WO2024064679A1 - Méthodes et systèmes d'attribution d'état fonctionnel de variants génomiques - Google Patents

Méthodes et systèmes d'attribution d'état fonctionnel de variants génomiques Download PDF

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WO2024064679A1
WO2024064679A1 PCT/US2023/074575 US2023074575W WO2024064679A1 WO 2024064679 A1 WO2024064679 A1 WO 2024064679A1 US 2023074575 W US2023074575 W US 2023074575W WO 2024064679 A1 WO2024064679 A1 WO 2024064679A1
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gene
genomic
genomic variant
variant
cancer
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PCT/US2023/074575
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Kimberly JOHNSON
Lisa HEPPLER
Oliver HOLMES
Pierre VANDEN BORRE
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Foundation Medicine, Inc.
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H15/00—ICT specially adapted for medical reports, e.g. generation or transmission thereof
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/40—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6869—Methods for sequencing

Definitions

  • the present disclosure relates generally to methods and systems for analyzing genomic profiling data, and more specifically to methods and systems for assigning a functional status to one or more genomic variants using genomic profiling data.
  • Genomic variants that occur relatively frequently in the population may be associated with a particular structural label, e.g., CD74-ROS1 fusion, and a known functional status, e.g., activating fusion.
  • the known functional status may correspond to a functional status group identified in scientific literature.
  • the functional status group may correspond to an activating variant, an inactivating variant, a functionally unknown variant, or a wildtype.
  • the functional status group may be associated with specific prognosis predictions, treatment recommendations, treatment outcomes, and the like. Accordingly, assigning a genomic variant to an appropriate functional status and determining a label for the genomic variant may allow a healthcare provider to efficiently determine a recommended course of treatment based on specific mechanisms that drive disease.
  • genomic variants in a sample from an individual may correspond to novel alterations or rearrangement events.
  • the novel alteration or rearrangement events may not be associated with a known label and may not clearly map to a functional status group.
  • the process of assigning a genomic variant to a functional status group and labeling the genomic variant is typically performed manually. This manual process can be time intensive and subject to human error. Accordingly, there is a need to provide an automatic process that assigns a genomic variant to a functional status group and labels the genomic variant, where the process can assign and label novel genomic variants that are not well classified in the art.
  • methods and systems for automatically assigning a genomic variant from a sample to a functional status group may be used to identify a treatment for the individual providing the sample.
  • the disclosed methods have the potential to improve healthcare outcomes for individuals (e.g., cancer patients) by providing healthcare providers with treatment recommendations based on an accurate identification of a functional status group for both novel and frequently occurring genomic variants.
  • methods and systems can further label the genomic variant and provide the label to a healthcare provider (e.g., in a report).
  • the healthcare provider may further base the treatment recommendation based on the label.
  • embodiments of the present disclosure provide improvements to patient care by providing an automatic process that assigns a genomic variant to a functional status group and labels the genomic variant, where the process can assign and label novel genomic variants that are not well classified in the art.
  • Embodiments of the present disclosure provide systems and methods for determining a functional status group for a genomic variant and identifying treatment for an individual based on the functional status group.
  • the method can comprise: providing a plurality of nucleic acid molecules obtained from a sample from a subject, ligating one or more adapters onto one or more nucleic acid molecules from the plurality of nucleic acid molecules; amplifying the one or more ligated nucleic acid molecules from the plurality of nucleic acid molecules; capturing amplified nucleic acid molecules from the amplified nucleic acid molecules; sequencing, by a sequencer, the captured nucleic acid molecules to obtain a plurality of sequence reads that represent the captured nucleic acid molecules; receiving, at one or more processors, sequence read data for the plurality of sequence reads; determining, using the one or more processors, a breakpoint of a genomic variant and a location of the breakpoint based on the sequence read data, the breakpoint associated with a rearrangement event and
  • the method can further comprise: in accordance with a determination that the rearrangement event is not in-strand, assigning the genomic variant to a first functional status group corresponding to a functionally unknown rearrangement group; and in accordance with a determination that the rearrangement event is in- strand and the sequence encoding the predetermined protein domain is in sequence read data corresponding to the rearrangement event, assigning the genomic variant to a second functional status group corresponding to a functionally unknown fusion group.
  • the method can further comprise performing, using the one or more processors, a third determination of whether the first gene and the second gene are known partner genes.
  • whether the first gene and the second gene are known partner genes is determined based on a predetermined association between the first gene and the second gene described in scientific literature.
  • determining whether the first gene and the second gene are known partner genes comprises comparing the second gene to a lookup table associated with the first gene.
  • the method can further comprise in accordance with a determination that the rearrangement event is in-strand, the sequence encoding the predetermined protein domain impacted by the rearrangement event, and the first gene is a known partner of the second gene, assigning the genomic variant to a third functional status group corresponding to an activating fusion group.
  • the subject is suspected of having or is determined to have cancer.
  • the cancer is a B cell cancer (multiple myeloma), a melanoma, breast cancer, lung cancer, bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain cancer, central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine cancer, endometrial cancer, cancer of an oral cavity, cancer of a pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel cancer, appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, a cancer of hematological tissue, an adenocarcinoma, an inflammatory myofibroblastic tumor, a gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), acute lymph
  • 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 can further comprise treating the subject with an anti-cancer therapy.
  • the anti-cancer therapy comprises a targeted anti- cancer therapy.
  • 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), atezoli
  • the method can further comprise 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.
  • 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
  • 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 between 10 and 100 loci, between 20 and 150 loci, between
  • 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 can further comprise generating, by the one or more processors, a report indicating the functional status of the genomic variant.
  • the method can further comprise transmitting the report to a healthcare provider.
  • the report is transmitted via a computer network or a peer-to-peer connection.
  • Embodiments of the present disclosure further comprise a method for identifying a treatment based on a genomic variant in a sample from an individual.
  • the method can comprise: receiving, at one or more processors, sequence read data associated with the genomic variant in the sample; determining, using the one or more processors, a breakpoint of the genomic variant and a location of the breakpoint based on the sequence read data, the breakpoint associated with a rearrangement event and further associated with a first gene and a second gene of the sample, the first gene corresponding to the genomic variant; if the breakpoint of the genomic variant is located outside a sequence encoding a predetermined protein domain, performing, using the one or more processors: a first determination of whether the rearrangement event is in-strand; and a second determination of whether the sequence encoding the predetermined protein domain is impacted by the rearrangement event; assigning, using the one or more processors, the genomic variant to a functional status group based on the first determination and the second determination; and identifying, using the one or more
  • the method can further comprise in accordance with a determination that the rearrangement event is not in-strand, assigning the genomic variant to a first functional status group; and in accordance with a determination that the rearrangement event is in-strand and the sequence encoding the predetermined protein domain is impacted by the rearrangement event, assigning the genomic variant to a second functional status group.
  • the first functional status group corresponds to a functionally unknown rearrangement group.
  • the method can further comprise comprising labeling the genomic variant as a rearrangement.
  • a functionally unknown rearrangement label is further based on an orientation of the first gene with respect to the second gene.
  • the second functional status group corresponds to a functionally unknown fusion group.
  • the method can further comprise labeling the genomic variant as a reciprocal fusion.
  • the method can further comprise performing, using the one or more processors, a third determination of whether the first gene and the second gene are known partner genes. In such examples, whether the first gene and the second gene are known partner genes is determined based on a predetermined association between the first gene and the second gene described in scientific literature. In one or more examples, determining whether the first gene and the second gene are known partner genes is based on a lookup table associated with the first gene.
  • the method can further comprise in accordance with a determination that the rearrangement event is in-strand, the sequence encoding the predetermined protein domain is impacted by the rearrangement event, and the first gene is a known partner of the second gene, assigning the genomic variant to a third functional status group.
  • the third functional status group corresponds to an activating fusion group.
  • the method can further comprise labeling the genomic variant as an activating fusion.
  • determining whether the sequence encoding the predetermined protein domain is impacted by the rearrangement event comprises: determining, whether a functional domain of the first gene is in the sequence read data corresponding to the rearrangement event.
  • the method can further comprise in accordance with a determination that the rearrangement event is in-strand, and further that the sequence encoding the predetermined protein domain is impacted by the rearrangement event, and the first gene is not a known partner gene of the second gene, flagging the genomic variant for manual review and forgoing labeling the genomic variant.
  • the method can further comprise forgoing assigning the genomic variant to the functional status group if the breakpoint of the genomic variant is located outside a predetermined sequence encoding a protein domain.
  • the method can further comprise determining, using the one or more processors, whether the first gene is an oncogene.
  • the first gene is ABL1, ALK, BRAF, FGFR1, FGFR2, FGFR3, MET, NTRK1, NTRK2, NTRK3, PDGFRA, PDGFRB, ROS1, RET, or RAFI.
  • the predetermined protein domain is a functional domain. In one or more examples, the predetermined protein domain is a kinase domain.
  • the method can further comprise performing, using the one or more processors, a fourth determination of whether the second gene is a coding gene.
  • the rearrangement event comprises a fusion event.
  • the method can further comprise determining the second gene is a known partner of the first gene in accordance with a determination that the first gene is ROS 1 and the second gene is one of CD74, CLIP1, EZR, GOPC, LRIG3, MY05A, PPFIBP1, PWWP2A, SLC34A2, SDC4, SHTN1 (KIAA1598), TPM3, and ZCCHC8.
  • the sequence read data for the individual is based on a targeted exome sequencing panel. In one or more examples, the sequence read data for the individual is derived from a single biopsy sample. In one or more examples, the sequence read data for the individual is derived from multiple biopsy samples. In one or more examples, the sequence read data for the individual is derived from single cell sequencing. In one or more examples, the sequence read data for the individual is derived from circulating tumor DNA in a liquid biopsy sample.
  • the method can further comprise assigning, using the one or more processors, a therapy for the individual based on the functional status group. In one or more examples, the method can further comprise administering, using the one or more processors, a treatment to the individual based on the functional status group. In one or more examples, the method can further comprise associating, using the one or more processors, the individual with a clinical trial based on the functional status group. In one or more examples, the method can further comprise monitoring, using the one or more processors, a prognosis of the individual based on the functional status group. In one or more examples, the method can further comprise predicting, using the one or more processors, one or more clinical outcomes based on the functional status group.
  • Embodiments of the present disclosure further provide systems for identifying a treatment for an individual based on a genomic variant.
  • the system can comprise: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to perform a method comprising: receiving, at one or more processors, sequence read data associated with a genomic variant in a sample from an individual; determining, using the one or more processors, a breakpoint of the genomic variant and a location of the breakpoint based on the sequence read data, the breakpoint associated with a rearrangement event and further associated with a first gene and a second gene of the sample, the first gene corresponding to the genomic variant; if the breakpoint of the genomic variant is located outside a sequence encoding a predetermined protein domain, performing, using the one or more processors: a first determination of whether the rearrangement event is in-strand; and a second determination of whether the sequence encoding
  • Embodiments of the present disclosure further provide non-transitory computer-readable storage mediums for identifying a treatment for an individual based on a genomic variant.
  • the 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, at one or more processors, sequence read data associated with a genomic variant in a sample from an individual; determine, using the one or more processors, a breakpoint of the genomic variant and a location of the breakpoint based on the sequence read data, the breakpoint associated with a rearrangement event and further associated with a first gene and a second gene of the sample, the first gene corresponding to the genomic variant; if the breakpoint of the genomic variant is located outside a sequence encoding a predetermined protein domain, perform, using the one or more processors: a first determination of whether the rearrangement event is in-strand; and a second determination of whether the sequence encoding
  • Embodiments of the present disclosure provide systems and methods for determining a functional status group for a genomic variant and identifying treatment for an individual based a genomic variant in a sample from the individual.
  • the method can comprise: receiving, at one or more processors, sequence read data associated with the genomic variant in the sample; determining, using the one or more processors, one or more breakpoints of the genomic variant and a location of the breakpoint based on the sequence read data, the breakpoint associated with a rearrangement event; if the one or more breakpoints of the genomic variant are located outside of a sequence encoding a predetermined protein domain, performing, using the one or more processors: a determination of whether the sequence encoding the predetermined protein domain is in the rearrangement event; assigning, using the one or more processors, the genomic variant to a functional status group based on the determination; and identifying, using the one or more processors, the treatment based on the assigned functional status group of the genomic variant.
  • the rearrangement event is an intragenic event.
  • the method can further comprise performing a second determination to identify a rearrangement type, wherein assigning the genomic variant to the functional status group is based on the rearrangement type.
  • the rearrangement type comprises a deletion, a duplication, or an inversion.
  • the method can further comprise in accordance with a determination that the sequence encoding the predetermined protein domain is in the rearrangement event, assigning the genomic variant to a first functional status group. In such examples, assigning the genomic variant to a functional status group is further based on the genomic variant. In one or more examples, the method can further comprise forgoing assigning the genomic variant to the functional status group if the breakpoint of the genomic variant is located outside a predetermined sequence encoding a protein domain. [0037] In one or more examples, the method can further comprise determining, using the one or more processors, whether the first gene is an oncogene. In one or more examples, a gene corresponding to the genomic variant is EGFR, BRAF, FGFR1, FGFR2, MET, or PDGFRA. In one or more examples, the predetermined protein domain is a functional domain.
  • the sequence read data for the individual is based on a targeted exome sequencing panel. In one or more examples, the sequence read data for the individual is derived from a single biopsy sample. In one or more examples, the sequence read data for the individual is derived from multiple biopsy samples. In one or more examples, the sequence read data for the individual is derived from single cell sequencing. In one or more examples, the sequence read data for the individual is derived from circulating tumor DNA in a liquid biopsy sample.
  • the method can further comprise assigning, using the one or more processors, a therapy for the individual based on the functional status group. In one or more examples, the method can further comprise administering, using the one or more processors, a treatment to the individual based on the functional status group. In one or more examples, the method can further comprise associating, using the one or more processors, the individual with a clinical trial based on the functional status group. In one or more examples, the method can further comprise monitoring, using the one or more processors, a prognosis of the individual based on the functional status group. In one or more examples, the method can further comprise predicting, using the one or more processors, one or more clinical outcomes based on the functional status group.
  • Embodiments of the present disclosure further provide systems for identifying a treatment for an individual based on a genomic variant.
  • the system can comprise: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to perform a method comprising: receiving, at one or more processors, sequence read data associated with a genomic variant in a sample from an individual; determining, using the one or more processors, one or more breakpoints of the genomic variant and a location of the one or more breakpoints based on the sequence read data, the one or more breakpoints associated with a rearrangement event; if the one or more breakpoints of the genomic variant are located outside of a sequence encoding a predetermined protein domain, performing, using the one or more processors: a determination of whether the sequence encoding the predetermined protein domain is in the rearrangement event; assigning, using the one or more processors, the genomic variant to
  • Embodiments of the present disclosure further provide non-transitory computer-readable storage mediums for identifying a treatment for an individual based on a genomic variant.
  • the 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, at one or more processors, sequence read data associated with a genomic variant in a sample from an individual; determine, using the one or more processors, one or more breakpoints of the genomic variant and a location of the one or more breakpoints based on the sequence read data, the one or more breakpoints associated with a rearrangement event; if the one or more breakpoints of the genomic variant are located outside of a sequence encoding a predetermined protein domain, perform, using the one or more processors: a determination of whether the sequence encoding the predetermined protein domain is in the rearrangement event; assign, using the one or more processors, the genomic variant to a
  • Embodiments of the present disclosure further provide methods for diagnosing a disease, the method comprising: diagnosing that a subject has the disease based on a determination of a functional status group of a genomic variant in a sample from the subject, wherein the functional status group of the genomic variant is determined according to any of the methods described above.
  • Embodiments of the present disclosure further provide methods of selecting an anticancer therapy, the method comprising: responsive to determining a functional status group of a genomic variant in a sample from a subject, selecting an anti-cancer therapy for the subject, wherein the functional status group of the genomic variant is determined according to any of the methods described above.
  • Embodiments of the present disclosure further provide methods of treating a cancer in a subject, comprising: responsive to determining a functional status group of a genomic variant in a sample from the subject, administering an effective amount of an anti-cancer therapy to the subject, wherein the functional status group of the genomic variant is determined according to any of the methods described above.
  • Embodiments of the present disclosure further provide methods for monitoring cancer progression or recurrence in a subject, the method comprising: determining a first functional status group of a genomic variant in a first sample obtained from the subject at a first time point according to any of the methods described above; determining a second functional status group of a genomic variant in a second sample obtained from the subject at a second time point; and comparing the first functional status group to the second functional status group, thereby monitoring the cancer progression or recurrence.
  • the second functional status group of the genomic variant for the second sample is determined according to any of the methods described above.
  • the methods for monitoring cancer progression or recurrence can further comprise selecting an anti-cancer therapy for the subject in response to the cancer progression. In one or more examples, the methods for monitoring cancer progression or recurrence can further comprise administering an anti-cancer therapy to the subject in response to the cancer progression. In one or more examples, the methods for monitoring cancer progression or recurrence can further comprise adjusting an anti-cancer therapy for the subject in response to the cancer progression. In one or more examples, the methods for monitoring cancer progression or recurrence can further comprise a dosage of the anti-cancer therapy or selecting a different anti-cancer therapy in response to the cancer progression. In such examples, the method can further comprise administering the adjusted anti-cancer therapy to the subject.
  • the first time point is before the subject has been administered an anti-cancer therapy, and wherein the second time point is after the subject has been administered the anti-cancer therapy.
  • the subject has a cancer, is at risk of having a cancer, is being routine tested for cancer, or is suspected of having a cancer.
  • the cancer is a solid tumor.
  • the cancer is a hematological cancer.
  • the anti-cancer therapy comprises chemotherapy, radiation therapy, immunotherapy, a targeted therapy, or surgery.
  • Embodiments according to the methods described above further comprise determining, identifying, or applying the functional status group of the genomic variant of the sample as a diagnostic value associated with the sample.
  • Embodiments according to the methods described above further comprise generating a genomic profile for the subject based on the determination of the functional status group of the genomic variant.
  • the genomic profile for the subject further comprises results from a comprehensive genomic profiling (CGP) test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof.
  • the genomic profile for the subject further comprises results from a nucleic acid sequencing-based test.
  • methods according to the present disclosure further comprise selecting an anticancer therapy, administering an anti-cancer therapy, or applying an anti-cancer therapy to the subject based on the generated genomic profile.
  • the determination of a functional status group of a genomic variant in a sample is used in making suggested treatment decisions for the subject. In one or more examples of the methods described above the determination of a functional status group of a genomic variant in a sample is used in applying or administering a treatment to the subject.
  • FIG. 1A provides a non-limiting example of an exemplary process for assigning a functional status to a genomic variant of a sample from an individual and identifying a treatment for the individual, according to embodiments of the present disclosure.
  • FIG. IB provides a non-limiting example of an exemplary process for assigning a functional status to a genomic variant of a sample from an individual and identifying a treatment for the individual, according to embodiments of the present disclosure.
  • FIG. 2A provides a non-limiting example of an exemplary pair of reference genes.
  • FIG. 2B provides a non-limiting example of an exemplary transcript of the pair of reference genes.
  • FIG. 2C provides a non-limiting example of an exemplary transcript of the pair of reference genes.
  • FIG. 2D provides a non-limiting example of an exemplary transcript of the pair of reference genes.
  • FIG. 2E provides a non-limiting example of an exemplary transcript of the pair of reference genes.
  • FIG. 3 provides a non-limiting example of a process for assigning a functional status to a genomic variant of a sample from an individual, according to embodiments of the present disclosure.
  • FIG. 4 provides a non-limiting example of a process for assigning a functional status to a genomic variant of a sample from an individual, according to embodiments of the present disclosure.
  • FIG. 5 provides a non-limiting example of a process for assigning a functional status to a genomic variant of a sample from an individual, according to embodiments of the present disclosure.
  • FIG. 6 provides a non-limiting example of a process for identifying a treatment for an individual based on the functional status of a genomic variant of a sample from an individual, according to embodiments of the present disclosure.
  • FIG. 7 depicts an exemplary computing device or system, according to embodiments of the present disclosure.
  • FIG. 8 depicts an exemplary computer system or computer network, according to embodiments of the present disclosure.
  • the functional status group may correspond to an activating variant, an inactivating variant, a functionally unknown variant, or a wildtype.
  • the activating variants may be associated with promoting pathway activations.
  • the inactivating variants may be associated with inhibiting a pathway.
  • the activating variants and the inactivating variants may be determined to be a pathogenic variant or a likely pathogenic variant.
  • Functionally unknown variants or functionally uncharacterized variants may correspond to variants (e.g., fusions or other rearrangements) that are not yet characterized in the literature.
  • functionally unknown variants may be determined to be a pathogenic or likely pathogenic variant.
  • the functionally unknown variant may be determined to have an uncharacterized pathogenic status.
  • the wildtype variants may correspond to a variant that behaves as if there were no alteration.
  • the wildtype variants may be determined to be a benign variant.
  • the functional status assignment of the genomic variant may be used to identify a treatment for the individual providing the sample.
  • the disclosed methods have the potential to improve healthcare outcomes for individuals (e.g., cancer patients) by providing healthcare providers with treatment recommendations based on an accurate identification of a functional status group for both novel and frequently occurring genomic variants.
  • methods and systems can further label the genomic variant and provide the label to a healthcare provider (e.g., in a report).
  • the healthcare provider may further base the treatment recommendation based on the label.
  • embodiments of the present disclosure provide improvements to patient care by providing an automatic process that assigns a genomic variant to a functional status group and labels the genomic variant, where the process can assign and label novel genomic variants that are not well classified in the art.
  • Embodiments of the present disclosure further comprise a method for identifying a treatment based on a genomic variant in a sample from an individual.
  • the method can comprise: receiving, at one or more processors, sequence read data associated with the genomic variant in the sample; determining, using the one or more processors, a breakpoint of the genomic variant and a location of the breakpoint based on the sequence read data, the breakpoint associated with a rearrangement event and further associated with a first gene and a second gene of the sample, the first gene corresponding to the genomic variant; if the breakpoint of the genomic variant is located outside a sequence encoding a predetermined protein domain, performing, using the one or more processors: a first determination of whether the rearrangement event is in-strand; and a second determination of whether the sequence encoding the predetermined protein domain is impacted by the rearrangement event; assigning, using the one or more processors, the genomic variant to a functional status group based on the first determination and the second determination; and identifying, using the one or more
  • the method can further comprise in accordance with a determination that the rearrangement event is not in-strand, assigning the genomic variant to a first functional status group; and in accordance with a determination that the rearrangement event is in-strand and the sequence encoding the predetermined protein domain is impacted by the rearrangement event, assigning the genomic variant to a second functional status group.
  • assigning the genomic variant to the first functional status group comprises associating the genomic variant with a functionally unknown rearrangement group.
  • the method can further comprise comprising labeling the genomic variant as a rearrangement.
  • a functionally unknown rearrangement label is further based on an orientation of the first gene with respect to the second gene.
  • the method can further comprise assigning the genomic variant to the second functional status group comprises associating the genomic variant with a functionally unknown fusion group.
  • the method can further comprise labeling the genomic variant as a reciprocal fusion.
  • the method can further comprise performing, using the one or more processors, a third determination of whether the first gene and the second gene are known partner genes.
  • whether the first gene and the second gene are known partner genes is determined based on a predetermined association between the first gene and the second gene described in scientific literature.
  • determining whether the first gene and the second gene are known partner genes is based on a lookup table associated with the first gene.
  • the method can further comprise in accordance with a determination that the rearrangement event is in-strand, the sequence encoding the predetermined protein domain is impacted by the rearrangement event, and the first gene is a known partner of the second gene, assigning the genomic variant to a third functional status group.
  • assigning the genomic variant to the third functional status group comprises associating the genomic variant with an activating fusion group.
  • the method can further comprise labeling the genomic variant as an activating fusion.
  • determining whether the sequence encoding the predetermined protein domain is impacted by the rearrangement event comprises: determining, whether a functional domain of the first gene is in the sequence read data corresponding to the rearrangement event.
  • the method can further comprise in accordance with a determination that the rearrangement event is in-strand, and further that the sequence encoding the predetermined protein domain is impacted by the rearrangement event, and the first gene is not a known partner gene of the second gene, flagging the genomic variant for manual review and forgoing labeling the genomic variant.
  • the method can further comprise forgoing assigning the genomic variant to the functional status group if the breakpoint of the genomic variant is located outside a predetermined sequence encoding a protein domain.
  • the method can further comprise determining, using the one or more processors, whether the first gene is an oncogene.
  • the first gene is ABL1, ALK, BRAF, FGFR1, FGFR2, FGFR3, MET, NTRK1, NTRK2, NTRK3, PDGFRA, PDGFRB, ROS1, RET, or RAFI.
  • the predetermined protein domain is a functional domain. In one or more examples, the predetermined protein domain is a kinase domain.
  • the disclosed methods and systems can improve patient outcomes, based on, for example, targeted treatment recommendations based on the functional status of one or more genomic variants identified in a patient’ s sample.
  • embodiments of the present disclosure provide improvements to patient care by providing an automatic process for assigning a genomic variant to a functional status group and labeling the genomic variant that can handle novel genomic variants that are not well classified in the art.
  • a healthcare provider may be able to base treatment decisions, make predictions regarding a patient’s response to treatment, make predictions regarding a patient’s prognosis, and the like based on the functional status and/or the label assigned the genomic variant. Definitions
  • “About” and “approximately” shall generally mean an acceptable degree of error for the quantity measured given the nature or precision of the measurements. Exemplary degrees of error are within 20 percent (%), typically, within 10%, and more typically, within 5% of a given value or range of values.
  • the terms “comprising” (and any form or variant of comprising, such as “comprise” and “comprises”), “having” (and any form or variant of having, such as “have” and “has”), “including” (and any form or variant of including, such as “includes” and “include”), or “containing” (and any form or variant of containing, such as “contains” and “contain”), are inclusive or open-ended and do not exclude additional, un-recited additives, components, integers, elements, or method steps.
  • the terms “individual,” “patient,” or “subject” are used interchangeably and refer to any single animal, e.g., a mammal (including such non-human animals as, for example, dogs, cats, horses, rabbits, zoo animals, cows, pigs, sheep, and non-human primates) for which treatment is desired.
  • a mammal including such non-human animals as, for example, dogs, cats, horses, rabbits, zoo animals, cows, pigs, sheep, and non-human primates
  • the individual, patient, or subject herein is a human.
  • cancer and “tumor” are used interchangeably herein. These terms refer to the presence of cells possessing characteristics typical of cancer-causing cells, such as uncontrolled proliferation, immortality, metastatic potential, rapid growth and proliferation rate, and certain characteristic morphological features. Cancer cells are often in the form of a tumor, but such cells can exist alone within an animal, or can be a non-tumorigenic cancer cell, such as a leukemia cell. These terms include a solid tumor, a soft tissue tumor, or a metastatic lesion. As used herein, the term “cancer” includes premalignant, as well as malignant cancers.
  • treatment refers to clinical intervention (e.g., administration of an anti-cancer agent or anticancer therapy) in an attempt to alter the natural course of the individual being treated, and can be performed either for prophylaxis or during the course of clinical pathology.
  • Desirable effects of treatment include, but are not limited to, preventing occurrence or recurrence of disease, alleviation of symptoms, diminishment of any direct or indirect pathological consequences of the disease, preventing metastasis, decreasing the rate of disease progression, amelioration or palliation of the disease state, and remission or improved prognosis.
  • genomic interval refers to a portion of a genomic sequence.
  • subject interval refers to a subgenomic interval or an expressed subgenomic interval (e.g., the transcribed sequence of a subgenomic interval).
  • variant sequence As used herein, the terms “variant sequence” or “variant” are used interchangeably and refer to a modified nucleic acid sequence relative to a corresponding “normal” or “wild-type” sequence. In some instances, a variant sequence may be a “short variant sequence” (or “short variant”), i.e., a variant sequence of less than about 50 base pairs in length.
  • allele frequency and “allele fraction” are used interchangeably herein and refer to the fraction of sequence reads corresponding to a particular allele relative to the total number of sequence reads for a genomic locus.
  • variant allele frequency and “variant allele fraction” are used interchangeably herein and refer to the fraction of sequence reads corresponding to a particular variant allele relative to the total number of sequence reads for a genomic locus.
  • pathogenic variant may refer to a variant that has a known and documented relevance to disease, e.g., recognized in the scientific literature as being an oncogenic driver of disease.
  • the term “likely pathogenic variant” may refer to a variant that is potentially relevant to pathogenicity, e.g., as supported by scientific literature, research, and or the genomic data from the one or more samples.
  • VUS variable of unknown significance
  • functionally unknown variant may refer to a variant that does not have a well-known or documented association with disease, e.g., there may not be sufficient evidence to identify the variant as a known pathogenic or likely pathogenic variant.
  • the term “benign variant” may refer to a variant that is determined to not be relevant to disease.
  • activating variant may refer to a variant that is associated with promoting pathway activations.
  • activating variant may refer to a variant that is associated with inhibiting a pathway.
  • the disclosed methods for assigning a genomic variant from a sample to a functional status group may be used to identify a treatment for the individual providing the sample.
  • the disclosed methods have the potential to improve healthcare outcomes for individuals (e.g., cancer patients) by providing healthcare providers with treatment recommendations based on an accurate identification of a functional status group for both novel and frequently occurring genomic variants.
  • methods and systems can further label the genomic variant and provide the label (e.g., a structural label) to a healthcare provider (e.g., in a report).
  • the healthcare provider may further base the treatment recommendation based on the label.
  • embodiments of the present disclosure provide improvements to patient care by providing an automatic process that assigns a genomic variant to a functional status group and labels the genomic variant, where the process can assign and label novel genomic variants that are not well classified in the art.
  • FIG. 1A provides a non-limiting example of a process 100A for assigning a genomic variant to a functional status group and identifying a treatment based on the assignment.
  • Process 100A can be performed, for example, using one or more electronic devices implementing a software platform.
  • process 100A is performed using a client-server system, and the blocks of process 100A are divided up in any manner between the server and a client device.
  • the blocks of process 100A are divided up between the server and multiple client devices.
  • process 100A is not so limited.
  • process 100A 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 100A.
  • process 100A may be performed for rearrangement events that are determined to be intergenic and affect more than one gene.
  • the system can receive sequence read data associated with a genomic variant in a sample from an individual.
  • the sequence read data may be derived from single region sequencing (e.g., sequencing of a single tissue biopsy sample collected from the tumor of the individual).
  • the genomic data comprising sequence read data may be derived from multi-region sequencing (e.g., sequencing of multiple tissue biopsy samples collected from the tumor of the individual).
  • the genomic data comprising sequence read data may be derived from single cell sequencing data as opposed to bulk tumor sequencing.
  • the genomic data comprising sequence read data may be derived from sequencing the circulating tumor DNA in a liquid biopsy sample.
  • the genomic data comprising sequence read data may be derived from targeted sequencing, e.g., targeted exome sequencing.
  • the genomic data comprising sequence read data may be derived from, e.g., whole genome or whole exome sequencing, as opposed to targeted exome sequencing to increase the number of genomic features (e.g., the number of short variants) detected.
  • the sequence read data may be received by the system as a BAM file.
  • the sequence read data may be indicative of a presence or absence of one or more short variants (SVs) in a patient sample.
  • the sequence read data may also be indicative of the presence or absence of genomic events, such as copy number alterations, rearrangements, insertions, deletions, fusions, chromosomal aneuploidy, whole genome doubling, Catalogue Of Somatic Mutations In Cancer (COSMIC) mutational signatures, or any combination thereof.
  • the sequence read data can be indicative of features associated with a genomic event such as a location of the genomic event, whether the genomic event is in- strand, an orientation of the genomic event, a directionality of the genomic event, genes involved in the genomic event, and the like.
  • the system can determine a breakpoint of the genomic variant and a location of the breakpoint based on the sequence read data, the breakpoint associated with a mutation event (e.g., a rearrangement event).
  • a mutation event e.g., a rearrangement event
  • process 100 may apply to other mutation events, e.g., an insertion event, a deletion event, and the like.
  • the rearrangement event may be associated with a primary gene and a secondary gene of the sample.
  • the primary gene may correspond to a gene associated with the genomic variant.
  • the system may determine whether the breakpoint of the genomic variant is located outside of a predetermined sequence encoding a protein domain. For example, if the breakpoint is located within the predetermined sequence encoding the protein domain (e.g., the functional or pathologically activating domain), then the system may determine that genomic variant likely does not have a clinical effect on the patient’s health because the protein domain is disrupted.
  • the system may determine that the disruption of the protein domain deactivates the pathogenic functionality of the protein. If the breakpoint is located outside the predetermined sequence encoding the protein domain, then the system may determine that the genomic variant may have a clinical effect on the patient’s health because the protein’s functional (e.g., pathogenic) domain is intact.
  • the system may determine the genomic variant does have a clinical effect on the patient’s health. For example, for tumor suppressor genes, having the breakpoint located within the protein will inactivate the protein and lead to a pathogenic functional status due to the break in the protein’s functional domain.
  • the location of the breakpoint of the genomic variant may be obtained via a computational pipeline for processing nucleic acid sequencing data.
  • the location of the breakpoint of the genomic variant may be determined by the system based on information provided by the computational pipeline.
  • the computational pipeline may indicate the location of the breakpoint in the genomic data for the sample based on an identification of the position of the first or last intact amino acid codon.
  • the position of the breakpoint may correspond to the position at which the sequence read begins (e.g., for a 5’ disruption) and/or ends (e.g., for a 3’ disruption).
  • the location indicated by the pipeline may correspond to an integer value.
  • the location indicated by the pipeline may be compared to a predetermined threshold to determine whether the breakpoint is located inside or outside the sequence that encodes for a predetermined protein domain. While this example is described with respect to data obtained via a computational pipeline, a skilled artisan will understand that the location of the breakpoint of the genomic variant may be obtained using other methods known in the art. [0105] If the system determines that the breakpoint of the genomic variant is located outside a sequence that encodes a predetermined protein domain perform, the system can move to block 106 of FIG. 1A. At block 106, the system can perform a plurality of determinations regarding the genomic variant.
  • the determinations may be based on data obtained via the computational pipeline for analyzing sequence read data.
  • the system may flag the genomic variant for manual processing.
  • the system can determine whether the rearrangement event is in-strand. For example, in an in-strand event the sequence reads of the genes will be oriented in a correct direction, while sequence reads of an out-of-strand event will be oriented in an incorrect direction.
  • FIG. 2A illustrates an exemplary pair of reference genes comprising gene 1 and gene 2.
  • FIG. 2B illustrates an exemplary transcript (e.g., an amplified copy of the genomic region corresponding to the pair of reference genes) where the primary gene (gene 2) and the secondary gene (gene 1) correctly map to the reference pair of genes.
  • the transcript comprises gene 1, gene 2, and breakpoint 202.
  • the position of the genes are in the canonical orientation, such that the genes are in the correct relative location, e.g., gene 1 comes before gene 2.
  • the genes are instrand, such that the directionality of the sequence reads overlapping the pair of genes is correct.
  • FIG. 2C illustrates an exemplary transcript with a non-canonical (e.g., reciprocal) orientation, in-strand event, where the positions of the two genes are incorrect such that gene 2 comes before gene 1; but the orientation of the sequence reads overlapping the pair of genes is correct (e.g., instrand).
  • a reciprocal event may refer to a non-canonical event.
  • FIG. 2D illustrates an exemplary transcript with a canonical orientation, out-of-strand event, where the relative locations of the two genes are correct such that gene 1 comes before gene 2; but the directionality of the sequence reads of gene 2 are incorrect (e.g., out-of-strand).
  • 2E illustrates another exemplary transcript with a canonical orientation, out-of-strand event, where the relative positions of the two genes are correct such that gene 1 comes before gene 2, but the directionality of the sequence reads of gene 1 are incorrect (e.g., out-of-strand).
  • the determination of whether the rearrangement event is instrand or out-of-strand may be obtained via the computational pipeline for analyzing sequence read data.
  • the computational pipeline may indicate whether the rearrangement event is in-strand or out-of-strand.
  • a skilled artisan could use other methods known in the art to determine whether the rearrangement event is in-strand or out-of-strand, such as manual inspection of the directionality of the sequence reads.
  • the system can determine whether the sequence encoding the predetermined protein domain is impacted by the rearrangement event, e.g., based on the sequence read data corresponding to the rearrangement event.
  • the sequence read data corresponding to the rearrangement event may comprise the sequence encoding the predetermined protein domain (e.g., such that the sequence encoding the predetermined protein domain is in the sequence read data corresponding to the rearrangement event).
  • the system may determine whether the sequence encoding the functional domain of the protein is intact and retained in the sequence read data following the rearrangement event.
  • determining whether the sequence encoding the predetermined protein domain is impacted by the rearrangement event may be associated with determining whether the rearrangement event is in-frame, as discussed above with respect to, for example, FIG. 2B and FIG. 2C.
  • FIG. 2B illustrates canonically oriented example where the sequence read data corresponding to the rearrangement event comprises the sequence encoding the predetermined protein domain
  • FIG. 2C illustrates a non-canonically oriented example where the sequence read data corresponding to the rearrangement event may not comprise the sequence encoding the predetermined protein domain.
  • determining whether an event is canonically or non-canonically oriented may allow the system to determine if the functional domain of the primary gene is retained in the fusion product of the rearrangement event.
  • the functional element of the protein domain e.g., kinase domain
  • the system may need to determine whether the kinase domain is retained in the rearrangement event based on the location of the breakpoint.
  • the system may determine whether the protein domain is disrupted based on a location of the breakpoint with respect to the sequence encoding the protein domain.
  • a determination of whether the sequence encoding the predetermined protein domain is included in the sequence read data corresponding to the rearrangement event may be obtained via the computational pipeline for analyzing sequence read data.
  • the computational pipeline may indicate whether the sequence encoding the protein domain is retained as an intact sequence in the rearrangement event. While this example relies on data obtained via the computational pipeline, a skilled artisan could use other methods known in the art to determine whether the protein domain is retained in the rearrangement event.
  • additional determinations may be associated with block 106.
  • the system may further determine whether the primary gene and the secondary gene associated with the rearrangement event are known partner genes in the fusion event (e.g., block 112B in FIG. IB).
  • partner genes may refer to genes that drive expression of the target gene, encode proteins that facilitate oligomerization, recruit additional cofactors, and/or separate or disrupt the functional domain in the primary gene.
  • the partner genes may not be identified in literature to be considered functional/applicable to the fusion event.
  • the known partner genes may be determined based on partner genes identified in research and scientific literature.
  • known partner genes of ROS 1 include, and is not limited to CD74, CLIP1, EZR, GOPC, LRIG3, MY05A, PPFIBP1, PWWP2A, SLC34A2, SDC4, SHTN1 (KIAA1598), TPM3, and ZCCHC8.
  • the partner genes may be determined based on a database or look-up table comprising the partner genes identified in research and scientific literature. For example, the system may access the database to determine whether the primary gene and the secondary gene are known partner genes.
  • the system may further determine whether the secondary gene is a coding gene (e.g., block 114B in FIG. IB). In one or more examples, determining whether a gene is a coding gene may be based on scientific literature. The number and types of determinations associated with block 106 is not intended to limit the scope of this disclosure and more or less determinations may be made by the system.
  • the system can assign the genomic variant to a functional status group based on blocks 108 and 110.
  • the system may assign the genomic variant to a functionally unknown rearrangement functional status group.
  • the functional status group may be associated with a first type of functionally unknown rearrangements.
  • the first type of functionally unknown rearrangements may be associated with a particular genomic event (e.g., insertion, deletion, etc.).
  • the functionally unknown rearrangement group may be associated with one or more therapies and/or clinical trials.
  • the system may assign the genomic variant to a functionally unknown fusion functional status group associated with non-canonical fusions.
  • the system may assign the genomic variant to an activating fusion functional status group associated with canonical fusions.
  • the system may not assign the genomic variant to a functional status group. Instead, the system may flag the genomic alteration for manual processing, whereby an individual may review the genomic alteration and determine the functional status group assignment for the genomic alteration. In one or more examples, the system may assign the genomic alteration to a functionally unknown fusion group because the primary and secondary genes are not known partner genes.
  • the system may not assign the genomic variant to a functional status group. Instead, the system may flag the genomic alteration for manual processing, whereby an individual may review the genomic alteration and determine the functional status group assignment for the genomic alteration.
  • a genomic variant may be determined to be functionally inactive and/or not pathogenic, e.g., such as for oncogenes.
  • the genomic variant may be determined to be functionally active and/or pathogenic, e.g., such as for tumor suppressor genes.
  • the system can identify a treatment based on the assigned functional status group of the genomic variant.
  • certain alterations may be associated with specific approved therapies.
  • a activating or canonical fusion e.g., an in-strand fusion where the rearrangement event includes the sequence encoding the predetermined protein domain, of ROS 1 and CD74 as a 5’ partner may be associated with ROS1 inhibitors such as entrectinib, lorlatinib, rizotinib, ceritinib, cabozantinib, and brigatinib.
  • a non-canonical fusion e.g., an in-strand but reciprocal fusion where the rearrangement event involving ROS 1 does not include the ROS 1 sequence encoding the predetermined protein domain and an unknown 3’ partner
  • ROS 1 inhibitors such as entrectinib, lorlatinib, crizotinib, ceritinib, cabozantinib, and brigatinib.
  • an out-of-strand fusion where the rearrangement event includes the sequence encoding the predetermined protein domain of ROS 1 and the known partner CD74 may not be associated with ROS1 inhibitors. While these examples are discussed with respect to specific genes, a skilled artisan will understand that other genes and associated therapies may be used without departing from the scope of this disclosure.
  • the system can generate a label for the genomic variant based on blocks 104 and 106.
  • the system can label the genomic variant as a rearrangement.
  • the system may label the genomic variant as secondary gene-primary gene rearrangement.
  • the primary gene is ROS1 and the secondary gene is KCNIP3
  • the out-of-strand rearrangement event that includes the sequence encoding the predetermined protein domain may be labeled as KCNIP3-ROS1 rearrangement.
  • the variant may be labeled as FLJ40288-ROS1 rearrangement.
  • the system may label the genomic variant as primary gene- secondary gene rearrangement.
  • the primary gene is ROS 1 and the secondary gene is LHFPL4
  • an out-of-strand rearrangement event that does not include the sequence encoding the predetermined protein domain may be labeled as ROS1- LHFPL4 rearrangement. While these examples are discussed with respect to specific genes, a skilled artisan will understand that other genes may be used without departing from the scope of this disclosure.
  • the system can label the genomic variant as a non-canonical fusion.
  • the system may label the genomic variant as primary gene- secondary gene non-canonical fusion.
  • the primary gene is ROS1 and the secondary gene is TSPAN3, an in-strand rearrangement event that does not include the sequence encoding the predetermined protein domain, the rearrangement may be labeled as ROS1-TSPAN3 non-canonical fusion.
  • the genomic variant could alternatively be labeled as a reciprocal fusion.
  • the system may label the genomic variant as a canonical fusion.
  • the primary gene is ROS1 and the secondary gene is CD74
  • an in-strand rearrangement event that includes the sequence encoding the predetermined protein domain with a known secondary gene may be labeled as CD74-ROS1 fusion or CD74-ROS1 canonical fusion.
  • the genomic variant could alternatively be labeled as an activating fusion.
  • the system may label the genomic variant as a functionally unknown fusion. In some embodiments, the system may label forgo labeling the variant. Instead, the system may flag the genomic alteration for manual processing, whereby an individual may review the genomic alteration and determine an appropriate label.
  • the system may not label the genomic variant. Instead, the system may flag the genomic alteration for manual processing, whereby an individual may review the genomic alteration and determine an appropriate functional status and/or label, as necessary.
  • FIG. IB provides a non-limiting example of a process 100B for assigning a genomic variant to a functional status group.
  • Process 100B can be performed, for example, using one or more electronic devices implementing a software platform.
  • process 100B is performed using a client-server system, and the blocks of process 100B are divided up in any manner between the server and a client device.
  • the blocks of process 100B are divided up between the server and multiple client devices.
  • portions of process 100B are described herein as being performed by particular devices of a client-server system, it will be appreciated that process 100B is not so limited.
  • process 100B is performed using only a client device or only multiple client devices.
  • process 100B some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the process 100B. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting. In one or more examples, process 100B may be performed for rearrangement events that are determined to be intergenic and affect more than one gene. [0126]
  • the system can receive sequence read data associated with a genomic variant in a sample from an individual. In one or more examples block 102 of FIG. IB can correspond to block 102 of FIG. 1A.
  • the system can determine a breakpoint of the genomic variant and a location of the breakpoint based on the sequence read data, the breakpoint associated with a rearrangement event.
  • the breakpoint may further be associated with a primary gene and a secondary gene of the sample.
  • block 104 of FIG. IB can correspond to block 104 of FIG. 1A.
  • the system can move to block 106 of FIG. IB.
  • the system can perform a plurality of determinations regarding the genomic variant.
  • block 106 of FIG. IB can correspond to block 106 of FIG. 1A.
  • the system can determine whether the rearrangement event is instrand.
  • block 108 of FIG. IB can correspond to block 108 of FIG. 1A.
  • the system can determine whether the sequence encoding the predetermined protein domain is impacted by the rearrangement event, e.g., based on the sequence read data corresponding to the rearrangement event.
  • block 110 of FIG. IB can correspond to block 110 of FIG. 1A.
  • the system may further determine whether the primary gene and the secondary gene associated with the rearrangement event are known partner genes.
  • the known partner genes may be determined based on partner genes identified in research and scientific literature.
  • the partner genes may be determined based on a database or look-up table comprising the partner genes identified in research and scientific literature. For example, the system may access the database to determine whether the primary gene and the secondary gene are known partner genes. In one or more examples, the partner genes may not be specifically identified in scientific literature.
  • the system may determine whether the secondary gene is a coding gene. In one or more examples, if the secondary gene is a non-coding gene, then the secondary gene may not encode a protein. In such examples, the secondary gene may be determined to not encode a functional rearrangement event, e.g., the system may determine that the rearrangement event has an unknown pathogenic significance.
  • the number and types of determinations associated with block 106 is not intended to limit the scope of this disclosure and more or less determinations may be made by the system with respect to the genomic variant.
  • the system can assign the genomic variant to a functional status group based on blocks 108, 110, 112B, and 114B in FIG. IB, blocks 430, 440, 442, 444, and 446 of FIG. 4, and blocks 530, 540, 542, 544, and 546 of FIG. 5.
  • block 116 of FIG. IB can correspond to block 116 of FIG. 1A.
  • the system can identify a treatment based on the assigned functional status group of the genomic variant.
  • block 118 of FIG. IB can correspond to block 118 of FIG. 1A.
  • the system can generate a label for the genomic variant based on blocks 104 and 106, as discussed above with respect to FIG. 1A, as discussed below with respect to FIG. 4 and FIG. 5.
  • the system may further perform process 300 shown in FIG. 3.
  • Process 300 may be performed prior to performing process 100A and/or 100B.
  • process 300 may be performed to determine whether a genomic variant should be assigned to a functional group based on the blocks described with respect to process 100A and/or 100B.
  • Process 300 can be performed, for example, using one or more electronic devices implementing a software platform.
  • process 300 is performed using a client-server system, and the blocks of process 300 are divided up in any manner between the server and a client device. In other examples, the blocks of process 300 are divided up between the server and multiple client devices.
  • process 300 is not so limited. In other examples, process 300 is performed using only a client device or only multiple client devices. In process 300, some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the process 300. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting. [0133] At block 302 of FIG. 3, the system can determine whether the mutation event is.
  • the mutation event may be intragenic if it affects a single gene (e.g., both breakpoints are within the same transcript). In one or more examples intragenic events may be associated with deletions, duplications, and inversions. As shown in the figure, the intragenic event may affect gene A. In one or more examples, the system may determine whether the mutation event is intragenic based on information received from the computational pipeline (e.g., both breakpoints are within the same gene or transcript). If the system determines that the event is intragenic, the system may proceed to block 304. At block 304 of FIG. 3, the system may determine whether the mutation event affects a baited oncogene.
  • the system may determine whether the gene variant associated with the mutation event corresponds to a gene that was baited in test performed on the sample from the individual. If the system determines that the mutation event is associated with a baited oncogene from the individual’s sample, the system may proceed to block 306. At block 306 of FIG. 3, the system may perform an oncogene intragenic functional status group assignment process. In one or more examples, block 308 may correspond to process 600 described with respect to FIG. 6.
  • the system may proceed to block 308.
  • the system may determine whether the mutation event affects more than one gene. For example, a first breakpoint may be within a first gene and a second breakpoint may be in a second, different gene or an intergenic space. If the system determines that the event is affects more than one gene, the system may proceed to block 310.
  • the system may determine whether the mutation event affects a baited oncogene, similar to the determination at block 304. If the system determines that the mutation event is associated with a baited oncogene from an individual’s sample, the system may proceed to block 312.
  • block 312 may correspond to process 100A and/or 100B described with respect to FIG. 1A and FIG. IB, respectively. In one or more examples, block 312 may correspond to processes 400 described with respect to FIG. 4 and/or 500 described with respect to and FIG. 5. [0135] FIG. 4 and FIG. 5 illustrate process 400 and 500, respectively. For example, processes 400 and 500 may be performed to determine whether a genomic variant should be assigned to a functional group based on the blocks described with respect to process 400 and/or 500.
  • Processes 400 and 500 can be performed, for example, using one or more electronic devices implementing a software platform.
  • processes 400 and 500 are performed using a client-server system, and the blocks of processes 400 and 500 are divided up in any manner between the server and a client device.
  • the blocks of processes 400 and 500 are divided up between the server and multiple client devices.
  • portions of processes 400 and 500 are described herein as being performed by particular devices of a clientserver system, it will be appreciated that processes 400 and 500 are not so limited.
  • processes 400 and 500 are 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 processes 400 and 500. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.
  • the system can determine whether the rearrangement event impacts a genomic variant. In one or more examples, this determination may be based on sequence read data from a sample from an individual. The sequence read data may be obtained as described above with respect to block 104.
  • the system may receive an indication of whether the rearrangement event impacts a genomic variant via a computational pipeline for analyzing sequence read data.
  • an output of the computational pipeline may indicate one or more alterations detected in a sample and whether the alteration corresponds to a rearrangement event that is intergenic.
  • the sequence read data may indicate whether the rearrangement event impacts a particular genomic variant (e.g., a primary gene) and a secondary gene.
  • the genomic variant may correspond to intergenic genomic variants, including but not limited to variants in ABL1, ALK, BRAF, FGFR1, FGFR2, FGFR3, MET, NTRK1, NTRK2, NTRK3, PDGFRA, PDGFRB, ROS1, RET, RAFI, and the like.
  • the system can determine whether the breakpoint of the genomic variant is located outside of a sequence encoding a predetermined protein domain.
  • the predetermined protein domain may be associated with a gene within which the genomic variant occurs. For example, ROS1 encodes a kinase domain.
  • the rearrangement may be determined to not be oncogenic if the breakpoint is in the protein domain, e.g., because the functional domain of the protein is disabled.
  • the system may proceed to block 430, where the system may not label the rearrangement nor assign the rearrangement to a functional status group. In such examples, the system may flag the alteration for manual review.
  • the location of the breakpoint of the genomic variant may be obtained via a computational pipeline for analyzing sequence read data.
  • the location of the breakpoint of the genomic variant may be determined by the system based on information provided by the computational pipeline.
  • the computational pipeline may indicate the location of the breakpoint in the genomic data for the sample.
  • the location indicated by the pipeline may correspond to an integer value, a floating-point value, or any other suitable value type.
  • the location indicated by the pipeline may be compared to a predetermined threshold (e.g., a genomic location threshold) to determine whether the breakpoint is located inside or outside the sequence encoding a predetermined protein domain. While this example is described with respect to data obtained via a computational pipeline, a skilled artisan will understand that the location of the breakpoint of the genomic variant may be obtained using other methods known in the art.
  • the system may proceed to blocks 406 and 408.
  • the system can determine whether the secondary gene is a coding gene.
  • block 406 may correspond to block 114B described with respect to FIG. IB.
  • the system can determine if the rearrangement event is in-strand. In one or more examples, block 406 may correspond to block 108 described with respect to FIG. 1A. If the system determines that the partner gene is not a coding gene and/or that the rearrangement event is not in-strand, then the system may proceed to block 416.
  • the system may determine whether the sequence read data corresponding to the rearrangement event comprises the sequence encoding the predetermined protein domain.
  • block 416 may correspond to block 110 described with respect to FIG. 1A.
  • the system may determine that the sequence read data corresponding to the rearrangement event comprises the sequence encoding the predetermined protein domain and the system may label the rearrangement at block 434.
  • the naming convention at block 434 may label the genomic variant as “secondary gene-primary gene rearrangement.” For example, if the primary gene is ROS1 and the secondary gene is FLJ40288 (which is a non-coding partner), the variant may be labeled as FLJ40288-ROS1 rearrangement. As another example, where the rearrangement event is an out-of- strand event, if the primary gene is ROS1 and the secondary gene is KCNIP3, the variant may be labeled as KCNIP3-ROS1 rearrangement.
  • the labeled genomic variant may be provided to a healthcare professional (e.g., in a report) who can use the label to inform treatment decisions.
  • a healthcare professional e.g., in a report
  • the healthcare professional may understand based on the label that the FLJ40288-ROS1 rearrangement includes a rearrangement event with a non-coding partner gene, suggesting that the detected event lacks a partner that encodes a protein product with an oligomerization domain and that the detected event may not be an oncogenic driver in the tumor.
  • the healthcare professional may understand based on the label that the ROS1-CD74 non-canonical fusion corresponds to a reciprocal event where the rearrangement event comprises the 5’ portion of ROS1 and 3’ portion of the known partner CD74, and that while the detected event may not be oncogenic, it may indicate that additional or orthogonal testing could detect a canonical ROS 1 fusion.
  • the healthcare professional may understand based on the label that the KCNIP3-ROS1 rearrangement corresponds to an out-of-strand event where the rearrangement event comprises the ROS 1 kinase domain, such that the detected event may not be oncogenic and additional testing may be warranted depending on the clinical context.
  • the system may label the rearrangement at block 432.
  • the naming convention at block 432 may label the genomic variant as “primary gene- secondary gene rearrangement.” For example, if the primary gene is ROS1 and the secondary gene is LHFPL4, for an out-of-strand rearrangement event that does not include the sequence encoding the predetermined protein domain, the system may label the rearrangement as ROS1-LHFPL4 rearrangement.
  • the labeled genomic variant may be provided to a healthcare professional (e.g., in a report) who can use the label to inform treatment decisions.
  • the healthcare professional may understand based on the label that the genomic variant comprises an out-of-strand rearrangement, affecting ROS1 and LHFPL4 genes, where the rearrangement event does not comprise the protein domain.
  • the label suggests that additional testing to identify the presence of an oncogenic fusion may be warranted, depending on the clinical context.
  • the system can proceed to block 440.
  • the system can assign a functional status group to the rearrangement.
  • the system may assign the genomic variant to a functionally unknown rearrangement functional status group, indicating that the functional implications of the genomic variant is not well-defined or well-known based on research and/or literature.
  • the functionally unknown rearrangement may be determined to be a potentially pathogenic variant.
  • the system may use the functional status to identify specific content, therapies, and clinical trials to be shared with a healthcare professional (e.g., via a report), and the healthcare professional can use the content provided in the report to inform treatment decisions.
  • the system can proceed to block 410.
  • the system may determine whether the sequence read data corresponding to the rearrangement event comprises the sequence encoding the predetermined protein domain.
  • block 410 may correspond to block 110 described with respect to FIG. 1A.
  • the system may label the genomic variant at block 436.
  • the naming convention at block 436 may label the genomic variant as “primary gene- secondary gene non-canonical fusion.” For example, if the primary gene is ROS1 and the secondary gene is TSPAN3, an in-strand rearrangement that does not include the sequence encoding the predetermined protein domain may be labeled as ROS1- TSPAN3 non-canonical fusion.
  • the labeled genomic variant may be provided to a healthcare professional (e.g., in a report) who can use the label to inform treatment decisions or determine if additional testing is necessary to detect if an oncogenic ROS1 fusion is present in the sample.
  • the system may label the genomic variant as a reciprocal fusion or the like.
  • any suitable label may be used that is indicative that the rearrangement is a non- canonical fusion.
  • “non-canonical fusion” and “reciprocal fusion” may be used interchangeably.
  • the system can proceed to block 442.
  • the system can assign a functional status group to the rearrangement.
  • the system may assign the genomic variant to a functionally unknown fusion (e.g., non-canonical fusion) functional status group.
  • a functionally unknown fusion may refer to an in-strand, non-canonical (reciprocal) orientation, that lacks a key functional domain (e.g., kinase domain), with an unknown partner lacking an oligomerization domain.
  • the functionally unknown fusion may be identified as potentially pathogenic.
  • the system may use the functional status to identify specific content, therapies, and clinical trials associated with the non-canonical fusion to a healthcare professional (e.g., via a report), and the healthcare professional can use the content provided in the report to inform treatment decisions.
  • the system may proceed to block 412.
  • the system may determine whether the secondary gene is a known partner gene.
  • the known partner genes may be determined based on partner genes identified in research and scientific literature.
  • the partner genes may be determined based on a database or look-up table comprising the partner genes identified in research and scientific literature. For example, at block 414, the system may access a database or look up table to determine whether the primary gene and the secondary gene are known partner genes. If the system determines that the secondary gene is not a known partner gene, the system may proceed to block 444.
  • the system may forgo labeling or assigning the genomic variant to a functional status group.
  • the system may instead flag the genomic variant for manual processing.
  • the system may assign the variant to a functionally unknown functional status group because the primary and secondary genes are not known partner genes.
  • the system may proceed to block 438.
  • the system may label the genomic variant accordingly.
  • the naming convention at block 438 may label the genomic variant as “secondary gene-primary gene canonical fusion.” For example, if the primary gene is ROS1 and the secondary gene is CD74, then the system would label the genomic variant as CD74- ROS1 canonical fusion.
  • the labeled genomic variant may be provided to a healthcare professional (e.g., in a report) who can use the label to inform treatment decisions.
  • a healthcare professional e.g., in a report
  • any suitable label may be used that communicates the rearrangement is a canonical fusion.
  • the system can proceed to block 446.
  • the system can assign a functional status group to the rearrangement.
  • the system may assign the genomic variant to an activating fusion (e.g., associated with a canonical fusion) functional status group.
  • an activating fusion or canonical fusion may refer to an in-strand, canonical orientation, inclusion of the key functional domain (e.g., kinase domain), known partner or partner capable of oligomerization.
  • the activating fusion functional status group may be determined to be pathogenic or likely pathogenic.
  • the system may use the functional status to provide specific content, therapies, and clinical trials associated with the canonical fusion to a healthcare professional (e.g., via a report), and the healthcare professional can use the content provided in the report to inform treatment decisions.
  • process 400 can be used to assign various genomic variants to a functional group.
  • process 400 can be used to assign variants in ALK, MET, NTRK1, NTRK2, NTRK3, PDGFRA, PDGFRB, ROS1, RET, RAFI, and others to a functional group.
  • FIG. 5 provides an example of process 500 for assigning a genomic variant to a functional group with respect to a ROS1 genomic variant, e.g., where ROS1 corresponds to the primary gene.
  • one or more blocks of process 500 may correspond to one or more like-numbered blocks described above with respect to FIG. 4.
  • the system can determine whether the rearrangement event impacts ROSE In one or more examples, this determination may be based on sequence read data from a sample from an individual. In one or more examples, the system may receive an indication from a computational pipeline regarding whether the sample includes a genomic rearrangement event that impacts ROSE
  • the system can determine whether the breakpoint of the ROS 1 rearrangement event is located outside of a sequence encoding a kinase domain, where the kinase domain is the functional domain of ROSE If the breakpoint is within the sequence encoding the kinase domain, the breakpoint can effectively disable the functional element of the ROS 1 kinase. Accordingly, the ROS1 rearrangement may be determined to not be oncogenic, e.g., because the kinase domain is broken.
  • the system may proceed to block 530, where the system may not label the rearrangement nor assign the rearrangement to a functional status group. In such examples, the system may flag the alteration for manual review.
  • the system may proceed to blocks 506 and 508.
  • the system can determine whether the secondary gene is a coding gene.
  • block 506 may correspond to block 114B described with respect to FIG. IB.
  • the system can determine if the ROS 1 rearrangement event is in-strand.
  • block 508 may correspond to block 108 described with respect to FIG. 1A. If the system determines that the partner gene is not a coding gene and/or that the ROS 1 rearrangement event is not in-strand, then the system may proceed to block 516.
  • the system may determine whether the sequence read data corresponding to the ROS 1 rearrangement event comprises the sequence encoding the kinase domain.
  • block 516 may be correspond to block 110 described with respect to FIG. 1A. If the system determines that the ROS 1 rearrangement event includes the sequence encoding the predetermined protein domain, then the system may label the ROS1 rearrangement at block 534. In one or more examples, the naming convention at block 534 may label the ROS1 variant as “secondary gene-ROSl rearrangement.” For example, if the secondary gene is FLJ40288 (which is a non-coding partner), the variant may be labeled as FLJ40288-ROS1 rearrangement.
  • the variant may be labeled as KCNIP3-ROS1 rearrangement.
  • the ROS1 variant may be provided to a healthcare professional (e.g., in a report) who can use the label to inform treatment decisions.
  • the system may label the ROS 1 rearrangement at block 532.
  • the naming convention at block 532 may label the ROS 1 variant as “ROS 1- secondary gene rearrangement.”
  • the secondary gene is LHFPL4
  • an out-of-strand rearrangement that does not include the sequence encoding the predetermined protein domain may be labeled as ROS1-LHFPL4 rearrangement.
  • the labeled genomic variant may be provided to a healthcare professional (e.g., in a report) who can use the label to inform treatment decisions.
  • the healthcare professional may understand based on the label that the genomic variant does not include the sequence encoding the predetermined protein domain affecting the ROS 1 and the secondary gene.
  • the label suggests that additional testing may be warranted, depending on the clinical context.
  • the system can proceed to block 540.
  • the system can assign a functional status group to the ROS1 rearrangement.
  • the system may assign the ROS1 variant to a functionally unknown functional status group, indicating that the functional implications of the ROS 1 variant is not well-defined or well-known based on research and/or literature.
  • the system may use the functional status to provide specific content, therapies, and clinical trials to a healthcare professional (e.g., via a report), and the healthcare professional can use the content provided in the report to inform treatment decisions.
  • the system may determine whether the sequence read data corresponding to the ROS 1 rearrangement event comprises the sequence encoding the kinase domain.
  • block 510 may correspond to block 110 described with respect to FIG. 1A.
  • the system may label the ROS 1 variant at block 536.
  • the naming convention at block 536 may label the genomic variant as “ROS 1 -secondary gene non-canonical fusion.”
  • the primary gene is ROS 1 and the secondary gene is TSPAN3
  • an in-strand rearrangement that does not include the sequence encoding the predetermined protein domain may be labeled as ROS1-TSPAN3 non-canonical fusion.
  • the labeled ROS1 variant may be provided to a healthcare professional (e.g., in a report) who can use the label to inform treatment decisions.
  • the system instead of labeling the genomic variant as a non-canonical fusion, the system may label the genomic variant as a reciprocal fusion or the like. A skilled artisan will understand that any suitable label may be used that is indicative that the ROS 1 rearrangement is a non-canonical fusion.
  • the system can proceed to block 542.
  • the system can assign a functional status group to the ROS 1 rearrangement.
  • the system may assign the ROS 1 variant to a functionally unknown fusion functional status group.
  • the system may use the functional status to provide specific content, therapies, and clinical trials associated with the functionally unknown fusion group to a healthcare professional (e.g., via a report), and the healthcare professional can use the content provided in the report to inform treatment decisions.
  • the system may proceed to block 512.
  • the system may determine whether the secondary gene is a known partner gene of ROS 1.
  • the known partner genes may be determined based on partner genes identified in research and scientific literature.
  • the partner genes may be determined based on a database or look-up table comprising the partner genes identified in research and scientific literature.
  • the system may access a database to determine whether the secondary gene corresponds to known partner genes of ROS 1, including but not limited to, CD74, CLIP1, EZR, GOPC, LRIG3, MY05A, PPFIBP1, PWWP2A, SLC34A2, SDC4, SHTN1 (KIAA1598), TPM3, and ZCCHC8. If the system determines that the secondary gene is not a known partner gene, the system may proceed to block 544. At block 544, the system may forgo labeling or assigning the ROS 1 variant to a functional status group. In one or more examples, the system may instead flag the ROS 1 variant for manual processing.
  • known partner genes of ROS 1 including but not limited to, CD74, CLIP1, EZR, GOPC, LRIG3, MY05A, PPFIBP1, PWWP2A, SLC34A2, SDC4, SHTN1 (KIAA1598), TPM3, and ZCCHC8. If the system determines that the secondary gene is not a known partner gene,
  • the system may proceed to block 538.
  • the system may label the ROS1 variant accordingly.
  • the naming convention at block 538 may label the ROS 1 variant as “secondary gene-ROS 1 canonical fusion.”
  • the system may label the variant as CD74-ROS1 canonical fusion.
  • the labeled genomic variant may be provided to a healthcare professional (e.g., in a report) who can use the label to inform treatment decisions.
  • a healthcare professional e.g., in a report
  • any suitable label may be used that communicates the ROS 1 rearrangement is a canonical fusion.
  • the system can proceed to block 546.
  • the system can assign a functional status group to the ROS 1 rearrangement.
  • the system may assign the ROS 1 variant to an activating fusion functional status group.
  • the system may use the functional status to provide specific content, therapies, and clinical trials associated with the activating fusion to a healthcare professional (e.g., via a report), and the healthcare professional can use the content provided in the report to inform treatment decisions.
  • FIG. 6 provides a non-limiting example of a process 600 for assigning a genomic variant to a functional status group and identifying a treatment for an individual.
  • process 600 may correspond to block 306 of FIG. 3.
  • process 600 may be performed for intragenic rearrangement events.
  • process 600 may be performed for an intragenic rearrangement event that affects a baited oncogene.
  • Process 600 can be performed, for example, using one or more electronic devices implementing a software platform.
  • process 600 is performed using a clientserver system, and the blocks of process 600 are divided up in any manner between the server and a client device.
  • the blocks of process 600 are divided up between the server and multiple client devices.
  • process 600 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 600. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.
  • the system can receive sequence read data associated with a sample from an individual.
  • the sequence read data may be derived from single region sequencing (e.g., sequencing of a single tissue biopsy sample collected from the tumor of the individual).
  • the genomic data comprising sequence read data may be derived from multi-region sequencing (e.g., sequencing of multiple tissue biopsy samples collected from the tumor of the individual).
  • the genomic data comprising sequence read data may be derived from single cell sequencing data as opposed to bulk tumor sequencing.
  • the genomic data comprising sequence read data may be derived from sequencing the circulating tumor DNA and/or RNA in a liquid biopsy sample.
  • the genomic data comprising sequence read data may be derived from targeted sequencing, e.g., targeted exome sequencing.
  • the genomic data comprising sequence read data may be derived from, e.g., whole genome or whole exome sequencing, as opposed to targeted exome sequencing to increase the number of genomic features (e.g., the number of short variants) detected.
  • the sequence read data may be received by the system as a BAM file.
  • the sequence read data may be indicative of a presence or absence of one or more short variants (SVs) in a patient sample.
  • the sequence read data may also be indicative of the presence or absence of genomic events, such as copy number alterations, rearrangements, insertions, deletions, fusions, chromosomal aneuploidy, whole genome doubling, Catalogue Of Somatic Mutations In Cancer (COSMIC) mutational signatures, or any combination thereof.
  • the sequence read data can be indicative of features associated with a genomic event such as a location of the genomic event, whether the genomic event is in- strand, an orientation of the genomic event, a directionality of the genomic event, genes involved in the genomic event, and the like.
  • the system can determine one or more breakpoints of the genomic variant and a location of the one or more breakpoints based on the sequence read data, the one or more breakpoints associated with a rearrangement event. In one or more examples, the system may determine whether the one or more breakpoints of the genomic variant are located outside of a sequence encoding a predetermined protein domain.
  • the location of the breakpoint of the genomic variant may be obtained via a computational pipeline for analyzing sequence read data.
  • the location of the breakpoint of the genomic variant may be determined by the system based on information provided by the computational pipeline.
  • the computational pipeline may indicate the location of the breakpoint in the genomic data for the sample.
  • the location indicated by the pipeline may correspond to, for example, an integer value, floating point value, and the like.
  • the location indicated by the pipeline may be compared to a predetermined threshold to determine whether the breakpoint is located inside or outside the sequence encoding a predetermined protein domain. While this example is described with respect to data obtained via a computational pipeline, a skilled artisan will understand that the location of the breakpoint of the genomic variant may be obtained using other methods known in the art.
  • the system can move to block 606 of FIG. 6.
  • the system can perform determinations regarding the genomic variant. While particular determinations are shown in the figures, a skilled artisan will understand that more or less determinations may be performed without departing from the scope of this disclosure. In one or more examples the determinations may be based on data obtained via the computational pipeline for analyzing sequence read data.
  • the system can determine whether the sequence encoding the predetermined protein domain is impacted by the rearrangement event, e.g., based on the sequence read data corresponding to the rearrangement event.
  • the sequence read data corresponding to the rearrangement event may comprise the sequence encoding the predetermined protein domain (e.g., such that the sequence encoding the predetermined protein domain is in the sequence read data corresponding to the rearrangement event).
  • the system may determine whether the functional domain of the genomic variant is retained in the sequence read data following the rearrangement event.
  • a determination of whether the sequence encoding the predetermined protein domain is included in the sequence read data corresponding to the rearrangement event may be obtained via the computational pipeline for processing nucleic acid sequencing data. While this example relies on data obtained via the computational pipeline, a skilled artisan could use other methods known in the art to determine whether the rearrangement event is in-strand or out-of- strand.
  • the system can further determine a rearrangement type associated with the genomic variant. For example, the system may determine whether the genomic variant corresponds to a deletion, a duplication, or an inversion.
  • the system can assign the genomic variant to a functional status group based on block 610.
  • the functional status group may further be based on a rearrangement type.
  • the label applied to the genomic variant may be based on block 610 and the rearrangement type.
  • the system may not assign the genomic variant to a functional status group. Instead, the system may flag the genomic alteration for manual processing, whereby an individual may review the genomic alteration and determine the functional status group assignment for the genomic alteration.
  • such a genomic variant may be determined to be functionally inactive and/or not be pathogenic, e.g., such as for oncogenes.
  • the system can identify a treatment based on the assigned functional status group of the genomic variant. For example, certain alterations may be associated with specific approved therapies. For example, an intragenic duplication of sequence encoding the FGFR2 kinase domain may be associated with kinase inhibitors such as pemigatinib. As another example, an intragenic deletion of sequence encoding a portion of the FGFR2 gene prior to the kinase domain may be associated with kinase inhibitors such as pemigatinib.
  • the system can generate a label for the genomic variant based on the determinations associated with blocks 604 and 606.
  • an intragenic event involving the duplication of sequence encoding the BRAF kinase domain may be labeled kinase domain duplication.
  • an intragenic event involving the deletion of sequence encoding the BRAF autoinhibitory domain may be labeled deletion exons 3-6.
  • the disclosed methods may be used to identify variants in the ABL1, ACVR1B, AKT1, AKT2, AKT3, ALK, ALOX12B, AMER1, APC, AR, ARAF, ARFRP1, ARID1A, ASXL1, ATM, ATR, ATRX, AURKA, AURKB, AXIN1, AXL, BAP1, BARD1, BCL2, BCL2L1, BCL2L2, BCL6, BCOR, BCORL1, BCR, BRAF, BRCA1, BRCA2, BRD4, BRIP1, BTG1, BTG2, BTK, CALR, CARD11, CASP8, CBFB, CBL, CCND1, CCND2, CCND3, CCNE1, CD22, CD274, CD70, CD74, CD79A, CD79B, CDC73, CDH1, CDK12, CDK4, CDK6, CDK8, CDKN1A, CDKN1B, CDKN2A, CDKN2B, CDKN2C, CE
  • the disclosed methods may be used to identify variants in the ABL.
  • 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 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 assigning a genomic variant to a functional status may be used to diagnose (or as part of a diagnosis of) the presence of disease or other condition (e.g., cancer, genetic disorders (such as Down Syndrome and Fragile X), neurological disorders, or any other disease type where detection of variants, e.g., copy number alternations, are relevant to diagnosing, treating, or predicting said disease) in a subject (e.g., a patient).
  • the disclosed methods may be applicable to diagnosis of any of a variety of cancers as described elsewhere herein.
  • the disclosed methods for assigning a genomic variant to a functional status may be used to predict genetic disorders in fetal DNA. (e.g., for invasive or non-invasive prenatal testing). For example, sequence read data obtained by sequencing fetal DNA extracted from samples obtained using invasive amniocentesis, chorionic villus sampling (cVS), or fetal umbilical cord sampling techniques, or obtained using non-invasive sampling of cell-free DNA (cfDNA) samples (which comprises a mix of maternal cfDNA and fetal cfDNA), may be processed according to the disclosed methods to identify variants, e.g., copy number alterations, associated with, e.g., Down Syndrome (trisomy 21), trisomy 18, trisomy 13, and extra or missing copies of the X and Y chromosomes.
  • the disclosed methods for assigning a genomic variant to a functional status may be used to select a subject (e.g., a patient) for a clinical trial based on the functional status determined for one or more gene loci.
  • patient selection for clinical trials based on, e.g., identification of the functional status at one or more gene loci may accelerate the development of targeted therapies and improve the healthcare outcomes for treatment decisions.
  • the disclosed methods for assigning a genomic variant to a functional status 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 assigning a genomic variant to a functional status may be used in treating a disease (e.g., a cancer) in a subject.
  • a disease e.g., a cancer
  • an effective amount of an anti-cancer therapy or anti-cancer treatment may be administered to the subject.
  • the disclosed methods for assigning a genomic variant to a functional status may be used for monitoring disease progression or recurrence (e.g., cancer or tumor progression or recurrence) in a subject.
  • the methods may be used to determine a functional status in a first sample obtained from the subject at a first time point, and used to determine a functional status in a second sample obtained from the subject at a second time point, where comparison of the first determination of the functional status and the second determination of the functional status allows one to monitor disease progression or recurrence.
  • the first time point is chosen before the subject has been administered a therapy or treatment
  • the second time point is chosen after the subject has been administered the therapy or treatment.
  • the disclosed methods may be used for adjusting a therapy or treatment (e.g., an anti-cancer treatment or anti-cancer therapy) for a subject, e.g., by adjusting a treatment dose and/or selecting a different treatment in response to a change in the determination of the functional status.
  • a therapy or treatment e.g., an anti-cancer treatment or anti-cancer therapy
  • the functional status determined using the disclosed methods may be used as a prognostic or diagnostic indicator associated with the sample.
  • the prognostic or diagnostic indicator may comprise an indicator of the presence of a disease (e.g., cancer) in the sample, an indicator of the probability that a disease (e.g., cancer) is present in the sample, an indicator of the probability that the subject from which the sample was derived will develop a disease (e.g., cancer) (z.e., a risk factor), or an indicator of the likelihood that the subject from which the sample was derived will respond to a particular therapy or treatment.
  • a disease e.g., cancer
  • an indicator of the probability that a disease e.g., cancer
  • an indicator of the probability that the subject from which the sample was derived will develop a disease e.g., cancer
  • a risk factor z.e., a risk factor
  • the disclosed methods for assigning a genomic variant to a functional status 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 nextgeneration sequencing (NGS) approach used to assess hundreds of genes (including relevant cancer biomarkers) in a single assay.
  • CGP comprehensive genomic profiling
  • NGS nextgeneration sequencing
  • Inclusion of the disclosed methods for assigning a genomic variant to a functional status 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 determining the functional status of a genomic variant 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.
  • 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).
  • CTCs circulating tumor cells
  • the sample may be a liquid biopsy sample and may comprise cell- free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.
  • the sample may comprise one or more premalignant or malignant cells.
  • Premalignant refers to a cell or tissue that is not yet malignant but is poised to become malignant.
  • the sample may be acquired from a solid tumor, a soft tissue tumor, or a metastatic lesion.
  • the sample may be acquired from a hematologic malignancy or pre-malignancy.
  • the sample may comprise a tissue or cells from a surgical margin.
  • the sample may comprise tumor-infiltrating lymphocytes.
  • the sample may comprise one or more non- malignant cells.
  • the sample may be, or is part of, a primary tumor or a metastasis (e.g., a metastasis biopsy sample).
  • the sample may be obtained from a site (e.g., a tumor site) with the highest percentage of tumor (e.g., tumor cells) as compared to adjacent sites (e.g., sites adjacent to the tumor).
  • the sample may be obtained from a site (e.g., a tumor site) with the largest tumor focus (e.g., the largest number of tumor cells as visualized under a microscope) as compared to adjacent sites (e.g., sites adjacent to the tumor).
  • the disclosed methods may further comprise analyzing a primary control (e.g., a normal tissue sample). In some instances, the disclosed methods may further comprise determining if a primary control is available and, if so, isolating a control nucleic acid (e.g., DNA) from said primary control. In some instances, the sample may comprise any normal control (e.g., a normal adjacent tissue (NAT)) if no primary control is available. In some instances, the sample may be or may comprise histologically normal tissue. In some instances, the method includes evaluating a sample, e.g., a histologically normal sample (e.g., from a surgical tissue margin) using the methods described herein.
  • a primary control e.g., a normal tissue sample.
  • the disclosed methods may further comprise determining if a primary control is available and, if so, isolating a control nucleic acid (e.g., DNA) from said primary control.
  • the sample may comprise any normal control (e.g.,
  • the disclosed methods may further comprise acquiring a sub-sample enriched for non-tumor cells, e.g., by macro-dissecting non-tumor tissue from said NAT in a sample not accompanied by a primary control. In some instances, the disclosed methods may further comprise determining that no primary control and no NAT is available, and marking said sample for analysis without a matched control.
  • samples obtained from histologically normal tissues may still comprise a genetic alteration such as a variant sequence as described herein.
  • the methods may thus further comprise re-classifying a sample based on the presence of the detected genetic alteration.
  • multiple samples e.g., from different subjects are processed simultaneously.
  • tissue samples e.g., solid tissue samples, soft tissue samples, metastatic lesions, or liquid biopsy samples.
  • tissues include, but are not limited to, connective tissue, muscle tissue, nervous tissue, epithelial tissue, and blood.
  • Tissue samples may be collected from any of the organs within an animal or human body.
  • human organs include, but are not limited to, the brain, heart, lungs, liver, kidneys, pancreas, spleen, thyroid, mammary glands, uterus, prostate, large intestine, small intestine, bladder, bone, skin, etc.
  • the nucleic acids extracted from the sample may comprise deoxyribonucleic acid (DNA) molecules.
  • DNA DNA that may be suitable for analysis by the disclosed methods include, but are not limited to, genomic DNA or fragments thereof, mitochondrial DNA or fragments thereof, cell-free DNA (cfDNA), and circulating tumor DNA (ctDNA).
  • Cell-free DNA (cfDNA) is comprised of fragments of DNA that are released from normal and/or cancerous cells during apoptosis and necrosis, and circulate in the blood stream and/or accumulate in other bodily fluids.
  • Circulating tumor DNA ctDNA is comprised of fragments of DNA that are released from cancerous cells and tumors that circulate in the blood stream and/or accumulate in other bodily fluids.
  • DNA is extracted from nucleated cells from the sample.
  • a sample may have a low nucleated cellularity, e.g., when the sample is comprised mainly of erythrocytes, lesional cells that contain excessive cytoplasm, or tissue with fibrosis.
  • a sample with low nucleated cellularity may require more, e.g., greater, tissue volume for DNA extraction.
  • the nucleic acids extracted from the sample may comprise ribonucleic acid (RNA) molecules.
  • RNA ribonucleic acid
  • examples of RNA that may be suitable for analysis by the disclosed methods include, but are not limited to, total cellular RNA, total cellular RNA after depletion of certain abundant RNA sequences (e.g., ribosomal RNAs), cell-free RNA (cfRNA), messenger RNA (mRNA) or fragments thereof, the poly(A)-tailed mRNA fraction of the total RNA, ribosomal RNA (rRNA) or fragments thereof, transfer RNA (tRNA) or fragments thereof, and mitochondrial RNA or fragments thereof.
  • 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.
  • the sample comprises nucleic acid (e.g., DNA, RNA (or a cDNA derived from the RNA), or both), e.g., from a tumor or from normal tissue.
  • nucleic acid e.g., DNA, RNA (or a cDNA derived from the RNA), or both
  • the sample may further comprise a non-nucleic acid component, e.g., cells, protein, carbohydrate, or lipid, e.g., from the tumor or normal tissue.
  • the sample is obtained (e.g., collected) from a subject (e.g., patient) with a condition or disease (e.g., a hyperproliferative disease or a non-cancer indication) or suspected of having the condition or disease.
  • a condition or disease e.g., a hyperproliferative disease or a non-cancer indication
  • the hyperproliferative disease is a cancer.
  • the cancer is a solid tumor or a metastatic form thereof.
  • the cancer is a hematological cancer, e.g., a leukemia or lymphoma.
  • the subject has a cancer or is at risk of having a cancer.
  • the subject has a genetic predisposition to a cancer (e.g., having a genetic mutation that increases his or her baseline risk for developing a cancer).
  • the subject has been exposed to an environmental perturbation (e.g., radiation or a chemical) that increases his or her risk for developing a cancer.
  • the subject is in need of being monitored for development of a cancer.
  • the subject is in need of being monitored for cancer progression or regression, e.g., after being treated with an anti-cancer therapy (or anti-cancer treatment).
  • the subject is in need of being monitored for relapse of cancer.
  • the subject is in need of being monitored for minimum residual disease (MRD).
  • the subject has been, or is being treated, for cancer.
  • the subject has not been treated with an anti-cancer therapy (or anti-cancer treatment).
  • the subject e.g., a patient
  • a post-targeted therapy sample e.g., specimen
  • the post-targeted therapy sample is a sample obtained after the completion of the targeted therapy.
  • the patient has not been previously treated with a targeted therapy.
  • the sample comprises a resection, e.g., an original resection, or a resection following recurrence (e.g., following a disease recurrence post-therapy).
  • a resection e.g., an original resection
  • 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, Pro mega Corporation, Madison, WI)). Protocols for RNA isolation are disclosed in, e.g., the Maxwell® 16 Total RNA Purification Kit Technical Bulletin (Promega Literature #TB351, August 2009, Promega Corporation, Madison, WI).
  • a typical DNA extraction procedure for example, comprises (i) collection of the fluid sample, cell sample, or tissue sample from which DNA is to be extracted, (ii) disruption of cell membranes (z.e., cell lysis), if necessary, to release DNA and other cytoplasmic components, (iii) treatment of the fluid sample or lysed sample with a concentrated salt solution to precipitate proteins, lipids, and RNA, followed by centrifugation to separate out the precipitated proteins, lipids, and RNA, and (iv) purification of DNA from the supernatant to remove detergents, proteins, salts, or other reagents used during the cell membrane lysis step.
  • Disruption of cell membranes may be performed using a variety of mechanical shear (e.g., by passing through a French press or fine needle) or ultrasonic disruption techniques.
  • the cell lysis step often comprises the use of detergents and surfactants to solubilize lipids the cellular and nuclear membranes.
  • the lysis step may further comprise use of proteases to break down protein, and/or the use of an RNase for digestion of RNA in the sample.
  • Examples of suitable techniques for DNA purification include, but are not limited to, (i) precipitation in ice-cold ethanol or isopropanol, followed by centrifugation (precipitation of DNA may be enhanced by increasing ionic strength, e.g., by addition of sodium acetate), (ii) phenol-chloroform extraction, followed by centrifugation to separate the aqueous phase containing the nucleic acid from the organic phase containing denatured protein, and (iii) solid phase chromatography where the nucleic acids adsorb to the solid phase (e.g., silica or other) depending on the pH and salt concentration of the buffer.
  • the solid phase e.g., silica or other
  • cellular and histone proteins bound to the DNA may be removed either by adding a protease or by having precipitated the proteins with sodium or ammonium acetate, or through extraction with a phenol-chloroform mixture prior to a DNA precipitation step.
  • DNA may be extracted using any of a variety of suitable commercial DNA extraction and purification kits. Examples include, but are not limited to, the QIAamp (for isolation of genomic DNA from human samples) and DNAeasy (for isolation of genomic DNA from animal or plant samples) kits from Qiagen (Germantown, MD) or the Maxwell® and ReliaPrepTM series of kits from Promega (Madison, WI).
  • the sample may comprise a formalin-fixed (also known as formaldehyde-fixed, or paraformaldehyde-fixed), paraffin-embedded (FFPE) tissue preparation.
  • FFPE formalin-fixed
  • the FFPE sample may be a tissue sample embedded in a matrix, e.g., an FFPE block.
  • Methods to isolate nucleic acids (e.g., DNA) from formaldehyde- or paraformaldehyde-fixed, paraffin-embedded (FFPE) tissues are disclosed in, e.g., Cronin, et al., (2004) Am J Pathol.
  • the Maxwell® 16 FFPE Plus LEV DNA Purification Kit is used with the Maxwell® 16 Instrument for purification of genomic DNA from 1 to 10 pm sections of FFPE tissue. DNA is purified using silica-clad paramagnetic particles (PMPs), and eluted in low elution volume.
  • PMPs silica-clad paramagnetic particles
  • the E.Z.N.A.® FFPE DNA Kit uses a spin column and buffer system for isolation of genomic DNA.
  • QIAamp® DNA FFPE Tissue Kit uses QIAamp® DNA Micro technology for purification of genomic and mitochondrial DNA.
  • the disclosed methods may further comprise determining or acquiring a yield value for the nucleic acid extracted from the sample and comparing the determined value to a reference value. For example, if the determined or acquired value is less than the reference value, the nucleic acids may be amplified prior to proceeding with library construction.
  • the disclosed methods may further comprise determining or acquiring a value for the size (or average size) of nucleic acid fragments in the sample, and comparing the determined or acquired value to a reference value, e.g., a size (or average size) of at least 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 base pairs (bps).
  • a reference value e.g., a size (or average size) of at least 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 base pairs (bps).
  • one or more parameters described herein may be adjusted or selected in response to this determination.
  • the nucleic acids are typically dissolved in a slightly alkaline buffer, e.g., Tris-EDTA (TE) buffer, or in ultra-pure water.
  • a slightly alkaline buffer e.g., Tris-EDTA (TE) buffer
  • the isolated nucleic acids may be fragmented or sheared by using any of a variety of techniques known to those of skill in the art.
  • genomic DNA can be fragmented by physical shearing methods, enzymatic cleavage methods, chemical cleavage methods, and other methods known to those of skill in the art. Methods for DNA shearing are described in Example 4 in International Patent Application Publication No. WO 2012/092426. In some instances, alternatives to DNA shearing methods can be used to avoid a ligation step during library preparation.
  • the nucleic acids isolated from the sample may be used to construct a library (e.g., a nucleic acid library as described herein).
  • the nucleic acids are fragmented using any of the methods described above, optionally subjected to repair of chain end damage, and optionally ligated to synthetic adapters, primers, and/or barcodes (e.g., amplification primers, sequencing adapters, flow cell adapters, substrate adapters, sample barcodes or indexes, and/or unique molecular identifier sequences), size-selected (e.g., by preparative gel electrophoresis), and/or amplified (e.g., using PCR, a non-PCR amplification technique, or an isothermal amplification technique).
  • synthetic adapters, primers, and/or barcodes e.g., amplification primers, sequencing adapters, flow cell adapters, substrate adapters, sample barcodes or indexes, and/or unique molecular identifier sequences
  • the fragmented and adapter-ligated group of nucleic acids is used without explicit size selection or amplification prior to hybridization-based selection of target sequences.
  • the nucleic acid is amplified by any of a variety of specific or non-specific nucleic acid amplification methods known to those of skill in the art.
  • the nucleic acids are amplified, e.g., by a whole-genome amplification method such as random-primed strand-displacement amplification. Examples of nucleic acid library preparation techniques for next-generation sequencing are described in, e.g., van Dijk, et al. (2014), Exp. Cell Research 322: 12 - 20, and Illumina’s genomic DNA sample preparation kit.
  • the resulting nucleic acid library may contain all or substantially all of the complexity of the genome.
  • the term “substantially all” in this context refers to the possibility that there can in practice be some unwanted loss of genome complexity during the initial steps of the procedure.
  • the methods described herein also are useful in cases where the nucleic acid library comprises a portion of the genome, e.g., where the complexity of the genome is reduced by design. In some instances, any selected portion of the genome can be used with a method described herein. For example, in certain embodiments, the entire exome or a subset thereof is isolated.
  • the library may include at least 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, or 5% of the genomic DNA.
  • the library may consist of cDNA copies of genomic DNA that includes copies of at least 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, or 5% of the genomic DNA.
  • the amount of nucleic acid used to generate the nucleic acid library may be less than 5 micrograms, less than 1 microgram, less than 500 ng, less than 200 ng, less than 100 ng, less than 50 ng, less than 10 ng, less than 5 ng, or less than 1 ng.
  • a library (e.g., a nucleic acid library) includes a collection of nucleic acid molecules.
  • the nucleic acid molecules of the library can include a target nucleic acid molecule (e.g., a tumor nucleic acid molecule, a reference nucleic acid molecule and/or a control nucleic acid molecule; also referred to herein as a first, second and/or third nucleic acid molecule, respectively).
  • the nucleic acid molecules of the library can be from a single subject or individual.
  • a library can comprise nucleic acid molecules derived from more than one subject (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30 or more subjects).
  • two or more libraries from different subjects can be combined to form a library having nucleic acid molecules from more than one subject (where the nucleic acid molecules derived from each subject are optionally ligated to a unique sample barcode corresponding to a specific subject).
  • the subject is a human having, or at risk of having, a cancer or tumor.
  • the library may comprise one or more subgenomic intervals.
  • a subgenomic interval can be a single nucleotide position, e.g., a nucleotide position for which a variant at the position is associated (positively or negatively) with a tumor phenotype.
  • a subgenomic interval comprises more than one nucleotide position. Such instances include sequences of at least 2, 5, 10, 50, 100, 150, 250, or more than 250 nucleotide positions in length.
  • Subgenomic intervals can comprise, e.g., one or more entire genes (or portions thereof), one or more exons or coding sequences (or portions thereof), one or more introns (or portion thereof), one or more microsatellite region (or portions thereof), or any combination thereof.
  • a subgenomic interval can comprise all or a part of a fragment of a naturally occurring nucleic acid molecule, e.g., a genomic DNA molecule.
  • a subgenomic interval can correspond to a fragment of genomic DNA which is subjected to a sequencing reaction.
  • a subgenomic interval is a continuous sequence from a genomic source.
  • a subgenomic interval includes sequences that are not contiguous in the genome, e.g., subgenomic intervals in cDNA can include 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.
  • the methods described herein may comprise contacting a nucleic acid library with a plurality of target capture reagents in order to select and capture a plurality of specific target sequences (e.g., gene sequences or fragments thereof) for analysis.
  • a target capture reagent i.e., a molecule which can bind to and thereby allow capture of a target molecule
  • a target capture reagent is used to select the subject intervals to be analyzed.
  • a target capture reagent can be a bait molecule, e.g., a nucleic acid molecule (e.g., a DNA molecule or RNA molecule) which can hybridize to (i.e., is complementary to) a target molecule, and thereby allows capture of the target nucleic acid.
  • the target capture reagent e.g., a bait molecule (or bait sequence)
  • the target nucleic acid is a genomic DNA molecule, an RNA molecule, a cDNA molecule derived from an RNA molecule, a microsatellite DNA sequence, and the like.
  • the target capture reagent is suitable for solution-phase hybridization to the target. In some instances, the target capture reagent is suitable for solid-phase hybridization to the target. In some instances, the target capture reagent is suitable for both solution-phase and solid-phase hybridization to the target.
  • the design and construction of target capture reagents is described in more detail in, e.g., International Patent Application Publication No. WO 2020/236941, the entire content of which is incorporated herein by reference.
  • the methods described herein provide for optimized sequencing of a large number of genomic loci (e.g., genes or gene products (e.g., mRNA), micro satellite loci, etc.) from samples (e.g., cancerous tissue specimens, liquid biopsy samples, and the like) from one or more subjects by the appropriate selection of target capture reagents to select the target nucleic acid molecules to be sequenced.
  • a target capture reagent may hybridize to a specific target locus, e.g., a specific target gene locus or fragment thereof.
  • a target capture reagent may hybridize to a specific group of target loci, e.g., a specific group of gene loci or fragments thereof.
  • a plurality of target capture reagents comprising a mix of target- specific and/or group- specific target capture reagents may be used.
  • the number of target capture reagents (e.g., bait molecules) in the plurality of target capture reagents (e.g., a bait set) contacted with a nucleic acid library to capture a plurality of target sequences for nucleic acid sequencing is greater than 10, greater than 50, greater than 100, greater than 200, greater than 300, greater than 400, greater than 500, greater than 600, greater than 700, greater than 800, greater than 900, greater than 1,000, greater than 1,250, greater than 1,500, greater than 1,750, greater than 2,000, greater than 3,000, greater than 4,000, greater than 5,000, greater than 10,000, greater than 25,000, or greater than 50,000.
  • the overall length of the target capture reagent sequence can be between about 70 nucleotides and 1000 nucleotides. In one instance, the target capture reagent length is between about 100 and 300 nucleotides, 110 and 200 nucleotides, or 120 and 170 nucleotides, in length. In addition to those mentioned above, intermediate oligonucleotide lengths of about 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 300, 400, 500, 600, 700, 800, and 900 nucleotides in length can be used in the methods described herein. In some embodiments, oligonucleotides of about 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, or 230 bases can be used.
  • each target capture reagent sequence can include: (i) a target-specific capture sequence (e.g., a gene locus or micro satellite locus-specific complementary sequence), (ii) an adapter, primer, barcode, and/or unique molecular identifier sequence, and (iii) universal tails on one or both ends.
  • a target-specific capture sequence e.g., a gene locus or micro satellite locus-specific complementary sequence
  • an adapter, primer, barcode, and/or unique molecular identifier sequence e.g., a 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).
  • 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 contacting said hybridization mixture with a binding entity that allows for separation of said plurality of target capture reagent/nucleic acid molecule hybrids from the hybridization mixture, thereby providing
  • 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 (z.e., the library catch).
  • the contacting step can be effected in, e.g., solution-based hybridization.
  • the method includes repeating the hybridization step for one or more additional rounds of solution-based hybridization.
  • the method further includes subjecting the library catch to one or more additional rounds of solution-based hybridization with the same or a different collection of target capture reagents.
  • the contacting step is effected using a solid support, e.g., an array.
  • Suitable solid supports for hybridization are described in, e.g., Albert, T.J. et al. (2007) Nat. Methods 4(l l):903-5; Hodges, E. et al. (2007) Nat. Genet. 39(12): 1522-7; and Okou, D.T. et al. (2007) Nat. Methods 4(11):907-9, the contents of which are incorporated herein by reference in their entireties.
  • Hybridization methods that can be adapted for use in the methods herein are described in the art, e.g., as described in International Patent Application Publication No. WO 2012/092426. Methods for hybridizing target capture reagents to a plurality of target nucleic acids are described in more detail in, e.g., International Patent Application Publication No. WO 2020/236941, the entire content of which is incorporated herein by reference.
  • the methods and systems disclosed herein can be used in combination with, or as part of, a method or system for sequencing nucleic acids (e.g., a next-generation sequencing system) to generate a plurality of sequence reads that overlap one or more gene loci within a subgenomic interval in the sample and thereby determine, e.g., gene allele sequences at a plurality of gene loci.
  • a method or system for sequencing nucleic acids e.g., a next-generation sequencing system
  • next-generation sequencing (or “NGS”) as used herein may also be referred to as “massively parallel sequencing” (or “MPS”), and refers to any sequencing method that determines the nucleotide sequence of either individual nucleic acid molecules (e.g., as in single molecule sequencing) or clonally expanded proxies for individual nucleic acid molecules in a high throughput fashion (e.g., wherein greater than 10 3 , 10 4 , 10 5 or more than 10 5 molecules are sequenced simultaneously).
  • next-generation sequencing methods are known in the art, and are described in, e.g., Metzker, M. (2010) Nature Biotechnology Reviews 11:31-46, which is incorporated herein by reference.
  • Other examples of sequencing methods suitable for use when implementing the methods and systems disclosed herein are described in, e.g., International Patent Application Publication No. WO 2012/092426.
  • the sequencing may comprise, for example, whole genome sequencing (WGS), whole exome sequencing, targeted sequencing, or direct sequencing.
  • GGS whole genome sequencing
  • sequencing may be performed using, e.g., Sanger sequencing.
  • the sequencing may comprise a paired-end sequencing technique that allows both ends of a fragment to be sequenced and generates high-quality, alignable sequence data for detection of, e.g., genomic rearrangements, repetitive sequence elements, gene fusions, and novel transcripts.
  • sequencing may comprise Illumina MiSeq sequencing.
  • sequencing may comprise Illumina HiSeq sequencing.
  • sequencing may comprise Illumina NovaSeq sequencing. Optimized methods for sequencing a large number of target genomic loci in nucleic acids extracted from a sample are described in more detail in, e.g., International Patent Application Publication No. WO 2020/236941, the entire content of which is incorporated herein by reference.
  • the disclosed methods comprise one or more of the steps of: (a) acquiring a library comprising a plurality of normal and/or tumor nucleic acid molecules from a sample; (b) simultaneously or sequentially contacting the library with one, two, three, four, five, or more than five pluralities of target capture reagents under conditions that allow hybridization of the target capture reagents to the target nucleic acid molecules, thereby providing a selected set of captured normal and/or tumor nucleic acid molecules (z.e., a library catch); (c) separating the selected subset of the nucleic acid molecules (e.g., the library catch) from the hybridization mixture, e.g., by contacting the hybridization mixture with a binding entity that allows for separation of the target capture reagent/nucleic acid molecule hybrids from the hybridization mixture, (d) sequencing the library catch to acquiring a plurality of reads (e.g., sequence reads) that overlap one or more subject intervals (e.g.
  • acquiring sequence reads for one or more subject intervals may comprise sequencing at least 1, at least 5, at least 10, at least 20, at least 30, at least 40, at least 50, at least 100, at least 150, at least 200, at least 250, at least 300, at least 350, at least 400, at least 450, at least 500, at least 550, at least 600, at least 650, at least 700, at least 750, at least 800, at least 850, at least 900, at least 950, at least 1,000, at least 1,250, at least 1,500, at least 1,750, at least 2,000, at least 2,250, at least 2,500, at least 2,750, at least 3,000, at least 3,500, at least 4,000, at least 4,500, or at least 5,000 loci, e.g., genomic loci, gene loci, microsatellite loci, etc.
  • acquiring a sequence read for one or more subject intervals may comprise sequencing a subject interval for any number of loci within the range described in this paragraph,
  • acquiring a sequence read for one or more subject intervals comprises sequencing a subject interval with a sequencing method that provides a sequence read length (or average sequence read length) of at least 20 bases, at least 30 bases, at least 40 bases, at least 50 bases, at least 60 bases, at least 70 bases, at least 80 bases, at least 90 bases, at least 100 bases, at least 120 bases, at least 140 bases, at least 160 bases, at least 180 bases, at least 200 bases, at least 220 bases, at least 240 bases, at least 260 bases, at least 280 bases, at least 300 bases, at least 320 bases, at least 340 bases, at least 360 bases, at least 380 bases, or at least 400 bases.
  • a sequencing method that provides a sequence read length (or average sequence read length) of at least 20 bases, at least 30 bases, at least 40 bases, at least 50 bases, at least 60 bases, at least 70 bases, at least 80 bases, at least 90 bases, at least 100 bases, at least 120 bases, at least 140 bases, at least 160 bases, at least 180 bases, at
  • acquiring a sequence read for the one or more subject intervals may comprise sequencing a subject interval with a sequencing method that provides a sequence read length (or average sequence read length) of any number of bases within the range described in this paragraph, e.g., a sequence read length (or average sequence read length) of 56 bases.
  • acquiring a sequence read for one or more subject intervals may comprise sequencing with at least lOOx or more coverage (or depth) on average.
  • acquiring a sequence read for one or more subject intervals may comprise sequencing with at least lOOx, at least 150x, at least 200x, at least 250x, at least 500x, at least 750x, at least l,000x, at least 1,500 x, at least 2,000x, at least 2,500x, at least 3,000x, at least 3,500x, at least 4,000x, at least 4,500x, at least 5,000x, at least 5,500x, or at least 6,000x or more coverage (or depth) on average.
  • acquiring a sequence read for one or more subject intervals may comprise sequencing with an average coverage (or depth) having any value within the range of values described in this paragraph, e.g., at least 160x.
  • acquiring a read for the one or more subject intervals comprises sequencing with an average sequencing depth having any value ranging from at least lOOx to at least 6,000x for greater than about 90%, 92%, 94%, 95%, 96%, 97%, 98%, or 99% of the gene loci sequenced.
  • acquiring a read for the subject interval comprises sequencing with an average sequencing depth of at least 125x for at least 99% of the gene loci sequenced.
  • acquiring a read for the subject interval comprises sequencing with an average sequencing depth of at least 4,100x for at least 95% of the gene loci sequenced.
  • the relative abundance of a nucleic acid species in the library can be estimated by counting the relative number of occurrences of their cognate sequences (e.g., the number of sequence reads for a given cognate sequence) in the data generated by the sequencing experiment.
  • the disclosed methods and systems provide nucleotide sequences for a set of subject intervals (e.g., gene loci), as described herein.
  • the sequences are provided without using a method that includes a matched normal control (e.g., a wild-type control) and/or a matched tumor control (e.g., primary versus metastatic).
  • the level of sequencing depth as used herein refers to the number of reads (e.g., unique reads) obtained after detection and removal of duplicate reads (e.g., PCR duplicate reads).
  • duplicate reads are evaluated, e.g., to support detection of copy number alteration (CNAs).
  • Alignment is the process of matching a read with a location, e.g., a genomic location or locus.
  • NGS reads may be aligned to a known reference sequence (e.g., a wild-type sequence).
  • NGS reads may be assembled de novo. Methods of sequence alignment for NGS reads are described in, e.g., Trapnell, C. and Salzberg, S.L. Nature Biotech., 2009, 27:455-457. Examples of de novo sequence assemblies are described in, e.g., Warren R., et al., Bioinformatics, 2007, 23:500-501; Butler, J.
  • Misalignment e.g., the placement of base-pairs from a short read at incorrect locations in the genome
  • misalignment of reads due to sequence context can lead to reduction in sensitivity of mutation detection
  • sequence context e.g., the presence of repetitive sequence
  • Other examples of sequence context that may cause misalignment include short-tandem repeats, interspersed repeats, low complexity regions, insertions - deletions (indels), and paralogs.
  • misalignment may introduce artifactual reads of “mutated” alleles by placing reads of actual reference genome base sequences at the wrong location. Because mutation-calling algorithms for multigene analysis should be sensitive to even low-abundance mutations, sequence misalignments may increase false positive discovery rates and/or reduce specificity.
  • the methods and systems disclosed herein may integrate the use of multiple, individually-tuned, alignment methods or algorithms to optimize base-calling performance in sequencing methods, particularly in methods that rely on massively parallel sequencing (MPS) of a large number of diverse genetic events at a large number of diverse genomic loci.
  • the disclosed methods and systems may comprise the use of one or more global alignment algorithms.
  • the disclosed methods and systems may comprise the use of one or more local alignment algorithms. Examples of alignment algorithms that may be used include, but are not limited to, the Burrows-Wheeler Alignment (BWA) software bundle (see, e.g., Li, et al.
  • BWA Burrows-Wheeler Alignment
  • the methods and systems disclosed herein may also comprise the use of a sequence assembly algorithm, e.g., the Arachne sequence assembly algorithm (see, e.g., Batzoglou, et al. (2002), “ARACHNE: A Whole-Genome Shotgun Assembler”, Genome Res. 12: 177-189).
  • a sequence assembly algorithm e.g., the Arachne sequence assembly algorithm (see, e.g., Batzoglou, et al. (2002), “ARACHNE: A Whole-Genome Shotgun Assembler”, Genome Res. 12: 177-189).
  • the alignment method used to analyze sequence reads is not individually customized or tuned for detection of different variants (e.g., point mutations, insertions, deletions, and the like) at different genomic loci.
  • different alignment methods are used to analyze reads that are individually customized or tuned for detection of at least a subset of the different variants detected at different genomic loci.
  • different alignment methods are used to analyze reads that are individually customized or tuned to detect each different variant at different genomic loci.
  • tuning can be a function of one or more of: (i) the genetic locus (e.g., gene loci, micro satellite locus, or other subject interval) being sequenced, (ii) the tumor type associated with the sample, (iii) the variant being sequenced, or (iv) a characteristic of the sample or the subject.
  • the 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).
  • 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 perform a method comprising: receiving, at one or more processors, sequence read data associated with a genomic variant in a sample from an individual; determining, using the one or more processors, a breakpoint of the genomic variant and a location of the breakpoint based on the sequence read data, the breakpoint associated with a rearrangement event and further associated with a first gene and a second gene of the sample, the first gene corresponding to the genomic variant; if the breakpoint of the genomic variant is located outside a sequence encoding a predetermined protein domain, performing, using the one or more processors: a first determination of whether the rearrangement event is in-strand; and
  • 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 assigning a genomic variant to a functional status in any of a variety of samples as described herein (e.g., a tissue sample, biopsy sample, hematological sample, or liquid biopsy sample derived from the subject).
  • samples as described herein (e.g., a tissue sample, biopsy sample, hematological sample, or liquid biopsy sample derived from the subject).
  • the plurality of gene loci for which sequencing data is processed to assign a genomic variant to the functional status may comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10 gene loci.
  • the nucleic acid sequence data is acquired using a next generation sequencing technique (also referred to as a massively parallel sequencing technique) having a read-length of less than 400 bases, less than 300 bases, less than 200 bases, less than 150 bases, less than 100 bases, less than 90 bases, less than 80 bases, less than 70 bases, less than 60 bases, less than 50 bases, less than 40 bases, or less than 30 bases.
  • a next generation sequencing technique also referred to as a massively parallel sequencing technique having a read-length of less than 400 bases, less than 300 bases, less than 200 bases, less than 150 bases, less than 100 bases, less than 90 bases, less than 80 bases, less than 70 bases, less than 60 bases, less than 50 bases, less than 40 bases, or less than 30 bases.
  • the assigning a genomic variant to a functional status is used to select, initiate, adjust, or terminate a treatment for cancer in the subject (e.g., a patient) from which the sample was derived, as described elsewhere herein.
  • the disclosed systems may further comprise sample processing and library preparation workstations, microplate-handling robotics, fluid dispensing systems, temperature control modules, environmental control chambers, additional data storage modules, data communication modules (e.g., Bluetooth®, WiFi, intranet, or internet communication hardware and associated software), display modules, one or more local and/or cloud-based software packages (e.g., instrument / system control software packages, sequencing data analysis software packages), etc., or any combination thereof.
  • the systems may comprise, or be part of, a computer system or computer network as described elsewhere herein.
  • FIG. 7 illustrates an example of a computing device or system in accordance with one embodiment.
  • Device 700 can be a host computer connected to a network.
  • Device 700 can be a client computer or a server.
  • device 700 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) 710, input devices 720, output devices 730, memory or storage devices 740, communication devices 760, and nucleic acid sequencers 770.
  • Software 750 residing in memory or storage device 740 may comprise, e.g., an operating system as well as software for executing the methods described herein.
  • Input device 720 and output device 730 can generally correspond to those described herein, and can either be connectable or integrated with the computer.
  • Input device 720 can be any suitable device that provides input, such as a touch screen, keyboard or keypad, mouse, or voice-recognition device.
  • Output device 730 can be any suitable device that provides output, such as a touch screen, haptics device, or speaker.
  • Storage 740 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 760 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 780, Ethernet connection, or any other wire transfer technology) or wirelessly (e.g., Bluetooth®, Wi-Fi®, or any other wireless technology).
  • Software module 750 which can be stored as executable instructions in storage 740 and executed by processor(s) 710, 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 750 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 740, 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 750 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 700 may be connected to a network (e.g., network 804, as shown in FIG. 8 and/or described below), which can be any suitable type of interconnected communication system.
  • the network can implement any suitable communications protocol and can be secured by any suitable security protocol.
  • the network can comprise network links of any suitable arrangement that can implement the transmission and reception of network signals, such as wireless network connections, T1 or T3 lines, cable networks, DSE, or telephone lines.
  • Device 700 can be implemented using any operating system, e.g., an operating system suitable for operating on the network.
  • Software module 750 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) 710.
  • Device 700 can further include a sequencer 770, which can be any suitable nucleic acid sequencing instrument.
  • FIG. 8 illustrates an example of a computing system in accordance with one embodiment.
  • device 700 e.g., as described above and illustrated in FIG. 7
  • network 804 which is also connected to device 806.
  • device 806 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 700 and 806 may communicate, e.g., using suitable communication interfaces via network 804, such as a Local Area Network (LAN), Virtual Private Network (VPN), or the Internet.
  • network 804 can be, for example, the Internet, an intranet, a virtual private network, a cloud network, a wired network, or a wireless network.
  • Devices 700 and 806 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 700 and 806 may communicate, e.g., using suitable communication interfaces, via a second network, such as a mobile/cellular network.
  • Communication between devices 700 and 806 may further include or communicate with various servers such as a mail server, mobile server, media server, telephone server, and the like.
  • Devices 700 and 806 can communicate directly (instead of, or in addition to, communicating via network 804), e.g., via wireless or hardwired communications, such as Ethernet, IEEE 802.11b wireless, or the like.
  • devices 700 and 806 communicate via communications 808, which can be a direct connection or can occur via a network (e.g., network 804).
  • One or all of devices 700 and 806 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 804 according to various examples described herein.
  • logic e.g., http web server logic
  • devices 700 and 806 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 804 according to various examples described herein.
  • a method comprising: providing a plurality of nucleic acid molecules obtained from a sample from a subject; ligating one or more adapters onto one or more nucleic acid molecules from the plurality of nucleic acid molecules; amplifying the one or more ligated nucleic acid molecules from the plurality of nucleic acid molecules; capturing amplified nucleic acid molecules from the amplified nucleic acid molecules; sequencing, by a sequencer, the captured nucleic acid molecules to obtain a plurality of sequence reads that represent the captured nucleic acid molecules; receiving, at one or more processors, sequence read data for the plurality of sequence reads; determining, using the one or more processors, a breakpoint of a genomic variant and a location of the breakpoint based on the sequence read data, the breakpoint associated with a rearrangement event and further associated with a first gene and a second gene of the sample, the first gene corresponding to the genomic variant; if the breakpoint of the genomic variant is located outside a sequence encoding a predetermined protein
  • the cancer is a B cell cancer (multiple myeloma), a melanoma, breast cancer, lung cancer, bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain cancer, central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine cancer, endometrial cancer, cancer of an oral cavity, cancer of a pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel cancer, appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, a cancer of hematological tissue, an adenocarcinoma, an inflammatory myofibroblastic tumor, a gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MP
  • the cancer comprises acute lymphoblastic leukemia (Philadelphia chromosome positive), acute lymphoblastic leukemia (precursor B-cell), acute myeloid leukemia (FLT3+), acute myeloid leukemia (with an IDH2 mutation), anaplastic large cell lymphoma, basal cell carcinoma, B-cell chronic lymphocytic leukemia, bladder cancer, breast cancer (HER2 overexpressed/amplified), breast cancer (HER2+), breast cancer (HR+, HER2-), cervical cancer, cholangiocarcinoma, chronic lymphocytic leukemia, chronic lymphocytic leukemia (with 17p deletion), chronic myelogenous leukemia, chronic myelogenous leukemia (Philadelphia chromosome positive), classical Hodgkin lymphoma, colorectal cancer, colorectal cancer (dMMR/MSI-H), colorectal cancer (KRAS wild type), cryopyrin-associated periodic syndrome,
  • the targeted anti-cancer therapy comprises abemaciclib (Verzenio), abiraterone acetate (Zytiga), acalabrutinib (Calquence), ado-trastuzumab emtansine (Kadcyla), afatinib dimaleate (Gilotrif), aldesleukin (Proleukin), alectinib (Alecensa), alemtuzumab (Campath), alitretinoin (Panretin), alpelisib (Piqray), amivantamab-vmjw (Rybrevant), anastrozole (Arimidex), apalutamide (Erleada), asciminib hydrochloride (Scemblix), atezolizumab (Tecentriq), avapritinib (Ayvakit), avelumab (Bavencio), axicabtagene
  • the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
  • the sample is a liquid biopsy sample and comprises cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.
  • cfDNA cell-free DNA
  • ctDNA circulating tumor DNA
  • tumor nucleic acid molecules are derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules are derived from a normal portion of the heterogeneous tissue biopsy sample.
  • the sample comprises a liquid biopsy sample
  • the tumor nucleic acid molecules are derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample
  • the non-tumor nucleic acid molecules are derived from a non-tumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.
  • the one or more 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
  • the one or more gene loci comprise ABL, ALK, ALL, B4GALNT1, BAFF, BCL2, BRAF, BRCA, BTK, CD19, CD20, CD3, CD30, CD319, CD38, CD52, CDK4, CDK6, CML, CRACC, CS1, CTLA-4, dMMR, EGFR, ERBB 1, ERBB2, FGFR1-3, FLT3, GD2, HDAC, HER1, HER2, HR, IDH2, IL-ip, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSLH, mTOR, PARP, PD-1, PDGFR, PDGFRa, PDGFRP, PD-L1, PI3K5, PIGF, PTCH, RAF, RANKL, RET, R0S1, SLAMF7, VEGF, VEGFA, VEGFB, or any combination thereof.
  • a method for identifying a treatment based on a genomic variant in a sample from an individual comprising: receiving, at one or more processors, sequence read data associated with the genomic variant in the sample; determining, using the one or more processors, a breakpoint of the genomic variant and a location of the breakpoint based on the sequence read data, the breakpoint associated with a rearrangement event and further associated with a first gene and a second gene of the sample, the first gene corresponding to the genomic variant; if the breakpoint of the genomic variant is located outside a sequence encoding a predetermined protein domain, performing, using the one or more processors: a first determination of whether the rearrangement event is in-strand; and a second determination of whether the sequence encoding the predetermined protein domain is impacted by the rearrangement event; assigning, using the one or more processors, the genomic variant to a functional status group based on the first determination and the second determination; and identifying, using the one or more processors, the treatment based on the assigned functional status group
  • determining whether the sequence encoding the predetermined protein domain is impacted by the rearrangement event comprises: determining, whether a functional domain of the first gene is in the sequence read data corresponding to the rearrangement event.
  • a method for diagnosing a disease comprising: diagnosing that a subject has the disease based on a determination of a functional status group of a genomic variant in a sample from the subject, wherein the functional status group of the genomic variant is determined according to the method of any one of clauses 35 to 67.
  • a method of selecting an anti-cancer therapy comprising: responsive to determining a functional status group of a genomic variant in a sample from a subject, selecting an anti-cancer therapy for the subject, wherein the functional status group of the genomic variant is determined according to the method of any one of clauses 35 to 67.
  • a method of treating a cancer in a subject comprising: responsive to determining a functional status group of a genomic variant in a sample from the subject, administering an effective amount of an anti-cancer therapy to the subject, wherein the functional status group of the genomic variant is determined according to the method of any one of clauses 35 to 67.
  • a method for monitoring cancer progression or recurrence in a subject comprising: determining a first functional status group of a genomic variant in a first sample obtained from the subject at a first time point according to the method of any one of clauses 35 to 67; determining a second functional status group of a genomic variant in a second sample obtained from the subject at a second time point; and comparing the first functional status group to the second functional status group, thereby monitoring the cancer progression or recurrence.
  • genomic profile for the subject further comprises results from a comprehensive genomic profiling (CGP) test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof.
  • CGP genomic profiling
  • a system comprising: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to perform a method comprising: receiving, at one or more processors, sequence read data associated with a genomic variant in a sample from an individual; determining, using the one or more processors, a breakpoint of the genomic variant and a location of the breakpoint based on the sequence read data, the breakpoint associated with a rearrangement event and further associated with a first gene and a second gene of the sample, the first gene corresponding to the genomic variant; if the breakpoint of the genomic variant is located outside a sequence encoding a predetermined protein domain, performing, using the one or more processors: a first determination of whether the rearrangement event is in-strand; and a second determination of whether the sequence encoding the predetermined protein domain is impacted by the rearrangement event; assigning, using the one or more processors, the genomic variant to a functional status group
  • 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, at one or more processors, sequence read data associated with a genomic variant in a sample from an individual; determine, using the one or more processors, a breakpoint of the genomic variant and a location of the breakpoint based on the sequence read data, the breakpoint associated with a rearrangement event and further associated with a first gene and a second gene of the sample, the first gene corresponding to the genomic variant; if the breakpoint of the genomic variant is located outside a sequence encoding a predetermined protein domain, perform, using the one or more processors: a first determination of whether the rearrangement event is in-strand; and a second determination of whether the sequence encoding the predetermined protein domain is impacted by the rearrangement event; assign, using the one or more processors, the genomic variant to a functional status group based on the first determination and the second determination;
  • a method for identifying a treatment based on a genomic variant in a sample from an individual comprising: receiving, at one or more processors, sequence read data associated with the genomic variant in the sample; determining, using the one or more processors, one or more breakpoints of the genomic variant and a location of the breakpoint based on the sequence read data, the breakpoint associated with a rearrangement event; if the one or more breakpoints of the genomic variant are located outside of a sequence encoding a predetermined protein domain, performing, using the one or more processors: a determination of whether the sequence encoding the predetermined protein domain is in the rearrangement event; assigning, using the one or more processors, the genomic variant to a functional status group based on the determination; and identifying, using the one or more processors, the treatment based on the assigned functional status group of the genomic variant.
  • a method for diagnosing a disease comprising: diagnosing that a subject has the disease based on a determination of a functional status group of a genomic variant in a sample from the subject, wherein the functional status group of the genomic variant is determined according to the method of any one of clauses 92 to 111.
  • a method of selecting an anti-cancer therapy comprising: responsive to determining a functional status group of a genomic variant in a sample from a subject, selecting an anti-cancer therapy for the subject, wherein the functional status group of the genomic variant is determined according to the method of any one of clauses 92 to 111.
  • a method of treating a cancer in a subject comprising: responsive to determining a functional status group of a genomic variant in a sample from the subject, administering an effective amount of an anti-cancer therapy to the subject, wherein the functional status group of the genomic variant is determined according to the method of any one of clauses 92 to 111.
  • a method for monitoring cancer progression or recurrence in a subject comprising: determining a first functional status group of a genomic variant in a first sample obtained from the subject at a first time point according to the method of any one of clauses 92 to 111; determining a second functional status group of a genomic variant in a second sample obtained from the subject at a second time point; and comparing the first functional status group to the second functional status group, thereby monitoring the cancer progression or recurrence.
  • a system comprising: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to perform a method comprising: receiving, at one or more processors, sequence read data associated with a genomic variant in a sample from an individual; determining, using the one or more processors, one or more breakpoints of the genomic variant and a location of the one or more breakpoints based on the sequence read data, the one or more breakpoints associated with a rearrangement event; if the one or more breakpoints of the genomic variant are located outside of a sequence encoding a predetermined protein domain, performing, using the one or more processors: a determination of whether the sequence encoding the predetermined protein domain is in the rearrangement event; assigning, using the one or more processors, the genomic variant to a functional status group based on the determination; and identifying, using the one or more processors, the treatment based on the assigned functional status group of the genomic
  • 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, at one or more processors, sequence read data associated with a genomic variant in a sample from an individual; determine, using the one or more processors, one or more breakpoints of the genomic variant and a location of the one or more breakpoints based on the sequence read data, the one or more breakpoints associated with a rearrangement event; if the one or more breakpoints of the genomic variant are located outside of a sequence encoding a predetermined protein domain, perform, using the one or more processors: a determination of whether the sequence encoding the predetermined protein domain is in the rearrangement event; assign, using the one or more processors, the genomic variant to a functional status group based on the determination; and identify, using the one or more processors, the treatment based on the assigned functional status group of the genomic variant.

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Abstract

Des modes de réalisation de la présente divulgation concernent des systèmes et des méthodes permettant de déterminer un état fonctionnel d'un variant génomique. Par exemple, des méthodes selon la présente divulgation comprennent la réception de données de lecture de séquence pour une pluralité de lectures de séquence et la détermination d'un point de cassure d'un variant génomique et d'un emplacement du point de rupture sur la base des données de lecture de séquence, du point de cassure associé à un événement de réarrangement et également associé à un premier gène et à un second gène de l'échantillon. Si le point de cassure du variant génomique se situe hors d'une séquence codant un domaine de protéine prédéterminé, le système peut effectuer une première détermination que l'événement de réarrangement est en brin et une seconde détermination que la séquence codant le domaine de protéine prédéterminé est affectée par l'événement de réarrangement. Sur la base des première et seconde déterminations, le système peut attribuer la variante génomique à un groupe d'états fonctionnels.
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