g., by a whole-genome amplification method such as random-primed strand-displacement amplification. Examples of nucleic acid library preparation techniques for next-generation sequencing are described in, e.g., van Dijk, et al. (2014), Exp. Cell Research 322:12 - 20, and Illumina’s genomic DNA sample preparation kit.
[0166] In some instances, the resulting nucleic acid library may contain all or substantially all of the complexity of the genome. The term “substantially all” in this context refers to the possibility that there can in practice be some unwanted loss of genome complexity during the initial steps of the procedure. The methods described herein also are useful in cases where the nucleic acid library comprises a portion of the genome, e.g., where the complexity of the genome is reduced by design. In some instances, any selected portion of the genome can be used with a method described herein. For example, in certain embodiments, the entire exome or a subset thereof is isolated. In some instances, the library may include at least 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, or 5% of the genomic DNA. In some instances, the library may consist of cDNA copies of genomic DNA that includes copies of at least 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, or 5% of the genomic DNA. In certain instances, the amount of nucleic acid used to generate the nucleic acid library may be less than 5 micrograms, less than 1 microgram, less than 500 ng, less than 200 ng, less than 100 ng, less than 50 ng, less than 10 ng, less than 5 ng, or less than 1 ng.
[0167] In some instances, a library (e.g., a nucleic acid library) includes a collection of nucleic acid molecules. As described herein, the nucleic acid molecules of the library can include a target nucleic acid molecule (e.g., a tumor nucleic acid molecule, a reference nucleic acid molecule and/or a control nucleic acid molecule; also referred to herein as a first, second and/or third nucleic acid molecule, respectively). The nucleic acid molecules of the library can be from a single subject or individual. In some instances, a library can comprise nucleic acid molecules derived from more than one subject (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30 or more subjects). For example, two or more libraries from different subjects can be combined to form a library having nucleic acid molecules from more than one subject (where the nucleic acid molecules derived from each subject are optionally ligated to a unique sample barcode corresponding to a specific subject). In some instances, the subject is a human having, or at risk of having, a cancer or tumor.
[0168] In some instances, the library (or a portion thereof) may comprise one or more subgenomic intervals. In some instances, a subgenomic interval can be a single nucleotide position, e.g., a nucleotide position for which a variant at the position is associated (positively or negatively) with a tumor phenotype. In some instances, a subgenomic interval comprises more than one nucleotide position. Such instances include sequences of at least 2, 5, 10, 50, 100, 150, 250, or more than 250 nucleotide positions in length. Subgenomic intervals can comprise, e.g., one or more entire genes (or portions thereof), one or more exons or coding sequences (or portions thereof), one or more introns (or portion thereof), one or more microsatellite region (or portions thereof), or any combination thereof. A subgenomic interval can comprise all or a part of a fragment of a naturally occurring nucleic acid molecule, e.g., a genomic DNA molecule. For example, a subgenomic interval can correspond to a fragment of genomic DNA which is subjected to a sequencing reaction. In some instances, a subgenomic interval is a continuous sequence from a genomic source. In some instances, a subgenomic interval includes sequences that are not contiguous in the genome, e.g., subgenomic intervals in cDNA can include exonexonjunctions formed as a result of splicing. In some instances, the subgenomic interval comprises a tumor nucleic acid molecule. In some instances, the subgenomic interval comprises a non-tumor nucleic acid molecule.
Targeting gene loci for analysis
[0169] 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. [0170] In some instances, the set of genomic loci evaluated by the disclosed methods comprises a plurality of, e.g., genes, which in mutant form, are associated with an effect on cell division, growth or survival, or are associated with a cancer, e.g., a cancer described herein.
[0171] In some instances, the set of gene loci evaluated by the disclosed methods comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, or more than 100 gene loci.
[0172] In some instances, the selected gene loci (also referred to herein as target gene loci or target sequences), or fragments thereof, may include subject intervals comprising non-coding sequences, coding sequences, intragenic regions, or intergenic regions of the subject genome. For example, the subject intervals can include a non-coding sequence or fragment thereof (e.g., a promoter sequence, enhancer sequence, 5’ untranslated region (5’ UTR), 3’ untranslated region (3’ UTR), or a fragment thereof), a coding sequence of fragment thereof, an exon sequence or fragment thereof, an intron sequence or a fragment thereof.
Target capture reagents
[0173] The methods described herein may comprise contacting a nucleic acid library with a plurality of target capture reagents in order to select and capture a plurality of specific target sequences (e.g., gene sequences or fragments thereof) for analysis. In some instances, a target capture reagent (i.e., a molecule which can bind to and thereby allow capture of a target molecule) is used to select the subject intervals to be analyzed. For example, a target capture reagent can be a bait molecule, e.g., a nucleic acid molecule (e.g., a DNA molecule or RNA molecule) which can hybridize to (i.e., is complementary to) a target molecule, and thereby allows capture of the target nucleic acid. In some instances, the target capture reagent, e.g., a bait molecule (or bait sequence), is a capture oligonucleotide (or capture probe). In some instances, the target nucleic acid is a genomic DNA molecule, an RNA molecule, a cDNA molecule derived from an RNA molecule, a microsatellite DNA sequence, and the like. In some instances, the target capture reagent is suitable for solution-phase hybridization to the target. In some instances, the target capture reagent is suitable for solid-phase hybridization to the target. In some instances, the target capture reagent is suitable for both solution-phase and solid-phase hybridization to the target. The design and construction of target capture reagents is described in more detail in, e.g., International Patent Application Publication No. WO 2020/236941, the entire content of which is incorporated herein by reference.
[0174] The methods described herein provide for optimized sequencing of a large number of genomic loci (e.g., genes or gene products (e.g., mRNA), micro satellite loci, etc.) from samples (e.g., cancerous tissue specimens, liquid biopsy samples, and the like) from one or more subjects by the appropriate selection of target capture reagents to select the target nucleic acid molecules to be sequenced. In some instances, a target capture reagent may hybridize to a specific target locus, e.g., a specific target gene locus or fragment thereof. In some instances, a target capture reagent may hybridize to a specific group of target loci, e.g., a specific group of gene loci or fragments thereof. In some instances, a plurality of target capture reagents comprising a mix of target- specific and/or group- specific target capture reagents may be used.
[0175] In some instances, the number of target capture reagents (e.g., bait molecules) in the plurality of target capture reagents (e.g., a bait set) contacted with a nucleic acid library to capture a plurality of target sequences for nucleic acid sequencing is greater than 10, greater than 50, greater than 100, greater than 200, greater than 300, greater than 400, greater than 500, greater than 600, greater than 700, greater than 800, greater than 900, greater than 1,000, greater than 1,250, greater than 1,500, greater than 1,750, greater than 2,000, greater than 3,000, greater than 4,000, greater than 5,000, greater than 10,000, greater than 25,000, or greater than 50,000.
[0176] In some instances, the overall length of the target capture reagent sequence can be between about 70 nucleotides and 1000 nucleotides. In one instance, the target capture reagent length is between about 100 and 300 nucleotides, 110 and 200 nucleotides, or 120 and 170 nucleotides, in length. In addition to those mentioned above, intermediate oligonucleotide lengths of about 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 300, 400, 500, 600, 700, 800, and 900 nucleotides in length can be used in the methods described herein. In some embodiments, oligonucleotides of about 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, or 230 bases can be used.
[0177] In some instances, each target capture reagent sequence can include: (i) a target- specific capture sequence (e.g., a gene locus or micro satellite locus- specific complementary sequence), (ii) an adapter, primer, barcode, and/or unique molecular identifier sequence, and (iii) universal tails on one or both ends. As used herein, the term "target capture reagent" can refer to the targetspecific target capture sequence or to the entire target capture reagent oligonucleotide including the target- specific target capture sequence.
[0178] In some instances, the target- specific capture sequences in the target capture reagents are between about 40 nucleotides and 1000 nucleotides in length. In some instances, the targetspecific capture sequence is between about 70 nucleotides and 300 nucleotides in length. In some instances, the target- specific sequence is between about 100 nucleotides and 200 nucleotides in length. In yet other instances, the target- specific sequence is between about 120 nucleotides and 170 nucleotides in length, typically 120 nucleotides in length. Intermediate lengths in addition to those mentioned above also can be used in the methods described herein, such as target- specific sequences of about 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 300, 400, 500, 600, 700, 800, and 900 nucleotides in length, as well as target- specific sequences of lengths between the above-mentioned lengths.
[0179] In some instances, the target capture reagent may be designed to select a subject interval containing one or more rearrangements, e.g., an intron containing a genomic rearrangement. In such instances, the target capture reagent is designed such that repetitive sequences are masked to increase the selection efficiency. In those instances where the rearrangement has a known juncture sequence, complementary target capture reagents can be designed to recognize the juncture sequence to increase the selection efficiency.
[0180] In some instances, the disclosed methods may comprise the use of target capture reagents designed to capture two or more different target categories, each category having a different target capture reagent design strategy. In some instances, the hybridization-based capture methods and target capture reagent compositions disclosed herein may provide for the capture and homogeneous coverage of a set of target sequences, while minimizing coverage of genomic sequences outside of the targeted set of sequences. In some instances, the target sequences may include the entire exome of genomic DNA or a selected subset thereof. In some instances, the target sequences may include, e.g., a large chromosomal region e.g., a whole chromosome arm). The methods and compositions disclosed herein provide different target capture reagents for achieving different sequencing depths and patterns of coverage for complex sets of target nucleic acid sequences.
[0181] Typically, DNA molecules are used as target capture reagent sequences, although RNA molecules can also be used. In some instances, a DNA molecule target capture reagent can be single stranded DNA (ssDNA) or double- stranded DNA (dsDNA). In some instances, an RNA- DNA duplex is more stable than a DNA-DNA duplex and therefore provides for potentially better capture of nucleic acids.
[0182] In some instances, the disclosed methods comprise providing a selected set of nucleic acid molecules (e.g., a library catch) captured from one or more nucleic acid libraries. For example, the method may comprise: providing one or a plurality of nucleic acid libraries, each comprising a plurality of nucleic acid molecules (e.g., a plurality of target nucleic acid molecules and/or reference nucleic acid molecules) extracted from one or more samples from one or more subjects; contacting the one or a plurality of libraries (e.g., in a solution-based hybridization reaction) with one, two, three, four, five, or more than five pluralities of target capture reagents (e.g., oligonucleotide target capture reagents) to form a hybridization mixture comprising a plurality of target capture reagent/nucleic acid molecule hybrids; separating the plurality of target capture reagent/nucleic acid molecule hybrids from said hybridization mixture, e.g., by contacting said hybridization mixture with a binding entity that allows for separation of said plurality of target capture reagent/nucleic acid molecule hybrids from the hybridization mixture, thereby providing a library catch (e.g., a selected or enriched subgroup of nucleic acid molecules from the one or a plurality of libraries).
[0183] In some instances, the disclosed methods may further comprise amplifying the library catch (e.g., by performing PCR). In other instances, the library catch is not amplified.
[0184] In some instances, the target capture reagents can be part of a kit which can optionally comprise instructions, standards, buffers or enzymes or other reagents.
Hybridization conditions
[0185] As noted above, the methods disclosed herein may include the step of contacting the library (e.g., the nucleic acid library) with a plurality of target capture reagents to provide a selected library target nucleic acid sequences (z.e., the library catch). The contacting step can be effected in, e.g., solution-based hybridization. In some instances, the method includes repeating the hybridization step for one or more additional rounds of solution-based hybridization. In some instances, the method further includes subjecting the library catch to one or more additional rounds of solution-based hybridization with the same or a different collection of target capture reagents.
[0186] In some instances, the contacting step is effected using a solid support, e.g., an array. Suitable solid supports for hybridization are described in, e.g., Albert, T.J. et al. (2007) Nat. Methods 4(11):903-5; Hodges, E. et al. (2007) Nat. Genet. 39(12): 1522-7; and Okou, D.T. et al. (2007) Nat. Methods 4(11 ):907-9, the contents of which are incorporated herein by reference in their entireties.
[0187] Hybridization methods that can be adapted for use in the methods herein are described in the art, e.g., as described in International Patent Application Publication No. WO 2012/092426. Methods for hybridizing target capture reagents to a plurality of target nucleic acids are described in more detail in, e.g., International Patent Application Publication No. WO 2020/236941, the entire content of which is incorporated herein by reference.
Sequencing methods
[0188] The methods and systems disclosed herein can be used in combination with, or as part of, a method or system for sequencing nucleic acids (e.g., a next-generation sequencing system) to generate a plurality of sequence reads that overlap one or more gene loci within a subgenomic interval in the sample and thereby determine, e.g., gene allele sequences at a plurality of gene loci. “Next-generation sequencing” (or “NGS”) as used herein may also be referred to as “massively parallel sequencing” (or “MPS”), and refers to any sequencing method that determines the nucleotide sequence of either individual nucleic acid molecules (e.g., as in single molecule sequencing) or clonally expanded proxies for individual nucleic acid molecules in a high throughput fashion (e.g., wherein greater than 103, 104, 105 or more than 105 molecules are sequenced simultaneously).
[0189] Next-generation sequencing methods are known in the art, and are described in, e.g., Metzker, M. (2010) Nature Biotechnology Reviews 11:31-46, which is incorporated herein by reference. Other examples of sequencing methods suitable for use when implementing the methods and systems disclosed herein are described in, e.g., International Patent Application Publication No. WO 2012/092426. In some instances, the sequencing may comprise, for example, whole genome sequencing (WGS), whole exome sequencing, targeted sequencing, or direct sequencing. In some instances, sequencing may be performed using, e.g., Sanger sequencing. In some instances, the sequencing may comprise a paired-end sequencing technique that allows both ends of a fragment to be sequenced and generates high-quality, alignable sequence data for detection of, e.g., genomic rearrangements, repetitive sequence elements, gene fusions, and novel transcripts.
[0190] The disclosed methods and systems may be implemented using sequencing platforms such as the Roche/454 Genome Sequencer (GS) FLX System, Illumina/Solexa Genome Analyzer (GA), Illumina’s HiSeq® 2500, HiSeq® 3000, HiSeq® 4000 and NovaSeq® 6000 Sequencing Systems, Life/APG’s Support Oligonucleotide Ligation Detection (SOLiD) system, Polonator’s G.007 system, Helicos BioSciences’ HeliScope Gene Sequencing system, or Pacific Biosciences’ PacBio® RS platform. In some instances, sequencing may comprise Illumina MiSeq™ sequencing. In some instances, sequencing may comprise Illumina HiSeq® sequencing. In some instances, sequencing may comprise Illumina NovaSeq® sequencing. Optimized methods for sequencing a large number of target genomic loci in nucleic acids extracted from a sample are described in more detail in, e.g., International Patent Application Publication No. WO 2020/236941, the entire content of which is incorporated herein by reference.
[0191] In certain instances, the disclosed methods comprise one or more of the steps of: (a) acquiring a library comprising a plurality of normal and/or tumor nucleic acid molecules from a sample; (b) simultaneously or sequentially contacting the library with one, two, three, four, five, or more than five pluralities of target capture reagents under conditions that allow hybridization of the target capture reagents to the target nucleic acid molecules, thereby providing a selected set of captured normal and/or tumor nucleic acid molecules (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., one or more target sequences) from said library catch that may comprise a mutation (or alteration), e.g., a variant sequence comprising a somatic mutation or germline mutation; (e) aligning said sequence reads using an alignment method as described elsewhere herein; and/or (f) assigning a nucleotide value for a nucleotide position in the subject interval (e.g., calling a mutation using, e.g., a Bayesian method or other method described herein) from one or more sequence reads of the plurality.
[0192] In some instances, acquiring sequence reads for one or more subject intervals may comprise sequencing at least 1, at least 5, at least 10, at least 20, at least 30, at least 40, at least 50, at least 100, at least 150, at least 200, at least 250, at least 300, at least 350, at least 400, at least 450, at least 500, at least 550, at least 600, at least 650, at least 700, at least 750, at least 800, at least 850, at least 900, at least 950, at least 1,000, at least 1,250, at least 1,500, at least 1,750, at least 2,000, at least 2,250, at least 2,500, at least 2,750, at least 3,000, at least 3,500, at least 4,000, at least 4,500, or at least 5,000 loci, e.g., genomic loci, gene loci, microsatellite loci, etc. In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing a subject interval for any number of loci within the range described in this paragraph, e.g., for at least 2,850 gene loci.
[0193] In some instances, acquiring a sequence read for one or more subject intervals comprises sequencing a subject interval with a sequencing method that provides a sequence read length (or average sequence read length) of at least 20 bases, at least 30 bases, at least 40 bases, at least 50 bases, at least 60 bases, at least 70 bases, at least 80 bases, at least 90 bases, at least 100 bases, at least 120 bases, at least 140 bases, at least 160 bases, at least 180 bases, at least 200 bases, at least 220 bases, at least 240 bases, at least 260 bases, at least 280 bases, at least 300 bases, at least 320 bases, at least 340 bases, at least 360 bases, at least 380 bases, or at least 400 bases. In some instances, acquiring a sequence read for the one or more subject intervals may comprise sequencing a subject interval with a sequencing method that provides a sequence read length (or average sequence read length) of any number of bases within the range described in this paragraph, e.g., a sequence read length (or average sequence read length) of 56 bases.
[0194] In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing with at least lOOx or more coverage (or depth) on average. In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing with at least lOOx, at least 150x, at least 200x, at least 250x, at least 500x, at least 750x, at least l,000x, at least 1,500 x, at least 2,000x, at least 2,500x, at least 3,000x, at least 3,500x, at least 4,000x, at least 4,500x, at least 5,000x, at least 5,500x, or at least 6,000x or more coverage (or depth) on average. In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing with an average coverage (or depth) having any value within the range of values described in this paragraph, e.g., at least 160x.
[0195] In some instances, acquiring a read for the one or more subject intervals comprises sequencing with an average sequencing depth having any value ranging from at least lOOx to at least 6,000x for greater than about 90%, 92%, 94%, 95%, 96%, 97%, 98%, or 99% of the gene loci sequenced. For example, in some instances acquiring a read for the subject interval comprises sequencing with an average sequencing depth of at least 125x for at least 99% of the gene loci sequenced. As another example, in some instances acquiring a read for the subject interval comprises sequencing with an average sequencing depth of at least 4,100x for at least 95% of the gene loci sequenced.
[0196] In some instances, the relative abundance of a nucleic acid species in the library can be estimated by counting the relative number of occurrences of their cognate sequences (e.g., the number of sequence reads for a given cognate sequence) in the data generated by the sequencing experiment.
[0197] In some instances, the disclosed methods and systems provide nucleotide sequences for a set of subject intervals (e.g., gene loci), as described herein. In certain instances, the sequences are provided without using a method that includes a matched normal control (e.g., a wild-type control) and/or a matched tumor control (e.g., primary versus metastatic).
[0198] In some instances, the level of sequencing depth as used herein (e.g., an X-fold level of sequencing depth) refers to the number of reads (e.g., unique reads) obtained after detection and removal of duplicate reads (e.g., PCR duplicate reads). In other instances, duplicate reads are evaluated, e.g., to support detection of copy number alteration (CNAs). Alignment
[0199] Alignment is the process of matching a read with a location, e.g., a genomic location or locus. In some instances, NGS reads may be aligned to a known reference sequence (e.g., a wild-type sequence). In some instances, NGS reads may be assembled de novo. Methods of sequence alignment for NGS reads are described in, e.g., Trapnell, C. and Salzberg, S.L. Nature Biotech., 2009, 27:455-457. Examples of de novo sequence assemblies are described in, e.g., Warren R., et al., Bioinformatics, 2007, 23:500-501; Butler, J. et al., Genome Res., 2008, 18:810-820; and Zerbino, D.R. and Birney, E., Genome Res., 2008, 18:821-829. Optimization of sequence alignment is described in the art, e.g., as set out in International Patent Application Publication No. WO 2012/092426. Additional description of sequence alignment methods is provided in, e.g., International Patent Application Publication No. WO 2020/236941, the entire content of which is incorporated herein by reference.
[0200] Misalignment (e.g., the placement of base-pairs from a short read at incorrect locations in the genome), e.g., misalignment of reads due to sequence context (e.g., the presence of repetitive sequence) around an actual cancer mutation can lead to reduction in sensitivity of mutation detection, can lead to a reduction in sensitivity of mutation detection, as reads for the alternate allele may be shifted off the histogram peak of alternate allele reads. Other examples of sequence context that may cause misalignment include short-tandem repeats, interspersed repeats, low complexity regions, insertions - deletions (indels), and paralogs. If the problematic sequence context occurs where no actual mutation is present, misalignment may introduce artifactual reads of “mutated” alleles by placing reads of actual reference genome base sequences at the wrong location. Because mutation-calling algorithms for multigene analysis should be sensitive to even low-abundance mutations, sequence misalignments may increase false positive discovery rates and/or reduce specificity.
[0201] In some instances, the methods and systems disclosed herein may integrate the use of multiple, individually-tuned, alignment methods or algorithms to optimize base-calling performance in sequencing methods, particularly in methods that rely on massively parallel sequencing (MPS) of a large number of diverse genetic events at a large number of diverse genomic loci. In some instances, the disclosed methods and systems may comprise the use of one or more global alignment algorithms. In some instances, the disclosed methods and systems may comprise the use of one or more local alignment algorithms. Examples of alignment algorithms that may be used include, but are not limited to, the Burrows-Wheeler Alignment (BWA) software bundle (see, e.g., Li, et al. (2009), “Fast and Accurate Short Read Alignment with Burrows-Wheeler Transform”, Bioinformatics 25: 1754-60; Li, et al. (2010), Fast and Accurate Long-Read Alignment with Burrows-Wheeler Transform”, Bioinformatics epub.
PMID: 20080505), the Smith- Waterman algorithm (see, e.g., Smith, et al. (1981), "Identification of Common Molecular Subsequences", J. Molecular Biology 147(1): 195-197), the Striped Smith-Waterman algorithm (see, e.g., Farrar (2007), “Striped Smith-Waterman Speeds Database Searches Six Times Over Other SIMD Implementations”, Bioinformatics 23(2): 156-161), the Needleman-Wunsch algorithm (Needleman, et al. (1970) "A General Method Applicable to the Search for Similarities in the Amino Acid Sequence of Two Proteins", J. Molecular Biology 48(3):443-53), or any combination thereof.
[0202] In some instances, the methods and systems disclosed herein may also comprise the use of a sequence assembly algorithm, e.g., the Arachne sequence assembly algorithm (see, e.g., Batzoglou, et al. (2002), “ARACHNE: A Whole-Genome Shotgun Assembler”, Genome Res. 12:177-189).
[0203] In some instances, the alignment method used to analyze sequence reads is not individually customized or tuned for detection of different variants (e.g., point mutations, insertions, deletions, and the like) at different genomic loci. In some instances, different alignment methods are used to analyze reads that are individually customized or tuned for detection of at least a subset of the different variants detected at different genomic loci. In some instances, different alignment methods are used to analyze reads that are individually customized or tuned to detect each different variant at different genomic loci. In some instances, tuning can be a function of one or more of: (i) the genetic locus (e.g., gene loci, micro satellite locus, or other subject interval) being sequenced, (ii) the tumor type associated with the sample, (iii) the variant being sequenced, or (iv) a characteristic of the sample or the subject. The selection or use of alignment conditions that are individually tuned to a number of specific subject intervals to be sequenced allows optimization of speed, sensitivity, and specificity. The method is particularly effective when the alignment of reads for a relatively large number of diverse subject intervals are optimized. [0204] In some instances, the method includes the use of an alignment method optimized for rearrangements in combination with other alignment methods optimized for subject intervals not associated with rearrangements.
[0205] In some instances, the methods disclosed herein further comprise selecting or using an alignment method for analyzing, e.g., aligning, a sequence read, wherein said alignment method is a function of, is selected responsive to, or is optimized for, one or more of: (i) tumor type, e.g., the tumor type in the sample; (ii) the location (e.g., a gene locus) of the subject interval being sequenced; (iii) the type of variant (e.g., a point mutation, insertion, deletion, substitution, copy number variation (CNV), rearrangement, or fusion) in the subject interval being sequenced; (iv) the site (e.g., nucleotide position) being analyzed; (v) the type of sample (e.g., a sample described herein); and/or (vi) adjacent sequence(s) in or near the subject interval being evaluated (e.g., according to the expected propensity thereof for misalignment of the subject interval due to, e.g., the presence of repeated sequences in or near the subject interval).
[0206] In some instances, the methods disclosed herein allow for the rapid and efficient alignment of troublesome reads, e.g., a read having a rearrangement. Thus, in some instances where a read for a subject interval comprises a nucleotide position with a rearrangement, e.g., a translocation, the method can comprise using an alignment method that is appropriately tuned and that includes: (i) selecting a rearrangement reference sequence for alignment with a read, wherein said rearrangement reference sequence aligns with a rearrangement (in some instances, the reference sequence is not identical to the genomic rearrangement); and (ii) comparing, e.g., aligning, a read with said rearrangement reference sequence.
[0207] In some instances, alternative methods may be used to align troublesome reads. These methods are particularly effective when the alignment of reads for a relatively large number of diverse subject intervals is optimized. By way of example, a method of analyzing a sample can comprise: (i) performing a comparison (e.g., an alignment comparison) of a read using a first set of parameters (e.g., using a first mapping algorithm, or by comparison with a first reference sequence), and determining if said read meets a first alignment criterion (e.g., the read can be aligned with said first reference sequence, e.g., with less than a specific number of mismatches); (ii) if said read fails to meet the first alignment criterion, performing a second alignment comparison using a second set of parameters, (e.g., using a second mapping algorithm, or by comparison with a second reference sequence); and (iii) optionally, determining if said read meets said second criterion (e.g., the read can be aligned with said second reference sequence, e.g., with less than a specific number of mismatches), wherein said second set of parameters comprises use of, e.g., said second reference sequence, which, compared with said first set of parameters, is more likely to result in an alignment with a read for a variant (e.g., a rearrangement, insertion, deletion, or translocation).
[0208] In some instances, the alignment of sequence reads in the disclosed methods may be combined with a mutation calling method as described elsewhere herein. As discussed herein, reduced sensitivity for detecting actual mutations may be addressed by evaluating the quality of alignments (manually or in an automated fashion) around expected mutation sites in the genes or genomic loci (e.g., gene loci) being analyzed. In some instances, the sites to be evaluated can be obtained from databases of the human genome (e.g., the HG19 human reference genome) or cancer mutations (e.g., COSMIC). Regions that are identified as problematic can be remedied with the use of an algorithm selected to give better performance in the relevant sequence context, e.g., by alignment optimization (or re-alignment) using slower, but more accurate alignment algorithms such as Smith-Waterman alignment. In cases where general alignment algorithms cannot remedy the problem, customized alignment approaches may be created by, e.g., adjustment of maximum difference mismatch penalty parameters for genes with a high likelihood of containing substitutions; adjusting specific mismatch penalty parameters based on specific mutation types that are common in certain tumor types (e.g. C~^T in melanoma); or adjusting specific mismatch penalty parameters based on specific mutation types that are common in certain sample types (e.g. substitutions that are common in FFPE).
[0209] Reduced specificity (increased false positive rate) in the evaluated subject intervals due to misalignment can be assessed by manual or automated examination of all mutation calls in the sequencing data. Those regions found to be prone to spurious mutation calls due to misalignment can be subjected to alignment remedies as discussed above. In cases where no algorithmic remedy is found possible, “mutations” from the problem regions can be classified or screened out from the panel of targeted loci. Mutation calling
[0210] Base calling refers to the raw output of a sequencing device, e.g., the determined sequence of nucleotides in an oligonucleotide molecule. Mutation calling refers to the process of selecting a nucleotide value, e.g., A, G, T, or C, for a given nucleotide position being sequenced. Typically, the sequence reads (or base calling) for a position will provide more than one value, e.g., some reads will indicate a T and some will indicate a G. Mutation calling is the process of assigning a correct nucleotide value, e.g., one of those values, to the sequence. Although it is referred to as “mutation” calling, it can be applied to assign a nucleotide value to any nucleotide position, e.g., positions corresponding to mutant alleles, wild-type alleles, alleles that have not been characterized as either mutant or wild-type, or to positions not characterized by variability.
[0211] In some instances, the disclosed methods may comprise the use of customized or tuned mutation calling algorithms or parameters thereof to optimize performance when applied to sequencing data, particularly in methods that rely on massively parallel sequencing (MPS) of a large number of diverse genetic events at a large number of diverse genomic loci (e.g., gene loci, micro satellite regions, etc.) in samples, e.g., samples from a subject having cancer. Optimization of mutation calling is described in the art, e.g., as set out in International Patent Application Publication No. WO 2012/092426.
[0212] 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.
[0213] 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).
[0214] 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.
[0215] After alignment, detection of substitutions can be performed using a mutation calling method (e.g., a Bayesian mutation calling method) which is applied to each base in each of the subject intervals, e.g., exons of a gene or other locus to be evaluated, where presence of alternate alleles is observed. This method will compare the probability of observing the read data in the presence of a mutation with the probability of observing the read data in the presence of basecalling error alone. Mutations can be called if this comparison is sufficiently strongly supportive of the presence of a mutation.
[0216] 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).
[0217] Indel calling is a process of finding bases in the sequencing data that differ from the reference sequence by insertion or deletion, typically including an associated confidence score or statistical evidence metric. Methods of indel calling can include the steps of identifying candidate indels, calculating genotype likelihood through local re-alignment, and performing LD-based genotype inference and calling. Typically, a Bayesian approach is used to obtain potential indel candidates, and then these candidates are tested together with the reference sequence in a Bayesian framework.
[0218] Algorithms to generate candidate indels are described in, e.g., McKenna, A., et al., Genome Res. 2010; 20(9): 1297-303; Ye, K., et al., Bioinformatics, 2009; 25(21):2865-71; Lunter, G., and Goodson, M., Genome Res. 2011; 21(6):936-9; and Li, H., et al. (2009), Bioinformatics 25(16):2078-9.
[0219] Methods for generating indel calls and individual-level genotype likelihoods include, e.g., the Dindel algorithm (Albers, C.A., et al., Genome Res. 2011 ;21(6):961 -73) . For example, the Bayesian EM algorithm can be used to analyze the reads, make initial indel calls, and generate genotype likelihoods for each candidate indel, followed by imputation of genotypes using, e.g., QCALL (Le S.Q. and Durbin R. Genome Res. 2011 ;21(6):952-60). Parameters, such as prior expectations of observing the indel can be adjusted (e.g., increased or decreased), based on the size or location of the indels.
[0220] 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.
[0221] In some instances, the mutation calling method used to analyze sequence reads is not individually customized or fine-tuned for detection of different mutations at different genomic loci. In some instances, different mutation calling methods are used that are individually customized or fine-tuned for at least a subset of the different mutations detected at different genomic loci. In some instances, different mutation calling methods are used that are individually customized or fine-tuned for each different mutant detected at each different genomic loci. The customization or tuning can be based on one or more of the factors described herein, e.g., the type of cancer in a sample, the gene or locus in which the subject interval to be sequenced is located, or the variant to be sequenced. This selection or use of mutation calling methods individually customized or fine-tuned for a number of subject intervals to be sequenced allows for optimization of speed, sensitivity and specificity of mutation calling.
[0222] In some instances, a nucleotide value is assigned for a nucleotide position in each of X unique subject intervals using a unique mutation calling method, and X is at least 2, at least 3, at least 4, at least 5, at least 10, at least 15, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 200, at least 300, at least 400, at least 500, at least 1000, at least 1500, at least 2000, at least 2500, at least 3000, at least 3500, at least 4000, at least 4500, at least 5000, or greater. The calling methods can differ, and thereby be unique, e.g., by relying on different Bayesian prior values.
[0223] In some instances, assigning said nucleotide value is a function of a value which is or represents the prior (e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type.
[0224] In some instances, the method comprises assigning a nucleotide value (e.g., calling a mutation) for at least 10, 20, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1,000 nucleotide positions, wherein each assignment is a function of a unique value (as opposed to the value for the other assignments) which is or represents the prior (e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type.
[0225] In some instances, assigning said nucleotide value is a function of a set of values which represent the probabilities of observing a read showing said variant at said nucleotide position if the variant is present in the sample at a specified frequency (e.g., 1%, 5%, 10%, etc.) and/or if the variant is absent (e.g., observed in the reads due to base-calling error alone).
[0226] In some instances, the mutation calling methods described herein can include the following: (a) acquiring, for a nucleotide position in each of said X subject intervals: (i) a first value which is or represents the prior (e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type X; and (ii) a second set of values which represent the probabilities of observing a read showing said variant at said nucleotide position if the variant is present in the sample at a frequency (e.g., 1%, 5%, 10%, etc.) and/or if the variant is absent (e.g., observed in the reads due to base-calling error alone); and (b) responsive to said values, assigning a nucleotide value (e.g., calling a mutation) from said reads for each of said nucleotide positions by weighing, e.g., by a Bayesian method described herein, the comparison among the values in the second set using the first value (e.g. , computing the posterior probability of the presence of a mutation), thereby analyzing said sample.
[0227] Additional description of exemplary nucleic acid sequencing methods, mutation calling methods, and methods for analysis of genetic variants is provided in, e.g., U.S. Patent No. 9,340,830, U.S. Patent No. 9,792,403, U.S. Patent No. 11,136,619, U.S. Patent No. 11,118,213, and International Patent Application Publication No. WO 2020/236941, the entire contents of each of which is incorporated herein by reference.
Systems
[0228] Also disclosed herein are systems designed to implement any of the disclosed methods for determining ITH scores in a sample from a subject. The systems may comprise, e.g., one or more processors, and a memory unit communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: receive sequence read data derived from a sample from a subject diagnosed with a cancer; process the sequence read data to identify one or more genomic features; provide the one or more genomic features as input to a trained machine learning model configured to predict an intra-tumor heterogeneity (ITH) score for the sample based on the one or more genomic features, wherein the trained machine learning model is trained using a training data set comprising both genomic and non-genomic data for samples from a cohort of individuals diagnosed with the cancer; and output the predicted intra-tumor heterogeneity (ITH) score for the sample.
[0229] In some instances, the disclosed systems may further comprise a sequencer, e.g., a next generation sequencer (also referred to as a massively parallel sequencer). Examples of next generation (or massively parallel) sequencing platforms include, but are not limited to, Roche/454’s Genome Sequencer (GS) FLX system, Illumina/Solexa’ s Genome Analyzer (GA), Illumina’s HiSeq® 2500, HiSeq® 3000, HiSeq® 4000 and NovaSeq® 6000 sequencing systems, Life/APG’s Support Oligonucleotide Ligation Detection (SOLiD) system, Polonator’s G.007 system, Helicos BioSciences’ HeliScope Gene Sequencing system, ThermoFisher Scientific’s Ion Torrent Genexus system, or Pacific Biosciences’ PacBio® RS system. [0230] In some instances, the disclosed systems may be used for determining ITH scores 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).
[0231] In some instances, the disclosed methods for determining ITH scores comprise determining the clonality of short variants identified in a plurality of gene loci. In some instances, the plurality of gene loci may comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 0, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, or more than 1000 gene loci (or any number of gene loci within the range of 1 to more than 1000 gene loci).
[0232] In some instance, the nucleic acid sequence data is acquired using a next generation sequencing technique (also referred to as a massively parallel sequencing technique) having a read-length of less than 400 bases, less than 300 bases, less than 200 bases, less than 150 bases, less than 100 bases, less than 90 bases, less than 80 bases, less than 70 bases, less than 60 bases, less than 50 bases, less than 40 bases, or less than 30 bases.
[0233] In some instances, the determination of an ITH score for a sample from a subject 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.
[0234] In some instances, the disclosed systems may further comprise sample processing and library preparation workstations, microplate-handling robotics, fluid dispensing systems, temperature control modules, environmental control chambers, additional data storage modules, data communication modules (e.g., Bluetooth®, WiFi, intranet, or internet communication hardware and associated software), display modules, one or more local and/or cloud-based software packages (e.g., instrument / system control software packages, sequencing data analysis software packages), etc., or any combination thereof. In some instances, the systems may comprise, or be part of, a computer system or computer network as described elsewhere herein.
Machine learning
[0235] Any of a variety of machine learning approaches & algorithms (where a machine learning model, as referred to herein, comprises a trained machine learning algorithm) may be used in implementing the disclosed methods. For example, the machine learning model may comprise a supervised learning model (z.e., a model trained using labeled sets of training data), an unsupervised learning model (z.e., a model trained using unlabeled sets of training data), a semisupervised learning model (z.e., a model trained using a combination of labeled and unlabeled training data), a self- supervised learning model, or any combination thereof. In some examples, the machine learning model can comprise a deep learning model (z.e., a model comprising many layers of coupled "nodes" that may be trained in a supervised, unsupervised, or semi-supervised manner).
[0236] In some instances, one or more machine learning models (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10 machine learning models), or a combination thereof, may be utilized to implement the disclosed methods.
[0237] In some instances, the one or more machine learning models may comprise statistical methods for analyzing data. The machine learning models may be used for classification and/or regression of data. The machine learning models can include, for example, neural networks, support vector machines, decision trees, ensemble learning (e.g., bagging-based learning, such as random forest, and/or boosting-based learning), k-nearest neighbors algorithms, linear regression-based models, and/or logistic regression-based models. The machine learning models can comprise regularization, such as LI regularization and/or L2 regularization. The machine learning models can include the use of dimensionality reduction techniques (e.g., principal component analysis, matrix factorization techniques, and/or autoencoders) and/or clustering techniques (e.g., hierarchical clustering, k-means clustering, distribution-based clustering, such as Gaussian mixture models, or density -based clustering, such as DBSCAN or OPTICS). The one or more machine learning models can comprise solving, e.g., optimizing, an objective function over multiple iterations based on a training data set. The iterative solving approach can be used even when the machine learning model comprises a model for which there exists a closed- form solution (e.g., linear regression).
[0238] In some instances, the machine learning models can comprise artificial neural networks (ANNs), e.g., deep learning models. For example, the one or more machine learning models/algorithms used for implementing the disclosed methods may include an ANN which can comprise any of a variety of computational motifs / architectures known to those of skill in the art, including, but not limited to, feedforward connections (e.g., skip connections), recurrent connections, fully connected layers, convolutional layers, and/or pooling functions (e.g., attention, including self-attention). The artificial neural networks can comprise differentiable non-linear functions trained by backpropagation.
[0239] Artificial neural networks, e.g., deep learning models, generally comprise an interconnected group of nodes organized into multiple layers of nodes. For example, the ANN architecture may comprise at least an input layer, one or more hidden layers (z.e., intermediate layers), and an output layer. The ANN or deep learning model may comprise any total number of layers (e.g., 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, or more than 20 layers in total), and any number of hidden layers (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, or more than 20 hidden layers), where the hidden layers function as trainable feature extractors that allow mapping of a set of input data to a preferred output value or set of output values. Each layer of the neural network comprises a plurality of nodes (e.g., at least 10, 25, 50, 75 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10,000, or more than 10,000 nodes). A node receives input data (e.g., genomic feature data (such as variant sequence data, methylation status data, etc.), non-genomic feature data (e.g., digital pathology image feature data), or other types of input data (e.g., patient- specific clinical data)) that comes either directly from one or more input data nodes or from the output of one or more nodes in previous layers, and performs a specific operation, e.g., a summation operation. In some cases, a connection from an input to a node is associated with a weight (or weighting factor). In some cases, the node may, for example, sum up the products of all pairs of inputs, Xi, and their associated weights, Wi. In some cases, the weighted sum is offset with a bias, b. In some cases, the output of a node may be gated using a threshold or activation function, f, where f may be a linear or non-linear function. The activation function may be, for example, a rectified linear unit (ReLU) activation function or other function such as a saturating hyperbolic tangent, identity, binary step, logistic, arcTan, softsign, parameteric rectified linear unit, exponential linear unit, softPlus, bent identity, softExponential, Sinusoid, Sine, Gaussian, or sigmoid function, or any combination thereof.
[0240] The weighting factors, bias values, and threshold values, or other computational parameters of the neural network (or other machine learning architecture), can be "taught" or "learned" in a training phase using one or more sets of training data (e.g., 1, 2, 3, 4, 5, or more than 5 sets of training data) and a specified training approach configured to solve, e.g., minimize, a loss function. For example, the adjustable parameters for an ANN (e.g., deep learning model) may be determined based on input data from a training data set using an iterative solver (such as a gradient-based method, e.g., backpropagation), so that the output value(s) that the ANN computes (e.g., a classification of a sample or a prediction of a disease outcome) are consistent with the examples included in the training data set. The training of the model (i.e., determination of the adjustable parameters of the model using an iterative solver) may or may not be performed using the same hardware as that used for deployment of the trained model.
[0241] In some instances, the disclosed methods may comprise retraining any of the machine learning models (e.g., iteratively retraining a previously trained model using one or more training data sets that differ from those used to train the model initially). In some instances, retraining the machine learning model may comprise using a continuous, e.g., online, machine learning model, i.e., where the model is periodically or continuously updated or retrained based on new training data. The new training data may be provided by, e.g., a single deployed local operational system, a plurality of deployed local operational systems, or a plurality of deployed, geographically-distributed operational systems. In some instances, the disclosed methods may employ, for example, pre-trained ANNs, and the pre-trained ANNs can be fine-tuned according to an additional dataset that is inputted into the pre-trained ANN.
Computer systems and networks
[0242] FIG. 5 illustrates an example of a computing device or system in accordance with one embodiment. Device 500 can be a host computer connected to a network. Device 500 can be a client computer or a server. As shown in FIG. 5, device 500 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) 510, input devices 520, output devices 530, memory or storage devices 540, communication devices 560, and nucleic acid sequencers 570. Software 550 residing in memory or storage device 540 may comprise, e.g., an operating system as well as software for executing the methods described herein. Input device 520 and output device 530 can generally correspond to those described herein, and can either be connectable or integrated with the computer. [0243] Input device 520 can be any suitable device that provides input, such as a touch screen, keyboard or keypad, mouse, or voice-recognition device. Output device 530 can be any suitable device that provides output, such as a touch screen, haptics device, or speaker.
[0244] Storage 540 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 560 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 580, Ethernet connection, or any other wire transfer technology) or wirelessly (e.g., Bluetooth®, Wi-Fi®, or any other wireless technology).
[0245] Software module 550, which can be stored as executable instructions in storage 540 and executed by processor(s) 510, 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).
[0246] Software module 550 can also be stored and/or transported within any non-transitory computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described herein, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions. In the context of this disclosure, a computer-readable storage medium can be any medium, such as storage 540, that can contain or store processes for use by or in connection with an instruction execution system, apparatus, or device. Examples of computer- readable storage media may include memory units like hard drives, flash drives and distribute modules that operate as a single functional unit. Also, various processes described herein may be embodied as modules configured to operate in accordance with the embodiments and techniques described above. Further, while processes may be shown and/or described separately, those skilled in the art will appreciate that the above processes may be routines or modules within other processes.
[0247] Software module 550 can also be propagated within any transport medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described above, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions. In the context of this disclosure, a transport medium can be any medium that can communicate, propagate or transport programming for use by or in connection with an instruction execution system, apparatus, or device. The transport readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic or infrared wired or wireless propagation medium.
[0248] Device 500 may be connected to a network (e.g., network 604, as shown in FIG. 6 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.
[0249] Device 500 can be implemented using any operating system, e.g., an operating system suitable for operating on the network. Software module 550 can be written in any suitable programming language, such as C, C++, Java or Python. In various embodiments, application software embodying the functionality of the present disclosure can be deployed in different configurations, such as in a client/server arrangement or through a Web browser as a Web-based application or Web service, for example. In some embodiments, the operating system is executed by one or more processors, e.g., processor(s) 510.
[0250] Device 500 can further include a sequencer 570, which can be any suitable nucleic acid sequencing instrument.
[0251] FIG. 6 illustrates an example of a computing system in accordance with one embodiment. In system 600, device 500 e.g., as described above and illustrated in FIG. 5) is connected to network 604, which is also connected to device 606. In some embodiments, device 606 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. [0252] Devices 500 and 606 may communicate, e.g., using suitable communication interfaces via network 604, such as a Local Area Network (LAN), Virtual Private Network (VPN), or the Internet. In some embodiments, network 604 can be, for example, the Internet, an intranet, a virtual private network, a cloud network, a wired network, or a wireless network. Devices 500 and 606 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 500 and 606 may communicate, e.g., using suitable communication interfaces, via a second network, such as a mobile/cellular network. Communication between devices 500 and 606 may further include or communicate with various servers such as a mail server, mobile server, media server, telephone server, and the like. In some embodiments, Devices 500 and 606 can communicate directly (instead of, or in addition to, communicating via network 604), e.g., via wireless or hardwired communications, such as Ethernet, IEEE 802.11b wireless, or the like. In some embodiments, devices 500 and 606 communicate via communications 608, which can be a direct connection or can occur via a network (e.g., network 604).
[0253] One or all of devices 500 and 606 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 604 according to various examples described herein.
EXAMPLES
[0254] The following examples are included for illustrative purposes only and are not intended to limit the scope of the present disclosure.
Example 1 - Generation of Training and/or Validation Data Through Spatially-Precise Sampling of Tissue Samples
[0255] This section provides a non-limiting example of a process used to generate training and/or validation data for training and/or validating a machine learning model configured to predict ITH scores based on input data comprising genomic features identified in sequencing data derived from a sample from a subject e.g., a patient). Independently of the trained ITH score prediction model, tumor heterogeneity and variant clonality for a cohort of patient samples are being studied using complete genomic profiling (targeted sequencing) and digital pathology tools. [0256] The use of small, precisely positioned (image-guided) sample punches effectively boosts the tumor purity of the sample (through “precision enrichment”), enables high-efficiency, spatially-confirmed detection of somatic alterations and biomarkers, while being cost-reducing relative to both macro -enrichment methods and no enrichment methods (i.e. curling the block without enrichment). The ability to selectively sample small regions of tumor tissue (e.g., approximately 1.5 mm diameter) embedded in surrounding non-tumor tissue enables postenrichment histologic quality control of target capture. The technique is fast and technically facile, and eliminates patient specimen waste.
[0257] Currently, most pathology review and laboratory efforts are focused on enriching samples to create a single tumor derivative sample for performing complete genomic profiling (CGP) (e.g., when more than one tissue punch is taken, the punch samples are aggregated for testing). The emphasis on single specimen enrichment schemes leads to identification of core clonal biomarkers, while spatially-localized subclonal alterations and biomarkers may go undetected or over-detected. Sources of heterogeneity in CGP include tumor spatial biological heterogeneity (i.e., true spatial genomic diversity) and laboratory processing-related heterogeneity (e.g., contamination, fixation and/or processing artifacts, etc.). It has generally been assumed that, regardless of where a tumor specimen is “enriched”, the key actionable tumor driver alterations and biomarkers are detectable within the test’s performance specifications as long as all QC requirements are met. However, more accurate detection of tumor biological heterogeneity has clinical utility. For example, optimal selection of molecular markers for longitudinal tumor recurrence tracking with liquid biopsy sampling necessitates consideration of heterogeneity in primary tumors.
[0258] The Pilot and Phase I multi-punch study will prospectively generate genomics data (DNA and RNA sequencing data based on co-extraction of nucleic acids) for at least 2 and up to 5 spatially-distinct additional tissue sample punches from each incoming clinical sample tissue block depending on the size of the tissue block. In these studies, the spatial-targeting capability of precision enrichment (PE) will be leveraged to perform CGP on distinct, spatially-defined replicates (two or more) from single tumor aliquots. The goal of the study is to characterize spatial genomic heterogeneity of clinically-actionable biomarkers, including those subject to longitudinal tracking. Specific endpoints of this study include: (i) to measure spatial heterogeneity in reportable variants and biomarkers, (ii) to measure spatial heterogeneity in “tracker” monitoring mutations, (iii) to measure spatial heterogeneity in clonal hematopoiesis of indeterminate potential (CHIP), including measurement of the CHIP/tumor ratio, (iv) to measure spatial heterogeneity in other biomarkers, and (v) to measure histologic heterogeneity (e.g., mixed gynecological tumors; sarcomatoid carcinomas; heterologous elements, etc.). In addition, these studies will be used to evaluate how spatial replicate testing can inform determinations of somatic-germline-zygosity (SGZ), clonal hematopoiesis of indeterminate potential (CHIP), tumor mutational burden (TMB), transplant biomarkers, chimerism, tumor purity, and other biomarker calls.
[0259] In addition to processing each of the sample punches independently, another sample specimen will be derived by combining DNA/RNA extracted from all the sample punches for a give tissue block. This sample punch cocktail specimen will also be processed as part of the research study.
[0260] A hematoxylin and eosin (H&E) stained slide from each tissue block will be digitally scanned both prior to and after performing precision sample multi-punching. These whole slide images will be available for digital review and the development of joint digital pathology and genomics derived clonality and tumor heterogeneity algorithms for solid biopsy specimens. The digital pathology-derived ITH score (see Example 2 below) will be analytically validated using results of the multi-punch study.
[0261] Other orthogonal data acquisition modalities that may be used to generate training data and/or to validate the disclosed methods for determining ITH scores (including validation of the digital pathology-derived ITH scores (see Example 2 below) can include, but are not limited to, single cell sequencing data, spatial “omics” data (e.g., spatially -resolved proteomics and/or transcriptomics data derived using, e.g., the CosMx™ system (NanoString, Seattle, WA), Xenium system (10X Genomics, Pleasanton, CA), or MERFISH system (Vizgen, Cambridge, MA)), stimulated Raman spectroscopy data, HiFi sequencing (PacBio, Menlo Park, CA) data, optical coherence tomography (OCT) data, cytometry-time-of-flight (CyTOF) data, multiplexed ion beam imaging (MIBI) data, and/or cyclic immunofluorescence (CycIF) data generated for a cohort of tumor specimens. [0262] FIG. 7 provides a non-limiting schematic illustration of the multi-punch process for generating a data set to, e.g., validate algorithms for determining short variant clonality and intratumor heterogeneity. For each tumor sample (e.g., a high tumor content, formalin-fixed, paraffin embedded (FFPE) tumor tissue block), an H&E stained thin tissue section is prepared (702) and imaged (704) to identify target areas for analysis. At step 706, multiple precision sample punches of the tumor tissue block are performed using the imaged tissue section to guide the punching process. At step 708, nucleic acid molecules are extracted from each of the precision sample punches and used to prepare sequencing libraries for downstream hybrid target capture and sequencing. At step 710, the sequence read data for each precision sample punch is processed using a computational biology / nucleic acid sequencing data analysis pipeline to identify genomic features, e.g., short variants, copy number alterations, etc., that are present in the precision punch samples. This data is reported at step 712 for each precision punch sample and may include, for example, a list of short variants, copy number alterations, etc., identified in the sequence read data as well as an estimate of cancer cell fraction (CCF) and tumor heterogeneity.
[0263] FIG. 8 provides a non-limiting example of a pathology image of a tumor tissue section that illustrates the precision punching process for sampling tumor tissue sections at two locations (DI and D2) as part of generating a data set to validate algorithms for determining short variant clonality and intra-tumor heterogeneity. Each precision sample punch excises a tissue sample of approximately 1.5 mm in diameter.
[0264] FIG. 9 provides a non-limiting example of a pathology image of the tissue section shown in FIG. 8 after performing precision punching to collect intra-tumor samples. Imaging of the tissue sample post-punching enables accurate measurement of the spacing between punch samples.
Example 2 - Training and. Deployment of a Machine Learning Model to Predict ITH Scores
[0265] In the disclosed methods, a tumor specimen ITH score is inferred by a trained machine learning model based on input comprising a plurality of genomic features identified in genomic profiling / targeted sequencing data, including short variant features, copy number features, phylogeny features, or a combination thereof. The machine learning model is trained on training data comprising both genomic data (e.g., short variant feature data, copy number feature data, and/or phylogeny feature data) and non-genomic data (e.g., corresponding intra-tumor heterogeneity scores derived from digital pathology images) for a cohort of subjects (e.g., a cohort of cancer patients). In some instances, the predicted ITH score for a sample may be compared to at least one predetermined ITH score threshold to classify the tumor specimen as, e.g., ITH high or ITH low.
[0266] Examples of sequence read data-derived genomic features that may be utilized in implementing the disclosed methods include, but are not limited to:
• Short variant features, for example: o Total number of short variants (e.g., a count of all short variants detected in the tumor specimen, including known, likely, and/or variants of unknown significance (VUS) and synonymous/noncoding short variants). o Total number of clonal short variants (e.g., a count of the number of short variants considered to be present in every cell of the tumor). o Total number of sub-clonal short variants (e.g., a count of the number of short variants considered to be present in only some of the tumor cells). o A cancer cell fraction (CCF)-derived tumor heterogeneity score. o The presence or absence of a genome doubling event.
■ If genome doubling is present, one may include a total number of pre-genome doubling clonal short variants identified in the sample (for example, one way of estimating the number of pre-genome doubling variants is to count the total number of clonal short variants identified with a multiplicity equal to the major copy number of the genomic region in which the respective short variant occurs).
■ If genome doubling present, one may include a total number of post-genome doubling clonal short variants identified in the sample (for example, one way of estimating the number of post-genome doubling variants is to count the total number of clonal short variants with a multiplicity less than the major copy number of the genomic region in which the respective short variants occurs). ■ Timing of genome doubling (e.g., a measure of the time from initiation of the tumor until the whole genome doubling event has occurred, which can be estimated, for example, based on the ratio of the number of clonal short variants to the number of sub-clonal short variants, as well as by other comparable approaches). o COSMIC insertion-deletion features (e.g., the number of COSMIC insertion-deletion events identified). o dN/dS - the ratio of the number of non-synonymous short variants identified to the number of synonymous short variants identified in a given protein coding gene (this is a measure of the evolutionary pressure/natural selection that a given gene is undergoing; dN/dS can be measured for each of the baited genes in a complete genomic profiling / targeted sequencing assay such that the dN/dS value for each gene interrogated in the assay is a genomic feature).
• Copy-number derived features, for example: o Copy number features as extracted from a copy number model (see, e.g., Macintyre el al. (2018), “Copy-Number Signatures and Mutational Processes in Ovarian Carcinoma”, Nature Genetics 50: 1262-1270). Examples include, but are not limited to, a number of breakpoints per 10 Mb of genomic sequence, a number of breakpoints per 25 Mb of genomic sequence, a number of breakpoints per 50 Mb of genomic sequence, a number of breakpoints per 100 Mb of genomic sequence, a number of breakpoints per chromosome arm for each chromosome, a chromosome arm-level copy number change, a magnitude of a copy number change between any two adjacent copy number segments; a length of each copy number segment; the copy number of segments; a count of contiguous oscillating copy number chains, a fraction of genomic sequence with a copy number that is different from a sample ploidy, a fraction of genomic sequence with a copy number that is different from an integer value, or any combination thereof. o Chromosomal instability features (e.g., for the genomic loci baited in a complete genomic profiling / targeted sequencing assay for solid tumor testing, arm-level aneuploidy can be derived by comparing the log ratio of sequence read counts in tumor DNA to a process matched normal control and calculating signal-to-noise metrics to measure chromosome arm copy number; a chromosome arm can be considered lost or gained if greater than 50% of the arm has been altered; the presence or absence of chromosome arm-level copy number gains and losses can be incorporated as input genomic features in the disclosed methods). o Copy number deviation v.1 (e.g. , the fraction of the genome with a copy number different from the specimen ploidy, weighted across all the autosomal chromosomes, as given by: where Segmenti indicates the ith copy number segment). o Copy number deviation v.2 (e.g., the fraction of the genome with a copy number different from an integer state, as given by: where Segmenti indicates the ith copy number segment, and Segmenti Copy Number is the absolute copy number of Segmenti. o Fraction of genome altered (e.g., the ratio of the sum of the lengths of genomic segments with copy number greater than a specified value, x, and the sum of the lengths of all measured genomic segments). o Fraction of genome altered with a copy number that deviates from an integer state.
• Phylogeny derived features, for example: o Total number of branching events (e.g. , the number of branch points in a phylogenetic tree generated based on the heterogeneous genetic make-up of a tumor sample). o Total number of genomic clusters (e.g., the number of groups of cells (clones) that have the same genomic content). o Number of unique phylogenetic trees representing the specimen (e.g., number of unique phylogenetic trees that correctly account for the sequence read data from the specimen; as noted elsewhere herein, the larger the number of subclones present in the specimen, the larger the uncertainty in the structure of the phylogenetic tree, and the more likely it is that multiple phylogenetic trees can adequately describe the genomic data). [0267] FIG. 10A provides a non-limiting schematic illustration of a process for training a machine learning model to predict ITH scores based on a training data set comprising both genomic feature data and non-genomic data (e.g., digital pathology-derived ITH score data). In this example, the machine learning model is a supervised model that is trained using labeled data, i.e., the training data set comprises genomic feature data (as described above) for a plurality of training sample (e.g., samples from a cohort of patients diagnosed with cancer) and corresponding ITH scores as determined from digital pathology images of the same set of training samples.
[0268] As indicated in FIG. 10A, in some instances the digital pathology-derived ITH scores for the cohort of patient samples can be used as the ground truth labels for the training data set used to train the ITH score prediction model. Methods for evaluating intratumor heterogeneity based on processing of whole slide digital pathology images have been described in detail in U.S. Provisional Patent Application No. 63/463,464, which is incorporated herein by reference in its entirety. In brief, multiple instance learning (a supervised learning approach in which the training data set comprises a set of labeled “bags”, each containing many instances of training example data; see, e.g., Carbonneau et al. (2016), “Multiple Instance Learning: A Survey of Problem Characteristics and Applications”, arXiv: 1612.03365), graph neural networks (neural network models that capture the dependence of graphs via message passing between the nodes of graphs; see, e.g., Zhou et al. (2020), “Graph Neural Networks: A Review of Methods and Applications”, Al Open 1:57-81), or generative adversarial networks (machine learning frameworks for supervised, semi-supervised, unsupervised, or reinforcement learning that comprise two neural networks (a generator and a discriminator) that interact with each other in a zero sum manner to generate output that is consistent with patterns or statistical distributions present in the training data; see, e.g., Creswell et al. (2018), “Generative Adversarial Networks: An Overview”, IEEE Signal Processing 35( 1 ):53-65) can be used to quantify the extent of intra-tumor heterogeneity within a tumor specimen (thereby creating a visual ITH score heatmap as a by-product). The multiple instance learning algorithm can also be enhanced by an additive approach to increase the transparency of the model and facilitate indication of what genomic feature(s) lead to what clinical decision(s). The digital pathology-derived ITH score is not only influenced by genomic features but also by their presence or absence and the spatial relationship between various morphological features, tertiary lymphoid structures, fibroblasts, granulocytes, lymphocytes, stromal cells, vasculature, etc.
[0269] Alternatively, it is also possible to include the digital pathology-derived ITH score as a feature in the training data used to train the model, and use a structured genomic feature (e.g., one of the genomic features from the list of features described above) as the ground truth label.
[0270] As indicated in FIG. 10A, the above-described structured data (e.g., short variant (SV), copy number alteration (CAN) and phylogeny-derived data) and unstructured features (e.g., digital pathology-derived ITH scores used as ground truth labels) can be collated and used to train a machine learning model to quantify a specimen’s intra-tumor heterogeneity, and to subsequently classify the tumor specimen as having, e.g., a high or low intra-tumor heterogeneity score. Examples of machine learning models that can be tested include, but are not limited to, regression-based models (such as logistic regression models), regularization-based models (such as elastic net models or ridge regression models), instance-based models (such as support vector machines or k nearest neighbor models), Bayesian-based models (such as naive-based models or Gaussian naive-based models), clustering-based models (such as expectation maximization (EM) models), ensemble-based models (such as adaptive boosting (AdaBoost) models, bagging models, or gradient boosting machine learning models), or neural-network based models (such as back propagation networks, or stochastic gradient descent networks). Deep learning models (such as convolutional neural networks, recurrent neural networks, or auto-encoders) can also be leveraged.
[0271] Large language models (LLMs) and generative machine learning models can boost existing models by providing seamless integration of heterogeneous data and concepts through their ability to represent complex data as vectors and to capture context-dependent representations. These can be further improved by augmented language models (ALMs) that can combine the flexibility and scale of LLMs with additional mechanisms to improve their reasoning and reliability e.g., augmentation of an existing multi-genomic feature model with the co-mutational landscape of the tumor type that incorporates clonality context, such that prior knowledge of co-occurrence and the mutual exclusivity of driver mutations, passenger mutations, etc., can be used to enhance model performance). [0272] FIG. 10B provides a non-limiting schematic illustration of the deployment of a trained machine learning model to predict ITH scores based on input genomic feature data. As indicated, the trained model can be configured to accept genomic feature data for a tumor specimen as input and output a prediction of an ITH score for the tumor specimen. In some instances, the trained model may be further configured to compare the predicted ITH score (e.g., a continuousvalued ITH score) to at least one predetermined ITH score threshold (determined as described elsewhere herein) and thereby classify the tumor specimen as, e.g., ITH high or ITH low.
[0273] It is possible that some of the features described in the structured genomics feature set are correlated, so it would be worthwhile evaluating this to generate a smaller core set of features for training the Al model and generating an intra-tumor heterogeneity score.
[0274] In addition to the genomic features described above, in some instances, fragmentomics features, DNA methylation features, or other measures of epigenetic modification, can also be incorporated as part of the feature set used as input for training and deploying a machine learning model configured to generate an intra-tumor heterogeneity score. Fragmentomics features, in particular, may provide enhanced model performance for predicting ITH scores for liquid biopsy specimens as compared to predictions for solid biopsy specimens.
[0275] The disclosed methods can be implemented for use in estimating intra-tumor heterogeneity of tumor tissue specimens, or for estimating heterogeneity of liquid tumor specimens e.g., liquid biopsy specimens). The clinical applications of determining an ITH score as a biomarker have been described in, for example, U.S. Provisional Patent Application Nos. 63/322,954 and 63/323,017, the contents of each which are incorporated herein by reference in their entireties.
EXEMPLARY IMPLEMENTATIONS
[0276] Exemplary implementations of the methods and systems described herein include:
1. A method comprising: providing a plurality of nucleic acid molecules obtained from a sample from a subject diagnosed with a cancer; ligating one or more adapters onto one or more nucleic acid molecules from the plurality of nucleic acid molecules; amplifying the one or more ligated nucleic acid molecules from the plurality of nucleic acid molecules; capturing amplified nucleic acid molecules from the amplified nucleic acid molecules; sequencing, by a sequencer, the captured nucleic acid molecules to obtain a plurality of sequence reads that represent the captured nucleic acid molecules; receiving, at one or more processors, sequence read data for the plurality of sequence reads; processing, using the one or more processors, the sequence read data to identify one or more genomic features; providing, using the one or more processors, the one or more genomic features as input to a trained machine learning model configured to predict an intra-tumor heterogeneity (ITH) score for the sample based on the one or more genomic features, wherein the trained machine learning model is trained using a training data set comprising both genomic and digital pathology image data for samples from a cohort of individuals diagnosed with the cancer; and outputting, using the one or more processors, the predicted intra-tumor heterogeneity (ITH) score for the sample.
2. The method of clause 1, further comprising comparing the predicted intra-tumor heterogeneity (ITH) score for the sample to at least one predetermined ITH score threshold to classify the sample.
3. The method of clause 2, wherein the predicted intra-tumor heterogeneity (ITH) score for the sample is compared to a single predetermined ITH score threshold, and the sample is classified as ITH-High if the predicted ITH score is greater than or equal to the single predetermined ITH score threshold; or the sample is classified as ITH-Low if the predicted ITH score is less than the single predetermined ITH score threshold. 4. The method of clause 3, wherein a sample classification of ITH-High is indicative of a shorter duration of the subject’s therapeutic response to an anti-cancer therapy.
5. The method of clause 4, wherein a sample classification of ITH-High is indicative of a shorter duration of the subject’s therapeutic response to an anti-cancer therapy comprising a targeted anti-cancer therapy selected, at least in part, based on knowledge of a driver mutation identified in the sample.
6. The method of clause 3, wherein a sample classification of ITH-Low is indicative of a longer duration of the subject’s therapeutic response to an anti-cancer therapy.
7. The method of clause 6, wherein a sample classification of ITH-Low is indicative of a longer duration of the subject’s therapeutic response to an anti-cancer therapy comprising a targeted anti-cancer therapy selected, at least in part, based on knowledge of a driver mutation identified in the sample.
8. The method of any one of clauses 1 to 7, wherein the cancer is a B cell cancer (multiple myeloma), a melanoma, breast cancer, lung cancer, bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain cancer, central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine cancer, endometrial cancer, cancer of an oral cavity, cancer of a pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel cancer, appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, a cancer of hematological tissue, an adenocarcinoma, an inflammatory myofibroblastic tumor, a gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), acute lymphocytic leukemia (ALL), acute myelocytic leukemia (AML), chronic myelocytic leukemia (CML), chronic lymphocytic leukemia (CLL), polycythemia Vera, Hodgkin lymphoma, nonHodgkin lymphoma (NHL), soft-tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endothelio sarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, bladder carcinoma, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, neuroblastoma, retinoblastoma, follicular lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, hepatocellular carcinoma, thyroid cancer, gastric cancer, head and neck cancer, small cell cancer, essential thrombocythemia, agnogenic myeloid metaplasia, hypereosinophilic syndrome, systemic mastocytosis, familiar hypereosinophilia, chronic eosinophilic leukemia, neuroendocrine cancers, or a carcinoid tumor.
9. The method of any one of clauses 1 to 7, wherein the cancer comprises acute lymphoblastic leukemia (Philadelphia chromosome positive), acute lymphoblastic leukemia (precursor B-cell), acute myeloid leukemia (FLT3+), acute myeloid leukemia (with an IDH2 mutation), anaplastic large cell lymphoma, basal cell carcinoma, B-cell chronic lymphocytic leukemia, bladder cancer, breast cancer (HER2 overexpressed/amplified), breast cancer (HER2+), breast cancer (HR+, HER2-), cervical cancer, cholangiocarcinoma, chronic lymphocytic leukemia, chronic lymphocytic leukemia (with 17p deletion), chronic myelogenous leukemia, chronic myelogenous leukemia (Philadelphia chromosome positive), classical Hodgkin lymphoma, colorectal cancer, colorectal cancer (dMMR/MSI-H), colorectal cancer (KRAS wild type), cryopyrin-associated periodic syndrome, a cutaneous T-cell lymphoma, dermatofibrosarcoma protuberans, a diffuse large B-cell lymphoma, fallopian tube cancer, a follicular B-cell non-Hodgkin lymphoma, a follicular lymphoma, gastric cancer, gastric cancer (HER2+), gastroesophageal junction (GEJ) adenocarcinoma, a gastrointestinal stromal tumor, a gastrointestinal stromal tumor (KIT+), a giant cell tumor of the bone, a glioblastoma, granulomatosis with polyangiitis, a head and neck squamous cell carcinoma, a hepatocellular carcinoma, Hodgkin lymphoma, juvenile idiopathic arthritis, lupus erythematosus, a mantle cell lymphoma, medullary thyroid cancer, melanoma, a melanoma with a BRAF V600 mutation, a melanoma with a BRAF V600E or V600K mutation, Merkel cell carcinoma, multicentric Castleman's disease, multiple hematologic malignancies including Philadelphia chromosome-positive ALL and CML, multiple myeloma, myelofibrosis, a non-Hodgkin’ s lymphoma, a nonresectable subependymal giant cell astrocytoma associated with tuberous sclerosis, a non-small cell lung cancer, a non-small cell lung cancer (ALK+), a non-small cell lung cancer (PD-L1+), a non-small cell lung cancer (with ALK fusion or ROS1 gene alteration), a non- small cell lung cancer (with BRAF V600E mutation), a non- small cell lung cancer (with an EGFR exon 19 deletion or exon 21 substitution (L858R) mutations), a nonsmall cell lung cancer (with an EGFR T790M mutation), ovarian cancer, ovarian cancer (with a BRCA mutation), pancreatic cancer, a pancreatic, gastrointestinal, or lung origin neuroendocrine tumor, a pediatric neuroblastoma, a peripheral T-cell lymphoma, peritoneal cancer, prostate cancer, a renal cell carcinoma, rheumatoid arthritis, a small lymphocytic lymphoma, a soft tissue sarcoma, a solid tumor (MSI-H/dMMR), a squamous cell cancer of the head and neck, a squamous non- small cell lung cancer, thyroid cancer, a thyroid carcinoma, urothelial cancer, a urothelial carcinoma, or Waldenstrom's macroglobulinemia.
10. The method of clause 8 or clause 9, further comprising treating the subject with an anticancer therapy.
11. The method of clause 10, wherein the anti-cancer therapy comprises a targeted anti-cancer therapy.
12. The method of clause 11, wherein the targeted anti-cancer therapy comprises abemaciclib (Verzenio), abiraterone acetate (Zytiga), acalabrutinib (Calquence), ado-trastuzumab emtansine (Kadcyla), afatinib dimaleate (Gilotrif), aldesleukin (Proleukin), alectinib (Alecensa), alemtuzumab (Campath), alitretinoin (Panretin), alpelisib (Piqray), amivantamab-vmjw (Rybrevant), anastrozole (Arimidex), apalutamide (Erleada), asciminib hydrochloride (Scemblix), atezolizumab (Tecentriq), avapritinib (Ayvakit), avelumab (Bavencio), axicabtagene ciloleucel (Yescarta), axitinib (Inlyta), belantamab mafodotin-blmf (Blenrep), belimumab (Benlysta), belinostat (Beleodaq), belzutifan (Welireg), bevacizumab (Avastin), bexarotene (Targretin), binimetinib (Mektovi), blinatumomab (Blincyto), bortezomib (Velcade), bosutinib (Bosulif), brentuximab vedotin (Adcetris), brexucabtagene autoleucel (Tecartus), brigatinib (Alunbrig), cabazitaxel (Jevtana), cabozantinib (Cabometyx), cabozantinib (Cabometyx, Cometriq), canakinumab (Haris), capmatinib hydrochloride (Tabrecta), carfilzomib (Kyprolis), cemiplimab-rwlc (Libtayo), ceritinib (LDK378/Zykadia), cetuximab (Erbitux), cobimetinib (Cotellic), copanlisib hydrochloride (Aliqopa), crizotinib (Xalkori), dabrafenib (Tafinlar), dacomitinib (Vizimpro), daratumumab (Darzalex), daratumumab and hyaluronidase-fihj (Darzalex Faspro), darolutamide (Nubeqa), dasatinib (Sprycel), denileukin diftitox (Ontak), denosumab (Xgeva), dinutuximab (Unituxin), dostarlimab-gxly (Jemperli), durvalumab (Imfinzi), duvelisib (Copiktra), elotuzumab (Empliciti), enasidenib mesylate (Idhifa), encorafenib (Braftovi), enfortumab vedotin-ejfv (Padcev), entrectinib (Rozlytrek), enzalutamide (Xtandi), erdafitinib (Balversa), erlotinib (Tarceva), everolimus (Afinitor), exemestane (Aromasin), fam-trastuzumab deruxtecan-nxki (Enhertu), fedratinib hydrochloride (Inrebic), fulvestrant (Faslodex), gefitinib (Iressa), gemtuzumab ozogamicin (Mylotarg), gilteritinib (Xospata), glasdegib maleate (Daurismo), hyaluronidase-zzxf (Phesgo), ibrutinib (Imbruvica), ibritumomab tiuxetan (Zevalin), idecabtagene vicleucel (Abecma), idelalisib (Zydelig), imatinib mesylate (Gleevec), infigratinib phosphate (Truseltiq), inotuzumab ozogamicin (Besponsa), iobenguane 1131 (Azedra), ipilimumab (Yervoy), isatuximab-irfc (Sarclisa), ivosidenib (Tibsovo), ixazomib citrate (Ninlaro), lanreotide acetate (Somatuline Depot), lapatinib (Tykerb), larotrectinib sulfate (Vitrakvi), lenvatinib mesylate (Lenvima), letrozole (Femara), lisocabtagene maraleucel (Breyanzi), loncastuximab tesirine-lpyl (Zynlonta), lorlatinib (Lorbrena), lutetium Lu 177-dotatate (Lutathera), margetuximab-cmkb (Margenza), midostaurin (Rydapt), mobocertinib succinate (Exkivity), mogamulizumab-kpkc (Poteligeo), moxetumomab pasudotox-tdfk (Lumoxiti), naxitamab-gqgk (Danyelza), necitumumab (Portrazza), neratinib maleate (Nerlynx), nilotinib (Tasigna), niraparib tosylate monohydrate (Zejula), nivolumab (Opdivo), obinutuzumab (Gazyva), ofatumumab (Arzerra), olaparib (Lynparza), olaratumab (Lartruvo), osimertinib (Tagrisso), palbociclib (Ibrance), panitumumab (Vectibix), panobinostat (Farydak), pazopanib (Votrient), pembrolizumab (Keytruda), pemigatinib (Pemazyre), pertuzumab (Perjeta), pexidartinib hydrochloride (Turalio), polatuzumab vedotin-piiq (Polivy), ponatinib hydrochloride (Iclusig), pralatrexate (Folotyn), pralsetinib (Gavreto), radium 223 dichloride (Xofigo), ramucirumab (Cyramza), regorafenib (Stivarga), ribociclib (Kisqali), ripretinib (Qinlock), rituximab (Rituxan), rituximab and hyaluronidase human (Rituxan Hycela), romidepsin (Istodax), rucaparib camsylate (Rubraca), ruxolitinib phosphate (Jakafi), sacituzumab govitecan-hziy (Trodelvy), seliciclib, selinexor (Xpovio), selpercatinib (Retevmo), selumetinib sulfate (Koselugo), siltuximab (Sylvant), sipuleucel-T (Provenge), sirolimus protein-bound particles (Fyarro), sonidegib (Odomzo), sorafenib (Nexavar), sotorasib (Lumakras), sunitinib (Sutent), tafasitamab-cxix (Monjuvi), tagraxofusp-erzs (Elzonris), talazoparib tosylate (Talzenna), tamoxifen (Nolvadex), tazemetostat hydrobromide (Tazverik), tebentafusp-tebn (Kimmtrak), temsirolimus (Torisel), tepotinib hydrochloride (Tepmetko), tisagenlecleucel (Kymriah), tisotumab vedotin-tftv (Tivdak), tocilizumab (Actemra), tofacitinib (Xeljanz), tositumomab (Bexxar), trametinib (Mekinist), trastuzumab (Herceptin), tretinoin (Vesanoid), tivozanib hydrochloride (Fotivda), toremifene (Fareston), tucatinib (Tukysa), umbralisib tosylate (Ukoniq), vandetanib (Caprelsa), vemurafenib (Zelboraf), venetoclax (Venclexta), vismodegib (Erivedge), vorinostat (Zolinza), zanubrutinib (Brukinsa), ziv-aflibercept (Zaltrap), or any combination thereof.
13. The method of any one of clauses 1 to 12, further comprising obtaining the sample from the subject.
14. The method of any one of clauses 1 to 13, wherein the sample comprises a tissue biopsy sample, a liquid biopsy sample, or a normal control.
15. The method of clause 14, wherein the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
16. The method of clause 14, wherein the sample is a liquid biopsy sample and comprises circulating tumor cells (CTCs).
17. The method of clause 14, wherein the sample is a liquid biopsy sample and comprises cell- free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.
18. The method of any one of clauses 1 to 17, wherein the plurality of nucleic acid molecules comprises a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules.
19. The method of clause 18, 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.
20. The method of clause 18, wherein the sample comprises a liquid biopsy sample, and wherein the tumor nucleic acid molecules are derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample, and the non-tumor nucleic acid molecules are derived from a non- tumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample. 21. The method of any one of clauses 1 to 20, wherein the one or more adapters comprise amplification primers, flow cell adaptor sequences, substrate adapter sequences, or sample index sequences.
22. The method of any one of clauses 1 to 21, wherein the captured nucleic acid molecules are captured from the amplified nucleic acid molecules by hybridization to one or more bait molecules.
23. The method of clause 22, 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.
24. The method of any one of clauses 1 to 23, wherein amplifying nucleic acid molecules comprises performing a polymerase chain reaction (PCR) amplification technique, a non-PCR amplification technique, or an isothermal amplification technique.
25. The method of any one of clauses 1 to 24, 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.
26. The method of clause 25, wherein the sequencing comprises massively parallel sequencing, and the massively parallel sequencing technique comprises next generation sequencing (NGS).
27. The method of any one of clauses 1 to 26, wherein the sequencer comprises a next generation sequencer.
28. The method of any one of clauses 1 to 27, wherein one or more of the plurality of sequencing reads overlap one or more gene loci within one or more subgenomic intervals in the sample.
29. The method of clause 28, wherein the one or more gene loci comprises between 10 and 20 loci, between 10 and 40 loci, between 10 and 60 loci, between 10 and 80 loci, between 10 and 100 loci, between 10 and 150 loci, between 10 and 200 loci, between 10 and 250 loci, between 10 and 300 loci, between 10 and 350 loci, between 10 and 400 loci, between 10 and 450 loci, between 10 and 500 loci, between 20 and 40 loci, between 20 and 60 loci, between 20 and 80 loci, between 20 and 100 loci, between 20 and 150 loci, between 20 and 200 loci, between 20 and 250 loci, between 20 and 300 loci, between 20 and 350 loci, between 20 and 400 loci, between 20 and 500 loci, between 40 and 60 loci, between 40 and 80 loci, between 40 and 100 loci, between 40 and 150 loci, between 40 and 200 loci, between 40 and 250 loci, between 40 and 300 loci, between 40 and 350 loci, between 40 and 400 loci, between 40 and 500 loci, between 60 and 80 loci, between 60 and 100 loci, between 60 and 150 loci, between 60 and 200 loci, between 60 and 250 loci, between 60 and 300 loci, between 60 and 350 loci, between 60 and 400 loci, between 60 and 500 loci, between 80 and 100 loci, between 80 and 150 loci, between 80 and 200 loci, between 80 and 250 loci, between 80 and 300 loci, between 80 and 350 loci, between 80 and 400 loci, between 80 and 500 loci, between 100 and 150 loci, between 100 and 200 loci, between 100 and 250 loci, between 100 and 300 loci, between 100 and 350 loci, between 100 and 400 loci, between 100 and 500 loci, between 150 and 200 loci, between 150 and 250 loci, between 150 and 300 loci, between 150 and 350 loci, between 150 and 400 loci, between 150 and 500 loci, between 200 and 250 loci, between 200 and 300 loci, between 200 and 350 loci, between 200 and 400 loci, between 200 and 500 loci, between 250 and 300 loci, between 250 and 350 loci, between 250 and 400 loci, between 250 and 500 loci, between 300 and 350 loci, between 300 and 400 loci, between 300 and 500 loci, between 350 and 400 loci, between 350 and 500 loci, or between 400 and 500 loci.
30. The method of clause 28 or clause 29, wherein the one or more gene loci comprise ABL1, ACVR1B, AKT1, AKT2, AKT3, ALK, ALOX12B, AMER1, APC, AR, ARAF, ARFRP1, ARID1A, ASXL1, ATM, ATR, ATRX, AURKA, AURKB, AXIN1, AXL, BAP1, BARD1, BCL2, BCL2L1, BCL2L2, BCL6, BCOR, BCORL1, BCR, BRAF, BRCA1, BRCA2, BRD4, BRIP1, BTG1, BTG2, BTK, CALR, CARD11, CASP8, CBFB, CBL, CCND1, CCND2, CCND3, CCNE1, CD22, CD274, CD70, CD74, CD79A, CD79B, CDC73, CDH1, CDK12, CDK4, CDK6, CDK8, CDKN1A, CDKN1B, CDKN2A, CDKN2B, CDKN2C, CEBPA, CHEK1, CHEK2, CIC, CREBBP, CRKL, CSF1R, CSF3R, CTCF, CTNNA1, CTNNB1, CUL3, CUL4A, CXCR4, CYP17A1, DAXX, DDR1, DDR2, DIS3, DNMT3A, DOT1L, EED, EGFR, EMSY (Cl lorf30), EP300, EPHA3, EPHB1, EPHB4, ERBB2, ERBB3, ERBB4, ERCC4, ERG, ERRFI1, ESRI, ETV4, ETV5, ETV6, EWSR1, EZH2, EZR, FAM46C, FANCA, FANCC, FANCG, FANCL, FAS, FBXW7, FGF10, FGF12, FGF14, FGF19, FGF23, FGF3, FGF4, FGF6, FGFR1, FGFR2, FGFR3, FGFR4, FH, FLCN, FLT1, FLT3, FOXL2, FUBP1, GABRA6, GATA3, GATA4, GATA6, GID4 (C17orf39), GNA11, GNA13, GNAQ, GNAS, GRM3, GSK3B, H3F3A, HDAC1, HGF, HNF1A, HRAS, HSD3B1, ID3, IDH1, IDH2, IGF1R, IKBKE, IKZF1, INPP4B, IRF2, IRF4, IRS2, JAK1, JAK2, JAK3, JUN, KDM5A, KDM5C, KDM6A, KDR, KEAP1, KEL, KIT, KLHL6, KMT2A (MLL), KMT2D (MLL2), KRAS, LTK, LYN, MAF, MAP2K1, MAP2K2, MAP2K4, MAP3K1, MAP3K13, MAPK1, MCL1, MDM2, MDM4, MED12, MEF2B, MEN1, MERTK, MET, MITF, MKNK1, MLH1, MPL, MRE11A, MSH2, MSH3, MSH6, MST1R, MTAP, MTOR, MUTYH, MYB, MYC, MYCL, MYCN, MYD88, NBN, NF1, NF2, NFE2L2, NFKBIA, NKX2-1, NOTCH1, NOTCH2, NOTCH3, NPM1, NRAS, NT5C2, NTRK1, NTRK2, NTRK3, NUTM1, P2RY8, PALB2, PARK2, PARP1, PARP2, PARP3, PAX5, PBRM1, PDCD1, PDCD1LG2, PDGFRA, PDGFRB, PDK1, PIK3C2B, PIK3C2G, PIK3CA, PIK3CB, PIK3R1, PIM1, PMS2, POLDI, POLE, PPARG, PPP2R1A, PPP2R2A, PRDM1, PRKAR1A, PRKCI, PTCHI, PTEN, PTPN11, PTPRO, QKI, RAC1, RAD21, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54L, RAFI, RARA, RBI, RBM10, REL, RET, RICTOR, RNF43, ROS1, RPTOR, RSPO2, SDC4, SDHA, SDHB, SDHC, SDHD, SETD2, SF3B1, SGK1, SLC34A2, SMAD2, SMAD4, SMARCA4, SMARCB1, SMO, SNCAIP, SOCS1, SOX2, SOX9, SPEN, SPOP, SRC, STAG2, STAT3, STK11, SUFU, SYK, TBX3, TEK, TERC, TERT, TET2, TGFBR2, TIPARP, TMPRSS2, TNFAIP3, TNFRSF14, TP53, TSC1, TSC2, TYRO3, U2AF1, VEGFA, VHL, WHSCI, WHSC1L1, WT1, XPO1, XRCC2, ZNF217, ZNF703, or any combination thereof.
31. The method of clause 28 or clause 29, wherein the one or more gene loci comprise ABL, ALK, ALL, B4GALNT1, BAFF, BCL2, BRAF, BRCA, BTK, CD19, CD20, CD3, CD30, CD319, CD38, CD52, CDK4, CDK6, CML, CRACC, CS1, CTLA-4, dMMR, EGFR, ERBB1, ERBB2, FGFR1-3, FLT3, GD2, HDAC, HER1, HER2, HR, IDH2, IL-ip, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSI-H, mTOR, PARP, PD-1, PDGFR, PDGFRa, PDGFRP, PD-L1, PI3K5, PIGF, PTCH, RAF, RANKL, RET, ROS1, SLAMF7, VEGF, VEGFA, VEGFB, or any combination thereof.
32. The method of any one of clauses 1 to 31, further comprising generating, by the one or more processors, a report indicating the ITH score determined for the sample.
33. The method of clause 32, further comprising transmitting the report to a healthcare provider. 34. The method of clause 33, wherein the report is transmitted via a computer network or a peer- to-peer connection.
35. A method for determining an intra-tumor heterogeneity (ITH) score comprising: receiving, at one or more processors, sequence read data derived from a sample from a subject diagnosed with a cancer; processing, using the one or more processors, the sequence read data to identify one or more genomic features; providing, using the one or more processors, the one or more genomic features as input to a trained machine learning model configured to predict an intra-tumor heterogeneity (ITH) score for the sample based on the one or more genomic features, wherein the trained machine learning model is trained using a training data set comprising both genomic and digital pathology image data for samples from a cohort of individuals diagnosed with the cancer; and outputting, using the one or more processors, the predicted intra-tumor heterogeneity (ITH) score for the sample.
36. The method of clause 35, further comprising comparing the predicted intra-tumor heterogeneity (ITH) score for the sample to at least one predetermined ITH score threshold to classify the sample.
37. The method of clause 36, wherein the predicted intra-tumor heterogeneity (ITH) score for the sample is compared to a single predetermined ITH score threshold, and the sample is classified as ITH-High if the predicted ITH score is greater than or equal to the single predetermined ITH score threshold; or the sample is classified as ITH-Low if the predicted ITH score is less than the single predetermined ITH score threshold.
38. The method of clause 36, wherein the predicted intra-tumor heterogeneity (ITH) score for the sample is compared to a first predetermined ITH score threshold and a second predetermined ITH score threshold, and the sample is classified as ITH-High if the predicted ITH score is greater than or equal to the first predetermined ITH score threshold; the sample is classified as ITH-Low if the predicted ITH score is less than or equal to the second predetermined ITH score threshold; or the sample is classified as ITH-indeterminate if the predicted ITH score is less than the first predetermined ITH score threshold and greater than the second ITH score threshold.
39. The method of clause 37 or clause 38, wherein a sample classification of ITH-High is indicative of a shorter duration of the subject’s therapeutic response to an anti-cancer therapy.
40. The method of clause 39, wherein a sample classification of ITH-High is indicative of a shorter duration of the subject’s therapeutic response to an anti-cancer therapy comprising a targeted anti-cancer therapy selected, at least in part, based on knowledge of a driver mutation identified in the sample.
41. The method of clause 37, or clause 38, wherein a sample classification of ITH-Low is indicative of a longer duration of the subject’s therapeutic response to an anti-cancer therapy.
42. The method of clause 41, wherein a sample classification of ITH-Low is indicative of a longer duration of the subject’s therapeutic response to an anti-cancer therapy comprising a targeted anti-cancer therapy selected, at least in part, based on knowledge of a driver mutation identified in the sample.
43. The method of any one of clauses 2 to 42, wherein the at least one predetermined ITH score threshold is determined based on a statistical analysis of the cohort of individuals diagnosed with the cancer and their associated survival time data.
44. The method of clause 43, wherein the associated survival time data comprises mean overall survival data, median overall survival data, one-year survival data, hazard ratio data, progression free survival data, or any combination thereof.
45. The method of any one of clauses 2 to 44, wherein the at least one predetermined ITH score threshold is different for different anti-cancer therapies. 46. The method of any one of clauses 2 to 45, wherein the at least one predetermined ITH score threshold is different for different cancers.
47. The method of any one of clauses 1 to 46, wherein the one of more genomic features comprise one or more short variant features, one or more copy number features, one or more phylogeny features, or any combination thereof.
48. The method of clause 47, wherein the one or more genomic features comprise one or more short variant features, and the one or more short variant features comprise a total number of short variants detected, a number of clonal variants detected, a number of sub-clonal variants detected, a cancer cell fraction (CCF)-derived tumor heterogeneity score, a presence or absence of a genome doubling event, a number of pre-genome doubling event clonal variants detected, a number of post-genome doubling event clonal variants detected, an estimate of elapsed time between initiation of the disease and a genome doubling event, a COSMIC insertion-deletion feature, a ratio of non- synonymous short variants detected to synonymous short variants detected in one or more protein coding genes, or any combination thereof.
49. The method of clause 47, wherein the one or more genomic features comprise one or more copy number features, and the one or more copy number features comprise a number of breakpoints per 10 Mb of genomic sequence, a number of breakpoints per 25 Mb of genomic sequence, a number of breakpoints per 50 Mb of genomic sequence, a number of breakpoints per 100 Mb of genomic sequence, a number of breakpoints per chromosome arm for each chromosome, a chromosome arm-level copy number change, a magnitude of a copy number change between any two adjacent copy number segments; a length of each copy number segment; the copy number of segments; a count of contiguous oscillating copy number chains, a fraction of genomic sequence with a copy number that is different from a sample ploidy, a fraction of genomic sequence with a copy number that is different from an integer value, or any combination thereof.
50. The method of clause 47, wherein the one or more genomic features comprise one or more phylogeny features, and the one or more phylogeny features comprise a total number of branching events, a total number of genomic clusters, a number of unique phylogenetic trees that correctly account for the sequence read data from the sample, or any combination thereof. 51. The method of any one of clauses 1 to 50, wherein the trained machine learning model comprises a trained regression-based machine learning model, regularization-based machine learning model, instance-based machine learning model, Bayesian-based machine learning model, clustering-based machine learning model, ensemble-based machine learning model, neural network-based machine learning model, graph neural network model, generative adversarial network model, or deep learning-based machine learning model.
52. The method of any one of clauses 1 to 51, wherein the genomic data included in the training data set comprises data for the one or more genomic features identified by processing sequence read data for the cohort of individuals diagnosed with the cancer.
53. The method of any one of clauses 1 to 52, wherein ground truth data labels used for training the machine learning model comprise intra-tumor heterogeneity scores derived from the digital pathology image data.
54. The method of any one of clauses 1 to 53, wherein the trained machine learning model is trained using a training data set that further comprises single cell sequencing data, long-read sequencing data, spatial omics data, cyclic immunofluorescence data, stimulated Raman spectroscopy data, optical coherence tomography data, cytometry by time-of-flight (CyTOF) data, multiplexed ion beam imaging data, or any combination thereof.
55. The method of any one of clauses 1 to 54, wherein the sample comprises a tissue biopsy sample, or a liquid biopsy sample.
56. The method of clause 55, wherein the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
57. The method of clause 55, wherein the sample is a liquid biopsy sample and comprises circulating tumor cells (CTCs).
58. The method of clause 55, wherein the sample is a liquid biopsy sample and comprises cell- free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.
59. The method of any one of clauses 1 to 58, wherein the cancer comprises ovarian cancer, prostate cancer, pancreatic cancer, non-small cell lung cancer, colorectal cancer, or melanoma. 60. The method of any one of clauses 1 to 59, wherein the predicted ITH score for the sample is a pan-cancer ITH score.
61. The method of any one of clauses 1 to 60, wherein the determination of ITH score is used to diagnose or confirm a diagnosis of cancer in the subject.
62. The method of any one of clauses 1 to 61, further comprising selecting an anti-cancer therapy to administer to the subject based on the determination of ITH score.
63. The method of any one of clauses 1 to 62, further comprising determining an effective amount of an anti-cancer therapy to administer to the subject based on the determination of ITH score.
64. The method of any one of clauses 1 to 63, further comprising administering an anti-cancer therapy to the subject based on the determination of ITH score.
65. The method of clause 64, wherein the anti-cancer therapy comprises chemotherapy, radiation therapy, immunotherapy, a targeted therapy, or surgery.
66. A method for diagnosing a disease, the method comprising: diagnosing that a subject has the disease based on a determination of ITH score for a sample from the subject, wherein ITH score is determined according to the method of any one of clauses 1 to 65.
67. A method of selecting an anti-cancer therapy, the method comprising: responsive to determining an ITH score for a sample from a subject, selecting an anticancer therapy for the subject, wherein the ITH score is determined according to the method of any one of clauses 1 to 65.
68. A method of treating a cancer in a subject, comprising: responsive to determining an ITH score for a sample from the subject, administering an effective amount of an anti-cancer therapy to the subject, wherein the ITH score is determined according to the method of any one of clauses 1 to 65. 69. A method for monitoring cancer progression or recurrence in a subject, the method comprising: determining a first ITH score in a first sample obtained from the subject at a first time point according to the method of any one of clauses 1 to 65; determining a second ITH score in a second sample obtained from the subject at a second time point; and comparing the first ITH score to the second ITH score, thereby monitoring the cancer progression or recurrence.
70. The method of clause 69, wherein the second ITH score for the second sample is determined according to the method of any one of clauses 1 to 65.
71. The method of clause 69 or clause 70, further comprising selecting an anti-cancer therapy for the subject in response to the cancer progression.
72. The method of clause 69 or clause 70, further comprising administering an anti-cancer therapy to the subject in response to the cancer progression.
73. The method of clause 69 or clause 70, further comprising adjusting an anti-cancer therapy for the subject in response to the cancer progression.
74. The method of any one of clauses 71 to 73, further comprising adjusting a dosage of the anticancer therapy or selecting a different anti-cancer therapy in response to the cancer progression.
75. The method of clause 74, further comprising administering the adjusted anti-cancer therapy to the subject.
76. The method of any one of clauses 69 to 75, wherein the first time point is before the subject has been administered an anti-cancer therapy, and wherein the second time point is after the subject has been administered the anti-cancer therapy.
77. The method of any one of clauses 69 to 76, wherein the subject has a cancer, is at risk of having a cancer, is being routine tested for cancer, or is suspected of having a cancer.
78. The method of any one of clauses 69 to 77, wherein the cancer is a solid tumor. 79. The method of any one of clauses 69 to 77, wherein the cancer is a hematological cancer.
80. The method of any one of clauses 71 to 79, wherein the anti-cancer therapy comprises chemotherapy, radiation therapy, immunotherapy, a targeted therapy, or surgery.
81. The method of any one of clauses 1 to 65, further comprising determining, identifying, or applying the ITH score for the sample as a diagnostic value associated with the sample.
82. The method of any one of clauses 1 to 65, further comprising generating a genomic profile for the subject based on the determination of ITH score.
83. The method of clause 82, wherein the genomic profile for the subject further comprises results from a comprehensive genomic profiling (CGP) test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof.
84. The method of clause 82 or clause 83, wherein the genomic profile for the subject further comprises results from a nucleic acid sequencing-based test.
85. The method of any one of clauses 82 to 84, further comprising selecting an anti-cancer therapy, administering an anti-cancer therapy, or applying an anti-cancer therapy to the subject based on the generated genomic profile.
86. The method of any one of clauses 1 to 85, wherein the determination of an ITH score for the sample is used in making suggested treatment decisions for the subject.
87. The method of any one of clauses 1 to 85, wherein the determination of an ITH score for the sample is used in applying or administering a treatment to the subject.
88. A system comprising: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: receive sequence read data derived from a sample from a subject diagnosed with a cancer; process the sequence read data to identify one or more genomic features; provide the one or more genomic features as input to a trained machine learning model configured to predict an intra-tumor heterogeneity (ITH) score for the sample based on the one or more genomic features, wherein the trained machine learning model is trained using a training data set comprising both genomic and digital pathology image data for samples from a cohort of individuals diagnosed with the cancer; and output the predicted intra-tumor heterogeneity (ITH) score for the sample.
89. A non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: receive sequence read data derived from a sample from a subject diagnosed with a cancer; process the sequence read data to identify one or more genomic features; provide the one or more genomic features as input to a trained machine learning model configured to predict an intra-tumor heterogeneity (ITH) score for the sample based on the one or more genomic features, wherein the trained machine learning model is trained using a training data set comprising both genomic and digital pathology image data for samples from a cohort of individuals diagnosed with the cancer; and output the predicted intra-tumor heterogeneity (ITH) score for the sample.
[0277] It should be understood from the foregoing that, while particular implementations of the disclosed methods and systems have been illustrated and described, various modifications can be made thereto and are contemplated herein. It is also not intended that the invention be limited by the specific examples provided within the specification. While the invention has been described with reference to the aforementioned specification, the descriptions and illustrations of the preferable embodiments herein are not meant to be construed in a limiting sense. Furthermore, it shall be understood that all aspects of the invention are not limited to the specific depictions, configurations or relative proportions set forth herein which depend upon a variety of conditions and variables. Various modifications in form and detail of the embodiments of the invention will be apparent to a person skilled in the art. It is therefore contemplated that the invention shall also cover any such modifications, variations and equivalents.

Claims

CLAIMS What is claimed is:
1. A method comprising: providing a plurality of nucleic acid molecules obtained from a sample from a subject diagnosed with a cancer; ligating one or more adapters onto one or more nucleic acid molecules from the plurality of nucleic acid molecules; amplifying the one or more ligated nucleic acid molecules from the plurality of nucleic acid molecules; capturing amplified nucleic acid molecules from the amplified nucleic acid molecules; sequencing, by a sequencer, the captured nucleic acid molecules to obtain a plurality of sequence reads that represent the captured nucleic acid molecules; receiving, at one or more processors, sequence read data for the plurality of sequence reads; processing, using the one or more processors, the sequence read data to identify one or more genomic features; providing, using the one or more processors, the one or more genomic features as input to a trained machine learning model configured to predict an intra-tumor heterogeneity (ITH) score for the sample based on the one or more genomic features, wherein the trained machine learning model is trained using a training data set comprising both genomic and digital pathology image data for samples from a cohort of individuals diagnosed with the cancer; and outputting, using the one or more processors, the predicted intra-tumor heterogeneity (ITH) score for the sample.
2. The method of claim 1, further comprising comparing the predicted intra-tumor heterogeneity (ITH) score for the sample to at least one predetermined ITH score threshold to classify the sample.
3. The method of claim 2, wherein the predicted intra-tumor heterogeneity (ITH) score for the sample is compared to a single predetermined ITH score threshold, and the sample is classified as ITH-High if the predicted ITH score is greater than or equal to the single predetermined ITH score threshold; or the sample is classified as ITH-Low if the predicted ITH score is less than the single predetermined ITH score threshold.
4. The method of claim 2, wherein the predicted intra-tumor heterogeneity (ITH) score for the sample is compared to a first predetermined ITH score threshold and a second predetermined ITH score threshold, and the sample is classified as ITH-High if the predicted ITH score is greater than or equal to the first predetermined ITH score threshold; the sample is classified as ITH-Low if the predicted ITH score is less than or equal to the second predetermined ITH score threshold; or the sample is classified as ITH-indeterminate if the predicted ITH score is less than the first predetermined ITH score threshold and greater than the second ITH score threshold.
5. The method of claim 3, wherein a sample classification of ITH-High is indicative of a shorter duration of the subject’s therapeutic response to an anti-cancer therapy.
6. The method of claim 3, wherein a sample classification of ITH-Low is indicative of a longer duration of the subject’s therapeutic response to an anti-cancer therapy.
7. The method of claim 2, wherein the at least one predetermined ITH score threshold is determined based on a statistical analysis of the cohort of individuals diagnosed with the cancer and their associated survival time data.
8. The method of claim 2, wherein the at least one predetermined ITH score threshold is different for different anti-cancer therapies and/or is different for different cancers.
9. The method of claim 1, wherein the one of more genomic features comprise one or more short variant features, one or more copy number features, one or more phylogeny features, or any combination thereof.
10. The method of claim 1, wherein the trained machine learning model comprises a trained regression-based machine learning model, regularization-based machine learning model, instance-based machine learning model, Bayesian -based machine learning model, clusteringbased machine learning model, ensemble-based machine learning model, neural network-based machine learning model, graph neural network model, generative adversarial network model, or deep learning-based machine learning model.
11. The method of claim 1, wherein the genomic data included in the training data set comprises data for the one or more genomic features identified by processing sequence read data for the cohort of individuals diagnosed with the cancer.
12. The method of claim 1, wherein ground truth data labels used for training the machine learning model comprise intra-tumor heterogeneity scores derived from the digital pathology image data.
13. The method of claim 1, wherein the trained machine learning model is trained using a training data set that further comprises single cell sequencing data, long-read sequencing data, spatial omics data, cyclic immunofluorescence data, stimulated Raman spectroscopy data, optical coherence tomography data, cytometry by time-of-flight (CyTOF) data, multiplexed ion beam imaging data, or any combination thereof.
14. The method of claim 1, wherein the sample comprises a tissue biopsy sample, or a liquid biopsy sample.
15. The method of claim 1, wherein the cancer comprises ovarian cancer, prostate cancer, pancreatic cancer, non-small cell lung cancer, colorectal cancer, or melanoma.
16. The method of claim 1, wherein the predicted ITH score for the sample is a pan-cancer ITH score.
17. The method of claim 1, wherein the determination of ITH score is used to diagnose or confirm a diagnosis of cancer in the subject.
18. The method of claim 1, further comprising selecting an anti-cancer therapy to administer to the subject, determining an effective amount of an anti-cancer therapy to administer to the subject, and/or administering an anti-cancer therapy to the subject based on the determination of ITH score.
19. A system comprising: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: receive sequence read data derived from a sample from a subject diagnosed with a cancer; process the sequence read data to identify one or more genomic features; provide the one or more genomic features as input to a trained machine learning model configured to predict an intra-tumor heterogeneity (ITH) score for the sample based on the one or more genomic features, wherein the trained machine learning model is trained using a training data set comprising both genomic and digital pathology image data for samples from a cohort of individuals diagnosed with the cancer; and output the predicted intra-tumor heterogeneity (ITH) score for the sample.
20. A non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: receive sequence read data derived from a sample from a subject diagnosed with a cancer; process the sequence read data to identify one or more genomic features; provide the one or more genomic features as input to a trained machine learning model configured to predict an intra-tumor heterogeneity (ITH) score for the sample based on the one or more genomic features, wherein the trained machine learning model is trained using a training data set comprising both genomic and digital pathology image data for samples from a cohort of individuals diagnosed with the cancer; and output the predicted intra-tumor heterogeneity (ITH) score for the sample.
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US20170091937A1 (en) * 2014-06-10 2017-03-30 Ventana Medical Systems, Inc. Methods and systems for assessing risk of breast cancer recurrence
WO2023183750A1 (en) * 2022-03-23 2023-09-28 Foundation Medicine, Inc. Methods and systems for determining tumor heterogeneity

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