WO2024112752A1 - Procédés pour identifier des associations de thérapie de maladie faussement positives et améliorer le rapport clinique pour des patients - Google Patents

Procédés pour identifier des associations de thérapie de maladie faussement positives et améliorer le rapport clinique pour des patients Download PDF

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WO2024112752A1
WO2024112752A1 PCT/US2023/080685 US2023080685W WO2024112752A1 WO 2024112752 A1 WO2024112752 A1 WO 2024112752A1 US 2023080685 W US2023080685 W US 2023080685W WO 2024112752 A1 WO2024112752 A1 WO 2024112752A1
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cosmic
genomic
sample
deficiency
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PCT/US2023/080685
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Brennan DECKER
Ethan S. SOKOL
Dexter X. JIN
Garrett M. Frampton
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Foundation Medicine, Inc.
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6806—Preparing nucleic acids for analysis, e.g. for polymerase chain reaction [PCR] assay
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • 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
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/156—Polymorphic or mutational markers
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G16B30/10—Sequence alignment; Homology search

Definitions

  • the present disclosure relates generally to methods and systems for analyzing genomic profiling data, and more specifically to methods and systems to identify false positive disease therapy associations for patients due to the presence of passenger mutations and thereby improve clinical reporting for patients.
  • DDR DNA damage repair
  • BER base excision repair
  • NER nucleotide excision repair
  • MMR mismatch repair
  • HR homologous recombination
  • NHEJ non-homologous end joining
  • Homologous recombination deficiency is a biological state defined by loss of function in the homologous recombination repair (HRR) apparatus, often through biallelic inactivation in specific genes of the HRR pathway.
  • Polyadenosine diphosphate-ribose polymerase inhibitors PARPi are synthetic cancer therapeutics that cause catastrophic genomic damage and cell death only in cells that exhibit homologous recombination deficiency.
  • PARP inhibitors include, but are not limited to, olaparib (Lynparza), niraparib (Zejula), rucaparib (Rubraca), and talazoparib (Talzenna).
  • Approved companion diagnostic (CDx) tests and non-CDx therapy associations for PARPi leverage a variety of biomarkers to identify patients whose tumors are HRD positive, and who may therefore benefit from PARPi therapy.
  • HRD biomarkers for PARPi therapy association include genomic loss of heterozygosity (gLOH) in ovarian cancers, and the presence of at least one pathogenic mutation in a specified set of genes associated with the HRR pathway. Specific disease therapy associations vary by drug and by cancer type.
  • HRR pathway genes do not always cause biallelic loss of function and a corresponding homologous repair deficiency, thus causing some patients to receive a positive indication for PARPi treatment when that class of drugs is unlikely to be effective.
  • monoallelic or subclonal passenger mutations in HRR pathway genes are commonly found by chance when a tumor arises from an oncogenic process (e.g., POLE-induced mutagenesis, ultraviolet radiation-induced mutagenesis, tobacco carcinogenesis, and APOB EC mutational processes) that causes accumulation of a large total number of mutations.
  • an oncogenic process e.g., POLE-induced mutagenesis, ultraviolet radiation-induced mutagenesis, tobacco carcinogenesis, and APOB EC mutational processes
  • This distinction is clinically important, as patients with passenger mutations in HRR pathway genes and a high total number of mutations are much more likely to respond to immunotherapy than to PARPi.
  • DDR deficiency biomarkers e.g., HRD biomarkers
  • HRD biomarkers e.g., HRD biomarkers
  • the disclosed methods and systems apply an evidence-backed heuristic model that leverages genomic features that are indicative of cancers comprising a high total number of mutations (e.g., tumor mutational burden (TMB), COSMIC single base substitution (SBS) signatures, indel signatures, micro satellite instability (MSI) status, etc.) and that are indicative of passenger DDR mutations (e.g., passenger HRD mutations) (e.g., genomic features such as germline status, clonality, zygosity predictions, etc.) to identify patients for whom PARPi therapy associations should be de-emphasized and immunotherapy associations (or other treatment associations) should be highlighted to optimize clinical care of these cancer patients.
  • TMB tumor mutational burden
  • SBS COSMIC single base substitution
  • MSI micro satellite instability
  • passenger DDR mutations e.g., passenger HRD mutations
  • genomic features such as germline status, clonality, zygosity predictions, etc.
  • Disclosed herein are methods comprising: providing a plurality of nucleic acid molecules obtained from a sample from a subject; ligating one or more adapters onto one or more nucleic acid molecules from the plurality of nucleic acid molecules; amplifying the one or more ligated nucleic acid molecules from the plurality of nucleic acid molecules; capturing amplified nucleic acid molecules from the amplified nucleic acid molecules; sequencing, by a sequencer, the captured nucleic acid molecules to obtain a plurality of sequence reads that represent the captured nucleic acid molecules; receiving, at one or more processors, sequence read data for the plurality of sequence reads; identifying, using the one or more processors, a therapy-associated mutation in at least one gene associated with DNA damage repair (DDR) deficiency based on the sequence read data; generating, using the one or more processors, genomic feature data for a plurality of genomic features based on the sequence read data; and analyzing, using the one or more processors, the identified therapy-
  • the prediction comprises a prediction that the subject is a candidate for treatment with a DDR deficiency-associated drug.
  • the DDR deficiency comprises homologous recombination deficiency
  • the prediction comprises a prediction that the subject is a candidate for treatment with a homologous recombination deficiency-associated drug.
  • the method further comprises outputting a recommendation that the subject be treated with the homologous recombination deficiency- associated drug.
  • the homologous recombination deficiency-associated drug comprises a first generation PARP inhibitor, a second generation PARP inhibitor, a platinum-based chemotherapy, an ATR inhibitor, or a CHK1 inhibitor.
  • the prediction comprises a prediction that the subject is not a candidate for treatment with a DDR deficiency-associated drug.
  • the DDR deficiency comprises homologous recombination deficiency, and the prediction comprises a prediction that the subject is not a candidate for treatment with a homologous recombination deficiency-associated drug.
  • the therapy-associated mutation in at least one gene associated with homologous recombination deficiency comprises a pathogenic mutation.
  • at least one gene associated with homologous recombination deficiency comprises ATM, BRCA1, BRCA2, BARD1, BRIP1, CDK12, CHEK1, CHEK2, FANCL, PALB2, RAD51B, RAD51C, RAD51D, RAD54L, RAD54B, or any combination thereof.
  • the DDR deficiency comprises homologous recombination deficiency
  • the genomic feature data for the plurality of genomic features comprises a determination of germline status for the therapy-associated mutation in at least one gene associated with homologous recombination deficiency, a determination of homozygous status for the therapy-associated mutation in at least one gene associated with homologous recombination deficiency, a determination of subclonal status for the therapy-associated mutation in at least one gene associated with homologous recombination deficiency a determination that a tumor mutational burden for the sample is greater than a predetermined tumor mutational burden threshold, a determination that a single base substitution signature associated with high tumor mutational burden is present in the sequence read data, a determination that an indel signature associated with high tumor mutational burden is present in the sequence read data, a determination of high microsatellite instability status, a determination that a homologous recombination deficiency mutational signature is
  • the subject is suspected of having or is determined to have cancer.
  • the cancer is a B cell cancer (multiple myeloma), a melanoma, breast cancer, lung cancer, bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain cancer, central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine cancer, endometrial cancer, cancer of an oral cavity, cancer of a pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel cancer, appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, a cancer of hematological tissue, an adenocarcinoma, an inflammatory myofibroblastic tumor, a gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), mye
  • MM multiple myeloma
  • 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 (Scemblix), atezolizum
  • the method further comprises obtaining the sample from the subject.
  • the sample comprises a tissue biopsy sample, a liquid biopsy sample, or a normal control.
  • the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
  • the sample is a liquid biopsy sample and comprises circulating tumor cells (CTCs).
  • the sample is a liquid biopsy sample and comprises cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.
  • the plurality of nucleic acid molecules comprises a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules.
  • the tumor nucleic acid molecules are derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules are derived from a normal portion of the heterogeneous tissue biopsy sample.
  • the sample comprises a liquid biopsy sample, and wherein the tumor nucleic acid molecules are derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample, and the non-tumor nucleic acid molecules are derived from a nontumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.
  • ctDNA circulating tumor DNA
  • cfDNA nontumor, cell-free DNA
  • the one or more adapters comprise amplification primers, flow cell adaptor sequences, substrate adapter sequences, or sample index sequences.
  • the captured nucleic acid molecules are captured from the amplified nucleic acid molecules by hybridization to one or more bait molecules.
  • the one or more bait molecules comprise one or more nucleic acid molecules, each comprising a region that is complementary to a region of a captured nucleic acid molecule.
  • amplifying nucleic acid molecules comprises performing a polymerase chain reaction (PCR) amplification technique, a non-PCR amplification technique, or an isothermal amplification technique.
  • PCR polymerase chain reaction
  • the sequencing comprises use of a massively parallel sequencing (MPS) technique, whole genome sequencing (WGS), whole exome sequencing, targeted sequencing, direct sequencing, or Sanger sequencing technique.
  • MPS massively parallel sequencing
  • WGS whole genome sequencing
  • GNS whole exome sequencing
  • targeted sequencing targeted sequencing
  • direct sequencing direct sequencing
  • Sanger sequencing technique e.g., a sequencing with a massively parallel sequencing
  • NGS next generation sequencing
  • the sequencer comprises a next generation sequencer.
  • one or more of the plurality of sequencing reads overlap one or more gene loci within one or more subgenomic intervals in the sample.
  • the one or more gene loci comprises between 10 and 20 loci, between 10 and 40 loci, between 10 and 60 loci, between 10 and 80 loci, between 10 and 100 loci, between 10 and 150 loci, between 10 and 200 loci, between 10 and 250 loci, between 10 and 300 loci, between 10 and 350 loci, between 10 and 400 loci, between 10 and 450 loci, between 10 and 500 loci, between 20 and 40 loci, between 20 and 60 loci, between 20 and 80 loci, between 20 and 100 loci, between 20 and 150 loci, between 20 and 200 loci, between 20 and 250 loci, between 20 and 300 loci, between 20 and 350 loci, between 20 and 400 loci, between 20 and 500 loci, between 40 and 60 loci, between 40 and 80 loci, between 40 and 100 loci, between 40 and 150 loci, between 40 and 200 and 200 loci, between 20 and 250 loci
  • the one or more gene loci comprise ABL1, ACVR1B, AKT1, AKT2, AKT3, ALK, ALOX12B, AMER1, APC, AR, ARAF, ARFRP1, ARID1A, ASXL1, ATM, ATR, ATRX, AURKA, AURKB, AXIN1, AXL, BAP1, BARD1, BCL2, BCL2L1, BCL2L2, BCL6, BCOR, BCORL1, BCR, BRAF, BRCA1, BRCA2, BRD4, BRIP1, BTG1, BTG2, BTK, CALR, CARD11, CASP8, CBFB, CBL, CCND1, CCND2, CCND3, CCNE1, CD22, CD274, CD70, CD74, CD79A, CD79B, CDC73, CDH1, CDK12, CDK4, CDK6, CDK8, CDKN1A, CDKN1B, CDKN2A, CDKN2B, CDKN2C, CEBPA,
  • the one or more gene loci comprise ABL, ALK, ALL, B4GALNT1, BAFF, BCL2, BRAF, BRCA, BTK, CD19, CD20, CD3, CD30, CD319, CD38, CD52, CDK4, CDK6, CML, CRACC, CS1, CTLA-4, dMMR, EGFR, ERBB1, ERBB2, FGFR1- 3, FLT3, GD2, HDAC, HER1, HER2, HR, IDH2, IL-ip, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSLH, 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 prediction for identifying the subject as a candidate for treatment with a DDR deficiency-associated treatment.
  • the method further comprises transmitting the report to a healthcare provider.
  • the report is transmitted via a computer network or a peer-to-peer connection.
  • DDR DNA damage repair
  • the method comprising: receiving, at one or more processors, sequence read data for a plurality of sequence reads derived from the sample from the subject; identifying, using the one or more processors, a therapy-associated mutation in at least one gene associated with DNA damage repair (DDR) deficiency based on the sequence read data; generating, using the one or more processors, genomic feature data for a plurality of genomic features based on the sequence read data; and analyzing, using the one or more processors, the identified therapy-associated mutation and the genomic feature data for the plurality of genomic features using a classification model configured to process genomic feature data as input and output a prediction for identifying the subject as a candidate for treatment with a DDR deficiency-associated treatment.
  • a classification model configured to process genomic feature data as input and output a prediction for identifying the subject as a candidate for treatment with a DDR deficiency-associated treatment.
  • the prediction comprises a prediction that the subject is a candidate for treatment with a DDR deficiency-associated drug.
  • the DDR deficiency comprises homologous recombination deficiency
  • the prediction comprises a prediction that the subject is a candidate for treatment with a homologous recombination deficiency-associated drug.
  • the method further comprises outputting a recommendation that the subject be treated with the homologous recombination deficiency- associated drug.
  • the homologous recombination deficiency-associated drug comprises a first generation PARP inhibitor, a second generation PARP inhibitor, a platinum-based chemotherapy, an ATR inhibitor, or a CHK1 inhibitor.
  • the prediction comprises a prediction that the subject is not a candidate for treatment with a DDR deficiency-associated drug.
  • the DDR deficiency comprises homologous recombination deficiency
  • the prediction comprises a prediction that the subject is not a candidate for treatment with a homologous recombination deficiency-associated drug.
  • the therapy-associated mutation in at least one gene associated with homologous recombination deficiency comprises a pathogenic mutation.
  • the at least one gene associated with homologous recombination deficiency comprises ATM, BRCA1, BRCA2, BARD1, BRIP1, CDK12, CHEK1, CHEK2, FANCL, PALB2, RAD51B, RAD51C, RAD51D, RAD54L, RAD54B, or any combination thereof.
  • the DDR deficiency comprises homologous recombination deficiency
  • the genomic feature data for the plurality of genomic features comprises a determination of germline status for the therapy-associated mutation in at least one gene associated with homologous recombination deficiency, a determination of homozygous status for the therapy-associated mutation in at least one gene associated with homologous recombination deficiency, a determination of subclonal status for the therapy-associated mutation in at least one gene associated with homologous recombination deficiency a determination that a tumor mutational burden for the sample is greater than a predetermined tumor mutational burden threshold, a determination that a single base substitution signature associated with high tumor mutational burden is present in the sequence read data, a determination that an indel signature associated with high tumor mutational burden is present in the sequence read data, a determination of high microsatellite instability status, a determination that a homologous recombination deficiency mutational signature is
  • the genomic feature data comprises a determination of germline status for the therapy-associated mutation in at least one gene associated with homologous recombination deficiency
  • the determination of germline status comprises: identifying one or more proxy genomic sequences for a genomic sequence comprising the therapy-associated mutation in at least one gene associated with homologous recombination deficiency; comparing an observed frequency of the sequence comprising the therapy-associated mutation in at least one gene associated with homologous recombination deficiency to a centrality measure of observed frequencies of the one or more proxy genomic sequences; and based on the comparison, characterizing the genomic sequence of interest as either germline or somatic.
  • the one or more proxy genomic sequences comprise a single nucleotide polymorphism (SNP). In some embodiments, the one or more proxy genomic sequences comprise an allele of the at least one gene associated with homologous recombination deficiency.
  • SNP single nucleotide polymorphism
  • the centrality measure of observed frequencies of the one or more proxy genomic sequences comprises a mean, median, or mode of the observed frequencies of the one or more proxy genomic sequences.
  • the genomic feature data comprises a determination that a tumor mutational burden for the sample is greater than a predetermined tumor mutational burden threshold, and wherein evaluation of the tumor mutational burden comprises: determining a sequence of a set of subgenomic intervals from the sample using hybrid capture and nextgeneration sequencing, wherein the set of subgenomic intervals are from a predetermined set of genes; and determining a value for the tumor mutational burden by counting a number of one or more somatic alterations in the set of subgenomic intervals, and excluding from said number of one or more somatic alterations: (i) a functional alteration in a subgenomic interval, wherein the functional alteration is an alteration that, compared with a reference sequence, has an effect on cell division, growth or survival, and wherein the functional alteration is identified as such based on inclusion in a database of functional alterations; and (ii) a germline alteration in a subgenomic interval, thereby evaluating the tumor mutational burden in the sample
  • the genomic feature data comprises a determination that a tumor mutational burden for the sample is greater than a predetermined tumor mutational burden threshold, and wherein the predetermined tumor mutational burden threshold is equal to 20, 30, 40, 50, 60, 70, or 80 mutations.
  • the genomic feature data comprises a determination that a single base substitution signature associated with high tumor mutational burden is present in the sequence read data, and wherein the single base substitution signature comprises a COSMIC single base substitution mutational signature, an ultraviolet (UV) mutational signature, a POLE mutational signature, a mismatch repair mutational signature, an APOBEC mutational signature, an alkylating agent mutational signature, a tobacco signature, or any combination thereof.
  • the single base substitution signature comprises a COSMIC single base substitution mutational signature, an ultraviolet (UV) mutational signature, a POLE mutational signature, a mismatch repair mutational signature, an APOBEC mutational signature, an alkylating agent mutational signature, a tobacco signature, or any combination thereof.
  • the COSMIC single base substitution mutational signature comprises a COSMIC SBS1 signature, a COSMIC SBS2 signature, a COSMIC SBS3 signature, a COSMIC SBS4 signature, a COSMIC SBS5 signature, a COSMIC SBS6 signature, a COSMIC SBS7 signature, a COSMIC SBS8 signature, a COSMIC SBS9 signature, a COSMIC SBS10 signature, a COSMIC SBS11 signature, a COSMIC SBS12 signature, a COSMIC SBS13 signature, a COSMIC SBS14 signature, a COSMIC SBS15 signature, a COSMIC SBS16 signature, a COSMIC SBS17 signature, a COSMIC SBS18 signature, a COSMIC SBS19 signature, a COSMIC SBS20 signature, a COSMIC SBS21 signature, a COSMIC SBS22 signature, a COSMIC SBS23 signature, a COSMIC
  • the ultraviolet (UV) mutational signature comprises a COSMIC signature 7.
  • the POLE mutational signature comprises a COSMIC signature 10.
  • the classification model comprises a heuristic model.
  • the heuristic model comprises a decision tree.
  • the classification model comprises a machine learning model.
  • the machine learning model comprises a supervised learning model.
  • the supervised learning model comprises an artificial neural network, a decision tree model, a random forest model, a k-nearest neighbors model, or a support vector machine model.
  • the machine learning model has been trained using a training data set comprising data generated for the plurality of genomic features in samples from a cohort of patients that have been diagnosed with DDR deficiency.
  • the DDR deficiency comprises homologous recombination deficiency.
  • the sample comprises a tissue biopsy sample or a liquid biopsy sample.
  • the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
  • the plurality of sequence reads are derived from the sample using a targeted sequencing method. In some embodiments, the plurality of sequence reads are derived from the sample using a whole exome sequencing method. In some embodiments, the plurality of sequence reads are derived from the sample using a whole genome sequencing method.
  • the prediction is used to diagnose or confirm a diagnosis of disease in the subject.
  • the disease is cancer.
  • the method further comprises selecting an anti-cancer therapy to administer to the subject based on the prediction.
  • the method further comprises determining an effective amount of an anti-cancer therapy to administer to the subject based on the prediction.
  • the method further comprises administering the anti-cancer therapy to the subject based on the prediction.
  • the anti-cancer therapy comprises chemotherapy, radiation therapy, immunotherapy, a targeted therapy, or surgery.
  • the method further comprises the determining, identifying, or applying the prediction for identifying the subject as a candidate for treatment with a DDR deficiency-associated treatment as a diagnostic value associated with the sample.
  • the method further comprises generating a genomic profile for the subject based on the prediction.
  • the genomic profile for the subject further comprises results from a comprehensive genomic profiling (CGP) test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof.
  • the genomic profile for the subject further comprises results from a nucleic acid sequencing-based test.
  • the 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.
  • the prediction is used in making suggested treatment decisions for the subject. In some embodiments, the prediction is used in applying or administering a treatment to the subject.
  • Also disclosed herein are systems comprising: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: receive sequence read data for a plurality of sequence reads derived from the sample from the subject; identify a therapy- associated mutation in at least one gene associated with DNA damage repair (DDR) deficiency based on the sequence read data; generate genomic feature data for a plurality of genomic features based on the sequence read data; and analyze the identified therapy-associated mutation and the genomic feature data for the plurality of genomic features using a classification model configured to process genomic feature data as input and output a prediction for identifying the subject as a candidate for treatment with a DDR deficiency-associated treatment.
  • DDR DNA damage repair
  • non-transitory computer-readable storage media storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: receive sequence read data for a plurality of sequence reads derived from the sample from the subject, identify a therapy-associated mutation in at least one gene associated with DNA damage repair (DDR) deficiency based on the sequence read data; generate genomic feature data for a plurality of genomic features based on the sequence read data; and analyze the identified therapy-associated mutation and the genomic feature data for the plurality of genomic features using a classification model configured to process genomic feature data as input and output a prediction for identifying the subject as a candidate for treatment with a DDR deficiency-associated treatment.
  • DDR DNA damage repair
  • FIG. 1 provides a non-limiting example of a process flowchart for outputting a prediction for identifying a subject as being a candidate for treatment with a DDR deficiency-associated treatment.
  • FIG. 2 depicts an exemplary computing device or system in accordance with one embodiment of the present disclosure.
  • FIG. 3 depicts an exemplary computer system or computer network, in accordance with some instances of the systems described herein.
  • FIG. 4 provides a non-limiting example of a process flowchart for outputting a prediction for identifying a subject as being a candidate for treatment with a HRD -associated treatment.
  • FIGS. 5A-C provide non-limiting examples of plots of percent genomic loss of heterozygosity (% gLOH) for different types of cancer that harbor BRCA1 and/or BRCA2 mutations.
  • FIG. 6A provides a non-limiting example of a Venn diagram illustrating the degree of overlap between patients who are HR deficient and patients who have a microsatellite instability status of MSLH.
  • FIG. 6B provides a non-limiting example of a plot of logio(Pval) versus log2(OR) for patients diagnosed with different types of cancer.
  • FIGS. 7A-C provide non-limiting examples of plots of different genomic features as a function of tumor mutational burden (TMB) range.
  • FIG. 7A plot of BRCAm prevalence versus TMB range.
  • FIG. 7B plot of allelic status proportion versus TMB range.
  • FIG. 7C plot of percent genomic loss of heterozygosity (% gLOH) versus TMB range.
  • FIGS. 8A-B provide non-limiting examples of plots of prostate specific antigen (PSA) concentration in blood samples from two different prostate cancer patients as a function of time following the initiation of different treatments.
  • FIG. 8A plot of PSA concentration data for patient 1.
  • FIG. 8B plot of PSA concentration data for patient 2.
  • DETAILED DESCRIPTION
  • DDR deficiency biomarkers e.g., HRD biomarkers
  • HRD biomarkers e.g., HRD biomarkers
  • the disclosed methods and systems apply an evidence-backed heuristic model that leverages genomic features that are indicative of cancers comprising a high total number of mutations (e.g., tumor mutational burden (TMB), COSMIC single base substitution (SBS) signatures, microsatellite instability (MSI) status, etc.) and that are indicative of passenger DDR mutations (e.g., passenger HRD mutations) (e.g., genomic features such as germline status, clonality, zygosity predictions, etc.) to identify patients for whom PARPi therapy associations should be de-emphasized and immunotherapy associations (or other treatment associations) should be highlighted to optimize clinical care of these cancer patients.
  • TMB tumor mutational burden
  • SBS COSMIC single base substitution
  • MSI microsatellite instability
  • passenger DDR mutations e.g., passenger HRD mutations
  • genomic features such as germline status, clonality, zygosity predictions, etc.
  • methods for identifying a subject as a candidate for treatment with a DNA damage repair (DDR) deficiency-associated treatment comprising: receiving, at one or more processors, sequence read data for a plurality of sequence reads derived from the sample from the subject; identifying, using the one or more processors, a therapy-associated mutation in at least one gene associated with DNA damage repair (DDR) deficiency based on the sequence read data; generating, using the one or more processors, genomic feature data for a plurality of genomic features based on the sequence read data; and analyzing, using the one or more processors, the identified therapy-associated mutation and the genomic feature data for the plurality of genomic features using a classification model configured to process genomic feature data as input and output a prediction for identifying the subject as a candidate for treatment with a DDR deficiency-associated treatment.
  • a classification model configured to process genomic feature data as input and output a prediction for identifying the subject as a candidate for treatment with a DDR deficiency-associated treatment.
  • the prediction comprises a prediction that the subject is a candidate for treatment with a DDR deficiency-associated drug.
  • the DDR deficiency comprises, e.g., homologous recombination deficiency
  • the prediction comprises a prediction that the subject is a candidate for treatment with a homologous recombination deficiency- associated drug.
  • the prediction comprises a prediction that the subject is not a candidate for treatment with a DDR deficiency-associated drug.
  • the DDR deficiency comprises, e.g., homologous recombination deficiency
  • the prediction comprises a prediction that the subject is not a candidate for treatment with a homologous recombination deficiency- associated drug.
  • the DDR deficiency comprises, e.g., homologous recombination deficiency
  • the at least one gene associated with homologous recombination deficiency comprises ATM, BRCA1, BRCA2, BARD1, BRIP1, CDK12, CHEK1, CHEK2, FANCL, PALB2, RAD51B, RAD51C, RAD51D, RAD54L, RAD54B, or any combination thereof.
  • the DDR deficiency comprises, e.g., homologous recombination deficiency
  • the genomic feature data for the plurality of genomic features comprises a determination of germline status for the therapy-associated mutation in at least one gene associated with homologous recombination deficiency, a determination of homozygous status for the therapy-associated mutation in at least one gene associated with homologous recombination deficiency, a determination of subclonal status for the therapy-associated mutation in at least one gene associated with homologous recombination deficiency a determination that a tumor mutational burden for the sample is greater than a predetermined tumor mutational burden threshold, a determination that a single base substitution signature associated with high tumor mutational burden is present in the sequence read data, a determination that an indel signature associated with high tumor mutational burden is present in the sequence read data, a determination of high microsatellite instability status, a determination that a homologous recombination defic
  • “About” and “approximately” shall generally mean an acceptable degree of error for the quantity measured given the nature or precision of the measurements. Exemplary degrees of error are within 20 percent (%), typically, within 10%, and more typically, within 5% of a given value or range of values.
  • the terms “comprising” (and any form or variant of comprising, such as “comprise” and “comprises”), “having” (and any form or variant of having, such as “have” and “has”), “including” (and any form or variant of including, such as “includes” and “include”), or “containing” (and any form or variant of containing, such as “contains” and “contain”), are inclusive or open-ended and do not exclude additional, un-recited additives, components, integers, elements, or method steps.
  • the terms “individual,” “patient,” or “subject” are used interchangeably and refer to any single animal, e.g., a mammal (including such non-human animals as, for example, dogs, cats, horses, rabbits, zoo animals, cows, pigs, sheep, and non-human primates) for which treatment is desired.
  • a mammal including such non-human animals as, for example, dogs, cats, horses, rabbits, zoo animals, cows, pigs, sheep, and non-human primates
  • the individual, patient, or subject herein is a human.
  • cancer and “tumor” are used interchangeably herein. These terms refer to the presence of cells possessing characteristics typical of cancer-causing cells, such as uncontrolled proliferation, immortality, metastatic potential, rapid growth and proliferation rate, and certain characteristic morphological features. Cancer cells are often in the form of a tumor, but such cells can exist alone within an animal, or can be a non-tumorigenic cancer cell, such as a leukemia cell. These terms include a solid tumor, a soft tissue tumor, or a metastatic lesion. As used herein, the term “cancer” includes premalignant, as well as malignant cancers.
  • genomic interval refers to a portion of a genomic sequence.
  • subject interval refers to a subgenomic interval or an expressed subgenomic interval (e.g., the transcribed sequence of a subgenomic interval).
  • variant sequence As used herein, the terms “variant sequence” or “variant” are used interchangeably and refer to a modified nucleic acid sequence relative to a corresponding “normal” or “wild-type” sequence. In some instances, a variant sequence may be a “short variant sequence” (or “short variant”), i.e., a variant sequence of less than about 50 base pairs in length.
  • allele frequency and “allele fraction” are used interchangeably herein and refer to the fraction of sequence reads corresponding to a particular allele relative to the total number of sequence reads for a genomic locus.
  • variant allele frequency and “variant allele fraction” are used interchangeably herein and refer to the fraction of sequence reads corresponding to a particular variant allele relative to the total number of sequence reads for a genomic locus.
  • the term “passenger mutation” refers to a mutation that has minimal biological consequence, but that occurs in a cell that coincidentally or subsequently may become a detectable clone in a tumor.
  • the section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.
  • the disclosed methods for identifying false-positive disease therapy associations utilize an evidence-backed heuristic model that takes into account genomic features that are (i) indicative of cancers comprising a high total number of mutations and that are (ii) indicative of passenger DDR mutations (e.g., passenger HRD mutations) to identify patients for whom a given disease therapy association (e.g., PARPi treatment for HRD positive patients) should be de-emphasized, and immunotherapy associations (or other treatment associations) should be highlighted to optimize the clinical care of these cancer patients.
  • a given disease therapy association e.g., PARPi treatment for HRD positive patients
  • FIG. 1 provides a non-limiting example of a flowchart for a process 100 for outputting a prediction for identifying a subject as being a candidate for treatment with a DDR deficiency- associated treatment.
  • 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.
  • some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted.
  • additional steps may be performed in combination with the process 100.
  • sequence read data for a plurality of sequence reads derived from a sample from a subject is received, e.g., by one or more processors configured to perform a computer-implemented method based on process 100.
  • the sample may comprise, e.g., a tissue biopsy sample or a liquid biopsy sample.
  • the sample may be a liquid biopsy sample, and may comprise, e.g., blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
  • the sequence read data may be derived from sequencing of a single tissue biopsy sample collected from a single region of a tumor in the subject. In some instances, the sequence read data may be derived from sequencing of a plurality of tissue biopsy samples collected from multiple regions of a tumor in the subject. In some instances, the sequence read data may be derived from a single cell sequencing method as opposed to a bulk tumor sequencing method. In some instances, the sequence read data may be derived from sequencing circulating tumor DNA in a liquid biopsy sample.
  • the plurality of sequence reads may be derived from the sample using a targeted sequencing method. In some instances, the plurality of sequence reads may be derived from the sample using from a whole exome sequencing method. In some instances, the plurality of sequence reads may be derived from the sample using a whole genome sequencing method.
  • a therapy-associated mutation in at least one gene associated with DNA damage repair (DDR) deficiency is identified based on the sequence read data.
  • DDR DNA damage repair
  • the DDR deficiency may comprise a homologous recombination deficiency (HRD), and the therapy-associated mutation in the at least one gene associated with homologous recombination deficiency may comprise a pathogenic mutation.
  • the DDR deficiency may comprise a homologous recombination deficiency (HRD)
  • the at least one gene associated with homologous recombination deficiency may comprise ATM, BRCA1, BRCA2, BARD1, BRIP1, CDK12, CHEK1, CHEK2, FANCL, PALB2, RAD51B, RAD51C, RAD51D, RAD54L, RAD54B, or any combination thereof.
  • genomic feature data for a plurality of genomic features associated with the DDR deficiency is generated based on the sequence read data.
  • the DDR deficiency may comprise homologous recombination deficiency
  • the genomic feature data for the plurality of genomic features may comprise a determination of germline status for the therapy-associated mutation in at least one gene associated with homologous recombination deficiency, a determination of homozygous status for the therapy-associated mutation in at least one gene associated with homologous recombination deficiency, a determination of subclonal status for the therapy-associated mutation in at least one gene associated with homologous recombination deficiency a determination that a tumor mutational burden for the sample is greater than a predetermined tumor mutational burden threshold, a determination that a single base substitution signature associated with high tumor mutational burden is present in the sequence read data, a determination that an indel signature associated with high tumor mutational burden is present in the sequence read data, a determination of high microsatellite instability status, a determination that a homologous recombination deficiency
  • the genomic feature data may comprise a determination of germline status for the therapy-associated mutation in at least one gene associated with homologous recombination deficiency
  • the determination of germline status may comprise: identifying one or more proxy genomic sequences for a genomic sequence comprising the therapy- associated mutation in at least one gene associated with homologous recombination deficiency; comparing an observed frequency of the sequence comprising the therapy-associated mutation in at least one gene associated with homologous recombination deficiency to a centrality measure of observed frequencies of the one or more proxy genomic sequences; and based on the comparison, characterizing the genomic sequence of interest as either germline or somatic.
  • one or more proxy genomic sequences may comprise 1, 2, 3, 4, 5, or more than 5 proxy genomic sequences.
  • the one or more proxy genomic sequences comprise a single nucleotide polymorphism (SNP).
  • the one or more proxy genomic sequences comprise an allele of the at least one gene associated with homologous recombination deficiency.
  • the centrality measure of observed frequencies of the one or more proxy genomic sequences may comprise a mean, median, or mode of the observed frequencies of the one or more proxy genomic sequences.
  • the genomic feature data may comprise a determination that a tumor mutational burden for the sample is greater than a predetermined tumor mutational burden threshold, and where evaluation of the tumor mutational burden comprises: determining a sequence of a set of subgenomic intervals from the sample using hybrid capture and nextgeneration sequencing, wherein the set of subgenomic intervals are from a predetermined set of genes; and determining a value for the tumor mutational burden by counting a number of one or more somatic alterations in the set of subgenomic intervals, and excluding from said number of one or more somatic alterations: (i) a functional alteration in a subgenomic interval, wherein the functional alteration is an alteration that, compared with a reference sequence, has an effect on cell division, growth or survival, and wherein the functional alteration is identified
  • the genomic feature data may comprise a determination that a tumor mutational burden for the sample is greater than a predetermined tumor mutational burden threshold, and wherein the predetermined tumor mutational burden threshold is equal to 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 mutations.
  • the genomic feature data may comprise a determination that a mutation occurs in a single gene (e.g., a determination that a single mutation occurs in a single gene). In some instances, the genomic feature data may comprise a determination that a single mutation occurs in each of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10 genes. In some instances, the genomic feature data may comprise a determination that two, three, four, or more than four mutations occur in each of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10 genes.
  • the genomic feature data may comprise a determination that a single base substitution signature associated with high tumor mutational burden is present in the sequence read data, and the single base substitution signature may comprise a COSMIC single base substitution mutational signature, an ultraviolet (UV) mutational signature, a POLE mutational signature, a mismatch repair (MMR) mutational signature, an APOB EC mutational signature, an alkylating agent mutational signature, a tobacco signature, or any combination thereof.
  • UV ultraviolet
  • POLE mutational signature a mismatch repair (MMR) mutational signature
  • MMR mismatch repair
  • APOB EC mutational signature an alkylating agent mutational signature
  • tobacco signature or any combination thereof.
  • the single base substitution signature may comprise a COSMIC single base substitution mutational signature
  • the COSMIC single base substitution mutational signature may comprise a COSMIC SBS1 signature, a COSMIC SBS2 signature, a COSMIC SBS3 signature, a COSMIC SBS4 signature, a COSMIC SBS5 signature, a COSMIC SBS6 signature, a COSMIC SBS7 signature, a COSMIC SBS8 signature, a COSMIC SBS9 signature, a COSMIC SBS10 signature, a COSMIC SBS11 signature, a COSMIC SBS12 signature, a COSMIC SBS13 signature, a COSMIC SBS14 signature, a COSMIC SBS15 signature, a COSMIC SBS16 signature, a COSMIC SBS17 signature, a COSMIC SBS18 signature, a COSMIC SBS19 signature, a COSMIC SBS20 signature, a COSMIC SBS21 signature, a COSMIC
  • COSMIC SBS82 signature a COSMIC SBS83 signature
  • COSMIC SBS84 signature a COSMIC SBS84 signature
  • COSMIC SBS85 signature a COSMIC SBS86 signature
  • COSMIC SBS87 signature a COSMIC SBS87 signature
  • COSMIC SBS88 signature a COSMIC SBS89 signature
  • COSMIC SBS90 signature a COSMIC SBS90 signature
  • COSMIC SBS91 signature a COSMIC SBS92 signature
  • COSMIC SBS93 signature a COSMIC SBS91 signature
  • COSMIC SBS92 signature a COSMIC SBS92 signature
  • COSMIC SBS93 signature a COSMIC SBS93 signature
  • COSMIC SBS94 signature or any combination thereof.
  • the single base substitution signature may comprise an ultraviolet (UV) mutational signature (e.g., a characteristic genomic mutational pattern associated with elevated tumor mutational burden (TMB) via the formation of pyrimidine-pyrimidine photodimers).
  • UV ultraviolet
  • the ultraviolet (UV) mutational signature may comprise a COSMIC signature 7.
  • the single base substitution signature may comprise a POLE mutational signature (z.e., a mutational signature associated with the POLE mutational process that comprises high proportions of C>A mutations at TCT loci, C>T mutations at TCG loci and T>G mutations at TTT loci).
  • the POLE mutational signature may comprise a COSMIC signature 10.
  • the single base substitution signature may comprise a mismatch repair (MMR) mutational signature (e.g., a characteristic genomic mutational pattern associated with DNA mismatch repair (MMR) deficiency).
  • MMR mismatch repair
  • the single base substitution signature may comprise an APOBEC mutational signature (e.g., a characteristic genomic mutational pattern that is generated by the activity of the APOBEC3A (A3A) and APOBEC3B (A3B) cytosine deaminases).
  • an APOBEC mutational signature e.g., a characteristic genomic mutational pattern that is generated by the activity of the APOBEC3A (A3A) and APOBEC3B (A3B) cytosine deaminases).
  • the single base substitution signature may comprise an alkylating agent mutational signature (e.g., characteristic genomic mutational patterns that are generated by the action of alkylating agents add an alkyl group (CnHin+i) to DNA.
  • alkylating agent mutational signature e.g., characteristic genomic mutational patterns that are generated by the action of alkylating agents add an alkyl group (CnHin+i) to DNA.
  • an alkylating agent mutational signature e.g., characteristic genomic mutational patterns that are generated by the action of alkylating agents add an alkyl group (CnHin+i) to DNA.
  • an alkylating agent mutational signature e.g., characteristic genomic mutational patterns that are generated by the action of alkylating agents add an alkyl group (CnHin+i) to DNA.
  • 06- alkylguanine causes G > A/C > T mutations
  • O4-alkylthymine causes T > C/A > G mutations
  • the genomic feature data may comprise a determination that an HRD mutational signature (HRDsig) is present in the sequence read data.
  • HRDsig an HRD mutational signature
  • an HRD mutational signature may comprise a combination of indel and copy number features in the sequence read data.
  • an HRD mutational signature may comprise a plurality of low copy number segments.
  • an HRD mutational signature may comprise heterozygous deletion(s), high genome- wide loss of heterozygosity (gLOH-high), and/or high genomic instability score (GIS).
  • an HRD mutational signature may comprise heterozygous deletion(s), heterozygous duplication(s), and high genome- wide loss of heterozygosity (gLOH-high).
  • an HRD mutational signature may comprise heterozygous deletion(s) and copy neutral loss of heterozygosity.
  • the identified therapy-associated mutation and the genomic feature data for the plurality of genomic features are analyzed using a classification model configured to process genomic feature data as input and output a prediction for identifying the subject as a candidate for treatment with a DDR deficiency-associated treatment.
  • the classification model may comprise a heuristic model.
  • the heuristic model may comprise, e.g., a decision tree.
  • the classification model may comprise a machine learning model.
  • the machine learning model may comprise a supervised learning model.
  • the supervised learning model may comprise an artificial neural network, a decision tree model, a random forest model, a k-nearest neighbors model, or a support vector machine model.
  • the classification model comprises a machine learning model, and the machine learning model has been trained using a training data set comprising data generated for the plurality of genomic features in samples from a cohort of patients that have been diagnosed with DDR deficiency.
  • the DDR deficiency may comprise homologous recombination deficiency.
  • the prediction output by the classification model may comprise a prediction that the subject is a candidate for treatment with a DDR deficiency-associated drug.
  • the DDR deficiency may comprise homologous recombination deficiency
  • the prediction may comprise a prediction that the subject is a candidate for treatment with a homologous recombination deficiency-associated drug.
  • the method may further comprise outputting a recommendation that the subject be treated with the homologous recombination deficiency-associated drug.
  • the homologous recombination deficiency-associated drug may comprise a first generation PARP inhibitor, a second generation PARP inhibitor, a platinum-based chemotherapy, an ATR inhibitor, or a CHK1 inhibitor.
  • the prediction output by the classification model may comprise a prediction that the subject is not a candidate for treatment with a DDR deficiency-associated drug.
  • the DDR deficiency may comprise homologous recombination deficiency
  • the prediction may comprise a prediction that the subject is not a candidate for treatment with a homologous recombination deficiency-associated drug.
  • the disclosed methods may further comprise one or more of the steps of: (i) obtaining the sample from the subject (e.g., a subject suspected of having or determined to have cancer), (ii) extracting nucleic acid molecules (e.g., a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules) from the sample, (iii) ligating one or more adapters to the nucleic acid molecules extracted from the sample (e.g., one or more amplification primers, flow cell adaptor sequences, substrate adapter sequences, or sample index sequences), (iv) performing a methylation conversion reaction to convert, e.g., non-methylated cytosine to uracil, (v) amplifying the nucleic acid molecules (e.g., using a polymerase chain reaction (PCR) amplification technique, a non-PCR amplification technique, or an isothermal amplification technique), (vi) capturing nucleic acid molecules from the amplified
  • PCR polymerase
  • the report comprises output from the methods described herein. In some instances, all or a portion of the report may be displayed in the graphical user interface of an online or web-based healthcare portal. In some instances, the report is transmitted via a computer network or peer-to-peer connection.
  • the disclosed methods may be used with any of a variety of samples.
  • the sample may comprise a tissue biopsy sample, a liquid biopsy sample, or a normal control.
  • the sample may be a liquid biopsy sample and may comprise blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
  • the sample may be a liquid biopsy sample and may comprise circulating tumor cells (CTCs).
  • the sample may be a liquid biopsy sample and may comprise cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.
  • the nucleic acid molecules extracted from a sample may comprise a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules.
  • the tumor nucleic acid molecules may be derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules may be derived from a normal portion of the heterogeneous tissue biopsy sample.
  • the sample may comprise a liquid biopsy sample, and the tumor nucleic acid molecules may be derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample while the non-tumor nucleic acid molecules may be derived from a non-tumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.
  • ctDNA circulating tumor DNA
  • the disclosed methods for discriminating between patients with tumors that are truly DDR deficient (e.g., HRD positive) and patients that exhibit false-positive DDR deficient status (e.g., that appear HRD positive but that are actually HRD negative) 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).
  • the disclosed methods may be applicable to diagnosis of any of a variety of cancers as described elsewhere herein.
  • the disclosed methods for discriminating between patients with tumors that are truly DDR deficient (e.g., HRD positive) and patients that exhibit false-positive DDR deficient status (e.g., that appear HRD positive but that are actually HRD negative) may be used to select a subject (e.g., a patient) for a clinical trial based on their determined status for DDR deficiency.
  • patient selection for clinical trials based on, e.g., determination of DDR deficiency status may accelerate the development of targeted therapies and improve the healthcare outcomes for treatment decisions.
  • the disclosed methods for discriminating between patients with tumors that are truly DDR deficient (e.g., HRD positive) and patients that exhibit false-positive DDR deficient status (e.g., that appear HRD positive but that are actually HRD negative) may be used to select an appropriate therapy or treatment (e.g., an anti-cancer therapy or anti-cancer treatment) for a subject.
  • an appropriate therapy or treatment e.g., an anti-cancer therapy or anti-cancer treatment
  • the anti-cancer therapy or treatment may comprise use of a poly (ADP-ribose) polymerase inhibitor (PARPi), a platinum compound, chemotherapy, radiation therapy, a targeted therapy (e.g., immunotherapy), surgery, or any combination thereof.
  • PARPi poly (ADP-ribose) polymerase inhibitor
  • the targeted therapy may comprise abemaciclib (Verzenio), abiraterone acetate (Zytiga), acalabrutinib (Calquence), ado-trastuzumab emtansine (Kadcyla), afatinib dimaleate (Gilotrif), aldesleukin (Proleukin), alectinib (Alecensa), alemtuzumab (Campath), alitretinoin (Panretin), alpelisib (Piqray), amivantamab-vmjw (Rybrevant), anastrozole (Arimidex), apalutamide (Erleada), asciminib hydrochloride (Scemblix), atezolizumab (Tecentriq), avapritinib (Ayvakit), avelumab (Bavencio),
  • the disclosed methods for discriminating between patients with tumors that are truly DDR deficient (e.g., HRD positive) and patients that exhibit false-positive DDR deficient status (e.g., that appear HRD positive but that are actually HRD negative) may be used in treating a disease (e.g., a cancer) in a subject.
  • a disease e.g., a cancer
  • an effective amount of an anti-cancer therapy or anti-cancer treatment may be administered to the subject.
  • the status of DDR deficiency determined using the disclosed methods may be used as a prognostic or diagnostic indicator associated with the sample.
  • the prognostic or diagnostic indicator may comprise an indicator of the presence of a disease (e.g., cancer) in the sample, an indicator of the probability that a disease (e.g., cancer) is present in the sample, an indicator of the probability that the subject from which the sample was derived will develop a disease (e.g., cancer) (i.e., a risk factor), or an indicator of the likelihood that the subject from which the sample was derived will respond to a particular therapy or treatment.
  • the disclosed methods for discriminating between patients with tumors that are truly DDR deficient (e.g., HRD positive) and patients that exhibit false-positive DDR deficient status (e.g., that appear HRD positive but that are actually HRD negative) may be implemented as part of a genomic profiling process that comprises identification of the presence of variant sequences at one or more gene loci in a sample derived from a subject as part of detecting, monitoring, predicting a risk factor, or selecting a treatment for a particular disease, e.g., cancer.
  • the variant panel selected for genomic profiling may comprise the detection of variant sequences at a selected set of gene loci.
  • the variant panel selected for genomic profiling may comprise detection of variant sequences at a number of gene loci through comprehensive genomic profiling (CGP), which is a next-generation sequencing (NGS) approach used to assess hundreds of genes (including relevant cancer biomarkers) in a single assay.
  • CGP comprehensive genomic profiling
  • NGS next-generation sequencing
  • Inclusion of the disclosed methods for discriminating between patients with tumors that are truly DDR deficient (e.g., HRD positive) and patients that exhibit false-positive DDR deficient status (e.g., that appear HRD positive but that are actually HRD negative) as part of a genomic profiling process (or inclusion of the output from the disclosed methods as part of the genomic profile of the subject) can improve the validity of, e.g., disease detection calls and treatment decisions, made on the basis of the genomic profile by, for example, confirming the presence of DDR deficient status for a given patient.
  • a genomic profile may comprise information on the presence of genes (or variant sequences thereof), copy number variations, epigenetic traits, proteins (or modifications thereof), and/or other biomarkers in an individual’s genome and/or proteome, as well as information on the individual’s corresponding phenotypic traits and the interaction between genetic or genomic traits, phenotypic traits, and environmental factors.
  • a genomic profile for the subject may comprise results from a comprehensive genomic profiling (CGP) test, a nucleic acid sequencing-based test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof.
  • CGP genomic profiling
  • the method can further include administering or applying a treatment or therapy (e.g., an anti-cancer agent, anti-cancer treatment, or anti-cancer therapy) to the subject based on the generated genomic profile.
  • a treatment or therapy e.g., an anti-cancer agent, anti-cancer treatment, or anti-cancer therapy
  • An anti-cancer agent or anti-cancer treatment may refer to a compound that is effective in the treatment of cancer cells.
  • anti-cancer agents or anti-cancer therapies include, but not limited to, alkylating agents, antimetabolites, natural products, hormones, chemotherapy, radiation therapy, immunotherapy, surgery, or a therapy configured to target a defect in a specific cell signaling pathway, e.g., a defect in a DNA mismatch repair (MMR) pathway.
  • MMR DNA mismatch repair
  • the disclosed methods and systems may be used with any of a variety of samples (also referred to herein as specimens) comprising nucleic acids (e.g., DNA or RNA) that are collected from a subject (e.g., a patient).
  • samples also referred to herein as specimens
  • nucleic acids e.g., DNA or RNA
  • a sample examples include, but are not limited to, a tumor sample, a tissue sample, a biopsy sample (e.g., a tissue biopsy, a liquid biopsy, or both), a blood sample (e.g., a peripheral whole blood sample), a blood plasma sample, a blood serum sample, a lymph sample, a saliva sample, a sputum sample, a urine sample, a gynecological fluid sample, a circulating tumor cell (CTC) sample, a cerebral spinal fluid (CSF) sample, a pericardial fluid sample, a pleural fluid sample, an ascites (peritoneal fluid) sample, a feces (or stool) sample, or other body fluid, secretion, and/or excretion sample (or cell sample derived therefrom).
  • the sample may be frozen sample or a formalin-fixed paraffin-embedded (FFPE) sample.
  • FFPE formalin-fixed paraffin-embedded
  • the sample may be collected by tissue resection (e.g., surgical resection), needle biopsy, bone marrow biopsy, bone marrow aspiration, skin biopsy, endoscopic biopsy, fine needle aspiration, oral swab, nasal swab, vaginal swab or a cytology smear, scrapings, washings or lavages (such as a ductal lavage or bronchoalveolar lavage), etc.
  • tissue resection e.g., surgical resection
  • needle biopsy e.g., bone marrow biopsy, bone marrow aspiration, skin biopsy, endoscopic biopsy, fine needle aspiration, oral swab, nasal swab, vaginal swab or a cytology smear
  • fine needle aspiration e.g., oral swab, nasal swab, vaginal swab or a cytology smear
  • scrapings e.
  • the sample is a liquid biopsy sample, and may comprise, e.g., whole blood, blood plasma, blood serum, urine, stool, sputum, saliva, or cerebrospinal fluid.
  • the sample may be a liquid biopsy sample and may comprise circulating tumor cells (CTCs).
  • the sample may be a liquid biopsy sample and may comprise cell- free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.
  • the sample may be, or is part of, a primary tumor or a metastasis (e.g., a metastasis biopsy sample).
  • the sample may be obtained from a site (e.g., a tumor site) with the highest percentage of tumor (e.g., tumor cells) as compared to adjacent sites (e.g., sites adjacent to the tumor).
  • the sample may be obtained from a site (e.g., a tumor site) with the largest tumor focus (e.g., the largest number of tumor cells as visualized under a microscope) as compared to adjacent sites (e.g., sites adjacent to the tumor).
  • the disclosed methods may further comprise analyzing a primary control (e.g., a normal tissue sample). In some instances, the disclosed methods may further comprise determining if a primary control is available and, if so, isolating a control nucleic acid (e.g., DNA) from said primary control. In some instances, the sample may comprise any normal control (e.g., a normal adjacent tissue (NAT)) if no primary control is available. In some instances, the sample may be or may comprise histologically normal tissue. In some instances, the method includes evaluating a sample, e.g., a histologically normal sample (e.g., from a surgical tissue margin) using the methods described herein.
  • a primary control e.g., a normal tissue sample.
  • the disclosed methods may further comprise determining if a primary control is available and, if so, isolating a control nucleic acid (e.g., DNA) from said primary control.
  • the sample may comprise any normal control (e.g.,
  • the disclosed methods may further comprise acquiring a sub-sample enriched for non-tumor cells, e.g., by macro-dissecting non-tumor tissue from said NAT in a sample not accompanied by a primary control. In some instances, the disclosed methods may further comprise determining that no primary control and no NAT is available, and marking said sample for analysis without a matched control. [0131] 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.
  • tissue samples e.g., solid tissue samples, soft tissue samples, metastatic lesions, or liquid biopsy samples.
  • tissues include, but are not limited to, connective tissue, muscle tissue, nervous tissue, epithelial tissue, and blood.
  • Tissue samples may be collected from any of the organs within an animal or human body.
  • human organs include, but are not limited to, the brain, heart, lungs, liver, kidneys, pancreas, spleen, thyroid, mammary glands, uterus, prostate, large intestine, small intestine, bladder, bone, skin, etc.
  • the nucleic acids extracted from the sample may comprise deoxyribonucleic acid (DNA) molecules.
  • DNA DNA that may be suitable for analysis by the disclosed methods include, but are not limited to, genomic DNA or fragments thereof, mitochondrial DNA or fragments thereof, cell-free DNA (cfDNA), and circulating tumor DNA (ctDNA).
  • Cell-free DNA (cfDNA) is comprised of fragments of DNA that are released from normal and/or cancerous cells during apoptosis and necrosis, and circulate in the blood stream and/or accumulate in other bodily fluids.
  • Circulating tumor DNA ctDNA is comprised of fragments of DNA that are released from cancerous cells and tumors that circulate in the blood stream and/or accumulate in other bodily fluids.
  • DNA is extracted from nucleated cells from the sample.
  • a sample may have a low nucleated cellularity, e.g., when the sample is comprised mainly of erythrocytes, lesional cells that contain excessive cytoplasm, or tissue with fibrosis.
  • a sample with low nucleated cellularity may require more, e.g., greater, tissue volume for DNA extraction.
  • the nucleic acids extracted from the sample may comprise ribonucleic acid (RNA) molecules.
  • RNA ribonucleic acid
  • examples of RNA that may be suitable for analysis by the disclosed methods include, but are not limited to, total cellular RNA, total cellular RNA after depletion of certain abundant RNA sequences (e.g., ribosomal RNAs), cell-free RNA (cfRNA), messenger RNA (mRNA) or fragments thereof, the poly(A)-tailed mRNA fraction of the total RNA, ribosomal RNA (rRNA) or fragments thereof, transfer RNA (tRNA) or fragments thereof, and mitochondrial RNA or fragments thereof.
  • ribosomal RNAs e.g., ribosomal RNAs
  • cfRNA cell-free RNA
  • mRNA messenger RNA
  • rRNA transfer RNA
  • tRNA transfer RNA
  • RNA may be extracted from the sample and converted to complementary DNA (cDNA) using, e.g., a reverse transcription reaction.
  • cDNA complementary DNA
  • the cDNA is produced by random-primed cDNA synthesis methods.
  • the cDNA synthesis is initiated at the poly(A) tail of mature mRNAs by priming with oligo(dT)-containing oligonucleotides. Methods for depletion, poly(A) enrichment, and cDNA synthesis are well known to those of skill in the art.
  • the sample may comprise a tumor content (e.g., comprising tumor cells or tumor cell nuclei), or a non-tumor content (e.g., immune cells, fibroblasts, and other nontumor cells).
  • the tumor content of the sample may constitute a sample metric.
  • the sample may comprise a tumor content of at least 5-50%, 10-40%, 15-25%, or 20-30% tumor cell nuclei.
  • the sample may comprise a tumor content of at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, or at least 50% tumor cell nuclei.
  • the percent tumor cell nuclei (e.g., sample fraction) is determined (e.g., calculated) by dividing the number of tumor cells in the sample by the total number of all cells within the sample that have nuclei.
  • a different tumor content calculation may be required due to the presence of hepatocytes having nuclei with twice, or more than twice, the DNA content of other, e.g., non-hepatocyte, somatic cell nuclei.
  • the sensitivity of detection of a genetic alteration e.g., a variant sequence, or a determination of, e.g., micro satellite instability, may depend on the tumor content of the sample. For example, a sample having a lower tumor content can result in lower sensitivity of detection for a given size sample.
  • the sample comprises nucleic acid (e.g., DNA, RNA (or a cDNA derived from the RNA), or both), e.g., from a tumor or from normal tissue.
  • the sample may further comprise a non-nucleic acid component, e.g., cells, protein, carbohydrate, or lipid, e.g., from the tumor or normal tissue.
  • the sample is obtained (e.g., collected) from a subject (e.g., patient) with a condition or disease (e.g., a hyperproliferative disease or a non-cancer indication) or suspected of having the condition or disease.
  • a condition or disease e.g., a hyperproliferative disease or a non-cancer indication
  • the hyperproliferative disease is a cancer.
  • the cancer is a solid tumor or a metastatic form thereof.
  • the cancer is a hematological cancer, e.g., a leukemia or lymphoma.
  • the subject has a cancer or is at risk of having a cancer.
  • the subject has a genetic predisposition to a cancer (e.g., having a genetic mutation that increases his or her baseline risk for developing a cancer).
  • the subject has been exposed to an environmental perturbation (e.g., radiation or a chemical) that increases his or her risk for developing a cancer.
  • the subject is in need of being monitored for development of a cancer.
  • the subject is in need of being monitored for cancer progression or regression, e.g., after being treated with an anti-cancer therapy (or anti-cancer treatment).
  • the subject is in need of being monitored for relapse of cancer.
  • the subject is in need of being monitored for minimum residual disease (MRD).
  • the subject has been, or is being treated, for cancer.
  • the subject has not been treated with an anti-cancer therapy (or anti-cancer treatment).
  • the subject e.g., a patient
  • a post-targeted therapy sample e.g., specimen
  • the post-targeted therapy sample is a sample obtained after the completion of the targeted therapy.
  • the patient has not been previously treated with a targeted therapy.
  • the sample comprises a resection, e.g., an original resection, or a resection following recurrence (e.g., following a disease recurrence post-therapy).
  • the sample is acquired from a subject having a cancer.
  • exemplary cancers include, but are not limited to, B cell cancer (e.g., multiple myeloma), melanomas, breast cancer, lung cancer (such as non-small cell lung carcinoma or NSCLC), bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain or central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine or endometrial cancer, cancer of the oral cavity or pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel or appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, cancer of hematological tissues, adenocarcinomas, inflammatory myofibroblastic tumors, gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM),
  • B cell cancer
  • the cancer comprises acute lymphoblastic leukemia (Philadelphia chromosome positive), acute lymphoblastic leukemia (precursor B-cell), acute myeloid leukemia (FLT3+), acute myeloid leukemia (with an IDH2 mutation), anaplastic large cell lymphoma, basal cell carcinoma, B-cell chronic lymphocytic leukemia, bladder cancer, breast cancer (HER2 overexpressed/amplified), breast cancer (HER2+), breast cancer (HR+, HER2-), cervical cancer, cholangiocarcinoma, chronic lymphocytic leukemia, chronic lymphocytic leukemia (with 17p deletion), chronic myelogenous leukemia, chronic myelogenous leukemia (Philadelphia chromosome positive), classical Hodgkin lymphoma, colorectal cancer, colorectal cancer (dMMR and MSI-H), colorectal cancer (KRAS wild type), cryopyrin-associated periodic syndrome,
  • the cancer is a hematologic malignancy (or premaligancy).
  • a hematologic malignancy refers to a tumor of the hematopoietic or lymphoid tissues, e.g., a tumor that affects blood, bone marrow, or lymph nodes.
  • Exemplary hematologic malignancies include, but are not limited to, leukemia (e.g., acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), chronic myelogenous leukemia (CML), hairy cell leukemia, acute monocytic leukemia (AMoL), chronic myelomonocytic leukemia (CMML), juvenile myelomonocytic leukemia (JMML), or large granular lymphocytic leukemia), lymphoma (e.g., AIDS-related lymphoma, cutaneous T-cell lymphoma, Hodgkin lymphoma (e.g., classical Hodgkin lymphoma or nodular lymphocyte- predominant Hodgkin lymphoma), mycosis fungoides, non-Hodgkin lymphoma (e.g., B-cell non-Hodgkin lymphoma (e.g.
  • DNA or RNA may be extracted from tissue samples, biopsy samples, blood samples, or other bodily fluid samples using any of a variety of techniques known to those of skill in the art (see, e.g., Example 1 of International Patent Application Publication No. WO 2012/092426; Tan, et al. (2009), “DNA, RNA, and Protein Extraction: The Past and The Present”, J. Biomed. Biotech. 2009:574398; the technical literature for the Maxwell® 16 LEV Blood DNA Kit (Promega Corporation, Madison, WI); and the Maxwell 16 Buccal Swab LEV DNA Purification Kit Technical Manual (Promega Literature #TM333, January 1, 2011, Promega Corporation, Madison, WI)).
  • a typical DNA extraction procedure 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.
  • DNA may be extracted using any of a variety of suitable commercial DNA extraction and purification kits. Examples include, but are not limited to, the QIAamp (for isolation of genomic DNA from human samples) and DNAeasy (for isolation of genomic DNA from animal or plant samples) kits from Qiagen (Germantown, MD) or the Maxwell® and ReliaPrepTM series of kits from Promega (Madison, WI). [0151] 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.
  • formalin-fixed also known as formaldehyde-fixed, or paraformaldehyde-fixed
  • FFPE paraffin-embedded
  • the FFPE sample may be a tissue sample embedded in a matrix, e.g., an FFPE block.
  • nucleic acids e.g., DNA
  • 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.
  • the RecoverAllTM 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.
  • PMPs silica-clad paramagnetic particles
  • the E.Z.N.A.® FFPE DNA Kit uses a spin column and buffer system for isolation of genomic DNA.
  • QIAamp® DNA FFPE Tissue Kit uses QIAamp® DNA Micro technology for purification of genomic and mitochondrial DNA.
  • the disclosed methods may further comprise determining or acquiring a yield value for the nucleic acid extracted from the sample and comparing the determined value to a reference value. For example, if the determined or acquired value is less than the reference value, the nucleic acids may be amplified prior to proceeding with library construction.
  • the disclosed methods may further comprise determining or acquiring a value for the size (or average size) of nucleic acid fragments in the sample, and comparing the determined or acquired value to a reference value, e.g., a size (or average size) of at least 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 base pairs (bps).
  • a reference value e.g., a size (or average size) of at least 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 base pairs (bps).
  • one or more parameters described herein may be adjusted or selected in response to this determination.
  • the nucleic acids are typically dissolved in a slightly alkaline buffer, e.g., Tris-EDTA (TE) buffer, or in ultra-pure water.
  • a slightly alkaline buffer e.g., Tris-EDTA (TE) buffer
  • the isolated nucleic acids may be fragmented or sheared by using any of a variety of techniques known to those of skill in the art.
  • genomic DNA can be fragmented by physical shearing methods, enzymatic cleavage methods, chemical cleavage methods, and other methods known to those of skill in the art. Methods for DNA shearing are described in Example 4 in International Patent Application Publication No. WO 2012/092426. In some instances, alternatives to DNA shearing methods can be used to avoid a ligation step during library preparation.
  • the nucleic acids isolated from the sample may be used to construct a library (e.g., a nucleic acid library as described herein).
  • the nucleic acids are fragmented using any of the methods described above, optionally subjected to repair of chain end damage, and optionally ligated to synthetic adapters, primers, and/or barcodes (e.g., amplification primers, sequencing adapters, flow cell adapters, substrate adapters, sample barcodes or indexes, and/or unique molecular identifier sequences), size-selected (e.g., by preparative gel electrophoresis), and/or amplified (e.g., using PCR, a non-PCR amplification technique, or an isothermal amplification technique).
  • synthetic adapters, primers, and/or barcodes e.g., amplification primers, sequencing adapters, flow cell adapters, substrate adapters, sample barcodes or indexes, and/or unique molecular identifier sequences
  • the fragmented and adapter-ligated group of nucleic acids is used without explicit size selection or amplification prior to hybridization-based selection of target sequences.
  • the nucleic acid is amplified by any of a variety of specific or non-specific nucleic acid amplification methods known to those of skill in the art.
  • the nucleic acids are amplified, e.g., by a whole-genome amplification method such as random-primed strand-displacement amplification. Examples of nucleic acid library preparation techniques for next-generation sequencing are described in, e.g., van Dijk, et al. (2014), Exp. Cell Research 322: 12 - 20, and Illumina’s genomic DNA sample preparation kit.
  • the resulting nucleic acid library may contain all or substantially all of the complexity of the genome.
  • the term “substantially all” in this context refers to the possibility that there can in practice be some unwanted loss of genome complexity during the initial steps of the procedure.
  • the methods described herein also are useful in cases where the nucleic acid library comprises a portion of the genome, e.g., where the complexity of the genome is reduced by design. In some instances, any selected portion of the genome can be used with a method described herein. For example, in certain embodiments, the entire exome or a subset thereof is isolated.
  • the library may include at least 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, or 5% of the genomic DNA.
  • the library may consist of cDNA copies of genomic DNA that includes copies of at least 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, or 5% of the genomic DNA.
  • the amount of nucleic acid used to generate the nucleic acid library may be less than 5 micrograms, less than 1 microgram, less than 500 ng, less than 200 ng, less than 100 ng, less than 50 ng, less than 10 ng, less than 5 ng, or less than 1 ng.
  • a library (e.g., a nucleic acid library) includes a collection of nucleic acid molecules.
  • the nucleic acid molecules of the library can include a target nucleic acid molecule (e.g., a tumor nucleic acid molecule, a reference nucleic acid molecule and/or a control nucleic acid molecule; also referred to herein as a first, second and/or third nucleic acid molecule, respectively).
  • the nucleic acid molecules of the library can be from a single subject or individual.
  • a library can comprise nucleic acid molecules derived from more than one subject (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30 or more subjects).
  • two or more libraries from different subjects can be combined to form a library having nucleic acid molecules from more than one subject (where the nucleic acid molecules derived from each subject are optionally ligated to a unique sample barcode corresponding to a specific subject).
  • the subject is a human having, or at risk of having, a cancer or tumor.
  • the library may comprise one or more subgenomic intervals.
  • a subgenomic interval can be a single nucleotide position, e.g., a nucleotide position for which a variant at the position is associated (positively or negatively) with a tumor phenotype.
  • a subgenomic interval comprises more than one nucleotide position. Such instances include sequences of at least 2, 5, 10, 50, 100, 150, 250, or more than 250 nucleotide positions in length.
  • Subgenomic intervals can comprise, e.g., one or more entire genes (or portions thereof), one or more exons or coding sequences (or portions thereof), one or more introns (or portion thereof), one or more microsatellite region (or portions thereof), or any combination thereof.
  • a subgenomic interval can comprise all or a part of a fragment of a naturally occurring nucleic acid molecule, e.g., a genomic DNA molecule.
  • a subgenomic interval can correspond to a fragment of genomic DNA which is subjected to a sequencing reaction.
  • a subgenomic interval is a continuous sequence from a genomic source.
  • a subgenomic interval includes sequences that are not contiguous in the genome, e.g., subgenomic intervals in cDNA can include exonexonjunctions formed as a result of splicing.
  • the subgenomic interval comprises a tumor nucleic acid molecule.
  • the subgenomic interval comprises a non-tumor nucleic acid molecule.
  • the methods described herein can be used in combination with, or as part of, a method for evaluating a plurality or set of subject intervals (e.g., target sequences), e.g., from a set of genomic loci (e.g., gene loci or fragments thereof), as described herein.
  • a plurality or set of subject intervals e.g., target sequences
  • genomic loci e.g., gene loci or fragments thereof
  • the set of genomic loci evaluated by the disclosed methods comprises a plurality of, e.g., genes, which in mutant form, are associated with an effect on cell division, growth or survival, or are associated with a cancer, e.g., a cancer described herein.
  • the set of gene loci evaluated by the disclosed methods comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, or more than 100 gene loci.
  • the selected gene loci may include subject intervals comprising non-coding sequences, coding sequences, intragenic regions, or intergenic regions of the subject genome.
  • the subject intervals can include a non-coding sequence or fragment thereof (e.g., a promoter sequence, enhancer sequence, 5’ untranslated region (5’ UTR), 3’ untranslated region (3’ UTR), or a fragment thereof), a coding sequence of fragment thereof, an exon sequence or fragment thereof, an intron sequence or a fragment thereof.
  • a non-coding sequence or fragment thereof e.g., a promoter sequence, enhancer sequence, 5’ untranslated region (5’ UTR), 3’ untranslated region (3’ UTR), or a fragment thereof
  • a coding sequence of fragment thereof e.g., an exon sequence or fragment thereof, an intron sequence or a fragment thereof.
  • the methods described herein may comprise contacting a nucleic acid library with a plurality of target capture reagents in order to select and capture a plurality of specific target sequences (e.g., gene sequences or fragments thereof) for analysis.
  • a target capture reagent z.e., a molecule which can bind to and thereby allow capture of a target molecule
  • a target capture reagent is used to select the subject intervals to be analyzed.
  • a target capture reagent can be a bait molecule, e.g., a nucleic acid molecule (e.g., a DNA molecule or RNA molecule) which can hybridize to (z.e., is complementary to) a target molecule, and thereby allows capture of the target nucleic acid.
  • the target capture reagent e.g., a bait molecule (or bait sequence)
  • the target nucleic acid is a genomic DNA molecule, an RNA molecule, a cDNA molecule derived from an RNA molecule, a microsatellite DNA sequence, and the like.
  • the target capture reagent is suitable for solution-phase hybridization to the target. In some instances, the target capture reagent is suitable for solid-phase hybridization to the target. In some instances, the target capture reagent is suitable for both solution-phase and solid-phase hybridization to the target.
  • the design and construction of target capture reagents is described in more detail in, e.g., International Patent Application Publication No. WO 2020/236941, the entire content of which is incorporated herein by reference.
  • the methods described herein provide for optimized sequencing of a large number of genomic loci (e.g., genes or gene products (e.g., mRNA), micro satellite loci, etc.) from samples (e.g., cancerous tissue specimens, liquid biopsy samples, and the like) from one or more subjects by the appropriate selection of target capture reagents to select the target nucleic acid molecules to be sequenced.
  • a target capture reagent may hybridize to a specific target locus, e.g., a specific target gene locus or fragment thereof.
  • a target capture reagent may hybridize to a specific group of target loci, e.g., a specific group of gene loci or fragments thereof.
  • a plurality of target capture reagents comprising a mix of target- specific and/or group- specific target capture reagents may be used.
  • the number of target capture reagents (e.g., bait molecules) in the plurality of target capture reagents (e.g., a bait set) contacted with a nucleic acid library to capture a plurality of target sequences for nucleic acid sequencing is greater than 10, greater than 50, greater than 100, greater than 200, greater than 300, greater than 400, greater than 500, greater than 600, greater than 700, greater than 800, greater than 900, greater than 1,000, greater than 1,250, greater than 1,500, greater than 1,750, greater than 2,000, greater than 3,000, greater than 4,000, greater than 5,000, greater than 10,000, greater than 25,000, or greater than 50,000.
  • the overall length of the target capture reagent sequence can be between about 70 nucleotides and 1000 nucleotides. In one instance, the target capture reagent length is between about 100 and 300 nucleotides, 110 and 200 nucleotides, or 120 and 170 nucleotides, in length. In addition to those mentioned above, intermediate oligonucleotide lengths of about 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 300, 400, 500, 600, 700, 800, and 900 nucleotides in length can be used in the methods described herein. In some embodiments, oligonucleotides of about 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, or 230 bases can be used.
  • each target capture reagent sequence can include: (i) a target-specific capture sequence (e.g., a gene locus or micro satellite locus-specific complementary sequence), (ii) an adapter, primer, barcode, and/or unique molecular identifier sequence, and (iii) universal tails on one or both ends.
  • a target-specific capture sequence e.g., a gene locus or micro satellite locus-specific complementary sequence
  • an adapter, primer, barcode, and/or unique molecular identifier sequence e.g., a target-specific capture sequence
  • universal tails e.g., a target-specific capture sequence
  • target capture reagent can refer to the targetspecific target capture sequence or to the entire target capture reagent oligonucleotide including the target- specific target capture sequence.
  • the target-specific capture sequences in the target capture reagents are between about 40 nucleotides and 1000 nucleotides in length. In some instances, the targetspecific capture sequence is between about 70 nucleotides and 300 nucleotides in length. In some instances, the target- specific sequence is between about 100 nucleotides and 200 nucleotides in length. In yet other instances, the target- specific sequence is between about 120 nucleotides and 170 nucleotides in length, typically 120 nucleotides in length.
  • target-specific sequences of about 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 300, 400, 500, 600, 700, 800, and 900 nucleotides in length, as well as target- specific sequences of lengths between the above-mentioned lengths.
  • the target capture reagent may be designed to select a subject interval containing one or more rearrangements, e.g., an intron containing a genomic rearrangement.
  • the target capture reagent is designed such that repetitive sequences are masked to increase the selection efficiency.
  • complementary target capture reagents can be designed to recognize the juncture sequence to increase the selection efficiency.
  • the disclosed methods may comprise the use of target capture reagents designed to capture two or more different target categories, each category having a different target capture reagent design strategy.
  • the hybridization-based capture methods and target capture reagent compositions disclosed herein may provide for the capture and homogeneous coverage of a set of target sequences, while minimizing coverage of genomic sequences outside of the targeted set of sequences.
  • the target sequences may include the entire exome of genomic DNA or a selected subset thereof.
  • the target sequences may include, e.g., a large chromosomal region (e.g., a whole chromosome arm).
  • the methods and compositions disclosed herein provide different target capture reagents for achieving different sequencing depths and patterns of coverage for complex sets of target nucleic acid sequences.
  • DNA molecules are used as target capture reagent sequences, although RNA molecules can also be used.
  • a DNA molecule target capture reagent can be single stranded DNA (ssDNA) or double- stranded DNA (dsDNA).
  • ssDNA single stranded DNA
  • dsDNA double- stranded DNA
  • an RNA- DNA duplex is more stable than a DNA-DNA duplex and therefore provides for potentially better capture of nucleic acids.
  • the disclosed methods comprise providing a selected set of nucleic acid molecules (e.g., a library catch) captured from one or more nucleic acid libraries.
  • the method may comprise: providing one or a plurality of nucleic acid libraries, each comprising a plurality of nucleic acid molecules (e.g., a plurality of target nucleic acid molecules and/or reference nucleic acid molecules) extracted from one or more samples from one or more subjects; contacting the one or a plurality of libraries (e.g., in a solution-based hybridization reaction) with one, two, three, four, five, or more than five pluralities of target capture reagents (e.g., oligonucleotide target capture reagents) to form a hybridization mixture comprising a plurality of target capture reagent/nucleic acid molecule hybrids; separating the plurality of target capture reagent/nucleic acid molecule hybrids from said hybridization mixture, e.g., by
  • the disclosed methods may further comprise amplifying the library catch (e.g., by performing PCR). In other instances, the library catch is not amplified.
  • the target capture reagents can be part of a kit which can optionally comprise instructions, standards, buffers or enzymes or other reagents.
  • the methods disclosed herein may include the step of contacting the library (e.g., the nucleic acid library) with a plurality of target capture reagents to provide a selected library target nucleic acid sequences (i.e., the library catch).
  • the contacting step can be effected in, e.g., solution-based hybridization.
  • the method includes repeating the hybridization step for one or more additional rounds of solution-based hybridization.
  • the method further includes subjecting the library catch to one or more additional rounds of solution-based hybridization with the same or a different collection of target capture reagents.
  • the contacting step is effected using a solid support, e.g., an array.
  • a solid support e.g., an array.
  • Suitable solid supports for hybridization are described in, e.g., Albert, T.J. et al. (2007) Nat. Methods 4(1 l):903-5; Hodges, E. et al. (2007) Nat. Genet. 39(12): 1522-7; and Okou, D.T. et al. (2007) Nat. Methods 4(11 ):907-9, the contents of which are incorporated herein by reference in their entireties.
  • Hybridization methods that can be adapted for use in the methods herein are described in the art, e.g., as described in International Patent Application Publication No. WO 2012/092426. Methods for hybridizing target capture reagents to a plurality of target nucleic acids are described in more detail in, e.g., International Patent Application Publication No. WO 2020/236941, the entire content of which is incorporated herein by reference.
  • the methods and systems disclosed herein can be used in combination with, or as part of, a method or system for sequencing nucleic acids (e.g., a next-generation sequencing system) to generate a plurality of sequence reads that overlap one or more gene loci within a subgenomic interval in the sample and thereby determine, e.g., gene allele sequences at a plurality of gene loci.
  • a method or system for sequencing nucleic acids e.g., a next-generation sequencing system
  • next-generation sequencing (or “NGS”) as used herein may also be referred to as “massively parallel sequencing” (or “MPS”), and refers to any sequencing method that determines the nucleotide sequence of either individual nucleic acid molecules (e.g., as in single molecule sequencing) or clonally expanded proxies for individual nucleic acid molecules in a high throughput fashion (e.g., wherein greater than 10 3 , 10 4 , 10 5 or more than 10 5 molecules are sequenced simultaneously).
  • next-generation sequencing methods are known in the art, and are described in, e.g., Metzker, M. (2010) Nature Biotechnology Reviews 11:31-46, which is incorporated herein by reference.
  • Other examples of sequencing methods suitable for use when implementing the methods and systems disclosed herein are described in, e.g., International Patent Application Publication No. WO 2012/092426.
  • the sequencing may comprise, for example, whole genome sequencing (WGS), whole exome sequencing, targeted sequencing, or direct sequencing.
  • GGS whole genome sequencing
  • sequencing may be performed using, e.g., Sanger sequencing.
  • the sequencing may comprise a paired-end sequencing technique that allows both ends of a fragment to be sequenced and generates high-quality, alignable sequence data for detection of, e.g., genomic rearrangements, repetitive sequence elements, gene fusions, and novel transcripts.
  • sequencing may comprise Illumina MiSeq sequencing.
  • sequencing may comprise Illumina HiSeq sequencing.
  • sequencing may comprise Illumina NovaSeq sequencing. Optimized methods for sequencing a large number of target genomic loci in nucleic acids extracted from a sample are described in more detail in, e.g., International Patent Application Publication No. WO 2020/236941, the entire content of which is incorporated herein by reference.
  • the disclosed methods comprise one or more of the steps of: (a) acquiring a library comprising a plurality of normal and/or tumor nucleic acid molecules from a sample; (b) simultaneously or sequentially contacting the library with one, two, three, four, five, or more than five pluralities of target capture reagents under conditions that allow hybridization of the target capture reagents to the target nucleic acid molecules, thereby providing a selected set of captured normal and/or tumor nucleic acid molecules (z.e., a library catch); (c) separating the selected subset of the nucleic acid molecules (e.g., the library catch) from the hybridization mixture, e.g., by contacting the hybridization mixture with a binding entity that allows for separation of the target capture reagent/nucleic acid molecule hybrids from the hybridization mixture, (d) sequencing the library catch to acquiring a plurality of reads (e.g., sequence reads) that overlap one or more subject intervals (e.g.
  • acquiring sequence reads for one or more subject intervals may comprise sequencing at least 1, at least 5, at least 10, at least 20, at least 30, at least 40, at least 50, at least 100, at least 150, at least 200, at least 250, at least 300, at least 350, at least 400, at least 450, at least 500, at least 550, at least 600, at least 650, at least 700, at least 750, at least 800, at least 850, at least 900, at least 950, at least 1,000, at least 1,250, at least 1,500, at least 1,750, at least 2,000, at least 2,250, at least 2,500, at least 2,750, at least 3,000, at least 3,500, at least 4,000, at least 4,500, or at least 5,000 loci, e.g., genomic loci, gene loci, microsatellite loci, etc.
  • loci e.g., genomic loci, gene loci, microsatellite loci, etc.
  • acquiring a sequence read for one or more subject intervals may comprise sequencing a subject interval for any number of loci within the range described in this paragraph, e.g., for at least 2,850 gene loci.
  • 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.
  • acquiring a sequence read for the one or more subject intervals may comprise sequencing a subject interval with a sequencing method that provides a sequence read length (or average sequence read length) of any number of bases within the range described in this paragraph, e.g., a sequence read length (or average sequence read length) of 56 bases.
  • acquiring a sequence read for one or more subject intervals may comprise sequencing with at least lOOx or more coverage (or depth) on average.
  • acquiring a sequence read for one or more subject intervals may comprise sequencing with at least lOOx, at least 150x, at least 200x, at least 250x, at least 500x, at least 750x, at least l,000x, at least 1,500 x, at least 2,000x, at least 2,500x, at least 3,000x, at least 3,500x, at least 4,000x, at least 4,500x, at least 5,000x, at least 5,500x, or at least 6,000x or more coverage (or depth) on average.
  • acquiring a sequence read for one or more subject intervals may comprise sequencing with an average coverage (or depth) having any value within the range of values described in this paragraph, e.g., at least 160x.
  • acquiring a read for the one or more subject intervals comprises sequencing with an average sequencing depth having any value ranging from at least lOOx to at least 6,000x for greater than about 90%, 92%, 94%, 95%, 96%, 97%, 98%, or 99% of the gene loci sequenced.
  • acquiring a read for the subject interval comprises sequencing with an average sequencing depth of at least 125x for at least 99% of the gene loci sequenced.
  • acquiring a read for the subject interval comprises sequencing with an average sequencing depth of at least 4,100x for at least 95% of the gene loci sequenced.
  • the relative abundance of a nucleic acid species in the library can be estimated by counting the relative number of occurrences of their cognate sequences (e.g., the number of sequence reads for a given cognate sequence) in the data generated by the sequencing experiment.
  • the disclosed methods and systems provide nucleotide sequences for a set of subject intervals (e.g., gene loci), as described herein.
  • the sequences are provided without using a method that includes a matched normal control (e.g., a wild-type control) and/or a matched tumor control (e.g., primary versus metastatic).
  • the level of sequencing depth as used herein refers to the number of reads (e.g., unique reads) obtained after detection and removal of duplicate reads (e.g., PCR duplicate reads).
  • duplicate reads are evaluated, e.g., to support detection of copy number alteration (CNAs).
  • Alignment is the process of matching a read with a location, e.g., a genomic location or locus.
  • NGS reads may be aligned to a known reference sequence (e.g., a wild-type sequence).
  • NGS reads may be assembled de novo. Methods of sequence alignment for NGS reads are described in, e.g., Trapnell, C. and Salzberg, S.L. Nature Biotech., 2009, 27:455-457. Examples of de novo sequence assemblies are described in, e.g., Warren R., et al., Bioinformatics, 2007, 23:500-501; Butler, J.
  • Misalignment e.g., the placement of base-pairs from a short read at incorrect locations in the genome
  • misalignment of reads due to sequence context can lead to reduction in sensitivity of mutation detection
  • sequence context e.g., the presence of repetitive sequence
  • Other examples of sequence context that may cause misalignment include short-tandem repeats, interspersed repeats, low complexity regions, insertions - deletions (indels), and paralogs.
  • misalignment may introduce artifactual reads of “mutated” alleles by placing reads of actual reference genome base sequences at the wrong location. Because mutation-calling algorithms for multigene analysis should be sensitive to even low-abundance mutations, sequence misalignments may increase false positive discovery rates and/or reduce specificity.
  • the methods and systems disclosed herein may integrate the use of multiple, individually-tuned, alignment methods or algorithms to optimize base-calling performance in sequencing methods, particularly in methods that rely on massively parallel sequencing (MPS) of a large number of diverse genetic events at a large number of diverse genomic loci.
  • the disclosed methods and systems may comprise the use of one or more global alignment algorithms.
  • the disclosed methods and systems may comprise the use of one or more local alignment algorithms. Examples of alignment algorithms that may be used include, but are not limited to, the Burrows-Wheeler Alignment (BWA) software bundle (see, e.g., Li, et al.
  • BWA Burrows-Wheeler Alignment
  • the methods and systems disclosed herein may also comprise the use of a sequence assembly algorithm, e.g., the Arachne sequence assembly algorithm (see, e.g., Batzoglou, et al. (2002), “ARACHNE: A Whole-Genome Shotgun Assembler”, Genome Res. 12: 177-189).
  • the alignment method used to analyze sequence reads is not individually customized or tuned for detection of different variants (e.g., point mutations, insertions, deletions, and the like) at different genomic loci.
  • different alignment methods are used to analyze reads that are individually customized or tuned for detection of at least a subset of the different variants detected at different genomic loci.
  • 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
  • the alignment of sequence reads in the disclosed methods may be combined with a mutation calling method as described elsewhere herein.
  • reduced sensitivity for detecting actual mutations may be addressed by evaluating the quality of alignments (manually or in an automated fashion) around expected mutation sites in the genes or genomic loci (e.g., gene loci) being analyzed.
  • the sites to be evaluated can be obtained from databases of the human genome (e.g., the HG19 human reference genome) or cancer mutations (e.g., COSMIC).
  • Regions that are identified as problematic can be remedied with the use of an algorithm selected to give better performance in the relevant sequence context, e.g., by alignment optimization (or re-alignment) using slower, but more accurate alignment algorithms such as Smith- Waterman alignment.
  • customized alignment approaches may be created by, e.g., adjustment of maximum difference mismatch penalty parameters for genes with a high likelihood of containing substitutions; adjusting specific mismatch penalty parameters based on specific mutation types that are common in certain tumor types e.g. C- T in melanoma); or adjusting specific mismatch penalty parameters based on specific mutation types that are common in certain sample types e.g. substitutions that are common in FFPE).
  • Reduced specificity (increased false positive rate) in the evaluated subject intervals due to misalignment can be assessed by manual or automated examination of all mutation calls in the sequencing data. Those regions found to be prone to spurious mutation calls due to misalignment can be subjected to alignment remedies as discussed above. In cases where no algorithmic remedy is found possible, “mutations” from the problem regions can be classified or screened out from the panel of targeted loci.
  • the methods may include the use of an alignment method optimized for aligning sequence reads for DNA that has been converted using, e.g., a bisulfite reaction, to convert unmethylated cytosine residues to uracil (which is interpreted as a thymine in sequencing results).
  • sequence reads may be aligned to two genomes in silico, e.g., converted and unconverted versions of the reference genome, using such alignment tools. Methylation occurs primarily at CpG sites, but may also occur less frequently at non-CpG sites (e.g., CHG or CHH sites).
  • the sequence read data may be obtained using a nucleic acid sequencing method comprising the use of a bisulfite- or enzymatic-conversion reaction (e.g., during library preparation) to convert non-methylated cytosine to uracil (see, e.g., Li, el al. (2011), “DNA Methylation Detection: Bisulfite Genomic Sequencing Analysis”, Methods Mol. Biol. 791: 11-21).
  • sequence read data may be obtained using a nucleic acid sequencing method comprising the use of alternative chemical and/or enzymatic reactions (e.g., during library preparation) to convert non-methylated cytosine to uracil (or to convert methylated cytosine to dihydrouracil).
  • enzymatic deamination of non-methylated cytosine using APOBEC to form uracil can be performed using, e.g., the Enzymatic Methyl-seq Kit from New England BioLabs (Ipswich, MA) which uses prior treatment with ten-eleven translocation methylcytosine dioxygenase 2 (TET2) to oxidize 5-mC and 5-hmC, thereby providing greater protection of the methylated cytosine from deamination by APOBEC).
  • TET2 ten-eleven translocation methylcytosine dioxygenase 2
  • sequence read data may be obtained using a nucleic acid sequencing method comprising the use of Methylated DNA Immunoprecipitation (MeDIP).
  • MeDIP Methylated DNA Immunoprecipitation
  • Examples of alignment tools optimized for aligning sequence reads for converted DNA include, but are not limited to, NovoAlign (Novocraft Technologies, Selangor, Malaysia), and the Bismark tool (Krueger, et al. (2011), “Bismark: A Flexible Aligner and Methylation Caller for Bisulfite-Seq Applications”, Bioinformatics 27(11): 1571-1572).
  • 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).
  • LD/imputation based analysis examples are described in, e.g., Browning, B.L. and Yu, Z. Am. J. Hum. Genet. 2009, 85(6):847-61.
  • 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. 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.
  • a mutation calling method e.g., a Bayesian
  • 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 Bayesian EM algorithm can be used to analyze the reads, make initial indel calls, and generate genotype likelihoods for each candidate indel, followed by imputation of genotypes using, e.g., QCALL (Le S.Q. and Durbin R. Genome Res. 2011;21(6):952-60). Parameters, such as prior expectations of observing the indel can be adjusted (e.g., increased or decreased), based on the size or location of the indels.
  • the mutation calling method used to analyze sequence reads is not individually customized or fine-tuned for detection of different mutations at different genomic loci.
  • different mutation calling methods are used that are individually customized or fine-tuned for at least a subset of the different mutations detected at different genomic loci.
  • different mutation calling methods are used that are individually customized or fine-tuned for each different mutant detected at each different genomic loci.
  • the customization or tuning can be based on one or more of the factors described herein, e.g., the type of cancer in a sample, the gene or locus in which the subject interval to be sequenced is located, or the variant to be sequenced. This selection or use of mutation calling methods individually customized or fine-tuned for a number of subject intervals to be sequenced allows for optimization of speed, sensitivity and specificity of mutation calling.
  • a nucleotide value is assigned for a nucleotide position in each of X unique subject intervals using a unique mutation calling method, and X is at least 2, at least 3, at least 4, at least 5, at least 10, at least 15, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 200, at least 300, at least 400, at least 500, at least 1000, at least 1500, at least 2000, at least 2500, at least 3000, at least 3500, at least 4000, at least 4500, at least 5000, or greater.
  • the calling methods can differ, and thereby be unique, e.g., by relying on different Bayesian prior values.
  • assigning said nucleotide value is a function of a value which is or represents the prior (e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type.
  • the method comprises assigning a nucleotide value (e.g., calling a mutation) for at least 10, 20, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1,000 nucleotide positions, wherein each assignment is a function of a unique value (as opposed to the value for the other assignments) which is or represents the prior (e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type.
  • a nucleotide value e.g., calling a mutation
  • assigning said nucleotide value is a function of a set of values which represent the probabilities of observing a read showing said variant at said nucleotide position if the variant is present in the sample at a specified frequency (e.g., 1%, 5%, 10%, etc.) and/or if the variant is absent (e.g., observed in the reads due to base-calling error alone).
  • the mutation calling methods described herein can include the following: (a) acquiring, for a nucleotide position in each of said X subject intervals: (i) a first value which is or represents the prior (e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type X; and (ii) a second set of values which represent the probabilities of observing a read showing said variant at said nucleotide position if the variant is present in the sample at a frequency (e.g., 1%, 5%, 10%, etc.) and/or if the variant is absent (e.g., observed in the reads due to base-calling error alone); and (b) responsive to said values, assigning a nucleotide value (e.g., calling a mutation) from said reads for each of said nucleotide positions by weighing, e.g., by a Bay
  • the methods described herein may comprise the use of a methylation status calling method, e.g., to call the methylation status of the CpG sites based on the sequence reads and fragments (complementary pairs of forward and reverse sequence reads) derived from DNA that has been subjected to a chemical or enzymatic conversion reaction, e.g., to convert unmethylated cytosine residues to uracil (which is interpreted as a thymine in sequencing results).
  • a methylation status calling method include, but are not limited to, the Bismark tool (Krueger, et al.
  • truly DDR deficient e.g., HRD positive
  • false-positive DDR deficient status e.g., that appear HRD positive but that are actually HRD negative
  • the systems may comprise, e.g., one or more processors, and a memory unit communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: receive sequence read data for a plurality of sequence reads derived from the sample from the subject; identify a therapy- associated mutation in at least one gene associated with DNA damage repair (DDR) deficiency based on the sequence read data; generate genomic feature data for a plurality of genomic features based on the sequence read data; and analyze the identified therapy-associated mutation and the genomic feature data for the plurality of genomic features using a classification model configured to process genomic feature data as input and output a prediction for identifying the subject as a candidate for treatment with a DDR deficiency-associated treatment.
  • DDR DNA damage repair
  • the disclosed systems may further comprise a sequencer, e.g., a next generation sequencer (also referred to as a massively parallel sequencer).
  • a sequencer e.g., a next generation sequencer (also referred to as a massively parallel sequencer).
  • next generation (or massively parallel) sequencing platforms include, but are not limited to, Roche/454’s Genome Sequencer (GS) FLX system, Illumina/Solexa’ s Genome Analyzer (GA), Illumina’s HiSeq® 2500, HiSeq® 3000, HiSeq® 4000 and NovaSeq® 6000 sequencing systems, Life/APG’s Support Oligonucleotide Ligation Detection (SOLiD) system, Polonator’s G.007 system, Helicos BioSciences’ HeliScope Gene Sequencing system, ThermoFisher Scientific’s Ion Torrent Genexus system, or Pacific Biosciences’ PacBio® RS system.
  • GS Geno
  • the disclosed systems may be used for discriminating between patients with tumors that are truly DDR deficient (e.g., HRD positive) and patients that exhibit falsepositive DDR deficient status (e.g., that appear HRD positive but that are actually HRD negative) based on sequence read data derived from 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).
  • a tissue sample, biopsy sample, hematological sample, or liquid biopsy sample derived from the subject e.g., a tissue sample, biopsy sample, hematological sample, or liquid biopsy sample derived from the subject.
  • the nucleic acid sequence data is acquired using a next generation sequencing technique (also referred to as a massively parallel sequencing technique) having a read-length of less than 400 bases, less than 300 bases, less than 200 bases, less than 150 bases, less than 100 bases, less than 90 bases, less than 80 bases, less than 70 bases, less than 60 bases, less than 50 bases, less than 40 bases, or less than 30 bases.
  • a next generation sequencing technique also referred to as a massively parallel sequencing technique having a read-length of less than 400 bases, less than 300 bases, less than 200 bases, less than 150 bases, less than 100 bases, less than 90 bases, less than 80 bases, less than 70 bases, less than 60 bases, less than 50 bases, less than 40 bases, or less than 30 bases.
  • the determination of DDR deficient status is used to select, initiate, adjust, or terminate a treatment for cancer in the subject (e.g., a patient) from which the sample was derived, as described elsewhere herein.
  • the disclosed systems may further comprise sample processing and library preparation workstations, microplate-handling robotics, fluid dispensing systems, temperature control modules, environmental control chambers, additional data storage modules, data communication modules (e.g., Bluetooth®, WiFi, intranet, or internet communication hardware and associated software), display modules, one or more local and/or cloud-based software packages (e.g., instrument / system control software packages, sequencing data analysis software packages), etc., or any combination thereof.
  • the systems may comprise, or be part of, a computer system or computer network as described elsewhere herein.
  • FIG. 2 illustrates an example of a computing device or system in accordance with one embodiment.
  • Device 200 can be a host computer connected to a network.
  • Device 200 can be a client computer or a server.
  • device 200 can be any suitable type of microprocessor-based device, such as a personal computer, workstation, server or handheld computing device (portable electronic device) such as a phone or tablet.
  • the device can include, for example, one or more processor(s) 210, input devices 220, output devices 230, memory or storage devices 240, communication devices 260, and nucleic acid sequencers 270.
  • Software 250 residing in memory or storage device 240 may comprise, e.g., an operating system as well as software for executing the methods described herein.
  • Input device 220 and output device 230 can generally correspond to those described herein, and can either be connectable or integrated with the computer.
  • Input device 220 can be any suitable device that provides input, such as a touch screen, keyboard or keypad, mouse, or voice-recognition device.
  • Output device 230 can be any suitable device that provides output, such as a touch screen, haptics device, or speaker.
  • Storage 240 can be any suitable device that provides storage (e.g., an electrical, magnetic or optical memory including a RAM (volatile and non-volatile), cache, hard drive, or removable storage disk).
  • Communication device 260 can include any suitable device capable of transmitting and receiving signals over a network, such as a network interface chip or device.
  • the components of the computer can be connected in any suitable manner, such as via a wired media (e.g., a physical system bus 280, Ethernet connection, or any other wire transfer technology) or wirelessly (e.g., Bluetooth®, Wi-Fi®, or any other wireless technology).
  • a wired media e.g., a physical system bus 280, Ethernet connection, or any other wire transfer technology
  • wirelessly e.g., Bluetooth®, Wi-Fi®, or any other wireless technology
  • Software module 250 which can be stored as executable instructions in storage 240 and executed by processor(s) 210, can include, for example, an operating system and/or the processes that embody the functionality of the methods of the present disclosure (e.g., as embodied in the devices as described herein).
  • Software module 250 can also be stored and/or transported within any non-transitory computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described herein, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions.
  • a computer-readable storage medium can be any medium, such as storage 240, that can contain or store processes for use by or in connection with an instruction execution system, apparatus, or device. Examples of computer- readable storage media may include memory units like hard drives, flash drives and distribute modules that operate as a single functional unit.
  • various processes described herein may be embodied as modules configured to operate in accordance with the embodiments and techniques described above. Further, while processes may be shown and/or described separately, those skilled in the art will appreciate that the above processes may be routines or modules within other processes.
  • Software module 250 can also be propagated within any transport medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described above, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions.
  • a transport medium can be any medium that can communicate, propagate or transport programming for use by or in connection with an instruction execution system, apparatus, or device.
  • the transport readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic or infrared wired or wireless propagation medium.
  • Device 200 may be connected to a network (e.g., network 304, as shown in FIG. 3 and/or described below), which can be any suitable type of interconnected communication system.
  • the network can implement any suitable communications protocol and can be secured by any suitable security protocol.
  • the network can comprise network links of any suitable arrangement that can implement the transmission and reception of network signals, such as wireless network connections, T1 or T3 lines, cable networks, DSL, or telephone lines.
  • Device 200 can be implemented using any operating system, e.g., an operating system suitable for operating on the network.
  • Software module 250 can be written in any suitable programming language, such as C, C++, Java or Python.
  • application software embodying the functionality of the present disclosure can be deployed in different configurations, such as in a client/server arrangement or through a Web browser as a Web-based application or Web service, for example.
  • the operating system is executed by one or more processors, e.g., processor(s) 210.
  • Device 200 can further include a sequencer 270, which can be any suitable nucleic acid sequencing instrument.
  • FIG. 3 illustrates an example of a computing system in accordance with one embodiment.
  • device 200 e.g., as described above and illustrated in FIG. 2
  • network 304 which is also connected to device 306.
  • device 306 is a sequencer.
  • Exemplary sequencers can include, without limitation, Roche/454’s Genome Sequencer (GS) FLX System, Illumina/Solexa’s Genome Analyzer (GA), Illumina’s HiSeq® 2500, HiSeq® 3000, HiSeq® 4000 and NovaSeq® 6000 Sequencing Systems, Life/APG’s Support Oligonucleotide Ligation Detection (SOLiD) system, Polonator’s G.007 system, Helicos BioSciences’ HeliScope Gene Sequencing system, or Pacific Biosciences’ PacBio® RS system.
  • Devices 200 and 306 may communicate, e.g., using suitable communication interfaces via network 304, such as a Local Area Network (LAN), Virtual Private Network (VPN), or the Internet.
  • network 304 can be, for example, the Internet, an intranet, a virtual private network, a cloud network, a wired network, or a wireless network.
  • Devices 200 and 306 may communicate, in part or in whole, via wireless or hardwired communications, such as Ethernet, IEEE 802.1 lb wireless, or the like. Additionally, devices 200 and 306 may communicate, e.g., using suitable communication interfaces, via a second network, such as a mobile/cellular network.
  • a second network such as a mobile/cellular network.
  • Communication between devices 200 and 306 may further include or communicate with various servers such as a mail server, mobile server, media server, telephone server, and the like.
  • Devices 200 and 306 can communicate directly (instead of, or in addition to, communicating via network 304), e.g., via wireless or hardwired communications, such as Ethernet, IEEE 802.11b wireless, or the like.
  • devices 200 and 306 communicate via communications 308, which can be a direct connection or can occur via a network (e.g., network 304).
  • One or all of devices 200 and 306 generally include logic (e.g., http web server logic) or are programmed to format data, accessed from local or remote databases or other sources of data and content, for providing and/or receiving information via network 304 according to various examples described herein.
  • logic e.g., http web server logic
  • devices 200 and 306 are programmed to format data, accessed from local or remote databases or other sources of data and content, for providing and/or receiving information via network 304 according to various examples described herein.
  • This section provides a non-limiting example of the application of the disclosed methods to the detection of false-positive homologous recombination deficiency (HRD) therapy associations in cancer patients.
  • HRD biomarkers that are in current use or under developmental rely on the detection of specific genomic scars - or signatures - as a functional readout of HRD status. These functional readouts quantify downstream genomic patterns associated with lost homologous recombination (HR) function, and offer clinical utility in some settings. However, they are not currently deployed for all cancer types due to disease-specific differences in HRD biology that present challenges for establishing analytical and clinical validity of signature-based biomarkers.
  • HRD biomarkers for other cancer types, one can also use the presence of pathogenic mutations in HRD genes, e.g., in combination with signature-based biomarkers, to support HRD therapy associations with PARPi and inform treatment decisions. While this approach has demonstrated success in some clinical trials, monoallelic and/or subclonal pathogenic mutations can arise in HRD genes due to stochastic factors when a tumor sample includes many mutations due to a high mutation rate for the underlying oncogenic process. Since patients that exhibit such mutations are unlikely to respond to PARPi, there is a need for better biomarkers that would exclude such patients from PARPi therapy associations.
  • the disclosed methods are intended to close the gaps created by the current patchwork of CDx and non-CDx biomarkers for identifying patients with HRD positive status.
  • CGP genomic profiling
  • the disclosed methods comprise the calculation and/or extraction of genomic features for a given patient sample that will serve as input to a heuristic algorithm. Cutoffs and thresholds for each genomic feature can be evaluated based on data in comprehensive genomic profiling (CGP) and/or cancer genomics databases, and may vary for disease-specific or pan-cancer datasets.
  • CGP genomic profiling
  • genomic features that are likely to be informative include, but are not limited to, germline versus somatic call (e.g., based on the somatic-germline-zygosity (SGZ) algorithm), SGZ homozygous versus heterozygous zygosity call, tumor mutational burden (TMB), the presence of COSMIC single base substitution (SBS) mutational signatures associated with high TMB (e.g., the UV mutational signature, the POLE mutational signature, the mismatch repair (MMR) mutational signature, the APOBEC mutational signature, the alkylating agents mutational signature, etc.), the presence of a second co-occurring alteration in a gene (presumed to be biallelic), microsatellite instability status (e.g., as defined by a fraction unstable metric), a subclonal status call (e.g., as defined by a next generation sequencing analysis pipeline), or any combination thereof.
  • TMB tumor mutational burden
  • SBS COSMIC single base substitution
  • FIG. 4 provides a non-limiting example of a heuristic process 400 to assign possible driver status versus likely passenger status for HRD mutations in an individual CGP sample.
  • genomic data for a CGP sample for which an HRD-associated mutation has been detected is received.
  • HRDsig HRD mutational signature
  • HRDsig HRD mutational signature
  • the sample is HRD mutational signature (HRDsig) positive or the sample has two or more pathogenic alterations in the same gene
  • HRD mutation is predicted to be a possible driver mutation
  • an HRD drug association is reported at step 406.
  • HRD drug associations include, but are not limited to, first generation PARP inhibitors, second generation PARP inhibitors, platinum-based chemotherapy, ATR inhibitors, and CHK1 inhibitors.
  • the analysis proceeds to step 408 where the tumor mutation burden (TMB) is compared to a predetermined threshold, e.g., a threshold of 50 mutations. If the TMB is greater than the predetermined threshold, the HRD mutation is predicted as a likely passenger mutation at step 410, and an HRD drug association is not reported.
  • TMB tumor mutation burden
  • the analysis proceeds to step 412 where the presence of a single base substitution (SBS) mutational signature is evaluated. If the sample is determined to be SBS mutational signature positive (e.g., a non-HRD SBS mutational signature), the HRD mutation is predicted as a likely passenger mutation at step 410, and an HRD drug association is not reported. [0249] If the sample is determined to be SBS mutational signature negative, the analysis proceeds to step 414 where the micro satellite instability status of the sample is evaluated. If the sample is determined to be microsatellite instability status high (MSI-H), the HRD mutation is predicted as a likely passenger mutation at step 410, and an HRD drug association is not reported.
  • SBS single base substitution
  • step 416 the subclonal status of the HRD mutation is evaluated. If the HRD mutation is determined to be subclonal, the HRD mutation is predicted as a likely passenger mutation at step 410, and an HRD drug association is not reported. If the HRD mutation is determined to not be subclonal, the HRD mutation is predicted to be a possible driver mutation, and an HRD drug association is reported at step 406.
  • FIGS. 5A-C provide non-limiting examples of plots of percent genomic loss of heterozygosity (% gLOH) for different types of cancer that harbor BRCA1 and/or BRCA2 mutations.
  • biallelic mutations in BRCA1 and/or BRCA2 consistently show greater genomic loss of heterozygosity than the wild-type. Therefore, patients with these biallelic alterations should continue to have them reported.
  • FIGS. 7A-C provide exemplary plots of BRCA gene mutation (BRCAm), allelic status proportion, and percent genomic loss of heterozygosity (% gLOH) as a function of tumor mutational burden (TMB) range that illustrate this point.
  • FIG. 7A provides an exemplary plot of BRCA gene mutation (BRCAm) prevalence versus TMB range.
  • FIG. 7B provides an exemplary plot of allelic status proportion versus TMB range.
  • FIG. 7C provides an exemplary plot of percent genomic loss of heterozygosity (% gLOH) versus TMB range.
  • FIGS. 8A-B provide non-limiting examples of plots of prostate specific antigen (PSA) concentration in blood samples from two different prostate cancer patients who were monoallelic for BRCAm and exhibited MSI-H status as a function of time following the initiation of different treatments.
  • FIG. 8A provides an exemplary plot of PSA concentration data for patient 1.
  • FIG. 8B provides an exemplary plot of PSA concentration data for patient 2. The patients did not respond (based on PSA and radiographic imaging) to PARPi (Olaparib) treatment, but had a robust response to ICI (Pembrolizumab) treatment.
  • PARPi Oparib
  • the approach described herein to identifying passenger mutations is not limited to HRD genes. It could also be applied to future therapy associations with mutations in other genes, especially those involving tumor suppressors or synthetic lethality targets (e.g., ATM, MTAP, TP53, PTEN, etc.). Furthermore, inclusion of additional genomic features may improve this classification approach. Examples of additional genomic features that may be utilized include, but are not limited to, copy number signatures, gLOH, HRDsig, and indel signatures.
  • a method comprising: providing a plurality of nucleic acid molecules obtained from a sample from a subject; ligating one or more adapters onto one or more nucleic acid molecules from the plurality of nucleic acid molecules; amplifying the one or more ligated nucleic acid molecules from the plurality of nucleic acid molecules; capturing amplified nucleic acid molecules from the amplified nucleic acid molecules; sequencing, by a sequencer, the captured nucleic acid molecules to obtain a plurality of sequence reads that represent the captured nucleic acid molecules; receiving, at one or more processors, sequence read data for the plurality of sequence reads; identifying, using the one or more processors, a therapy-associated mutation in at least one gene associated with DNA damage repair (DDR) deficiency based on the sequence read data; generating, using the one or more processors, genomic feature data for a plurality of genomic features based on the sequence read data; and analyzing, using the one or more processors, the identified therapy-associated mutation and the genomic feature data for
  • the homologous recombination deficiency-associated drug comprises a first generation PARP inhibitor, a second generation PARP inhibitor, a platinumbased chemotherapy, an ATR inhibitor, or a CHK1 inhibitor.
  • the DDR deficiency comprises homologous recombination deficiency
  • the genomic feature data for the plurality of genomic features comprises a determination of germline status for the therapy-associated mutation in at least one gene associated with homologous recombination deficiency, a determination of homozygous status for the therapy-associated mutation in at least one gene associated with homologous recombination deficiency, a determination of subclonal status for the therapy- associated mutation in at least one gene associated with homologous recombination deficiency a determination that a tumor mutational burden for the sample is greater than a predetermined tumor mutational burden threshold, a determination that a single base substitution signature associated with high tumor mutational burden is present in the sequence read data, a determination that an indel signature associated with high tumor mutational burden is present in the sequence read data, a determination of high microsatellite instability status, a determination that a homologous recombination defici
  • the cancer is a B cell cancer (multiple myeloma), a melanoma, breast cancer, lung cancer, bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain cancer, central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine cancer, endometrial cancer, cancer of an oral cavity, cancer of a pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel cancer, appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, a cancer of hematological tissue, an adenocarcinoma, an inflammatory myofibroblastic tumor, a gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MP
  • the cancer comprises acute lymphoblastic leukemia (Philadelphia chromosome positive), acute lymphoblastic leukemia (precursor B-cell), acute myeloid leukemia (FLT3+), acute myeloid leukemia (with an IDH2 mutation), anaplastic large cell lymphoma, basal cell carcinoma, B-cell chronic lymphocytic leukemia, bladder cancer, breast cancer (HER2 overexpressed/amplified), breast cancer (HER2+), breast cancer (HR+, HER2-), cervical cancer, cholangiocarcinoma, chronic lymphocytic leukemia, chronic lymphocytic leukemia (with 17p deletion), chronic myelogenous leukemia, chronic myelogenous leukemia (Philadelphia chromosome positive), classical Hodgkin lymphoma, colorectal cancer, colorectal cancer (dMMR/MSI-H), colorectal cancer (KRAS wild type), cryopyrin-associated periodic syndrome,
  • the targeted anti-cancer therapy comprises abemaciclib (Verzenio), abiraterone acetate (Zytiga), acalabrutinib (Calquence), ado-trastuzumab emtansine (Kadcyla), afatinib dimaleate (Gilotrif), aldesleukin (Proleukin), alectinib (Alecensa), alemtuzumab (Campath), alitretinoin (Panretin), alpelisib (Piqray), amivantamab-vmjw (Rybrevant), anastrozole (Arimidex), apalutamide (Erleada), asciminib hydrochloride (Scemblix), atezolizumab (Tecentriq), avapritinib (Ayvakit), avelumab (Bavencio), axicabtagene
  • the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
  • tumor nucleic acid molecules are derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules are derived from a normal portion of the heterogeneous tissue biopsy sample.
  • the sample comprises a liquid biopsy sample
  • the tumor nucleic acid molecules are derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample
  • the non-tumor nucleic acid molecules are derived from a non- tumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.
  • the one or more bait molecules comprise one or more nucleic acid molecules, each comprising a region that is complementary to a region of a captured nucleic acid molecule.
  • amplifying nucleic acid molecules comprises performing a polymerase chain reaction (PCR) amplification technique, a non-PCR amplification technique, or an isothermal amplification technique.
  • PCR polymerase chain reaction
  • the one or more gene loci comprises between 10 and 20 loci, between 10 and 40 loci, between 10 and 60 loci, between 10 and 80 loci, between 10 and 100 loci, between 10 and 150 loci, between 10 and 200 loci, between 10 and 250 loci, between 10 and 300 loci, between 10 and 350 loci, between 10 and 400 loci, between 10 and 450 loci, between 10 and 500 loci, between 20 and 40 loci, between 20 and 60 loci, between 20 and 80 loci, between 20 and 100 loci, between 20 and 150 loci, between 20 and 200 loci, between 20 and 250 loci, between 20 and 300 loci, between 20 and 350 loci, between 20 and 400 loci, between 20 and 500 loci, between 40 and 60 loci, between 40 and 80 loci, between 40 and 100 loci, between 40 and 150 loci, between 40 and 200 loci, between 40 and 250 loci, between 40 and 300 loci, between 40 and 350 loci, between 40 and 400 loci, between 40 and 500 loci, between 40 and 60 loci,
  • the one or more gene loci comprise ABL, ALK, ALL, B4GALNT1, BAFF, BCL2, BRAF, BRCA, BTK, CD19, CD20, CD3, CD30, CD319, CD38, CD52, CDK4, CDK6, CML, CRACC, CS1, CTLA-4, dMMR, EGFR, ERBB 1, ERBB2, FGFR1-3, FLT3, GD2, HDAC, HER1, HER2, HR, IDH2, IL-ip, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSLH, mTOR, PARP, PD-1, PDGFR, PDGFRa, PDGFRP, PD-L1, PI3K5, PIGF, PTCH, RAF, RANKL, RET, ROS1, SLAMF7, VEGF, VEGFA, VEGFB, or any combination thereof.
  • a method for identifying a subject as a candidate for treatment with a DNA damage repair (DDR) deficiency-associated treatment comprising: receiving, at one or more processors, sequence read data for a plurality of sequence reads derived from the sample from the subject; identifying, using the one or more processors, a therapy-associated mutation in at least one gene associated with DNA damage repair (DDR) deficiency based on the sequence read data; generating, using the one or more processors, genomic feature data for a plurality of genomic features based on the sequence read data; and analyzing, using the one or more processors, the identified therapy-associated mutation and the genomic feature data for the plurality of genomic features using a classification model configured to process genomic feature data as input and output a prediction for identifying the subject as a candidate for treatment with a DDR deficiency-associated treatment.
  • DDR DNA damage repair
  • the homologous recombination deficiency-associated drug comprises a first generation PARP inhibitor, a second generation PARP inhibitor, a platinumbased chemotherapy, an ATR inhibitor, or a CHK1 inhibitor.
  • the DDR deficiency comprises homologous recombination deficiency
  • the genomic feature data for the plurality of genomic features comprises a determination of germline status for the therapy-associated mutation in at least one gene associated with homologous recombination deficiency, a determination of homozygous status for the therapy-associated mutation in at least one gene associated with homologous recombination deficiency, a determination of subclonal status for the therapy- associated mutation in at least one gene associated with homologous recombination deficiency a determination that a tumor mutational burden for the sample is greater than a predetermined tumor mutational burden threshold, a determination that a single base substitution signature associated with high tumor mutational burden is present in the sequence read data, a determination that an indel signature associated with high tumor mutational burden is present in the sequence read data, a determination of high microsatellite instability status, a determination that a homologous recombination de
  • the genomic feature data comprises a determination of germline status for the therapy-associated mutation in at least one gene associated with homologous recombination deficiency
  • the determination of germline status comprises: identifying one or more proxy genomic sequences for a genomic sequence comprising the therapy-associated mutation in at least one gene associated with homologous recombination deficiency; comparing an observed frequency of the sequence comprising the therapy-associated mutation in at least one gene associated with homologous recombination deficiency to a centrality measure of observed frequencies of the one or more proxy genomic sequences; and based on the comparison, characterizing the genomic sequence of interest as either germline or somatic.
  • the genomic feature data comprises a determination that a tumor mutational burden for the sample is greater than a predetermined tumor mutational burden threshold
  • evaluation of the tumor mutational burden comprises: determining a sequence of a set of subgenomic intervals from the sample using hybrid capture and next-generation sequencing, wherein the set of subgenomic intervals are from a predetermined set of genes; and determining a value for the tumor mutational burden by counting a number of one or more somatic alterations in the set of subgenomic intervals, and excluding from said number of one or more somatic alterations:
  • a functional alteration in a subgenomic interval wherein the functional alteration is an alteration that, compared with a reference sequence, has an effect on cell division, growth or survival, and wherein the functional alteration is identified as such based on inclusion in a database of functional alterations;
  • the genomic feature data comprises a determination that a tumor mutational burden for the sample is greater than a predetermined tumor mutational burden threshold, and wherein the predetermined tumor mutational burden threshold is equal to 20, 30, 40, 50, 60, 70, or 80 mutations.
  • the genomic feature data comprises a determination that a single base substitution signature associated with high tumor mutational burden is present in the sequence read data, and wherein the single base substitution signature comprises a COSMIC single base substitution mutational signature, an ultraviolet (UV) mutational signature, a POLE mutational signature, a mismatch repair mutational signature, an APOBEC mutational signature, an alkylating agent mutational signature, a tobacco signature, or any combination thereof.
  • the single base substitution signature comprises a COSMIC single base substitution mutational signature, an ultraviolet (UV) mutational signature, a POLE mutational signature, a mismatch repair mutational signature, an APOBEC mutational signature, an alkylating agent mutational signature, a tobacco signature, or any combination thereof.
  • the COSMIC single base substitution mutational signature comprises a COSMIC SBS1 signature, a COSMIC SBS2 signature, a COSMIC SBS3 signature, a COSMIC SBS4 signature, a COSMIC SBS5 signature, a COSMIC SBS6 signature, a COSMIC SBS7 signature, a COSMIC SBS8 signature, a COSMIC SBS9 signature, a COSMIC SBS10 signature, a COSMIC SBS11 signature, a COSMIC SBS12 signature, a COSMIC SBS13 signature, a COSMIC SBS14 signature, a COSMIC SBS15 signature, a COSMIC SBS16 signature, a COSMIC SBS17 signature, a COSMIC SBS18 signature, a COSMIC SBS19 signature, a COSMIC SBS20 signature, a COSMIC SBS21 signature, a COSMIC SBS22 signature, a COSMIC SBS23 signature, a COS
  • the supervised learning model comprises an artificial neural network, a decision tree model, a random forest model, a k-nearest neighbors model, or a support vector machine model.
  • the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
  • 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.
  • 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 for a plurality of sequence reads derived from the sample from the subject; identify a therapy-associated mutation in at least one gene associated with DNA damage repair (DDR) deficiency based on the sequence read data; generate genomic feature data for a plurality of genomic features based on the sequence read data; and analyze the identified therapy-associated mutation and the genomic feature data for the plurality of genomic features using a classification model configured to process genomic feature data as input and output a prediction for identifying the subject as a candidate for treatment with a DDR deficiency-associated treatment.
  • DDR DNA damage repair
  • a non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: receive sequence read data for a plurality of sequence reads derived from the sample from the subject, identify a therapy-associated mutation in at least one gene associated with DNA damage repair (DDR) deficiency based on the sequence read data; generate genomic feature data for a plurality of genomic features based on the sequence read data; and analyze the identified therapy-associated mutation and the genomic feature data for the plurality of genomic features using a classification model configured to process genomic feature data as input and output a prediction for identifying the subject as a candidate for treatment with a DDR deficiency-associated treatment.
  • DDR DNA damage repair

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Abstract

L'invention décrit des procédés de discrimination entre des patients atteints de tumeurs qui sont véritablement déficients en DDR et des patients qui présentent un état déficient en DDR faussement positif. Les procédés peuvent comprendre, par exemple, la réception de données de lecture de séquence pour une pluralité de lectures de séquence dérivées de l'échantillon provenant du sujet ; l'identification d'une mutation associée à une thérapie dans au moins un gène associé à une déficience de réparation de dommages à l'ADN (DDR) sur la base des données de lecture de séquence ; la génération de données de caractéristiques génomiques pour une pluralité de caractéristiques génomiques sur la base des données de lecture de séquence ; et l'analyse de la mutation associée à une thérapie identifiée et les données de caractéristiques génomiques pour la pluralité de caractéristiques génomiques à l'aide d'un modèle de classification configuré pour traiter des données de caractéristiques génomiques en tant qu'entrée et délivrer une prédiction pour identifier le sujet en tant que candidat pour un traitement avec un traitement associé à une déficience en DDR.
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180363066A1 (en) * 2016-02-29 2018-12-20 Foundation Medicine, Inc. Methods and systems for evaluating tumor mutational burden
US20200255909A1 (en) * 2019-02-12 2020-08-13 Tempus Integrated machine-learning framework to estimate homologous recombination deficiency
US20210262016A1 (en) * 2018-11-13 2021-08-26 Myriad Genetics, Inc. Methods and systems for somatic mutations and uses thereof

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180363066A1 (en) * 2016-02-29 2018-12-20 Foundation Medicine, Inc. Methods and systems for evaluating tumor mutational burden
US20210262016A1 (en) * 2018-11-13 2021-08-26 Myriad Genetics, Inc. Methods and systems for somatic mutations and uses thereof
US20200255909A1 (en) * 2019-02-12 2020-08-13 Tempus Integrated machine-learning framework to estimate homologous recombination deficiency

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
BESEMER J, BORODOVSKY MARK, : "Heuristic approach to deriving models for gene finding", NUCLEIC ACIDS RESEARCH, OXFORD UNIVERSITY PRESS, GB, vol. 27, no. 19, 1 October 1999 (1999-10-01), GB , pages 3911 - 3920, XP093178833, ISSN: 1362-4962, DOI: 10.1093/nar/27.19.3911 *
SHA DAN, JIN ZHAOHUI, BUDCZIES JAN, KLUCK KLAUS, STENZINGER ALBRECHT, SINICROPE FRANK A.: "Tumor Mutational Burden as a Predictive Biomarker in Solid Tumors", CANCER DISCOVERY, AMERICAN ASSOCIATION FOR CANCER RESEARCH, US, vol. 10, no. 12, 1 December 2020 (2020-12-01), US , pages 1808 - 1825, XP093141396, ISSN: 2159-8274, DOI: 10.1158/2159-8290.CD-20-0522 *

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