Methods and systems for determining genomic variant clonality using multi-region sequencing assays and algorithms

Abstract

Methods for determining genomic variant clonality using multi-region sequencing assays and algorithms are described. For example, the methods can include receiving, at one or more processors, sequence read data for a sample obtained from a subject. The sequence read data is obtained from nucleic acid molecules (extracted, e.g., via needle punch) from a plurality of target regions within the sample (e.g., spatially distinct regions of a tissue biopsy sample). For each target region within the sample, a set of reads from the sequence read data associated with the respective target region is identified, then a presence of a candidate genomic variant within the target region is detected based on the set of reads. A total number of target regions in which the presence of the candidate genomic variant is compared to a first predetermined threshold. Based on the comparison, clonality of the candidate genomic variant is determined.

Description

Docket No.: 197102019540METHODS AND SYSTEMS FOR DETERMINING GENOMIC VARIANT CLONALITY USING MULTI-REGION SEQUENCING ASSAYS AND ALGORITHMSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application No. 63 / 690,148, filed September 3, 2024, which is hereby incorporated by reference in its entirety.FIELD

[0002] The present disclosure relates generally to methods and systems for analyzing genomic profiling data, and more specifically to determining genomic variant clonality using genomic profiling data.BACKGROUND

[0003] Tumor spatial profiling of real- world cancer specimens for the heterogeneity of actionable genomic alterations (AGAs) is relevant to the diagnosis and treatment of cancer patients. Small biopsy procedures and laboratory tumor microdissection practices may impact the detection of AGAs of various categories, such as patient- specific genomic variants, via sequencing. Multiple factors may be considered when selecting which genomic variants to detect via sequencing — one such factor is the evolutionary origin, and specifically, the clonality, of each genomic variants. If a candidate genomic variant is clonal (e.g., the genomic variant is present in most of the cells within the tumor), then the genomic variant is likely driving tumor initiation and / or tumor progression and may respond favorably to treatment. For example, the genomic variant EGFR L858R (in NSCLC), the genomic variant BRAF V600E (in melanoma), and the genomic variant IDH1 R132C (in cholangiocarcinoma) are known clonal drivers for their respective tumor types and have clinically-approved targeted therapies against them.Conversely, if a candidate genomic variant is sub-clonal (e.g., the genomic variant is present in only some cells in the tumor), the detection of the genomic variant in the baseline tumor specimen may be attributed to tissue geospatial sampling bias, and there may be no guarantee that the genomic variant is driving tumor initiation and / or tumor progression.1MOFO-359739214Docket No.: 197102019540

[0004] Traditional sequencing techniques to detect the presence of genomic variants in a sample from a patient involve extracting and sequencing biological analytes (e.g., tissue, DNA, RNA, blood) from one location of the sample. However, when used for detecting the clonality of genomic variants, these traditional sequencing techniques fall short because it is difficult to predict whether a genomic variant is present in many / most of the cells within the tumor (e.g., whether the genomic variant is clonal) when only one location is sampled. These traditional sequencing techniques fail to determine genomic variant clonality with confidence due, at least in part, to the limited sampling locations and, as such, the traditional sequencing techniques fail to detect clonal variants driving tumor initiation and / or tumor progression. Thus, the need exists for novel approaches to determine genomic variant clonality with greater confidence in order to detect clonal variants and treat the associated tumors accordingly.BRIEF SUMMARY OF THE INVENTION

[0005] Disclosed herein are methods and systems for determining genomic variant clonality using genomic profiling data. The methods and systems described herein involve the use of multi-region sequencing assays and algorithms to determine genomic variant clonality. For example, multiple target regions of a sample can be sampled (e.g., using needle punch extraction techniques) and sequenced to determine clonality in one or more of the target regions. Based on the determined clonality, a given genomic variant (e.g., a candidate genomic variant) can be selected and / or monitored. The use of multi-region sequencing techniques to determine clonality increases confidence in the clonality determination relative to traditional sequencing techniques, since sampling and sequencing multiple regions of a sample enables a more accurate estimate of whether the genomic variant is present in most of the cells within the tumor (e.g., whether the genomic variant is clonal) relative to sampling from just one region of a sample. Thus, the methods and systems described herein provide improved techniques for determining genomic variant clonality with greater confidence in order to detect clonal variants and treat the associated tumors accordingly.

[0006] Embodiments of the present disclosure may receive, at one or more processors, sequence read data for a sample obtained from a subject. The sequence read data may be obtained via multi-region sequencing techniques (e.g., needle punch extraction) from a plurality of target2MOFO-359739214Docket No.: 197102019540 regions within the sample (e.g., spatially distinct regions of a tissue biopsy sample). For each target region within the sample, a set of reads from the sequence read data associated with the respective target region may be identified, then a presence of a candidate genomic variant within the target region may be detected based on the set of reads. A total number of target regions in which the presence of the candidate genomic variant may be compared to a first predetermined threshold (e.g., a percentage of the plurality of target regions). Based on the comparison, clonality of the candidate genomic variant may be determined.

[0007] In some instances, determining a clonality of the candidate genomic variant may include confirming clonality between the plurality of target regions and / or determining that the candidate genomic variant is a clonal or subclonal variant. In some instances, if the candidate genomic variant is determined to be a clonal variant, the system may select the candidate genomic variant for monitoring (e.g., for inclusion in a monitoring assay). Clonality-informed selection of genomic variants for monitoring can improve the efficiency of monitoring and treatment because clonal variants are more likely than subclonal variants to drive tumor initiation and / or tumor progression and are more likely to respond favorably to treatment.

[0008] In some embodiments, provided herein is a method for determining genomic variant clonality, the method comprising: obtaining a first plurality of nucleic acid molecules from a first target region of a sample obtained from a subject; obtaining a second plurality of nucleic acid molecules from a second target region of the sample, wherein the second target region of the sample is from a different spatial location within the sample than the first target region; sequencing, by a sequencer, the first plurality of nucleic acid molecules or amplicons thereof to obtain a first plurality of sequence reads; sequencing, by the sequencer, the second plurality of nucleic acid molecules or amplicons thereof to obtain a second plurality of sequence reads; detecting, using one or more processors, a presence of a candidate genomic variant within the first and / or second target region(s) based on the first and second pluralities of sequence reads; determining, using the one or more processors, a number of target regions in which the presence of the candidate genomic variant is detected; comparing the number of target regions to a first predetermined threshold; and based on the comparison, determining a clonality of the candidate genomic variant.3MOFO-359739214Docket No.: 197102019540

[0009] In some embodiments, provided herein is a method for determining genomic variant clonality, the method comprising: receiving, at one or more processors, sequence read data for a sample obtained from a subject, wherein the sequence read data is associated with a plurality of target regions within the sample, wherein each target region of the plurality is from a different spatial location within the sample; for each target region of the plurality of target regions within the sample: identifying, using the one or more processors, a set of reads from the sequence read data associated with the target region; and detecting, using the one or more processors, a presence of a candidate genomic variant within the target region based on the set of reads; determining, using the one or more processors, a number of target regions in which the presence of the candidate genomic variant is detected; comparing the number of target regions to a first predetermined threshold; and based on the comparison, determining a clonality of the candidate genomic variant.

[0010] In some embodiments, provided herein is a method for monitoring progression or recurrence of cancer in a subject, comprising: receiving, at one or more processors, sequence read data for a sample obtained from a subject, wherein the sequence read data is associated with a plurality of target regions within the sample; for each target region of the plurality of target regions: identifying, using the one or more processors, a set of reads from the sequence read data associated with the target region; and detecting, using the one or more processors, a presence of a candidate genomic variant within the target region based on the set of reads; determining, using the one or more processors, a number of target regions in which the presence of the candidate genomic variant is detected; comparing the number of target regions to a first predetermined threshold; based on the comparison, determining a clonality of the candidate genomic variant; and in accordance with a determination of the clonality of the candidate genomic variant, detecting the candidate genomic variant in nucleic acid molecules from a liquid biopsy sample obtained from the subject, wherein the liquid biopsy sample comprises cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), cell-free RNA, or any combination thereof, and wherein the candidate genomic variant is detected in the cfDNA, the ctDNA, nucleic acid molecules from the CTCs, the cell-free RNA, or amplicons thereof.4MOFO-359739214Docket No.: 197102019540

[0011] In some embodiments, provided herein is a method for monitoring response to treatment in a subject having cancer, comprising: receiving, at one or more processors, sequence read data for a sample obtained from a subject, wherein the sequence read data is associated with a plurality of target regions within the sample; for each target region of the plurality of target regions: identifying, using the one or more processors, a set of reads from the sequence read data associated with the target region; and detecting, using the one or more processors, a presence of a candidate genomic variant within the target region based on the set of reads; determining, using the one or more processors, a number of target regions in which the presence of the candidate genomic variant is detected; comparing the number of target regions to a first predetermined threshold; based on the comparison, determining a clonality of the candidate genomic variant; administering one or more anti-cancer therapies to the subject; and in accordance with a determination of the clonality of the candidate genomic variant, detecting the candidate genomic variant in nucleic acid molecules from a liquid biopsy sample obtained from the subject after administration of the one or more anti-cancer therapies for cancer, wherein the liquid biopsy sample comprises cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), cell-free RNA, or any combination thereof, and wherein the candidate genomic variant is detected in the cfDNA, the ctDNA, nucleic acid molecules from the CTCs, the cell-free RNA, or amplicons thereof.

[0012] In some embodiments, provided herein is a method for treating or delaying progression of cancer in a subject, comprising: receiving, at one or more processors, sequence read data for a sample obtained from a subject, wherein the sequence read data is associated with a plurality of target regions within the sample; for each target region of the plurality of target regions: identifying, using the one or more processors, a set of reads from the sequence read data associated with the target region; and detecting, using the one or more processors, a presence of a candidate genomic variant within the target region based on the set of reads; determining, using the one or more processors, a number of target regions in which the presence of the candidate genomic variant is detected; comparing the number of target regions to a first predetermined threshold; based on the comparison, determining a clonality of the candidate genomic variant; and in accordance with a determination of the clonality of the candidate genomic variant,5MOFO-359739214Docket No.: 197102019540 administering to the subject an effective amount of a targeted therapeutic agent, wherein the targeted therapeutic agent is selected based on the candidate genomic variant.

[0013] In some embodiments, provided herein is a method for determining genomic variant clonality, the method comprising: obtaining a first plurality of nucleic acid molecules from a first target region of a sample obtained from a subject; obtaining a plurality of nucleic acid molecules from one or more additional target regions of the sample, wherein the one or more additional target regions of the sample are each from a different spatial location and from a spatial location different from the first target region; sequencing, by a sequencer, the first plurality of nucleic acid molecules or amplicons thereof to obtain a first plurality of sequence reads; sequencing, by the sequencer, the plurality of nucleic acid molecules or amplicons thereof from the one or more additional target region to obtain a plurality of sequence reads for each additional target region; detecting, using one or more processors, a presence of a candidate genomic variant within the first and / or each additional target region(s) based on the pluralities of sequence reads; determining, using the one or more processors, a number of target regions in which the presence of the candidate genomic variant is detected; and determining the candidate genomic variant as being clonal when a ratio of the first target region and each additional target region having the candidate genomic variant to all target regions is greater than or equal to a threshold. In some embodiments, the threshold is 0.5.

[0014] In some embodiments, provided herein is a method for determining genomic variant clonality, the method comprising: obtaining a first plurality of nucleic acid molecules from a first target region of a sample obtained from a subject; obtaining a plurality of nucleic acid molecules from one or more additional target regions of the sample, wherein the one or more additional target regions of the sample are each from a different spatial location and from a spatial location different from the first target region; sequencing, by a sequencer, the first plurality of nucleic acid molecules or amplicons thereof to obtain a first plurality of sequence reads; sequencing, by the sequencer, the plurality of nucleic acid molecules or amplicons thereof from the one or more additional target region to obtain a plurality of sequence reads for each additional target region; detecting, using one or more processors, a presence of a candidate genomic variant within the first and / or each additional target region(s) based on the pluralities of sequence reads;6MOFO-359739214Docket No.: 197102019540 determining, using the one or more processors, a number of target regions in which the presence of the candidate genomic variant is detected; and determining the candidate genomic variant as being clonal if at least one of: (1) a ratio of the first target region and each additional target region having the candidate genomic variant to all target regions is equal to or great than a first threshold; or (2) a clonality of all target regions determined using a cancer cell fraction (CCF) score is greater than or equal to a second threshold. In some embodiments, the first threshold is 0.5. In some embodiments, the second threshold is 0.5. In some embodiments, the CCF score represents an allele frequency of the candidate genomic variant proportional to tumor purity of the sample. In some embodiments, the CCF score is proportional to a ratio of an allele frequency of the candidate genomic variant to: (1) a number of mutant copies of its gene and / or (2) tumor purity of the sample.

[0015] In some embodiments according to any one of the embodiments disclosed herein, determining a clonality comprises confirming clonality between or among the plurality of target regions. In some embodiments, the number of target regions in which the presence of the candidate genomic variant is detected is greater than or equal to the first predetermined threshold. In some embodiments, determining the clonality of the candidate genomic variant comprises determining that the candidate genomic variant is a clonal variant. In some embodiments, the number of target regions in which the presence of the candidate genomic variant is detected is less than the first predetermined threshold. In some embodiments, determining the clonality of the candidate genomic variant comprises determining that the candidate genomic variant is a subclonal variant. In some embodiments, the first predetermined threshold is two target regions. In some embodiments, the first predetermined threshold is about 50% of a total number of target regions in the plurality of target regions. In some embodiments, the method further comprises selecting the target regions of the plurality. In some embodiments, the target regions of the plurality are selected to maximize distance between target regions of the plurality. In some embodiments, the sample is a tissue sample comprising tumor cells. In some embodiments, the sample is a tumor biopsy sample. In some embodiments, the sample is a formalin-fixed paraffin-embedded (FFPE) sample. In some embodiments, the plurality of target regions, or tissues, cells, or nucleic acid molecules therefrom, are extracted from the sample using a needle punch method. In some embodiments, each target region of the plurality of target7MOFO-359739214Docket No.: 197102019540 regions is visually distinct from other target regions of the plurality. In some embodiments, at least 20% of the target regions of the plurality of target regions comprise tumor cells. In some embodiments, the plurality of target regions includes at least three target regions. In some embodiments, the first and the second pluralities of nucleic acid molecules comprise tumor nucleic acids. In some embodiments, the sample comprises at least one liquid biopsy sample. In some embodiments, the at least one liquid biopsy sample comprises circulating tumor DNA (ctDNA) and / or circulating tumor cells (CTCs). In some embodiments, the first and second pluralities of sequence reads or the sequence read data are obtained from nucleic acid molecules from cancer cells. In some embodiments, the first and second pluralities of sequence reads or the sequence read data are obtained using next generation sequencing. In some embodiments, the next generation sequencing is performed on one or more of: the whole genome, whole transcriptome, whole exome, a plurality of specific regions of the genome, or a plurality of specific regions of the transcriptome. In some embodiments, the method further comprises estimating, using the one or more processors, a cancer cell fraction (CCF) of the candidate genomic variant based on a tumor purity of the sample; and comparing the CCF to a second predetermined threshold. In some embodiments, the second predetermined threshold is 0.5. In some embodiments, the number of target regions in which the presence of the candidate genomic variant is detected is greater than or equal to the first predetermined threshold, wherein the CCF of the candidate genomic variant is greater than or equal to the second predetermined threshold, and wherein determining a clonality of the candidate genomic variant comprises determining that the candidate genomic variant is clonal. In some embodiments, the method further comprises in accordance with a determination of the clonality of the candidate genomic variant, monitoring the candidate genomic variant in a monitoring assay. In some embodiments, the method further comprises in accordance with a determination of the clonality of the candidate genomic variant, detecting the candidate genomic variant in nucleic acid molecules or amplicons thereof from a liquid biopsy sample obtained from the subject, wherein the liquid biopsy sample comprises cell- free DNA (cfDNA), circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), cell-free RNA, or any combination thereof, and wherein the candidate genomic variant is detected in the cfDNA, the ctDNA, nucleic acid molecules from the CTCs, the cell-free RNA, or amplicons thereof. In some embodiments, the candidate genomic variant is detected by quantitative polymerase chain reaction (qPCR). In some embodiments, detection of the candidate genomic8MOFO-359739214Docket No.: 197102019540 variant by qPCR comprises use of a primer pair specific for the candidate genomic variant. In some embodiments, the method further comprises generating an output indicative of the clonality of the candidate genomic variant. In some embodiments, the output comprises a report recommending that the candidate genomic variant be added to or removed from a monitoring assay. In some embodiments, the output comprises a confidence level of the determination of the clonality of the candidate genomic variant. In some embodiments, the method further comprises in accordance with a determination of the clonality of the candidate genomic variant, administering to the subject an effective amount of a targeted therapeutic agent, wherein the targeted therapeutic agent is selected based on the candidate genomic variant or a polypeptide encoded by the candidate genomic variant. In some embodiments, the candidate genomic variant is a biomarker associated with cancer. In some embodiments, the subject has, has been diagnosed with, is suspected of having, or is being screened for having cancer. In some embodiments, the cancer is a B cell cancer, a melanoma, breast cancer, lung cancer, bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain cancer, central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine cancer, endometrial cancer, cancer of an oral cavity, cancer of a pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel cancer, appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, a cancer of hematological tissue, an adenocarcinoma, an inflammatory myofibroblastic tumor, a gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), acute lymphocytic leukemia (ALL), acute myelocytic leukemia (AML), chronic myelocytic leukemia (CML), chronic lymphocytic leukemia (CLL), polycythemia Vera, Hodgkin lymphoma, non-Hodgkin lymphoma (NHL), soft-tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endothelio sarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, bladder carcinoma, epithelial carcinoma, glioma, astrocytoma, medulloblastoma,9MOFO-359739214Docket No.: 197102019540 craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, neuroblastoma, retinoblastoma, follicular lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, hepatocellular carcinoma, thyroid cancer, gastric cancer, head and neck cancer, small cell cancer, essential thrombocythemia, agnogenic myeloid metaplasia, hypereosinophilic syndrome, systemic mastocytosis, familiar hypereosinophilia, chronic eosinophilic leukemia, neuroendocrine cancers, or a carcinoid tumor.

[0016] It should be appreciated that all combinations of the foregoing concepts and additional concepts discussed in greater detail below (provided such concepts are not mutually inconsistent) are contemplated as being part of the inventive subject matter disclosed herein. In particular, all combinations of claimed subject matter appearing at the end of this disclosure are contemplated as being part of the inventive subject matter disclosed herein.INCORPORATION BY REFERENCE

[0017] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference in their entirety to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference in its entirety. In the event of a conflict between a term herein and a term in an incorporated reference, the term herein controls.BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Various aspects of the disclosed methods, devices, and systems are set forth with particularity in the appended claims. A better understanding of the features and advantages of the disclosed methods, devices, and systems will be obtained by reference to the following detailed description of illustrative embodiments and the accompanying drawings, of which:

[0019] FIG. 1 provides a non-limiting example of a process for determining genomic variant clonality.

[0020] FIG. 2 provides a non-limiting example of a process for determining whether a genomic variant is a clonal variant or a subclonal variant.10MOFO-359739214Docket No.: 197102019540

[0021] FIG. 3A provides a non-limiting example of a flowchart illustrating a multi-region sequencing assay for determining genomic variant clonality.

[0022] FIG. 3B provides a non-limiting example of the flowchart illustrating a multi-region sequencing assay for determining genomic variant clonality, the assay being represented in graphical form.

[0023] FIG. 4 provides a non-limiting example of a process for estimating cancer cell fraction of a genomic variant to be used in determining clonality of the genomic variant.

[0024] FIG. 5 depicts an exemplary computing device or system in accordance with one embodiment of the present disclosure.

[0025] FIG. 6 depicts an exemplary computer system or computer network, in accordance with some instances of the systems described herein.DETAILED DESCRIPTION

[0026] Methods and systems for determining genomic variant clonality are described. The methods and systems described herein involve the use of multi-region sequencing assays and algorithms to determine genomic variant clonality. For example, multiple target regions of a sample can be sampled (e.g., using needle punch extraction techniques) and sequenced to determine clonality in one or more of the target regions. Based on the determined clonality, a given genomic variant (e.g., a candidate genomic variant) can be selected and / or monitored.

[0027] The disclosed methods and systems provide a number of technical advantages. For example, the use of multi-region sequencing techniques to determine clonality increases confidence in the clonality determination relative to traditional sequencing techniques, since sampling and sequencing multiple regions of a sample enables a more accurate estimate of whether the genomic variant is present in most of the cells within the tumor (e.g., whether the genomic variant is clonal) relative to sampling from just one region of a sample. Thus, the methods and systems described herein provide improved techniques for determining genomic variant clonality with greater confidence in order to detect clonal variants and treat the associated11MOFO-359739214Docket No.: 197102019540 tumors accordingly. Further, the detection of clonal variants may lead to the identification of new tumor-inducing variants, which could potentially be targeted via a treatment (e.g., an anticancer therapy). For example, cancer genomes may be screened for the clonal variants (e.g., spatially-concordant somatic genomic alterations) to identify novel diagnostic and therapeutic targets.

[0028] In some instances, the methods for determining genomic variant clonality described herein can be performed using one or more exemplary systems (e.g., one or more electronic devices). For example, an exemplary system configured to implement the methods can receive, at one or more processors, sequence read data for a sample (e.g., tissue biopsy sample containing tumor tissue) obtained from a subject. The sequence read data can be obtained (e.g., via needle punch extraction techniques) from a plurality of target regions within the sample (e.g., spatially distinct regions of the tissue biopsy sample). For each target region within the sample, the system can identify, using the one or more processors, a set of reads from the sequence read data associated with the respective target region, then detect, using the one or more processors, a presence of a candidate genomic variant (e.g., a biomarker associated with cancer) within the target region based on the set of reads. The system can determine, using the one or more processors, a total number of target regions in which the presence of the candidate genomic variant is detected, then compare the number of target regions to a first predetermined threshold. Based on the comparison, the system can determine a clonality of the candidate genomic variant.

[0029] In some instances, determining a clonality of the candidate genomic variant can include confirming clonality between the plurality of target regions and / or determining that the candidate genomic variant is a clonal or subclonal variant. In some instances, if the candidate genomic variant is determined to be a clonal variant, the system may select the candidate genomic variant for monitoring (e.g., for inclusion in a monitoring assay).

[0030] Further, in addition to using multi-region sequencing techniques on the plurality of target regions of the sample, clonality of the candidate genomic variant can also be determined using algorithmic techniques, such as calculating a cell cancer fraction (CCF) of the sample. The combined approach of detecting clonal variants using both multi-region sequencing and an12MOFO-359739214Docket No.: 197102019540 algorithm (e.g., the CCF algorithm) can be used to further increase the confidence level of the determination of genomic variant clonality.Definitions

[0031] Unless otherwise defined, all of the technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art in the field to which this disclosure belongs.

[0032] As used in this specification and the appended claims, the singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise. Any reference to “or” herein is intended to encompass “and / or” unless otherwise stated.

[0033] ‘ ‘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.

[0034] As used herein, the terms "comprising" (and any form or variant of comprising, such as "comprise" and "comprises"), "having" (and any form or variant of having, such as "have" and "has"), "including" (and any form or variant of including, such as "includes" and "include"), or "containing" (and any form or variant of containing, such as "contains" and "contain"), are inclusive or open-ended and do not exclude additional, un-recited additives, components, integers, elements, or method steps.

[0035] The term “sample,” as used herein, refers to a composition that is obtained or derived from a subject and / or individual of interest that contains a cellular and / or other molecular entity that is to be characterized and / or identified, for example, based on physical, biochemical, chemical, and / or physiological characteristics. For example, the phrase “disease sample” and variations thereof refers to any sample obtained from a subject of interest that would be expected or is known to contain the cellular and / or molecular entity that is to be characterized. Samples include, but are not limited to, tissue samples, primary or cultured cells or cell lines, cell supernatants, cell lysates, platelets, serum, plasma, vitreous fluid, lymph fluid, synovial fluid,13MOFO-359739214Docket No.: 197102019540 follicular fluid, seminal fluid, amniotic fluid, milk, whole blood, plasma, serum, blood-derived cells, urine, cerebro-spinal fluid, saliva, sputum, tears, perspiration, mucus, tumor lysates, and tissue culture medium, tissue extracts such as homogenized tissue, tumor tissue, cellular extracts, and combinations thereof. In some instances, the sample is a whole blood sample, a plasma sample, a serum sample, or a combination thereof. In some embodiments, the sample is from a tumor (e.g., a “tumor sample”), such as from a biopsy. In some embodiments, the sample is a formalin-fixed paraffin-embedded (FFPE) sample.

[0036] A “tumor cell” as used herein, refers to any tumor cell present in a tumor or a sample thereof. Tumor cells may be distinguished from other cells that may be present in a tumor sample, for example, stromal cells and tumor-infiltrating immune cells, using methods known in the art and / or described herein.

[0037] As used herein, the terms “individual,” “patient,” or “subject” are used interchangeably and refer to any single animal, e.g., a mammal (including such non-human animals as, for example, dogs, cats, horses, rabbits, zoo animals, cows, pigs, sheep, and non-human primates) for which treatment is desired. In particular embodiments, the individual, patient, or subject herein is a human.

[0038] The terms “cancer” and “tumor” are used interchangeably herein. These terms refer to the presence of cells possessing characteristics typical of cancer-causing cells, such as uncontrolled proliferation, immortality, metastatic potential, rapid growth and proliferation rate, and certain characteristic morphological features. Cancer cells are often in the form of a tumor, but such cells can exist alone within an animal, or can be a non-tumorigenic cancer cell, such as a leukemia cell. These terms include a solid tumor, a soft tissue tumor, or a metastatic lesion. As used herein, the term “cancer” includes premalignant, as well as malignant cancers.

[0039] As used herein, “treatment” (and grammatical variations thereof such as “treat” or “treating”) refers to clinical intervention e.g., administration of an anti-cancer agent or anticancer therapy) in an attempt to alter the natural course of the individual being treated, and can be performed either for prophylaxis or during the course of clinical pathology. Desirable effects of treatment include, but are not limited to, preventing occurrence or recurrence of disease,14MOFO-359739214Docket No.: 197102019540 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.

[0040] As used herein, the term “subgenomic interval” (or “subgenomic sequence interval”) refers to a portion of a genomic sequence.

[0041] As used herein, the term "subject interval" refers to a subgenomic interval or an expressed subgenomic interval (e.g., the transcribed sequence of a subgenomic interval).

[0042] 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.

[0043] The terms “allele frequency” and “allele fraction” are used interchangeably herein and refer to the fraction of sequence reads corresponding to a particular allele relative to the total number of sequence reads for a genomic locus.

[0044] The terms “variant allele frequency” and “variant allele fraction” are used interchangeably herein and refer to the fraction of sequence reads corresponding to a particular variant allele relative to the total number of sequence reads for a genomic locus.

[0045] The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.Methods for determining genomic variant clonality

[0046] The disclosed methods for determining genomic variant clonality involve the use of multi-region sequencing assays and algorithms to determine genomic variant clonality based on genomic profiling data. Based on the determined clonality, a given genomic variant (e.g., a candidate genomic variant) can be selected and / or monitored.15MOFO-359739214Docket No.: 197102019540

[0047] In some embodiments, a sample (e.g., a tissue biopsy sample containing tumor tissue) may be obtained from a subject. Using a multi-region sequencing technique, clinicians may identify target regions that are spatially distant and / or visually distinct (e.g., histologically or morphologically different regions) to identify diverse regions within the sample. Target regions or tissue / cells therefrom may be extracted from the sample (e.g., using needle punch or an equivalent technique).

[0048] Nucleic acid molecules (e.g., DNA and / or RNA) may be obtained or extracted from each target region and independently sequenced to obtain a set of sequence reads for each target region. Upon obtaining genomic data comprising the sequence reads for each target region, the genomic data can be analyzed to determine how many of the target regions’ sequence reads contain a candidate genomic variant. If the candidate genomic variant is present in many and / or most target regions of the sample, then the candidate genomic variant can be considered a clonal variant. An exemplary technique for determining clonality of a candidate genomic variant is described in greater detail at process 100 of FIG. 1, below.

[0049] FIG. 1 provides a non-limiting example of a process 100 for determining genomic variant clonality. Process 100 can be performed, for example, using one or more electronic devices implementing a software platform. In some examples, process 100 is performed using a client-server system (e.g., cloud infrastructure, local virtual private network (VPN), Software as a Service (SaaS), or any other distributed computing system), and the blocks of process 100 are divided up in any manner between the server and a client device. In other examples, the blocks of process 100 are divided up between the server and multiple client devices. Thus, while portions of process 100 are described herein as being performed by particular devices of a clientserver system, it will be appreciated that process 100 is not so limited. In other examples, process 100 is performed using only a client device or only multiple client devices. In process 100, some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the process 100. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.16MOFO-359739214Docket No.: 197102019540

[0050] At block 102, an exemplary system (e.g., one or more electronic devices) receives, at one or more processors, sequence read data for a sample obtained from a subject, wherein the sequence read data is associated with a plurality of target regions within the sample. The sequence read data in some embodiments is generated by a sequencer such as a next generation sequencer (NGS). This sequence read data may then be aligned and processed by a bioinformatics analysis pipeline to generate results associated with the sequence read data.These results may include substitutions, deletions, inversions, rearrangement calls, copy number variations associated with a particular molecule (e.g., loss or gain), genomic stability, tumor mutational burden, loss of zygosity, homologous recombination deficiency (HRD), tumor heterogeneity, tumor fraction, allele frequency, etc. However, the sequence read data may be obtained from the sample of the subject using any of the techniques described herein.

[0051] The sample from which the sequence read data is generated may comprise a tissue biopsy sample, a liquid biopsy sample, or a normal control sample. In some embodiments, the sample is a tissue biopsy sample and may comprise tissue from a local tumor (e.g., non- metastatic). In some embodiments, the sample is a tissue biopsy sample and may comprise metastatic tissue. In other embodiments, the sample is a blood sample and cell free DNA is used as the input to the sequencer to generate the sequence read data.

[0052] In some embodiments, the sample can be divided into target regions. Biological analytes (e.g., tissue, DNA, RNA, blood, etc.) can be extracted from each target region and sequenced to obtain sequence read data. (See flowchart 300 of FIGS. 3A-3B for more details.) In some embodiments, the sample can comprise a tissue sample obtained from a target region of the plurality of target regions (e.g., tumor tissue that contains at least a portion of a tumor). In some embodiments, tissue sample can include nucleic acid molecules from cancer cells of the tumor from the target region. The sequence read data from a given target region can be obtained from the nucleic acid molecules from the cancer cells of the tumor from the target region.

[0053] At block 104, the system performs actions pertaining to one or more target regions of the plurality of target regions within the sample. For example, the system can, for each target region of the plurality of target regions within the sample, identify a set of reads associated with the target region from the sequence read data received at block 102 (see block 104a), and / or detect a 17MOFO-359739214Docket No.: 197102019540 presence of a candidate genomic variant within the respective target region based on the set of reads identified at block 104a (see block 104b). In some embodiments, the system can perform additional actions pertaining to the target regions, including but not limited to: counting a total number of target regions from which the sequence read data is collected, identifying a distance between a given target region and other target regions of the plurality of target regions, analyzing the plurality of target regions to determine similarities and differences between target regions (e.g.. using machine vision to determine visual differences between a given target region and other target regions of the plurality of target regions), labeling target regions with tumor identifiers (e.g., contains tumor tissue of a given tumor type, does not contain tumor tissue of a given tumor type) and / or sampling identifiers (e.g., tissue biopsy sample from the colon of the subject, liquid biopsy sample containing blood from the subject), determining summary statistics for the plurality of target regions (e.g., 20% of target regions in the sample contain tumor tissue of a given tumor type, half of the samples were collected at Time A and half of the samples were collected at Time B), etc.

[0054] At block 104a, the system can identify, using one or more processors, a set of reads from the sequence read data. The set of reads can be associated with a target region of the plurality of target regions within the sample. In some embodiments, the sequence read data can be received by the system at block 102. In some embodiments, the sequence read data can be obtained for each target region of the plurality of target regions via an assay, such as a multi-region sequencing assay (see flowchart 300 of FIGS. 3A-3B for more details). Alternatively, in some embodiments, the sequence read data can be provided to the system by an outside data source, such as a server and / or a database for storing genomic information.

[0055] At block 104b, the system can detect, using one or more processors, a presence of a candidate genomic variant within a target region of the plurality of target regions based on a set of reads. The set of reads can be associated with the target region of the plurality of target regions within the sample. In some embodiments, the set of reads can be identified by the system at block 104a. Alternatively, in some embodiments, the set of reads can be provided to the system by an outside data source, such as a server and / or a database for storing genomic information.18MOFO-359739214Docket No.: 197102019540

[0056] In some embodiments, the candidate genomic variant can be a biomarker, such as a biomarker associated with cancer. In some embodiments, the candidate genomic variant can be associated with a cancer driven by heterogeneity (e.g., heterogeneity of short variants and / or copy number alterations), such as prostate, pancreatic, ovarian, breast, skin, lung, and / or colorectal cancer.

[0057] At block 106, the system can determine, using one or more processors, a number of target regions in which the presence of a candidate genomic variant is detected. In some embodiments, the number of target regions can be an integer, percentage, fraction, value, and / or quantity corresponding to one or more target regions of the plurality of target regions within the sample.

[0058] At block 108, the system can compare a number of target regions (e.g., a number of target regions in which the presence of a candidate genomic variant is detected) to a first predetermined threshold. In some embodiments, the number of target regions can be determined by the system at block 106. Alternatively, in some embodiments, the number of genomic variants can be provided to the system by an outside data source, such as a server and / or a database for storing genomic information.

[0059] Based on the comparison, at block 110, the system can determine a clonality (e.g., of the candidate genomic variant). In some embodiments, determining a clonality (e.g. of the candidate genomic variant) comprises confirming clonality between / among the plurality of target regions of the sample. The determination of clonality of the candidate genomic variant, as performed at block 110, is described in greater detail with respect to process 200 of FIG. 2.

[0060] FIG. 2 provides a non-limiting example of a process 200 for determining whether a genomic variant is a clonal variant or a subclonal variant. Process 200 can be performed, for example, using one or more electronic devices implementing a software platform. In some examples, process 200 is performed using a client-server system (e.g., cloud infrastructure, local virtual private network (VPN), Software as a Service (SaaS), or any other distributed computing system), and the blocks of process 200 are divided up in any manner between the server and a client device. In other examples, the blocks of process 200 are divided up between the server and multiple client devices. Thus, while portions of process 200 are described herein as being19MOFO-359739214Docket No.: 197102019540 performed by particular devices of a client-server system, it will be appreciated that process 200 is not so limited. In other examples, process 200 is performed using only a client device or only multiple client devices. In process 200, 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 200. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.

[0061] At block 202, an exemplary system (e.g., one or more electronic devices) receives a number of target regions in which the presence of a candidate genomic variant is detected. In some embodiments, the number of target regions can be determined by the system see block 106 of FIG. 1). Alternatively, in some embodiments, the number of target regions can be provided to the system by an outside data source, such as a server and / or a database for storing genomic information. Block 202 of FIG. 2 can share any characteristics of block 106 of FIG. 1, and vice versa.

[0062] At block 204, the system can compare the number of target regions to a first predetermined threshold to answer the question: is the number of target regions greater than or equal to a first predetermined threshold? In some embodiments, the first predetermined threshold can be a number, integer, percentage, fraction, value, and / or quantity associated with one or more target regions. For example, the first predetermined threshold can be about two target regions, about three target regions, about four target regions, about five target regions, about six target regions, about seven target regions, about eight target regions, about nine target regions, about ten target regions, about twenty target regions, about thirty target regions, about forty target regions, about fifty target regions, about sixty target regions, about seventy target regions, about eighty target regions, about ninety target regions, and / or about one hundred target regions. In some embodiments, the first predetermined threshold can be about 1%, about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, about 35%, about 40%, about 45%, about 50%, about 55%, about 60%, about 65%, about 70%, about 75%, about 80%, about 85%, about 90%, about 95%, and / or about 99% of a total number of target regions in a plurality of20MOFO-359739214Docket No.: 197102019540 target regions (e.g., the plurality of target regions within the sample). Block 204 of FIG. 2 can share any characteristics of block 108 of FIG. 1, and vice versa.

[0063] At block 206a, if the number of target regions is less than the first predetermined threshold, the system can determine that the candidate genomic variant is a subclonal variant. At block 206b, if the number of target regions is greater than or equal to the first predetermined threshold, the system can determine that the candidate genomic variant is a subclonal variant. Blocks 206a and 206b of FIG. 2 can share any characteristics of block 110 of FIG. 1, and vice versa.

[0064] Referring back to FIG. 1, once genomic variant clonality is determined (e.g., at block 110), the system can perform any number of additional processing and / or analysis steps based on the determination of genomic variant clonality. In some embodiments, after process 100 is performed, the candidate genomic variant can be selected for monitoring (e.g., in a monitoring assay). For example, if the candidate genomic variant is determined to be a clonal variant, it may be selected by the system for further monitoring. In some embodiments, in accordance with a determination of the clonality of the candidate genomic variant, the system can perform further detection steps to detect the candidate genomic variant. For example, the system can detect the candidate genomic variant in nucleic acid molecules from a liquid biopsy sample obtained from the subject. The liquid biopsy sample cam comprise cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), cell-free RNA, or any combination thereof, such that the candidate genomic variant can be detected in the cfDNA, the ctDNA, nucleic acid molecules from the CTCs, the cell-free RNA, or amplicons thereof. In some embodiments, the candidate genomic variant is detected by quantitative polymerase chain reaction (qPCR). For example, the system can detect the candidate genomic variant by qPCR by using a primer pair specific for the candidate genomic variant. In some embodiments, the system can generate an output indicative of the clonality of the candidate genomic variant. For example, the output can include a report recommending that the candidate genomic variant be added to or removed from a monitoring assay, a confidence level of the determination of the clonality of the candidate genomic variant, and / or a recommendation that the subject be administered an effective amount21MOFO-359739214Docket No.: 197102019540 of a targeted therapeutic agent, wherein the targeted therapeutic agent is selected based on the candidate genomic variant.

[0065] FIG. 3A provides a non-limiting example of a flowchart 300 illustrating a multi-region sequencing assay for determining genomic variant clonality. FIG. 3B provides a non-limiting example of the flowchart 300 represented in graphical form.

[0066] At step 302, a sample 352 is extracted from a subject 350. In some embodiments, the sample 352 can comprise a tissue or tumor biopsy sample, a liquid biopsy sample, or a normal control. For example, the sample 352 can be a formalin-fixed paraffin-embedded (FFPE) or tissue block sample. Sample 352 can share any characteristics of other samples described herein, and vice versa. In some embodiments, the sample is or comprises biological tissue or fluid. In some embodiments, the sample is a tissue sample comprising tumor cells and, optionally, nontumor cells. The sample can contain compounds that are not naturally intermixed with the tissue in nature such as preservatives, anticoagulants, buffers, fixatives, nutrients, antibiotics or the like. In one embodiment, the sample is preserved as a frozen sample or as a formaldehyde- or paraformaldehyde-fixed paraffin-embedded (FFPE) tissue preparation. For example, the sample can be embedded in a matrix, e.g., an FFPE block or a frozen sample. In another embodiment, the sample is a blood or blood constituent sample. In yet another embodiment, the sample is a bone marrow aspirate sample. In another embodiment, the sample comprises cell-free DNA (cfDNA) or circulating cell-free DNA (ccfDNA), e.g., tumor cfDNA or tumor ccfDNA

[0067] At step 304, a plurality of target regions within the sample 352 can be identified. The identification of the target regions can be performed by a human (e.g., by a clinician viewing the sample 352 under a microscope to identify an appropriate spacing between target regions), and / or by a computerized process (e.g. by a machine configured to receive image data pertaining to the sample 352 and divide the sample 352 into visually distinct target regions). As shown by the dashed circles in FIG. 3A labeled A-H, the target regions can be spatially distinct regions of the sample 352. In some embodiments, the target regions can be needle punched to extract tissue and / or other biological material (e.g., tumor tissue). In some embodiments, nucleic acid molecules such as DNA and RNA and / or other biological analytes can be extracted from one or more target regions. The target regions can share any characteristics of other target22MOFO-359739214Docket No.: 197102019540 regions described herein, and vice versa. In some embodiments, the target regions are selected, e.g., to maximize distance between target regions of the plurality. In some embodiments, the target regions are spatially distinct, e.g., from different spatial locations within the sample. In some embodiments, the target regions are morphologically / histologically distinct or morphologically / histologically similar, e.g., containing distinct or similar cell types / histologies or morphologies.

[0068] In some embodiments, the total number of target regions sampled varies depending on various factors, including but not limited to: the type(s) of sample to be collected (e.g., liquid biopsy, tissue biopsy), the tissue(s) to be sampled (e.g., skin, liver, blood), the type or heterogeneity (known or suspected) of the cancer, the characteristics of the patient (e.g., age, weight, health conditions), etc. In some embodiments, the plurality of target regions can include at least two target regions, at least three target regions, at least four target regions, at least five target regions, at least six target regions, at least seven target regions, at least eight target regions, at least nine target regions, at least ten target regions, at least twenty target regions, at least thirty target regions, at least forty target regions, at least fifty target regions, at least sixty target regions, at least seventy target regions, at least eighty target regions, at least ninety target regions, and / or at least one hundred target regions. In some embodiments, the plurality of target regions can include up to two target regions, up to three target regions, up to four target regions, up to five target regions, up to six target regions, up to seven target regions, up to eight target regions, up to nine target regions, up to ten target regions, up to twenty target regions, up to thirty target regions, up to forty target regions, up to fifty target regions, up to sixty target regions, up to seventy target regions, up to eighty target regions, up to ninety target regions, and / or up to one hundred target regions.

[0069] In some embodiments, each target region of the plurality of target regions is spatially distinct from other target regions of the plurality of target regions. In some embodiments, the target regions may be selected to maximize the distance between target regions. In some embodiments, the sample may be collected and / or the target regions may be selected such that no target region overlaps with another target region, and that there is at least a minimum spacing between each target region. For example, the sample may be a tissue sample (e.g., FFPE23MOFO-359739214Docket No.: 197102019540 sample), and each target region may be from a different spatial location in a tissue sample. The plurality of target regions may be sampled using a needle punch method. In some embodiments, the spatially distinct target regions may be identified on FFPE slides via computational means (e.g., by applying computer vision functions assisted by a pre-trained neural network). In some embodiments, each target region of the plurality of target regions is visually distinct from other target regions of the plurality of target regions. For example, a clinician may be able to select different target regions to sample based on a visual determination of difference (e.g., cell size, cell shape, color, texture, structure, body part, etc.) between the target regions. In some embodiments, digital pathology computational algorithms may be used to analyze FFPE slides to identify target regions (e.g., spatially distinct and / or visually distinct target regions).

[0070] In some embodiments, some target regions may contain different types of tissue than other target regions. For example, some target regions may contain tumor tissue, whereas other target regions may not contain tumor tissue. In some embodiments, at least 1%, 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%, and / or at least 99% of the target regions of the plurality of target regions comprise cells indicative of a tumor (e.g., tumor cells). In some embodiments, up to 1%, up to 10%, up to 20%, up to 30%, up to 40%, up to 50%, up to 60%, up to 70%, up to 80%, up to 90%, and / or up to 99% of the target regions of the plurality of target regions comprise cells indicative of a tumor (e.g., tumor cells). In some embodiments, the target regions may also contain non-tumor and / or stromal tissue.

[0071] At step 306, nucleic acids or amplicons thereof from each target region can be sequenced. In some embodiments, the nucleic acids can be extracted from tissue from / in the target regions (e.g., tissue extracted via needle punching at step 304). The sequencing of the nucleic acids can be performed using any sequencing modality described herein, including but not limited to: next generation sequencing (e.g., whole genome sequencing, whole exome sequencing), DNA sequencing, RNA sequencing, etc. The sequencing can be performed on one or more of: the whole genome, whole transcriptome, whole exome, a plurality of specific regions of the genome, or a plurality of specific regions of the transcriptome. Step 306 can optionally further comprise extracting nucleic acid molecules from each target region, or from tissues, cells, or nucleic acid24MOFO-359739214Docket No.: 197102019540 molecules obtained from each target region. In some embodiments, nucleic acid molecules extracted from the target region (or tissues / cells therefrom) are sequenced. In some embodiments, nucleic acid molecules extracted from the target region (or tissues / cells therefrom) are subjected to one or more processes (including but not limited to amplification, fragmentation, adapter ligation, end repair, A-tailing, library preparation, and the like), and nucleic acid molecules resulting from these process(es) are sequenced. In some embodiments, amplicons derived from nucleic acid molecules extracted from the target region (or tissues / cells therefrom) are sequenced. In some embodiments, one or more libraries derived from nucleic acid molecules extracted from the target region (or tissues / cells therefrom) are sequenced.

[0072] At step 308, sequence read data can be obtained for each target region. The sequence read data in some embodiments is generated by a sequencer (e.g., a next generation sequencer that sequences nucleic acids at step 306). This sequence read data may then be aligned and processed by a bioinformatics analysis pipeline to generate results associated with the sequence read data. These results may include substitutions, deletions, inversions, rearrangement calls, copy number variations associated with a particular molecule (e.g., loss or gain), genomic stability, tumor mutational burden, loss of zygosity, homologous recombination deficiency (HRD), tumor heterogeneity, tumor fraction, allele frequency, etc. However, the sequence read data may be obtained from the sample of the subject using any of the techniques described herein.

[0073] After process 300 is performed, the sequence read data may optionally be received by the system configured to perform process 100 of FIG. 1. For example, the sequence read data for a sample obtained from a subject, as received by the system’s processors at block 102 of FIG. 1, may be the same sequence read data that is obtained at step 308 of FIG. 3A. In some embodiments, the sequence read data may be transmitted to and / or stored at a database, cloud server, and / or other computer-readable storage medium. In some embodiments, the sequence read data may be shared with a clinician.

[0074] Further, in addition to using multi-region sequencing techniques on the plurality of target regions of the sample, clonality of the candidate genomic variant can also be determined using algorithmic techniques, such as calculating a cell cancer fraction (CCF) of the sample. The 25MOFO-359739214Docket No.: 197102019540 combined approach of detecting clonal variants using both multi-region sequencing and an algorithm (e.g., the CCF algorithm) can be used to further increase the confidence level of the determination of genomic variant clonality. One such example of a combined approach to detecting clonal variants is described in further detail at process 400 of FIG. 4, below.

[0075] FIG. 4 provides a non-limiting example of a process 400 for estimating cancer cell fraction of a genomic variant to be used in determining clonality of the genomic variant. Process 400 can be performed, for example, using one or more electronic devices implementing a software platform. In some examples, process 400 is performed using a client-server system (e.g., cloud infrastructure, local virtual private network (VPN), Software as a Service (SaaS), or any other distributed computing system), and the blocks of process 400 are divided up in any manner between the server and a client device. In other examples, the blocks of process 400 are divided up between the server and multiple client devices. Thus, while portions of process 400 are described herein as being performed by particular devices of a client-server system, it will be appreciated that process 400 is not so limited. In other examples, process 400 is performed using only a client device or only multiple client devices. In process 400, 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 400. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.

[0076] At block 402, system (e.g., one or more electronic devices) receives data pertaining to a candidate genomic variant for which clonality is to be determined. In some embodiments, the data can include a number of target regions of a sample in which the candidate genomic variant was detected. In some embodiments, the number of target regions can be determined by the system (see block 106 of FIG. 1). Alternatively, in some embodiments, the number of target regions can be provided to the system by an outside data source, such as a server and / or a database for storing genomic information. Block 402 of FIG. 4 can share any characteristics of block 402 of FIG. 2, and vice versa.

[0077] In some embodiments, the data at block 402 can further include a cancer cell fraction (CCF) of the candidate genomic variant. Incorporating both CCF and target region data (e.g., 26MOFO-359739214Docket No.: 197102019540 multi-region analysis based on needle punches of the sample) into the genomic variant clonality determination can improve the confidence of the determination. In some embodiments, a candidate genomic variant may be considered a clonal variant if it fulfills certain requirements with respect to either the CCF or its presence in the target regions. For example, in some embodiments, if the CCF of the candidate genomic variant is greater than or equal to 0.5, the candidate genomic variant can be considered a clonal variant. In some embodiments, a candidate genomic variant may be considered a clonal variant if it fulfills certain requirements with respect to both the CCF and its presence in the target regions. For example, in some embodiments, a candidate genomic variant should be detected in greater than or equal to 50% of the target regions of the sample, and the CCF of the candidate genomic variant should be greater than or equal to 0.5, in order for the candidate genomic variant to be considered a clonal variant.

[0078] In some embodiments, the system can calculate a CCF of the candidate genomic variant based on various factors (e.g., tumor purity of the sample in which the candidate genomic variant was detected). In some embodiments, the system can estimate the CCF of the candidate genomic variant using the following equation:

[0079] In the above equation, f= allele frequency of the candidate genomic variant, m = number of mutant copies of the gene, p = tumor purity, and NT = total copies of the gene. Alternatively, in some embodiments, the system can estimate the CCF of the candidate genomic variant using any other suitable equation and / or algorithm.

[0080] At block 404, the system can compare the number of target regions to a first predetermined threshold to answer the question: is the number of target regions greater than or equal to a first predetermined threshold? Block 404 of FIG. 4 can share any characteristics of block 204 of FIG. 2, and vice versa.

[0081] At block 406, if the number of target regions is less than the first predetermined threshold, the system can determine that the candidate genomic variant is a subclonal variant. Block 406 can share any characteristics of block 206a of FIG. 2, and vice versa.27MOFO-359739214Docket No.: 197102019540

[0082] At block 408, if the number of target regions is greater than or equal to the first predetermined threshold, the system can perform an additional comparison. The system can compare the CCF of the candidate genomic variant to a second predetermined threshold to answer the question: is the CCF greater than or equal to a second predetermined threshold? In some embodiments, the second predetermined threshold can be a number, integer, percentage, fraction, value, and / or quantity associated with the CCF. For example, the second predetermined threshold can be a value of about 0.01, about 0.1, about 0.2, about 0.3, about 0.4, about 0.5, about 0.6, about 0.7, about 0.8, about 0.9, and / or about 0.99. In some embodiments, the second predetermined threshold can be a percentage of about 1%, about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, about 35%, about 40%, about 45%, about 50%, about 55%, about 60%, about 65%, about 70%, about 75%, about 80%, about 85%, about 90%, about 95%, and / or about 99%.

[0083] After block 408, if the CCF associated with the candidate genomic variant is less than the second predetermined threshold, the process 400 can return to block 406. The system can determine that the candidate genomic variant is a subclonal variant. Thus, if the number of target regions in which the presence of a candidate genomic variant is detected is greater that or equal to a first predetermined threshold, but the CCF of the candidate genomic variant is less than the second predetermined threshold, the system can determine the candidate genomic variant to be a subclonal variant.

[0084] Alternatively, if the CCF associated with the candidate genomic variant is greater than or equal to the second predetermined threshold, the process 400 can continue to block 410. The system can determine that the candidate genomic variant is a clonal variant. Thus, if the number of target regions in which the presence of a candidate genomic variant is detected is greater that or equal to a first predetermined threshold, and the CCF of the candidate genomic variant is greater than or equal to the second predetermined threshold, the system can determine the candidate genomic variant to be a clonal variant. Block 410 can share any characteristics of block 206b of FIG. 2, and vice versa.

[0085] Once clonality of the candidate genomic variant is determined based on the number of target regions and / or the CCF (e.g., at block 406 and / or 410), the system can perform any 28MOFO-359739214Docket No.: 197102019540 number of additional processing and / or analysis steps based on the determination of genomic variant clonality. (See additional processing steps of process 100 of FIG. 1, above.).

[0086] In some embodiments, a candidate genomic variant of the present disclosure may be further assayed, detected, or monitored. In some embodiments, the candidate genomic variant has been determined to be a clonal variant as described herein. For example, the candidate genomic variant or amplicons thereof can be detected in nucleic acid molecules from a sample, e.g., a liquid biopsy sample. In some embodiments, the candidate genomic variant or amplicons thereof is detected by quantitative polymerase chain reaction (qPCR) or real-time PCR (RT- PCR), e.g., using one or more primer pair(s) specific for the candidate genomic variant or a genomic locus thereof. In some embodiments, the candidate genomic variant or amplicons thereof is detected by sequencing.

[0087] In some aspects, the present disclosure provides methods for monitoring progression or recurrence of cancer in a subject. In some embodiments, the methods comprise detecting a candidate genomic variant of the present disclosure in nucleic acid molecules from a liquid biopsy sample obtained from the subject. In some embodiments, the liquid biopsy sample comprises cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), cell-free RNA, or any combination thereof. In some embodiments, the candidate genomic variant is detected in the cfDNA, the ctDNA, nucleic acid molecules from the CTCs, the cell-free RNA, or amplicons thereof. In some embodiments, the candidate genomic variant is detected or selected for detection / monitoring in accordance with a determination of the clonality of the candidate genomic variant, e.g., that the candidate genomic variant is a clonal variant. In some embodiments, the methods comprise monitoring a candidate genomic variant in a monitoring assay, e.g., in accordance with a determination of the clonality of the candidate genomic variant (such as that the variant is clonal).

[0088] In some embodiments, the methods further comprise administering to the subject an effective amount of a targeted therapeutic agent, e.g., in accordance with a determination of the clonality of the candidate genomic variant (such as if the candidate genomic variant is clonal). In some aspects, the present disclosure provides methods for monitoring response to treatment in a subject having cancer. In some embodiments, the methods comprise administering one or 29MOFO-359739214Docket No.: 197102019540 more anti-cancer therapies to a subject. For example, the anti-cancer therapies can include one or more targeted therapeutic agent(s). In some embodiments, a targeted therapeutic agent is selected based on a candidate genomic variant (e.g., a clonal variant) of the present disclosure being present in a sample obtained from the subject. In some embodiments, the targeted therapeutic agent binds to or inhibits one or more activities (e.g., enzymatic activities) of a polypeptide product of the candidate genomic variant. In some embodiments, the targeted therapeutic agent inhibits or reduces expression level of a polypeptide product of the candidate genomic variant. In some embodiments, the targeted therapeutic agent is approved, indicated, or being tested for treatment of a cancer that expresses the candidate genomic variant. In some embodiments, the methods further comprise ceasing a treatment of the subject, e.g., in accordance with a determination of the clonality of the candidate genomic variant (such as if the candidate genomic variant is subclonal).

[0089] In some embodiments, a sample of the present disclosure is obtained from a subject that has cancer, has been diagnosed with cancer, is thought or suspected to have cancer, or is being screened for cancer. In some embodiments of any of the methods provided herein, the cancer is a carcinoma, a sarcoma, a lymphoma, a leukemia, a myeloma, a germ cell cancer, or a blastoma. In some embodiments, the cancer is a solid tumor. In some embodiments, the cancer is a hematologic malignancy. In some embodiments, the cancer is a B cell cancer, a melanoma, breast cancer, lung cancer, bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain cancer, central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine cancer, endometrial cancer, cancer of an oral cavity, cancer of a pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel cancer, appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, a cancer of hematological tissue, an adenocarcinoma, an inflammatory myofibroblastic tumor, a gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), acute lymphocytic leukemia (ALL), acute myelocytic leukemia (AML), chronic myelocytic leukemia (CML), chronic lymphocytic leukemia (CLL), polycythemia Vera, Hodgkin lymphoma, non-Hodgkin lymphoma (NHL), soft- tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteogenic sarcoma, chordoma,30MOFO-359739214Docket No.: 197102019540 angiosarcoma, endotheliosarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, bladder carcinoma, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, neuroblastoma, retinoblastoma, follicular lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, hepatocellular carcinoma, thyroid cancer, gastric cancer, head and neck cancer, small cell cancer, essential thrombocythemia, agnogenic myeloid metaplasia, hypereosinophilic syndrome, systemic mastocytosis, familiar hypereosinophilia, chronic eosinophilic leukemia, neuroendocrine cancers, or a carcinoid tumor.

[0090] In some embodiments, the cancer is appendix adenocarcinoma, bladder adenocarcinoma, bladder urothelial (transitional cell) carcinoma, breast cancer not otherwise specified (NOS), breast carcinoma NOS, breast invasive ductal carcinoma (IDC), breast invasive lobular carcinoma (ILC), cervix squamous cell carcinoma (SCC), colon adenocarcinoma (CRC), esophagus adenocarcinoma, esophagus carcinoma NOS, esophagus squamous cell carcinoma (SCC), eye intraocular melanoma, gallbladder adenocarcinoma, gastroesophageal junction adenocarcinoma, intra-hepatic cholangiocarcinoma, kidney cancer NOS, liver hepatocellular carcinoma (HCC), lung cancer NOS, lung adenocarcinoma, lung large cell carcinoma, lung nonsmall cell lung carcinoma (NSCLC) NOS, lung small cell undifferentiated carcinoma, lung squamous cell carcinoma (SCC), ovary cancer NOS, pancreas cancer NOS, pancreas ductal adenocarcinoma, pancreatobiliary carcinoma, prostate cancer NOS, prostate acinar adenocarcinoma, prostate ductal adenocarcinoma, rectum adenocarcinoma (CRC), skin melanoma, small intestine adenocarcinoma, soft tissue sarcoma NOS, stomach adenocarcinoma NOS, unknown primary cancer NOS, unknown primary adenocarcinoma, unknown primary carcinoma (CUP) NOS, unknown primary neuroendocrine tumor, unknown primary squamous cell carcinoma (SCC), or uterus endometrial adenocarcinoma NOS.31MOFO-359739214Docket No.: 197102019540

[0091] In some instances, the gene panel may comprise 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, at least 150, at least 200, at least 250, at least 300, at least 350, at least 400, at least 450, at least 500, or more than 500 genes.

[0092] In some instances, the disclosed methods may be used to determine genomic variant clonality by assessing genomic features (e.g., the presence or absence of a candidate genomic variant) in 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, at least 150, at least 200, at least 250, at least 300, at least 350, at least 400, at least 450, at least 500, or more than 500 gene loci.

[0093] In some instances, 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, CEBPA, CHEK1, CHEK2, CIC, CREBBP, CRKL, CSF1R, CSF3R, CTCF, CTNNA1, CTNNB1, CUL3, CUL4A, CXCR4, CYP17A1, DAXX, DDR1, DDR2, DIS3, DNMT3A, DOT1L, EED, EGFR, EMSY (Cl lorf30), EP300, EPHA3, EPHB1, EPHB4, ERBB2, ERBB3, ERBB4, ERCC4, ERG, ERRFI1, ESRI, ETV4, ETV5, ETV6, EWSR1, EZH2, EZR, FAM46C, FANCA, FANCC, FANCG, FANCL, FAS, FBXW7, FGF10, FGF12, FGF14, FGF19, FGF23, FGF3, FGF4, FGF6, FGFR1, FGFR2, FGFR3, FGFR4, FH, FLCN, FLT1, FLT3, FOXL2, FUBP1, GABRA6, GATA3, GATA4, GATA6, GID4 (C17orf39), GNA11, GNA13, GNAQ, GNAS, GRM3, GSK3B, H3F3A, HDAC1, HGF, HNF1A, HRAS, HSD3B1, ID3, IDH1, IDH2, IGF1R, IKBKE, IKZF1, INPP4B, IRF2, IRF4, IRS2, JAK1, JAK2, JAK3, JUN, KDM5A, KDM5C, KDM6A, KDR, KEAP1, KEL, KIT, KLHL6, KMT2A (MLL), KMT2D (MLL2), KRAS, LTK, LYN, MAF, MAP2K1, MAP2K2, MAP2K4, MAP3K1, MAP3K13, MAPK1, MCL1, MDM2, MDM4,32MOFO-359739214Docket No.: 197102019540MED12, MEF2B, MEN1, MERTK, MET, MITF, MKNK1, MLH1, MPL, MRE11A, MSH2, MSH3, MSH6, MST1R, MTAP, MTOR, MUTYH, MYB, MYC, MYCL, MYCN, MYD88, NBN, NF1, NF2, NFE2L2, NFKBIA, NKX2-1, NOTCH1, NOTCH2, NOTCH3, NPM1, NRAS, NT5C2, NTRK1, NTRK2, NTRK3, NUTM1, P2RY8, PALB2, PARK2, PARP1, PARP2, PARP3, PAX5, PBRM1, PDCD1, PDCD1LG2, PDGFRA, PDGFRB, PDK1, PIK3C2B, PIK3C2G, PIK3CA, PIK3CB, PIK3R1, PIM1, PMS2, POLDI, POLE, PPARG, PPP2R1A, PPP2R2A, PRDM1, PRKAR1A, PRKCI, PTCHI, PTEN, PTPN11, PTPRO, QKI, RAC1, RAD21, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54L, RAFI, RARA, RBI, RBM10, REL, RET, RICTOR, RNF43, ROS1, RPTOR, RSPO2, SDC4, SDHA, SDHB, SDHC, SDHD, SETD2, SF3B1, SGK1, SLC34A2, SMAD2, SMAD4, SMARCA4, SMARCB1, SMO, SNCAIP, SOCS1, SOX2, SOX9, SPEN, SPOP, SRC, STAG2, STAT3, STK11, SUFU, SYK, TBX3, TEK, TERC, TERT, TET2, TGFBR2, TIPARP, TMPRSS2, TNFAIP3, TNFRSF14, TP53, TSC1, TSC2, TYRO3, U2AF1, VEGFA, VHL, WHSCI, WHSC1L1, WT1, XPO1, XRCC2, ZNF217, or ZNF703 gene locus, or any combination thereof.

[0094] In some instances, the disclosed methods may be used to identify variants in the ABL, ALK, ALL, B4GALNT1, BAFF, BCL2, BRAF, BRCA, BTK, CD19, CD20, CD3, CD30, CD319, CD38, CD52, CDK4, CDK6, CML, CRACC, CS1, CTLA-4, dMMR, EGFR, ERBB1, ERBB2, FGFR1-3, FLT3, GD2, HDAC, HER1, HER2, HR, IDH2, IL-ip, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSI-H, mTOR, PARP, PD-1, PDGFR, PDGFRa, PDGFRP, PD-L1, PI3K5, PIGF, PTCH, RAF, RANKL, RET, ROS1, SLAMF7, VEGF, VEGFA, or VEGFB gene locus, or any combination thereof.Methods of use

[0095] In some instances, the disclosed methods may further comprise one or more of the steps of: (i) obtaining the sample from the subject (e.g., a subject suspected of having or determined to have cancer), (ii) extracting nucleic acid molecules (e.g., a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules) from the sample, (iii) ligating one or more adapters to the nucleic acid molecules extracted from the sample (e.g., one or more amplification primers, flow cell adaptor sequences, substrate adapter sequences, or sample index sequences), (iv) performing a methylation conversion reaction to convert, e.g., non-methylated cytosine to 33MOFO-359739214Docket No.: 197102019540 uracil, (v) amplifying the nucleic acid molecules (e.g., using a polymerase chain reaction (PCR) amplification technique, a non-PCR amplification technique, or an isothermal amplification technique), (vi) capturing nucleic acid molecules from the amplified nucleic acid molecules (e.g., by hybridization to one or more bait molecules, where the bait molecules each comprise one or more nucleic acid molecules that each comprising a region that is complementary to a region of a captured nucleic acid molecule), (vii) sequencing the nucleic acid molecules extracted from the sample (or library proxies derived therefrom) using, e.g., a next-generation (massively parallel) sequencing technique, a whole genome sequencing (WGS) technique, a whole exome sequencing technique, a targeted sequencing technique, a direct sequencing technique, or a Sanger sequencing technique) using, e.g., a next-generation (massively parallel) sequencer, (viii) combining the nucleic acid sequence data (including, e.g., variant data, copy number data, methylation status data, etc., of the sequenced nucleic acid molecules) with other biomarker data modalities including, but not limited to, proteomics-based biomarker data (e.g., the detection of specific polypeptides, such as proteins) or fragmentomics-based biomarker data (e.g., the detection of certain attributes related to nucleic acid fragments, such as fragment size or the sequences of fragment ends), to determine, for example, the presence of ctDNA in the sample and / or to determine a diagnostic, prognostic, and / or treatment response prediction for the subject, and (ix) generating, displaying, transmitting, and / or delivering a report (e.g., an electronic, webbased, or paper report) to the subject (or patient), a caregiver, a healthcare provider, a physician, an oncologist, an electronic medical record system, a hospital, a clinic, a third-party payer, an insurance company, or a government office. In some instances, the report comprises output from the methods described herein. In some instances, all or a portion of the report may be displayed in the graphical user interface of an online or web-based healthcare portal. In some instances, the report is transmitted via a computer network or peer-to-peer connection.

[0096] The disclosed methods may be used with any of a variety of samples. For example, in some instances, the sample may comprise a tissue biopsy sample, a liquid biopsy sample, or a normal control. In some instances, the sample may be a tissue biopsy sample and may comprise tissue from a local tumor (e.g., non-metastatic) and / or metastatic tissue. In some instances, the sample may be a liquid biopsy sample and may comprise blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva. In some instances, the sample may be a liquid biopsy sample and34MOFO-359739214Docket No.: 197102019540 may comprise circulating tumor cells (CTCs). In some instances, the sample may be a liquid biopsy sample and may comprise cell-free DNA (cfDNA). In some instances, the cell-free DNA (cfDNA), or a portion thereof, may comprise circulating tumor DNA (ctDNA). In some instances, the liquid biopsy sample may comprise a combination of cell-free DNA (cfDNA) and circulating tumor DNA (ctDNA).

[0097] In some instances, the nucleic acid molecules extracted from a sample may comprise a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules. In some instances, the tumor nucleic acid molecules may be obtained from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules may be obtained from a normal portion of the heterogeneous tissue biopsy sample. In some instances, the sample may comprise a tissue biopsy sample, and the tumor nucleic acid molecules may be obtained from metastatic tissue. In some instances, the sample may comprise a liquid biopsy sample, and the tumor nucleic acid molecules may be obtained from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample while the non-tumor nucleic acid molecules may be obtained from a non-tumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.

[0098] In some instances, the disclosed methods for determining genomic variant clonality may be used to diagnose (or as part of a diagnosis of) the presence of disease or other condition (e.g., cancer, genetic disorders (such as Down Syndrome and Fragile X), neurological disorders, or any other disease type where detection of variants, e.g., copy number alternations, are relevant to diagnosing, treating, or predicting said disease) in a subject (e.g., a patient). In some instances, the disclosed methods may be applicable to diagnosis of any of a variety of cancers as described elsewhere herein.

[0099] In some instances, the disclosed methods for determining genomic variant clonality 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, 35MOFO-359739214Docket No.: 197102019540 associated with, e.g., Down Syndrome (trisomy 21), trisomy 18, trisomy 13, and extra or missing copies of the X and Y chromosomes.

[0100] In some instances, the disclosed methods for determining genomic variant clonality may be used to select a subject (e.g., a patient) for a clinical trial based on the genomic features (e.g., the presence or absence of a candidate genomic variant) determined for one or more gene loci. In some instances, patient selection for clinical trials based on, e.g., identification of genomic features (e.g., the presence or absence of a candidate genomic variant) at one or more gene loci, may accelerate the development of targeted therapies and improve the healthcare outcomes for treatment decisions.

[0101] In some instances, the disclosed methods for determining genomic variant clonality may be used to select an appropriate therapy or treatment (e.g., an anti-cancer therapy or anti-cancer treatment) for a subject. In some instances, for example, the anti-cancer therapy or treatment may comprise use of a poly (ADP-ribose) polymerase inhibitor (PARPi), a platinum compound, chemotherapy, radiation therapy, a targeted therapy, an immunotherapy, a neoantigen-based therapy, surgery, or any combination thereof.

[0102] In some instances, the anti-cancer therapy or treatment may comprise a targeted anticancer therapy or treatment (e.g., a monoclonal antibody-based therapy, an enzyme inhibitorbased therapy, an antibody-drug conjugate therapy, a hormone therapy, and / or a targeted radiotherapy) that targets specific molecules required for cancer cell growth, division, and spreading. In some instances, the targeted anti-cancer therapy or treatment may comprise abemaciclib (Verzenio), abiraterone acetate (Zytiga), acalabrutinib (Calquence), ado-trastuzumab emtansine (Kadcyla), afatinib dimaleate (Gilotrif), alectinib (Alecensa), alemtuzumab (Campath), alitretinoin (Panretin), alpelisib (Piqray), amivantamab-vmjw (Rybrevant), anastrozole (Arimidex), apalutamide (Erleada), asciminib hydrochloride (Scemblix), atezolizumab (Tecentriq), avapritinib (Ayvakit), avelumab (Bavencio), axicabtagene ciloleucel (Yescarta), axitinib (Inlyta), belantamab mafodotin-blmf (Blenrep), belimumab (Benlysta), belinostat (Beleodaq), belzutifan (Welireg), bevacizumab (Avastin), bexarotene (Targretin), binimetinib (Mektovi), blinatumomab (Blincyto), bortezomib (Velcade), bosutinib (Bosulif), brentuximab vedotin (Adcetris), brexucabtagene autoleucel (Tecartus), brigatinib (Alunbrig),36MOFO-359739214Docket No.: 197102019540 cabazitaxel (Jevtana), cabozantinib (Cabometyx), cabozantinib (Cabometyx, Cometriq), canakinumab (Ilans), capmatinib hydrochloride (Tabrecta), carfilzomib (Kyprolis), cemiplimab- rwlc (Libtayo), ceritinib (LDK378 / Zykadia), cetuximab (Erbitux), cobimetinib (Cotellic), crizotinib (Xalkori), dabrafenib (Tafinlar), dacomitinib (Vizimpro), daratumumab (Darzalex), daratumumab and hyaluronidase-fihj (Darzalex Faspro), darolutamide (Nubeqa), dasatinib (Sprycel), denileukin diftitox (Ontak), denosumab (Xgeva), dinutuximab (Unituxin), dostarlimab-gxly (Jemperli), durvalumab (Imfinzi), duvelisib (Copiktra), elotuzumab (Empliciti), enasidenib mesylate (Idhifa), encorafenib (Braftovi), enfortumab vedotin-ejfv (Padcev), entrectinib (Rozlytrek), enzalutamide (Xtandi), erdafitinib (Balversa), erlotinib (Tarceva), everolimus (Afinitor), exemestane (Aromasin), fam-trastuzumab deruxtecan-nxki (Enhertu), fedratinib hydrochloride (Inrebic), fulvestrant (Faslodex), gefitinib (Iressa), gemtuzumab ozogamicin (Mylotarg), gilteritinib (Xospata), glasdegib maleate (Daurismo), hyaluronidase-zzxf (Phesgo), ibrutinib (Imbruvica), ibritumomab tiuxetan (Zevalin), idecabtagene vicleucel (Abecma), idelalisib (Zydelig), imatinib mesylate (Gleevec), infigratinib phosphate (Truseltiq), inotuzumab ozogamicin (Besponsa), ipilimumab (Yervoy), isatuximab-irfc (Sarclisa), ivosidenib (Tibsovo), ixazomib citrate (Ninlaro), lanreotide acetate (Somatuline Depot), lapatinib (Tykerb), larotrectinib sulfate (Vitrakvi), lenvatinib mesylate (Lenvima), letrozole (Femara), lisocabtagene maraleucel (Breyanzi), loncastuximab tesirine-lpyl (Zynlonta), lorlatinib (Lorbrena), lutetium Lu 177-dotatate (Lutathera), margetuximab-cmkb (Margenza), midostaurin (Rydapt), mobocertinib succinate (Exkivity), mogamulizumab-kpkc (Poteligeo), moxetumomab pasudotox-tdfk (Lumoxiti), naxitamab-gqgk (Danyelza), necitumumab (Portrazza), neratinib maleate (Nerlynx), nilotinib (Tasigna), niraparib tosylate monohydrate (Zejula), nivolumab (Opdivo), obinutuzumab (Gazyva), ofatumumab (Arzerra), olaparib (Lynparza), olaratumab (Lartruvo), osimertinib (Tagrisso), palbociclib (Ibrance), panitumumab (Vectibix), pazopanib (Votrient), pembrolizumab (Keytruda), pemigatinib (Pemazyre), pertuzumab (Perjeta), pexidartinib hydrochloride (Turalio), polatuzumab vedotin-piiq (Polivy), ponatinib hydrochloride (Iclusig), pralatrexate (Folotyn), pralsetinib (Gavreto), radium 223 dichloride (Xofigo), ramucirumab (Cyramza), regorafenib (Stivarga), ribociclib (Kisqali), ripretinib (Qinlock), rituximab (Rituxan), rituximab and hyaluronidase human (Rituxan Hycela), romidepsin (Istodax), rucaparib camsylate (Rubraca), ruxolitinib phosphate (Jakafi), sacituzumab govitecan-hziy (Trodelvy), seliciclib, selinexor (Xpovio), selpercatinib (Retevmo), selumetinib sulfate (Koselugo), siltuximab (Sylvant),37MOFO-359739214Docket No.: 197102019540 sirolimus protein-bound particles (Fyarro), sonidegib (Odomzo), sorafenib (Nexavar), sotorasib (Lumakras), sunitinib (Sutent), tafasitamab-cxix (Monjuvi), tagraxofusp-erzs (Elzonris), talazoparib tosylate (Talzenna), tamoxifen (Nolvadex), tazemetostat hydrobromide (Tazverik), tebentafusp-tebn (Kimmtrak), temsirolimus (Torisel), tepotinib hydrochloride (Tepmetko), tisagenlecleucel (Kymriah), tisotumab vedotin-tftv (Tivdak), tocilizumab (Actemra), tofacitinib (Xeljanz), tositumomab (Bexxar), trametinib (Mekinist), trastuzumab (Herceptin), tretinoin (Vesanoid), tivozanib hydrochloride (Fotivda), toremifene (Fareston), tucatinib (Tukysa), umbralisib tosylate (Ukoniq), vandetanib (Caprelsa), vemurafenib (Zelboraf), venetoclax (Venclexta), vismodegib (Erivedge), vorinostat (Zolinza), zanubrutinib (Brukinsa), ziv- aflibercept (Zaltrap), or any combination thereof.

[0103] In some instances, the anti-cancer therapy or treatment may comprise an immunotherapy (e.g., a cancer treatment that acts by stimulating the immune system to fight cancer). In some instances, the immunotherapy can be, for example, an immune system modulator (e.g., a cytokine, such as an interferon or interleukin), an immune checkpoint inhibitor (such as an anti- PD-1 or anti-PD-Ll antibody), a T-cell transfer therapy (e.g., a tumor infiltrating lymphocyte (TIL) therapy in lymphocytes extracted from a patient’ s tumor are selected for their ability to recognize tumor cells and propagated prior to reintroduction into the patient, or a CAR T-cell therapy in which a patient’s T-cells are modified to express the CAR protein prior to reintroduction into the patient), a monoclonal antibody -based therapy (e.g., a monoclonal antibody that binds to cell surface markers on cancer cells to facilitate recognition by the immune system), or a cancer treatment vaccine (e.g., a vaccine based on tumor cells, tumor- associated neoantigens, or dendritic cells, etc., that stimulates the immune system to fight cancer).

[0104] In some instances, the anti-cancer therapy or treatment may comprise a neoantigen-based therapy. Non-limiting examples of neoantigen-based therapies include T-cell receptor (TCR) engineered T-cell (TCR-T) therapies, chimeric antigen receptor T-cell (CAR-T) therapies, TCR bispecific antibody therapies, and cancer vaccines. TCR-T therapies are produced by genetically engineering a patient’s T-cells to express T-cell receptors that are specific to neoantigens of interest, and then infusing them back into the patient. CAR-T therapies are produced by38MOFO-359739214Docket No.: 197102019540 genetically engineering a patient’s T-cells to express chimeric antigen receptor molecules which contain an intracellular signaling and co-signaling domain as well as an extracellular antigenbinding domain; CAR-T therapies don’t always rely on neoantigen presentation, but can be designed to be directed towards neoantigens. TCR bispecific antibody therapies are small, engineered antibody molecules that comprise a neoantigen- specific TCR on one end and a CD3- directed single-chain variable fragment on the other end. Cancer vaccines can include RNA molecules, DNA molecules, peptides, or a combination thereof that are designed to boost the immune system’s ability to find and destroy neoantigen-presenting cells.

[0105] In some instances, the disclosed methods for determining genomic variant clonality may be used in treating a disease (e.g., a cancer) in a subject. For example, in response to determining that a candidate genomic variant is a clonal variant using any of the methods disclosed herein, an effective amount of an anti-cancer therapy or anti-cancer treatment may be administered to the subject.

[0106] In some instances, the disclosed methods for determining genomic variant clonality may be used for monitoring disease progression or recurrence (e.g., cancer or tumor progression or recurrence) in a subject. For example, in some instances, the methods may be used to determine clonality of a candidate genomic variant in a first sample obtained from the subject at a first time point, and used to determine clonality of the candidate genomic variant in a second sample obtained from the subject at a second time point, where comparison of the first determination of clonality of the candidate genomic variant and the second determination of clonality of the candidate genomic variant allows one to monitor disease progression or recurrence. In some instances, the first time point is chosen before the subject has been administered a therapy or treatment, and the second time point is chosen after the subject has been administered the therapy or treatment.

[0107] In some instances, the disclosed methods may be used for adjusting a therapy or treatment (e.g., an anti-cancer treatment or anti-cancer therapy) for a subject, e.g., by adjusting a treatment dose and / or selecting a different treatment in response to a change in the determination of clonality of a candidate genomic variant.39MOFO-359739214Docket No.: 197102019540

[0108] In some instances, the genomic variant clonality determined using the disclosed methods may be used as a prognostic or diagnostic indicator associated with the sample. For example, in some instances, the prognostic or diagnostic indicator may comprise an indicator of the presence of a disease (e.g., cancer) in the sample, an indicator of the probability that a disease (e.g., cancer) is present in the sample, an indicator of the probability that the subject from which the sample was obtained 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 obtained will respond to a particular therapy or treatment.

[0109] In some instances, the disclosed methods for determining genomic variant clonality 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 obtained from a subject as part of detecting, monitoring, predicting a risk factor, or selecting a treatment for a particular disease, e.g., cancer. In some instances, the variant panel selected for genomic profiling may comprise the detection of variant sequences at a selected set of gene loci. In some instances, the variant panel selected for genomic profiling may comprise detection of variant sequences at a number of gene loci through comprehensive genomic profiling (CGP), which is a nextgeneration sequencing (NGS) approach used to assess hundreds of genes (including relevant cancer biomarkers) in a single assay. Inclusion of the disclosed methods for determining genomic variant clonality as part of a genomic profiling process (or inclusion of the output from the disclosed methods for determining genomic variant clonality as part of the genomic profile of the subject) can improve the validity of, e.g., disease detection calls and treatment decisions, made on the basis of the genomic profile by, for example, independently confirming the presence of one or more clonal variants and / or subclonal variants in a given patient sample.

[0110] In some instances, a genomic profile may comprise information on the presence of genes (or variant sequences thereof), copy number variations, epigenetic traits, proteins (or modifications thereof), and / or other biomarkers in an individual’s genome and / or proteome, as well as information on the individual’s corresponding phenotypic traits and the interaction between genetic or genomic traits, phenotypic traits, and environmental factors.40MOFO-359739214Docket No.: 197102019540

[0111] In some instances, a genomic profile for the subject may comprise results from a comprehensive genomic profiling (CGP) test, a nucleic acid sequencing-based test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof.

[0112] In some instances, the method can further include administering or applying a treatment or therapy (e.g., an anti-cancer agent, anti-cancer treatment, or anti-cancer therapy) to the subject based on the generated genomic profile. An anti-cancer agent or anti-cancer treatment may refer to a compound that is effective in the treatment of cancer cells. Examples of anti-cancer agents or anti-cancer therapies include, but not limited to, alkylating agents, antimetabolites, natural products, hormones, chemotherapy, radiation therapy, immunotherapy, surgery, or a therapy configured to target a defect in a specific cell signaling pathway, e.g., a defect in a DNA mismatch repair (MMR) pathway.Samples

[0113] The disclosed methods and systems may be used with any of a variety of samples (also referred to herein as specimens) comprising nucleic acids (e.g., DNA or RNA) that are collected from a subject (e.g., a patient). Examples of a sample include, but are not limited to, a tumor sample, a tissue sample, a biopsy sample (e.g., a tissue biopsy, a liquid biopsy, or both), a blood sample (e.g., a peripheral whole blood sample), a blood plasma sample, a blood serum sample, a lymph sample, a saliva sample, a sputum sample, a urine sample, a gynecological fluid sample, a circulating tumor cell (CTC) sample, a cerebral spinal fluid (CSF) sample, a pericardial fluid sample, a pleural fluid sample, an ascites (peritoneal fluid) sample, a feces (or stool) sample, or other body fluid, secretion, and / or excretion sample (or cell sample derived therefrom). In certain instances, the sample may be frozen sample or a formalin-fixed paraffin-embedded (FFPE) sample.

[0114] In some instances, the sample may be collected by tissue resection (e.g., surgical resection), needle biopsy, bone marrow biopsy, bone marrow aspiration, skin biopsy, endoscopic biopsy, fine needle aspiration, oral swab, nasal swab, vaginal swab or a cytology smear, scrapings, washings or lavages (such as a ductal lavage or bronchoalveolar lavage), etc.41MOFO-359739214Docket No.: 197102019540

[0115] In some instances, the sample is a liquid biopsy sample, and may comprise, e.g., whole blood, blood plasma, blood serum, urine, stool, sputum, saliva, or cerebrospinal fluid. In some instances, the sample may be a liquid biopsy sample and may comprise circulating tumor cells (CTCs). In some instances, the sample may be a liquid biopsy sample and may comprise cell- free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.

[0116] In some instances, the sample may comprise one or more premalignant or malignant cells. Premalignant, as used herein, refers to a cell or tissue that is not yet malignant but is poised to become malignant. In certain instances, the sample may be acquired from a solid tumor, a soft tissue tumor, or a metastatic lesion. In certain instances, the sample may be acquired from a hematologic malignancy or pre-malignancy. In other instances, the sample may comprise a tissue or cells from a surgical margin. In certain instances, the sample may comprise tumor-infiltrating lymphocytes. In some instances, the sample may comprise one or more non- malignant cells. In some instances, the sample may be, or is part of, a primary tumor or a metastasis (e.g., a metastasis biopsy sample). In some instances, the sample may be obtained from a site (e.g., a tumor site) with the highest percentage of tumor (e.g., tumor cells) as compared to adjacent sites (e.g., sites adjacent to the tumor). In some instances, the sample may be obtained from a site (e.g., a tumor site) with the largest tumor focus (e.g., the largest number of tumor cells as visualized under a microscope) as compared to adjacent sites (e.g., sites adjacent to the tumor).

[0117] In some instances, the disclosed methods may further comprise analyzing a primary control (e.g., a normal tissue sample). In some instances, the disclosed methods may further comprise determining if a primary control is available and, if so, isolating a control nucleic acid (e.g., DNA) from said primary control. In some instances, the sample may comprise any normal control (e.g., a normal adjacent tissue (NAT)) if no primary control is available. In some instances, the sample may be or may comprise histologically normal tissue. In some instances, the method includes evaluating a sample, e.g., a histologically normal sample (e.g., from a surgical tissue margin) using the methods described herein. In some instances, the disclosed methods may further comprise acquiring a sub-sample enriched for non-tumor cells, e.g., by macro-dissecting non-tumor tissue from said NAT in a sample not accompanied by a primary42MOFO-359739214Docket No.: 197102019540 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.

[0118] In some instances, samples obtained from histologically normal tissues (e.g., otherwise histologically normal surgical tissue margins) may still comprise a genetic alteration such as a variant sequence as described herein. The methods may thus further comprise re-classifying a sample based on the presence of the detected genetic alteration. In some instances, multiple samples (e.g., from different subjects) are processed simultaneously.

[0119] The disclosed methods and systems may be applied to the analysis of nucleic acids extracted from any of variety of tissue samples (or disease states thereof), e.g., solid tissue samples, soft tissue samples, metastatic lesions, or liquid biopsy samples. Examples of tissues include, but are not limited to, connective tissue, muscle tissue, nervous tissue, epithelial tissue, and blood. Tissue samples may be collected from any of the organs within an animal or human body. Examples of human organs include, but are not limited to, the brain, heart, lungs, liver, kidneys, pancreas, spleen, thyroid, mammary glands, uterus, prostate, large intestine, small intestine, bladder, bone, skin, etc.

[0120] In some instances, the nucleic acids extracted from the sample may comprise deoxyribonucleic acid (DNA) molecules. Examples of DNA that may be suitable for analysis by the disclosed methods include, but are not limited to, genomic DNA or fragments thereof, mitochondrial DNA or fragments thereof, cell-free DNA (cfDNA), and circulating tumor DNA (ctDNA). Cell-free DNA (cfDNA) is comprised of fragments of DNA that are released from normal and / or cancerous cells during apoptosis and necrosis, and circulate in the blood stream and / or accumulate in other bodily fluids. Circulating tumor DNA (ctDNA) is comprised of fragments of DNA that are released from cancerous cells and tumors that circulate in the blood stream and / or accumulate in other bodily fluids.

[0121] In some instances, DNA is extracted from nucleated cells from the sample. In some instances, a sample may have a low nucleated cellularity, e.g., when the sample is comprised mainly of erythrocytes, lesional cells that contain excessive cytoplasm, or tissue with fibrosis. In43MOFO-359739214Docket No.: 197102019540 some instances, a sample with low nucleated cellularity may require more, e.g., greater, tissue volume for DNA extraction.

[0122] In some instances, the nucleic acids extracted from the sample may comprise ribonucleic acid (RNA) molecules. Examples of RNA that may be suitable for analysis by the disclosed methods include, but are not limited to, total cellular RNA, total cellular RNA after depletion of certain abundant RNA sequences (e.g., ribosomal RNAs), cell-free RNA (cfRNA), messenger RNA (mRNA) or fragments thereof, the poly(A)-tailed mRNA fraction of the total RNA, ribosomal RNA (rRNA) or fragments thereof, transfer RNA (tRNA) or fragments thereof, and mitochondrial RNA or fragments thereof. In some instances, RNA may be extracted from the sample and converted to complementary DNA (cDNA) using, e.g., a reverse transcription reaction. In some instances, the cDNA is produced by random-primed cDNA synthesis methods. In other instances, the cDNA synthesis is initiated at the poly (A) tail of mature mRNAs by priming with oligo(dT)-containing oligonucleotides. Methods for depletion, poly(A) enrichment, and cDNA synthesis are well known to those of skill in the art.

[0123] In some instances, the sample may comprise a tumor content (e.g., comprising tumor cells or tumor cell nuclei), or a non-tumor content (e.g., immune cells, fibroblasts, and other nontumor cells). In some instances, the tumor content of the sample may constitute a sample metric. In some instances, the sample may comprise a tumor content of at least 5-50%, 10-40%, 15-25%, or 20-30% tumor cell nuclei. In some instances, the sample may comprise a tumor content of at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, or at least 50% tumor cell nuclei. In some instances, the percent tumor cell nuclei (e.g., sample fraction) is determined (e.g., calculated) by dividing the number of tumor cells in the sample by the total number of all cells within the sample that have nuclei. In some instances, for example when the sample is a liver sample comprising hepatocytes, a different tumor content calculation may be required due to the presence of hepatocytes having nuclei with twice, or more than twice, the DNA content of other, e.g., non-hepatocyte, somatic cell nuclei. In some instances, the sensitivity of detection of a genetic alteration, e.g., a variant sequence, or a determination of, e.g., micro satellite instability, may depend on the tumor content of the sample. For example, a sample having a lower tumor content can result in lower sensitivity of detection for a given size sample.44MOFO-359739214Docket No.: 197102019540

[0124] In some instances, as noted above, the sample comprises nucleic acid (e.g., DNA, RNA (or a cDNA derived from the RNA), or both), e.g., from a tumor or from normal tissue. In certain instances, the sample may further comprise a non-nucleic acid component, e.g., cells, protein, carbohydrate, or lipid, e.g., from the tumor or normal tissue.Subjects

[0125] In some instances, the sample is obtained (e.g., collected) from a subject (e.g., patient) with a condition or disease (e.g., a hyperproliferative disease or a non-cancer indication) or suspected of having the condition or disease. In some instances, the hyperproliferative disease is a cancer. In some instances, the cancer is a solid tumor or a metastatic form thereof. In some instances, the cancer is a hematological cancer, e.g., a leukemia or lymphoma.

[0126] In some instances, the subject has a cancer or is at risk of having a cancer. For example, in some instances, the subject has a genetic predisposition to a cancer (e.g., having a genetic mutation that increases his or her baseline risk for developing a cancer). In some instances, the subject has been exposed to an environmental perturbation (e.g., radiation or a chemical) that increases his or her risk for developing a cancer. In some instances, the subject is in need of being monitored for development of a cancer. In some instances, the subject is in need of being monitored for cancer progression or regression, e.g., after being treated with an anti-cancer therapy (or anti-cancer treatment). In some instances, the subject is in need of being monitored for relapse of cancer. In some instances, the subject is in need of being monitored for minimum residual disease (MRD). In some instances, the subject has been, or is being treated, for cancer. In some instances, the subject has not been treated with an anti-cancer therapy (or anti-cancer treatment).

[0127] In some instances, the subject (e.g., a patient) is being treated, or has been previously treated, with one or more targeted therapies. In some instances, e.g., for a patient who has been previously treated with a targeted therapy, a post-targeted therapy sample (e.g., specimen) is obtained (e.g., collected). In some instances, the post-targeted therapy sample is a sample obtained after the completion of the targeted therapy.45MOFO-359739214Docket No.: 197102019540

[0128] In some instances, the patient has not been previously treated with a targeted therapy. In some instances, e.g., for a patient who has not been previously treated with a targeted therapy, the sample comprises a resection, e.g., an original resection, or a resection following recurrence (e.g., following a disease recurrence post-therapy).Cancers

[0129] In some instances, the sample is acquired from a subject having a cancer. Exemplary cancers include, but are not limited to, B cell cancer (e.g., multiple myeloma), melanomas, breast cancer, lung cancer (such as non-small cell lung carcinoma or NSCLC), bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain or central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine or endometrial cancer, cancer of the oral cavity or pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel or appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, cancer of hematological tissues, adenocarcinomas, inflammatory myofibroblastic tumors, gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), acute lymphocytic leukemia (ALL), acute myelocytic leukemia (AML), chronic myelocytic leukemia (CML), chronic lymphocytic leukemia (CLL), polycythemia Vera, Hodgkin lymphoma, nonHodgkin lymphoma (NHL), soft-tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endothelio sarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, bladder carcinoma, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, neuroblastoma, retinoblastoma, follicular lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, hepatocellular carcinoma, thyroid cancer, gastric cancer, head46MOFO-359739214Docket No.: 197102019540 and neck cancer, small cell cancers, essential thrombocythemia, agnogenic myeloid metaplasia, hypereosinophilic syndrome, systemic mastocytosis, familiar hypereosinophilia, chronic eosinophilic leukemia, neuroendocrine cancers, carcinoid tumors, and the like.

[0130] In some instances, the cancer comprises acute lymphoblastic leukemia (Philadelphia chromosome positive), acute lymphoblastic leukemia (precursor B-cell), acute myeloid leukemia (FLT3+), acute myeloid leukemia (with an IDH2 mutation), anaplastic large cell lymphoma, basal cell carcinoma, B-cell chronic lymphocytic leukemia, bladder cancer, breast cancer (HER2 overexpressed / amplified), breast cancer (HER2+), breast cancer (HR+, HER2-), cervical cancer, cholangiocarcinoma, chronic lymphocytic leukemia, chronic lymphocytic leukemia (with 17p deletion), chronic myelogenous leukemia, chronic myelogenous leukemia (Philadelphia chromosome positive), classical Hodgkin lymphoma, colorectal cancer, colorectal cancer (dMMR and MSI-H), colorectal cancer (KRAS wild type), cryopyrin-associated periodic syndrome, a cutaneous T-cell lymphoma, dermatofibrosarcoma protuberans, a diffuse large B- cell lymphoma, fallopian tube cancer, a follicular B-cell non-Hodgkin lymphoma, a follicular lymphoma, gastric cancer, gastric cancer (HER2+), a gastroesophageal junction (GEJ) adenocarcinoma, a gastrointestinal stromal tumor, a gastrointestinal stromal tumor (KIT+), a giant cell tumor of the bone, a glioblastoma, granulomatosis with polyangiitis, a head and neck squamous cell carcinoma, a hepatocellular carcinoma, Hodgkin lymphoma, juvenile idiopathic arthritis, lupus erythematosus, a mantle cell lymphoma, medullary thyroid cancer, melanoma, a melanoma with a BRAF V600 mutation, a melanoma with a BRAF V600E or V600K mutation, Merkel cell carcinoma, multicentric Castleman's disease, multiple hematologic malignancies including Philadelphia chromosome-positive ALL and CML, multiple myeloma, myelofibrosis, a non-Hodgkin’ s lymphoma, a nonresectable subependymal giant cell astrocytoma associated with tuberous sclerosis, a non-small cell lung cancer, a non-small cell lung cancer (ALK+), a non-small cell lung cancer (PD-L1+), a non-small cell lung cancer (with ALK fusion or ROS1 gene alteration), a non-small cell lung cancer (with BRAF V600E mutation), a non-small cell lung cancer (with an EGFR exon 19 deletion or exon 21 substitution (L858R) mutations), a non- small cell lung cancer (with an EGFR T790M mutation), ovarian cancer, ovarian cancer (with a BRCA mutation), pancreatic cancer, a pancreatic, gastrointestinal, or lung origin neuroendocrine tumor, a pediatric neuroblastoma, a peripheral T-cell lymphoma, peritoneal cancer, prostate47MOFO-359739214Docket No.: 197102019540 cancer, a renal cell carcinoma, rheumatoid arthritis, a small lymphocytic lymphoma, a soft tissue sarcoma, a solid tumor (MSI-H / dMMR), a squamous cell cancer of the head and neck, a squamous non- small cell lung cancer, thyroid cancer, a thyroid carcinoma, urothelial cancer, a urothelial carcinoma, or Waldenstrom's macroglobulinemia.

[0131] In some instances, the cancer is a hematologic malignancy (or premaligancy). As used herein, a hematologic malignancy refers to a tumor of the hematopoietic or lymphoid tissues, e.g., a tumor that affects blood, bone marrow, or lymph nodes. Exemplary hematologic malignancies include, but are not limited to, leukemia (e.g., acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), chronic myelogenous leukemia (CML), hairy cell leukemia, acute monocytic leukemia (AMoL), chronic myelomonocytic leukemia (CMML), juvenile myelomonocytic leukemia (JMML), or large granular lymphocytic leukemia), lymphoma (e.g., AIDS-related lymphoma, cutaneous T-cell lymphoma, Hodgkin lymphoma (e.g., classical Hodgkin lymphoma or nodular lymphocyte- predominant Hodgkin lymphoma), mycosis fungoides, non-Hodgkin lymphoma (e.g., B-cell non-Hodgkin lymphoma (e.g., Burkitt lymphoma, small lymphocytic lymphoma (CLL / SLL), diffuse large B-cell lymphoma, follicular lymphoma, immunoblastic large cell lymphoma, precursor B-lymphoblastic lymphoma, or mantle cell lymphoma) or T-cell non-Hodgkin lymphoma (mycosis fungoides, anaplastic large cell lymphoma, or precursor T-lymphoblastic lymphoma)), primary central nervous system lymphoma, Sezary syndrome, Waldenstrom macroglobulinemia), chronic myeloproliferative neoplasm, Langerhans cell histiocytosis, multiple myeloma / plasma cell neoplasm, myelodysplastic syndrome, or myelodysplastic / myeloproliferative neoplasm.Nucleic acid extraction and processing

[0132] 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 48MOFO-359739214Docket No.: 197102019540Kit Technical Manual (Promega Literature #TM333, January 1, 2011, Promega Corporation, Madison, WI)). Protocols for RNA isolation are disclosed in, e.g., the Maxwell® 16 Total RNA Purification Kit Technical Bulletin (Promega Literature #TB351, August 2009, Promega Corporation, Madison, WI).

[0133] 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.

[0134] Disruption of cell membranes may be performed using a variety of mechanical shear (e.g., by passing through a French press or fine needle) or ultrasonic disruption techniques. The cell lysis step often comprises the use of detergents and surfactants to solubilize lipids the cellular and nuclear membranes. In some instances, the lysis step may further comprise use of proteases to break down protein, and / or the use of an RNase for digestion of RNA in the sample.

[0135] 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.

[0136] In some instances, cellular and histone proteins bound to the DNA may be removed either by adding a protease or by having precipitated the proteins with sodium or ammonium acetate, or through extraction with a phenol-chloroform mixture prior to a DNA precipitation step.

[0137] In some instances, DNA may be extracted using any of a variety of suitable commercial DNA extraction and purification kits. Examples include, but are not limited to, the QIAamp (for 49MOFO-359739214Docket No.: 197102019540 isolation of genomic DNA from human samples) and DNAeasy (for isolation of genomic DNA from animal or plant samples) kits from Qiagen (Germantown, MD) or the Maxwell® and ReliaPrep™ series of kits from Promega (Madison, WI).

[0138] As noted above, in some instances the sample may comprise a formalin-fixed (also known as formaldehyde-fixed, or paraformaldehyde-fixed), paraffin-embedded (FFPE) tissue preparation. For example, the FFPE sample may be a tissue sample embedded in a matrix, e.g., an FFPE block. Methods to isolate nucleic acids e.g., DNA) from formaldehyde- or paraformaldehyde-fixed, paraffin-embedded (FFPE) tissues are disclosed in, e.g., Cronin, et al., (2004) Am J Pathol. 164(l):35-42; Masuda, et al., (1999) Nucleic Acids Res. 27 (22): 4436-4443; Specht, et al., (2001) Am J Pathol. 158(2):419-429; the Ambion RecoverAll™ Total Nucleic Acid Isolation Protocol (Ambion, Cat. No. AM1975, September 2008); the Maxwell® 16 FFPE Plus LEV DNA Purification Kit Technical Manual (Promega Literature #TM349, February 2011); the E.Z.N.A.® FFPE DNA Kit Handbook (OMEGA bio-tek, Norcross, GA, product numbers D3399-00, D3399-01, and D3399-02, June 2009); and the QIAamp® DNA FFPE Tissue Handbook (Qiagen, Cat. No. 37625, October 2007). For example, the RecoverAll™ Total Nucleic Acid Isolation Kit uses xylene at elevated temperatures to solubilize paraffin- embedded samples and a glass-fiber filter to capture nucleic acids. The Maxwell® 16 FFPE Plus LEV DNA Purification Kit is used with the Maxwell® 16 Instrument for purification of genomic DNA from 1 to 10 pm sections of FFPE tissue. DNA is purified using silica-clad paramagnetic particles (PMPs), and eluted in low elution volume. The E.Z.N.A.® FFPE DNA Kit uses a spin column and buffer system for isolation of genomic DNA. QIAamp® DNA FFPE Tissue Kit uses QIAamp® DNA Micro technology for purification of genomic and mitochondrial DNA.

[0139] In some instances, the disclosed methods may further comprise determining or acquiring a yield value for the nucleic acid extracted from the sample and comparing the determined value to a reference value. For example, if the determined or acquired value is less than the reference value, the nucleic acids may be amplified prior to proceeding with library construction. In some instances, the disclosed methods may further comprise determining or acquiring a value for the size (or average size) of nucleic acid fragments in the sample, and comparing the determined or acquired value to a reference value, e.g., a size (or average size) of at least 100, 200, 300, 400,50MOFO-359739214Docket No.: 197102019540500, 600, 700, 800, 900, or 1000 base pairs (bps). In some instances, one or more parameters described herein may be adjusted or selected in response to this determination.

[0140] After isolation, the nucleic acids are typically dissolved in a slightly alkaline buffer, e.g., Tris-EDTA (TE) buffer, or in ultra-pure water. In some instances, the isolated nucleic acids (e.g., genomic DNA) may be fragmented or sheared by using any of a variety of techniques known to those of skill in the art. For example, genomic DNA can be fragmented by physical shearing methods, enzymatic cleavage methods, chemical cleavage methods, and other methods known to those of skill in the art. Methods for DNA shearing are described in Example 4 in International Patent Application Publication No. WO 2012 / 092426. In some instances, alternatives to DNA shearing methods can be used to avoid a ligation step during library preparation.Library preparation

[0141] In some instances, the nucleic acids isolated from the sample may be used to construct a library (e.g., a nucleic acid library as described herein). In some instances, the nucleic acids are fragmented using any of the methods described above, optionally subjected to repair of chain end damage, and optionally ligated to synthetic adapters, primers, and / or barcodes (e.g., amplification primers, sequencing adapters, flow cell adapters, substrate adapters, sample barcodes or indexes, and / or unique molecular identifier sequences), size-selected (e.g., by preparative gel electrophoresis), and / or amplified (e.g., using PCR, a non-PCR amplification technique, or an isothermal amplification technique). In some instances, the fragmented and adapter-ligated group of nucleic acids is used without explicit size selection or amplification prior to hybridization-based selection of target sequences. In some instances, the nucleic acid is amplified by any of a variety of specific or non-specific nucleic acid amplification methods known to those of skill in the art. In some instances, the nucleic acids are amplified, e.g., by a whole-genome amplification method such as random-primed strand-displacement amplification. Examples of nucleic acid library preparation techniques for next-generation sequencing are described in, e.g., van Dijk, et al. (2014), Exp. Cell Research 322:12 - 20, and Illumina’s genomic DNA sample preparation kit.51MOFO-359739214Docket No.: 197102019540

[0142] In some instances, the resulting nucleic acid library may contain all or substantially all of the complexity of the genome. The term “substantially all” in this context refers to the possibility that there can in practice be some unwanted loss of genome complexity during the initial steps of the procedure. The methods described herein also are useful in cases where the nucleic acid library comprises a portion of the genome, e.g., where the complexity of the genome is reduced by design. In some instances, any selected portion of the genome can be used with a method described herein. For example, in certain embodiments, the entire exome or a subset thereof is isolated. In some instances, the library may include at least 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, or 5% of the genomic DNA. In some instances, the library may consist of cDNA copies of genomic DNA that includes copies of at least 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, or 5% of the genomic DNA. In certain instances, the amount of nucleic acid used to generate the nucleic acid library may be less than 5 micrograms, less than 1 microgram, less than 500 ng, less than 200 ng, less than 100 ng, less than 50 ng, less than 10 ng, less than 5 ng, or less than 1 ng.

[0143] In some instances, a library (e.g., a nucleic acid library) includes a collection of nucleic acid molecules. As described herein, the nucleic acid molecules of the library can include a target nucleic acid molecule (e.g., a tumor nucleic acid molecule, a reference nucleic acid molecule and / or a control nucleic acid molecule; also referred to herein as a first, second and / or third nucleic acid molecule, respectively). The nucleic acid molecules of the library can be from a single subject or individual. In some instances, a library can comprise nucleic acid molecules obtained from more than one subject (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30 or more subjects). For example, two or more libraries from different subjects can be combined to form a library having nucleic acid molecules from more than one subject (where the nucleic acid molecules obtained from each subject are optionally ligated to a unique sample barcode corresponding to a specific subject). In some instances, the subject is a human having, or at risk of having, a cancer or tumor.

[0144] In some instances, the library (or a portion thereof) may comprise one or more subgenomic intervals. In some instances, a subgenomic interval can be a single nucleotide position, e.g., a nucleotide position for which a variant at the position is associated (positively or52MOFO-359739214Docket No.: 197102019540 negatively) with a tumor phenotype. In some instances, a subgenomic interval comprises more than one nucleotide position. Such instances include sequences of at least 2, 5, 10, 50, 100, 150, 250, or more than 250 nucleotide positions in length. Subgenomic intervals can comprise, e.g., one or more entire genes (or portions thereof), one or more exons or coding sequences (or portions thereof), one or more introns (or portion thereof), one or more microsatellite region (or portions thereof), or any combination thereof. A subgenomic interval can comprise all or a part of a fragment of a naturally occurring nucleic acid molecule, e.g., a genomic DNA molecule. For example, a subgenomic interval can correspond to a fragment of genomic DNA which is subjected to a sequencing reaction. In some instances, a subgenomic interval is a continuous sequence from a genomic source. In some instances, a subgenomic interval includes sequences that are not contiguous in the genome, e.g., subgenomic intervals in cDNA can include exonexonjunctions formed as a result of splicing. In some instances, the subgenomic interval comprises a tumor nucleic acid molecule. In some instances, the subgenomic interval comprises a non-tumor nucleic acid molecule.Targeting gene loci for analysis

[0145] 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.

[0146] In some instances, the set of genomic loci evaluated by the disclosed methods comprises a plurality of, e.g., genes, which in mutant form, are associated with an effect on cell division, growth or survival, or are associated with a cancer, e.g., a cancer described herein.

[0147] In some instances, the set of gene loci evaluated by the disclosed methods comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, or more than 100 gene loci.

[0148] In some instances, the selected gene loci (also referred to herein as target gene loci or target sequences), or fragments thereof, may include subject intervals comprising non-coding sequences, coding sequences, intragenic regions, or intergenic regions of the subject genome.53MOFO-359739214Docket No.: 197102019540For example, the subject intervals can include a non-coding sequence or fragment thereof (e.g., a promoter sequence, enhancer sequence, 5’ untranslated region (5’ UTR), 3’ untranslated region (3’ UTR), or a fragment thereof), a coding sequence of fragment thereof, an exon sequence or fragment thereof, an intron sequence or a fragment thereof.Target capture reagents

[0149] The methods described herein may comprise contacting a nucleic acid library with a plurality of target capture reagents in order to select and capture a plurality of specific target sequences (e.g., gene sequences or fragments thereof) for analysis. In some instances, a target capture reagent (i.e., a molecule which can bind to and thereby allow capture of a target molecule) is used to select the subject intervals to be analyzed. For example, a target capture reagent can be a bait molecule, e.g., a nucleic acid molecule (e.g., a DNA molecule or RNA molecule) which can hybridize to (i.e., is complementary to) a target molecule, and thereby allows capture of the target nucleic acid. In some instances, the target capture reagent, e.g., a bait molecule (or bait sequence), is a capture oligonucleotide (or capture probe). In some instances, the target nucleic acid is a genomic DNA molecule, an RNA molecule, a cDNA molecule derived from an RNA molecule, a microsatellite DNA sequence, and the like. In some instances, the target capture reagent is suitable for solution-phase hybridization to the target. In some instances, the target capture reagent is suitable for solid-phase hybridization to the target. In some instances, the target capture reagent is suitable for both solution-phase and solid-phase hybridization to the target. The design and construction of target capture reagents is described in more detail in, e.g., International Patent Application Publication No. WO 2020 / 236941, the entire content of which is incorporated herein by reference.

[0150] The methods described herein provide for optimized sequencing of a large number of genomic loci (e.g., genes or gene products (e.g., mRNA), micro satellite loci, etc.) from samples (e.g., cancerous tissue specimens, liquid biopsy samples, and the like) from one or more subjects by the appropriate selection of target capture reagents to select the target nucleic acid molecules to be sequenced. In some instances, a target capture reagent may hybridize to a specific target locus, e.g., a specific target gene locus or fragment thereof. In some instances, a target capture reagent may hybridize to a specific group of target loci, e.g., a specific group of gene loci or 54MOFO-359739214Docket No.: 197102019540 fragments thereof. In some instances, a plurality of target capture reagents comprising a mix of target- specific and / or group- specific target capture reagents may be used.

[0151] In some instances, the number of target capture reagents (e.g., bait molecules) in the plurality of target capture reagents (e.g., a bait set) contacted with a nucleic acid library to capture a plurality of target sequences for nucleic acid sequencing is greater than 10, greater than 50, greater than 100, greater than 200, greater than 300, greater than 400, greater than 500, greater than 600, greater than 700, greater than 800, greater than 900, greater than 1,000, greater than 1,250, greater than 1,500, greater than 1,750, greater than 2,000, greater than 3,000, greater than 4,000, greater than 5,000, greater than 10,000, greater than 25,000, or greater than 50,000.

[0152] In some instances, the overall length of the target capture reagent sequence can be between about 70 nucleotides and 1000 nucleotides. In one instance, the target capture reagent length is between about 100 and 300 nucleotides, 110 and 200 nucleotides, or 120 and 170 nucleotides, in length. In addition to those mentioned above, intermediate oligonucleotide lengths of about 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 300, 400, 500, 600, 700, 800, and 900 nucleotides in length can be used in the methods described herein. In some embodiments, oligonucleotides of about 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, or 230 bases can be used.

[0153] In some instances, each target capture reagent sequence can include: (i) a target- specific capture sequence (e.g., a gene locus or micro satellite locus- specific complementary sequence), (ii) an adapter, primer, barcode, and / or unique molecular identifier sequence, and (iii) universal tails on one or both ends. As used herein, the term "target capture reagent" can refer to the targetspecific target capture sequence or to the entire target capture reagent oligonucleotide including the target- specific target capture sequence.

[0154] In some instances, the target- specific capture sequences in the target capture reagents are between about 40 nucleotides and 1000 nucleotides in length. In some instances, the targetspecific capture sequence is between about 70 nucleotides and 300 nucleotides in length. In some instances, the target- specific sequence is between about 100 nucleotides and 200 nucleotides in length. In yet other instances, the target- specific sequence is between about 120 nucleotides and55MOFO-359739214Docket No.: 197102019540170 nucleotides in length, typically 120 nucleotides in length. Intermediate lengths in addition to those mentioned above also can be used in the methods described herein, such as target- specific sequences of about 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 300, 400, 500, 600, 700, 800, and 900 nucleotides in length, as well as target- specific sequences of lengths between the above-mentioned lengths.

[0155] In some instances, the target capture reagent may be designed to select a subject interval containing one or more rearrangements, e.g., an intron containing a genomic rearrangement. In such instances, the target capture reagent is designed such that repetitive sequences are masked to increase the selection efficiency. In those instances where the rearrangement has a known juncture sequence, complementary target capture reagents can be designed to recognize the juncture sequence to increase the selection efficiency.

[0156] In some instances, the disclosed methods may comprise the use of target capture reagents designed to capture two or more different target categories, each category having a different target capture reagent design strategy. In some instances, the hybridization-based capture methods and target capture reagent compositions disclosed herein may provide for the capture and homogeneous coverage of a set of target sequences, while minimizing coverage of genomic sequences outside of the targeted set of sequences. In some instances, the target sequences may include the entire exome of genomic DNA or a selected subset thereof. In some instances, the target sequences may include, e.g., a large chromosomal region e.g., a whole chromosome arm). The methods and compositions disclosed herein provide different target capture reagents for achieving different sequencing depths and patterns of coverage for complex sets of target nucleic acid sequences.

[0157] Typically, DNA molecules are used as target capture reagent sequences, although RNA molecules can also be used. In some instances, a DNA molecule target capture reagent can be single stranded DNA (ssDNA) or double- stranded DNA (dsDNA). In some instances, an RNA- DNA duplex is more stable than a DNA-DNA duplex and therefore provides for potentially better capture of nucleic acids.56MOFO-359739214Docket No.: 197102019540

[0158] In some instances, the disclosed methods comprise providing a selected set of nucleic acid molecules (e.g., a library catch) captured from one or more nucleic acid libraries. For example, the method may comprise: providing one or a plurality of nucleic acid libraries, each comprising a plurality of nucleic acid molecules (e.g., a plurality of target nucleic acid molecules and / or reference nucleic acid molecules) extracted from one or more samples from one or more subjects; contacting the one or a plurality of libraries (e.g., in a solution-based hybridization reaction) with one, two, three, four, five, or more than five pluralities of target capture reagents (e.g., oligonucleotide target capture reagents) to form a hybridization mixture comprising a plurality of target capture reagent / nucleic acid molecule hybrids; separating the plurality of target capture reagent / nucleic acid molecule hybrids from said hybridization mixture, e.g., by contacting said hybridization mixture with a binding entity that allows for separation of said plurality of target capture reagent / nucleic acid molecule hybrids from the hybridization mixture, thereby providing a library catch (e.g., a selected or enriched subgroup of nucleic acid molecules from the one or a plurality of libraries).

[0159] In some instances, the disclosed methods may further comprise amplifying the library catch (e.g., by performing PCR). In other instances, the library catch is not amplified.

[0160] In some instances, the target capture reagents can be part of a kit which can optionally comprise instructions, standards, buffers or enzymes or other reagents.Hybridization conditions

[0161] As noted above, the methods disclosed herein may include the step of contacting the library (e.g., the nucleic acid library) with a plurality of target capture reagents to provide a selected library target nucleic acid sequences (z.e., the library catch). The contacting step can be effected in, e.g., solution-based hybridization. In some instances, the method includes repeating the hybridization step for one or more additional rounds of solution-based hybridization. In some instances, the method further includes subjecting the library catch to one or more additional rounds of solution-based hybridization with the same or a different collection of target capture reagents.57MOFO-359739214Docket No.: 197102019540

[0162] In some instances, the contacting step is effected using a solid support, e.g., an array. Suitable solid supports for hybridization are described in, e.g., Albert, T.J. et al. (2007) Nat. Methods 4(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.

[0163] Hybridization methods that can be adapted for use in the methods herein are described in the art, e.g., as described in International Patent Application Publication No. WO 2012 / 092426. Methods for hybridizing target capture reagents to a plurality of target nucleic acids are described in more detail in, e.g., International Patent Application Publication No. WO 2020 / 236941, the entire content of which is incorporated herein by reference.Sequencing methods

[0164] The methods and systems disclosed herein can be used in combination with, or as part of, a method or system for sequencing nucleic acids (e.g., a next-generation sequencing system) to generate a plurality of sequence reads that overlap one or more gene loci within a subgenomic interval in the sample and thereby determine, e.g., gene allele sequences at a plurality of gene loci. “Next-generation sequencing” (or “NGS”) as used herein may also be referred to as “massively parallel sequencing” (or “MPS”), and refers to any sequencing method that determines the nucleotide sequence of either individual nucleic acid molecules (e.g., as in single molecule sequencing) or clonally expanded proxies for individual nucleic acid molecules in a high throughput fashion (e.g., wherein greater than 103, 104, 105or more than 105molecules are sequenced simultaneously).

[0165] Next-generation sequencing methods are known in the art, and are described in, e.g., Metzker, M. (2010) Nature Biotechnology Reviews 11:31-46, which is incorporated herein by reference. Other examples of sequencing methods suitable for use when implementing the methods and systems disclosed herein are described in, e.g., International Patent Application Publication No. WO 2012 / 092426. In some instances, the sequencing may comprise, for example, whole genome sequencing (WGS), whole exome sequencing, targeted sequencing, or direct sequencing. In some instances, sequencing may be performed using, e.g., Sanger58MOFO-359739214Docket No.: 197102019540 sequencing. In some instances, the sequencing may comprise a paired-end sequencing technique that allows both ends of a fragment to be sequenced and generates high-quality, alignable sequence data for detection of, e.g., genomic rearrangements, repetitive sequence elements, gene fusions, and novel transcripts.

[0166] The disclosed methods and systems may be implemented using sequencing platforms such as the Roche / 454 Genome Sequencer (GS) FLX System, Illumina / Solexa Genome Analyzer (GA), Illumina’s HiSeq® 2500, HiSeq® 3000, HiSeq® 4000 and NovaSeq® 6000 Sequencing Systems, Life / APG’s Support Oligonucleotide Ligation Detection (SOLiD) system, Polonator’s G.007 system, Helicos BioSciences’ HeliScope Gene Sequencing system, or Pacific Biosciences’ PacBio® RS platform. In some instances, sequencing may comprise Illumina MiSeq™ sequencing. In some instances, sequencing may comprise Illumina HiSeq® sequencing. In some instances, sequencing may comprise Illumina NovaSeq® sequencing. Optimized methods for sequencing a large number of target genomic loci in nucleic acids extracted from a sample are described in more detail in, e.g., International Patent Application Publication No. WO 2020 / 236941, the entire content of which is incorporated herein by reference.

[0167] In certain instances, the disclosed methods comprise one or more of the steps of: (a) acquiring a library comprising a plurality of normal and / or tumor nucleic acid molecules from a sample; (b) simultaneously or sequentially contacting the library with one, two, three, four, five, or more than five pluralities of target capture reagents under conditions that allow hybridization of the target capture reagents to the target nucleic acid molecules, thereby providing a selected set of captured normal and / or tumor nucleic acid molecules (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., one or more target sequences) from said library catch that may comprise a mutation (or alteration), e.g., a variant sequence comprising a somatic mutation or germline mutation; (e) aligning said sequence reads using an alignment method as59MOFO-359739214Docket No.: 197102019540 described elsewhere herein; and / or (f) assigning a nucleotide value for a nucleotide position in the subject interval (e.g., calling a mutation using, e.g., a Bayesian method or other method described herein) from one or more sequence reads of the plurality.

[0168] In some instances, acquiring sequence reads for one or more subject intervals may comprise sequencing at least 1, at least 5, at least 10, at least 20, at least 30, at least 40, at least 50, at least 100, at least 150, at least 200, at least 250, at least 300, at least 350, at least 400, at least 450, at least 500, at least 550, at least 600, at least 650, at least 700, at least 750, at least 800, at least 850, at least 900, at least 950, at least 1,000, at least 1,250, at least 1,500, at least 1,750, at least 2,000, at least 2,250, at least 2,500, at least 2,750, at least 3,000, at least 3,500, at least 4,000, at least 4,500, or at least 5,000 loci, e.g., genomic loci, gene loci, microsatellite loci, etc. In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing a subject interval for any number of loci within the range described in this paragraph, e.g., for at least 2,850 gene loci.

[0169] In some instances, acquiring a sequence read for one or more subject intervals comprises sequencing a subject interval with a sequencing method that provides a sequence read length (or average sequence read length) of at least 20 bases, at least 30 bases, at least 40 bases, at least 50 bases, at least 60 bases, at least 70 bases, at least 80 bases, at least 90 bases, at least 100 bases, at least 120 bases, at least 140 bases, at least 160 bases, at least 180 bases, at least 200 bases, at least 220 bases, at least 240 bases, at least 260 bases, at least 280 bases, at least 300 bases, at least 320 bases, at least 340 bases, at least 360 bases, at least 380 bases, or at least 400 bases. In some instances, acquiring a sequence read for the one or more subject intervals may comprise sequencing a subject interval with a sequencing method that provides a sequence read length (or average sequence read length) of any number of bases within the range described in this paragraph, e.g., a sequence read length (or average sequence read length) of 56 bases.

[0170] In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing with at least lOOx or more coverage (or depth) on average. In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing with at least lOOx, at least 150x, at least 200x, at least 250x, at least 500x, at least 750x, at least l,000x, at least 1,500 x, at least 2,000x, at least 2,500x, at least 3,000x, at least 3,500x, at least 60MOFO-359739214Docket No.: 1971020195404,000x, at least 4,500x, at least 5,000x, at least 5,500x, or at least 6,000x or more coverage (or depth) on average. In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing with an average coverage (or depth) having any value within the range of values described in this paragraph, e.g., at least 160x.

[0171] In some instances, acquiring a read for the one or more subject intervals comprises sequencing with an average sequencing depth having any value ranging from at least lOOx to at least 6,000x for greater than about 90%, 92%, 94%, 95%, 96%, 97%, 98%, or 99% of the gene loci sequenced. For example, in some instances acquiring a read for the subject interval comprises sequencing with an average sequencing depth of at least 125x for at least 99% of the gene loci sequenced. As another example, in some instances acquiring a read for the subject interval comprises sequencing with an average sequencing depth of at least 4,100x for at least 95% of the gene loci sequenced.

[0172] In some instances, the relative abundance of a nucleic acid species in the library can be estimated by counting the relative number of occurrences of their cognate sequences (e.g., the number of sequence reads for a given cognate sequence) in the data generated by the sequencing experiment.

[0173] In some instances, the disclosed methods and systems provide nucleotide sequences for a set of subject intervals (e.g., gene loci), as described herein. In certain instances, the sequences are provided without using a method that includes a matched normal control (e.g., a wild-type control) and / or a matched tumor control (e.g., primary versus metastatic).

[0174] In some instances, the level of sequencing depth as used herein (e.g., an X-fold level of sequencing depth) refers to the number of reads (e.g., unique reads) obtained after detection and removal of duplicate reads (e.g., PCR duplicate reads). In other instances, duplicate reads are evaluated, e.g., to support detection of copy number alteration (CNAs).Alignment

[0175] Alignment is the process of matching a read with a location, e.g., a genomic location or locus. In some instances, NGS reads may be aligned to a known reference sequence (e.g., a61MOFO-359739214Docket No.: 197102019540 wild-type sequence). In some instances, NGS reads may be assembled de novo. Methods of sequence alignment for NGS reads are described in, e.g., Trapnell, C. and Salzberg, S.L. Nature Biotech., 2009, 27:455-457. Examples of de novo sequence assemblies are described in, e.g., Warren R., et al., Bioinformatics, 2007, 23:500-501; Butler, J. et al., Genome Res., 2008, 18:810-820; and Zerbino, D.R. and Birney, E., Genome Res., 2008, 18:821-829. Optimization of sequence alignment is described in the art, e.g., as set out in International Patent Application Publication No. WO 2012 / 092426. Additional description of sequence alignment methods is provided in, e.g., International Patent Application Publication No. WO 2020 / 236941, the entire content of which is incorporated herein by reference.

[0176] Misalignment (e.g., the placement of base-pairs from a short read at incorrect locations in the genome), e.g., misalignment of reads due to sequence context (e.g., the presence of repetitive sequence) around an actual cancer mutation can lead to reduction in sensitivity of mutation detection, can lead to a reduction in sensitivity of mutation detection, as reads for the alternate allele may be shifted off the histogram peak of alternate allele reads. Other examples of sequence context that may cause misalignment include short-tandem repeats, interspersed repeats, low complexity regions, insertions - deletions (indels), and paralogs. If the problematic sequence context occurs where no actual mutation is present, misalignment may introduce artifactual reads of “mutated” alleles by placing reads of actual reference genome base sequences at the wrong location. Because mutation-calling algorithms for multigene analysis should be sensitive to even low-abundance mutations, sequence misalignments may increase false positive discovery rates and / or reduce specificity.

[0177] In some instances, the methods and systems disclosed herein may integrate the use of multiple, individually-tuned, alignment methods or algorithms to optimize base-calling performance in sequencing methods, particularly in methods that rely on massively parallel sequencing (MPS) of a large number of diverse genetic events at a large number of diverse genomic loci. In some instances, the disclosed methods and systems may comprise the use of one or more global alignment algorithms. In some instances, the disclosed methods and systems may comprise the use of one or more local alignment algorithms. Examples of alignment algorithms that may be used include, but are not limited to, the Burrows-Wheeler Alignment62MOFO-359739214Docket No.: 197102019540(BWA) software bundle (see, e.g., Li, et al. (2009), “Fast and Accurate Short Read Alignment with Burrows-Wheeler Transform”, Bioinformatics 25: 1754-60; Li, et al. (2010), Fast and Accurate Long-Read Alignment with Burrows-Wheeler Transform”, Bioinformatics epub. PMID: 20080505), the Smith- Waterman algorithm (see, e.g., Smith, et al. (1981), "Identification of Common Molecular Subsequences", J. Molecular Biology 147(1): 195-197), the Striped Smith-Waterman algorithm (see, e.g., Farrar (2007), “Striped Smith-Waterman Speeds Database Searches Six Times Over Other SIMD Implementations”, Bioinformatics 23(2): 156-161), the Needleman-Wunsch algorithm (Needleman, et al. (1970) "A General Method Applicable to the Search for Similarities in the Amino Acid Sequence of Two Proteins", J. Molecular Biology 48(3):443-53), or any combination thereof.

[0178] In some instances, the methods and systems disclosed herein may also comprise the use of a sequence assembly algorithm, e.g., the Arachne sequence assembly algorithm (see, e.g., Batzoglou, et al. (2002), “ARACHNE: A Whole-Genome Shotgun Assembler”, Genome Res. 12:177-189).

[0179] In some instances, the alignment method used to analyze sequence reads is not individually customized or tuned for detection of different variants (e.g., point mutations, insertions, deletions, and the like) at different genomic loci. In some instances, different alignment methods are used to analyze reads that are individually customized or tuned for detection of at least a subset of the different variants detected at different genomic loci. In some instances, different alignment methods are used to analyze reads that are individually customized or tuned to detect each different variant at different genomic loci. In some instances, tuning can be a function of one or more of: (i) the genetic locus (e.g., gene loci, micro satellite locus, or other subject interval) being sequenced, (ii) the tumor type associated with the sample, (iii) the variant being sequenced, or (iv) a characteristic of the sample or the subject. The selection or use of alignment conditions that are individually tuned to a number of specific subject intervals to be sequenced allows optimization of speed, sensitivity, and specificity. The method is particularly effective when the alignment of reads for a relatively large number of diverse subject intervals are optimized.63MOFO-359739214Docket No.: 197102019540

[0180] In some instances, the method includes the use of an alignment method optimized for rearrangements in combination with other alignment methods optimized for subject intervals not associated with rearrangements.

[0181] In some instances, the methods disclosed herein further comprise selecting or using an alignment method for analyzing, e.g., aligning, a sequence read, wherein said alignment method is a function of, is selected responsive to, or is optimized for, one or more of: (i) tumor type, e.g., the tumor type in the sample; (ii) the location (e.g., a gene locus) of the subject interval being sequenced; (iii) the type of variant (e.g., a point mutation, insertion, deletion, substitution, copy number variation (CNV), rearrangement, or fusion) in the subject interval being sequenced; (iv) the site (e.g., nucleotide position) being analyzed; (v) the type of sample (e.g., a sample described herein); and / or (vi) adjacent sequence(s) in or near the subject interval being evaluated (e.g., according to the expected propensity thereof for misalignment of the subject interval due to, e.g., the presence of repeated sequences in or near the subject interval).

[0182] In some instances, the methods disclosed herein allow for the rapid and efficient alignment of troublesome reads, e.g., a read having a rearrangement. Thus, in some instances where a read for a subject interval comprises a nucleotide position with a rearrangement, e.g., a translocation, the method can comprise using an alignment method that is appropriately tuned and that includes: (i) selecting a rearrangement reference sequence for alignment with a read, wherein said rearrangement reference sequence aligns with a rearrangement (in some instances, the reference sequence is not identical to the genomic rearrangement); and (ii) comparing, e.g., aligning, a read with said rearrangement reference sequence.

[0183] In some instances, alternative methods may be used to align troublesome reads. These methods are particularly effective when the alignment of reads for a relatively large number of diverse subject intervals is optimized. By way of example, a method of analyzing a sample can comprise: (i) performing a comparison (e.g., an alignment comparison) of a read using a first set of parameters (e.g., using a first mapping algorithm, or by comparison with a first reference sequence), and determining if said read meets a first alignment criterion (e.g., the read can be aligned with said first reference sequence, e.g., with less than a specific number of mismatches); (ii) if said read fails to meet the first alignment criterion, performing a second alignment 64MOFO-359739214Docket No.: 197102019540 comparison using a second set of parameters, (e.g., using a second mapping algorithm, or by comparison with a second reference sequence); and (iii) optionally, determining if said read meets said second criterion (e.g., the read can be aligned with said second reference sequence, e.g., with less than a specific number of mismatches), wherein said second set of parameters comprises use of, e.g., said second reference sequence, which, compared with said first set of parameters, is more likely to result in an alignment with a read for a variant (e.g., a rearrangement, insertion, deletion, or translocation).

[0184] In some instances, the alignment of sequence reads in the disclosed methods may be combined with a mutation calling method as described elsewhere herein. As discussed herein, reduced sensitivity for detecting actual mutations may be addressed by evaluating the quality of alignments (manually or in an automated fashion) around expected mutation sites in the genes or genomic loci (e.g., gene loci) being analyzed. In some instances, the sites to be evaluated can be obtained from databases of the human genome (e.g., the HG19 human reference genome) or cancer mutations (e.g., COSMIC). Regions that are identified as problematic can be remedied with the use of an algorithm selected to give better performance in the relevant sequence context, e.g., by alignment optimization (or re-alignment) using slower, but more accurate alignment algorithms such as Smith-Waterman alignment. In cases where general alignment algorithms cannot remedy the problem, customized alignment approaches may be created by, e.g., adjustment of maximum difference mismatch penalty parameters for genes with a high likelihood of containing substitutions; adjusting specific mismatch penalty parameters based on specific mutation types that are common in certain tumor types (e.g. C~^T in melanoma); or adjusting specific mismatch penalty parameters based on specific mutation types that are common in certain sample types (e.g. substitutions that are common in FFPE).

[0185] Reduced specificity (increased false positive rate) in the evaluated subject intervals due to misalignment can be assessed by manual or automated examination of all mutation calls in the sequencing data. Those regions found to be prone to spurious mutation calls due to misalignment can be subjected to alignment remedies as discussed above. In cases where no algorithmic remedy is found possible, “mutations” from the problem regions can be classified or screened out from the panel of targeted loci.65MOFO-359739214Docket No.: 197102019540Mutation calling

[0186] Base calling refers to the raw output of a sequencing device, e.g., the determined sequence of nucleotides in an oligonucleotide molecule. Mutation calling refers to the process of selecting a nucleotide value, e.g., A, G, T, or C, for a given nucleotide position being sequenced. Typically, the sequence reads (or base calling) for a position will provide more than one value, e.g., some reads will indicate a T and some will indicate a G. Mutation calling is the process of assigning a correct nucleotide value, e.g., one of those values, to the sequence. Although it is referred to as “mutation” calling, it can be applied to assign a nucleotide value to any nucleotide position, e.g., positions corresponding to mutant alleles, wild-type alleles, alleles that have not been characterized as either mutant or wild-type, or to positions not characterized by variability.

[0187] In some instances, the disclosed methods may comprise the use of customized or tuned mutation calling algorithms or parameters thereof to optimize performance when applied to sequencing data, particularly in methods that rely on massively parallel sequencing (MPS) of a large number of diverse genetic events at a large number of diverse genomic loci (e.g., gene loci, micro satellite regions, etc.) in samples, e.g., samples from a subject having cancer. Optimization of mutation calling is described in the art, e.g., as set out in International Patent Application Publication No. WO 2012 / 092426.

[0188] 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.

[0189] 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 when66MOFO-359739214Docket No.: 197102019540 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).

[0190] 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.

[0191] After alignment, detection of substitutions can be performed using a mutation calling method (e.g., a Bayesian mutation calling method) which is applied to each base in each of the subject intervals, e.g., exons of a gene or other locus to be evaluated, where presence of alternate alleles is observed. This method will compare the probability of observing the read data in the presence of a mutation with the probability of observing the read data in the presence of basecalling error alone. Mutations can be called if this comparison is sufficiently strongly supportive of the presence of a mutation.

[0192] 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).

[0193] 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 identifying67MOFO-359739214Docket No.: 197102019540 candidate indels, calculating genotype likelihood through local re-alignment, and performing LD-based genotype inference and calling. Typically, a Bayesian approach is used to obtain potential indel candidates, and then these candidates are tested together with the reference sequence in a Bayesian framework.

[0194] Algorithms to generate candidate indels are described in, e.g., McKenna, A., et al., Genome Res. 2010; 20(9): 1297-303; Ye, K., et al., Bioinformatics, 2009; 25(21):2865-71; Lunter, G., and Goodson, M., Genome Res. 2011; 21(6):936-9; and Li, H., et al. (2009), Bioinformatics 25(16):2078-9.

[0195] Methods for generating indel calls and individual-level genotype likelihoods include, e.g., the Dindel algorithm (Albers, C.A., et al., Genome Res. 2011 ;21(6):961 -73) . For example, the Bayesian EM algorithm can be used to analyze the reads, make initial indel calls, and generate genotype likelihoods for each candidate indel, followed by imputation of genotypes using, e.g., QCALL (Le S.Q. and Durbin R. Genome Res. 2011 ;21(6):952-60). Parameters, such as prior expectations of observing the indel can be adjusted (e.g., increased or decreased), based on the size or location of the indels.

[0196] Methods have been developed that address limited deviations from allele frequencies of 50% or 100% for the analysis of cancer DNA. (see, e.g., SNVMix -Bioinformatics. 2010 March 15; 26(6): 730-736.) Methods disclosed herein, however, allow consideration of the possibility of the presence of a mutant allele at frequencies (or allele fractions) ranging from 1% to 100% (i.e., allele fractions ranging from 0.01 to 1.0), and especially at levels lower than 50%. This approach is particularly important for the detection of mutations in, for example, low-purity FFPE samples of natural (multi-clonal) tumor DNA.

[0197] In some instances, the mutation calling method used to analyze sequence reads is not individually customized or fine-tuned for detection of different mutations at different genomic loci. In some instances, different mutation calling methods are used that are individually customized or fine-tuned for at least a subset of the different mutations detected at different genomic loci. In some instances, different mutation calling methods are used that are individually customized or fine-tuned for each different mutant detected at each different68MOFO-359739214Docket No.: 197102019540 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.

[0198] In some instances, a nucleotide value is assigned for a nucleotide position in each of X unique subject intervals using a unique mutation calling method, and X is at least 2, at least 3, at least 4, at least 5, at least 10, at least 15, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 200, at least 300, at least 400, at least 500, at least 1000, at least 1500, at least 2000, at least 2500, at least 3000, at least 3500, at least 4000, at least 4500, at least 5000, or greater. The calling methods can differ, and thereby be unique, e.g., by relying on different Bayesian prior values.

[0199] In some instances, assigning said nucleotide value is a function of a value which is or represents the prior (e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type.

[0200] In some instances, the method comprises assigning a nucleotide value (e.g., calling a mutation) for at least 10, 20, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1,000 nucleotide positions, wherein each assignment is a function of a unique value (as opposed to the value for the other assignments) which is or represents the prior (e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type.

[0201] In some instances, assigning said nucleotide value is a function of a set of values which represent the probabilities of observing a read showing said variant at said nucleotide position if the variant is present in the sample at a specified frequency (e.g., 1%, 5%, 10%, etc.) and / or if the variant is absent (e.g., observed in the reads due to base-calling error alone).

[0202] In some instances, the mutation calling methods described herein can include the following: (a) acquiring, for a nucleotide position in each of said X subject intervals: (i) a first value which is or represents the prior (e.g., literature) expectation of observing a read showing a 69MOFO-359739214Docket No.: 197102019540 variant, e.g., a mutation, at said nucleotide position in a tumor of type X; and (ii) a second set of values which represent the probabilities of observing a read showing said variant at said nucleotide position if the variant is present in the sample at a frequency (e.g., 1%, 5%, 10%, etc.) and / or if the variant is absent (e.g., observed in the reads due to base-calling error alone); and (b) responsive to said values, assigning a nucleotide value (e.g., calling a mutation) from said reads for each of said nucleotide positions by weighing, e.g., by a Bayesian method described herein, the comparison among the values in the second set using the first value (e.g. , computing the posterior probability of the presence of a mutation), thereby analyzing said sample.

[0203] Additional description of exemplary nucleic acid sequencing methods, mutation calling methods, and methods for analysis of genetic variants is provided in, e.g., U.S. Patent No. 9,340,830, U.S. Patent No. 9,792,403, U.S. Patent No. 11,136,619, U.S. Patent No. 11,118,213, and International Patent Application Publication No. WO 2020 / 236941, the entire contents of each of which is incorporated herein by reference.Systems

[0204] Also disclosed herein are systems designed to implement any of the disclosed methods for determining genomic variant clonality in a sample from a subject. The systems may comprise, e.g., one or more processors, and a memory unit communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: receive, at the one or more processors, sequence read data for the sample, wherein the sequence read data is associated with a plurality of target regions within the sample; for each target region of the plurality of target regions within the sample: identify, using the one or more processors, a set of reads from the sequence read data associated with the target region; and detect, using the one or more processors, a presence of a candidate genomic variant within the target region based on the set of reads; determine, using the one or more processors, a number of target regions in which the presence of the candidate genomic variant is detected; compare the number of target regions to a first predetermined threshold; and based on the comparison, determine a clonality of the candidate genomic variant.70MOFO-359739214Docket No.: 197102019540

[0205] In some instances, the disclosed systems may further comprise a sequencer, e.g., a next generation sequencer (also referred to as a massively parallel sequencer). Examples of next generation (or massively parallel) sequencing platforms include, but are not limited to, Roche / 454’s Genome Sequencer (GS) FLX system, Illumina / Solexa’ s Genome Analyzer (GA), Illumina’s HiSeq® 2500, HiSeq® 3000, HiSeq® 4000 and NovaSeq® 6000 sequencing systems, Life / APG’s Support Oligonucleotide Ligation Detection (SOLiD) system, Polonator’s G.007 system, Helicos BioSciences’ HeliScope Gene Sequencing system, ThermoFisher Scientific’s Ion Torrent Genexus system, or Pacific Biosciences’ PacBio® RS system.

[0206] In some instances, the disclosed systems may be used for determining genomic variant clonality in any of a variety of samples as described herein (e.g., a tissue sample, biopsy sample, hematological sample, or liquid biopsy sample obtained from the subject).

[0207] In some instances, the plurality of gene loci for which sequencing data is processed to determine genomic variant clonality may comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 0, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, or more than 1000 gene loci (or any number of gene loci within the range of 1 to more than 1000 gene loci).

[0208] In some instance, the nucleic acid sequence data is acquired using a next generation sequencing technique (also referred to as a massively parallel sequencing technique) having a read-length of less than 400 bases, less than 300 bases, less than 200 bases, less than 150 bases, less than 100 bases, less than 90 bases, less than 80 bases, less than 70 bases, less than 60 bases, less than 50 bases, less than 40 bases, or less than 30 bases.

[0209] In some instances, the determination of genomic variant clonality (e.g., of a candidate genomic variant) is used to select, initiate, adjust, or terminate a treatment for cancer in the subject (e.g., a patient) from which the sample was obtained, as described elsewhere herein.

[0210] In some instances, the disclosed systems may further comprise sample processing and library preparation workstations, microplate-handling robotics, fluid dispensing systems, temperature control modules, environmental control chambers, additional data storage modules, data communication modules (e.g., Bluetooth®, WiFi, intranet, or internet communication hardware and associated software), display modules, one or more local and / or cloud-based 71MOFO-359739214Docket No.: 197102019540 software packages (e.g., instrument / system control software packages, sequencing data analysis software packages), etc., or any combination thereof. In some instances, the systems may comprise, or be part of, a computer system or computer network as described elsewhere herein.Machine learning

[0211] Any of a variety of machine learning approaches & algorithms (where a machine learning model, as referred to herein, comprises a trained machine learning algorithm) may be used in implementing the disclosed methods. For example, the machine learning model may comprise a supervised learning model (z.e., a model trained using labeled sets of training data), an unsupervised learning model (z.e., a model trained using unlabeled sets of training data), a semisupervised learning model (z.e., a model trained using a combination of labeled and unlabeled training data), a self- supervised learning model, or any combination thereof. In some examples, the machine learning model can comprise a deep learning model (z.e., a model comprising many layers of coupled "nodes" that may be trained in a supervised, unsupervised, or semi-supervised manner).

[0212] In some instances, one or more machine learning models (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10 machine learning models), or a combination thereof, may be utilized to implement the disclosed methods.

[0213] In some instances, the one or more machine learning models may comprise statistical methods for analyzing data. The machine learning models may be used for classification and / or regression of data. The machine learning models can include, for example, neural networks, support vector machines, decision trees, ensemble learning (e.g., bagging-based learning, such as random forest, and / or boosting-based learning), ^-nearest neighbors algorithms, linear regression-based models, and / or logistic regression-based models. The machine learning models can comprise regularization, such as LI regularization and / or L2 regularization. The machine learning models can include the use of dimensionality reduction techniques (e.g., principal component analysis, matrix factorization techniques, and / or autoencoders) and / or clustering techniques (e.g., hierarchical clustering, / .-means clustering, distribution-based clustering, such as Gaussian mixture models, or density -based clustering, such as DBSCAN or OPTICS). The72MOFO-359739214Docket No.: 197102019540 one or more machine learning models can comprise solving, e.g., optimizing, an objective function over multiple iterations based on a training data set. The iterative solving approach can be used even when the machine learning model comprises a model for which there exists a closed-form solution (e.g., linear regression).

[0214] In some instances, the machine learning models can comprise artificial neural networks (ANNs), e.g., deep learning models. For example, the one or more machine learning models / algorithms used for implementing the disclosed methods may include an ANN which can comprise any of a variety of computational motifs / architectures known to those of skill in the art, including, but not limited to, feedforward connections (e.g., skip connections), recurrent connections, fully connected layers, convolutional layers, and / or pooling functions (e.g., attention, including self-attention). The artificial neural networks can comprise differentiable non-linear functions trained by backpropagation.

[0215] Artificial neural networks, e.g., deep learning models, generally comprise an interconnected group of nodes organized into multiple layers of nodes. For example, the ANN architecture may comprise at least an input layer, one or more hidden layers (i.e., intermediate layers), and an output layer. The ANN or deep learning model may comprise any total number of layers (e.g., 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, or more than 20 layers in total), and any number of hidden layers (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, or more than 20 hidden layers), where the hidden layers function as trainable feature extractors that allow mapping of a set of input data to a preferred output value or set of output values. Each layer of the neural network comprises a plurality of nodes (e.g., at least 10, 25, 50, 75 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10,000, or more than 10,000 nodes). A node receives input data (e.g., genomic feature data (such as variant sequence data, methylation status data, etc.), non-genomic feature data (e.g., digital pathology image feature data), or other types of input data (e.g., patient- specific clinical data)) that comes either directly from one or more input data nodes or from the output of one or more nodes in previous layers, and performs a specific operation, e.g., a summation operation. In some cases, a connection from an input to a node is associated with a weight (or weighting factor). In some cases, the node may, for example, sum up the products of all pairs of inputs, Xi, and their associated weights, Wi. In some73MOFO-359739214Docket No.: 197102019540 cases, the weighted sum is offset with a bias, b. In some cases, the output of a node may be gated using a threshold or activation function, / , where / may be a linear or non-linear function. The activation function may be, for example, a rectified linear unit (ReLU) activation function or other function such as a saturating hyperbolic tangent, identity, binary step, logistic, arcTan, softsign, parameteric rectified linear unit, exponential linear unit, softPlus, bent identity, softExponential, Sinusoid, Sine, Gaussian, or sigmoid function, or any combination thereof.

[0216] The weighting factors, bias values, and threshold values, or other computational parameters of the neural network (or other machine learning architecture), can be "taught" or "learned" in a training phase using one or more sets of training data (e.g., 1, 2, 3, 4, 5, or more than 5 sets of training data) and a specified training approach configured to solve, e.g., minimize, a loss function. For example, the adjustable parameters for an ANN (e.g., deep learning model) may be determined based on input data from a training data set using an iterative solver (such as a gradient-based method, e.g., backpropagation), so that the output value(s) that the ANN computes (e.g., a classification of a sample or a prediction of a disease outcome) are consistent with the examples included in the training data set. The training of the model (i.e., determination of the adjustable parameters of the model using an iterative solver) may or may not be performed using the same hardware as that used for deployment of the trained model.

[0217] In some instances, the disclosed methods may comprise retraining any of the machine learning models (e.g., iteratively retraining a previously trained model using one or more training data sets that differ from those used to train the model initially). In some instances, retraining the machine learning model may comprise using a continuous, e.g., online, machine learning model, i.e., where the model is periodically or continuously updated or retrained based on new training data. The new training data may be provided by, e.g., a single deployed local operational system, a plurality of deployed local operational systems, or a plurality of deployed, geographically-distributed operational systems. In some instances, the disclosed methods may employ, for example, pre-trained ANNs, and the pre-trained ANNs can be fine-tuned according to an additional dataset that is inputted into the pre-trained ANN.74MOFO-359739214Docket No.: 197102019540Computer systems and networks

[0218] FIG. 5 illustrates an example of a computing device or system in accordance with one embodiment. Device 500 can be a host computer connected to a network. Device 500 can be a client computer or a server. As shown in FIG. 5, device 500 can be any suitable type of microprocessor-based device, such as a personal computer, workstation, server or handheld computing device (portable electronic device) such as a phone or tablet. The device can include, for example, one or more processor(s) 510, input devices 520, output devices 530, memory or storage devices 540, communication devices 560, and nucleic acid sequencers 570. Software 550 residing in memory or storage device 540 may comprise, e.g., an operating system as well as software for executing the methods described herein. Input device 520 and output device 530 can generally correspond to those described herein, and can either be connectable or integrated with the computer.

[0219] Input device 520 can be any suitable device that provides input, such as a touch screen, keyboard or keypad, mouse, or voice-recognition device. Output device 530 can be any suitable device that provides output, such as a touch screen, haptics device, or speaker.

[0220] Storage 540 can be any suitable device that provides storage (e.g., an electrical, magnetic or optical memory including a RAM (volatile and non-volatile), cache, hard drive, or removable storage disk). Communication device 560 can include any suitable device capable of transmitting and receiving signals over a network, such as a network interface chip or device. The components of the computer can be connected in any suitable manner, such as via a wired media (e.g., a physical system bus 580, Ethernet connection, or any other wire transfer technology) or wirelessly (e.g., Bluetooth®, Wi-Fi®, or any other wireless technology).

[0221] Software module 550, which can be stored as executable instructions in storage 540 and executed by processor(s) 510, can include, for example, an operating system and / or the processes that embody the functionality of the methods of the present disclosure (e.g., as embodied in the devices as described herein).

[0222] Software module 550 can also be stored and / or transported within any non-transitory computer-readable storage medium for use by or in connection with an instruction execution 75MOFO-359739214Docket No.: 197102019540 system, apparatus, or device, such as those described herein, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions. In the context of this disclosure, a computer-readable storage medium can be any medium, such as storage 540, that can contain or store processes for use by or in connection with an instruction execution system, apparatus, or device. Examples of computer- readable storage media may include memory units like hard drives, flash drives and distribute modules that operate as a single functional unit. Also, various processes described herein may be embodied as modules configured to operate in accordance with the embodiments and techniques described above. Further, while processes may be shown and / or described separately, those skilled in the art will appreciate that the above processes may be routines or modules within other processes.

[0223] Software module 550 can also be propagated within any transport medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described above, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions. In the context of this disclosure, a transport medium can be any medium that can communicate, propagate or transport programming for use by or in connection with an instruction execution system, apparatus, or device. The transport readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic or infrared wired or wireless propagation medium.

[0224] Device 500 may be connected to a network (e.g., network 604, as shown in FIG. 6 and / or described below), which can be any suitable type of interconnected communication system. The network can implement any suitable communications protocol and can be secured by any suitable security protocol. The network can comprise network links of any suitable arrangement that can implement the transmission and reception of network signals, such as wireless network connections, T1 or T3 lines, cable networks, DSE, or telephone lines.

[0225] Device 500 can be implemented using any operating system, e.g., an operating system suitable for operating on the network. Software module 550 can be written in any suitable programming language, such as C, C++, Java or Python. In various embodiments, application software embodying the functionality of the present disclosure can be deployed in different76MOFO-359739214Docket No.: 197102019540 configurations, such as in a client / server arrangement or through a Web browser as a Web-based application or Web service, for example. In some embodiments, the operating system is executed by one or more processors, e.g., processor(s) 510.

[0226] Device 500 can further include a sequencer 570, which can be any suitable nucleic acid sequencing instrument.

[0227] FIG. 6 illustrates an example of a computing system in accordance with one embodiment. In system 600, device 500 (e.g., as described above and illustrated in FIG. 5) is connected to network 604, which is also connected to device 606. In some embodiments, device 606 is a sequencer. Exemplary sequencers can include, without limitation, Roche / 454’s Genome Sequencer (GS) FLX System, Illumina / Solexa’ s Genome Analyzer (GA), Illumina’s HiSeq® 2500, HiSeq® 3000, HiSeq® 4000 and NovaSeq® 6000 Sequencing Systems, Life / APG’s Support Oligonucleotide Ligation Detection (SOLiD) system, Polonator’s G.007 system, Helicos BioSciences’ HeliScope Gene Sequencing system, or Pacific Biosciences’ PacBio® RS system.

[0228] Devices 500 and 606 may communicate, e.g., using suitable communication interfaces via network 604, such as a Local Area Network (LAN), Virtual Private Network (VPN), or the Internet. In some embodiments, network 604 can be, for example, the Internet, an intranet, a virtual private network, a cloud network, a wired network, or a wireless network. Devices 500 and 606 may communicate, in part or in whole, via wireless or hardwired communications, such as Ethernet, IEEE 802.1 lb wireless, or the like. Additionally, devices 500 and 606 may communicate, e.g., using suitable communication interfaces, via a second network, such as a mobile / cellular network. Communication between devices 500 and 606 may further include or communicate with various servers such as a mail server, mobile server, media server, telephone server, and the like. In some embodiments, Devices 500 and 606 can communicate directly (instead of, or in addition to, communicating via network 604), e.g., via wireless or hardwired communications, such as Ethernet, IEEE 802.11b wireless, or the like. In some embodiments, devices 500 and 606 communicate via communications 608, which can be a direct connection or can occur via a network (e.g., network 604).77MOFO-359739214Docket No.: 197102019540

[0229] One or all of devices 500 and 606 generally include logic (e.g., http web server logic) or are programmed to format data, accessed from local or remote databases or other sources of data and content, for providing and / or receiving information via network 604 according to various examples described herein.EXEMPLARY IMPLEMENTATIONS

[0230] Exemplary implementations of the methods and systems described herein include:Embodiment 1. A method for determining genomic variant clonality, the method comprising: obtaining a first plurality of nucleic acid molecules from a first target region of a sample obtained from a subject; obtaining a second plurality of nucleic acid molecules from a second target region of the sample, wherein the second target region of the sample is from a different spatial location within the sample than the first target region; sequencing, by a sequencer, the first plurality of nucleic acid molecules or amplicons thereof to obtain a first plurality of sequence reads; sequencing, by the sequencer, the second plurality of nucleic acid molecules or amplicons thereof to obtain a second plurality of sequence reads; detecting, using one or more processors, a presence of a candidate genomic variant within the first and / or second target region(s) based on the first and second pluralities of sequence reads; determining, using the one or more processors, a number of target regions in which the presence of the candidate genomic variant is detected; comparing the number of target regions to a first predetermined threshold; and based on the comparison, determining a clonality of the candidate genomic variant.Embodiment 2. A method for determining genomic variant clonality, the method comprising: receiving, at one or more processors, sequence read data for a sample obtained from a subject, wherein the sequence read data is associated with a plurality of target regions within the sample, wherein each target region of the plurality is from a different spatial location within the sample;78MOFO-359739214Docket No.: 197102019540 for each target region of the plurality of target regions within the sample: identifying, using the one or more processors, a set of reads from the sequence read data associated with the target region; and detecting, using the one or more processors, a presence of a candidate genomic variant within the target region based on the set of reads; determining, using the one or more processors, a number of target regions in which the presence of the candidate genomic variant is detected; comparing the number of target regions to a first predetermined threshold; and based on the comparison, determining a clonality of the candidate genomic variant.Embodiment 3. The method of embodiment 1 or embodiment 2, wherein determining a clonality comprises confirming clonality between or among the plurality of target regions.Embodiment 4. The method of any one of embodiments 1-3, wherein the number of target regions in which the presence of the candidate genomic variant is detected is greater than or equal to the first predetermined threshold.Embodiment 5. The method of embodiment 4, wherein determining the clonality of the candidate genomic variant comprises determining that the candidate genomic variant is a clonal variant.Embodiment 6. The method of any one of embodiments 1-3, wherein the number of target regions in which the presence of the candidate genomic variant is detected is less than the first predetermined threshold.Embodiment 7. The method of embodiment 6, wherein determining the clonality of the candidate genomic variant comprises determining that the candidate genomic variant is a subclonal variant.Embodiment 8. The method of any one of embodiments 1-7, wherein the first predetermined threshold is two target regions.Embodiment 9. The method of any one of embodiments 1-7, wherein the first predetermined threshold is about 50% of a total number of target regions in the plurality of target regions.79MOFO-359739214Docket No.: 197102019540Embodiment 10. The method of any one of embodiments 1-9, further comprising selecting the target regions of the plurality.Embodiment 11. The method of embodiment 10, wherein the target regions of the plurality are selected to maximize distance between target regions of the plurality.Embodiment 12. The method of any one of embodiments 1-11, wherein the sample is a tissue sample comprising tumor cells.Embodiment 13. The method of embodiment 12, wherein the sample is a tumor biopsy sample.Embodiment 14. The method of embodiment 12 or embodiment 13, wherein the sample is a formalin-fixed paraffin-embedded (FFPE) sample.Embodiment 15. The method of any one of embodiments 12-14, wherein the plurality of target regions, or tissues, cells, or nucleic acid molecules therefrom, are extracted from the sample using a needle punch method.Embodiment 16. The method of any one of embodiments 1-15, wherein each target region of the plurality of target regions is visually distinct from other target regions of the plurality.Embodiment 17. The method of any one of embodiments 1-16, wherein at least 20% of the target regions of the plurality of target regions comprise tumor cells.Embodiment 18. The method of any one of embodiments 1-17, wherein the plurality of target regions includes at least three target regions.Embodiment 19. The method of any one of embodiments 1-18, wherein the first and the second pluralities of nucleic acid molecules comprise tumor nucleic acids.Embodiment 20. The method of any one of embodiments 1-11, wherein the sample comprises at least one liquid biopsy sample.80MOFO-359739214Docket No.: 197102019540Embodiment 21. The method of embodiment 20, wherein the at least one liquid biopsy sample comprises circulating tumor DNA (ctDNA) and / or circulating tumor cells (CTCs).Embodiment 22. The method of embodiment 19 or embodiment 21, wherein the first and second pluralities of sequence reads or the sequence read data are obtained from nucleic acid molecules from cancer cells.Embodiment 23. The method of any one of embodiments 1-22, wherein the first and second pluralities of sequence reads or the sequence read data are obtained using next generation sequencing.Embodiment 24. The method of embodiment 23, wherein the next generation sequencing is performed on one or more of: the whole genome, whole transcriptome, whole exome, a plurality of specific regions of the genome, or a plurality of specific regions of the transcriptome.Embodiment 25. The method of any one of embodiments 1-24, further comprising: estimating, using the one or more processors, a cancer cell fraction (CCF) of the candidate genomic variant based on a tumor purity of the sample; and comparing the CCF to a second predetermined threshold.Embodiment 26. The method of embodiment 25, wherein the second predetermined threshold is 0.5.Embodiment 27. The method of embodiment 25 or embodiment 26, wherein the number of target regions in which the presence of the candidate genomic variant is detected is greater than or equal to the first predetermined threshold, wherein the CCF of the candidate genomic variant is greater than or equal to the second predetermined threshold, and wherein determining a clonality of the candidate genomic variant comprises determining that the candidate genomic variant is clonal.Embodiment 28. The method of any one of embodiments 1-27, further comprising: in accordance with a determination of the clonality of the candidate genomic variant, monitoring the candidate genomic variant in a monitoring assay.81MOFO-359739214Docket No.: 197102019540Embodiment 29. The method of any one of embodiments 1-28, further comprising: in accordance with a determination of the clonality of the candidate genomic variant, detecting the candidate genomic variant in nucleic acid molecules or amplicons thereof from a liquid biopsy sample obtained from the subject, wherein the liquid biopsy sample comprises cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), cell-free RNA, or any combination thereof, and wherein the candidate genomic variant is detected in the cfDNA, the ctDNA, nucleic acid molecules from the CTCs, the cell-free RNA, or amplicons thereof.Embodiment 30. The method of embodiment 29, wherein the candidate genomic variant is detected by quantitative polymerase chain reaction (qPCR).Embodiment 31. The method of embodiment 30, wherein detection of the candidate genomic variant by qPCR comprises use of a primer pair specific for the candidate genomic variant.Embodiment 32. The method of any one of embodiments 1-31, further comprising: generating an output indicative of the clonality of the candidate genomic variant.Embodiment 33. The method of embodiment 32, wherein the output comprises a report recommending that the candidate genomic variant be added to or removed from a monitoring assay.Embodiment 34. The method of embodiment 32 or embodiment 33, wherein the output comprises a confidence level of the determination of the clonality of the candidate genomic variant.Embodiment 35. The method of any one of embodiments 1-34, further comprising: in accordance with a determination of the clonality of the candidate genomic variant, administering to the subject an effective amount of a targeted therapeutic agent, wherein the targeted therapeutic agent is selected based on the candidate genomic variant or a polypeptide encoded by the candidate genomic variant.82MOFO-359739214Docket No.: 197102019540Embodiment 36. The method of any one of embodiments 1-35, wherein the candidate genomic variant is a biomarker associated with cancer.Embodiment 37. The method of any one of embodiments 1-36, wherein the subject has, has been diagnosed with, is suspected of having, or is being screened for having cancer.Embodiment 38. The method of embodiment 37, wherein the cancer is a B cell cancer, a melanoma, breast cancer, lung cancer, bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain cancer, central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine cancer, endometrial cancer, cancer of an oral cavity, cancer of a pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel cancer, appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, a cancer of hematological tissue, an adenocarcinoma, an inflammatory myofibroblastic tumor, a gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), acute lymphocytic leukemia (ALL), acute myelocytic leukemia (AML), chronic myelocytic leukemia (CML), chronic lymphocytic leukemia (CLL), polycythemia Vera, Hodgkin lymphoma, nonHodgkin lymphoma (NHL), soft-tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endotheliosarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, bladder carcinoma, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, neuroblastoma, retinoblastoma, follicular lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, hepatocellular carcinoma, thyroid cancer, gastric cancer, head and neck cancer, small cell cancer, essential thrombocythemia, agnogenic myeloid metaplasia,83MOFO-359739214Docket No.: 197102019540 hypereosinophilic syndrome, systemic mastocytosis, familiar hypereosinophilia, chronic eosinophilic leukemia, neuroendocrine cancers, or a carcinoid tumor.Embodiment 39. A method for monitoring progression or recurrence of cancer in a subject, comprising: receiving, at one or more processors, sequence read data for a sample obtained from a subject, wherein the sequence read data is associated with a plurality of target regions within the sample; for each target region of the plurality of target regions: identifying, using the one or more processors, a set of reads from the sequence read data associated with the target region; and detecting, using the one or more processors, a presence of a candidate genomic variant within the target region based on the set of reads; determining, using the one or more processors, a number of target regions in which the presence of the candidate genomic variant is detected; comparing the number of target regions to a first predetermined threshold; based on the comparison, determining a clonality of the candidate genomic variant; and in accordance with a determination of the clonality of the candidate genomic variant, detecting the candidate genomic variant in nucleic acid molecules from a liquid biopsy sample obtained from the subject, wherein the liquid biopsy sample comprises cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), cell-free RNA, or any combination thereof, and wherein the candidate genomic variant is detected in the cfDNA, the ctDNA, nucleic acid molecules from the CTCs, the cell-free RNA, or amplicons thereof.Embodiment 40. A method for monitoring response to treatment in a subject having cancer, comprising: receiving, at one or more processors, sequence read data for a sample obtained from a subject, wherein the sequence read data is associated with a plurality of target regions within the sample; for each target region of the plurality of target regions:84MOFO-359739214Docket No.: 197102019540 identifying, using the one or more processors, a set of reads from the sequence read data associated with the target region; and detecting, using the one or more processors, a presence of a candidate genomic variant within the target region based on the set of reads; determining, using the one or more processors, a number of target regions in which the presence of the candidate genomic variant is detected; comparing the number of target regions to a first predetermined threshold; based on the comparison, determining a clonality of the candidate genomic variant; administering one or more anti-cancer therapies to the subject; and in accordance with a determination of the clonality of the candidate genomic variant, detecting the candidate genomic variant in nucleic acid molecules from a liquid biopsy sample obtained from the subject after administration of the one or more anti-cancer therapies for cancer, wherein the liquid biopsy sample comprises cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), cell-free RNA, or any combination thereof, and wherein the candidate genomic variant is detected in the cfDNA, the ctDNA, nucleic acid molecules from the CTCs, the cell-free RNA, or amplicons thereof.Embodiment 41. A method for treating or delaying progression of cancer in a subject, comprising: receiving, at one or more processors, sequence read data for a sample obtained from a subject, wherein the sequence read data is associated with a plurality of target regions within the sample; for each target region of the plurality of target regions: identifying, using the one or more processors, a set of reads from the sequence read data associated with the target region; and detecting, using the one or more processors, a presence of a candidate genomic variant within the target region based on the set of reads; determining, using the one or more processors, a number of target regions in which the presence of the candidate genomic variant is detected; comparing the number of target regions to a first predetermined threshold; based on the comparison, determining a clonality of the candidate genomic variant; and85MOFO-359739214Docket No.: 197102019540 in accordance with a determination of the clonality of the candidate genomic variant, administering to the subject an effective amount of a targeted therapeutic agent, wherein the targeted therapeutic agent is selected based on the candidate genomic variant.Embodiment 42. A method for determining genomic variant clonality, the method comprising: obtaining a first plurality of nucleic acid molecules from a first target region of a sample obtained from a subject; obtaining a plurality of nucleic acid molecules from one or more additional target regions of the sample, wherein the one or more additional target regions of the sample are each from a different spatial location and from a spatial location different from the first target region; sequencing, by a sequencer, the first plurality of nucleic acid molecules or amplicons thereof to obtain a first plurality of sequence reads; sequencing, by the sequencer, the plurality of nucleic acid molecules or amplicons thereof from the one or more additional target region to obtain a plurality of sequence reads for each additional target region; detecting, using one or more processors, a presence of a candidate genomic variant within the first and / or each additional target region(s) based on the pluralities of sequence reads; determining, using the one or more processors, a number of target regions in which the presence of the candidate genomic variant is detected; and determining the candidate genomic variant as being clonal when a ratio of the first target region and each additional target region having the candidate genomic variant to all target regions is greater than or equal to a threshold.Embodiment 43. The method of embodiment 42, wherein the threshold is 0.5.Embodiment 44. A method for determining genomic variant clonality, the method comprising: obtaining a first plurality of nucleic acid molecules from a first target region of a sample obtained from a subject;86MOFO-359739214Docket No.: 197102019540 obtaining a plurality of nucleic acid molecules from one or more additional target regions of the sample, wherein the one or more additional target regions of the sample are each from a different spatial location and from a spatial location different from the first target region; sequencing, by a sequencer, the first plurality of nucleic acid molecules or amplicons thereof to obtain a first plurality of sequence reads; sequencing, by the sequencer, the plurality of nucleic acid molecules or amplicons thereof from the one or more additional target region to obtain a plurality of sequence reads for each additional target region; detecting, using one or more processors, a presence of a candidate genomic variant within the first and / or each additional target region(s) based on the pluralities of sequence reads; determining, using the one or more processors, a number of target regions in which the presence of the candidate genomic variant is detected; and determining the candidate genomic variant as being clonal if at least one of:(1) a ratio of the first target region and each additional target region having the candidate genomic variant to all target regions is equal to or great than a first threshold; or(2) a clonality of all target regions determined using a cancer cell fraction (CCF) score is greater than or equal to a second threshold.Embodiment 45. The method of embodiment 44, wherein the first threshold is 0.5.Embodiment 46. The method of embodiment 44 or embodiment 45, wherein the second threshold is 0.5.Embodiment 47. The method of any one of embodiments 44-46, wherein the CCF score represents an allele frequency of the candidate genomic variant proportional to tumor purity of the sample.Embodiment 48. The method of any one of embodiments 44-47, wherein the CCF score is proportional to a ratio of an allele frequency of the candidate genomic variant to: (1) a number of mutant copies of its gene and / or (2) tumor purity of the sample.

[0231] It should be understood from the foregoing that, while particular implementations of the disclosed methods and systems have been illustrated and described, various modifications can be 87MOFO-359739214Docket No.: 197102019540 made thereto and are contemplated herein. It is also not intended that the invention be limited by the specific examples provided within the specification. While the invention has been described with reference to the aforementioned specification, the descriptions and illustrations of the preferable embodiments herein are not meant to be construed in a limiting sense. Furthermore, it shall be understood that all aspects of the invention are not limited to the specific depictions, configurations or relative proportions set forth herein which depend upon a variety of conditions and variables. Various modifications in form and detail of the embodiments of the invention will be apparent to a person skilled in the art. It is therefore contemplated that the invention shall also cover any such modifications, variations and equivalents.88MOFO-359739214

Claims

Docket No.: 197102019540CLAIMSWhat is claimed is:

1. A method for determining genomic variant clonality, the method comprising: obtaining a first plurality of nucleic acid molecules from a first target region of a sample obtained from a subject; obtaining a second plurality of nucleic acid molecules from a second target region of the sample, wherein the second target region of the sample is from a different spatial location within the sample than the first target region; sequencing, by a sequencer, the first plurality of nucleic acid molecules or amplicons thereof to obtain a first plurality of sequence reads; sequencing, by the sequencer, the second plurality of nucleic acid molecules or amplicons thereof to obtain a second plurality of sequence reads; detecting, using one or more processors, a presence of a candidate genomic variant within the first and / or second target region(s) based on the first and second pluralities of sequence reads; determining, using the one or more processors, a number of target regions in which the presence of the candidate genomic variant is detected; comparing the number of target regions to a first predetermined threshold; and based on the comparison, determining a clonality of the candidate genomic variant.

2. A method for determining genomic variant clonality, the method comprising: receiving, at one or more processors, sequence read data for a sample obtained from a subject, wherein the sequence read data is associated with a plurality of target regions within the sample, wherein each target region of the plurality is from a different spatial location within the sample; for each target region of the plurality of target regions within the sample: identifying, using the one or more processors, a set of reads from the sequence read data associated with the target region; and89MOFO-359739214Docket No.: 197102019540 detecting, using the one or more processors, a presence of a candidate genomic variant within the target region based on the set of reads; determining, using the one or more processors, a number of target regions in which the presence of the candidate genomic variant is detected; comparing the number of target regions to a first predetermined threshold; and based on the comparison, determining a clonality of the candidate genomic variant.

3. A method for determining genomic variant clonality, the method comprising: obtaining a first plurality of nucleic acid molecules from a first target region of a sample obtained from a subject; obtaining a plurality of nucleic acid molecules from one or more additional target regions of the sample, wherein the one or more additional target regions of the sample are each from a different spatial location and from a spatial location different from the first target region; sequencing, by a sequencer, the first plurality of nucleic acid molecules or amplicons thereof to obtain a first plurality of sequence reads; sequencing, by the sequencer, the plurality of nucleic acid molecules or amplicons thereof from the one or more additional target region to obtain a plurality of sequence reads for each additional target region; detecting, using one or more processors, a presence of a candidate genomic variant within the first and / or each additional target region(s) based on the pluralities of sequence reads; determining, using the one or more processors, a number of target regions in which the presence of the candidate genomic variant is detected; and determining the candidate genomic variant as being clonal if at least one of:(1) a ratio of the first target region and each additional target region having the candidate genomic variant to all target regions is equal to or great than a first threshold; or(2) a clonality of all target regions determined using a cancer cell fraction (CCF) score is greater than or equal to a second threshold.

4. The method of claim 1, wherein the number of target regions in which the presence of the candidate genomic variant is detected is greater than or equal to the first predetermined90MOFO-359739214Docket No.: 197102019540 threshold, and wherein determining the clonality of the candidate genomic variant comprises determining that the candidate genomic variant is a clonal variant.

5. The method of claim 1, wherein the number of target regions in which the presence of the candidate genomic variant is detected is less than the first predetermined threshold, and wherein determining the clonality of the candidate genomic variant comprises determining that the candidate genomic variant is a subclonal variant.

6. The method of claim 1, further comprising selecting the target regions of the plurality; optionally wherein the target regions of the plurality are selected to maximize distance between target regions of the plurality.

7. The method of claim 1, wherein the sample is a tissue sample comprising tumor cells; optionally wherein the sample is a tumor biopsy sample and / or a formalin-fixed paraffin- embedded (FFPE) sample.

8. The method of claim 7, wherein the plurality of target regions, or tissues, cells, or nucleic acid molecules therefrom, are extracted from the sample using a needle punch method.

9. The method of claim 1, wherein the sample comprises at least one liquid biopsy sample; optionally wherein the at least one liquid biopsy sample comprises circulating tumor DNA (ctDNA) and / or circulating tumor cells (CTCs) and / or wherein the first and second pluralities of sequence reads or the sequence read data are obtained from nucleic acid molecules from cancer cells.

10. The method of claim 1, wherein each target region of the plurality of target regions is visually distinct from other target regions of the plurality.

11. The method of claim 1, wherein at least 20% of the target regions of the plurality of target regions comprise tumor cells.

12. The method of claim 1, wherein the plurality of target regions includes at least three target regions.91MOFO-359739214Docket No.: 19710201954013. The method of claim 1, wherein the first and the second pluralities of nucleic acid molecules comprise tumor nucleic acids.

14. The method of claim 1, wherein the first and second pluralities of sequence reads or the sequence read data are obtained using next generation sequencing; optionally wherein the next generation sequencing is performed on one or more of: the whole genome, whole transcriptome, whole exome, a plurality of specific regions of the genome, or a plurality of specific regions of the transcriptome.

15. The method of claim 1, further comprising: estimating, using the one or more processors, a cancer cell fraction (CCF) of the candidate genomic variant based on a tumor purity of the sample; and comparing the CCF to a second predetermined threshold; optionally wherein the second predetermined threshold is 0.5.

16. The method of claim 15, wherein the number of target regions in which the presence of the candidate genomic variant is detected is greater than or equal to the first predetermined threshold, wherein the CCF of the candidate genomic variant is greater than or equal to the second predetermined threshold, and wherein determining a clonality of the candidate genomic variant comprises determining that the candidate genomic variant is clonal.

17. The method of claim 1, further comprising: in accordance with a determination of the clonality of the candidate genomic variant, monitoring the candidate genomic variant in a monitoring assay.

18. The method of claim 1, further comprising: in accordance with a determination of the clonality of the candidate genomic variant, detecting the candidate genomic variant in nucleic acid molecules or amplicons thereof from a liquid biopsy sample obtained from the subject, wherein the liquid biopsy sample comprises cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), cell-free RNA, or any combination thereof, and wherein the candidate genomic variant is detected in the cfDNA, the ctDNA, nucleic acid92MOFO-359739214Docket No.: 197102019540 molecules from the CTCs, the cell-free RNA, or amplicons thereof; optionally wherein the candidate genomic variant is detected by quantitative polymerase chain reaction (qPCR) using a primer pair specific for the candidate genomic variant.

19. The method of claim 1, further comprising: generating an output indicative of the clonality of the candidate genomic variant; optionally wherein the output comprises a report recommending that the candidate genomic variant be added to or removed from a monitoring assay.

20. The method of claim 1, further comprising: in accordance with a determination of the clonality of the candidate genomic variant, administering to the subject an effective amount of a targeted therapeutic agent, wherein the targeted therapeutic agent is selected based on the candidate genomic variant or a polypeptide encoded by the candidate genomic variant.93MOFO-359739214

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