WO2025059560A1

WO2025059560A1 - Methods and systems for predicting an outcome of an early-stage disease based on genomic instability features

Info

Publication number: WO2025059560A1
Application number: PCT/US2024/046746
Authority: WO
Inventors: Ericka EBOT
Original assignee: Foundation Medicine Inc
Current assignee: Foundation Medicine Inc
Priority date: 2023-09-15
Filing date: 2024-09-13
Publication date: 2025-03-20
Anticipated expiration: 2026-03-15

Abstract

Methods and systems for predicting an outcome of an early-stage disease in a subject are described. Embodiments of the present disclosure may receive sequence read data obtained from a sample of a subject and determine one or more features indicative of genomic instability based on the sequence read data. The one or more features indicative of genomic instability may include one or more copy number features associated with the sample, among other features described herein. The system may determine a risk score associated with the subject by inputting the one or more features indicative of genomic instability into a statistical model (e.g., a machine-learning model), and predict the outcome for the subject based on the determined risk score.

Description

METHODS AND SYSTEMS FOR PREDICTING AN OUTCOME OF AN EARLY-STAGE DISEASE BASED ON GENOMIC INSTABILITY FEATURES

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the priority benefit of United States Provisional Patent Application Serial No. 63/538,559, filed September 15, 2023, the contents of which are incorporated herein by reference in their entirety.

FIELD OF THE INVENTION

[0002] The present disclosure relates generally to methods and systems for analyzing genomic profiling data, and more specifically to methods and systems for predicting an outcome of an early- stage disease for a patient using genomic profiling data such as genomic instability features.

BACKGROUND

[0003] The TNM (Tumor, lymph Node, Metastasis) cancer staging system is currently used to predict prognosis and guide treatment decisions for patients with various cancers, including non- small-cell lung cancer (NSCLC). For example, among patients with resectable (i.e., surgically removable), early-stage (i.e., non-metastatic) NSCLC, the primary (first line) treatment generally involves surgery to remove the tumor. However, the prognosis for patients with early-stage NSCLC may vary greatly: 30% to 55% of patients with early-stage NSCLC may develop recurrence and die of their disease despite curative resection (see, e.g., Uramoto and Tanaka (2014), “Recurrence after surgery in patients with NSCLC,” Translational Lung Cancer Research 3(4): 242-9). As such, additional factors beyond the TNM cancer staging system are needed to identify NSCLC patients with poor prognosis who may require additional or more aggressive treatment at earlier stages (i.e., adjuvant and/or neoadjuvant therapy such as chemotherapy or radiation therapy). Understanding the underlying genomic mechanisms driving disease progression and metastasis in NSCLC may assist in the prognosis and treatment of NSCLC patients. [0004] Previous studies have identified molecular subtypes of lung adenocarcinoma, which is a form of NSCLC, that have prognostic implications using gene expression profiling and gene mutation status (see, e.g., Ruiz-Cordero el al. (2020), “Simplified molecular classification of lung adenocarcinomas based on EGFR, KRAS, and TP53 mutations,” BMC Cancer 20:83). However, these previously reported biomarkers lack the predictive ability required to accurately predict prognosis for early-stage NSCLC patients, thus presenting a challenge in determining whether such patients require or may benefit from additional or more aggressive treatment.

Thus, the need exists for novel approaches to identify the specific prognostic features that are capable of predicting disease outcomes for early-stage diseases (e.g., early-stage NSCLC) and treating them accordingly.

BRIEF SUMMARY OF THE INVENTION

[0005] Methods and systems for predicting an outcome of an early-stage disease in a subject are described. Embodiments of the present disclosure may receive sequence read data obtained from a sample of a subject and determine or identify one or more features indicative of genomic instability based on the sequence read data. The one or more features indicative of genomic instability may include one or more copy number features associated with the sample, among other features described herein. The system may determine a risk score associated with the subject by inputting the one or more features indicative of genomic instability into a statistical model (e.g., a machine-learning model), and predict the outcome for the subject based on the determined risk score.

[0006] Previous studies have identified features indicative of genomic instability that are associated with organ-specific metastasis across various cancer types (see, e.g., Nguyen, et al. (2022), “Genomic characterization of metastatic patterns from prospective clinical sequencing of 25,000 patients,” Cell 185(3): 563-575). In particular, a clinic-genomic analysis of MSK-MET has identified somatic alterations associated with organ- specific metastasis and highlights that genomic instability correlates with metastatic burden in a cancer type-dependent manner. For example, oncogenic alteration frequency and chromosomal instability are increased in metastases of lung cancer patients. Metastatic tumors are significantly more chromosomally unstable, as measured by a higher fraction of genome altered (FGA). Additionally, metastatic tumors are more homogeneous, with a higher fraction of clonal mutations compared to primary, non- metastatic tumors.

[0007] Embodiments of the present disclosure may use features indicative of genomic instability, along with other features, to predict patient outcomes by leveraging statistical model(s) such as machine-learning model(s). In some embodiments, the features indicative of genomic instability may include chromosome arm-level features, chromosome cytoband-level features, and/or genome- wide features, such as chromosome arm- level gain/loss/loss of heterozygosity (LOH) data, chromosome cytoband-level gain/loss/LOH data, genome- wide copy number feature data, and/or genome- wide copy number signature data.

[0008] In some embodiments, the features indicative of genomic instability may be related to biomarkers such as tumor mutational burden (TMB) and/or fraction of genome altered (FGA). For example, the features indicative of genomic instability may include FGA gain/loss data. In some embodiments, the model may be further configured to receive genomic alteration data, such as gene-specific short variant data, gene-specific copy number data, and/or gene-specific rearrangement data as input. As described herein, these features may be selected using a number of techniques and criteria, such as variance, association with patient outcome based on a univariate analysis, and/or clustering.

[0009] Further, as described herein, different machine-learning (ML) models may be configured to predict different types of patient outcomes, such as overall survival and/or risk of metastasis. In some embodiments, the ML model is a statistical model such as a random forest model, a logistic regression model, and/or a deep learning model. In such embodiments, the statistical model may be configured to output a risk score in the form of a hazard ratio for estimating a probability of survival relative to a control group (e.g., the risk of death from Stage III NSCLC relative to Stage I NSCLC is 2), and the risk score is used to predict an outcome such as disease recurrence, metastatic progression, or survival at a given time. In some embodiments, the ML model is a statistical model such as a random survival forest model, a Cox regression model, a support vector machine, an accelerated failure time model, and/or a deep learning model (e.g., an Artificial Neural Network (ANN) or a Convolutional Neural Network (CNN)). In such embodiments, the statistical model may be configured to output a risk score in the form of a hazard ratio or a survival curve estimating a probability of survival across a time range (e.g., the probability of survival at 12 months is 75%), and the risk score is used to predict an outcome such as time to recurrence or overall survival.

[0010] Embodiments of the present disclosure provide a number of technical advantages. Previously reported biomarkers lack the predictive ability required to accurately predict prognosis for early-stage NSCLC patients, which presents a challenge in determining whether such patients require additional or more aggressive treatment at earlier stages. The systems and methods described herein may identify biomarkers that are highly predictive of risk of disease recurrence/metastasis in patients with early-stage diseases (e.g., early-stage NSCLC) by utilizing genomic data (e.g., copy number data) and clinical outcome data (e.g., 4eline-genomic databases). Further, the techniques disclosed herein for selecting features for the statistical model may limit the input features to those that are more clinically relevant and also reduce the dimensionality and volume of the inputs, thus ensuring the model to produce accurate predictions using a smaller and leaner dataset in an efficient manner and improving the functioning of the computing devices executing such models. Such feature selection techniques may also improve the prediction stability (e.g., predictions on new samples) and interpretability.

[0011] 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

[0012] 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

[0013] 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:

[0014] FIG. 1 provides a non-limiting example of a process for predicting an outcome of an early-stage disease in a subject.

[0015] FIG. 2 provides a non-limiting example of a data set of tumor samples, arranged by collection date.

[0016] FIG. 3 provides a non-limiting example of a bar chart illustrating the changes in the relative frequencies of cancer stages at initial diagnosis over time.

[0017] FIG. 4 provides a non-limiting example of a line graph illustrating the overall survival curves of subjects diagnosed with non-squamous NSCLC, arranged by cancer stage.

[0018] FIG. 5 provides a non-limiting example of a flowchart illustrating treatment options for non-metastatic NSCLC.

[0019] FIG. 6 depicts an exemplary computing device or system in accordance with one embodiment of the present disclosure.

[0020] FIG. 7 depicts an exemplary computer system or computer network, in accordance with some instances of the systems described herein.

DETAILED DESCRIPTION

[0021] Methods and systems for predicting an outcome of an early-stage disease in a subject are described. Embodiments of the present disclosure may receive sequence read data obtained from a sample of a subject and determine one or more features indicative of genomic instability based on the sequence read data. The one or more features indicative of genomic instability may include one or more copy number features associated with the sample, among other features described herein. The system may determine a risk score associated with the subject by inputting the one or more features indicative of genomic instability into a statistical model (e.g., a machine-learning model), and predict the outcome for the subject based on the determined risk score.

[0022] Previous studies have identified features indicative of genomic instability that are associated with organ-specific metastasis across various cancer types (see, e.g., Nguyen, et al. (Cell 2022), “Genomic characterization of metastatic patterns from prospective clinical sequencing of 25,000 patients,” Cell 185(3): 563-575). In particular, a clinic-genomic analysis of MSK-MET has identified somatic alterations associated with organ- specific metastasis and highlights that genomic instability correlates with metastatic burden in a cancer type-dependent manner. For example, oncogenic alteration frequency and chromosomal instability are increased in metastases of lung cancer patients. Metastatic tumors are significantly more chromosomally unstable, as measured by a higher fraction of genome altered (FGA). Additionally, metastatic tumors are more homogeneous, with a higher fraction of clonal mutations compared to primary, non-metastatic tumors.

[0023] Embodiments of the present disclosure may use features indicative of genomic instability, along with other features, to predict patient outcomes by leveraging statistical model(s) such as machine-learning model(s). In some embodiments, the features indicative of genomic instability may include chromosome arm-level features, chromosome cytoband-level features, and/or genome-wide features, such as chromosome arm-level gain/loss/LOH data, chromosome cytoband-level gain/loss/LOH data, genome-wide copy number feature data, and/or genomewide copy number signature data. In some embodiments, the features indicative of genomic instability may include biomarkers such as tumor mutational burden (TMB) and/or fraction of genome altered (FGA). In some embodiments, the model may be further configured to receive genomic alteration data, such as gene-specific short variant data, gene-specific copy number data, and/or gene- specific rearrangement data as input. As described herein, these features may be selected using a number of techniques and criteria, such as variance, association with patient outcome based on a univariate analysis, and/or clustering. [0024] Further, as described herein, different machine-learning (ML) models may be configured to predict different types of patient outcomes, such as overall survival and/or risk of metastasis. In some embodiments, the ML model is a statistical model such as a random forest model, a logistic regression model, and/or a deep learning model. In such embodiments, the statistical model may be configured to output a risk score in the form of a hazard ratio for estimating a probability of survival relative to a control group (e.g., the risk of death from Stage III NSCLC relative to Stage I NSCLC is 2), and the risk score is used to predict an outcome such as disease recurrence, metastatic progression, or survival at a given time. In some embodiments, the ML model is a statistical model such as a random survival forest model, a Cox regression model, a support vector machine, an accelerated failure time model, and/or a deep learning model. In such embodiments, the statistical model may be configured to output a risk score in the form of a hazard ratio or a survival curve estimating a probability of survival across a time range (e.g., the probability of survival at 12 months is 75%), and the risk score is used to predict an outcome such as time to recurrence or overall survival.

[0025] Embodiments of the present disclosure provide a number of technical advantages. Previously reported biomarkers lack the predictive ability required to accurately predict prognosis for early-stage NSCLC patients, which presents a challenge in determining whether such patients require additional treatment. The systems and methods described herein may identify biomarkers that are highly predictive of risk of disease recurrence/metastasis in patients with early-stage diseases (e.g., early-stage NSCLC) by utilizing genomic data (e.g., copy number data) and clinical outcome data (e.g., from genomic databases). Further, the techniques disclosed herein for selecting features for the statistical model may limit the input features to those that are more clinically relevant and also reduce the dimensionality and volume of the inputs, thus ensuring the model to produce accurate predictions using a smaller and leaner dataset in an efficient manner and improving the functioning of the computing devices executing such models.

Definitions

[0026] 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. [0027] 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.

[0028] ‘ ‘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.

[0029] 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.

[0030] 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.

[0031] 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 may exist alone within an animal, or may 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.

[0032] 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 may be performed either for prophylaxis or during the course of clinical pathology. Desirable effects of treatment include, but are not limited to, preventing occurrence or recurrence of disease, alleviation of symptoms, diminishment of any direct or indirect pathological consequences of the disease, preventing metastasis, decreasing the rate of disease progression, amelioration or palliation of the disease state, and remission or improved prognosis.

[0033] As used herein, the term “subgenomic interval” (or “subgenomic sequence interval”) refers to a portion of a genomic sequence.

[0034] 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).

[0035] 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.

[0036] 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.

[0037] 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.

[0038] The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.

Methods for predicting an outcome of an early-stage disease in a subject

[0039] FIG. 1 provides a non-limiting example of a process 100 for predicting an outcome of an early-stage disease in a subject who has been diagnosed as having a disease (e.g., cancer).

Process 100 may 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 client-server system, it will be appreciated that process 100 is not so limited. In other examples, process 100 is performed using only a client device or only multiple client devices. In process 100, some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the process 100. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.

[0040] At block 102, an exemplary system (e.g., one or more electronic devices) receives, at one or more processors, sequence read data obtained from a sample of the subject. 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.

[0041] 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 (i.e., 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 uses as the input to the sequencer to generate the sequence read data. In a liquid biopsy sample, a certain percentage of the cell free DNA is circulating tumor DNA (ctDNA) and it is this ctDNA for which the results pertain. [0042] At block 104, the system determines, using the one or more processors, one or more features indicative of genomic instability based on the sequence read data. The one or more features indicative of genomic instability may include, for example, one or more copy number features associated with the sample, one or more values associated with one or more complex biomarkers, or any combination thereof. Given the role of chromosomal instability and chromosome copy-number heterogeneity in cancer progression and metastasis, copy number features and chromosome arm-level features may be of particular importance when determining patient outcomes. Copy number features may be obtained from the copy number profile of a specimen and may summarize the variability in the copy number profile. These features may include the length of each genome segment, the number of breakpoints occurring across the genome, the segment copy number of each segment, etc. Chromosome arm-level features may capture copy number changes at the chromosome arm level (e.g., chrlp). Specimens with chromosomal instability may be associated with high levels of aneuploidy. In some embodiments, the one or more features indicative of genomic instability may include an aneuploidy score representing the gain/loss of a fraction of chromosome arms with copy number alterations. In some instances, additional features, e.g., one or more values associated with one or more genomic alterations, may also be determined based on the sequence read data. In some embodiments, the one or more features indicative of genomic instability may be at least partially indicative of an extent of chromosomal copy number changes across a genome of the subject.

[0043] In some embodiments, the one or more features indicative of genomic instability may include one or more copy number features and/or copy number signatures e.g., patterns of copy number features) associated with the sample. Examples of copy number features include, but are not limited to, a number of breakpoints per 10 Mb of genomic sequence, a number of breakpoints per 25 Mb of genomic sequence, a number of breakpoints per 50 Mb of genomic sequence, a number of breakpoints per 100 Mb of genomic sequence, a number of breakpoints per chromosome arm for each chromosome, a magnitude of a copy number change between any two adjacent copy number segments; a length of each copy number segment; the copy number of segments; a count of contiguous oscillating copy number chains, or any combination thereof. Examples of copy number signatures include, but are not limited to, a low aneuploidy signature, a chromosomal instability signature, a focal tandem duplication (FTD) signature, a homologous recombination deficient (HRD) signature, an HRD breast signature, an HRD prostate signature, an amplicon signature, a seismic amplification signature, a subclonal signature, an oscillating signature, a neuroendocrine signature, or any combination thereof. In some instances, copy number features and/or copy number signatures may be extracted from a copy number model of the genomic data associated with the sample. Methods for identifying copy number features and copy number signatures, and methods for their use, are described in U.S. Provisional Patent Application No. 63/413,559, the contents of which are incorporated herein by reference in their entirety.

[0044] In some embodiments, the one or more features indicative of genomic instability may include one or more chromosome arm-level features (e.g., chromosome arm- level gain/loss/LOH data), one or more chromosome cytoband-level features (e.g., cytoband-level gain/loss/LOH data), and one or more genome- wide features, (e.g., genome- wide copy number feature data and genome-wide copy number signature data). Chromosome arm-level features may also be referred to herein as “aneuploidy features.” The copy number features may be obtained from the copy number profile of a sample and may summarize the variability in the copy number profile. Additional copy number features may include the length of each genome segment, the number of breakpoints occurring across the genome, the segment copy number of each segment, etc. In some embodiments, one or more copy number features may be combined using modeling to create copy number signatures. In some embodiments, the one or more values associated with the one or more copy number features may comprise a binary value. For example, a digital value associated with gain data of a chromosomal arm, such as chromosome arm 9p, may be 0 (no gain of chromosome arm 9p) or 1 (gain of chromosome arm 9p). A digital value associated with loss of heterozygosity (LOH) of a chromosomal arm may be 0 (no LOH of the chromosomal arm) or 1 (LOH of the chromosomal arm). In some embodiments, the one or more values associated with the one or more copy number features may comprise a continuous value. For example, each chromosome may be broken down into cytobands (i.e., genomic regions of the chromosome), and the copy number features for a given cytoband, such as chromosome region 9p21.3, may include the number of copies of chromosome region 9p21.3 that are present in the sample.

[0045] In some embodiments, the one or more features indicative of genomic instability may include one or more values associated with one or more biomarkers, such as TMB and FGA. Tumor mutational burden (TMB) is the estimated number of non-inherited (somatic) mutations per megabase of genomic sequence. Methods for evaluating TMB are described in PCT International Patent Application Publication No. WO 2017/151524, the contents of which are incorporated herein by reference in their entirety. In some embodiments, the one or more values associated with the one or more biomarkers may include a continuous value. For example, as noted, a value associated with TMB may represent the number of somatic mutations per megabase of genomic sequence in the sample. A value associated with fraction of genome altered (FGA) may represent the percentage of regions in a predetermined set of chromosome regions that contain copy number alterations. In some embodiments, the one or more values associated with the one or more complex biomarkers may include a binary value. For example, a digital value associated with TMB may be represented as a 0 (low TMB) or a 1 (high TMB) based on how many tumor genome mutations the sample contains relative to a threshold number of tumor genome mutations (e.g., 10 mut/mb).

[0046] In some embodiments, additional features, e.g., one or more values associated with one or more genomic alterations, may also be determined. The one or more values associated with the one or more genomic alterations may be obtained from genomic alteration data, which is derived from the sequence read data, as mentioned above. To obtain the one or more values associated with the one or more genomic alterations, the system may analyze the sequence read data, using the one or more processors, to generate genomic alteration data. In some embodiments, genomic alteration data may comprise, for example, gene-specific short variant data as, gene-specific copy number data, gene- specific rearrangement data, or any combination thereof, any of which may be represented as the one or more values associated with the one or more genomic alterations. In some embodiments, genomic alteration data 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, or any combination thereof. In some embodiments, the one or more values associated with the one or more genomic alterations may include a binary value. For example, for each gene in a predetermined set of genes, a digital value of 0 (absent) or a digital value of 1 (present) may represent whether there is a known pathogenic or likely pathogenic alteration present in that gene. The known pathogenic or likely pathogenic alteration may be a predefined alteration that is gene- specific. In some embodiments, the one or more values associated with the one or more genomic alterations may include a continuous value. For example, genes with known pathogenic or likely pathogenic alterations may be weighted differently depending on whether the known pathogenic or likely pathogenic alterations are rearrangements, substitutions, deletions, duplications, inversions, or any combination thereof. In some instances, a longer alteration (e.g., more than 10 bps long) may be weighted differently, and thus may be associated with a higher or lower value, than a shorter alteration. In some embodiments, a genomic alteration (e.g., SNV) may be identified from the sequence read data, and the sequence identity associated with that alteration may be provided.

[0047] At block 106, the system determines, using the one or more processors, a risk score associated with the subject by inputting the one or more features indicative of genomic instability into a statistical model. In some instances, one or more features indicative of the presence of one or more genomic alterations may also be input into the statistical model and used to determine a risk score.

[0048] In some embodiments, the one or more features indicative of genomic instability (and, optionally, the one or more features indicative of the presence of a gene alteration) may, optionally, be normalized or standardized before they are inputted into the statistical model. As an example, for data sets containing continuous values corresponding to the features (e.g., values representing TMB), the values may be represented by Z-scores that are centered and scaled by the standard deviation of the data sets.

[0049] The statistical model may include a random forest model, a random survival forest model, a logistic regression model, a Cox regression model, a LASSO Cox regression model, a support vector machine, an accelerated failure time model, a deep learning model, or any combination thereof. In some embodiments (e.g., embodiments in which the statistical model includes a deep learning model), the statistical model may automatically update its capabilities over time by pulling in new clinical data that reflects the most recent studies. By inputting the one or more features indicative of genomic instability (and, optionally, the one or more features indicative of the presence of a gene alteration) — which may have been normalized, clustered, or otherwise filtered — into a suitable statistical model, the desired risk score may be calculated. The risk score may comprise a probability of disease recurrence, time to recurrence, metastatic progression, overall survival, survival at a given time, or any combination thereof.

[0050] Certain outcomes may be predicted using risk scores from specific statistical models. For example, in some embodiments, the model is a random forest model, a logistic regression model, a deep learning model (e.g., ANN, CNN, etc.), or any combination thereof. In such embodiments, the statistical model may be configured to output a risk score in the form of a hazard ratio for estimating a probability of survival relative to a control group (e.g., the risk of death from Stage III NSCLC relative to Stage I NSCLC is 2), and the predicted outcome is disease recurrence, metastatic progression, or survival at a given time. In some embodiments, the statistical model is a random survival forest model, a Cox regression model, a support vector machine, an accelerated failure time model, a deep learning model, or any combination thereof. In such embodiments, the statistical model may be configured to output a risk score in the form of a hazard ratio or a survival curve estimating a probability of survival across a time range (e.g., the probability of survival at 12 months is 75%), and the predicted outcome is time to recurrence or overall survival. In some embodiments, when the outcome is binomial (e.g., represents an event vs. no event), the risk score may be a linear predictor (e.g., in the case of a logistic regression model) or a probability (in the case of a random forest model). The hazard ratio may be an estimate obtained from survival and/or time-to-event models.

[0051] Optionally, at block 106, the system may assign, using the one or more processors, a weight to a feature of the one or more features indicative of genomic instability (and the one or more features indicative of the presence of a gene alteration, if included). For example, if genomic alterations in a specific gene are strongly correlated with greater genomic instability and/or greater risk of tumor metastasis, the genomic alterations in that specific gene may be weighted more heavily when determining the risk score.

[0052] Optionally, at block 106, the system may classify the risk score associated with the subject as high-risk or low-risk by comparing the risk score to a predetermined threshold score. The predetermined threshold score may be determined from the training data set used to train the statistical model. Subjects with high risk scores (i.e., above the predetermined threshold score) may be at greater risk of a negative outcome (e.g., death, metastasis, or disease recurrence). Subjects with low risk scores may be at lesser risk of a negative outcome.

[0053] At block 108, the system predicts, using the one or more processors, the outcome for the subject based on the risk score. The outcome may comprise disease recurrence, time to disease recurrence, metastatic progression, overall survival, survival at a given time, or any combination thereof. An outcome predicted based on a high risk score (e.g., exceeding a predefined threshold) may be indicative of a high risk (e.g., a high risk of metastatic progression). In some embodiments, an outcome indicative of a high risk may include, for example, higher likelihood of disease recurrence, higher likelihood of metastatic progression, lower likelihood of survival at a given time, shorter time to disease recurrence, or lower likelihood of overall survival across a time range, when compared to one or more predefined thresholds. An outcome predicted based on a low risk score (e.g., not exceeding a predefined threshold) may be indicative of a low risk (e.g., a low risk of metastatic progression). In some embodiments, an outcome indicative of a low risk may include, for example, lower likelihood of disease recurrence, lower likelihood of metastatic progression, higher likelihood of survival at a given time, longer time to disease recurrence, or higher likelihood of overall survival across a time range, when compared to one or more predefined thresholds.

[0054] Resectability may also be used by the system to predict the outcome for the subject. Resectability refers to the ability of a tumor to be surgically removed from the subject. Tumors are generally more resectable in early-stage disease than in late-stage disease, in which the tumor may have metastasized to other regions of the body. In some embodiments, if the tumor was not fully removed during surgery, the outcome of the subject may indicate a high risk (e.g., a high risk of disease recurrence). According, the statistical model may be configured to receive a resectability feature, among other features described herein, to output a risk score.

[0055] After process 100 is performed, the system may select a treatment for the disease (e.g., early-stage NSCLC) based on the outcome. Specifically, the system may select a treatment based on whether the outcome is indicative of a low risk or a high risk.

[0056] For an outcome indicative of a low risk, the subject may benefit from less intensive treatment. Because many cancer treatments may be associated with unpleasant side effects for the subject, selecting the appropriate treatment to treat the disease while minimizing the negative side effects experienced by the subject may be desirable. Thus, based on an outcome indicative of a low risk, the system may select a primary treatment, such as chemotherapy and/or surgery. In some embodiments, the primary treatment may further comprise radiation therapy, hormone therapy, immunotherapy, medication, or any combination thereof. For example, Atezolizumab (immunotherapy) and 17elinexorl7b (EGFR inhibitor), which are FDA-approved drugs for treating early-stage NSCLC, may be used as a primary treatment for certain patients.

[0057] Alternatively, for an outcome indicative of a high risk, the risk to the subject’s life may outweigh the unpleasant side effects of the cancer treatments, and as such, the subject may benefit from more intensive treatment. Thus, based on an outcome indicative of a high risk, the system may select both a primary treatment and a secondary treatment. Taken together, the primary treatment and the secondary treatment may improve the prognosis of the subject and reduce the risk of disease recurrence. In some embodiments, the secondary treatment may comprise chemotherapy, surgery, radiation therapy, hormone therapy, immunotherapy, medication, or any combination thereof. In some embodiments, the secondary treatment may include a neoadjuvant therapy delivered before the primary treatment to reduce the size or slow the growth of a tumor. In some embodiments, the secondary treatment may include an adjuvant therapy delivered after the primary treatment to destroy remaining cancer cells. In some embodiments, the secondary treatment may comprise a combination of adjuvant and/or neoadjuvant therapies. For example, the combination of cisplatin and pemetrexed and the combination of carboplatin and pemetrexed, all of which are chemotherapy drugs, may be provided to subjects as an adjuvant therapy.

[0058] Further, after process 100 is performed, the treatment may be selected based on the disease type (e.g., early-stage NSCLC may be treated by surgery, whereas metastatic NSCLC may no longer be surgically treatable) as well as the outcome. For example, the TNM cancer staging system may be used to guide treatment decisions for patients with various cancers, including NSCLC. In some embodiments, the TNM cancer stage may be used together with the predicted outcome to select a treatment for the disease. [0059] Further, after process 100 is performed, the system may predict a measurement of minimum residual disease (MRD) based on the risk score. MRD refers to the number of cancer cells remaining in the subject after treatment. In some embodiments, the predicted MRD may be used together with the predicted outcome to select a treatment for the disease.

[0060] To train the statistical model of process 100, the system may use a number of techniques and criteria to determine what the input features for the model may be. Training the statistical model may involve the removal of features with low variance, the selection of features based on univariate analysis, the clustering of correlated features, or any combination thereof.

[0061] In some embodiments, the one or more features indicative of genomic instability (and, optionally, the one or more features indicative of the presence of a gene alteration) may be processed by removing features with low variance in favor of features with high variance. In some embodiments, the system may select for features with high variance by obtaining a plurality of features, obtaining a plurality of variance values corresponding to the plurality of features, and then selecting the one or more features indicative of genomic instability from the plurality of features based on the plurality of variance values. The plurality of variance values may comprise a coefficient of variation or a standard deviation. By selecting features with high variance, the input set of the statistical model may be limited to inputs that are most clinically relevant, which in turn allows the statistical model to determine risk scores more efficiently.

[0062] In some embodiments, the one or more features indicative of genomic instability (and, optionally, the one or more features indicative of the presence of a gene alteration) may be processed based on univariate analysis. In some embodiments, the system may select for features based on univariate analysis by obtaining a plurality of features, obtaining a plurality of outcome association values corresponding to the plurality of features, and then selecting the one or more features indicative of genomic instability (and, optionally, the one or more features indicative of the presence of a gene alteration) from the plurality of features based on the plurality of outcome association values. The plurality of outcome association values may be obtained from the results of a univariate analysis such as a Cox proportional hazards regression model. For example, if the outcome to be predicted is overall survival, the outcome association values may indicate which features are associated with overall survival outcomes. Further, the selection of features based on univariate analysis may be based on a predefined threshold. For example, for a given number of outcome association values (e.g., 200), a predefined number of features (e.g., 100) may be selected based on their relative outcome association values. By selecting for features based on univariate analysis, the input set of the statistical model may be limited to reduce noise and dimensionality of the inputs, which in turn allows the statistical model to determine risk scores more efficiently.

[0063] In some embodiments, the one or more features indicative of genomic instability (and, optionally, the one or more features indicative of the presence of a gene alteration) may be processed by clustering. This may be performed using a hierarchical clustering technique, such as calculating a Pearson’s correlation coefficient between features in the data set, to group features with similar behaviors. Hierarchical clustering may be performed using Euclidean or correlation based distance measures. Other clustering approaches may include K- means clustering and non-negative matrix factorization. In some embodiments, the system may determine which features should be clustered with each other by obtaining a plurality of features, identifying one or more clusters of features based on the plurality of features, and selecting the one or more features indicative of genomic instability (and, optionally, the one or more features indicative of the presence of a gene alteration) from the plurality of features based on the one or more clusters of features. For example, a given number of features (e.g., 100) may be clustered into a smaller number of clusters (e.g., 10), and the clusters of features may then replace the individual features as the inputs of the statistical model. The behavior of the cluster may be quantified by choosing one feature of the cluster to represent the entire cluster, by averaging the values of all features of the cluster, or any combination thereof. By clustering the features with similar behaviors, the input set of the statistical model may be limited to reduce dimensionality of the inputs, which in turn allows the statistical model to determine risk scores more efficiently.

[0064] FIG. 2 provides a non-limiting example of a data set of tumor samples, arranged by collection date. Among the tumor samples of the 14,190 NSCLC subjects represented in the data set, 11% are Stage I, 22% are Stage II-IIIA, 9% are Stage IIIB-C, 54% are Stage IV, and the remaining 4% are not categorized. Among the tumor samples from subjects with early-stage disease (e.g., Stage I and Stage II-IIIA), the samples are further divided based on whether they were collected at the initial diagnosis (“initial dx”) or later in a clinical course of cancer treatment (“adv dx”), such as after progression to a late-stage (e.g., Stage IIIB-C and Stage IV) form of the disease or after reoccurrence of an early-stage of the disease. Among the tumor samples from subjects with early-stage disease that were collected at the initial diagnosis, as well as the tumor samples from subjects with late-stage disease, the samples are further divided based on whether a comprehensive genomic profiling (CGP) test was performed at the time of sample collection. The data set may be used to train a statistical model, such as the statistical model of process 100 in FIG. 1. For example, the data associated with tumor samples from subjects with early-stage disease that were collected at the initial diagnosis (the “specimen @ initial dx” entries circled in FIG. 2) may be used to train the statistical model to identify prognostic features for subjects with early-stage disease.

[0065] FIG. 3 provides a non-limiting example of a bar chart illustrating the changes in the relative frequencies of cancer stages at initial diagnosis over time. Each year from 2014 to 2022 is represented by the stage distribution among a selection of patients who received a CGP test at the time of initial diagnosis during that year. The data illustrates that, for the selection of patients who received the CGP test, the relative frequency of early-stage diagnosis (e.g., Stage I and Stage II-IIIA) is increasing over time. As such, there is an increasing need to develop early- stage outcome prediction techniques, such as the embodiments described herein, for the growing number of patients receiving early-stage diagnoses.

[0066] FIG. 4 provides a non-limiting example of a line graph illustrating the overall survival curves of subjects diagnosed with non-squamous NSCLC, arranged by cancer stage. The survival probability of subjects diagnosed at a given cancer stage (e.g., Stage I, Stage II-IIIA, Stage IIIB-C, and Stage IV) is plotted over a duration of 60 months. The data illustrates that subjects with earlier-stage diagnoses have greater survival probabilities throughout the 60 months than subjects with later-stage diagnoses. For example, subjects diagnosed at Stage I have roughly 50% probability of survival 60 months after diagnosis, while subjects diagnosed at Stage IV have less than 25% probability of survival at the corresponding time. On an individual subject level, the embodiments described herein may output such survival curves to estimate a subject’s probability of survival across a time range. Based on the survival curves, the subject’s outcome can be predicted, and appropriate cancer treatments can be recommended. [0067] FIG. 5 provides a non-limiting example of a flowchart illustrating treatment options for non-metastatic NSCLC. Following surgery for stage IA subjects, adjuvant therapy may not be provided at all in some embodiments. Following surgery for Stage IB-IIIA subjects, chemotherapy or another adjuvant therapy may be provided. For example, cisplatin-based adjuvant chemotherapy is the standard of care for subjects with resected high-risk non-metastatic NSCLC (e.g., Stage II-IIIA). Recent FDA approvals in the adjuvant setting include 21elinexor21b (EGFR-mutated tumors) and atezolizumab immunotherapy (PD-L1 >1% tumor cells). For Stage IIIB-C subjects or other subjects with unresectable non-metastatic NSCLC, anti-PD-Ll therapy with durvalumab after concurrent chemotherapy and radiotherapy is the standard of care for unresectable or inoperable non-metastatic NSCLC. Immunotherapy is being extensively studied in the preoperative/neoadjuvant setting. Whether or not these adjuvant therapies are recommended, as well as what types of adjuvant therapies are recommended, may vary depending on the genomic instability features of the subject.

[0068] 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.

[0069] In some instances, the disclosed methods may be used to predict an outcome of an early- stage disease in a subject by assessing features indicative of genomic instability 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. In some embodiments, the one or more gene loci may include ABL, ALK, ALL, B4GALNT1, BAFF, BCL2, BRAF, BRCA, BTK, CD19, CD20, CD3, CD30, CD319, CD38, CD52, CDK4, CDK6, CML, CRACC, CS1, CTLA-4, dMMR, EGFR, ERBB1, ERBB2, FGFR1-3, FLT3, GD2, HDAC, HER1, HER2, HR, IDH2, IL-ip, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSI-H, mTOR, PARP, PD-1, PDGFR, PDGFRa, PDGFRP, PD-L1, PI3K5, PIGF, PTCH, RAF, RANKL, RET, ROS1, SLAMF7, VEGF, VEGFA, VEGFB, 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 (Cllorf30), EP300, EPHA3, EPHB1, EPHB4, ERBB2, ERBB3, ERBB4, ERCC4, ERG, ERRFI1, ESRI, ETV4, ETV5, ETV6, EWSR1, EZH2, EZR, FAM46C, FANCA, FANCC, FANCG, FANCL, FAS, FBXW7, FGF10, FGF12, FGF14, FGF19, FGF23, FGF3, FGF4, FGF6, FGFR1, FGFR2, FGFR3, FGFR4, FH, FLCN, FLT1, FLT3, FOXL2, FUBP1, GABRA6, GATA3, GATA4, GATA6, GID4 (C17orf39), GNA11, GNA13, GNAQ, GNAS, GRM3, GSK3B, H3F3A, HDAC1, HGF, HNF1A, HRAS, HSD3B1, ID3, IDH1, IDH2, IGF1R, IKBKE, IKZF1, INPP4B, IRF2, IRF4, IRS2, JAK1, JAK2, JAK3, JUN, KDM5A, KDM5C, KDM6A, KDR, KEAP1, KEL, KIT, KLHL6, KMT2A (MLL), KMT2D (MLL2), KRAS, LTK, LYN, MAF, MAP2K1, MAP2K2, MAP2K4, MAP3K1, MAP3K13, MAPK1, MCL1, MDM2, MDM4, MED12, MEF2B, MEN1, MERTK, MET, MITF, MKNK1, MLH1, MPL, MRE11A, MSH2, MSH3, MSH6, MST1R, MTAP, MTOR, MUTYH, MYB, MYC, MYCL, MYCN, MYD88, NBN, NF1, NF2, NFE2L2, NFKBIA, NKX2-1, NOTCH1, NOTCH2, NOTCH3, NPM1, NRAS, NT5C2, NTRK1, NTRK2, NTRK3, NUTM1, P2RY8, PALB2, PARK2, PARP1, PARP2, PARP3, PAX5, PBRM1, PDCD1, PDCD1LG2, PDGFRA, PDGFRB, PDK1, PIK3C2B, PIK3C2G, PIK3CA, PIK3CB, PIK3R1, PIM1, PMS2, POLDI, POLE, PPARG, PPP2R1A, PPP2R2A, PRDM1, PRKAR1A, PRKCI, PTCHI, PTEN, PTPN11, PTPRO, QKI, RAC1, RAD21, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54L, RAFI, RARA, RBI, RBM10, REL, RET, RICTOR, RNF43, ROS1, RPTOR, RSPO2, SDC4, SDHA, SDHB, SDHC, SDHD, SETD2, SF3B1, SGK1, SLC34A2, SMAD2, SMAD4, SMARCA4, SMARCB1, SMO, SNCAIP, SOCS1, SOX2, SOX9, SPEN, SPOP, SRC, STAG2, STAT3, STK11, SUFU, SYK, TBX3, TEK, TERC, TERT, TET2, TGFBR2, TIPARP, TMPRSS2, TNFAIP3, TNFRSF14, TP53, TSC1, TSC2, TYRO3, U2AF1, VEGFA, VHL, WHSCI, WHSC1L1, WT1, XPO1, XRCC2, ZNF217, ZNF703, or any combination thereof. [0070] 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

[0071] In some instances, the disclosed methods may further comprise one or more of the steps of: (i) obtaining the sample from the subject (e.g., a subject suspected of having or determined to have cancer), (ii) extracting nucleic acid molecules (e.g., a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules) from the sample, (iii) ligating one or more adapters to the nucleic acid molecules extracted from the sample (e.g., one or more amplification primers, flow cell adaptor sequences, substrate adapter sequences, or sample index sequences), (iv) performing a methylation conversion reaction to convert, e.g., non-methylated cytosine to uracil, (v) amplifying the nucleic acid molecules (e.g., using a polymerase chain reaction (PCR) amplification technique, a non-PCR amplification technique, or an isothermal amplification technique), (vi) capturing nucleic acid molecules from the amplified nucleic acid molecules (e.g., by hybridization to one or more bait molecules, where the bait molecules each comprise one or more nucleic acid molecules that each comprising a region that is complementary to a region of a captured nucleic acid molecule), (vii) sequencing the nucleic acid molecules extracted from the sample (or library proxies derived therefrom) using, e.g., a next-generation (massively parallel) sequencing technique, a whole genome sequencing (WGS) technique, a whole exome sequencing technique, a targeted sequencing technique, a direct sequencing technique, or a Sanger sequencing technique) using, e.g., a next-generation (massively parallel) sequencer, (viii) combining the nucleic acid sequence data (including, e.g., variant data, copy number data, methylation status data, etc., of the sequenced nucleic acid molecules) with other biomarker data modalities including, but not limited to, proteomics-based biomarker data (e.g., the detection of specific polypeptides, such as proteins) or fragmentomics-based biomarker data (e.g., the detection of certain attributes related to nucleic acid fragments, such as fragment size or the sequences of fragment ends), to determine, for example, the presence of ctDNA in the sample and/or to determine a diagnostic, prognostic, and/or treatment response prediction for the subject, and (ix) generating, displaying, transmitting, and/or delivering a report (e.g., an electronic, webbased, or paper report) to the subject (or patient), a caregiver, a healthcare provider, a physician, an oncologist, an electronic medical record system, a hospital, a clinic, a third-party payer, an insurance company, or a government office. In some instances, the report comprises output from the methods described herein wherein for example genomic instability values and gene alterations are input into 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.

[0072] The disclosed methods may be used with any of a variety of samples. For example, in some instances, the sample may comprise a tissue biopsy sample, a liquid biopsy sample, or a normal control. In some instances, the sample may be a liquid biopsy sample and may comprise blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva. In some instances, the sample may be a liquid biopsy sample and may comprise circulating tumor cells (CTCs). In some instances, the sample may be a liquid biopsy sample and may comprise cell-free DNA (cfDNA). In some instances, the cell-free DNA (cfDNA), or a portion thereof, may comprise circulating tumor DNA (ctDNA). In some instances, the liquid biopsy sample may comprise a combination of cell-free DNA (cfDNA) and circulating tumor DNA (ctDNA).

[0073] In some instances, the disclosed methods for predicting an outcome may be used to diagnose (or as part of a diagnosis of) the presence of disease or other conditions (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.

[0074] In some instances, the disclosed methods for predicting an outcome may be used to predict genetic disorders in fetal DNA (e.g., for invasive or non-invasive prenatal testing). For example, sequence read data obtained by sequencing fetal DNA extracted from samples obtained using invasive amniocentesis, chorionic villus sampling (cVS), or fetal umbilical cord sampling techniques, or obtained using non-invasive sampling of cell-free DNA (cfDNA) samples (which comprises a mix of maternal cfDNA and fetal cfDNA), may be processed according to the disclosed methods to identify variants, e.g., copy number alterations, associated with, e.g., Down Syndrome (trisomy 21), trisomy 18, trisomy 13, and extra or missing copies of the X and Y chromosomes.

[0075] In some instances, the nucleic acid molecules extracted from a sample may comprise a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules. In some instances, the tumor nucleic acid molecules may be derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules may be derived from a normal portion of the heterogeneous tissue biopsy sample. In some instances, the sample may comprise a liquid biopsy sample, and the tumor nucleic acid molecules may be derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample while the non-tumor nucleic acid molecules may be derived from a non-tumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.

[0076] In some instances, the disclosed methods for predicting an outcome of an early-stage disease in a subject may be used to select a subject (e.g., a patient) for a clinical trial based on the risk score and/or the predicted outcome. In some instances, patient selection for clinical trials based on, e.g., the risk score and/or the predicted outcome, may accelerate the development of targeted therapies and improve the healthcare outcomes for treatment decisions.

[0077] In some instances, the disclosed methods for predicting an outcome of an early-stage disease in a subject may be used to select an appropriate therapy or treatment (e.g., an anticancer therapy or anti-cancer treatment) for a subject. In some instances, for example, the anticancer therapy or treatment may comprise use of a poly (ADP-ribose) polymerase inhibitor (PARPi), a platinum compound, chemotherapy, radiation therapy, a targeted therapy (e.g., immunotherapy), surgery, or any combination thereof.

[0078] 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 inhibitor- based therapy, an antibody-drug conjugate therapy, a hormone therapy, and/or a targeted radiotherapy) that targets specific molecules required for cancer cell growth, division, and spreading. In some instances, the targeted anti-cancer therapy or treatment may comprise abemaciclib (Verzenio), abiraterone acetate (Zytiga), acalabrutinib (Calquence), ado-trastuzumab emtansine (Kadcyla), afatinib dimaleate (Gilotrif), alectinib (Alecensa), alemtuzumab (Campath), alitretinoin (Panretin), alpelisib (Piqray), amivantamab-vmjw (Rybrevant), anastrozole (Arimidex), apalutamide (Erleada), asciminib hydrochloride (Scemblix), atezolizumab (Tecentriq), avapritinib (Ayvakit), avelumab (Bavencio), axicabtagene ciloleucel (Yescarta), axitinib (Inlyta), belantamab mafodotin-blmf (Blenrep), belimumab (Benlysta), belinostat (Beleodaq), belzutifan (Welireg), bevacizumab (Avastin), bexarotene (Targretin), binimetinib (Mektovi), blinatumomab (Blincyto), bortezomib (Velcade), bosutinib (Bosulif), brentuximab vedotin (Adcetris), brexucabtagene autoleucel (Tecartus), brigatinib (Alunbrig), cabazitaxel (Jevtana), cabozantinib (Cabometyx), cabozantinib (Cabometyx, Cometriq), canakinumab (Haris), capmatinib hydrochloride (Tabrecta), carfilzomib (Kyprolis), cemiplimab- rwlc (Libtayo), ceritinib (LDK378/Zykadia), cetuximab (Erbitux), cobimetinib (Cotellic), crizotinib (Xalkori), dabrafenib (Tafinlar), dacomitinib (Vizimpro), daratumumab (Darzalex), daratumumab and hyaluronidase-fihj (Darzalex Faspro), darolutamide (Nubeqa), dasatinib (Sprycel), denileukin diftitox (Ontak), denosumab (Xgeva), dinutuximab (Unituxin), dostarlimab-gxly (Jemperli), durvalumab (Imfinzi), duvelisib (Copiktra), elotuzumab (Empliciti), enasidenib mesylate (Idhifa), encorafenib (Braftovi), enfortumab vedotin-ejfv (Padcev), entrectinib (Rozlytrek), enzalutamide (Xtandi), erdafitinib (Balversa), erlotinib (Tarceva), everolimus (Afinitor), exemestane (Aromasin), fam-trastuzumab deruxtecan-nxki (Enhertu), fedratinib hydrochloride (Inrebic), fulvestrant (Faslodex), gefitinib (Iressa), gemtuzumab ozogamicin (Mylotarg), gilteritinib (Xospata), glasdegib maleate (Daurismo), hyaluronidase-zzxf (Phesgo), ibrutinib (Imbruvica), ibritumomab tiuxetan (Zevalin), idecabtagene vicleucel (Abecma), idelalisib (Zydelig), imatinib mesylate (Gleevec), infigratinib phosphate (Truseltiq), inotuzumab ozogamicin (Besponsa), ipilimumab (Yervoy), isatuximab-irfc (Sarclisa), ivosidenib (Tibsovo), ixazomib citrate (Ninlaro), lanreotide acetate (Somatuline Depot), lapatinib (Tykerb), larotrectinib sulfate (Vitrakvi), lenvatinib mesylate (Lenvima), letrozole (Femara), lisocabtagene maraleucel (Breyanzi), loncastuximab tesirine-lpyl (Zynlonta), lorlatinib (Lorbrena), lutetium Lu 177-dotatate (Lutathera), margetuximab-cmkb (Margenza), midostaurin (Rydapt), mobocertinib succinate (Exkivity), mogamulizumab-kpkc (Poteligeo), moxetumomab pasudotox-tdfk (Lumoxiti), naxitamab-gqgk (Danyelza), necitumumab (Portrazza), neratinib maleate (Nerlynx), nilotinib (Tasigna), niraparib tosylate monohydrate (Zejula), nivolumab (Opdivo), obinutuzumab (Gazyva), ofatumumab (Arzerra), olaparib (Lynparza), olaratumab (Lartruvo), osimertinib (Tagrisso), palbociclib (Ibrance), panitumumab (Vectibix), pazopanib (Votrient), pembrolizumab (Keytruda), pemigatinib (Pemazyre), pertuzumab (Perjeta), pexidartinib hydrochloride (Turalio), polatuzumab vedotin-piiq (Polivy), ponatinib hydrochloride (Iclusig), pralatrexate (Folotyn), pralsetinib (Gavreto), radium 223 dichloride (Xofigo), ramucirumab (Cyramza), regorafenib (Stivarga), ribociclib (Kisqali), ripretinib (Qinlock), rituximab (Rituxan), rituximab and hyaluronidase human (Rituxan Hycela), romidepsin (Istodax), rucaparib camsylate (Rubraca), ruxolitinib phosphate (Jakafi), sacituzumab govitecan-hziy (Trodelvy), seliciclib, selinexor (Xpovio), selpercatinib (Retevmo), selumetinib sulfate (Koselugo), siltuximab (Sylvant), sirolimus protein-bound particles (Fyarro), sonidegib (Odomzo), sorafenib (Nexavar), sotorasib (Lumakras), sunitinib (Sutent), tafasitamab-cxix (Monjuvi), tagraxofusp-erzs (Elzonris), talazoparib tosylate (Talzenna), tamoxifen (Nolvadex), tazemetostat hydrobromide (Tazverik), tebentafusp-tebn (Kimmtrak), temsirolimus (Torisel), tepotinib hydrochloride (Tepmetko), tisagenlecleucel (Kymriah), tisotumab vedotin-tftv (Tivdak), tocilizumab (Actemra), tofacitinib (Xeljanz), tositumomab (Bexxar), trametinib (Mekinist), trastuzumab (Herceptin), tretinoin (Vesanoid), tivozanib hydrochloride (Fotivda), toremifene (Fareston), tucatinib (Tukysa), umbralisib tosylate (Ukoniq), vandetanib (Caprelsa), vemurafenib (Zelboraf), venetoclax (Venclexta), vismodegib (Erivedge), vorinostat (Zolinza), zanubrutinib (Brukinsa), ziv- aflibercept (Zaltrap), or any combination thereof.

[0079] 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).

[0080] 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 by 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.

[0081] In some instances, the disclosed methods for predicting an outcome of an early-stage disease in a subject may be used in treating a disease (e.g., a cancer) in a subject. For example, in response to predicting an outcome of an early-stage disease in a subject 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.

[0082] In some instances, the disclosed methods for predicting an outcome of an early-stage disease in a subject 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 a first predicted outcome in a first sample obtained from the subject at a first time point, and used to determine a second predicted outcome in a second sample obtained from the subject at a second time point, where comparison of the predicted outcomes 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.

[0083] 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 predicted outcome.

[0084] In some instances, the outcome predicted using the disclosed methods may be based on a risk score associated with the subject. The value of the risk score may be used as a prognostic or diagnostic indicator associated with the sample. For example, in some instances, the prognostic or diagnostic indicator may comprise an indicator of the presence of a disease (e.g., cancer) in the sample, an indicator of the probability that a disease (e.g., cancer) is present in the sample, an indicator of the probability that the subject from which the sample was derived will develop a disease (e.g., cancer) (i.e., a risk factor), or an indicator of the likelihood that the subject from which the sample was derived will respond to a particular therapy or treatment.

[0085] In some instances, the disclosed methods for predicting an outcome of an early-stage disease in a subject may be implemented as part of a genomic profiling process that comprises prediction of the outcomes as part of detecting, monitoring, predicting a risk factor, or selecting a treatment for a particular disease, e.g., cancer. In some instances, the variant panel selected for genomic profiling may comprise the detection of variant sequences at a selected set of gene loci. In some instances, the variant panel selected for genomic profiling may comprise detection of variant sequences at a number of gene loci through comprehensive genomic profiling (CGP), which is a next-generation sequencing (NGS) approach used to assess hundreds of genes (including relevant cancer biomarkers) in a single assay. Inclusion of the disclosed methods for predicting an outcome of an early-stage disease in a subject as part of a genomic profiling process (or inclusion of the output from the disclosed methods for predicting an outcome of an early-stage disease in a subject as part of the genomic profile of the subject) may 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 predicted outcome in a given patient sample.

[0086] 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.

[0087] 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.

[0088] In some instances, the method may 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

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] In some instances, the disclosed methods may further comprise analyzing a primary control (e.g., a normal tissue sample). In some instances, the disclosed methods may further comprise determining if a primary control is available and, if so, isolating a control nucleic acid (e.g., DNA) from said primary control. In some instances, the sample may comprise any normal control (e.g., a normal adjacent tissue (NAT)) if no primary control is available. In some instances, the sample may be or may comprise histologically normal tissue. In some instances, the method includes evaluating a sample, e.g., a histologically normal sample (e.g., from a surgical tissue margin) using the methods described herein. In some instances, the disclosed methods may further comprise acquiring a sub-sample enriched for non-tumor cells, e.g., by macro-dissecting non-tumor tissue from said NAT in a sample not accompanied by a primary control. In some instances, the disclosed methods may further comprise determining that no primary control and no NAT is available, and marking said sample for analysis without a matched control.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] In some instances, DNA is extracted from nucleated cells from the sample. In some instances, a sample may have a low nucleated cellularity, e.g., when the sample is comprised mainly of erythrocytes, lesional cells that contain excessive cytoplasm, or tissue with fibrosis. In some instances, a sample with low nucleated cellularity may require more, e.g., greater, tissue volume for DNA extraction.

[0098] 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.

[0099] 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 may result in lower sensitivity of detection for a given size sample.

[0100] 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

[0101] 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.

[0102] 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). [0103] 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.

[0104] 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

[0105] In some instances, the sample is acquired from a subject having a cancer. Exemplary cancers include, but are not limited to, B cell cancer (e.g., multiple myeloma), melanomas, breast cancer, lung cancer (such as non-small cell lung carcinoma or NSCLC), bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain or central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine or endometrial cancer, cancer of the oral cavity or pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel or appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, cancer of hematological tissues, adenocarcinomas, inflammatory myofibroblastic tumors, gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), acute lymphocytic leukemia (ALL), acute myelocytic leukemia (AML), chronic myelocytic leukemia (CML), chronic lymphocytic leukemia (CLL), polycythemia Vera, Hodgkin lymphoma, nonHodgkin lymphoma (NHL), soft-tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endotheliosarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing’s tumor, leiomyosarcoma, rhabdomyosarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms’ tumor, bladder carcinoma, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, neuroblastoma, retinoblastoma, follicular lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, hepatocellular carcinoma, thyroid cancer, gastric cancer, head and neck cancer, small cell cancers, essential thrombocythemia, agnogenic myeloid metaplasia, hypereosinophilic syndrome, systemic mastocytosis, familiar hypereosinophilia, chronic eosinophilic leukemia, neuroendocrine cancers, carcinoid tumors, and the like.

[0106] 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 (NSCLC), 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 nonsmall cell lung cancer (with an EGFR exon 19 deletion or exon 21 substitution (L858R) mutations), a non-small cell lung cancer (with an EGFR T790M mutation), ovarian cancer, ovarian cancer (with a BRCA mutation), pancreatic cancer, a pancreatic, gastrointestinal, or lung origin neuroendocrine tumor, a pediatric neuroblastoma, a peripheral T-cell lymphoma, peritoneal cancer, prostate cancer, a renal cell carcinoma, rheumatoid arthritis, a small lymphocytic lymphoma, a soft tissue sarcoma, a solid tumor (MSI-H/dMMR), a squamous cell cancer of the head and neck, a squamous non-small cell lung cancer, thyroid cancer, a thyroid carcinoma, urothelial cancer, a urothelial carcinoma, or Waldenstrom’s macroglobulinemia.

[0107] 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

[0108] DNA or RNA may be extracted from tissue samples, biopsy samples, blood samples, or other bodily fluid samples using any of a variety of techniques known to those of skill in the art (see, e.g., Example 1 of International Patent Application Publication No. WO 2012/092426; Tan, et al. (2009), “DNA, RNA, and Protein Extraction: The Past and The Present”, J. Biomed. Biotech. 2009:574398; the technical literature for the Maxwell® 16 LEV Blood DNA Kit (Promega Corporation, Madison, WI); and the Maxwell 16 Buccal Swab LEV DNA Purification Kit Technical Manual (Promega Literature #TM333, January 1, 2011, Promega Corporation, Madison, WI)). Protocols for RNA isolation are disclosed in, e.g., the Maxwell® 16 Total RNA Purification Kit Technical Bulletin (Promega Literature #TB351, August 2009, Promega Corporation, Madison, WI).

[0109] 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 (i.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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] In some instances, DNA may be extracted using any of a variety of suitable commercial DNA extraction and purification kits. Examples include, but are not limited to, the QIAamp (for isolation of genomic DNA from human samples) and DNAeasy (for isolation of genomic DNA from animal or plant samples) kits from Qiagen (Germantown, MD) or the Maxwell® and ReliaPrep™ series of kits from Promega (Madison, WI).

[0114] 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 EEV DNA Purification Kit Technical Manual (Promega Eiterature #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 EEV 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. [0115] In some instances, the disclosed methods may further comprise determining or acquiring a yield value for the nucleic acid extracted from the sample and comparing the determined value to a reference value. For example, if the determined or acquired value is less than the reference value, the nucleic acids may be amplified prior to proceeding with library construction. In some instances, the disclosed methods may further comprise determining or acquiring a value for the size (or average size) of nucleic acid fragments in the sample, and comparing the determined or acquired value to a reference value, e.g., a size (or average size) of at least 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 base pairs (bps). In some instances, one or more parameters described herein may be adjusted or selected in response to this determination.

[0116] 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 may 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 may be used to avoid a ligation step during library preparation.

Library preparation

[0117] 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.

[0118] 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 may 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 may 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.

[0119] 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 may 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 may be from a single subject or individual. In some instances, a library may comprise nucleic acid molecules derived from more than one subject (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30 or more subjects). For example, two or more libraries from different subjects may be combined to form a library having nucleic acid molecules from more than one subject (where the nucleic acid molecules derived from each subject are optionally ligated to a unique sample barcode corresponding to a specific subject). In some instances, the subject is a human having, or at risk of having, a cancer or tumor.

[0120] In some instances, the library (or a portion thereof) may comprise one or more subgenomic intervals. In some instances, a subgenomic interval may be a single nucleotide position, e.g., a nucleotide position for which a variant at the position is associated (positively or negatively) with a tumor phenotype. In some instances, a subgenomic interval comprises more than one nucleotide position. Such instances include sequences of at least 2, 5, 10, 50, 100, 150, 250, or more than 250 nucleotide positions in length. Subgenomic intervals may 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 may 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 may 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 may 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

[0121] The methods described herein may 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.

[0122] 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.

[0123] 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.

[0124] In some instances, the selected gene loci (also referred to herein as target gene loci or target sequences), or fragments thereof, may include subject intervals comprising non-coding sequences, coding sequences, intragenic regions, or intergenic regions of the subject genome. For example, the subject intervals may 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

[0125] 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 may 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 may be a bait molecule, e.g., a nucleic acid molecule (e.g., a DNA molecule or RNA molecule) which may 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.

[0126] The methods described herein provide for optimized sequencing of a large number of genomic loci (e.g., genes or gene products (e.g., mRNA), micro satellite loci, etc.) from samples (e.g., cancerous tissue specimens, liquid biopsy samples, and the like) from one or more subjects by the appropriate selection of target capture reagents to select the target nucleic acid molecules to be sequenced. In some instances, a target capture reagent may hybridize to a specific target locus, e.g., a specific target gene locus or fragment thereof. In some instances, a target capture reagent may hybridize to a specific group of target loci, e.g., a specific group of gene loci or fragments thereof. In some instances, a plurality of target capture reagents comprising a mix of target- specific and/or group- specific target capture reagents may be used.

[0127] 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.

[0128] In some instances, the overall length of the target capture reagent sequence may 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 may 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 may be used.

[0129] In some instances, each target capture reagent sequence may 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” may refer to the target- specific target capture sequence or to the entire target capture reagent oligonucleotide including the target- specific target capture sequence. [0130] In some instances, the target- specific capture sequences in the target capture reagents are between about 40 nucleotides and 1000 nucleotides in length. In some instances, the targetspecific capture sequence is between about 70 nucleotides and 300 nucleotides in length. In some instances, the target- specific sequence is between about 100 nucleotides and 200 nucleotides in length. In yet other instances, the target- specific sequence is between about 120 nucleotides and 170 nucleotides in length, typically 120 nucleotides in length. Intermediate lengths in addition to those mentioned above also may 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.

[0131] 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 may be designed to recognize the juncture sequence to increase the selection efficiency.

[0132] 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. [0133] Typically, DNA molecules are used as target capture reagent sequences, although RNA molecules may also be used. In some instances, a DNA molecule target capture reagent may 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.

[0134] 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).

[0135] 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.

[0136] In some instances, the target capture reagents may be part of a kit which may optionally comprise instructions, standards, buffers or enzymes or other reagents.

Hybridization conditions

[0137] 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 (i.e., the library catch). The contacting step may 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.

[0138] In some instances, the contacting step is effected using a solid support, e.g., an array. Suitable solid supports for hybridization are described in, e.g., Albert, T.J. et al. (2007) Nat. Methods 4(11):903-5; Hodges, E. et al. (2007) Nat. Genet. 39(12): 1522-7; and Okou, D.T. et al. (2007) Nat. Methods 4(11 ):907-9, the contents of which are incorporated herein by reference in their entireties.

[0139] Hybridization methods that may 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

[0140] The methods and systems disclosed herein may be used in combination with, or as part of, a method or system for sequencing nucleic acids (e.g., a next-generation sequencing system) to generate a plurality of sequence reads that overlap one or more gene loci within a subgenomic interval in the sample and thereby determine, e.g., gene allele sequences at a plurality of gene loci. “Next-generation sequencing” (or “NGS”) as used herein may also be referred to as “massively parallel sequencing” (or “MPS”), and refers to any sequencing method that determines the nucleotide sequence of either individual nucleic acid molecules (e.g., as in single molecule sequencing) or clonally expanded proxies for individual nucleic acid molecules in a high throughput fashion (e.g., wherein greater than 10³, 10⁴, 10⁵ or more than 10⁵ molecules are sequenced simultaneously).

[0141] Next-generation sequencing methods are known in the art, and are described in, e.g., Metzker, M. (2010) Nature Biotechnology Reviews 11:31-46, which is incorporated herein by reference. Other examples of sequencing methods suitable for use when implementing the methods and systems disclosed herein are described in, e.g., International Patent Application Publication No. WO 2012/092426. In some instances, the sequencing may comprise, for example, whole genome sequencing (WGS), whole exome sequencing, targeted sequencing, or direct sequencing. In some instances, sequencing may be performed using, e.g., Sanger sequencing. In some instances, the sequencing may comprise a paired-end sequencing technique that allows both ends of a fragment to be sequenced and generates high-quality, alignable sequence data for detection of, e.g., genomic rearrangements, repetitive sequence elements, gene fusions, and novel transcripts.

[0142] 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.

[0143] In certain instances, the disclosed methods comprise one or more of the steps of: (a) acquiring a library comprising a plurality of normal and/or tumor nucleic acid molecules from a sample; (b) simultaneously or sequentially contacting the library with one, two, three, four, five, or more than five pluralities of target capture reagents under conditions that allow hybridization of the target capture reagents to the target nucleic acid molecules, thereby providing a selected set of captured normal and/or tumor nucleic acid molecules (i.e., a library catch); (c) separating the selected subset of the nucleic acid molecules (e.g., the library catch) from the hybridization mixture, e.g., by contacting the hybridization mixture with a binding entity that allows for separation of the target capture reagent/nucleic acid molecule hybrids from the hybridization mixture, (d) sequencing the library catch to acquiring a plurality of reads (e.g., sequence reads) that overlap one or more subject intervals e.g., one or more target sequences) from said library catch that may comprise a mutation (or alteration), e.g., a variant sequence comprising a somatic mutation or germline mutation; (e) aligning said sequence reads using an alignment method as described elsewhere herein; and/or (f) assigning a nucleotide value for a nucleotide position in the subject interval (e.g., calling a mutation using, e.g., a Bayesian method or other method described herein) from one or more sequence reads of the plurality.

[0144] 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.

[0145] 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.

[0146] In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing with at least lOOx or more coverage (or depth) on average. In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing with at least lOOx, at least 150x, at least 200x, at least 250x, at least 500x, at least 750x, at least l,000x, at least 1,500 x, at least 2,000x, at least 2,500x, at least 3,000x, at least 3,500x, at least 4,000x, at least 4,500x, at least 5,000x, at least 5,500x, or at least 6,000x or more coverage (or depth) on average. In some instances, acquiring a sequence read for one or more subject intervals may comprise sequencing with an average coverage (or depth) having any value within the range of values described in this paragraph, e.g., at least 160x.

[0147] 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.

[0148] In some instances, the relative abundance of a nucleic acid species in the library may 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.

[0149] 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).

[0150] 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

[0151] Alignment is the process of matching a read with a location, e.g., a genomic location or locus. In some instances, NGS reads may be aligned to a known reference sequence (e.g., a wild-type sequence). In some instances, NGS reads may be assembled de novo. Methods of sequence alignment for NGS reads are described in, e.g., Trapnell, C. and Salzberg, S.L. Nature Biotech., 2009, 27:455-457. Examples of de novo sequence assemblies are described in, e.g., Warren R., et al., Bioinformatics, 2007, 23:500-501; Butler, J. et al., Genome Res., 2008, 18:810-820; and Zerbino, D.R. and Birney, E., Genome Res., 2008, 18:821-829. Optimization of sequence alignment is described in the art, e.g., as set out in International Patent Application Publication No. WO 2012/092426. Additional description of sequence alignment methods is provided in, e.g., International Patent Application Publication No. WO 2020/236941, the entire content of which is incorporated herein by reference.

[0152] 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 may lead to reduction in sensitivity of mutation detection, may 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.

[0153] In some instances, the methods and systems disclosed herein may integrate the use of multiple, individually-tuned, alignment methods or algorithms to optimize base-calling performance in sequencing methods, particularly in methods that rely on massively parallel sequencing (MPS) of a large number of diverse genetic events at a large number of diverse genomic loci. In some instances, the disclosed methods and systems may comprise the use of one or more global alignment algorithms. In some instances, the disclosed methods and systems may comprise the use of one or more local alignment algorithms. Examples of alignment algorithms that may be used include, but are not limited to, the Burrows-Wheeler Alignment (BWA) software bundle (see, e.g., Li, et al. (2009), “Fast and Accurate Short Read Alignment with Burrows-Wheeler Transform”, Bioinformatics 25: 1754-60; Li, et al. (2010), Fast and Accurate Long-Read Alignment with Burrows-Wheeler Transform”, Bioinformatics epub.

PMID: 20080505), the Smith- Waterman algorithm (see, e.g., Smith, et al. (1981), "Identification of Common Molecular Subsequences", J. Molecular Biology 147(1): 195-197), the Striped Smith-Waterman algorithm (see, e.g., Farrar (2007), “Striped Smith-Waterman Speeds Database Searches Six Times Over Other SIMD Implementations”, Bioinformatics 23(2): 156-161), the Needleman-Wunsch algorithm (Needleman, et al. (1970) "A General Method Applicable to the Search for Similarities in the Amino Acid Sequence of Two Proteins", J. Molecular Biology 48(3):443-53), or any combination thereof.

[0154] 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).

[0155] 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 may 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.

[0156] 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.

[0157] 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).

[0158] 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 may 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.

[0159] 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 may 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 may be aligned with said first reference sequence, e.g., with less than a specific number of mismatches); (ii) if said read fails to meet the first alignment criterion, performing a second alignment comparison using a second set of parameters, (e.g., using a second mapping algorithm, or by comparison with a second reference sequence); and (iii) optionally, determining if said read meets said second criterion (e.g., the read may 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).

[0160] 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 may 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 may 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. CaT 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).

[0161] Reduced specificity (increased false positive rate) in the evaluated subject intervals due to misalignment may 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 may be subjected to alignment remedies as discussed above. In cases where no algorithmic remedy is found possible, “mutations” from the problem regions may be classified or screened out from the panel of targeted loc.

Alignment of Methyl-Seq Sequence Reads

[0162] In some instances, the methods may include the use of an alignment method optimized for aligning sequence reads for DNA that has been converted using, e.g., a bisulfite reaction, to convert unmethylated cytosine residues to uracil (which is interpreted as a thymine in sequencing results). In some instances, sequence reads may be aligned to two genomes in silico, e.g., converted and unconverted versions of the reference genome, using such alignment tools. Methylation occurs primarily at CpG sites, but may also occur less frequently at non-CpG sites (e.g., CHG or CHH sites).

[0163] In some instances, the sequence read data may be obtained using a nucleic acid sequencing method comprising the use of a bisulfite- or enzymatic-conversion reaction (e.g., during library preparation) to convert non-methylated cytosine to uracil (see, e.g., Li, et al. (2011), “DNA Methylation Detection: Bisulfite Genomic Sequencing Analysis”, Methods Mol. Biol. 791:11-21).

[0164] In some instances, the sequence read data may be obtained using a nucleic acid sequencing method comprising the use of alternative chemical and/or enzymatic reactions (e.g., during library preparation) to convert non-methylated cytosine to uracil (or to convert methylated cytosine to dihydrouracil). For example, enzymatic deamination of non-methylated cytosine using APOBEC to form uracil may be performed using, e.g., the Enzymatic Methyl-seq Kit from New England BioLabs (Ipswich, MA) which uses prior treatment with ten-eleven translocation methylcytosine dioxygenase 2 (TET2) to oxidize 5-mC and 5-hmC, thereby providing greater protection of the methylated cytosine from deamination by APOBEC). Liu, et al. (2019) recently described a bisulfite-free and base-level-resolution sequencing-based method, TET- Assisted Pyridine borane Sequencing (TAPS), for detection of 5mC and 5hmC. The method combines ten-eleven translocation methylcytosine dioxygenase (TET)-mediated oxidation of 5mC and 5hmC to 5-carboxylcytosine (5caC) with pyridine borane reduction of 5caC to dihydrouracil (DHU). Subsequent PCR amplification converts DHU to thymine, thereby enabling conversion of methylated cytosines to thymine (Liu, et al. (2019), “Bisulfite-Free Direct Detection of 5 -Methylcytosine and 5-Hydroxymethylcytosine at Base Resolution”, Nature Biotechnology, vol. 37, pp. 424-429).

[0165] In some instances, the sequence read data may be obtained using a nucleic acid sequencing method comprising the use of Methylated DNA Immunoprecipitation (MeDIP).

[0166] Examples of alignment tools optimized for aligning sequence reads for converted DNA include, but are not limited to, NovoAlign (Novocraft Technologies, Selangor, Malaysia), and the Bismark tool (Krueger, et al. (2011), “Bismark: A Flexible Aligner and Methylation Caller for Bisulfite-Seq Applications”, Bioinformatics 27(11): 1571- 1572).

Mutation calling

[0167] 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 may 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.

[0168] 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.

[0169] Methods for mutation calling may 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.

[0170] 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 may be used when evaluating samples from that cancer type. Such likelihood may 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).

[0171] 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.

[0172] After alignment, detection of substitutions may 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 may be called if this comparison is sufficiently strongly supportive of the presence of a mutation.

[0173] 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 may 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 may 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 may be orders of magnitude higher. These likelihoods may be derived from public databases of cancer mutations (e.g., COSMIC).

[0174] 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 may include the steps of identifying candidate indels, calculating genotype likelihood through local re-alignment, and performing LD-based genotype inference and calling. Typically, a Bayesian approach is used to obtain potential indel candidates, and then these candidates are tested together with the reference sequence in a Bayesian framework.

[0175] 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.

[0176] 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 may 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 may be adjusted (e.g., increased or decreased), based on the size or location of the indels.

[0177] 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. [0178] In some instances, the mutation calling method used to analyze sequence reads is not individually customized or fine-tuned for detection of different mutations at different genomic loci. In some instances, different mutation calling methods are used that are individually customized or fine-tuned for at least a subset of the different mutations detected at different genomic loci. In some instances, different mutation calling methods are used that are individually customized or fine-tuned for each different mutant detected at each different genomic loci. The customization or tuning may 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.

[0179] 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 may differ, and thereby be unique, e.g., by relying on different Bayesian prior values.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] In some instances, the mutation calling methods described herein may include the following: (a) acquiring, for a nucleotide position in each of said X subject intervals: (i) a first value which is or represents the prior (e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type X; and (ii) a second set of values which represent the probabilities of observing a read showing said variant at said nucleotide position if the variant is present in the sample at a frequency (e.g., 1%, 5%, 10%, etc.) and/or if the variant is absent (e.g., observed in the reads due to base-calling error alone); and (b) responsive to said values, assigning a nucleotide value (e.g., calling a mutation) from said reads for each of said nucleotide positions by weighing, e.g., by a Bayesian method described herein, the comparison among the values in the second set using the first value (e.g., computing the posterior probability of the presence of a mutation), thereby analyzing said sample.

[0184] 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.

Methylation Status Calling

[0185] In some instances, the methods described herein may comprise the use of a methylation status calling method, e.g., to call the methylation status of the CpG sites based on the sequence reads and fragments (complementary pairs of forward and reverse sequence reads) derived from DNA that has been subjected to a chemical or enzymatic conversion reaction, e.g., to convert unmethylated cytosine residues to uracil (which is interpreted as a thymine in sequencing results). Examples of such methylation status calling tools include, but are not limited to, the Bismark tool (Krueger, et al. (2011), “Bismark: A Flexible Aligner and Methylation Caller for Bisulfite-Seq Applications”, Bioinformatics 27(11): 1571-1572), TARGOMICS (Garinet, et al. (2017), “Calling Chromosome Alterations, DNA Methylation Statuses, and Mutations in Tumors by Simple Targeted Next-Generation Sequenci-g - A Solution for Transferring Integrated Pangenomic Studies into Routine Practice?”, J. Molecular Diagnostics 19(5):776-787), Bicycle (Grana, et al. (2018) “Bicycle: A Bioinformatics Pipeline to Analyze Bisulfite Sequencing Data”, Bioinformatics 34(8): 1414-5), SMAP (Gao, et al. (2015), “SMAP: A Streamlined Methylation Analysis Pipeline for Bisulfite Sequencing”, Gigascience 4:29), and MeDUSA (Wilson, et al. (2016), “Computational Analysis and Integration of MeDIP-Seq Methylome Data”, in: Kulski JK, editor, Next Generation Sequencing: Advances, Applications and Challenges. Rijeka: InTech, p. 153-69). See also, Rauluseviciute, et al. (2019), “DNA Methylation Data by Sequencing: Experimental Approaches and Recommendations for Tools and Pipelines for Data Analysis”, Clinical Epigenetics 11:193.

Systems

[0186] Also disclosed herein are systems designed to implement any of the disclosed methods for predicting an outcome of an early-stage disease based on 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 one or more processors, sequence read data obtained from a sample of a subject; determine, using the one or more processors, one or more features indicative of genomic instability based on the sequence read data, wherein the one or more features indicative of genomic instability include one or more copy number features associated with the sample; determine, using the one or more processors, a risk score associated with the subject by inputting the one or more features indicative of genomic instability into a statistical model; and predict, using the one or more processors, the outcome for the subject based on the determined risk score.

[0187] 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.

[0188] In some instances, the disclosed systems may be used for predicting an outcome of an early-stage disease based on any of a variety of samples as described herein (e.g., a tissue sample, biopsy sample, hematological sample, or liquid biopsy sample derived from the subject).

[0189] In some instances, the plurality of gene loci for which sequencing data is processed to predict an outcome of an early-stage disease 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).

[0190] 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.

[0191] In some instances, the prediction of an outcome of an early-stage disease is used to select, initiate, adjust, or terminate a treatment for cancer in the subject (e.g., a patient) from which the sample was derived, as described elsewhere herein.

[0192] In some instances, the disclosed systems may further comprise sample processing and library preparation workstations, microplate-handling robotics, fluid dispensing systems, temperature control modules, environmental control chambers, additional data storage modules, data communication modules (e.g., Bluetooth®, WiFi, intranet, or internet communication hardware and associated software), display modules, one or more local and/or cloud-based software packages (e.g., instrument / system control software packages, sequencing data analysis software packages), etc., or any combination thereof. In some instances, the systems may comprise, or be part of, a computer system or computer network as described elsewhere herein. Machine learning

[0193] 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).

[0194] 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.

[0195] 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). The one or more machine learning models can comprise solving, e.g., optimizing, an objective function over multiple iterations based on a training data set. The iterative solving approach can be used even when the machine learning model comprises a model for which there exists a closed-form solution (e.g., linear regression). [0196] 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.

[0197] 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 some cases, the weighted sum is offset with a bias, b. In some cases, the output of a node may be gated using a threshold or activation function,/, 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.

[0198] 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.

[0199] In some instances, the disclosed methods may comprise retraining any of the machine learning models (e.g., iteratively retraining a previously trained model using one or more training data sets that differ from those used to train the model initially). In some instances, retraining the machine learning model may comprise using a continuous, e.g., online, machine learning model, i.e., where the model is periodically or continuously updated or retrained based on new training data. The new training data may be provided by, e.g., a single deployed local operational system, a plurality of deployed local operational systems, or a plurality of deployed, geographically-distributed operational systems. In some instances, the disclosed methods may employ, for example, pre-trained ANNs, and the pre-trained ANNs can be fine-tuned according to an additional dataset that is inputted into the pre-trained ANN.

Computer systems and networks

[0200] FIG. 6 illustrates an example of a computing device or system in accordance with one embodiment. Device 600 may be a host computer connected to a network. Device 600 may be a client computer or a server. As shown in FIG. 6, device 600 may 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 may include, for example, one or more processor(s) 610, input devices 620, output devices 630, memory or storage devices 640, communication devices 660, and nucleic acid sequencers 670. Outcome prediction module 650 residing in memory or storage device 640 may comprise, e.g., an operating system as well as software for executing the methods described herein. Input device 620 and output device 630 may generally correspond to those described herein and may either be connectable or integrated with the computer.

[0201] Input device 620 may be any suitable device that provides input, such as a touch screen, keyboard or keypad, mouse, or voice-recognition device. Output device 630 may be any suitable device that provides output, such as a touch screen, haptics device, or speaker.

[0202] Storage 640 may 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 660 may 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 may be connected in any suitable manner, such as via a wired media (e.g., a physical system bus 680, Ethernet connection, or any other wire transfer technology) or wirelessly (e.g., Bluetooth®, Wi-Fi®, or any other wireless technology).

[0203] Outcome prediction module 650, which may be stored as executable instructions in storage 640 and executed by processor(s) 610, may 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).

[0204] Outcome prediction module 650 may also be stored and/or transported within any non- transitory computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described herein, that may 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 may be any medium, such as storage 640, that may 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.

[0205] Outcome prediction module 650 may 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 may 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 may be any medium that may communicate, propagate or transport programming for use by or in connection with an instruction execution system, apparatus, or device. The transport readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic or infrared wired or wireless propagation medium.

[0206] Device 600 may be connected to a network (e.g., network 704, as shown in FIG. 7 and/or described below), which may be any suitable type of interconnected communication system.

The network may implement any suitable communications protocol and may be secured by any suitable security protocol. The network may comprise network links of any suitable arrangement that may implement the transmission and reception of network signals, such as wireless network connections, T1 or T3 lines, cable networks, DSL, or telephone lines.

[0207] Device 600 may be implemented using any operating system, e.g., an operating system suitable for operating on the network. Outcome prediction module 650 may 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 may be deployed in different configurations, such as in a client/server arrangement or through a Web browser as a Web-based application or Web service, for example. In some embodiments, the operating system is executed by one or more processors, e.g., processor(s) 610.

[0208] Device 600 may further include a sequencer 670, which may be any suitable nucleic acid sequencing instrument. [0209] FIG. 7 illustrates an example of a computing system in accordance with one embodiment. In system 700, device 600 (e.g., as described above and illustrated in FIG. 6) is connected to network 704, which is also connected to device 706. In some embodiments, device 706 is a sequencer. Exemplary sequencers may 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.

[0210] Devices 600 and 706 may communicate, e.g., using suitable communication interfaces via network 704, such as a Local Area Network (LAN), Virtual Private Network (VPN), or the Internet. In some embodiments, network 704 may be, for example, the Internet, an intranet, a virtual private network, a cloud network, a wired network, or a wireless network. Devices 600 and 706 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 600 and 706 may communicate, e.g., using suitable communication interfaces, via a second network, such as a mobile/cellular network. Communication between devices 600 and 706 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 600 and 706 may communicate directly (instead of, or in addition to, communicating via network 704), e.g., via wireless or hardwired communications, such as Ethernet, IEEE 802.11b wireless, or the like. In some embodiments, devices 600 and 706 communicate via communications 708, which may be a direct connection or may occur via a network (e.g., network 704).

[0211] One or all of devices 600 and 706 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 704 according to various examples described herein.

EXEMPLARY IMPLEMENTATIONS

[0212] Exemplary implementations of the methods and systems described herein include: 1. A method, comprising: providing a plurality of nucleic acid molecules obtained from a sample from a subject; ligating one or more adapters onto one or more nucleic acid molecules from the plurality of nucleic acid molecules; amplifying the one or more ligated nucleic acid molecules from the plurality of nucleic acid molecules; capturing amplified nucleic acid molecules from the amplified nucleic acid molecules; sequencing, by a sequencer, the captured nucleic acid molecules to obtain a plurality of sequence reads that represent the captured nucleic acid molecules; receiving, at one or more processors, sequence read data for the plurality of sequence reads; inputting, using the one or more processors, the sequence read data into a model generated based on one or more features indicative of genomic instability, wherein the one or more features indicative of genomic instability include one or more copy number features associated with the sample; determining, using the one or more processors, a risk score associated with the subject by inputting the one or more features indicative of genomic instability into a statistical model; and predicting, using the one or more processors, an outcome for the subject based on the determined risk score.

2. The method of clause 1, wherein the one or more features indicative of genomic instability include one or more chromosome arm-level features, one or more chromosome cytoband-level features, one or more genome-wide features, or any combination thereof.

3. The method of clause 1 or clause 2, wherein the one or more features indicative of genomic instability comprise: chromosome arm-level gain/loss/loss of heterozygosity data, chromosome cytoband-level gain/loss/loss of heterozygosity data, genome-wide copy number feature data, genome-wide copy number signature data, or any combination thereof. 4. The method of any one of clauses 1 to 3, wherein the one or more features indicative of genomic instability are at least partially indicative of an extent of chromosomal copy number changes across a genome of the subject.

5. The method of any one of clauses 1 to 4, wherein the subject is determined to have cancer.

6. The method of clause 5, wherein the cancer is a B cell cancer (multiple myeloma), a melanoma, breast cancer, lung cancer, bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain cancer, central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine cancer, endometrial cancer, cancer of an oral cavity, cancer of a pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel cancer, appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, a cancer of hematological tissue, an adenocarcinoma, an inflammatory myofibroblastic tumor, a gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), acute lymphocytic leukemia (ALL), acute myelocytic leukemia (AML), chronic myelocytic leukemia (CML), chronic lymphocytic leukemia (CLL), polycythemia Vera, Hodgkin lymphoma, nonHodgkin lymphoma (NHL), soft-tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteogenic sarcoma, chordoma, angiosarcoma, 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.

7. The method of clause 5, wherein the cancer comprises acute lymphoblastic leukemia (Philadelphia chromosome positive), acute lymphoblastic leukemia (precursor B-cell), acute myeloid leukemia (FLT3+), acute myeloid leukemia (with an IDH2 mutation), anaplastic large cell lymphoma, basal cell carcinoma, B-cell chronic lymphocytic leukemia, bladder cancer, breast cancer (HER2 overexpressed/amplified), breast cancer (HER2+), breast cancer (HR+, HER2-), cervical cancer, cholangiocarcinoma, chronic lymphocytic leukemia, chronic lymphocytic leukemia (with 17p deletion), chronic myelogenous leukemia, chronic myelogenous leukemia (Philadelphia chromosome positive), classical Hodgkin lymphoma, colorectal cancer, colorectal cancer (dMMR/MSI-H), colorectal cancer (KRAS wild type), cryopyrin-associated periodic syndrome, a cutaneous T-cell lymphoma, dermatofibrosarcoma protuberans, a diffuse large B-cell lymphoma, fallopian tube cancer, a follicular B-cell non-Hodgkin lymphoma, a follicular lymphoma, gastric cancer, gastric cancer (HER2+), gastroesophageal junction (GEJ) adenocarcinoma, a gastrointestinal stromal tumor, a gastrointestinal stromal tumor (KIT+), a giant cell tumor of the bone, a glioblastoma, granulomatosis with polyangiitis, a head and neck squamous cell carcinoma, a hepatocellular carcinoma, Hodgkin lymphoma, juvenile idiopathic arthritis, lupus erythematosus, a mantle cell lymphoma, medullary thyroid cancer, melanoma, a melanoma with a BRAF V600 mutation, a melanoma with a BRAF V600E or V600K mutation, Merkel cell carcinoma, multicentric Castleman's disease, multiple hematologic malignancies including Philadelphia chromosome-positive ALL and CML, multiple myeloma, myelofibrosis, a non-Hodgkin’ s lymphoma, a nonresectable subependymal giant cell astrocytoma associated with tuberous sclerosis, a non-small cell lung cancer, a non-small cell lung cancer (ALK+), a non-small cell lung cancer (PD-L1+), a non-small cell lung cancer (with ALK fusion or ROS1 gene alteration), a non-small cell lung cancer (with BRAF V600E mutation), a non-small cell lung cancer (with an EGFR exon 19 deletion or exon 21 substitution (L858R) mutations), a non- small cell lung cancer (with an EGFR T790M mutation), ovarian cancer, ovarian cancer (with a BRCA mutation), pancreatic cancer, a pancreatic, gastrointestinal, or lung origin neuroendocrine tumor, a pediatric neuroblastoma, a peripheral T-cell lymphoma, peritoneal cancer, prostate cancer, a renal cell carcinoma, rheumatoid arthritis, a small lymphocytic lymphoma, a soft tissue sarcoma, a solid tumor (MSI-H/dMMR), a squamous cell cancer of the head and neck, a squamous non- small cell lung cancer, thyroid cancer, a thyroid carcinoma, urothelial cancer, a urothelial carcinoma, or Waldenstrom's macroglobulinemia.

8. The method of clause 7, further comprising treating the subject with an anti-cancer therapy in response to the predicted outcome.

9. The method of clause 8, wherein the anti-cancer therapy comprises a targeted anti-cancer therapy.

10. The method of clause 9, wherein the targeted anti-cancer therapy comprises abemaciclib (Verzenio), abiraterone acetate (Zytiga), acalabrutinib (Calquence), ado-trastuzumab emtansine (Kadcyla), afatinib dimaleate (Gilotrif), aldesleukin (Proleukin), alectinib (Alecensa), alemtuzumab (Campath), alitretinoin (Panretin), alpelisib (Piqray), amivantamab-vmjw (Rybrevant), anastrozole (Arimidex), apalutamide (Erleada), asciminib hydrochloride (Scemblix), atezolizumab (Tecentriq), avapritinib (Ayvakit), avelumab (Bavencio), axicabtagene ciloleucel (Yescarta), axitinib (Inlyta), belantamab mafodotin-blmf (Blenrep), belimumab (Benlysta), belinostat (Beleodaq), belzutifan (Welireg), bevacizumab (Avastin), bexarotene (Targretin), binimetinib (Mektovi), blinatumomab (Blincyto), bortezomib (Velcade), bosutinib (Bosulif), brentuximab vedotin (Adcetris), brexucabtagene autoleucel (Tecartus), brigatinib (Alunbrig), cabazitaxel (Jevtana), cabozantinib (Cabometyx), cabozantinib (Cabometyx, Cometriq), canakinumab (Haris), capmatinib hydrochloride (Tabrecta), carfilzomib (Kyprolis), cemiplimab-rwlc (Libtayo), ceritinib (LDK378/Zykadia), cetuximab (Erbitux), cobimetinib (Cotellic), copanlisib hydrochloride (Aliqopa), crizotinib (Xalkori), dabrafenib (Tafinlar), dacomitinib (Vizimpro), daratumumab (Darzalex), daratumumab and hyaluronidase-fihj (Darzalex Faspro), darolutamide (Nubeqa), dasatinib (Sprycel), denileukin diftitox (Ontak), denosumab (Xgeva), dinutuximab (Unituxin), dostarlimab-gxly (Jemperli), durvalumab (Imfinzi), duvelisib (Copiktra), elotuzumab (Empliciti), enasidenib mesylate (Idhifa), encorafenib (Braftovi), enfortumab vedotin-ejfv (Padcev), entrectinib (Rozlytrek), enzalutamide (Xtandi), erdafitinib (Balversa), erlotinib (Tarceva), everolimus (Afinitor), exemestane (Aromasin), fam-trastuzumab deruxtecan-nxki (Enhertu), fedratinib hydrochloride (Inrebic), fulvestrant (Faslodex), gefitinib (Iressa), gemtuzumab ozogamicin (Mylotarg), gilteritinib (Xospata), glasdegib maleate (Daurismo), hyaluronidase-zzxf (Phesgo), ibrutinib (Imbruvica), ibritumomab tiuxetan (Zevalin), idecabtagene vicleucel (Abecma), idelalisib (Zydelig), imatinib mesylate (Gleevec), infigratinib phosphate (Truseltiq), inotuzumab ozogamicin (Besponsa), iobenguane 1131 (Azedra), ipilimumab (Yervoy), isatuximab-irfc (Sarclisa), ivosidenib (Tibsovo), ixazomib citrate (Ninlaro), lanreotide acetate (Somatuline Depot), lapatinib (Tykerb), larotrectinib sulfate (Vitrakvi), lenvatinib mesylate (Lenvima), letrozole (Femara), lisocabtagene maraleucel (Breyanzi), loncastuximab tesirine-lpyl (Zynlonta), lorlatinib (Lorbrena), lutetium Lu 177-dotatate (Lutathera), margetuximab-cmkb (Margenza), midostaurin (Rydapt), mobocertinib succinate (Exkivity), mogamulizumab-kpkc (Poteligeo), moxetumomab pasudotox-tdfk (Lumoxiti), naxitamab-gqgk (Danyelza), necitumumab (Portrazza), neratinib maleate (Nerlynx), nilotinib (Tasigna), niraparib tosylate monohydrate (Zejula), nivolumab (Opdivo), obinutuzumab (Gazyva), ofatumumab (Arzerra), olaparib (Lynparza), olaratumab (Lartruvo), osimertinib (Tagrisso), palbociclib (Ibrance), panitumumab (Vectibix), panobinostat (Farydak), pazopanib (Votrient), pembrolizumab (Keytruda), pemigatinib (Pemazyre), pertuzumab (Perjeta), pexidartinib hydrochloride (Turalio), polatuzumab vedotin-piiq (Polivy), ponatinib hydrochloride (Iclusig), pralatrexate (Folotyn), pralsetinib (Gavreto), radium 223 dichloride (Xofigo), ramucirumab (Cyramza), regorafenib (Stivarga), ribociclib (Kisqali), ripretinib (Qinlock), rituximab (Rituxan), rituximab and hyaluronidase human (Rituxan Hycela), romidepsin (Istodax), rucaparib camsylate (Rubraca), ruxolitinib phosphate (Jakafi), sacituzumab govitecan-hziy (Trodelvy), seliciclib, selinexor (Xpovio), selpercatinib (Retevmo), selumetinib sulfate (Koselugo), siltuximab (Sylvant), sipuleucel-T (Provenge), sirolimus protein-bound particles (Fyarro), sonidegib (Odomzo), sorafenib (Nexavar), sotorasib (Lumakras), sunitinib (Sutent), tafasitamab-cxix (Monjuvi), tagraxofusp-erzs (Elzonris), talazoparib tosylate (Talzenna), tamoxifen (Nolvadex), tazemetostat hydrobromide (Tazverik), tebentafusp-tebn (Kimmtrak), temsirolimus (Torisel), tepotinib hydrochloride (Tepmetko), tisagenlecleucel (Kymriah), tisotumab vedotin-tftv (Tivdak), tocilizumab (Actemra), tofacitinib (Xeljanz), tositumomab (Bexxar), trametinib (Mekinist), trastuzumab (Herceptin), tretinoin (Vesanoid), tivozanib hydrochloride (Fotivda), toremifene (Fareston), tucatinib (Tukysa), umbralisib tosylate (Ukoniq), vandetanib (Caprelsa), vemurafenib (Zelboraf), venetoclax (Venclexta), vismodegib (Erivedge), vorinostat (Zolinza), zanubrutinib (Brukinsa), ziv-aflibercept (Zaltrap), or any combination thereof. 11. The method of any one of clauses 1 to 10, further comprising obtaining the sample from the subject.

12. The method of any one of clauses 1 to 11, wherein the sample comprises a tissue biopsy sample, a liquid biopsy sample, or a normal control.

13. The method of clause 12, wherein the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.

14. The method of clause 12, wherein the sample is a liquid biopsy sample and comprises circulating tumor cells (CTCs).

15. The method of clause 12, wherein the sample is a liquid biopsy sample and comprises cell- free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.

16. The method of any one of clauses 1 to 15, wherein the plurality of nucleic acid molecules comprises a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules.

17. The method of clause 16, wherein the tumor nucleic acid molecules are derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules are derived from a normal portion of the heterogeneous tissue biopsy sample.

18. The method of clause 16, wherein the sample comprises a liquid biopsy sample, and wherein the tumor nucleic acid molecules are derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample, and the non-tumor nucleic acid molecules are derived from a non- tumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.

19. The method of any one of clauses 1 to 18, wherein the one or more adapters comprise amplification primers, flow cell adaptor sequences, substrate adapter sequences, or sample index sequences.

20. The method of any one of clauses 1 to 19, wherein the captured nucleic acid molecules are captured from the amplified nucleic acid molecules by hybridization to one or more bait molecules. 21. The method of clause 20, wherein the one or more bait molecules comprise one or more nucleic acid molecules, each comprising a region that is complementary to a region of a captured nucleic acid molecule.

22. The method of any one of clauses 1 to 21, wherein amplifying nucleic acid molecules comprises performing a polymerase chain reaction (PCR) amplification technique, a non-PCR amplification technique, or an isothermal amplification technique.

23. The method of any one of clauses 1 to 22, wherein the sequencing comprises use of a massively parallel sequencing (MPS) technique, whole genome sequencing (WGS), whole exome sequencing, targeted sequencing, direct sequencing, or Sanger sequencing technique.

24. The method of clause 23, wherein the sequencing comprises massively parallel sequencing, and the massively parallel sequencing technique comprises next generation sequencing (NGS).

25. The method of any one of clauses 1 to 24, wherein the sequencer comprises a next generation sequencer.

26. The method of any one of clauses 1 to 25, wherein one or more of the plurality of sequencing reads overlap one or more gene loci within one or more subgenomic intervals in the sample.

27. The method of clause 26, wherein the one or more gene loci comprises between 10 and 20 loci, between 10 and 40 loci, between 10 and 60 loci, between 10 and 80 loci, between 10 and 100 loci, between 10 and 150 loci, between 10 and 200 loci, between 10 and 250 loci, between 10 and 300 loci, between 10 and 350 loci, between 10 and 400 loci, between 10 and 450 loci, between 10 and 500 loci, between 20 and 40 loci, between 20 and 60 loci, between 20 and 80 loci, between 20 and 100 loci, between 20 and 150 loci, between 20 and 200 loci, between 20 and 250 loci, between 20 and 300 loci, between 20 and 350 loci, between 20 and 400 loci, between 20 and 500 loci, between 40 and 60 loci, between 40 and 80 loci, between 40 and 100 loci, between 40 and 150 loci, between 40 and 200 loci, between 40 and 250 loci, between 40 and 300 loci, between 40 and 350 loci, between 40 and 400 loci, between 40 and 500 loci, between 60 and 80 loci, between 60 and 100 loci, between 60 and 150 loci, between 60 and 200 loci, between 60 and 250 loci, between 60 and 300 loci, between 60 and 350 loci, between 60 and 400 loci, between 60 and 500 loci, between 80 and 100 loci, between 80 and 150 loci, between 80 and 200 loci, between 80 and 250 loci, between 80 and 300 loci, between 80 and 350 loci, between 80 and 400 loci, between 80 and 500 loci, between 100 and 150 loci, between 100 and 200 loci, between 100 and 250 loci, between 100 and 300 loci, between 100 and 350 loci, between 100 and 400 loci, between 100 and 500 loci, between 150 and 200 loci, between 150 and 250 loci, between 150 and 300 loci, between 150 and 350 loci, between 150 and 400 loci, between 150 and 500 loci, between 200 and 250 loci, between 200 and 300 loci, between 200 and 350 loci, between 200 and 400 loci, between 200 and 500 loci, between 250 and 300 loci, between 250 and 350 loci, between 250 and 400 loci, between 250 and 500 loci, between 300 and 350 loci, between 300 and 400 loci, between 300 and 500 loci, between 350 and 400 loci, between 350 and 500 loci, or between 400 and 500 loci.

28. The method of clause 26 or clause 27, wherein the one or more gene loci comprise ABL1, ACVR1B, AKT1, AKT2, AKT3, ALK, ALOX12B, AMER1, APC, AR, ARAF, ARFRP1, ARID1A, ASXL1, ATM, ATR, ATRX, AURKA, AURKB, AXIN1, AXL, BAP1, BARD1, BCL2, BCL2L1, BCL2L2, BCL6, BCOR, BCORL1, BCR, BRAF, BRCA1, BRCA2, BRD4, BRIP1, BTG1, BTG2, BTK, CALR, CARD11, CASP8, CBFB, CBL, CCND1, CCND2, CCND3, CCNE1, CD22, CD274, CD70, CD74, CD79A, CD79B, CDC73, CDH1, CDK12, CDK4, CDK6, CDK8, CDKN1A, CDKN1B, CDKN2A, CDKN2B, CDKN2C, CEBPA, CHEK1, CHEK2, CIC, CREBBP, CRKL, CSF1R, CSF3R, CTCF, CTNNA1, CTNNB1, CUL3, CUL4A, CXCR4, CYP17A1, DAXX, DDR1, DDR2, DIS3, DNMT3A, DOT1L, EED, EGFR, EMSY (Cl lorf30), EP300, EPHA3, EPHB1, EPHB4, ERBB2, ERBB3, ERBB4, ERCC4, ERG, ERRFI1, ESRI, ETV4, ETV5, ETV6, EWSR1, EZH2, EZR, FAM46C, FANCA, FANCC, FANCG, FANCL, FAS, FBXW7, FGF10, FGF12, FGF14, FGF19, FGF23, FGF3, FGF4, FGF6, FGFR1, FGFR2, FGFR3, FGFR4, FH, FLCN, FLT1, FLT3, FOXL2, FUBP1, GABRA6, GATA3, GATA4, GATA6, GID4 (C17orf39), GNA11, GNA13, GNAQ, GNAS, GRM3, GSK3B, H3F3A, HDAC1, HGF, HNF1A, HRAS, HSD3B1, ID3, IDH1, IDH2, IGF1R, IKBKE, IKZF1, INPP4B, IRF2, IRF4, IRS2, JAK1, JAK2, JAK3, JUN, KDM5A, KDM5C, KDM6A, KDR, KEAP1, KEL, KIT, KLHL6, KMT2A (MLL), KMT2D (MLL2), KRAS, LTK, LYN, MAF, MAP2K1, MAP2K2, MAP2K4, MAP3K1, MAP3K13, MAPK1, MCL1, MDM2, MDM4, MED12, MEF2B, MEN1, MERTK, MET, MITF, MKNK1, MLH1, MPL, MRE11A, MSH2, MSH3, MSH6, MST1R, MTAP, MTOR, MUTYH, MYB, MYC, MYCL, MYCN, MYD88, NBN, NF1, NF2, NFE2L2, NFKBIA, NKX2-1, N0TCH1, NOTCH2, NOTCH3, NPM1, NRAS, NT5C2, NTRK1, NTRK2, NTRK3, NUTM1, P2RY8, PALB2, PARK2, PARP1, PARP2, PARP3, PAX5, PBRM1, PDCD1, PDCD1LG2, PDGFRA, PDGFRB, PDK1, PIK3C2B, PIK3C2G, PIK3CA, PIK3CB, PIK3R1, PIM1, PMS2, POLDI, POLE, PPARG, PPP2R1A, PPP2R2A, PRDM1, PRKAR1A, PRKCI, PTCHI, PTEN, PTPN11, PTPRO, QKI, RAC1, RAD21, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54L, RAFI, RARA, RBI, RBM10, REL, RET, RICTOR, RNF43, ROS1, RPTOR, RSPO2, SDC4, SDHA, SDHB, SDHC, SDHD, SETD2, SF3B1, SGK1, SLC34A2, SMAD2, SMAD4, SMARCA4, SMARCB1, SMO, SNCAIP, SOCS1, SOX2, SOX9, SPEN, SPOP, SRC, STAG2, STAT3, STK11, SUFU, SYK, TBX3, TEK, TERC, TERT, TET2, TGFBR2, TIPARP, TMPRSS2, TNFAIP3, TNFRSF14, TP53, TSC1, TSC2, TYRO3, U2AF1, VEGFA, VHL, WHSCI, WHSC1L1, WT1, XPO1, XRCC2, ZNF217, ZNF703, or any combination thereof.

29. The method of clause 26 or clause 27, wherein the one or more gene loci comprise ABL, ALK, ALL, B4GALNT1, BAFF, BCL2, BRAF, BRCA, BTK, CD19, CD20, CD3, CD30, CD319, CD38, CD52, CDK4, CDK6, CML, CRACC, CS1, CTLA-4, dMMR, EGFR, ERBB1, ERBB2, FGFR1-3, FLT3, GD2, HDAC, HER1, HER2, HR, IDH2, IL-ip, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSI-H, mTOR, PARP, PD-1, PDGFR, PDGFRa, PDGFRP, PD-L1, PI3K5, PIGF, PTCH, RAF, RANKL, RET, ROS1, SLAMF7, VEGF, VEGFA, VEGFB, or any combination thereof.

30. The method of any one of clauses 1 to 29, further comprising generating, by the one or more processors, a report indicating the predicted outcome.

31. The method of clause 30, further comprising transmitting the report to a healthcare provider.

32. The method of clause 31, wherein the report is transmitted via a computer network or a peer- to-peer connection.

33. A method for predicting an outcome for a subject, the method comprising: receiving, at one or more processors, sequence read data obtained from a sample of the subject; inputting, using the one or more processors, the sequence read data into a model generated based on one or more features indicative of genomic instability, wherein the one or more features indicative of genomic instability include one or more copy number features associated with the sample; determining, using the one or more processors, a risk score associated with the subject by inputting the one or more features indicative of genomic instability into a statistical model; and predicting, using the one or more processors, the outcome for the subject based on the determined risk score.

34. The method of clause 33, wherein the one or more features indicative of genomic instability include one or more chromosome arm-level features, one or more chromosome cytoband- level features, or any combination thereof.

35. The method of clause 33 or clause 34, wherein the one or more features indicative of genomic instability comprise: chromosome arm-level gain/loss/loss of heterozygosity data, chromosome cytoband-level gain/loss/loss of heterozygosity data, genome-wide copy number feature data, genome-wide copy number signature data, or any combination thereof.

36. The method of any one of the preceding clauses, wherein the one or more features indicative of genomic instability are at least partially indicative of an extent of chromosomal copy number changes across a genome of the subject.

37. The method of any one of the preceding clauses, wherein determining the risk score comprises determining a probability of disease recurrence, time to recurrence, metastatic progression, overall survival, survival at a given time, or any combination thereof.

38. The method of any one of the preceding clauses, wherein determining the risk score comprises determining a survival curve estimating a probability of survival across a time range by inputting the one or more features indicative of genomic instability into a random survival forest model. 39. The method of any one of the preceding clauses, wherein determining the risk score comprises determining a hazard ratio estimating a probability of survival relative to a control group by inputting the one or more features indicative of genomic instability into a Cox regression model or a LASSO Cox regression model.

40. The method of any one of the preceding clauses, wherein predicting the outcome comprises predicting disease recurrence, time to recurrence, metastatic progression, overall survival, survival at a given time, or a combination thereof.

41. The method of clause 40, wherein predicting disease recurrence, metastatic progression, or survival at a given time comprises using a random forest model, a logistic regression model, a deep learning model, or any combination thereof as the statistical model.

42. The method of clause 40, wherein predicting time to recurrence or overall survival comprises using a random survival forest model, a Cox regression model, a support vector machine, an accelerated failure time model, a deep learning model, or any combination thereof as the statistical model.

43. The method of any one of the preceding clauses, further comprising: analyzing the sequence read data, using the one or more processors, to generate genomic alteration data.

44. The method of clause 43, wherein the genomic alteration data comprises gene-specific short variant data, gene- specific copy number data, gene- specific rearrangement data, or any combination thereof.

45. The method of any one of the preceding clauses, wherein the one or more features indicative of genomic instability comprise a plurality of gene-specific values corresponding to a plurality of genes, and wherein each gene-specific value of the plurality of gene-specific values is indicative of whether there is a predefined alteration in a respective gene.

46. The method of clause 45, wherein each gene-specific value comprises a binary value indicative of a presence or absence of the predefined alteration in the respective gene.

47. The method of clause 45, wherein each gene-specific value comprises a continuous value indicative of an alteration type associated with the respective gene. 48. The method of any one of the preceding clauses, wherein the one or more features indicative of genomic instability comprise one or more values associated with tumor mutational burden (TMB).

49. The method of any one of the preceding clauses, wherein the one or more features indicative of genomic instability comprise one or more values associated with a fraction of genome altered (FGA).

50. The method of any one of the preceding clauses, further comprising: assigning, using the one or more processors, a weight to a feature of the one or more features indicative of genomic instability, based on the one or more features indicative of genomic instability.

51. The method of any one of the preceding clauses, wherein the one or more features indicative of genomic instability are selected by: obtaining a plurality of features; obtaining a plurality of variance values corresponding to the plurality of features; and selecting the one or more features indicative of genomic instability from the plurality of features based on the plurality of variance values.

52. The method of any one of the preceding clauses, wherein the one or more features indicative of genomic instability are selected by: obtaining a plurality of features; obtaining a plurality of outcome association values corresponding to the plurality of features; and selecting the one or more features indicative of genomic instability from the plurality of features based on the plurality of outcome association values.

53. The method of any one of the preceding clauses, wherein the one or more features indicative of genomic instability are selected by: obtaining a plurality of features; identifying one or more clusters of features based on the plurality of features; and selecting the one or more features indicative of genomic instability from the plurality of features based on the one or more clusters of features. 54. The method of any one of the preceding clauses, wherein the statistical model comprises one or more of a random forest model, a random survival forest model, a logistic regression model, a Cox regression model, a LASSO Cox regression model, a support vector machine, an accelerated failure time model, and a deep learning model.

55. The method of any one of the preceding clauses, wherein the subject has an early stage of a disease.

56. The method of clause 55, wherein the disease comprises one or more of non-small-cell lung cancer (NSCLC), breast cancer, prostate cancer, colorectal cancer, pancreatic cancer, uterine cancer, and ovarian cancer.

57. The method of clause 55, wherein the disease is NSCLC, and wherein the early stage of the disease comprises TNM Stages 1-3.

58. The method of any one of the preceding clauses, further comprising: selecting a treatment for the disease based on the predicted outcome.

59. The method of clause 58, wherein selecting the treatment for the disease comprises: in accordance with a determination that the predicted outcome is indicative of a low risk, selecting a first treatment; and in accordance with a determination that the predicted outcome is indicative of a high risk, selecting a second treatment in addition to the first treatment.

60. The method of clause 59, wherein the first treatment comprises chemotherapy, radiation therapy, hormone therapy, immunotherapy, surgery, medication, or any combination thereof.

61. The method of clause 59, wherein the second treatment comprises an adjuvant therapy or a neoadjuvant therapy, and wherein the adjuvant therapy or the neoadjuvant therapy comprises chemotherapy, radiation therapy, hormone therapy, immunotherapy, surgery, medication, or any combination thereof.

62. A method for diagnosing a disease, the method comprising: diagnosing that a subject has the disease based on a determination of a predicted outcome for a sample from the subject, wherein the outcome is determined according to the method of any one of clauses 33 to 61. 63. A method of selecting an anti-cancer therapy, the method comprising: responsive to determining a predicted outcome for a sample from a subject, selecting an anti-cancer therapy for the subject, wherein the predicted outcome is determined according to the method of any one of clauses 33 to 61.

64. A method of treating a cancer in a subject, comprising: responsive to determining a predicted outcome for a sample from the subject, administering an effective amount of an anti-cancer therapy to the subject, wherein the predicted outcome is determined according to the method of any one of clauses 33 to 61.

65. A method for monitoring cancer progression or recurrence in a subject, the method comprising: determining a first predicted outcome in a first sample obtained from the subject at a first time point according to the method of any one of clauses 33 to 61; determining a second predicted outcome in a second sample obtained from the subject at a second time point; and comparing the first predicted outcome to the second predicted outcome, thereby monitoring the cancer progression or recurrence.

66. The method of clause 65, wherein the second predicted outcome for the second sample is determined according to the method of any one of clauses 33 to 61.

67. The method of clause 65 or clause 66, further comprising selecting an anti-cancer therapy for the subject in response to the second predicted outcome.

68. The method of clause 65 or clause 67, further comprising administering an anti-cancer therapy to the subject in response to the second predicted outcome.

69. The method of clause 65 or clause 68, further comprising adjusting an anti-cancer therapy for the subject in response to the second predicted outcome. 70. The method of any one of clauses 67 to 69, further comprising adjusting a dosage of the anticancer therapy or selecting a different anti-cancer therapy in response to the second predicted outcome.

71. The method of clause 70, further comprising administering the adjusted anti-cancer therapy to the subject.

72. The method of any one of clauses 65 to 71, wherein the first time point is before the subject has been administered an anti-cancer therapy, and wherein the second time point is after the subject has been administered the anti-cancer therapy.

73. The method of any one of clauses 65 to 72, wherein the subject has a cancer, is at risk of having a cancer, is being routine tested for cancer, or is suspected of having a cancer.

74. The method of any one of clauses 65 to 73, wherein the cancer is a solid tumor.

75. The method of any one of clauses 65 to 73, wherein the cancer is a hematological cancer.

76. The method of any one of clauses 67 to 75, wherein the anti-cancer therapy comprises chemotherapy, radiation therapy, immunotherapy, a targeted therapy, or surgery.

77. The method of any one of clauses 33 to 61, further comprising determining, identifying, or applying the risk score for the sample as a diagnostic value associated with the sample.

78. The method of any one of clauses 33 to 61, further comprising generating a genomic profile for the subject that includes the first or second predicted outcome.

79. The method of clause 78, wherein the genomic profile for the subject further comprises results from a comprehensive genomic profiling (CGP) test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof.

80. The method of clause 78 or clause 79, wherein the genomic profile for the subject further comprises results from a nucleic acid sequencing-based test. 81. The method of any one of clauses 78 to 80, further comprising selecting an anti-cancer therapy, administering an anti-cancer therapy, or applying an anti-cancer therapy to the subject based on the generated genomic profile.

82. The method of any one of clauses 33 to 61, wherein the prediction of the outcome for the sample is used in making treatment decisions for the subject.

83. The method of any one of clauses 33 to 61, wherein the prediction of the outcome for the sample is used in applying or administering a treatment to the subject.

84. A system comprising: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to perform the method of any one of the clauses 33-61.

85. A non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to perform the method of any one of the clauses 33-61.

[0213] It should be understood from the foregoing that, while particular implementations of the disclosed methods and systems have been illustrated and described, various modifications may be made thereto and are contemplated herein. It is also not intended that the invention be limited by the specific examples provided within the specification. While the invention has been described with reference to the aforementioned specification, the descriptions and illustrations of the preferable embodiments herein are not meant to be construed in a limiting sense.

Furthermore, it shall be understood that all aspects of the invention are not limited to the specific depictions, configurations or relative proportions set forth herein which depend upon a variety of conditions and variables. Various modifications in form and detail of the embodiments of the invention will be apparent to a person skilled in the art. It is therefore contemplated that the invention shall also cover any such modifications, variations and equivalents.

Claims

CLAIMS What is claimed is:

1. A method for predicting an outcome for a subject, the method comprising: receiving, at one or more processors, sequence read data obtained from a sample of the subject; inputting, using the one or more processors, the sequence read data into a model generated based on one or more features indicative of genomic instability, wherein the one or more features indicative of genomic instability include one or more copy number features associated with the sample; determining, using the one or more processors, a risk score associated with the subject by inputting the one or more features indicative of genomic instability into a statistical model; and predicting, using the one or more processors, the outcome for the subject based on the determined risk score.

2. The method of claim 1, wherein the one or more features indicative of genomic instability include one or more chromosome arm-level features, one or more chromosome cytoband- level features, or any combination thereof.

3. The method of claim 1, wherein the one or more features indicative of genomic instability comprise: chromosome arm-level gain/loss/loss of heterozygosity data, chromosome cytoband-level gain/loss/loss of heterozygosity data, genome-wide copy number feature data, genome-wide copy number signature data, or any combination thereof.

4. The method of claim 1, wherein the one or more features indicative of genomic instability are at least partially indicative of an extent of chromosomal copy number changes across a genome of the subject.

5. The method of claim 1, wherein determining the risk score comprises determining a probability of disease recurrence, time to recurrence, metastatic progression, overall survival, survival at a given time, or any combination thereof.

6. The method of claim 1, wherein determining the risk score comprises determining a survival curve estimating a probability of survival across a time range by inputting the one or more features indicative of genomic instability into a random survival forest model.

7. The method of claim 1, wherein determining the risk score comprises determining a hazard ratio estimating a probability of survival relative to a control group by inputting the one or more features indicative of genomic instability into a Cox regression model or a LASSO Cox regression model.

8. The method of claim 1, wherein predicting the outcome comprises predicting disease recurrence, time to recurrence, metastatic progression, overall survival, survival at a given time, or a combination thereof.

9. The method of claim 8, wherein predicting disease recurrence, metastatic progression, or survival at a given time comprises using a random forest model, a logistic regression model, a deep learning model, or any combination thereof as the statistical model.

10. The method of claim 8, wherein predicting time to recurrence or overall survival comprises using a random survival forest model, a Cox regression model, a support vector machine, an accelerated failure time model, a deep learning model, or any combination thereof as the statistical model.

11. The method of claim 1, further comprising: analyzing the sequence read data, using the one or more processors, to generate genomic alteration data, wherein the genomic alteration data comprises gene-specific short variant data, gene-specific copy number data, gene-specific rearrangement data, or any combination thereof.

12. The method of claim 1, wherein the one or more features indicative of genomic instability comprise a plurality of gene-specific values corresponding to a plurality of genes, and wherein each gene-specific value of the plurality of gene-specific values is indicative of whether there is a predefined alteration in a respective gene.

13. The method of claim 12, wherein each gene-specific value comprises a binary value indicative of a presence or absence of the predefined alteration in the respective gene and/or a continuous value indicative of an alteration type associated with the respective gene.

14. The method of claim 1, wherein the one or more features indicative of genomic instability comprise one or more values associated with tumor mutational burden (TMB) and/or a fraction of genome altered (FGA).

15. The method of claim 1, further comprising: assigning, using the one or more processors, a weight to a feature of the one or more features indicative of genomic instability, based on the one or more features indicative of genomic instability.

16. The method of claim 1, wherein the statistical model comprises one or more of a random forest model, a random survival forest model, a logistic regression model, a Cox regression model, a LASSO Cox regression model, a support vector machine, an accelerated failure time model, and a deep learning model.

17. The method of claim 1, wherein the subject has an early stage of a disease comprising one or more of non-small-cell lung cancer (NSCLC), breast cancer, prostate cancer, colorectal cancer, pancreatic cancer, uterine cancer, and ovarian cancer.

18. The method of claim 17, wherein the disease is NSCLC, and wherein the early stage of the disease comprises TNM Stages 1-3.

19. The method of claim 1, further comprising: selecting a treatment for the disease based on the predicted outcome, wherein selecting the treatment for the disease comprises: in accordance with a determination that the predicted outcome is indicative of a low risk, selecting a first treatment; and in accordance with a determination that the predicted outcome is indicative of a high risk, selecting a second treatment in addition to the first treatment.

20. The method of claim 19, wherein the first treatment comprises chemotherapy, radiation therapy, hormone therapy, immunotherapy, surgery, medication, or any combination thereof, and wherein the second treatment comprises an adjuvant therapy or a neoadjuvant therapy comprising chemotherapy, radiation therapy, hormone therapy, immunotherapy, surgery, medication, or any combination thereof.