WO2024077041A2 - Procédés et systèmes d'identification de signatures de nombre de copies - Google Patents

Procédés et systèmes d'identification de signatures de nombre de copies Download PDF

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Publication number
WO2024077041A2
WO2024077041A2 PCT/US2023/075910 US2023075910W WO2024077041A2 WO 2024077041 A2 WO2024077041 A2 WO 2024077041A2 US 2023075910 W US2023075910 W US 2023075910W WO 2024077041 A2 WO2024077041 A2 WO 2024077041A2
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Prior art keywords
copy number
individual
cancer
signature
sample
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PCT/US2023/075910
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English (en)
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WO2024077041A3 (fr
Inventor
Jay Moore
Dexter X. JIN
Ethan S. SOKOL
Justin NEWBERG
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Foundation Medicine, Inc.
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Publication of WO2024077041A2 publication Critical patent/WO2024077041A2/fr
Publication of WO2024077041A3 publication Critical patent/WO2024077041A3/fr

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/10—Ploidy or copy number detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • 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
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

  • the present disclosure relates generally to methods and systems for analyzing genomic profiling data, and more specifically to methods and systems for identifying copy number signatures in samples from individuals using genomic profiling data.
  • disease e.g., cancer
  • biomarkers for disease diagnosis, prognosis, and prediction of treatment outcomes e.g., cancer
  • Advantages of the disclosed methods and systems over previous work include utilization of off-target sequence read data derived from a targeted sequencing assay to maximize coverage of copy number features while minimizing sequencing costs, and the identification of an expanded set of copy number signatures as compared to those described in previous work.
  • Disclosed herein are methods comprising: providing a plurality of nucleic acid molecules obtained from a sample from an individual diagnosed with or suspected of having a disease; 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, targeted sequence read data for the sample derived from the individual; determining, using the one or more processors, a copy number profile having a plurality of copy number features for the individual based on the targeted sequence read data for the sample; determining, using the one or more processors, a copy number feature value for each of the plurality of copy number features; performing, using the one or more processors, an analysis
  • the method further comprises confirming a diagnosis of the disease for the individual based on the dominant copy number signature or the one or more copy number signatures identified for the individual. In some embodiments, the method further comprises predicting a treatment outcome for the individual based on the dominant copy number signature or the one or more copy number signatures identified for the individual. In some embodiments, the method further comprises selecting a treatment for the individual based on the dominant copy number signature or the one or more copy number signatures identified for the individual. In some embodiments, the method further comprises adjusting a treatment dosage for the individual based on the dominant copy number signature or the one or more copy number signatures identified for the individual.
  • the method further comprises selecting the individual for inclusion in a clinical trial based on the dominant copy number signature or the one or more copy number signatures identified for the individual. In some embodiments, the method further comprises identifying the individual as a candidate for chemotherapy, radiation therapy, or surgery based on the dominant copy number signature or the one or more copy number signatures identified for the individual. In some embodiments, the method further comprises identifying the individual as a candidate for hospice care based on the dominant copy number signature or the one or more copy number signatures identified for the individual.
  • the copy number profile for the individual is determined based on single nucleotide polymorphisms (SNPs) located in the targeted sequence read data for the sample.
  • the single nucleotide polymorphisms (SNPs) are located in on- target sequence read data.
  • the copy number profile for the individual is determined based on off-target sequence reads in the targeted sequence read data for the sample.
  • the copy number profile for the individual is determined based on a combination of on-target and off-target sequence reads in the targeted sequence read data for the sample.
  • the analysis to identify one or more copy number signatures represented in the copy number profile of the individual comprises an analysis of both the probabilities determined for each of the one or more copy number feature subgroups and a presence of one or more genomic features detected in the targeted sequence read data for the individual using the second trained model.
  • the one or more genomic features comprise one or more short variants, one or more indels, one or more biallelic loss-of- function variants, one or more rearrangements, or any combination thereof.
  • 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 non- tumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.
  • ctDNA circulating tumor DNA
  • cfDNA non- tumor, 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 dominant copy number signature or the one or more copy number signature components identified for the individual. In some embodiments, the method further comprises transmitting the report to a healthcare provider. In some embodiments, the report is transmitted via a computer network or a peer-to-peer connection.
  • the method further comprises confirming a diagnosis of the disease for the individual based on the dominant copy number signature or the one or more copy number signatures identified for the individual. In some embodiments, the method further comprises predicting a treatment outcome for the individual based on the dominant copy number signature or the one or more copy number signatures identified for the individual. In some embodiments, the method further comprises selecting a treatment for the individual based on the dominant copy number signature or the one or more copy number signatures identified for the individual. In some embodiments, the method further comprises adjusting a treatment dosage for the individual based on the dominant copy number signature or the one or more copy number signatures identified for the individual.
  • the method further comprises selecting the individual for inclusion in a clinical trial based on the dominant copy number signature or the one or more copy number signatures identified for the individual. In some embodiments, the method further comprises identifying the individual as a candidate for chemotherapy, radiation therapy, or surgery based on the dominant copy number signature or the one or more copy number signatures identified for the individual. In some embodiments, the method further comprises identifying the individual as a candidate for hospice care based on the dominant copy number signature or the one or more copy number signatures identified for the individual.
  • the copy number profile for the individual is determined based on single nucleotide polymorphisms (SNPs) located in the targeted sequence read data for the sample.
  • the single nucleotide polymorphisms (SNPs) are located in on- target sequence read data.
  • the copy number profile for the individual is determined based on off-target sequence reads in the targeted sequence read data for the sample.
  • the copy number profile for the individual is determined based on a combination of on-target and off-target sequence reads in the targeted sequence read data for the sample.
  • the off-target sequence reads comprise an average sequencing coverage of less than l.Ox, 0.9x, 0.8x, 0.7x, 0.6x, 0.5x, 0.4x, 0.3x, 0.2x, or O.lx.
  • the analysis to identify one or more copy number signatures represented in the copy number profile of the individual comprises an analysis of both the probabilities determined for each of the one or more copy number feature subgroups and a presence of one or more genomic features detected in the targeted sequence read data for the individual using the second trained model.
  • the one or more genomic features comprise one or more short variants, one or more indels, one or more biallelic loss-of- function variants, one or more rearrangements, or any combination thereof.
  • the plurality of copy number features comprises a number of breakpoints per 10 Mb of genomic sequence, a number of breakpoints per 25 Mb of genomic sequence, a number of breakpoints per 50 Mb of genomic sequence, a number of breakpoints per 100 Mb of genomic sequence, a number of breakpoints per chromosome arm for each chromosome, a magnitude of a copy number change between any two adjacent copy number segments; a length of each copy number segment; the copy number of segments; a count of contiguous oscillating copy number chains, or any combination thereof.
  • the first trained model comprises a trained statistical model. In some embodiments, the first trained model comprises a trained machine learning model. In some embodiments, the first trained model is updated based on the one or more copy number signatures identified in the copy number profile of the individual. In some embodiments, the first trained model comprises a latent class analysis model or a mixture model. In some embodiments, the first trained model is trained on copy number feature values calculated for each of the plurality of copy number features based on copy number profiles for a cohort of individuals diagnosed with the disease.
  • the disease comprises a cancer.
  • the cancer comprises ovarian cancer, prostate cancer, or colorectal cancer.
  • the second trained model comprises a non-negative matrix factorization (NMF) model or a machine learning model.
  • NMF non-negative matrix factorization
  • the second trained model comprises a machine learning model, and the machine learning model comprises a supervised, semi-supervised, or unsupervised machine learning model.
  • the second trained model comprises a supervised machine learning model, and the supervised machine learning model comprises an XGBoost model.
  • the second trained model is trained on copy number feature subgroups identified in data for a cohort of individuals diagnosed with the disease. In some embodiments, the second trained model is trained on copy number feature subgroups identified in data for a cohort of individuals comprising individuals diagnosed with a plurality of different diseases. In some embodiments, the plurality of different diseases comprises a plurality of different cancers. In some embodiments, the plurality of different cancers comprises ovarian cancer, breast cancer, non-small cell lung carcinoma (NSCLC), esophageal cancer, prostate cancer, pancreatic cancer, colorectal cancer, or any combination thereof.
  • NSCLC non-small cell lung carcinoma
  • the second trained model is trained to identify copy number signatures based on the probabilities determined for each of the one or more copy number feature subgroups and based on the presence of one or more additional genomic features identified in the targeted sequence read data.
  • the one or more additional genomic features comprise one or more short variants, one or more indels, one or more biallelic loss-of-function variants, one or more rearrangements, or any combination thereof.
  • the one or more corresponding predetermined thresholds are determined based on an analysis of copy number signature scores for a cohort of individuals diagnosed with the disease. In some embodiments, the one or more corresponding predetermined thresholds are determined based on an analysis of copy number signature scores for a cohort of individuals comprising individuals diagnosed with a plurality of different diseases. In some embodiments, the one or more corresponding predetermined thresholds are determined based on an analysis of data for a cohort of individuals that comprises both copy number signature scores and clinical data. In some embodiments, the clinical data comprises the individual’s sex, age, gender, height, weight, clinical history, family history, sample type, tumor stage, tumor grade, or any combination thereof.
  • the identification of a dominant copy number signature or of one or more copy number signature components is used to diagnose or confirm a diagnosis of disease in the individual.
  • the disease is 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 Hodg
  • the method further comprises selecting an anti-cancer therapy to administer to the subject based on the identification of the dominant copy number signature or of the one or more copy number signature components. In some embodiments, the method further comprises determining an effective amount of an anti-cancer therapy to administer to the subject based on the identification of the dominant copy number signature or of the one or more copy number signature components. In some embodiments, the method further comprises administering the anti-cancer therapy to the subject based on the identification of the dominant copy number signature or of the one or more copy number signature components. In some embodiments, the anti-cancer therapy comprises chemotherapy, radiation therapy, immunotherapy, a targeted therapy, or surgery.
  • Disclosed herein are methods for diagnosing a disease the method comprising: diagnosing that a subject has the disease based on an identification of a dominant copy number signature or of one or more copy number signature components in a copy number profile for a sample from an individual, wherein the dominant copy number signature or the one or more copy number signature components are identified according to any of the methods described herein.
  • Disclosed herein are methods of selecting an anti-cancer therapy comprising: responsive to an identification of a dominant copy number signature or of one or more copy number signature components in a copy number profile for a sample from an individual, selecting an anti-cancer therapy for the individual, wherein the dominant copy number signature or the one or more copy number signature components are identified according to any of the methods described herein.
  • Disclosed herein are methods for monitoring cancer progression or recurrence in an individual comprising: identifying a first dominant copy number signature, or a first set of one or more copy number signature components, in a first copy number profile for a first sample obtained from an individual at a first time point according to any of the methods described herein; identifying a second dominant copy number signature, or a second set of one or more copy number signature components, in a second copy number profile for a second sample obtained from the individual at a second time point; and comparing the first dominant copy number signature, or the first set of one or more copy number signature components, to the second dominant copy number signature, or the second set of one or more copy number signature components, thereby monitoring the cancer progression or recurrence.
  • the second dominant copy number signature, or the second set of one or more copy number signature components, for the second sample is determined according to any of the methods described herein.
  • the method further comprises selecting an anti- cancer therapy for the individual in response to the cancer progression.
  • the method further comprises administering an anti-cancer therapy to the individual in response to the cancer progression.
  • the method further comprises adjusting an anticancer therapy for the individual in response to the cancer progression.
  • the method further comprises adjusting a dosage of the anti-cancer therapy or selecting a different anti-cancer therapy in response to the cancer progression.
  • the method further comprises administering the adjusted anti-cancer therapy to the individual.
  • the first time point is before the individual has been administered an anticancer therapy, and wherein the second time point is after the individual has been administered the anti-cancer therapy.
  • the individual has a cancer, is at risk of having a cancer, is being routine tested for cancer, or is suspected of having a cancer.
  • the cancer is a solid tumor.
  • the cancer is a hematological cancer.
  • the anti-cancer therapy comprises chemotherapy, radiation therapy, immunotherapy, a targeted therapy, or surgery.
  • the method further comprises determining, identifying, or applying the dominant copy number signature, or the one or more copy number signature components, for the sample as a diagnostic value associated with the sample.
  • the method further comprises generating a genomic profile for the individual based on the identification of a dominant copy number signature or of one or more copy number signature components in a copy number profile for the sample from the individual.
  • the genomic profile for the individual further comprises results from a comprehensive genomic profiling (CGP) test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof.
  • CGP genomic profiling
  • the genomic profile for the individual further comprises results from a nucleic acid sequencing-based test. In some embodiments, the method further comprises selecting an anti-cancer therapy, administering an anti-cancer therapy, or applying an anti-cancer therapy to the individual based on the generated genomic profile. In some embodiments, the identification of a dominant copy number signature or of one or more copy number signature components in a copy number profile for the sample from the individual is used in making suggested treatment decisions for the subject. In some embodiments, the identification of a dominant copy number signature or of one or more copy number signature components in a copy number profile for the sample from the individual is used in applying or administering a treatment to the subject.
  • PARPi poly (ADP-ribose) polymerase inhibitor
  • HRD homologous recombination deficient
  • the method further comprises administering an effective amount of a PARPi to the individual.
  • the PARPi comprises olaparib, rucaparib, niraparib, talazoparib, or veliparib.
  • the cancer is ovarian cancer. In some embodiments, the cancer is prostate cancer.
  • the method further comprising, prior to receiving the targeted sequence read data: providing a plurality of nucleic acid molecules obtained from a sample from the individual; 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; and sequencing, by a sequencer, the captured nucleic acid molecules to obtain a plurality of sequence reads that represent the captured nucleic acid molecules.
  • the sample lacks a loss-of-function mutation in BRCA1 and/or BRCA2 gene(s).
  • the sample comprises biallelic loss-of-function mutations in one or more of BARD1, PALB2, RAD51D, and RAD51C gene(s).
  • the sample further lacks a loss-of-function mutation in ATM, BARD1, BRIP1, CDK12, CHEK1, CHEK2, FANCL, PALB2, RAD51B, RAD51C, RAD51D, and RAD54L gene(s).
  • the HRD signature comprises a plurality of low copy number segments.
  • the HRD signature comprises heterozygous deletion(s), high genome-wide loss of heterozygosity (gLOH-high), and/or high genomic instability score (GIS).
  • the cancer is ovarian or breast cancer, and the HRD signature comprises heterozygous deletion(s), heterozygous duplication(s), and high genome-wide loss of heterozygosity (gLOH-high).
  • the cancer is prostate cancer, and the HRD signature comprises heterozygous deletion(s) and copy neutral loss of heterozygosity.
  • 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.
  • 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 targeted sequence read data for a sample derived from an individual diagnosed with or suspected of having a disease; determine a copy number profile having a plurality of copy number features for the individual based on the targeted sequence read data for the sample; determine a copy number feature value for each of the plurality of copy number features; perform an analysis of the copy number feature values for each of a plurality of copy number features using a first trained model to determine a probability that each of one or more copy number feature subgroups is represented in the copy number profile for the sample; perform an analysis of the probabilities determined for each of the one or more copy number feature subgroups using a second trained model to identify one or more copy number signatures represented in the copy number profile for the individual, and to determine one or more corresponding copy number signature scores; compare the copy number signature scores for the one or more identified copy
  • the copy number profile for the individual is determined based on off-target sequence reads in the targeted sequence read data for the sample. In some embodiments, the copy number profile for the individual is determined based on single nucleotide polymorphisms (SNPs) located in the targeted sequence read data for the sample. In some embodiments, the single nucleotide polymorphisms (SNPs) are located in on-target sequence read data. In some embodiments, the copy number profile for the individual is determined based on off-target sequence reads in the targeted sequence read data for the sample. In some embodiments, the copy number profile for the individual is determined based on a combination of on-target and off-target sequence reads in the targeted sequence read data for the sample. In some embodiments, the off-target sequence reads comprise an average coverage of less than 0.5x, 0.4x, 0.3x, 0.2x, or O.lx.
  • the analysis to identify one or more copy number signatures represented in the copy number profile of the individual comprises an analysis of both the probabilities determined for each of the one or more copy number feature subgroups and a presence of one or more genomic features detected in the targeted sequence read data for the individual using the second trained model.
  • the one or more genomic features comprise one or more short variants, one or more indels, one or more biallelic loss-of- function variants, one or more rearrangements, or any combination thereof.
  • the plurality of copy number features comprises a number of breakpoints per 10 Mb of genomic sequence, a number of breakpoints per 25 Mb of genomic sequence, a number of breakpoints per 50 Mb of genomic sequence, a number of breakpoints per 100 Mb of genomic sequence, a number of breakpoints per chromosome arm for each chromosome, a magnitude of a copy number change between any two adjacent copy number segments; a length of each copy number segment; the copy number of segments; a count of contiguous oscillating copy number chains, or any combination thereof.
  • the first trained model comprises a trained statistical model. In some embodiments, the first trained model comprises a trained machine learning model. In some embodiments, the first trained model is updated based on the one or more copy number signatures identified in the copy number profile of the individual. In some embodiments, the first trained model comprises a latent class analysis model or a mixture model.
  • the second trained model comprises a non-negative matrix factorization (NMF) model or a machine learning model.
  • NMF non-negative matrix factorization
  • the second trained model comprises a machine learning model
  • the machine learning model comprises a supervised, semi-supervised, or unsupervised machine learning model.
  • the second trained model comprises a supervised machine learning model
  • the supervised machine learning model comprises an XGBoost model.
  • the one or more corresponding predetermined thresholds are determined based on an analysis of copy number signature scores for a cohort of individuals diagnosed with the disease. In some embodiments, the one or more corresponding predetermined thresholds are determined based on an analysis of copy number signature scores for a cohort of individuals comprising individuals diagnosed with a plurality of different diseases. In some embodiments, the one or more corresponding predetermined thresholds are determined based on an analysis of data for a cohort of individuals that comprises both copy number signature scores and clinical data.
  • 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 targeted sequence read data for a sample derived from an individual diagnosed with or suspected of having a disease; determine a copy number profile having a plurality of copy number features for the individual based on the targeted sequence read data for the sample; determine a copy number feature value for each of a plurality of copy number features; perform an analysis of the copy number feature values for each of a plurality of copy number features using a first trained model to determine a probability that each of one or more copy number feature subgroups is represented in the copy number profile for the sample; perform an analysis of the probabilities determined for each of the one or more copy number feature subgroups using a second trained model to identify one or more copy number signatures represented in the copy number profile for the individual, and to determine one or more corresponding copy number signature scores; compare the copy number signature scores for the one or more identified copy number
  • the copy number profile for the individual is determined based on off-target sequence reads in the targeted sequence read data for the sample. In some embodiments, the copy number profile for the individual is determined based on single nucleotide polymorphisms (SNPs) located in the targeted sequence read data for the sample. In some embodiments, the single nucleotide polymorphisms (SNPs) are located in on-target sequence read data. In some embodiments, the copy number profile for the individual is determined based on off-target sequence reads in the targeted sequence read data for the sample. In some embodiments, the copy number profile for the individual is determined based on a combination of on-target and off-target sequence reads in the targeted sequence read data for the sample.
  • SNPs single nucleotide polymorphisms
  • the off-target sequence reads comprise an average coverage of less than 0.5x, 0.4x, 0.3x, 0.2x, or O.lx.
  • the analysis to identify one or more copy number signatures represented in the copy number profile of the individual comprises an analysis of both the probabilities determined for each of the one or more copy number feature subgroups and a presence of one or more genomic features detected in the targeted sequence read data for the individual using the second trained model.
  • the one or more genomic features comprise one or more short variants, one or more indels, one or more biallelic loss-of- function variants, one or more rearrangements, or any combination thereof.
  • the plurality of copy number features comprises a number of breakpoints per 10 Mb of genomic sequence, a number of breakpoints per 25 Mb of genomic sequence, a number of breakpoints per 50 Mb of genomic sequence, a number of breakpoints per 100 Mb of genomic sequence, a number of breakpoints per chromosome arm for each chromosome, a magnitude of a copy number change between any two adjacent copy number segments; a length of each copy number segment; the copy number of segments; a count of contiguous oscillating copy number chains, or any combination thereof.
  • the first trained model comprises a trained statistical model. In some embodiments, the first trained model comprises a trained machine learning model. In some embodiments, the first trained model is updated based on the one or more copy number signatures identified in the copy number profile of the individual. In some embodiments, the first trained model comprises a latent class analysis model or a mixture model.
  • the second trained model comprises a non-negative matrix factorization (NMF) model or a machine learning model.
  • NMF non-negative matrix factorization
  • the second trained model comprises a machine learning model
  • the machine learning model comprises a supervised, semi-supervised, or unsupervised machine learning model.
  • the second trained model comprises a supervised machine learning model
  • the supervised machine learning model comprises an XGBoost model.
  • the one or more corresponding predetermined thresholds are determined based on an analysis of copy number signature scores for a cohort of individuals diagnosed with the disease. In some embodiments, the one or more corresponding predetermined thresholds are determined based on an analysis of copy number signature scores for a cohort of individuals comprising individuals diagnosed with a plurality of different diseases. In some embodiments, the one or more corresponding predetermined thresholds are determined based on an analysis of data for a cohort of individuals that comprises both copy number signature scores and clinical data.
  • FIG. 1 provides a non-limiting example of a process flowchart for identifying one or more copy number signatures present in a copy number profile determined from targeted sequencing data for a sample collected from an individual (e.g., a cancer patient), according to one embodiment described herein.
  • FIG. 2 provides a non-limiting example of a process flowchart for training a first model and a second model for use in identifying one or more copy number signatures present in a copy number profile determined from targeted sequencing data for a sample collected from an individual (e.g., a cancer patient), according to one embodiment described herein.
  • FIG. 3 depicts an exemplary computing device or system in accordance with one embodiment of the present disclosure.
  • FIG. 4 depicts an exemplary computer system or computer network, in accordance with some instances of the systems described herein.
  • FIGS. 5A-5F show that HRDsig predicts PARPi benefit in ovarian and prostate clinical cohorts.
  • FIG. 5A Ovary CGDB cohort selection schematic.
  • FIG. 5B Kaplan-Meier curves showing HRDsig as predictor of response to PARPi treatment in ovarian cancer.
  • FIG. 5C Multivariate analysis of predictors of response in ovarian cancer.
  • FIG. 5D Prostate CGDB cohort selection schematic.
  • FIG. 5E Kaplan-Meier curves showing HRDsig as predictor of response to PARPi treatment in prostate cancer.
  • FIG. 5F Multivariate analysis of predictors of response in prostate cancer.
  • FIGS. 6A-6F show the landscape of copy number signatures in a pan-cancer cohort.
  • FIG. 6A Stacked bar plot showing the prevalence of copy number signatures across 46 tumor types. Low aneuploidy signature was excluded from this plot due to it being characterized as a low aberrant copy number profile.
  • FIG. 6B Bar plot depicting the prevalence of the low aneuploidy signature across tumor types.
  • FIG. 6C The size of each dot represents the prevalence of each signature in each tumor type. The color of dot represents the enrichment (red) or depletion (blue) of the signatures in each tumor type. Enrichment or depletion was determined using Fisher’s exact test and the odd’s ratio (OR) was log2 normalized.
  • FIG. 6A Stacked bar plot showing the prevalence of copy number signatures across 46 tumor types. Low aneuploidy signature was excluded from this plot due to it being characterized as a low aberrant copy number profile.
  • FIG. 6B Bar plot depicting the prevalence of the low aneuploidy signature
  • each dot represents the median sample segment copy number across signatures and tumor types.
  • the color of the dot represents the -log 10 transformed FDR-corrected P value.
  • FIG. 6E A plot showing the enrichment of biomarker positive calls in signature positive samples.
  • the size of each dot represents the prevalence of each biomarker positive call in each signature positive cohort.
  • the color of dot represents the enrichment (red) or depletion (blue) of the biomarker positive call in each copy number signature positive cohort. Enrichment or depletion was determined using Fisher’s exact test and the odd’s ratio (OR) was log2 normalized.
  • FIG. 6F A plot showing the enrichment of short variant mutational signatures in copy number signature positive samples.
  • the size of each dot represents the prevalence of mutational signature positive calls in copy number signature positive cohorts.
  • the y-axis represents the -log 10 transformed FDR-corrected P value. Mutational signatures above the horizontal dotted line are significant (FDR P value ⁇ 0.05).
  • FIG. 7 shows descriptions of 10 copy number signatures identified in pan-cancer samples.
  • FIG. 8 shows an exemplary copy number profile for each copy number signature. Each sample’s tumor type and relevant genomics are included.
  • FIG. 9 shows the percent change in mean for features in samples with a signature compared to samples without the signature.
  • Mann-Whitney U test result - log 10 transformed Pval of percent change in continuous genomic features compared in samples with and without the signature.
  • FIG. 10 shows that high amounts of FTD events occur across diseases with chromosomal instability signatures.
  • the size of each dot represents the prevalence of each copy number signature called positive in each disease group.
  • the color of the dot represents the mean number of FTD events overserved in samples in each disease group and copy number signature positive group.
  • FIGS. 11A & 11B show genomic associations with copy number signatures.
  • FIG. 11A A plot showing the enrichment of gene alterations in copy number signatures. The size of each dot represents the prevalence of the genes being altered in copy number signature positive samples.
  • the y-axis represents the -log 10 transformed FDR-corrected P value. Dots above the horizontal dotted line are significant (FDR P value ⁇ 0.05).
  • FIG. 11B A plot showing the enrichment of cytogenetic events (arm level gain or losses) across signatures.
  • each dot represents the prevalence of the cytogenetic arm level event occurring in copy number signature positive samples.
  • the y-axis represents the -log 10 transformed FDR-corrected P value. Dots above the horizontal dotted line are significant (FDR P value ⁇ 0.05).
  • FIGS. 12A-12F show that HRD copy number signatures capture genomic scarring (e.g., BRCAness).
  • FIG. 12A A schematic of model training and evaluation.
  • FIG. 12A A schematic of model training and evaluation.
  • FIG. 12B Performance of HRDsig and gLOH models in predicting approximate true positive (biallelic BRCA1/2) and approximate true negative (HRRwt) in all tumor types.
  • FIG. 12C HRDsig probability plotted for each disease, broken down by biallelic BRCA samples (red), other mutated HRR (other HRRm) (grey), and HRRwt (yellow).
  • FIG. 12D Frequency of HRD positive calls in disease groups. Stacked bars of percentage of biallelic BRCA, other HRRm or HRRwt prevalence within positively called group.
  • FIG. 12E HRDsig sensitivity in detecting biallelic BRCA1/2 alerations in all samples, as well as in key diseases.
  • FIG. 12F Gene alteration enrichment in BRCAwt samples based on HRDsig status. Y-axis is limited to -loglO P-value ⁇ 80.
  • FIG. 13 shows that HRDsig is called at a similar rate in liquid biopsy as compared to tissue biopsy.
  • Scatterplot shows the prevalence of HRDsig-positive calls in tissue biopsy and prevalence of HRDsig-positive calls in liquid biopsy samples. A cutoff of >50 assessable liquid biopsy samples was used. Size of the points is the number of assessable liquid biopsies in each tumor type.
  • FIG. 14 shows a comparison of PARPi treated ovarian cancer patients at treatment start.
  • FIG. 15 shows HRD stratification in PARPi response in BRCA1/2 wildtype ovarian cancer (CGDB).
  • FIG. 16 shows a comparison of PARPi treated prostate cancer patients at treatment start.
  • FIG. 17 shows time-to-treatment discontinuation (TTD) in ovarian cancer for genomewide loss of heterozygosity (gLOH) and genomic instability score (GIS).
  • TTD time-to-treatment discontinuation
  • GIS genomic instability score
  • FIG. 18 shows TTD in prostate cancer for gLOH and GIS.
  • Methods and systems are described for identifying copy number signature(s) in a copy number profile for a sample from an individual (e.g., a patient), and for identifying the dominant copy number signature, or a set of one or more copy number signatures that constitute significant components of the copy number profile for the individual.
  • Advantages of the disclosed methods and systems over previous work include utilization of off-target sequence read data derived from a targeted sequencing assay to maximize coverage of copy number features while minimizing sequencing costs, and the identification of an expanded set of copy number signatures as compared to those described in previous work.
  • methods comprise receiving targeted sequence read data for a sample derived from an individual diagnosed with or suspected of having a disease; determining a copy number profile having a plurality of copy number features for the individual based on the targeted sequence read data for the sample; determining a copy number feature value for each of the plurality of copy number features; performing an analysis of the copy number feature values for each of a plurality of copy number features using a first trained model to determine a probability that each of one or more copy number feature subgroups is represented in the copy number profile for the sample; performing an analysis of the probabilities determined for each of the one or more copy number feature subgroups using a second trained model to identify one or more copy number signatures represented in the copy number profile for the individual, and to determine one or more corresponding copy number signature scores; comparing the copy number signature scores for the one or more identified copy number signatures to one or more corresponding predetermined thresholds; and identifying, based on the comparison, a dominant copy number signature for the individual; or identifying,
  • the method further comprises confirming a diagnosis of the disease for the individual based on the dominant copy number signature or the one or more copy number signatures identified for the individual. In some instances, the method further comprises predicting a treatment outcome for the individual based on the dominant copy number signature or the one or more copy number signatures identified for the individual. In some instances, the method further comprises selecting a treatment for the individual based on the dominant copy number signature or the one or more copy number signatures identified for the individual. In some instances, the method further comprises adjusting a treatment dosage for the individual based on the dominant copy number signature or the one or more copy number signatures identified for the individual.
  • the method further comprises selecting the individual for inclusion in a clinical trial based on the dominant copy number signature or the one or more copy number signatures identified for the individual. In some instances, the method further comprises identifying the individual as a candidate for chemotherapy, radiation therapy, or surgery based on the dominant copy number signature or the one or more copy number signatures identified for the individual. In some instances, the method further comprises identifying the individual as a candidate for hospice care based on the dominant copy number signature or the one or more copy number signatures identified for the individual.
  • the copy number profile for the individual is determined based on single nucleotide polymorphisms (SNPs) located in the targeted sequence read data for the sample. In some instances, the single nucleotide polymorphisms (SNPs) are located in on-target sequence read data. In some instances, the copy number profile for the individual is determined based on off-target sequence reads in the targeted sequence read data for the sample. In some instances, the copy number profile for the individual is determined based on a combination of on-target and off-target sequence reads in the targeted sequence read data for the sample.
  • SNPs single nucleotide polymorphisms
  • the plurality of copy number features comprises a number of breakpoints per 10 Mb of genomic sequence, a number of breakpoints per 25 Mb of genomic sequence, a number of breakpoints per 50 Mb of genomic sequence, a number of breakpoints per 100 Mb of genomic sequence, a number of breakpoints per chromosome arm for each chromosome, a magnitude of a copy number change between any two adjacent copy number segments; a length of each copy number segment; the copy number of segments; a count of contiguous oscillating copy number chains, or any combination thereof.
  • the first trained model comprises a trained statistical model. In some instances, the first trained model comprises a trained machine learning model. In some instances, the first trained model is updated based on the one or more copy number signatures identified in the copy number profile of the individual. In some instances, the first trained model comprises a latent class analysis model or a mixture model.
  • the second trained model comprises a non-negative matrix factorization (NMF) model or a machine learning model.
  • NMF non-negative matrix factorization
  • the second trained model comprises a machine learning model
  • the machine learning model comprises a supervised, semi-supervised, or unsupervised machine learning model.
  • the second trained model comprises a supervised machine learning model
  • the supervised machine learning model comprises an XGBoost model.
  • advantages of the disclosed methods and systems over previous work include utilization of off-target sequence read data derived from a targeted sequencing assay to maximize coverage of copy number features while minimizing sequencing costs, and the identification of an expanded set of copy number signatures as compared to those described in previous work.
  • “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.
  • range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6, etc., as well as individual numbers within that range, for example, 1, 1.4, 2, 3, 3.6, 4, 5, 5.8, and 6. This applies regardless of the breadth of the range.
  • Numbers may be expressed herein as being “about” a particular value. Similarly, ranges may be expressed herein as from “about” one particular value and/or to “about” another particular value.
  • the terms “about” and “approximately” shall generally mean an acceptable degree of error or variation for a given value or range of values, such as, for example, a degree of error or variation that is within 20 percent (%), within 15%, within 10%, or within 5% of a given value or range of values.
  • the terms “individual,” “patient,” or “subject” are used interchangeably and refer to any single animal, e.g., a mammal (including such non-human animals as, for example, dogs, cats, horses, rabbits, zoo animals, cows, pigs, sheep, and non-human primates) for which treatment is desired.
  • a mammal including such non-human animals as, for example, dogs, cats, horses, rabbits, zoo animals, cows, pigs, sheep, and non-human primates
  • the individual, patient, or subject herein is a human.
  • cancer and “tumor” are used interchangeably herein. These terms refer to the presence of cells possessing characteristics typical of cancer-causing cells, such as uncontrolled proliferation, immortality, metastatic potential, rapid growth and proliferation rate, and certain characteristic morphological features. Cancer cells are often in the form of a tumor, but such cells can exist alone within an animal, or can be a non-tumorigenic cancer cell, such as a leukemia cell. These terms include a solid tumor, a soft tissue tumor, or a metastatic lesion. As used herein, the term “cancer” includes premalignant, as well as malignant cancers.
  • treatment refers to clinical intervention (e.g., administration of an anti-cancer agent or anticancer therapy) in an attempt to alter the natural course of the individual being treated, and can be performed either for prophylaxis or during the course of clinical pathology.
  • Desirable effects of treatment include, but are not limited to, preventing occurrence or recurrence of disease, alleviation of symptoms, diminishment of any direct or indirect pathological consequences of the disease, preventing metastasis, decreasing the rate of disease progression, amelioration or palliation of the disease state, and remission or improved prognosis.
  • 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.
  • on-target sequence read refers to a sequence read corresponding to a genomic locus targeted by a bait molecule or other locus-specific enrichment technique used in a targeted sequencing assay.
  • off-target sequence read refers to a sequence read corresponding to a genomic locus that is not targeted by a bait molecule or other locus-specific enrichment technique used in a targeted sequencing assay. Off-target sequence reads may arise, for example, due to the inefficiencies of enrichment strategies used for targeted sequencing.
  • breakpoint refers to a genomic region or locus where a sample genomic sequence may have a different copy number level than an adjacent segment. These may include sites of breakage where a chromosome breaks (and recombines).
  • breakpoint count per section of genomic sequence represents the number of breakpoints per a specified section of genomic sequence across the genome or a portion of the genome. For example, to determine the breakpoint count per 10 megabases (Mb) of sequence, a series of adjacent processing windows (or alternatively, a sliding processing window) of 10 Mb is analyzed for the genome, or a portion thereof, and the number of breakpoints for each frame of the processing window can then be assessed.
  • Mb megabases
  • change point copy number refers to the absolute difference in copy number between two adjacent genome segments. For example, adjacent segments modeled at copy numbers of 7 and 2 would have an absolute different - or change point copy number - of 5.
  • copy number signature refers to a pattern of copy number alterations accumulated across a genome that, in turn, are based on patterns of copy number features present in the genome.
  • copy number oscillation refers to copy number patterns in the DNA, such as repeating copy number patterns, that may arise through various processes including, but not limited to, chromothripsis. The number of segments with oscillating copy number represents a traversal of the genome, or a portion thereof, while counting the number of repeated alternating segments between two copy numbers.
  • chromothripsis refers to a mutational process in which a large number of clustered chromosomal rearrangements occur in a single event in localized and confined genomic regions in one or a few chromosomes. Chromothripsis is known to be involved in both cancer and congenital diseases.
  • the objective of the methods described herein is to determine a copy number profile and evaluate copy number features identified in genomic DNA extracted from a sample (e.g., a biopsy sample) from an individual (e.g. a patient) based on targeted sequencing data, and to use those evaluated copy number features to identify copy number signatures present in the individual’s genomic copy number profile that may represent important genomic characteristics, such as the presence of seismic amplifications (characterized by multiple rearrangements and discontinuous copy number levels), chromothripsis (characterized by a large number of clustered chromosomal rearrangements that occur in a single event in localized and confined genomic regions in one or a few chromosomes), etc.
  • the disclosed methods utilize off-target coverage of genomic loci (e.g., non-targeted single nucleotide polymorphisms (SNPs)) from targeted sequencing data to derive copy number features for a genomic sample.
  • Off-target coverage relies on the presence of off- target sequence reads in the targeted sequence read data due to, for example, the inefficiencies of the enrichment strategies used for performing the targeted sequencing.
  • the disclosed methods may utilize a combination of off-target and on-target coverage of genomic loci in targeted sequencing data.
  • the disclosed methods may utilize other sequencing data sets, e.g., whole genome sequencing data, low pass whole genome sequencing data, whole exome sequencing data, etc.
  • FIG. 1 provides a non-limiting example of a flowchart for a process 100 for identifying one or more copy number signatures present in a copy number profile determined from targeted sequencing data for a sample collected from an individual (e.g., a cancer patient).
  • Process 100 can be performed, for example, using one or more electronic devices implementing a software platform.
  • process 100 is performed using a client-server system, and the blocks of process 100 are divided up in any manner between the server and a client device.
  • the blocks of process 100 are divided up between the server and multiple client devices.
  • process 100 is 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. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.
  • a set of targeted sequencing data - derived by sequencing nucleic acid molecules e.g., DNA
  • a sample collected from an individual e.g., a patient
  • methods for collecting samples from individual, extracting nucleic acid molecules therefrom, and sequencing nucleic acid molecules are described elsewhere herein.
  • the sample may be a solid biopsy sample (e.g., a tumor sample).
  • the sample may be 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 cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.
  • the generation of targeted sequencing data may comprise use of a next-generation sequencing as described elsewhere herein.
  • the disclosed methods may be applied to non-targeted sequencing data, e.g., whole genome sequencing data, low pass whole genome sequencing data, whole exome sequencing data, etc.
  • a copy number profile is determined for the individual, where the copy number profile can include a plurality of copy number features.
  • Copy number profile determination may be performed using any of a variety of segmentation and/or copy number modeling techniques known to those of skill in the art, e.g., sliding window techniques and hidden Markov Model (HMM) techniques (see, e.g., Wang, et al. (2014), “Copy Number Variation Detection Using Next Generation Sequencing Read Counts”, BMC Bioinformatics 15: 109).
  • HMM hidden Markov Model
  • the copy number profile for the individual may be determined based on an analysis of off-target sequence reads in the targeted sequence read data for the sample. In some instances, the copy number profile for the individual may be determined based on an analysis of on-target sequence reads in the targeted sequence read data for the sample. In some instances, the copy number profile for the individual may be determined based on a combination of on-target and off-target sequence reads in the targeted sequence read data for the sample.
  • the copy number profile for the individual is determined based on single nucleotide polymorphisms (SNPs) located in the targeted sequence read data for the sample.
  • SNPs single nucleotide polymorphisms
  • the SNPs are located in the off-target sequence reads of the targeted sequence read data set.
  • the SNPs are located in the on-target sequence reads of the targeted sequence read data set.
  • the SNPs are located in a combination of off-target and on-target sequence reads of the targeted sequence read data set.
  • off-target sequence reads may arise in a targeted sequence read data set due to, for example, the inefficiencies of the enrichment strategies used for performing the targeted sequencing.
  • the off-target sequence reads may comprise an average sequencing coverage of less than 0.5x, 0.4x, 0.3x, 0.2x, or O.lx.
  • the copy number profile determined for an individual may comprise one or more copy number features (z.e., key attributes of the copy number profile). Examples of copy number features are described in more detail below.
  • the copy number profile may comprise at least 1, at least 2, at least 3, at least 4, at least 5, at least 10, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, at least 55, at least 60, at least 65, at least 70, at least 75, at least 80, or more than 80 copy number features.
  • a copy number value for each of the plurality of copy number features is determined.
  • copy number features to be evaluated include, but are not limited to, the number of breakpoints per 10 Mb of genomic sequence, the number of breakpoints per 25 Mb of genomic sequence, the number of breakpoints per 50 Mb of genomic sequence, the number of breakpoints per 100 Mb of genomic sequence, the number of breakpoints per chromosome arm for each chromosome, the magnitude of a copy number change between any two adjacent copy number segments; the length of each copy number segment; the copy number of each segment; change point copy number, the number of contiguous oscillating copy number chains, the length of oscillating copy number chains, segment minority allele frequency, or any combination thereof.
  • the copy number features may comprise features that address whole genome data, centromeric data, telomeric data, or any combination thereof.
  • the copy number features to be evaluated may include, but are not limited to, bp 10MB 1, bplOMB2, bpl0MB3, bplOMB4, bpl0MB5, bplOMB6, bplOOMBl, bpl00MB2, bpl00MB3, bpl00MB4, bpl00MB5, bpl00MB6, bp50MBl, bp50MB2, bp50MB3, bp50MB4, bp50MB5, bp50MB6, bp25MBl, bp25MB2, bp25MB3, bp25MB4, bp25MB5, bp25MB6, segsizel, segsize2, segsize3, segsize4, segsize5, segsize6, segsize7, segsize8, segsize9, se
  • a first trained model is used to analyze the copy number feature values for the plurality of copy number features and identify subgroups of copy number features, and corresponding probabilities that the identified subgroups are represented in the copy number profile of the individual.
  • the first trained model may comprise a trained statistical model.
  • the first trained model may comprise a trained machine learning model, e.g., a supervised, semi-supervised, or unsupervised machine learning model.
  • the first trained model may be updated based on the one or more copy number signatures identified in the copy number profile of the individual (e.g., the training data set for the model may be updated and used to retrain the first trained model).
  • the first trained model may comprise a latent class analysis model or a mixture model.
  • the first trained model may be trained on copy number feature values calculated for each of the plurality of copy number features based on copy number profiles for a cohort of individuals (e.g., a cohort of patients) diagnosed with the disease.
  • the disease may be cancer, e.g., ovarian cancer, prostate cancer, or colorectal cancer.
  • a second trained model is used to analyze the probabilities determined for each of one or more copy number feature subgroups to identify one or more copy number signatures represented in the copy number profile of the individual, and to determine their corresponding copy number signature scores.
  • the one or more copy number signatures represented in the copy number profile of the individual may comprise 0, at least 1, at least 2, at least 3, at least 4, at least 5, at least 10, at least 15, at least 20, or more than 20 copy number signatures.
  • the second trained model may comprise, e.g., a non-negative matrix factorization (NMF) model or a machine learning model, such as a supervised, semi- supervised, or unsupervised machine learning model.
  • NMF non-negative matrix factorization
  • the second trained model may comprise a supervised machine learning model such as an XGBoost model.
  • the second trained model may be trained on copy number feature subgroups identified in sequencing data for a cohort of individuals (e.g., a cohort of patients) diagnosed with a same disease as that for which the individual whose sample is being processed has been diagnosed.
  • the second trained model may be trained on copy number feature subgroups identified in sequencing data for a cohort of individuals comprising individuals diagnosed with a plurality of different diseases.
  • the disease with which the individual has been diagnosed may comprise a cancer.
  • the plurality of different diseases with which individuals of the cohort used to provide training data have been diagnosed may comprise a plurality of different cancers.
  • the plurality of different cancers may comprise, e.g., ovarian cancer, breast cancer, non-small cell lung carcinoma (NSCLC), esophageal cancer, prostate cancer, pancreatic cancer, colorectal cancer, or any combination thereof.
  • NSCLC non-small cell lung carcinoma
  • the second trained model may be trained to identify copy number signatures based on the probabilities determined for each of one or more copy number feature subgroups identified in the copy number profiles for the individuals of the cohort, and based on the presence of one or more additional genomic features identified in the sequence read date (e.g., targeted sequence read data) for the individuals of the cohort.
  • the one or more additional genomic features may comprise, e.g., one or more short variants, one or more biallelic loss-of-function variants, one or more rearrangements, other pathogenic alterations, or any combination thereof.
  • the copy number signature scores determined for the one or more copy number signatures represented in the copy number profile of the individual are compared to one or more corresponding predetermined thresholds (e.g., cutoff thresholds) to identify a dominant copy number signature, or to identify a set of component copy number signatures, for the copy number profile of the individual.
  • predetermined thresholds e.g., cutoff thresholds
  • the one or more corresponding predetermined thresholds are determined based on an analysis of copy number signature scores for a cohort of individuals (e.g., a cohort of patients) diagnosed with a same disease as that for which the individual whose sample is being processed has been diagnosed. In some instances, the one or more corresponding predetermined thresholds are determined based on an analysis of copy number signature scores for a cohort of individuals (e.g., a cohort of patients) comprising individuals diagnosed with a plurality of different diseases. In some instances, the one or more corresponding predetermined thresholds are determined based on an analysis of data for a cohort of individuals that comprises both copy number signature scores and clinical data.
  • the clinical data for the individual may comprise an individual’s sex, age, gender, height, weight, clinical history, family history, sample type, tumor stage, tumor grade, or any combination thereof.
  • any of the methods described herein may further comprise confirming a diagnosis of the disease for the individual based on the dominant copy number signature, or the one or more copy number signatures, identified for the individual.
  • any of the methods described herein may further comprise predicting a treatment outcome for the individual based on the dominant copy number signature, or the one or more copy number signatures, identified for the individual.
  • any of the methods described herein may further comprise selecting a treatment for the individual based on the dominant copy number signature or the one or more copy number signatures identified for the individual.
  • any of the methods described herein may further comprise adjusting a treatment dosage for the individual based on the dominant copy number signature or the one or more copy number signatures identified for the individual.
  • any of the methods described herein may further comprise selecting the individual for inclusion in a clinical trial based on the dominant copy number signature or the one or more copy number signatures identified for the individual.
  • any of the methods described herein may further comprise identifying the individual as a candidate for chemotherapy, radiation therapy, or surgery based on the dominant copy number signature or the one or more copy number signatures identified for the individual.
  • any of the methods described herein may further comprise identifying the individual as a candidate for hospice care based on the dominant copy number signature or the one or more copy number signatures identified for the individual.
  • FIG. 2 provides a non-limiting example of a flowchart for a process 200 for training a first model and a second model for use in identifying one or more copy number signatures present in a copy number profile determined from targeted sequencing data for a sample collected from an individual (e.g., a cancer patient).
  • a set of targeted sequencing data is received for each of a cohort of individuals (e.g., a cohort of patients), where the targeted sequencing data comprises a plurality of sequence reads obtained by sequencing nucleic acid molecules (e.g., DNA molecules) extracted from samples collected from each individual of the cohort.
  • sequencing nucleic acid molecules e.g., DNA molecules
  • the samples may be solid biopsy samples (e.g., a tumor samples).
  • the samples may be liquid biopsy samples, and may comprise, e.g., whole blood, blood plasma, blood serum, urine, stool, sputum, saliva, or cerebrospinal fluid.
  • the samples may be liquid biopsy samples and may comprise cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.
  • the generation of targeted sequencing data may comprise use of a next-generation sequencing as described elsewhere herein.
  • the disclosed methods may be applied to non-targeted sequencing data, e.g., whole genome sequencing data, low pass whole genome sequencing data, whole exome sequencing data, etc.
  • a copy number profile is determined for each individual of the cohort, where the copy number profiles can include a plurality of copy number features.
  • Copy number profile determination may be performed using any of a variety of segmentation and/or copy number modeling techniques known to those of skill in the art, e.g., sliding window techniques and hidden Markov Model (HMM) techniques (see, e.g., Wang, el al. (2014), “Copy Number Variation Detection Using Next Generation Sequencing Read Counts”, BMC Bioinformatics 15: 109).
  • HMM hidden Markov Model
  • the copy number profile for each individual of the cohort may be determined based on an analysis of off-target sequence reads in the targeted sequence read data for the sample from the individual. In some instances, the copy number profile for the individual may be determined based on an analysis of on-target sequence reads in the targeted sequence read data for the sample from the individual. In some instances, the copy number profile for the individual may be determined based on a combination of on-target and off-target sequence reads in the targeted sequence read data for the sample from the individual.
  • the copy number profile for each individual in the cohort is determined based on single nucleotide polymorphisms (SNPs) located in the targeted sequence read data for the sample from the individual.
  • SNPs single nucleotide polymorphisms
  • the SNPs are located in the off-target sequence reads of the targeted sequence read data set for the individual sample.
  • the SNPs are located in the on-target sequence reads of the targeted sequence read data set for the individual sample.
  • the SNPs are located in a combination of off-target and on-target sequence reads of the targeted sequence read data set for the individual sample.
  • off-target sequence reads may arise in a targeted sequence read data set for a sample from an individual due to, for example, the inefficiencies of the enrichment strategies used for performing the targeted sequencing.
  • the off-target sequence reads may comprise an average sequencing coverage of less than 0.5x, 0.4x, 0.3x, 0.2x, or O.lx.
  • the copy number profile determined for each individual of the cohort may comprise one or more copy number features (z.e., key attributes of the copy number profile). Examples of copy number features are described in more detail below.
  • the copy number profile for an individual of the cohort may comprise at least 1, at least 2, at least 3, at least 4, at least 5, at least 10, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, at least 55, at least 60, at least 65, at least 70, at least 75, at least 80, or more than 80 copy number features.
  • a copy number feature value is determined for each of the plurality of copy number features for the copy number profile of each individual in the cohort.
  • copy number features to be evaluated include, but are not limited to, the number of breakpoints per 10 Mb of genomic sequence, the number of breakpoints per 25 Mb of genomic sequence, the number of breakpoints per 50 Mb of genomic sequence, the number of breakpoints per 100 Mb of genomic sequence, the number of breakpoints per chromosome arm for each chromosome, the magnitude of a copy number change between any two adjacent copy number segments; the length of each copy number segment; the copy number of each segment; change point copy number , the number of contiguous oscillating copy number chains, the length of oscillating copy number chains, segment minority allele frequency, or any combination thereof.
  • a first model is trained to analyze the distribution of copy number feature values for the plurality of copy number profiles represented by the cohort of individuals, and identify subgroups of copy number features and their relative probabilities within the distribution of copy number feature values for the cohort.
  • the first trained model may comprise a trained statistical model.
  • the first trained model may comprise a trained machine learning model, e.g., a supervised, semi-supervised, or unsupervised machine learning model.
  • the first trained model may comprise a latent class analysis model or a mixture model.
  • the first trained model may be periodically or continuously updated based on an updated training data set that includes the one or more copy number signatures identified in the copy number profile of an individual sample according to the process illustrated in FIG. 1 e.g., the training data set for the model may be updated and used to retrain the first trained model).
  • the adjustable parameters of a machine learning model may be trained using the input data from a training data set and a gradient descent or backward propagation method so that the output value(s) of the model are consistent with the examples included in the training data set.
  • the training data used to train the first trained model may comprise copy number feature values calculated for each of a plurality of copy number features represented in the copy number profiles for the cohort of individuals (e.g., a cohort of patients). In some instances, the training data used to train the first trained model may further comprise the number of copy number feature subgroups identified in the copy number profiles for the individuals or for the cohort of individuals. In some instances, the cohort of individuals may comprise individuals diagnosed with a disease. In some instances, for example, the disease may be cancer, e.g., ovarian cancer, prostate cancer, or colorectal cancer.
  • a second model is trained to analyze copy number feature subgroups and their relative probabilities within the distribution of copy number feature values for the cohort to identify copy number signatures present in the copy number profiles for the cohort, and to determine their corresponding copy number signature scores.
  • the one or more copy number signatures represented in the copy number profiles of the individuals in the cohort may comprise at least 1, at least 2, at least 3, at least 4, at least 5, at least 10, at least 15, at least 20, or more than 20 copy number signatures.
  • the second trained model may comprise, e.g., a non-negative matrix factorization (NMF) model or a machine learning model, such as a supervised, semi- supervised, or unsupervised machine learning model.
  • NMF non-negative matrix factorization
  • the adjustable parameters of the machine learning model may be trained using the input data from a training data set and a gradient descent or backward propagation method so that the output value(s) of the model are consistent with the examples included in the training data set.
  • the second trained model may comprise a supervised machine learning model such as an XGBoost model.
  • XGBoost is an optimized distributed gradient boosting library designed to be highly efficient and flexible that implements machine learning algorithms under the Gradient Boosting framework.
  • XGBoost provides a parallel tree boosting approach to training (using an additive training technique) where, at each iteration, a new tree determines the gradients for the residuals between the target training values (e.g., for copy number signatures and copy number signature scores) and the current predicted values, and the algorithm then conducts a gradient descent-based refinement of the predicted values based on the gradients determined for the residuals.
  • the training data used to train the second trained model may comprise copy number feature subgroups identified in sequencing data for a cohort of individuals (e.g., a cohort of patients) diagnosed with a same disease as that for which the individual whose sample is being processed has been diagnosed.
  • the second trained model may be trained on training data comprising copy number feature subgroups identified in sequencing data for a cohort of individuals comprising individuals diagnosed with a plurality of different diseases.
  • the training data used to train the second model may further comprise copy number signatures identified in the copy number profiles for the cohort of individuals.
  • the disease with which the individual has been diagnosed may comprise a cancer.
  • the plurality of different diseases with which individuals of the cohort used to provide training data have been diagnosed may comprise a plurality of different cancers.
  • the plurality of different cancers may comprise, e.g., ovarian cancer, breast cancer, non-small cell lung carcinoma (NSCLC), esophageal cancer, prostate cancer, pancreatic cancer, colorectal cancer, or any combination thereof.
  • the second trained model may be trained to identify copy number signatures based on the probabilities determined for each of one or more copy number feature subgroups identified in the copy number profiles for the cohort of individuals and based on the presence of one or more additional genomic features identified in the sequence read data (e.g., targeted sequence read data) for the individuals of the cohort.
  • the one or more additional genomic features may comprise, e.g., one or more short variants, one or more biallelic loss-of-function variants, one or more rearrangements, other pathogenic alterations, or any combination thereof.
  • an analysis of the copy number signature score distribution is performed to calculate cutoff threshold(s) for determining a positive presence of a given copy number signature in a given copy number profile, and to identify dominant copy number signature(s).
  • Cutoff thresholds may be calculated, for example, using an elbow method to discriminate between baseline noise and signal, a signal-to-noise analysis, or other statistical approaches such as random sampling / bootstrapping.
  • the methods described herein may be used to confirm a diagnosis of disease for an individual based on the dominant copy number signature, or the one or more copy number signatures, identified for the individual.
  • the methods described herein may be used to predict a treatment outcome for an individual based on the dominant copy number signature, or the one or more copy number signatures, identified for the individual.
  • the methods described herein may be used to select a treatment for an individual based on the dominant copy number signature or the one or more copy number signatures identified for the individual.
  • the methods described herein may be used to identify an individual that may benefit from treatment with a poly (ADP-ribose) polymerase inhibitor (PARPi).
  • PARPi poly (ADP-ribose) polymerase inhibitor
  • the methods of the present disclosure comprise identifying a dominant copy number signature or one or more copy number signatures that are components of a copy number profile for the individual, in which the dominant copy number signature, one or more component copy number profile, and/or copy number profile comprise a homologous recombination deficient (HRD) signature.
  • HRD homologous recombination deficient
  • the examples provided infra demonstrate that the methods of the present disclosure allow for improved detection of an HRD signature in a sample, and that such HRD signatures have predictive value for patients that may benefit from PARPi treatment. Any of the methods of the present disclosure may find use in identifying an individual that may benefit from PARPi treatment.
  • the individual has ovarian or prostate cancer.
  • the individual is a human.
  • the sample comprising an HRD signature as set forth herein lacks a loss-of-function mutation in BRCA1 and/or BRCA2 gene(s). In some embodiments, the sample comprising an HRD signature as set forth herein lacks a loss-of-function mutation in BRCA1 and BRCA2 genes, e.g., is BRCAwt. In some embodiments, the sample comprising an HRD signature as set forth herein lacks a loss-of-function mutation in BRCA1, BRCA2, ATM, BARD1, BRIP1, CDK12, CHEK1, CHEK2, FANCE, PALB2, RAD51B, RAD51C, RAD51D, and/or RAD54L gene(s).
  • the sample comprising an HRD signature as set forth herein lacks a loss-of-function mutation in BRCA1, BRCA2, ATM, BARD1, BRIP1, CDK12, CHEK1, CHEK2, FANCE, PALB2, RAD51B, RAD51C, RAD51D, and RAD54E genes, e.g., is HRRwt.
  • the present disclosure demonstrates that the HRD signature identified a BRCA1/2 wildtype population enriched for biallelic alterations in other homologous recombination repair (HRR) genes and in some cases lacking any HRR alteration, suggesting that using the HRD signature allows for detection of a population not currently eligible for PARPi treatment in many indications.
  • HRR homologous recombination repair
  • the sample comprising an HRD signature as set forth herein lacks a loss-of-function mutation in BRCA1 and/or BRCA2 gene(s) and comprises biallelic loss-of- function mutations in one or more of BARD1, PALB2, RAD51D, and RAD51C gene(s).
  • the sample comprising an HRD signature as set forth herein lacks a loss-of- function mutation in BRCA1 and BRCA2 genes (e.g., is BRCAwt) and comprises biallelic loss- of-function mutations in one or more of BARD1, PAEB2, RAD51D, and RAD51 C gene(s).
  • the present disclosure demonstrates that the HRD signature identified a BRCAwt population strongly associated with biallelic alterations in BARD1, PAEB2, RAD51D, and/or RAD51 C.
  • an HRD signature of the present disclosure comprises a plurality of low copy number segments.
  • the HRD signature comprises heterozygous deletion(s), high genome- wide loss of heterozygosity (gLOH-high), and/or high genomic instability score (GIS).
  • GIS genomic instability score
  • an HRD signature of the present disclosure comprises a plurality of high copy number amplifications, e.g., amplifications of more than 70 copies gained.
  • the HRD signature further comprises heterozygous deletion(s) (e.g., segment copy numbers at half of ploidy) and/or duplication(s) (e.g., ploidy + 1 in diploid samples).
  • the HRD signature further comprises heterozygous deletion(s) (e.g., segment copy numbers at half of ploidy) and/or duplication(s) (e.g., ploidy + 1 in diploid samples), and is characterized by gLOH-high status.
  • gLOH-high refers to greater than or equal to 16% of the genome under LOH.
  • the HRD signature further comprises heterozygous deletion(s) (e.g., segment copy numbers at half of ploidy) without duplication(s) (e.g., segments at ploidy, or segment copy number 1.6 - 2.2 for diploid samples), e.g., copy neutral loss of heterozygosity.
  • the methods of the present disclosure further comprise administering an effective amount of a PARPi to the individual.
  • PARPi inhibits PARPI and PARP2.
  • PARPi inhibits PARPI, PARP2, and PARP3.
  • PARPi’ s are known in the art, and non-limiting examples of PARPi’ s are described below.
  • the PARPi comprises olaparib (also known as AZD2281, MK- 7339, KU0059436, or LYNPARZA®; AstraZeneca/Merck).
  • olaparib also known as AZD2281, MK- 7339, KU0059436, or LYNPARZA®; AstraZeneca/Merck.
  • the structure of olaparib is provided below.
  • the PARPi comprises rucaparib (also known as AG014699, PF- 01367338, or RUBRACA®; Clovis Oncology).
  • rucaparib also known as AG014699, PF- 01367338, or RUBRACA®; Clovis Oncology.
  • the structure of rucaparib is provided below.
  • the PARPi comprises niraparib (also known as MK-4827 or ZEJULA®; GlaxoSmithKline).
  • niraparib also known as MK-4827 or ZEJULA®; GlaxoSmithKline.
  • the structure of niraparib is provided below.
  • the PARPi comprises talazoparib (also known as BMN-673 or TALZENNA®; Pfizer).
  • talazoparib also known as BMN-673 or TALZENNA®; Pfizer.
  • the structure of talazoparib is provided below.
  • the PARPi comprises veliparib (also known as ABT-888).
  • ABT-888 veliparib
  • the structure of veliparib is provided below.
  • the methods described herein may be used to adjust a treatment dosage for an individual based on the dominant copy number signature or the one or more copy number signatures identified for the individual.
  • the methods described herein may be used to selecting an individual for inclusion in a clinical trial based on the dominant copy number signature or the one or more copy number signatures identified for the individual.
  • the methods described herein may be used to identify an individual as a candidate for chemotherapy, radiation therapy, or surgery based on the dominant copy number signature or the one or more copy number signatures identified for the individual.
  • the methods described herein may be used to identify an individual as a candidate for hospice care based on the dominant copy number signature or the one or more copy number signatures identified for the individual.
  • 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) 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), (v) capturing nucleic acid molecules from the amplified nucleic acid molecules (e.g., by hybridization to one or more bait molecules, where the bait molecules each comprise one or more nucleic acid
  • 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 identifying copy number signatures may be used to diagnose (or as part of a diagnosis of) the presence of disease or other condition (e.g., cancer, genetic disorders (such as Down Syndrome and Fragile X), neurological disorders, or any other disease type where detection of variants, e.g., copy number alternations, are relevant to diagnosing, treating, or predicting said disease) in a subject (e.g., a patient).
  • disease or other condition e.g., cancer, genetic disorders (such as Down Syndrome and Fragile X), neurological disorders, or any other disease type where detection of variants, e.g., copy number alternations, are relevant to diagnosing, treating, or predicting said disease
  • a subject e.g., a patient
  • the disclosed methods may be applicable to diagnosis of any of a variety of cancers as described elsewhere herein.
  • the disclosed methods for identifying copy number signatures may be used to select a subject (e.g., a patient) for a clinical trial based on the dominant copy number signature, or set of copy number signature components, identified in the subject’s copy number profile.
  • patient selection for clinical trials based on, e.g., identification of copy number signatures may accelerate the development of targeted therapies and improve the healthcare outcomes for treatment decisions.
  • the disclosed methods for identifying copy number signatures 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 identifying copy number signatures 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 anticancer treatment may be administered to the subject.
  • the disclosed methods for identifying copy number signatures may be used for monitoring disease progression or recurrence (e.g., cancer or tumor progression or recurrence) in a subject.
  • the methods may be used to identify a dominant copy number signature, or a set of component copy number signatures, in a first sample obtained from the subject at a first time point, and used to identify a dominant copy number signature, or a set of component copy number signatures, in a second sample obtained from the subject at a second time point, where comparison of the first determination of copy number signatures and the second determination of copy number signatures allows one to monitor disease progression or recurrence.
  • the first time point is chosen before the subject has been administered a therapy or treatment
  • the second time point is chosen after the subject has been administered the therapy or treatment.
  • the disclosed methods may be used for adjusting a therapy or treatment (e.g., an anti-cancer treatment or anti-cancer therapy) for a subject, e.g., by adjusting a treatment dose and/or selecting a different treatment in response to a change in the dominant copy number signature or set of component copy number signatures.
  • a therapy or treatment e.g., an anti-cancer treatment or anti-cancer therapy
  • the dominant copy number signature, or set of component copy number signatures, identified using the disclosed methods may be used as a prognostic or diagnostic indicator associated with the sample.
  • the prognostic or diagnostic indicator may comprise an indicator of the presence of a disease (e.g., cancer) in the sample, an indicator of the probability that a disease (e.g., cancer) is present in the sample, an indicator of the probability that the subject from which the sample was derived will develop a disease (e.g., cancer) (z.e., a risk factor), or an indicator of the likelihood that the subject from which the sample was derived will respond to a particular therapy or treatment.
  • the disclosed methods for identifying copy number signatures 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 identifying copy number signatures as part of a genomic profiling process can improve the validity of, e.g., disease detection calls and treatment decisions, made on the basis of the genomic profile by, for example, independently confirming the presence of a dominant copy number signature or set of component copy number signatures in a given patient sample.
  • a genomic profile may comprise information on the presence of genes (or variant sequences thereof), copy number variations, epigenetic traits, proteins (or modifications thereof), and/or other biomarkers in an individual’s genome and/or proteome, as well as information on the individual’s corresponding phenotypic traits and the interaction between genetic or genomic traits, phenotypic traits, and environmental factors.
  • a genomic profile for the subject may comprise results from a comprehensive genomic profiling (CGP) test, a nucleic acid sequencing-based test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof.
  • CGP genomic profiling
  • the method can further include administering or applying a treatment or therapy (e.g., an anti-cancer agent, anti-cancer treatment, or anti-cancer therapy) to the subject based on the generated genomic profile.
  • a treatment or therapy e.g., an anti-cancer agent, anti-cancer treatment, or anti-cancer therapy
  • An anti-cancer agent or anti-cancer treatment may refer to a compound that is effective in the treatment of cancer cells.
  • anti-cancer agents or anti-cancer therapies include, but not limited to, alkylating agents, antimetabolites, natural products, hormones, chemotherapy, radiation therapy, immunotherapy, surgery, or a therapy configured to target a defect in a specific cell signaling pathway, e.g., a defect in a DNA mismatch repair (MMR) pathway.
  • MMR DNA mismatch repair
  • the disclosed methods and systems may be used with any of a variety of samples (also referred to herein as specimens) comprising nucleic acids (e.g., DNA or RNA) that are collected from a subject (e.g., a patient).
  • samples also referred to herein as specimens
  • nucleic acids e.g., DNA or RNA
  • a sample examples include, but are not limited to, a tumor sample, a tissue sample, a biopsy sample (e.g., a tissue biopsy, a liquid biopsy, or both), a blood sample (e.g., a peripheral whole blood sample), a blood plasma sample, a blood serum sample, a lymph sample, a saliva sample, a sputum sample, a urine sample, a gynecological fluid sample, a circulating tumor cell (CTC) sample, a cerebral spinal fluid (CSF) sample, a pericardial fluid sample, a pleural fluid sample, an ascites (peritoneal fluid) sample, a feces (or stool) sample, or other body fluid, secretion, and/or excretion sample (or cell sample derived therefrom).
  • the sample may be frozen sample or a formalin-fixed paraffin-embedded (FFPE) sample.
  • FFPE formalin-fixed paraffin-embedded
  • the sample may be collected by tissue resection (e.g., surgical resection), needle biopsy, bone marrow biopsy, bone marrow aspiration, skin biopsy, endoscopic biopsy, fine needle aspiration, oral swab, nasal swab, vaginal swab or a cytology smear, scrapings, washings or lavages (such as a ductal lavage or bronchoalveolar lavage), etc.
  • tissue resection e.g., surgical resection
  • needle biopsy e.g., bone marrow biopsy, bone marrow aspiration, skin biopsy, endoscopic biopsy, fine needle aspiration, oral swab, nasal swab, vaginal swab or a cytology smear
  • fine needle aspiration e.g., oral swab, nasal swab, vaginal swab or a cytology smear
  • scrapings e.
  • the sample is a liquid biopsy sample, and may comprise, e.g., whole blood, blood plasma, blood serum, urine, stool, sputum, saliva, or cerebrospinal fluid.
  • the sample may be a liquid biopsy sample and may comprise circulating tumor cells (CTCs).
  • the sample may comprise one or more premalignant or malignant cells.
  • Premalignant refers to a cell or tissue that is not yet malignant but is poised to become malignant.
  • the sample may be acquired from a solid tumor, a soft tissue tumor, or a metastatic lesion.
  • the sample may be acquired from a hematologic malignancy or pre-malignancy.
  • the sample may comprise a tissue or cells from a surgical margin.
  • the sample may comprise tumor-infiltrating lymphocytes.
  • the sample may comprise one or more non- malignant cells.
  • the sample may be, or is part of, a primary tumor or a metastasis (e.g., a metastasis biopsy sample).
  • the sample may be obtained from a site (e.g., a tumor site) with the highest percentage of tumor (e.g., tumor cells) as compared to adjacent sites (e.g., sites adjacent to the tumor).
  • the sample may be obtained from a site (e.g., a tumor site) with the largest tumor focus (e.g., the largest number of tumor cells as visualized under a microscope) as compared to adjacent sites (e.g., sites adjacent to the tumor).
  • the disclosed methods may further comprise analyzing a primary control (e.g., a normal tissue sample). In some instances, the disclosed methods may further comprise determining if a primary control is available and, if so, isolating a control nucleic acid (e.g., DNA) from said primary control. In some instances, the sample may comprise any normal control (e.g., a normal adjacent tissue (NAT)) if no primary control is available. In some instances, the sample may be or may comprise histologically normal tissue. In some instances, the method includes evaluating a sample, e.g., a histologically normal sample (e.g., from a surgical tissue margin) using the methods described herein.
  • a primary control e.g., a normal tissue sample.
  • the disclosed methods may further comprise determining if a primary control is available and, if so, isolating a control nucleic acid (e.g., DNA) from said primary control.
  • the sample may comprise any normal control (e.g.,
  • the disclosed methods may further comprise acquiring a sub-sample enriched for non-tumor cells, e.g., by macro-dissecting non-tumor tissue from said NAT in a sample not accompanied by a primary control. In some instances, the disclosed methods may further comprise determining that no primary control and no NAT is available, and marking said sample for analysis without a matched control.
  • samples obtained from histologically normal tissues e.g., otherwise histologically normal surgical tissue margins
  • the methods may thus further comprise re-classifying a sample based on the presence of the detected genetic alteration.
  • multiple samples e.g., from different subjects are processed simultaneously.
  • tissue samples e.g., solid tissue samples, soft tissue samples, metastatic lesions, or liquid biopsy samples.
  • tissues include, but are not limited to, connective tissue, muscle tissue, nervous tissue, epithelial tissue, and blood.
  • Tissue samples may be collected from any of the organs within an animal or human body.
  • human organs include, but are not limited to, the brain, heart, lungs, liver, kidneys, pancreas, spleen, thyroid, mammary glands, uterus, prostate, large intestine, small intestine, bladder, bone, skin, etc.
  • the nucleic acids extracted from the sample may comprise deoxyribonucleic acid (DNA) molecules.
  • DNA DNA that may be suitable for analysis by the disclosed methods include, but are not limited to, genomic DNA or fragments thereof, mitochondrial DNA or fragments thereof, cell-free DNA (cfDNA), and circulating tumor DNA (ctDNA).
  • Cell-free DNA (cfDNA) is comprised of fragments of DNA that are released from normal and/or cancerous cells during apoptosis and necrosis, and circulate in the blood stream and/or accumulate in other bodily fluids.
  • Circulating tumor DNA ctDNA is comprised of fragments of DNA that are released from cancerous cells and tumors that circulate in the blood stream and/or accumulate in other bodily fluids.
  • DNA is extracted from nucleated cells from the sample.
  • a sample may have a low nucleated cellularity, e.g., when the sample is comprised mainly of erythrocytes, lesional cells that contain excessive cytoplasm, or tissue with fibrosis.
  • a sample with low nucleated cellularity may require more, e.g., greater, tissue volume for DNA extraction.
  • the nucleic acids extracted from the sample may comprise ribonucleic acid (RNA) molecules.
  • RNA ribonucleic acid
  • examples of RNA that may be suitable for analysis by the disclosed methods include, but are not limited to, total cellular RNA, total cellular RNA after depletion of certain abundant RNA sequences (e.g., ribosomal RNAs), cell-free RNA (cfRNA), messenger RNA (mRNA) or fragments thereof, the poly(A)-tailed mRNA fraction of the total RNA, ribosomal RNA (rRNA) or fragments thereof, transfer RNA (tRNA) or fragments thereof, and mitochondrial RNA or fragments thereof.
  • 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).
  • 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(11):903-5; Hodges, E. et al. (2007) Nat. Genet. 39(12): 1522-7; and Okou, D.T. et al. (2007) Nat. Methods 4(11 ):907-9, the contents of which are incorporated herein by reference in their entireties.
  • Hybridization methods that can be adapted for use in the methods herein are described in the art, e.g., as described in International Patent Application Publication No. 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.
  • different alignment methods are used to analyze reads that are individually customized or tuned to detect each different variant at different genomic loci.
  • tuning can be a function of one or more of: (i) the genetic locus (e.g., gene loci, micro satellite locus, or other subject interval) being sequenced, (ii) the tumor type associated with the sample, (iii) the variant being sequenced, or (iv) a characteristic of the sample or the subject.
  • the 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.
  • Base calling refers to the raw output of a sequencing device, e.g., the determined sequence of nucleotides in an oligonucleotide molecule.
  • Mutation calling refers to the process of selecting a nucleotide value, e.g., A, G, T, or C, for a given nucleotide position being sequenced. Typically, the sequence reads (or base calling) for a position will provide more than one value, e.g., some reads will indicate a T and some will indicate a G.
  • Mutation calling is the process of assigning a correct nucleotide value, e.g., one of those values, to the sequence.
  • mutant calling it can be applied to assign a nucleotide value to any nucleotide position, e.g., positions corresponding to mutant alleles, wild-type alleles, alleles that have not been characterized as either mutant or wild-type, or to positions not characterized by variability.
  • the disclosed methods may comprise the use of customized or tuned mutation calling algorithms or parameters thereof to optimize performance when applied to sequencing data, particularly in methods that rely on massively parallel sequencing (MPS) of a large number of diverse genetic events at a large number of diverse genomic loci (e.g., gene loci, micro satellite regions, etc.) in samples, e.g., samples from a subject having cancer.
  • MPS massively parallel sequencing
  • optimization of mutation calling is described in the art, e.g., as set out in International Patent Application Publication No. WO 2012/092426.
  • Methods for mutation calling can include one or more of the following: making independent calls based on the information at each position in the reference sequence (e.g., examining the sequence reads; examining the base calls and quality scores; calculating the probability of observed bases and quality scores given a potential genotype; and assigning genotypes (e.g., using Bayes’ rule)); removing false positives (e.g., using depth thresholds to reject SNPs with read depth much lower or higher than expected; local realignment to remove false positives due to small indels); and performing linkage disequilibrium (LD)/imputation- based analysis to refine the calls.
  • making independent calls based on the information at each position in the reference sequence e.g., examining the sequence reads; examining the base calls and quality scores; calculating the probability of observed bases and quality scores given a potential genotype; and assigning genotypes (e.g., using Bayes’ rule)
  • removing false positives e.g., using depth thresholds to reject SNP
  • Equations used to calculate the genotype likelihood associated with a specific genotype and position are described in, e.g., Li, H. and Durbin, R. Bioinformatics, 2010; 26(5): 589-95.
  • the prior expectation for a particular mutation in a certain cancer type can be used when evaluating samples from that cancer type.
  • Such likelihood can be derived from public databases of cancer mutations, e.g., Catalogue of Somatic Mutation in Cancer (COSMIC), HGMD (Human Gene Mutation Database), The SNP Consortium, Breast Cancer Mutation Data Base (BIC), and Breast Cancer Gene Database (BCGD).
  • Examples of LD/imputation based analysis are described in, e.g., Browning, B.L. and Yu, Z. Am. J. Hum. Genet. 2009, 85(6):847-61.
  • Examples of low-coverage SNP calling methods are described in, e.g., Li, Y., et al., Annu. Rev. Genomics Hum. Genet. 2009, 10:387-406.
  • detection of substitutions can be performed using a mutation calling method (e.g., a Bayesian mutation calling method) which is applied to each base in each of the subject intervals, e.g., exons of a gene or other locus to be evaluated, where presence of alternate alleles is observed.
  • a mutation calling method e.g., a Bayesian mutation calling method
  • This method will compare the probability of observing the read data in the presence of a mutation with the probability of observing the read data in the presence of basecalling error alone. Mutations can be called if this comparison is sufficiently strongly supportive of the presence of a mutation.
  • An advantage of a Bayesian mutation detection approach is that the comparison of the probability of the presence of a mutation with the probability of base-calling error alone can be weighted by a prior expectation of the presence of a mutation at the site. If some reads of an alternate allele are observed at a frequently mutated site for the given cancer type, then presence of a mutation may be confidently called even if the amount of evidence of mutation does not meet the usual thresholds. This flexibility can then be used to increase detection sensitivity for even rarer mutations/lower purity samples, or to make the test more robust to decreases in read coverage.
  • the likelihood of a random base-pair in the genome being mutated in cancer is ⁇ le-6.
  • the likelihood of specific mutations occurring at many sites in, for example, a typical multigenic cancer genome panel can be orders of magnitude higher. These likelihoods can be derived from public databases of cancer mutations (e.g., COSMIC).
  • Indel calling is a process of finding bases in the sequencing data that differ from the reference sequence by insertion or deletion, typically including an associated confidence score or statistical evidence metric.
  • Methods of indel calling can include the steps of identifying candidate indels, calculating genotype likelihood through local re-alignment, and performing LD-based genotype inference and calling.
  • a Bayesian approach is used to obtain potential indel candidates, and then these candidates are tested together with the reference sequence in a Bayesian framework.
  • Methods for generating indel calls and individual-level genotype likelihoods include, e.g., the Dindel algorithm (Albers, C.A., et al., Genome Res. 2011;21(6):961-73).
  • the Bayesian EM algorithm can be used to analyze the reads, make initial indel calls, and generate genotype likelihoods for each candidate indel, followed by imputation of genotypes using, e.g., QCALL (Le S.Q. and Durbin R. Genome Res. 2011;21(6):952-60).
  • Parameters, such as prior expectations of observing the indel can be adjusted ⁇ e.g., increased or decreased), based on the size or location of the indels.
  • the mutation calling method used to analyze sequence reads is not individually customized or fine-tuned for detection of different mutations at different genomic loci.
  • different mutation calling methods are used that are individually customized or fine-tuned for at least a subset of the different mutations detected at different genomic loci.
  • different mutation calling methods are used that are individually customized or fine-tuned for each different mutant detected at each different genomic loci.
  • the customization or tuning can be based on one or more of the factors described herein, e.g., the type of cancer in a sample, the gene or locus in which the subject interval to be sequenced is located, or the variant to be sequenced. This selection or use of mutation calling methods individually customized or fine-tuned for a number of subject intervals to be sequenced allows for optimization of speed, sensitivity and specificity of mutation calling.
  • a nucleotide value is assigned for a nucleotide position in each of X unique subject intervals using a unique mutation calling method, and X is at least 2, at least 3, at least 4, at least 5, at least 10, at least 15, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 200, at least 300, at least 400, at least 500, at least 1000, at least 1500, at least 2000, at least 2500, at least 3000, at least 3500, at least 4000, at least 4500, at least 5000, or greater.
  • the calling methods can differ, and thereby be unique, e.g., by relying on different Bayesian prior values.
  • assigning said nucleotide value is a function of a value which is or represents the prior (e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type.
  • the method comprises assigning a nucleotide value (e.g., calling a mutation) for at least 10, 20, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1,000 nucleotide positions, wherein each assignment is a function of a unique value (as opposed to the value for the other assignments) which is or represents the prior (e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type.
  • a nucleotide value e.g., calling a mutation
  • assigning said nucleotide value is a function of a set of values which represent the probabilities of observing a read showing said variant at said nucleotide position if the variant is present in the sample at a specified frequency (e.g., 1%, 5%, 10%, etc.) and/or if the variant is absent (e.g., observed in the reads due to base-calling error alone).
  • the mutation calling methods described herein can include the following: (a) acquiring, for a nucleotide position in each of said X subject intervals: (i) a first value which is or represents the prior (e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type X; and (ii) a second set of values which represent the probabilities of observing a read showing said variant at said nucleotide position if the variant is present in the sample at a frequency (e.g., 1%, 5%, 10%, etc.) and/or if the variant is absent (e.g., observed in the reads due to base-calling error alone); and (b) responsive to said values, assigning a nucleotide value (e.g., calling a mutation) from said reads for each of said nucleotide positions by weighing, e.g., by a Bay
  • the systems may comprise, e.g., one or more processors, and a memory unit communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: receive targeted sequence read data for a sample derived from an individual diagnosed with or suspected of having a disease; determine a copy number profile having a plurality of copy number features for the individual based on the targeted sequence read data for the sample; determine a copy number feature value for each of the plurality of copy number features; perform an analysis of the copy number feature values for each of a plurality of copy number features using a first trained model to determine a probability that each of one or more copy number feature subgroups is represented in the copy number profile for the sample; perform an analysis of the probabilities determined for each of the one or more copy number feature subgroups using a second trained model to identify one or more copy number signatures represented in the
  • 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 Genome
  • the disclosed systems may be used for identifying copy number signatures in sequencing data obtained from any of a variety of samples as described elsewhere herein (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 identification of a dominant copy number signature, or a set of component copy number signatures may be used to select, initiate, adjust, or terminate a treatment for cancer in the subject (e.g., a patient) from which the sample was derived, as described elsewhere herein.
  • the disclosed systems may further comprise sample processing and library preparation workstations, microplate-handling robotics, fluid dispensing systems, temperature control modules, environmental control chambers, additional data storage modules, data communication modules (e.g., Bluetooth®, WiFi, intranet, or internet communication hardware and associated software), display modules, one or more local and/or cloud-based software packages (e.g., instrument / system control software packages, sequencing data analysis software packages), etc., or any combination thereof.
  • the systems may comprise, or be part of, a computer system or computer network as described elsewhere herein.
  • FIG. 3 illustrates an example of a computing device or system in accordance with one embodiment.
  • Device 300 can be a host computer connected to a network.
  • Device 300 can be a client computer or a server.
  • device 300 can be any suitable type of microprocessor-based device, such as a personal computer, workstation, server or handheld computing device (portable electronic device) such as a phone or tablet.
  • the device can include, for example, one or more processor(s) 310, input devices 320, output devices 330, memory or storage devices 340, communication devices 360, and nucleic acid sequencers 370.
  • Software 350 residing in memory or storage device 340 may comprise, e.g., an operating system as well as software for executing the methods described herein.
  • Input device 320 and output device 330 can generally correspond to those described herein, and can either be connectable or integrated with the computer.
  • Input device 320 can be any suitable device that provides input, such as a touch screen, keyboard or keypad, mouse, or voice-recognition device.
  • Output device 330 can be any suitable device that provides output, such as a touch screen, haptics device, or speaker.
  • Storage 340 can be any suitable device that provides storage (e.g., an electrical, magnetic or optical memory including a RAM (volatile and non-volatile), cache, hard drive, or removable storage disk).
  • Communication device 360 can include any suitable device capable of transmitting and receiving signals over a network, such as a network interface chip or device.
  • the components of the computer can be connected in any suitable manner, such as via a wired media (e.g., a physical system bus 380, Ethernet connection, or any other wire transfer technology) or wirelessly (e.g., Bluetooth®, Wi-Fi®, or any other wireless technology).
  • a wired media e.g., a physical system bus 380, Ethernet connection, or any other wire transfer technology
  • wirelessly e.g., Bluetooth®, Wi-Fi®, or any other wireless technology
  • Software module 350 which can be stored as executable instructions in storage 340 and executed by processor(s) 310, can include, for example, an operating system and/or the processes that embody the functionality of the methods of the present disclosure (e.g., as embodied in the devices as described herein).
  • Software module 350 can also be stored and/or transported within any non-transitory computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described herein, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions.
  • a computer-readable storage medium can be any medium, such as storage 340, that can contain or store processes for use by or in connection with an instruction execution system, apparatus, or device. Examples of computer- readable storage media may include memory units like hard drives, flash drives and distribute modules that operate as a single functional unit.
  • various processes described herein may be embodied as modules configured to operate in accordance with the embodiments and techniques described above. Further, while processes may be shown and/or described separately, those skilled in the art will appreciate that the above processes may be routines or modules within other processes.
  • Software module 350 can also be propagated within any transport medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described above, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions.
  • a transport medium can be any medium that can communicate, propagate or transport programming for use by or in connection with an instruction execution system, apparatus, or device.
  • the transport readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic or infrared wired or wireless propagation medium.
  • Device 300 may be connected to a network (e.g., network 404, as shown in FIG. 4 and/or as described below), which can be any suitable type of interconnected communication system.
  • the network can implement any suitable communications protocol and can be secured by any suitable security protocol.
  • the network can comprise network links of any suitable arrangement that can implement the transmission and reception of network signals, such as wireless network connections, T1 or T3 lines, cable networks, DSL, or telephone lines.
  • Device 300 can be implemented using any operating system, e.g., an operating system suitable for operating on the network.
  • Software module 350 can be written in any suitable programming language, such as C, C++, Java or Python.
  • application software embodying the functionality of the present disclosure can be deployed in different configurations, such as in a client/server arrangement or through a Web browser as a Web-based application or Web service, for example.
  • the operating system is executed by one or more processors, e.g., processor(s) 310.
  • Device 300 can further include a sequencer 370, which can be any suitable nucleic acid sequencing instrument.
  • FIG. 4 illustrates an example of a computing system in accordance with one embodiment.
  • device 300 e.g., as described above and illustrated in FIG. 3
  • network 404 which is also connected to device 406.
  • device 406 is a sequencer.
  • Exemplary sequencers can include, without limitation, Roche/454’s Genome Sequencer (GS) FLX System, Illumina/Solexa’s Genome Analyzer (GA), Illumina’s HiSeq® 2500, HiSeq® 3000, HiSeq® 4000 and NovaSeq® 6000 Sequencing Systems, Life/APG’s Support Oligonucleotide Ligation Detection (SOLiD) system, Polonator’s G.007 system, Helicos BioSciences’ HeliScope Gene Sequencing system, or Pacific Biosciences’ PacBio® RS system.
  • Devices 300 and 406 may communicate, e.g., using suitable communication interfaces via network 404, such as a Local Area Network (LAN), Virtual Private Network (VPN), or the Internet.
  • network 404 can be, for example, the Internet, an intranet, a virtual private network, a cloud network, a wired network, or a wireless network.
  • Devices 300 and 406 may communicate, in part or in whole, via wireless or hardwired communications, such as Ethernet, IEEE 802.1 lb wireless, or the like. Additionally, devices 300 and 406 may communicate, e.g., using suitable communication interfaces, via a second network, such as a mobile/cellular network.
  • Communication between devices 300 and 406 may further include or communicate with various servers such as a mail server, mobile server, media server, telephone server, and the like.
  • Devices 300 and 406 can communicate directly (instead of, or in addition to, communicating via network 404), e.g., via wireless or hardwired communications, such as Ethernet, IEEE 802.11b wireless, or the like.
  • devices 300 and 406 communicate via communications 408, which can be a direct connection or can occur via a network (e.g., network 404).
  • One or all of devices 300 and 406 generally include logic (e.g., http web server logic) or are programmed to format data, accessed from local or remote databases or other sources of data and content, for providing and/or receiving information via network 404 according to various examples described herein.
  • logic e.g., http web server logic
  • devices 300 and 406 are programmed to format data, accessed from local or remote databases or other sources of data and content, for providing and/or receiving information via network 404 according to various examples described herein.
  • Example 1 Pan-cancer analysis of copy number signatures identifies broad prevalence and utility in predicting response to PARP inhibitors
  • This section provides a non-limiting example of how copy number signatures can be used to reveal the molecular states of various cancers and provide prognostic value for identifying treatments.
  • the landscapes of 10 copy number signatures found in a pan-cancer cohort of 259,415 samples were analyzed, identifying associations with genomic alterations and clinical characteristics. Copy number signatures were seen at a high prevalence across many cancer types, and all 10 signatures were present in 41/46 of cancer types analyzed, highlighting their broad relevance.
  • Copy number signatures generated by extracting patterns in aberrant copy numbers from the whole genome, identify complex biological processes relevant to tumor biology (Macintyre G et al. (2016) Nat Genet 50(9): 1262- 1270; Wang S et al. (2021) PLoS Genet 17(5):el009557; Pladsen AV et al. Commun Biol 2020, 3(1): 153; Maclachlan KH et al. Nat Commun 2021, 12(1):5172; Guan B et al. Front Cell Dev Biol 2021, 9:713499; Steele CD et al. Signatures of copy number alterations in human cancer. bioArxiv 2021).
  • HOSOC high grade serous ovarian cancer
  • HRD homologous recombination deficiency
  • This example describes the landscape of copy number signatures in a large real- world genomic dataset obtained from comprehensive genomic profiling (CGP) using a hybrid capture targeted gene panel assay. 259,415 samples across 46 tumor types were analyzed for copy number aberrations using feature quantification and signature extraction methods published by Macintyre and colleagues (Macintyre G et al. (2016) Nat Genet 50(9): 1262-1270). From this pan-cancer cohort, 10 biologically informative copy number signatures were characterized.
  • Tumor samples were sequenced (March 2014 to April 2021) by hybrid capture-based comprehensive genomic profiling as part of routine clinical care (Frampton GM et al. Nat Biotechnol 2013, 31(11): 1023- 1031). Sequencing of liquid biopsy ctDNA was performed on >20ng of ctDNA extracted from blood plasma to create adapter sequencing libraries before hybrid capture and sample-multiplexed sequencing to a median unique exon coverage depth of >6,000X for up to 324 genes (Woodhouse R et al. PLoS One 2020, 15(9):e0237802). Results were analyzed for base substitutions, short insertions and deletions (indels), copy number alterations, and rearrangements. Testing was performed in a CLIA certified/CAP- accredited laboratory.
  • This study used the nationwide (US-based) de-identified Flatiron Health-Foundation Medicine ovarian and metastatic prostate clinico-genomic databases.
  • the de-identified data originated from approximately 280 US cancer clinics (-800 sites of care).
  • Retrospective longitudinal clinical data were derived from electronic health record (EHR) data, comprising patient-level structured and unstructured data, curated via technology-enabled abstraction, and were linked to genomic data derived from comprehensive genomic profiling (CGP) tests in the databases by de-identified, deterministic matching (Singal G et al. JAMA 2019, 321(14): 1391- 1399).
  • TTD Median time to treatment discontinuation
  • HR hazard ratios
  • the data was divided into training and testing datasets (70:30). For feature selection, all the features were initially used to fit a model in training dataset. The features were then ranked by their importance based on their performance gain. The optimal number of features was selected until the ROC-AUC plateaued in the training dataset. The optimal model was determined using grid search over the hyper-parameter space with 4 repeated 10-fold cross-validation and the performances are evaluated by ROC-AUC.
  • genomic scarring of the prostate is different than other tumor types, so two XGB models were developed, one applied to pan-cancer and the other applied to prostate only. Both XGB models (pan-cancer and prostate-specific) were trained in the same process.
  • pan-cancer copy number signatures 2000 samples were selected from key diseases: ovary, breast, lung non-small cell lung carcinoma (NSCLC), esophageal, prostate, and pancreatic cancer (Ciriello G et al. (2013) Nat Genet 45(10): 1127-1133; Macintyre G et al. (2016) Nat Genet 50(9): 1262-1270). To ensure rarer diseases were included in the testing set, up to 200 samples were added from these tumor types. When ⁇ 200 samples were available in a disease group all samples were added. This cohort was split into 75% training and 25% testing.
  • Rank for non-negative matrix factorization was estimated by picking a rank between 2 and 15 where the sparseness in the basis matrix was higher in a randomized dataset than in the original dataset (Macintyre G et al. (2016) Nat Genet 50(9): 1262-1270). The dispersion and cophenetic coefficients were also calculated between consecutive runs to evaluate the different numbers of ranks (Brunet JP et al. Proc Natl Acad Sci U SA 2004, 101 ( 12) :4164-4169). Once a rank was selected, Nonnegative Double Singular Value Decomposition (NNSVD) was used to seed the NMF algorithm using Nimfa (vl.2.3) (Zitnik M, Zupan B: NIMFA : A Python Library for Nonnegative Matrix Factorization.
  • NSVD Nonnegative Double Singular Value Decomposition
  • Tumor fraction was estimated using a measure of tumor aneuploidy that incorporates observed deviations in coverage across the genome for a given sample. Calculated values for this metric were calibrated against a training set based on samples with well-defined tumor fractions to generate an estimate of the tumor fraction (Li M et al. Cancer Res 2021, 81 (13_Supplement): 2231).
  • telomereHunter 1.1.0 (Feuerbach L et al. BMC Bioinformatics 2019, 20(l):272). This software tool was run using the default parameters and a repeat threshold set to 7 for 49 bp paired-end reads. TelomereHunter extracts telomeric reads that contain seven instances of the four most common telomeric repeat types (TTAGGG, TCAGGG, TGAGGG, and TTGGGG) and determines the telomeric content by normalizing the telomere read count by all reads in the sample with a GC content of 48-52%.
  • a low aneuploidy signature was identified and was dominant in 70.5% of samples (FIG. 6B). Unlike all other copy number signatures, this signature was defined by the absence of copy number aberrations, such as few breakpoints ( ⁇ 2 per chromosome arm) and long segments (>42 Mb) (Fig SI & S2). The low aneuploidy signature was most prevalent in acute leukemias (90%) and appendiceal cancers (88%). This signature was also enriched for tumors that are associated with well characterized hypermutation phenotypes rather than chromosomal instability, including colorectal cancers (87%), skin cancers (82%) and diffuse large B-cell lymphoma (80%) (FIGS. 6B & 6C).
  • Diseases with low rates of low aneuploidy signature included prostate cancers, fallopian tube cancers, leiomyosarcomas, breast cancers, and ovarian cancers - diseases that are more canonically associated with copy number aberrations (Ciriello G et al. Nat Genet 2013, 45(10): 1127-1133).
  • a chromosomal instability signature was characterized by moderate quantities of breakpoints (4 per chromosome arm) and shorter segments ( ⁇ 24 Mb) (FIGS. 7 & 8) and was most prevalent in fallopian tube cancers (25%), ovarian cancers (22%), and prostate cancers (19%). Chromosomal instability signature samples correlated with two mechanisms of chromosomal instability, focal tandem duplications (FTD) and gLOH (FIG. 6E). Samples harboring the chromosomal instability signature had 13% higher ploidies (mean of 3.1) than samples harboring other signatures (mean of 2.7) (FIG. 9).
  • FTD events were 10 kb and 10 Mb amplifications with single copy number gain (ploidy +1) (Sokol ES et al. Oncologist 2019, 24(12): 1526-1533).
  • the FTD signature was defined by having many breakpoints (10 per chromosome arm), very short segments ( ⁇ 13 Mb), segments at ploidy+1 and changepoints of 1 copy in diploid samples, which are all characteristic of an FTD phenotype.
  • a signature characteristic of HRD in breast/gynecologic (GYN) tumors was defined by both heterozygous deletions (segment copy numbers at half of ploidy) and duplications (ploidy + 1 in diploid samples). Segment lengths represented wide range (7 - 51 Mb) and the profiles had few breakpoints (1 breakpoint per 25 Mb).
  • amplicon signature and a seismic amplification signature were identified in this cohort. Both signatures were defined by the presence of high copy number amplifications, including amplifications at >70 copies gained (FIG. 6D). Samples with these signatures had an average of 4.6 and 4.8 known and likely pathogenic amplification alterations, amplicon and seismic amplification signatures respectively, compared to 1.3 of these amplifications outside of the signatures (FIG. 6D). The amplicon signature was defined by very short segments (2 - 7 Mb) amongst long segments ( ⁇ 72 Mb), whereas the seismic amplification showed seismic amplification patterns, containing segments of 7 - 13 Mb length and lacking long unbroken segments (FIG. 8).
  • a subclonal signature was characterized by clusters of breakpoints (5 per 50 Mb windows) leading to patterns of very short segments (1.7 - 3.7 Mb) and was most prevalent in gist (20%), CNS non-gliomas (18%), leiomyosarcoma (18%), small cell cancer (18%), and mesothelioma (15%).
  • a neuroendocrine signature was defined by 7 - 13 Mb long segments with ploidy +1 copy number levels. The neuroendocrine signature positive samples averaged 10 cytogenetic gains vs 6.6 in samples outside of signature. The most prevalent diseases were adrenal gland cancers (11%), small cell cancers (11%), and hormone producing endocrine tumors (endocrine- neuro) cancers (10%).
  • An oscillating signature was characterized by having highly localized segmentation (3-5 breakpoints per 25 Mb) resulting in mix of shorter segments (1.7 - 7.1 Mb) and longer segments (42 - 72 Mb).
  • the oscillating signature was also defined by long chains of oscillating copy number states (including- 11 segment long chains).
  • the localized chromosome shattering seen with this signature is a hallmark of chromothripsis (Stephens PJ et al. Cell 2011, 144(l):27-40; Rausch T et al. Cell 2012, 148( l-2):59-71 ) and breakage fusion-bridges (Bignell GR et al. Genome Res 2007, 17(9): 1296-1303).
  • MEN 1 Multiple Neoplasia type 1, is known to predispose patients for multiple endocrine tumors (Bergman L et al. Br J Cancer 2000, 83(8): 1003-1008).
  • the HRDsig algorithm outperformed gLOH overall and across diseases (FIG. 12B). Importantly, the algorithm identified a population of HRDsig- positive samples even within the population lacking BRCA1/2 or other HRR alterations, especially in breast and ovarian cancers where non-genomic (e.g. methylation) mechanisms of HRD have been reported (FIGS. 12C & 12D) (Ruscito I et al. Eur J Cancer 2014, 50(12):2090- 2098; Yamashita N et al. Clin Breast Cancer 2015, 15(6):498-504).
  • HRRwt homologous recombination repair wildtype
  • mCRPC metastatic castration-resistant prostate cancer
  • bioArxiv 2021 settings by investigating over 259,000 samples pan-cancer. These results build upon Steele et al. by applying copy number signature calling to genomic profiles obtained from targeted panel sequencing assays - which currently have widespread clinical usage. Additional copy number signatures were discovered with strong disease bias and genomic associations. It was found that 29% of tumors had dominant copy number signature associated with chromosomal instability, with the highest rates observed in prostate cancers (56%), fallopian tube cancers (53%), and leiomyosarcomas (52%), whereas tumors harboring a dominant copy number signature associated with quiet copy number genomes were most often acute leukemias (90%), appendiceal cancers (88%) and colorectal cancers (87%), where copy number variant processes were less common.
  • PARP inhibitors improve outcomes in HR deficient patients in a number of tumor types.
  • Current patient selection strategies primarily rely on the presence of germline and/or tumor alterations in BRCA1 and BRCA2. These strategies have been successful in ovarian, prostate, pancreatic, and breast cancer; however, beyond these BRCA-associated cancer types, such a biomarker strategy may have pitfalls.
  • BRCA1/2 alterations even those of germline origin, may not contribute to the pathogenicity of diseases such as lung cancer or colorectal cancer.
  • BRCA1/2 alterations may represent monoallelic passenger alterations.
  • Sokol et al. reported that while a majority of BRCA1/2 alterations are biallelic in ovary (95%), breast (88%), prostate (87%), and pancreatic (79%) cancers, in most other tumor types, a minority of alterations are predicted biallelic, including low rates in melanoma (17%) and skin squamous cell carcinoma (33%) (Sokol ES et al. JCO Precis Oncol 2020, 4:442-465). These monoallelic alterations are not associated with elevated genomic scarring and with the remaining functional copy, likely do not result in functional HRD.
  • This example describes an HRD signature trained with a diverse set of copy number and indel features. While the highest rates of positivity were seen in fallopian tube, ovarian, peritoneal, breast, and prostate cancer, appreciable frequencies were seen across tumor types, with a 6% overall prevalence. HRDsig was associated with biallelic BRCA1/2 alterations was high across tumor types. Importantly, HRDsig identified a BRCA1/2 wildtype population enriched for biallelic alterations in other HRR genes and in some cases lacking any HRR alteration, suggesting that HRDsig is able to detect a BRCAness population not currently eligible for PARPi in most indications.
  • HRDsig was predictive of improved outcomes on PARPi efficacy, with similar or trending superior performance relative to gLOH and GIS.
  • sample numbers were sufficient to demonstrate predictive power of HRDsig in the BRCA wt population.
  • pan-cancer copy number signatures revealed distinct molecular subtypes across a wide array of cancers. Many of these signatures were present in almost all tumor types, reflecting an opportunity for the use of pattern-based scores in targeted therapy selection in pan-cancer trials. Even for HRD, which represents a well appreciated and targeted phenotype, it was found that patterns of scarring differed across tumor types with prostate- specific patterns, which requires a nuanced genomic scar-based signature.
  • the algorithm was developed using a hybrid capture targeted panel and may be applicable to other panel-based tests including liquid biopsies, which would be valuable in cases where limited tissue is available, or a post-progression sample is preferred.
  • a method comprising: providing a plurality of nucleic acid molecules obtained from a sample from an individual diagnosed with or suspected of having a disease; 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, targeted sequence read data for the sample derived from the individual; determining, using the one or more processors, a copy number profile having a plurality of copy number features for the individual based on the targeted sequence read data for the sample; determining, using the one or more processors, a copy number feature value for each of the plurality of copy number features; performing, using the one or more processors, an analysis of the copy number feature values for each
  • the one or more genomic features comprise one or more short variants, one or more indels, one or more biallelic loss-of-function variants, one or more rearrangements, or any combination thereof.
  • 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,
  • I l l lung cancer (with an EGFR exon 19 deletion or exon 21 substitution (L858R) mutations), a nonsmall cell lung cancer (with an EGFR T790M mutation), ovarian cancer, ovarian cancer (with a BRCA mutation), pancreatic cancer, a pancreatic, gastrointestinal, or lung origin neuroendocrine tumor, a pediatric neuroblastoma, a peripheral T-cell lymphoma, peritoneal cancer, prostate cancer, a renal cell carcinoma, rheumatoid arthritis, a small lymphocytic lymphoma, a soft tissue sarcoma, a solid tumor (MSI-H/dMMR), a squamous cell cancer of the head and neck, a squamous non-small cell lung cancer, thyroid cancer, a thyroid carcinoma, urothelial cancer, a urothelial carcinoma, or Waldenstrom's macroglobulinemia.
  • a pediatric neuroblastoma a peripheral T-cell lymph
  • 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.
  • sample is a liquid biopsy sample and comprises cell- free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.
  • cfDNA cell- free DNA
  • ctDNA circulating tumor DNA
  • 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 adapters comprise amplification primers, flow cell adaptor sequences, substrate adapter sequences, or sample index sequences.
  • 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,
  • a method comprising: receiving, at one or more processors, targeted sequence read data for a sample derived from an individual diagnosed with or suspected of having a disease; determining, using the one or more processors, a copy number profile having a plurality of copy number features for the individual based on the targeted sequence read data for the sample; determining, using the one or more processors, a copy number feature value for each of the plurality of copy number features; performing, using the one or more processors, an analysis of the copy number feature values for each of a plurality of copy number features using a first trained model to determine a probability that each of one or more copy number feature subgroups is represented in the copy number profile for the sample; performing, using the one or more processors, an analysis of the probabilities determined for each of the one or more copy number feature subgroups using a second trained model to identify one or more copy number signatures represented in the copy number profile for the individual, and to determine one or more corresponding copy number signature scores; comparing, using the one or more processors, the copy number signature scores
  • off-target sequence reads comprise an average sequencing coverage of less than l.Ox, 0.9x, 0.8x, 0.7x, 0.6x, 0.5x, 0.4x, 0.3x, 0.2x, or O.lx.
  • the plurality of copy number features comprises a number of breakpoints per 10 Mb of genomic sequence, a number of breakpoints per 25 Mb of genomic sequence, a number of breakpoints per 50 Mb of genomic sequence, a number of breakpoints per 100 Mb of genomic sequence, a number of breakpoints per chromosome arm for each chromosome, a magnitude of a copy number change between any two adjacent copy number segments; a length of each copy number segment; the copy number of segments; a count of contiguous oscillating copy number chains, or any combination thereof.
  • the plurality of different cancers comprises ovarian cancer, breast cancer, non-small cell lung carcinoma (NSCLC), esophageal cancer, prostate cancer, pancreatic cancer, colorectal cancer, or any combination thereof.
  • NSCLC non-small cell lung carcinoma
  • the one or more additional genomic features comprise one or more short variants, one or more indels, one or more biallelic loss-of-function variants, one or more rearrangements, or any combination thereof.
  • 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
  • clause 80 or clause 81 further comprising selecting an anti-cancer therapy to administer to the subject based on the identification of the dominant copy number signature or of the one or more copy number signature components.
  • a method for diagnosing a disease comprising: diagnosing that a subject has the disease based on an identification of a dominant copy number signature or of one or more copy number signature components in a copy number profile for a sample from an individual, wherein the dominant copy number signature or the one or more copy number signature components are identified according to the method of any one of clauses 43 to 78.
  • a method of selecting an anti-cancer therapy comprising: responsive to an identification of a dominant copy number signature or of one or more copy number signature components in a copy number profile for a sample from an individual, selecting an anti-cancer therapy for the individual, wherein the dominant copy number signature or the one or more copy number signature components are identified according to the method of any one of clauses 43 to 78.
  • a method of treating a cancer in an individual comprising: responsive to an identification of a dominant copy number signature or of one or more copy number signature components in a copy number profile for a sample from an individual, administering an effective amount of an anti-cancer therapy to the individual, wherein the dominant copy number signature or the one or more copy number signature components are identified according to the method of any one of clauses 43 to 78.
  • a method for monitoring cancer progression or recurrence in an individual comprising: identifying a first dominant copy number signature, or a first set of one or more copy number signature components, in a first copy number profile for a first sample obtained from an individual at a first time point according to the method of any one of clauses 43 to 78; identifying a second dominant copy number signature, or a second set of one or more copy number signature components, in a second copy number profile for a second sample obtained from the individual at a second time point; and comparing the first dominant copy number signature, or the first set of one or more copy number signature components, to the second dominant copy number signature, or the second set of one or more copy number signature components, thereby monitoring the cancer progression or recurrence.
  • any one of clauses 43 to 78 further comprising generating a genomic profile for the individual based on the identification of a dominant copy number signature or of one or more copy number signature components in a copy number profile for the sample from the individual.
  • the genomic profile for the individual further comprises results from a comprehensive genomic profiling (CGP) test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof.
  • CGP genomic profiling
  • the HRD signature comprises heterozygous deletion(s), high genome- wide loss of heterozygosity (gLOH-high), and/or high genomic instability score (GIS).
  • the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
  • 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 targeted sequence read data for a sample derived from an individual diagnosed with or suspected of having a disease; determine a copy number profile having a plurality of copy number features for the individual based on the targeted sequence read data for the sample; determine a copy number feature value for each of the plurality of copy number features; perform an analysis of the copy number feature values for each of a plurality of copy number features using a first trained model to determine a probability that each of one or more copy number feature subgroups is represented in the copy number profile for the sample; perform an analysis of the probabilities determined for each of the one or more copy number feature subgroups using a second trained model to identify one or more copy number signatures represented in the copy number profile for the individual, and to determine one or more corresponding copy number signature scores; compare the copy number signature scores for the one or more identified copy number signatures to one
  • the one or more genomic features comprise one or more short variants, one or more indels, one or more biallelic loss-of-function variants, one or more rearrangements, or any combination thereof.
  • the plurality of copy number features comprises a number of breakpoints per 10 Mb of genomic sequence, a number of breakpoints per 25 Mb of genomic sequence, a number of breakpoints per 50 Mb of genomic sequence, a number of breakpoints per 100 Mb of genomic sequence, a number of breakpoints per chromosome arm for each chromosome, a magnitude of a copy number change between any two adjacent copy number segments; a length of each copy number segment; the copy number of segments; a count of contiguous oscillating copy number chains, or any combination thereof.
  • 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 targeted sequence read data for a sample derived from an individual diagnosed with or suspected of having a disease; determine a copy number profile having a plurality of copy number features for the individual based on the targeted sequence read data for the sample; determine a copy number feature value for each of a plurality of copy number features; perform an analysis of the copy number feature values for each of a plurality of copy number features using a first trained model to determine a probability that each of one or more copy number feature subgroups is represented in the copy number profile for the sample; perform an analysis of the probabilities determined for each of the one or more copy number feature subgroups using a second trained model to identify one or more copy number signatures represented in the copy number profile for the individual, and to determine one or more corresponding copy number signature scores; compare the copy number signature scores for the one or more identified copy number signatures to one or more
  • the plurality of copy number features comprises a number of breakpoints per 10 Mb of genomic sequence, a number of breakpoints per 25 Mb of genomic sequence, a number of breakpoints per 50 Mb of genomic sequence, a number of breakpoints per 100 Mb of genomic sequence, a number of breakpoints per chromosome arm for each chromosome, a magnitude of a copy number change between any two adjacent copy number segments; a length of each copy number segment; the copy number of segments; a count of contiguous oscillating copy number chains, or any combination thereof.
  • NMF non-negative matrix factorization

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Abstract

L'invention concerne des procédés et des systèmes d'estimation de signatures de nombre de copies sur la base de données de séquençage d'acide nucléique. Les procédés peuvent comprendre les étapes consistant à, par exemple, déterminer un profil de nombre de copies pour un individu sur la base de données de lecture de séquence ; déterminer une valeur de caractéristique de nombre de copies pour chacune d'une pluralité de caractéristiques de nombre de copies ; analyser les valeurs de caractéristiques de nombre de copies à l'aide d'un premier modèle entraîné pour déterminer une probabilité qu'un ou plusieurs sous-groupes de caractéristiques de nombre de copies soient représentés dans le profil de nombre de copies ; analyser les probabilités déterminées à l'aide d'un second modèle entraîné pour identifier une ou plusieurs signatures de nombre de copies et leurs scores de signature de nombre de copies correspondants ; comparer les scores de signature de nombre de copies à des seuils prédéterminés correspondants ; et identifier, sur la base de la comparaison, une signature de nombre de copies dominant ou un ensemble d'une ou plusieurs signatures de nombre de copies qui sont des composants du profil de nombre de copies pour l'individu.
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