Probe sets for a liquid biopsy assay

Abstract

Compositions for enriching target nucleic acids, and methods of using the same, are provided. The composition includes a probe set and a plurality of nucleic acids. The probe set includes a first set of probes comprising a first plurality of probe species, each probe species targeting a respective genomic region in a first plurality of genomic regions and present in the composition at a first average molar concentration. The probe set further includes a second set of probes comprising a second plurality of probe species, each probe species targeting a respective genomic region in a second plurality of genomic regions and present in the composition at a second average molar concentration that is from five to eight times greater than the first average concentration. The plurality of nucleic acids comprises cell-free nucleic acids from a biological sample of a subject, or nucleic acids prepared therefrom.

Description

PROBE SETS FOR A LIQUID BIOPSY ASSAY CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 314,267, filed February 25, 2022, and U.S. Provisional Patent Application No. 63 / 387,262, filed December 13, 2022, the contents of which are hereby incorporated by reference herein, in their entireties, for all purposes. FIELD OF THE INVENTION

[0002] The present disclosure relates generally to improved probe sets and their use for enriching cell free DNA data to provide clinical support for personalized treatment of disorders, such as cancer. BACKGROUND

[0003] Precision oncology is the practice of tailoring cancer therapy to the unique genomic, epigenetic, and / or transcriptomic profile of an individual’s cancer. Personalized cancer treatment builds upon conventional therapeutic regimens used to treat cancer based only on the gross classification of the cancer, e.g., treating all breast cancer patients with a first therapy and all lung cancer patients with a second therapy. This field was borne out of many observations that different patients diagnosed with the same type of cancer, e.g., breast cancer, responded very differently to common treatment regimens. Over time, researchers have identified genomic, epigenetic, and transcriptomic markers that improve predictions as to how an individual cancer will respond to a particular treatment modality.

[0004] There is growing evidence that cancer patients who receive therapy guided by their genetics have better outcomes. For example, studies have shown that targeted therapies result in significantly improved progression-free cancer survival. See, e.g., Radovich M. et al., Oncotarget, 7(35):56491-500 (2016). Similarly, reports from the IMPACT trial—a large (n = 1307) retrospective analysis of consecutive, prospectively molecularly profiled patients with advanced cancer who participated in a large, personalized medicine trial—indicate that patients receiving targeted therapies matched to their tumor biology had a response rate of16.2%, as opposed to a response rate of 5.2% for patients receiving non-matched therapy. Tsimberidou AM et al., ASCO 2018, Abstract LBA2553 (2018).

[0005] In fact, therapy targeted to specific genomic alterations is already the standard of care in several tumor types, e.g., as suggested in the National Comprehensive Cancer Network (NCCN) guidelines for melanoma, colorectal cancer, and non-small cell lung cancer. In practice, implementation of these targeted therapies requires determining the status of the diagnostic marker in each eligible cancer patient. While this can be accomplished for the few, well known mutations associated with treatment recommendations in the NCCN guidelines using individual assays or small next generation sequencing (NGS) panels, the growing number of actionable genomic alterations and increasing complexity of diagnostic classifiers necessitates a more comprehensive evaluation of each patient’s cancer genome, epigenome, and / or transcriptome.

[0006] For instance, some evidence suggests that use of combination therapies where each component is matched to an actionable genomic alteration holds the greatest potential for treating individual cancers. To this point, a retroactive study of cancer patients treated with one or more therapeutic regimens revealed that patients who received therapies matched to a higher percentage of their genomic alterations experienced a greater frequency of stable disease (e.g., a longer time to recurrence), longer time to treatment failure, and greater overall survival. Wheeler JJ et al., Cancer Res., 76:3690-701 (2016). Thus, comprehensive evaluation of each cancer patient’s genome, epigenome, and / or transcriptome should maximize the benefits provided by precision oncology, by facilitating more fine-tuned combination therapies, use of novel off-label drug indications, and / or tissue agnostic immunotherapy. See, for example, Schwaederle M. et al., J Clin Oncol., 33(32):3817-25 (2015); Schwaederle M. et al., JAMA Oncol., 2(11):1452-59 (2016); and Wheler JJ et al., Cancer Res., 76(13):3690-701 (2016). Further, the use of comprehensive next generation sequencing analysis of cancer genomes facilitates better access and a larger patient pool for clinical trial enrollment. Coyne GO et al., Curr. Probl. Cancer, 41(3):182-93 (2017); and Markman M., Oncology, 31(3):158, 168.

[0007] The use of large NGS genomic analysis is growing in order to address the need for more comprehensive characterization of an individual’s cancer genome. See, for example, Fernandes GS et al., Clinics, 72(10):588-94. Recent studies indicate that of the patients for which large NGS genomic analysis is performed, 30-40% then receive clinical care based on the assay results, which is limited by at least the identification of actionable genomicalterations, the availability of medication for treatment of identified actionable genomic alterations, and the clinical condition of the subject. See, Ross JS et al., JAMA Oncol., 1(1):40-49 (2015); Ross JS et al., Arch. Pathol. Lab Med., 139:642-49 (2015); Hirshfield KM et al., Oncologist, 21(11):1315-25 (2016); and Groisberg R. et al., Oncotarget, 8:39254-67 (2017).

[0008] However, these large NGS genomic analyses are conventionally performed on solid tumor samples. For instance, each of the studies referenced in the paragraph above performed NGS analysis of FFPE tumor blocks from patients. Solid tissue biopsies remain the gold standard for diagnosis and identification of predictive biomarkers because they represent well-known and validated methodologies that provide a high degree of accuracy. Nevertheless, there are significant limitations to the use of solid tissue material for large NGS genomic analyses of cancers. For example, tumor biopsies are subject to sampling bias caused by spatial and / or temporal genetic heterogeneity, e.g., between two regions of a single tumor and / or between different cancerous tissues (such as between primary and metastatic tumor sites or between two different primary tumor sites). Such inter-tumor or intra-tumor heterogeneity can cause sub-clonal or emerging mutations to be overlooked when using localized tissue biopsies, with the potential for sampling bias to be exacerbated over time as sub-clonal populations further evolve and / or shift in predominance.

[0009] Additionally, the acquisition of solid tissue biopsies often requires invasive surgical procedures, e.g., when the primary tumor site is located at an internal organ. These procedures can be expensive, time consuming, and carry a significant risk to the patient, e.g., when the patient’s health is poor and may not be able to tolerate invasive medical procedures and / or the tumor is located in a particularly sensitive or inoperable location, such as in the brain or heart. Further, the amount of tissue, if any, that can be procured depends on multiple factors, including the location of the tumor, the size of the tumor, the fragility of the patient, and the risk of comorbidities related to biopsies, such as bleeding and infections. For instance, recent studies report that tissue samples in a majority of advanced non-small cell lung cancer patients are limited to small biopsies and cannot be obtained at all in up to 31% of patients. Ilie and Hofman, Transl. Lung Cancer Res., 5(4):420-23 (2016). Even when a tissue biopsy is obtained, the sample may be too scant for comprehensive testing.

[0010] Further, the method of tissue collection, preservation (e.g., formalin fixation), and / or storage of tissue biopsies can result in sample degradation and variable quality DNA. This, in turn, leads to inaccuracies in downstream assays and analysis, including next-generation sequencing (NGS) for the identification of biomarkers. Ilie and Hofman, Transl Lung Cancer Res., 5(4):420-23 (2016).

[0011] In addition, the invasive nature of the biopsy procedure, the time and cost associated with obtaining the sample, and the compromised state of cancer patients receiving therapy render repeat testing of cancerous tissues impracticable, if not impossible. As a result, solid tissue biopsy analysis is not amenable to many monitoring schemes that would benefit cancer patients, such as disease progression analysis, treatment efficacy evaluation, disease recurrence monitoring, and other techniques that require data from several time points.

[0012] Cell-free DNA (cfDNA) has been identified in various bodily fluids, e.g., blood serum, plasma, urine, etc. Chan et al., Ann. Clin. Biochem., 40(Pt 2):122-30 (2003). This cfDNA originates from necrotic or apoptotic cells of all types, including germline cells, hematopoietic cells, and diseased (e.g., cancerous) cells. Advantageously, genomic alterations in cancerous tissues can be identified from cfDNA isolated from cancer patients. See, e.g., Stroun et al., Oncology, 46(5):318-22 (1989); Goessl et al., Cancer Res., 60(21):5941-45 (2000); and Frenel et al., Clin. Cancer Res.21(20):4586-96 (2015). Thus, one approach to overcoming the problems presented by the use of solid tissue biopsies described above is to analyze cell-free nucleic acids (e.g., cfDNA) and / or nucleic acids in circulating tumor cells present in biological fluids, e.g., via a liquid biopsy.

[0013] Specifically, liquid biopsies offer several advantages over conventional solid tissue biopsy analysis. For instance, because bodily fluids can be collected in a minimally invasive or non-invasive fashion, sample collection is simpler, faster, safer, and less expensive than solid tumor biopsies. Such methods require only small amounts of sample (e.g., 10 mL or less of whole blood per biopsy) and reduce the discomfort and risk of complications experienced by patients during conventional tissue biopsies. In fact, liquid biopsy samples can be collected with limited or no assistance from medical professionals and can be performed at almost any location. Further, liquid biopsy samples can be collected from any patient, regardless of the location of their cancer, their overall health, and any previous biopsy collection. This allows for analysis of the cancer genome of patients from which a solid tumor sample cannot be easily and / or safely obtained. In addition, because cell-free DNA in the bodily fluids arise from many different types of tissues in the patient, the genomic alterations present in the pool of cell-free DNA are representative of various different clonal sub-populations of the cancerous tissue of the subject, facilitating a morecomprehensive analysis of the cancerous genome of the subject than is possible from one or more sections of a single solid tumor sample.

[0014] Liquid biopsies also enable serial genetic testing prior to cancer detection, during the early stages of cancer progression, throughout the course of treatment, and during remission, e.g., to monitor for disease recurrence. The ability to conduct serial testing via non-invasive liquid biopsies throughout the course of disease could prove beneficial for many patients, e.g., through monitoring patient response to therapies, the emergence of new actionable genomic alterations, and / or drug-resistance alterations. These types of information allow medical professionals to more quickly tailor and update therapeutic regimens, e.g., facilitating more timely intervention in the case of disease progression. See, e.g., Ilie and Hofman, Transl. Lung Cancer Res., 5(4):420-23 (2016).

[0015] Nevertheless, while liquid biopsies are promising tools for improving outcomes using precision oncology, there are significant challenges specific to the use of cell-free DNA for evaluation of a subject’s cancer genome. For instance, there is a highly variable signal-to- noise ratio from one liquid biopsy sample to the next. This occurs because cfDNA originates from a variety of different cells in a subject, both healthy and diseased. Depending on the stage and type of cancer in any particular subject, the fraction of cfDNA fragments originating from cancerous cells (the “tumor fraction” or “ctDNA fraction” of the sample / subject) can range from almost 0% to well over 50%. Other factors, including tumor type and mutation profile, can also impact the amount of DNA released from cancerous tissues. For instance, cfDNA clearance through the liver and kidneys is affected by a variety of factors, including renal dysfunction or other tissue damaging factors (e.g., chemotherapy, surgery, and / or radiotherapy).

[0016] This, in turn, leads to problems detecting and / or validating cancer-specific genomic alterations in a liquid sample. This is particularly true during early stages of the disease—when cancer therapies have much higher success rates—because the tumor fraction in the patient is lowest at this point. Thus, early-stage cancer patients can have ctDNA fractions below the limit of detection (LOD) for one or more informative genomic alterations, limiting clinical utility because of the risk of false negatives and / or providing an incomplete picture of the cancer genome of the patient. Further, because cancers, and even individual tumors, can be clonally diverse, actionable genomic alterations that arise in only a subset of clonal populations are diluted below the overall tumor fraction of the sample, further frustrating attempts to tailor combination therapies to the various actionable mutations in thepatient’s cancer genome. Consequently, most studies using liquid biopsy samples to date have focused on late-stage patients for assay validation and research.

[0017] Another challenge associated with liquid biopsies is the accurate determination of tumor fraction in a sample. This difficulty arises from at least the heterogeneity of cancers and the increased frequency of large chromosomal duplications and deletions found in cancers. As a result, the frequency of genomic alterations from cancerous tissues varies from locus to locus based on at least (i) their prevalence in different sub-clonal populations of the subject’s cancer, and (ii) their location within the genome, relative to large chromosomal copy number variations. The difficulty in accurately determining the tumor fraction of liquid biopsy samples affects accurate measurement of various cancer features shown to have diagnostic value for the analysis of solid tumor biopsies. These include allelic ratios, copy number variations, overall mutational burden, frequency of abnormal methylation patterns, etc., all of which are correlated with the percentage of DNA fragments that arise from cancerous tissue, as opposed to healthy tissue.

[0018] Altogether, these factors result in highly variable concentrations of ctDNA—from patient to patient and possibly from locus to locus—that confound accurate measurement of disease indicators and actionable genomic alterations. Further, the quantity and quality of cfDNA obtained from liquid biopsy samples are highly dependent on the particular methodology for collecting the samples, storing the samples, sequencing the samples, and standardizing the sequencing data.

[0019] While validation studies of existing liquid biopsy assays have shown high sensitivity and specificity, few studies have corroborated results with orthogonal methods, or between particular testing platforms, e.g., different NGS technologies and / or targeted panel sequencing versus whole genome / exome sequence. Reports of liquid biopsy-based studies are limited by comparison to non-comprehensive tissue testing algorithms including Sanger sequencing, small NGS hotspot panels, polymerase chain reaction (PCR), and fluorescent in situ hybridization (FISH), which may not contain all NCCN guideline genes in their reportable range, thus suffering in comparison to a more comprehensive liquid biopsy assay.

[0020] As the field of precision oncology continues to grow, many different nucleic acid sequencing-based assays have been developed to inform diagnosis, prognosis, and treatment of a range of cancer conditions. For example, assays have been developed for the initial identification of cancer, for determining cancer type and / or cancer stage, predicting theprimary tissue source of tumors of unknown origin, predicting therapeutic responses for particular treatment regimens, etc. Each of these assays have their own sequencing requirements, e.g., requiring sequencing information from a particular set of genomic loci at a particular minimal sequencing depth, and the performance of each assay has been validated using nucleic acids prepared from a particular sample type according to a particular protocol and sequenced according to a particular sequencing technology. As the clinical standard of care continues to become more particularized for each cancer type and for each individual cancer, more of these nucleic acid-based sequencing assays will be relied upon to inform treatment of a single patient. However, because each of these assays relies on different sequencing information collected using different preparation and sequencing methodologies, this will require increasing amounts of biological sample from the patient—which is particularly problematic where a solid tumor sample is required for one or more of the assays. This will also require performance of an increasing number of assays per patients, which increases healthcare costs and slows down clinical decisions.

[0021] The information disclosed in this Background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art. SUMMARY

[0022] Given the above background, there is a need in the art for improved compositions, methods, and systems for supporting clinical decisions in precision oncology using liquid biopsy assays. In particular, there is a need for improved liquid biopsy assays that can support a growing number of molecular analyses and their differing requirements, in a cost- effective manner. For example, liquid biopsy assays that use probe sets enriching for a wide range of genomic loci at different limits of detection. Advantageously, the present disclosure solves this and other needs in the art by providing probe sets and cell-free DNA hybridization reactions that facilitate enrichment of many genomic loci across different limits of detection, eliminating the need to perform multiple nucleic acid enrichment and sequencing assays.

[0023] For example, in some embodiments, the improved methods and compositions described herein are based on the discovery that different genomic variant limits of detection can be achieved in a liquid biopsy assay by using probe sets at different stoichiometric ratios. However, as shown herein, there is not a linear relationship between the concentration ofenrichment probes and locus sequencing coverage / limit of detection in a liquid biopsy assay. For example, as reported in Example 11, the median limit of detection for SNVs and MNVs in a panel-enriched sequencing reaction of cell free DNA for a first set of genes (‘non- enhanced’ genes) captured in a hybridization assay using 0.7 fmol of each probe was 0.42%, while the median limit of detection for SNVs and MNVs in the same panel-enriched sequencing reaction of cell free DNA for a second set of genes (‘enhanced’ genes) captured in the hybridization assay using 4.55 fmol of each probe was 0.25%. That is, despite that the probes for the enriched genes were present at 6.5-fold higher concentration than the probes for the non-enriched genes in the hybridization reaction, the limit of detection for SNVs and MNVs in the enhanced genes was less than 2-fold greater.

[0024] Further, it was discovered that when the concentration of probes targeting the second set of genes was increased in the hybridization reaction, sequencing coverage for the first set of genes decreased. For example, as shown in Figure 7 and reported in Example 3, increasing the molar concentration of enrichment probes targeting the second set of genes (the ‘enriched’ genes) in the hybridization reaction resulted in both increasing the sequence coverage for the second set of genes and decreasing the sequence coverage for the first set of genes.

[0025] Accordingly, in some embodiments, the advantages provided by the methods and compositions described herein are realized, at least in part, by the discovery of stoichiometric ratios that allow variant detection in cell free DNA to be tuned to different limits of detection for different genomic loci in a single liquid biopsy reaction. Accordingly, in some embodiments of the methods and compositions described herein, a first set of enrichment probes are used at a first concentration (e.g., to facilitate detection of genomic variants represented in cell-free DNA at a first limit of detection) and a second set of enrichment probes are used at a second concentration that is from five to eight times greater than the first concentration (e.g., to facilitate detection of genomic variants represented in cell-free DNA at a second, lower limit of detection).

[0026] For example, in one aspect, the present disclosure provides a composition for enriching target nucleic acids, the composition comprising a probe set and a plurality of nucleic acids. The probe set comprisesa first set of polynucleotide probes (e.g., non- enhanced probes) collectively targeting a first plurality of genomic regions at an average coverage of from 0.75X to 1.25X, where the first set of polynucleotide probes comprises a first plurality of polynucleotide probe species. Each respective polynucleotide probe speciesin the first plurality of polynucleotide probe species targets a respective genomic region in the first plurality of genomic regions, and the polynucleotide probe species in the first plurality of polynucleotide probe species are present in the composition at a first average molar concentration.

[0027] The probe set further comprises a second set of polynucleotide probes (e.g., enhanced probes) collectively targeting a second plurality of genomic regions at an average coverage of from 0.75X to 1.25X, where the second set of polynucleotide probes comprises a second plurality of polynucleotide probe species. Each respective polynucleotide probe species in the second plurality of polynucleotide probe species targets a respective genomic region in the second plurality of genomic regions, the polynucleotide probe species in the second plurality of polynucleotide probe species are present in the composition at a second average molar concentration, and the second average molar concentration is from five to eight times greater than the first average concentration.

[0028] The plurality of nucleic acids comprises cell-free nucleic acids from a first biological sample of a first subject, or nucleic acids prepared therefrom.

[0029] In another aspect, the present disclosure provides a method for enriching target nucleic acids. The method comprises contacting a plurality of nucleic acids comprising the target nucleic acids with a probe set under hybridizing conditions, where the probe set comprisesa first set of polynucleotide probes (e.g., non-enhanced probes) collectively targeting a first plurality of genomic regions at an average coverage of from 0.75X to 1.25X, the first set of polynucleotide probes comprising a first plurality of polynucleotide probe species. Each respective polynucleotide probe species in the first plurality of polynucleotide probe species targets a respective genomic region in the first plurality of genomic regions, and the polynucleotide probe species in the first plurality of polynucleotide probe species are present in the composition at a first average molar concentration. The probe set further comprises a second set of polynucleotide probes (e.g., enhanced probes) collectively targeting a second plurality of genomic regions at an average coverage of from 0.75X to 1.25X, the second set of polynucleotide probes comprising a second plurality of polynucleotide probe species. Each respective polynucleotide probe species in the second plurality of polynucleotide probe species targets a respective genomic region in the second plurality of genomic regions, the polynucleotide probe species in the second plurality of polynucleotide probe species are present in the composition at a second average molar concentration, and the second average molar concentration is from five to eight times greater than the first averageconcentration. The plurality of nucleic acids comprises cell-free nucleic acids from a first biological sample of a first subject, or nucleic acids prepared therefrom.

[0030] Additional aspects and advantages of the present disclosure will become readily apparent to those skilled in this art from the following detailed description, wherein only illustrative embodiments of the present disclosure are shown and described. As will be realized, the present disclosure is capable of other and different embodiments, and its several details are capable of modifications in various obvious respects, all without departing from the disclosure. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figures 1A and 1B collectively illustrate a block diagram of an example computing device for providing clinical support for personalized cancer therapy based on sequencing of cell-free DNA, in accordance with some embodiments of the present disclosure.

[0032] Figure 2A illustrates an example workflow for generating a clinical report based on information generated from analysis of one or more patient specimens, in accordance with some embodiments of the present disclosure.

[0033] Figure 2B illustrates an example of a distributed diagnostic environment for collecting and evaluating patient data for the purpose of precision oncology, in accordance with some embodiments of the present disclosure.

[0034] Figure 3 provides an example flow chart of processes and features for liquid biopsy sample collection and analysis for use in precision oncology, in accordance with some embodiments of the present disclosure.

[0035] Figures 4A, 4B, 4C, 4D, and 4E collectively illustrate example steps of a bioinformatics pipeline for precision oncology, in accordance with various embodiments of the present disclosure. Figure 4A provides an overview flow chart of processes and features in a bioinformatics pipeline, in accordance with some embodiments of the present disclosure. Figure 4B provides an overview of a bioinformatics pipeline executed with either a liquid biopsy sample alone or a liquid biopsy sample and a matched normal sample. Figure 4C illustrates that paired end reads from tumor and normal isolates are zipped and stored separately under the same order identifier, in accordance with some embodiments of thepresent disclosure. Figure 4D illustrates quality correction for FASTQ files, in accordance with some embodiments of the present disclosure. Figure 4E illustrates processes for obtaining tumor and normal BAM alignment files, in accordance with some embodiments of the present disclosure.

[0036] Figures 5A and 5B collectively illustrate a flow chart of processes and features for generating sequencing data for cell-free DNA using an improved probe set, in accordance with some embodiments of the present disclosure.

[0037] Figure 6A, 6B, 6C, 6D, 6E, 6F, 6G, 6H, 6I, 6J, 6K, 6L, and 6M collectively illustrate example nucleic acids targeted for enrichment and variant detection using one or more probes, in accordance with some embodiments of the present disclosure.

[0038] Figure 7 illustrates sequencing coverage obtained using probe sets including a first set of polynucleotide probes and a second set of polynucleotide probes at varying concentration ratios, in accordance with an embodiment of the present disclosure.

[0039] Figure 8 illustrates sensitivity of a probe set, including a first set of polynucleotide probes and a second set of polynucleotide probes, when performing singleplex and multiplex library hybridization enrichment, in accordance with an embodiment of the present disclosure.

[0040] Figure 9 illustrates a schematic of relative unique read coverage and total read coverage obtained using a first set of polynucleotide probes and a second set of polynucleotide probes with varying amounts of nucleic acid input, in accordance with some embodiments of the present disclosure.

[0041] Figures 10A and 10B collectively illustrate example microsatellite regions in the human genome useful for determining MSI status of a sample, in accordance with some embodiments of the present disclosure.

[0042] Figure 11 illustrates post-deduplicated coverage for each DNA input and probe ratio condition, split by target type, in accordance with an embodiment of the present disclosure. The Y-axis shows the coverage, where each dot represents 1 target region for one sample in the condition. The X-axis shows the conditions, with 30 ng DNA input and probe ratio on the left, and 10 ng DNA input and probe ratio on the right. The Enhanced and Nonenhanced targets were plotted separately to illustrate differences in coverage for each probe ratio. Coverage for the enhanced targets (left-hand boxplot for each pair of boxplots) appeared to reach an upper limit, as there was no increase in coverage in the enhanced targetswith increased probe ratio for enhanced. The nonenhanced coverage (right-hand boxplot for each pair of boxplots) appeared to decrease in the 1:5:1 probe ratio, relative to the 1:1:1 probe ratio.

[0043] Figure 12 illustrates pre-deduplicated coverage for each DNA input and probe ratio condition, split by target type, in accordance with an embodiment of the present disclosure. The Y-axis is the coverage, where each dot represents 1 target region for one sample in the condition. The X-axis shows the conditions, with 30 ng DNA input and probe- ratio on the left, and 10 ng DNA input and probe ratio on the right. The Enhanced and Nonenhanced targets were plotted separately to illustrate differences in coverage for each probe ratio. The enhanced targets (left-hand boxplot for each pair of boxplots) did not reach an upper limit, indicating that the probe-ratios cause differences in the coverage in some instances. However, there was an upper limit to the number of unique fragments identified in the samples, as shown in Figure 11.

[0044] Figure 13 illustrates enhanced pre-deduplicated read counts by enhanced PCR duplication rate, in accordance with an embodiment of the present disclosure. The enhanced pre-deduplicated read count (y-axis, 100 million) was plotted against the enhanced PCR- duplication rate (x-axis), where the dots correspond to one sample and are color coded based on condition (DNA input and probe ratio).

[0045] Figure 14 illustrates nonenhanced pre-deduplicated read count by enhanced PCR duplication rate, in accordance with an embodiment of the present disclosure. The nonenhanced pre-deduplicated read count (y-axis, 100 million) was plotted against the nonenhanced PCR-duplication rate (x-axis), where the dots correspond to one sample and are color coded based on condition (DNA input and probe ratio).

[0046] Figure 15 illustrates total read counts for each sample (y-axis, 100 million), in accordance with an embodiment of the present disclosure. Individual samples are shown as dots, with the boxplots shown for each probe ratio (legend) and DNA input (x-axis, 10 ng or 30 ng). The total read count was higher than the anticipated total read count expected for combined enhanced and nonenhanced probes.

[0047] Figure 16 illustrates unique read counts for each sample (y-axis, 100 million), in accordance with an embodiment of the present disclosure. Individual samples are shown as dots, with the boxplots shown for each probe ratio (legend) and DNA input (x-axis, 10 ng or 30 ng). The unique read count was higher than the anticipated unique read count expectedfor combined enhanced and nonenhanced probes, with no large change in unique reads identified for each probe ratio.

[0048] Figure 17 illustrates PCR duplication rate by UMI (y-axis), in accordance with an embodiment of the present disclosure. Individual samples are shown as dots, with the boxplots shown for each probe ratio (legend) and DNA input (x-axis, 10 ng or 30 ng). The PCR duplication rate was lower for the 30 ng DNA input, as expected.

[0049] Figure 18 illustrates on-target rate (y-axis), in accordance with an embodiment of the present disclosure. Individual samples are shown as dots, with the boxplots shown for each probe ratio (legend), and DNA input (x-axis, 10 ng or 30 ng). The on-target rate appeared to decline with higher probe ratios for the Enhanced probes, indicating that the enhanced probes cause more off-target reads in some instances.

[0050] Figure 19 illustrates pre-deduplicated on-target rate (y-axis), in accordance with an embodiment of the present disclosure. Individual samples are shown as dots, with the boxplots shown for each probe (legend), and DNA input (x-axis, 10 ng or 30 ng). The pre- deduplicated on-target rate was elevated compared to the post-deduplicated on-target-rate, and declined with probe-ratios favoring the enhanced probes, consistent with the post- deduplicated on-target-rate shown in Figure 18.

[0051] Figure 20 provides example genomic regions, useful for determining whether a patient is likely to be resistant to immune oncology therapy, targeted for enrichment and variant detection using one or more probes, in accordance with some embodiments of the present disclosure.

[0052] Figures 21A, 21B, 21C, 21D, and 21E collectively provide example microsatellite genomic regions, useful for determining a microsatellite stability status for a patient, targeted for enrichment and variant detection using one or more probes, in accordance with some embodiments of the present disclosure.

[0053] Figures 22A and 22B collectively illustrate pre- and post-deduplicated coverage for each DNA input and probe ratio condition, split by enhanced and non-enhanced targets, in accordance with an embodiment of the present disclosure. Pre-duplicated coverage is shown in Figure 22A. Post-duplicated coverage is shown in Figure 22B. The Y-axis is the coverage, where each dot represents 1 target region for one sample in the condition. The X- axis shows the conditions, with 30 ng DNA input and probe-ratio on the left, and 10 ng DNA input and probe ratio on the right. Enhanced (left-hand boxplot in each pair of boxplots) andnon-enhanced (right-hand boxplot in each pair of boxplots) coverage appeared to respectively increase and decrease in correlation with the ratio of enhanced to other probes.

[0054] Figures 23A and 23B collectively illustrate enhanced and non-enhanced pre- deduplicated read count (y-axis, 10 and 100 million respectively) by enhanced PCR duplication rate (x-axis, fraction), in accordance with an embodiment of the present disclosure. Enhanced pre-deduplicated read counts are shown in Figure 23A. Non-enhanced pre-deduplicated read counts are shown in Figure 23B. Dots represent single samples, with color based on condition (DNA input and probe ratio). Enhanced pre-deduplicated read count and PCR duplication rate appeared to generally increase with molar ratio. Non- enhanced pre-deduplicated read count and PCR duplication rate appeared to be mutually exclusive, with PCR duplication rate dependent predominantly on DNA input (lower rate for higher DNA input), while pre-deduplicated read count decreased, expectedly in accordance with some instances, with increasing molar ratio.

[0055] Figure 24 illustrates total read counts (y-axis, 100 million), in accordance with an embodiment of the present disclosure. Dots represent single samples, with boxplots shown for each probe ratio (legend) and DNA input (x-axis, 10 ng or 30 ng). There was a clear difference in total read count between the two DNA input weights.

[0056] Figure 25 illustrates unique read counts for each sample (y-axis, 10 million), in accordance with an embodiment of the present disclosure. Individual samples are shown as a dot, with the boxplots shown for each probe ratio (legend) and DNA input (x-axis, 10 ng or 30 ng).

[0057] Figure 26 illustrates PCR duplication rate by UMI (y-axis), in accordance with an embodiment of the present disclosure. Individual samples are shown as a dot, with the boxplots shown for each probe ratio (legend) and DNA input (x-axis, 10 ng or 30 ng). The PCR duplication rate was lower for the 30 ng DNA input, as expected in some instances.

[0058] Figure 27 illustrates on-target rate (y-axis), in accordance with an embodiment of the present disclosure. Individual samples are shown as a dot, with the boxplots shown for each probe ratio (legend), and DNA input (x-axis, 10 ng or 30 ng). The on-target rate appeared to decline with higher probe ratios for the enhanced probes, indicating that the enhanced probes cause more off-target reads in some instances.

[0059] Figure 28 illustrates pre-deduplicated on-target rate (y-axis), in accordance with an embodiment of the present disclosure. Individual samples are shown as a dot, with theboxplots shown for each probe (legend), and DNA input (x-axis, 10 ng or 30 ng). The pre- deduplicated on-target rate was similar to the post-deduplicated on-target rate, and declined with probe-ratios favoring the enhanced probes, consistent with the post-deduplicated on- target-rate illustrated in Figure 27.

[0060] Figures 29A and 29B collectively provide example viral genomic regions, useful for determining whether a patient has a clinically relevant viral infection, targeted for enrichment and variant detection using one or more probes, in accordance with some embodiments of the present disclosure.

[0061] Figure 30 illustrates enhanced vs. non-enhanced pre-deduplicated coverage ratio as a function of enhanced probe molar ratio, in accordance with an embodiment of the present disclosure. Given the equation for line of best fit, the optimal ratio to achieve 4:1 enhanced vs. non-enhanced coverage was 1:5.5:1. A R2value of 0.993 indicated high confidence in the line of best fit.

[0062] Figures 31A and 31B collectively illustrate pre- and post-deduplicated coverage for each sequencing depth and probe ratio condition, split by enhanced (left-hand boxplot in each pair of boxplots) and non-enhanced (right-hand boxplot in each pair of boxplots) targets to illustrate differences in coverage for each probe ratio, in accordance with an embodiment of the present disclosure. Pre-duplicated coverage is shown in Figure 31A. Post-duplicated coverage is shown in Figure 31B. The Y-axis is the coverage, where each dot represents 1 target region for one sample in the condition. The X-axis shows the conditions, with 1x depth and probe-ratio on the left, and 2.5x depth + probe ratio on the right

[0063] Figures 32A and 32B collectively illustrate enhanced and non-enhanced pre- deduplicated read count (y-axis, 100 million) by enhanced PCR duplication rate (x-axis, fraction), in accordance with an embodiment of the present disclosure. Dots represent single samples, with color based on condition (DNA input and probe ratio). Higher sequencing depth resulted in higher PCR duplication rate, as expected in some instances, but was more significant for enhanced (10% higher) vs. non-enhanced (5% higher).

[0064] Figure 33 illustrates total read counts (y-axis, 100 million), in accordance with an embodiment of the present disclosure. Dots represent single samples, with boxplots shown for each probe ratio (legend) and sequencing depth (x-axis, 1x or 2.5x). The total read count was higher than the anticipated total read count expected for combined enhanced and non-enhanced probes. Total read count appeared to correspond to sequencing depth, as expected in some instances.

[0065] Figure 34 illustrates unique read counts for each sample (y-axis, 100 million), in accordance with an embodiment of the present disclosure. Individual samples are shown as a dot, with the boxplots shown for each probe ratio (legend) and sequencing depth (x-axis, 1x or 2.5x).

[0066] Figure 35 illustrates PCR duplication rate by UMI (y-axis), in accordance with an embodiment of the present disclosure. Individual samples are shown as a dot, with the boxplots shown for each probe ratio (legend) and sequencing depth (x-axis, 1x or 2.5x).

[0067] Figure 36 illustrates on-target rate (y-axis), in accordance with an embodiment of the present disclosure. Individual samples are shown as a dot, with the boxplots shown for each probe ratio (legend), and sequencing depth (x-axis, 1x or 2.5x). On target rate was lower with a higher sequencing depth, indicating, in some instances, a higher number of reads (e.g., mapped away from target regions).

[0068] Figure 37 illustrates pre-deduplicated on-target rate (y-axis), in accordance with an embodiment of the present disclosure. Individual samples are shown as a dot, with the boxplots shown for each probe (legend), and sequencing depth (x-axis, 1x or 2.5x).

[0069] Figure 38 illustrates enhanced vs. non-enhanced pre-deduplicated median coverage ratio as a function of enhanced probe molar ratio, in accordance with an embodiment of the present disclosure. Given the equation for line of best fit, the optimal ratio to achieve 4:1 enhanced vs. non-enhanced coverage was 1:5.5:1. R2values of 0.974 and 0.979 for 1x and 2x sequencing depth respectively indicated high confidence in the line of best fit.

[0070] Figures 39A and 39B collectively illustrate pre- and post-deduplicated coverage for each DNA input, split by enhanced (left-hand boxplot), BRCA1 / 2 (center boxplot), and non-enhanced (right-hand boxplot) targets to illustrate differences in coverage, in accordance with an embodiment of the present disclosure. The Y-axis is the coverage, where each dot represents one target region for one sample in the condition. The X-axis shows the conditions.

[0071] Figures 40A, 40B, and 40C collectively illustrate enhanced, BRCA1 / 2, and non- enhanced pre-deduplicated read count (y-axis) by enhanced PCR duplication rate (x-axis, fraction), in accordance with an embodiment of the present disclosure. Enhanced targets areshown in Figure 40A. BRCA1 / 2 targets are shown in Figure 40B. Non-enhanced targets are shown in Figure 40C. Dots represent single samples, with color based on condition (DNA input and probe ratio). Higher DNA input resulted in lower PCR duplication rate, as expected in some instances, without change in pre-deduplicated read count.

[0072] Figure 41 illustrates total read counts (y-axis), in accordance with an embodiment of the present disclosure. Dots represent single samples, with boxplots shown for each probe ratio (legend) and DNA input (30 ng or 45 ng).

[0073] Figure 42 illustrates unique read counts for each sample (y-axis), in accordance with an embodiment of the present disclosure. Individual samples are shown as a dot, with the boxplots shown for each probe ratio (legend) and DNA input (x-axis, 30 ng or 45 ng).

[0074] Figure 43 illustrates PCR duplication rate by UMI (y-axis), in accordance with an embodiment of the present disclosure. Individual samples are shown as a dot, with the boxplots shown for each probe ratio (legend) and DNA input (x-axis, 30 ng or 45 ng).

[0075] Figure 44 illustrates on-target rate (y-axis), in accordance with an embodiment of the present disclosure. Individual samples are shown as a dot, with the boxplots shown for each probe ratio (legend), and DNA input (x-axis, 30 ng or 45 ng).

[0076] Figure 45 illustrates pre-deduplicated on-target rate (y-axis), in accordance with an embodiment of the present disclosure. Individual samples are shown as a dot, with the boxplots shown for each probe (legend), and DNA input (x-axis, 30 ng or 45 ng).

[0077] Figure 46A and 46B collectively illustrate pre- and post-deduplicated coverage for each DNA input, split by enhanced (left-hand boxplot), BRCA1 / 2 (center boxplot), and non- enhanced (right-hand boxplot) targets to illustrate differences in coverage for each DNA input, in accordance with an embodiment of the present disclosure. The Y-axis is the coverage, where each dot represents one target region for one sample in the condition. The X-axis shows the conditions.

[0078] Figure 47A, 47B, and 47C collectively illustrate enhanced, BRCA1 / 2, and non- enhanced pre-deduplicated read count (y-axis) by enhanced PCR duplication rate (x-axis, fraction), in accordance with an embodiment of the present disclosure. Enhanced targets are shown in Figure 47A. BRCA1 / 2 targets are shown in Figure 47B. Non-enhanced targets are shown in Figure 47C. Dots represent single samples, with color based on condition (DNA input and probe ratio).

[0079] Figure 48 illustrates total read counts (y-axis), in accordance with an embodiment of the present disclosure. Dots represent single samples, with boxplots shown for each probe ratio (legend) and DNA input (30 ng or 45 ng).

[0080] Figure 49 illustrates unique read counts for each sample (y-axis), in accordance with an embodiment of the present disclosure. Individual samples are shown as a dot, with the boxplots shown for each probe ratio (legend) and DNA input (x-axis, 30 ng or 45 ng).

[0081] Figure 50 illustrates PCR duplication rate by UMI (y-axis), in accordance with an embodiment of the present disclosure. Individual samples are shown as a dot, with the boxplots shown for each probe ratio (legend) and DNA input (x-axis, 30 ng or 45 ng).

[0082] Figure 51 illustrates on-target rate (y-axis), in accordance with an embodiment of the present disclosure. Individual samples are shown as a dot, with the boxplots shown for each probe ratio (legend), and DNA input (x-axis, 1x or 2.5x).

[0083] Figure 52 illustrates pre-deduplicated on-target rate (y-axis), in accordance with an embodiment of the present disclosure. Individual samples are shown as a dot, with the boxplots shown for each probe (legend), and DNA input (x-axis, 30 ng or 45 ng).

[0084] Figure 53 illustrates changes in allele frequency of insertion-deletion sites greater than 10 bp using control single and multiplex libraries for hybridization reactions, in accordance with an embodiment of the present disclosure.

[0085] Figure 54 provides example genomic regions, useful for determining whether a patient is likely to be resistant to androgen receptor therapy, e.g., androgen receptor antagonists or other therapies that target, modulate, or interact with the androgen receptor, targeted for enrichment and variant detection using one or more probes, in accordance with some embodiments of the present disclosure.

[0086] Figure 55 provides example genomic regions in the BRCA1 and BRCA2 genes, useful for determining whether a patient has a clinically relevant homologous recombination deficiency mutation, targeted for enrichment and variant detection using one or more probes, in accordance with some embodiments of the present disclosure.

[0087] Figures 56A, 56B, 56C, 56D, 56E, 56F, 56G, 56H, 56I, 56J, 56K, 56L, 56M, 56N, 56O, 56P, 56Q, 56R, 56S, 56T, 56U, 56V, 56W, and 56X collectively provide example genomic regions, useful for determining whether a patient has a clinically relevant copynumber variation, targeted for enrichment and variant detection using one or more probes, in accordance with some embodiments of the present disclosure.

[0088] Figures 57A, 57B, 57C, 57D, 57E, 57F, 57G, 57H, 57I, 57J, 57K, 57L, 57M, 57N, 57O, 57P, 57Q, 57R, 57S, 57T, 57U, 57V, 57W, 57X, and 57Y collectively provide example genomic regions, useful for determining whether a patient has a clinically relevant variant, targeted for enrichment and variant detection using one or more probes, in accordance with some embodiments of the present disclosure.

[0089] Figures 58A, 58B, 58C, 58D, 58E, 58F, 58G, 58H, 58I, 58J, 58K, 58L, 58M, 58N, 58O, 58P, 58Q, 58R, 58S, 58T, 58U, 58V, 58W, 58X, 58Y, 58Z, 58AA, 58AB, 58AC, 58AD, 58AE, 58AF, 58AG, 58AH, 58AI, 58AJ, 58AK, 58AL, 58AM, 58AN, 58AO, 58AP, 58AQ, 58AR, 58AS, 58AT, 58AU, 58AV, 58AW, 58AX, 58AY, 58AZ, 58BA, 58BB, 58BC, 58BD, 58BE, and 58BF, collectively provide example genomic regions, useful for determining whether a patient has a clinically relevant variant, targeted for enrichment and variant detection using one or more probes, in accordance with some embodiments of the present disclosure.

[0090] Like reference numerals refer to corresponding parts throughout the several views of the drawings. DETAILED DESCRIPTION Introduction

[0091] One aspect of the design of precision oncology and / or next generation sequencing assays is the selection and concentration of probes used to identify specific regions of a genome. In some instances, biological samples such as liquid biopsy assays include nucleic acids derived from a plurality of different genomic regions, where two or more genomic regions in the plurality of different genomic regions can have different limits of detection. For instance, as described in the Background section above, the fraction of cfDNA fragments originating from cancerous cells (the “tumor fraction” or “ctDNA fraction” of the sample / subject) in a liquid biopsy sample can range from almost 0% to well over 50%. In some instances, clonal heterogeneity can result in dilution of one or more clonal populations well below the overall tumor fraction of the sample. Moreover, the frequency of genomic alterations from cancerous tissues can vary from locus to locus based on at least (i) their prevalence in different sub-clonal populations of the subject’s cancer, and (ii) their location within the genome, relative to large chromosomal copy number variations. Accordingly, theamount of DNA released from cancerous tissues for different genomic targets can vary widely in a single given sample, hampering accurate measurements of disease indicators and actionable genomic alterations.

[0092] In particular, enrichment of regions having lower limits of detection can be difficult using standard probe panels for targeted genes, as such low LOD genomic regions may be underrepresented in the resulting sequencing data. Accordingly, the presently disclosed compositions, methods, and systems provide improved probe sets that account for different LODs between genomic regions having a baseline of detection (e.g., non-enhanced genes) and genomic regions having a LOD lower than the baseline (e.g., enhanced genes). In particular, the present disclosure describes improved probe sets obtained by tuning the ratio of probe molarity between enhanced and non-enhanced genomic regions.

[0093] Advantageously, as described in Examples 2, 3, and 13 with reference to Figures 7 and 8, the presently disclosed compositions and methods allow for the hybridization and capture of non-enhanced genes having a limit of detection no higher than an allele fraction of 0.01 (1%) and exemplary enhanced genes having a limit of detection no higher than an allele fraction of 0.0025 (0.25%). Using the improved ratios of enhanced probes and non-enhanced probes, in some implementations, sequencing coverage for enhanced genes is increased with no loss in sensitivity for either the enhanced or non-enhanced probes when performing hybridization enrichment of multiplexed libraries (e.g., DNA input). Accordingly, the compositions and methods described herein tune the sensitivity and specificity of probes for target nucleic acid enrichment in a locus-specific fashion to achieve higher accuracy of true variant calling in a liquid biopsy assay. The improved performance of the presently disclosed compositions and methods, including enhanced and non-enhanced probes, is further illustrated by the schematic in Figure 9, in which the coverage of unique reads per total reads sequenced is increased when using enhanced probes compared to non-enhanced probes. An even greater effect can be observed when the amount of the sample nucleic acid input (e.g., mass of DNA input) is increased relative to an amount of probes, where the coverage of unique reads per total reads sequenced is further increased for enhanced probes, with minimal loss of sensitivity for non-enhanced probes.

[0094] The methods and systems described herein also improve precision oncology methods for assigning and / or administering treatment because of the improved accuracy of variation detection. The identification of therapeutically actionable variants that can be included in a clinical report for patient and / or clinician review, and / or matched withappropriate therapies and / or clinical trials for treatment and / or monitoring, allows for more accurate assignment of treatments. Furthermore, the removal of false positive variant detection reduces the risk of patients undergoing unnecessary or potentially harmful regimens due to misdiagnoses. Definitions

[0095] As used herein, the term “subject” refers to any living or non-living organism including, but not limited to, a human (e.g., a male human, female human, fetus, pregnant female, child, or the like), a non-human mammal, or a non-human animal. Any human or non-human animal can serve as a subject, including but not limited to mammal, reptile, avian, amphibian, fish, ungulate, ruminant, bovine (e.g., cattle), equine (e.g., horse), caprine and ovine (e.g., sheep, goat), swine (e.g., pig), camelid (e.g., camel, llama, alpaca), monkey, ape (e.g., gorilla, chimpanzee), ursid (e.g., bear), poultry, dog, cat, mouse, rat, fish, dolphin, whale and shark. In some embodiments, a subject is a male or female of any age (e.g., a man, a woman, or a child).

[0096] As used herein, the terms “control,” “control sample,” “reference,” “reference sample,” “normal,” and “normal sample” describe a sample from a non-diseased tissue. In some embodiments, such a sample is from a subject that does not have a particular condition (e.g., cancer). In other embodiments, such a sample is an internal control from a subject, e.g., who may or may not have the particular disease (e.g., cancer), but is from a healthy tissue of the subject. For example, where a liquid or solid tumor sample is obtained from a subject with cancer, an internal control sample may be obtained from a healthy tissue of the subject, e.g., a white blood cell sample from a subject without a blood cancer or a solid germline tissue sample from the subject. Accordingly, a reference sample can be obtained from the subject or from a database, e.g., from a second subject who does not have the particular disease (e.g., cancer).

[0097] As used herein the term “cancer,” “cancerous tissue,” or “tumor” refers to an abnormal mass of tissue in which the growth of the mass surpasses, and is not coordinated with, the growth of normal tissue, including both solid masses (e.g., as in a solid tumor) or fluid masses (e.g., as in a hematological cancer). A cancer or tumor can be defined as “benign” or “malignant” depending on the following characteristics: degree of cellular differentiation including morphology and functionality, rate of growth, local invasion and metastasis. A “benign” tumor can be well differentiated, have characteristically slower growth than a malignant tumor and remain localized to the site of origin. In addition, in somecases a benign tumor does not have the capacity to infiltrate, invade or metastasize to distant sites. A “malignant” tumor can be a poorly differentiated (anaplasia), have characteristically rapid growth accompanied by progressive infiltration, invasion, and destruction of the surrounding tissue. Furthermore, a malignant tumor can have the capacity to metastasize to distant sites. Accordingly, a cancer cell is a cell found within the abnormal mass of tissue whose growth is not coordinated with the growth of normal tissue. Accordingly, a “tumor sample” refers to a biological sample obtained or derived from a tumor of a subject, as described herein.

[0098] Non-limiting examples of cancer types include ovarian cancer, cervical cancer, uveal melanoma, colorectal cancer, chromophobe renal cell carcinoma, liver cancer, endocrine tumor, oropharyngeal cancer, retinoblastoma, biliary cancer, adrenal cancer, neural cancer, neuroblastoma, basal cell carcinoma, brain cancer, breast cancer, non-clear cell renal cell carcinoma, glioblastoma, glioma, kidney cancer, gastrointestinal stromal tumor, medulloblastoma, bladder cancer, gastric cancer, bone cancer, non-small cell lung cancer, thymoma, prostate cancer, clear cell renal cell carcinoma, skin cancer, thyroid cancer, sarcoma, testicular cancer, head and neck cancer (e.g., head and neck squamous cell carcinoma), meningioma, peritoneal cancer, endometrial cancer, pancreatic cancer, mesothelioma, esophageal cancer, small cell lung cancer, Her2 negative breast cancer, ovarian serous carcinoma, HR+ breast cancer, uterine serous carcinoma, uterine corpus endometrial carcinoma, gastroesophageal junction adenocarcinoma, gallbladder cancer, chordoma, and papillary renal cell carcinoma.

[0099] As used herein, the terms “cancer state” or “cancer condition” refer to a characteristic of a cancer patient's condition, e.g., a diagnostic status, a type of cancer, a location of cancer, a primary origin of a cancer, a cancer stage, a cancer prognosis, and / or one or more additional characteristics of a cancer (e.g., tumor characteristics such as morphology, heterogeneity, size, etc.). In some embodiments, one or more additional personal characteristics of the subject are used further describe the cancer state or cancer condition of the subject, e.g., age, gender, weight, race, personal habits (e.g., smoking, drinking, diet), other pertinent medical conditions (e.g., high blood pressure, dry skin, other diseases), current medications, allergies, pertinent medical history, current side effects of cancer treatments and other medications, etc.

[0100] As used herein, the term “liquid biopsy” sample refers to a liquid sample obtained from a subject that includes cell-free DNA. Examples of liquid biopsy samples include, butare not limited to, blood, whole blood, plasma, serum, urine, cerebrospinal fluid, fecal material, saliva, sweat, tears, pleural fluid, pericardial fluid, or peritoneal fluid of the subject. In some embodiments, a liquid biopsy sample is a cell-free sample, e.g., a cell free blood sample. In some embodiments, a liquid biopsy sample is obtained from a subject with cancer. In some embodiments, a liquid biopsy sample is collected from a subject with an unknown cancer status, e.g., for use in determining a cancer status of the subject. Likewise, in some embodiments, a liquid biopsy is collected from a subject with a non-cancerous disorder, e.g., a cardiovascular disease. In some embodiments, a liquid biopsy is collected from a subject with an unknown status for a non-cancerous disorder, e.g., for use in determining a non-cancerous disorder status of the subject.

[0101] As used herein, the term “cell-free DNA” and “cfDNA” interchangeably refer to DNA fragments that circulate in a subject’s body (e.g., bloodstream) and originate from one or more healthy cells and / or from one or more cancer cells. These DNA molecules are found outside cells, in bodily fluids such as blood, whole blood, plasma, serum, urine, cerebrospinal fluid, fecal material, saliva, sweat, sweat, tears, pleural fluid, pericardial fluid, or peritoneal fluid of a subject, and are believed to be fragments of genomic DNA expelled from healthy and / or cancerous cells, e.g., upon apoptosis and lysis of the cellular envelope.

[0102] As used herein, the term “locus” refers to a position (e.g., a site) within a genome, e.g., on a particular chromosome. In some embodiments, a locus refers to a single nucleotide position, on a particular chromosome, within a genome. In some embodiments, a locus refers to a group of nucleotide positions within a genome. In some instances, a locus is defined by a mutation (e.g., substitution, insertion, deletion, inversion, or translocation) of consecutive nucleotides within a cancer genome. In some instances, a locus is defined by a gene, a sub- genic structure (e.g., a regulatory element, exon, intron, or combination thereof), or a predefined span of a chromosome. Because normal mammalian cells have diploid genomes, a normal mammalian genome (e.g., a human genome) will generally have two copies of every locus in the genome, or at least two copies of every locus located on the autosomal chromosomes, e.g., one copy on the maternal autosomal chromosome and one copy on the paternal autosomal chromosome.

[0103] As used herein, the term “allele” refers to a particular sequence of one or more nucleotides at a chromosomal locus. In a haploid organism, the subject has one allele at every chromosomal locus. In a diploid organism, the subject has two alleles at every chromosomal locus.

[0104] As used herein, the term “base pair” or “bp” refers to a unit consisting of two nucleobases bound to each other by hydrogen bonds. Generally, the size of an organism's genome is measured in base pairs because DNA is typically double stranded. However, some viruses have single-stranded DNA or RNA genomes.

[0105] As used herein, the terms “genomic alteration,” “mutation,” and “variant” refer to a detectable change in the genetic material of one or more cells. A genomic alteration, mutation, or variant can refer to various type of changes in the genetic material of a cell, including changes in the primary genome sequence at single or multiple nucleotide positions, e.g., a single nucleotide variant (SNV), a multi-nucleotide variant (MNV), an indel (e.g., an insertion or deletion of nucleotides), a DNA rearrangement (e.g., an inversion or translocation of a portion of a chromosome or chromosomes), a variation in the copy number of a locus (e.g., an exon, gene, or a large span of a chromosome) (CNV), a partial or complete change in the ploidy of the cell, as well as in changes in the epigenetic information of a genome, such as altered DNA methylation patterns. In some embodiments, a mutation is a change in the genetic information of the cell relative to a particular reference genome, or one or more ‘normal’ alleles found in the population of the species of the subject. For instance, mutations can be found in both germline cells (e.g., non-cancerous, ‘normal’ cells) of a subject and in abnormal cells (e.g., pre-cancerous or cancerous cells) of the subject. As such, a mutation in a germline of the subject (e.g., which is found in substantially all ‘normal cells’ in the subject) is identified relative to a reference genome for the species of the subject. However, many loci of a reference genome of a species are associated with several variant alleles that are significantly represented in the population of the subject and are not associated with a diseased state, e.g., such that they would not be considered ‘mutations.’ By contrast, in some embodiments, a mutation in a cancerous cell of a subject can be identified relative to either a reference genome of the subject or to the subject’s own germline genome. In certain instances, identification of both types of variants can be informative. For instance, in some instances, a mutation that is present in both the cancer genome of the subject and the germline of the subject is informative for precision oncology when the mutation is a so-called ‘driver mutation,’ which contributes to the initiation and / or development of a cancer. However, in other instances, a mutation that is present in both the cancer genome of the subject and the germline of the subject is not informative for precision oncology, e.g., when the mutation is a so-called ‘passenger mutation,’ which does not contribute to the initiation and / or development of the cancer. Likewise, in some instances, a mutation that is present inthe cancer genome of the subject but not the germline of the subject is informative for precision oncology, e.g., where the mutation is a driver mutation and / or the mutation facilitates a therapeutic approach, e.g., by differentiating cancer cells from normal cells in a therapeutically actionable way. However, in some instances, a mutation that is present in the cancer genome but not the germline of a subject is not informative for precision oncology, e.g., where the mutation is a passenger mutation and / or where the mutation fails to differentiate the cancer cell from a germline cell in a therapeutically actionable way.

[0106] As used herein, the term “reference allele” refers to the sequence of one or more nucleotides at a chromosomal locus that is either the predominant allele represented at that chromosomal locus within the population of the species (e.g., the “wild-type” sequence), or an allele that is predefined within a reference genome for the species.

[0107] As used herein, the term “variant allele” refers to a sequence of one or more nucleotides at a chromosomal locus that is either not the predominant allele represented at that chromosomal locus within the population of the species (e.g., not the “wild-type” sequence), or not an allele that is predefined within a reference sequence construct (e.g., a reference genome or set of reference genomes) for the species. In some instances, sequence isoforms found within the population of a species that do not affect a change in a protein encoded by the genome, or that result in an amino acid substitution that does not substantially affect the function of an encoded protein, are not variant alleles.

[0108] As used herein, the term “variant allele fraction,” “VAF,” “allelic fraction,” or “AF” refers to the number of times a variant or mutant allele was observed (e.g., a number of reads supporting a candidate variant allele) divided by the total number of times the position was sequenced (e.g., a total number of reads covering a candidate locus).

[0109] As used herein, the terms “variant fragment count” and “variant allele fragment count” interchangeably refer to a quantification, e.g., a raw or normalized count, of the number of sequences representing unique cell-free DNA fragments encompassing a variant allele in a sequencing reaction. That is, a variant fragment count represents a count of sequence reads representing unique molecules in the liquid biopsy sample, after duplicate sequence reads in the raw sequencing data have been collapsed, e.g., through the use of unique molecular indices (UMI) and bagging, etc. as described herein.

[0110] As used herein, the term “germline variants” refers to genetic variants inherited from maternal and paternal DNA. Germline variants may be determined through a matched tumor-normal calling pipeline.

[0111] As used herein, the term “somatic variants” refers to variants arising as a result of dysregulated cellular processes associated with neoplastic cells, e.g., a mutation. Somatic variants may be detected via subtraction from a matched normal sample.

[0112] As used herein, the term “single nucleotide variant” or “SNV” refers to a substitution of one nucleotide to a different nucleotide at a position (e.g., site) of a nucleotide sequence, e.g., a sequence read from an individual. A substitution from a first nucleobase X to a second nucleobase Y may be denoted as “X>Y.” For example, a cytosine to thymine SNV may be denoted as “C>T.”

[0113] As used herein, the term “insertions and deletions” or “indels” refers to a variant resulting from the gain or loss of DNA base pairs within an analyzed region.

[0114] As used herein, the term “copy number variation” or “CNV” refers to the process by which large structural changes in a genome associated with tumor aneuploidy and other dysregulated repair systems are detected. These processes are used to detect large scale insertions or deletions of entire genomic regions. CNV is defined as structural insertions or deletions greater than a certain base pair (“bp”) in size, such as 500 bp.

[0115] As used herein, the term “gene fusion” refers to the product of large-scale chromosomal aberrations resulting in the creation of a chimeric protein. These expressed products can be non-functional, or they can be highly over or underactive. This can cause deleterious effects in cancer such as hyper-proliferative or anti-apoptotic phenotypes.

[0116] As used herein, the term “loss of heterozygosity” refers to the loss of one copy of a segment (e.g., including part or all of one or more genes) of the genome of a diploid subject (e.g., a human) or loss of one copy of a sequence encoding a functional gene product in the genome of the diploid subject, in a tissue, e.g., a cancerous tissue, of the subject. As used herein, when referring to a metric representing loss of heterozygosity across the entire genome of the subject, loss of heterozygosity is caused by the loss of one copy of various segments in the genome of the subject. Loss of heterozygosity across the entire genome may be estimated without sequencing the entire genome of a subject, and such methods for such estimations based on gene panel targeting-based sequencing methodologies are described in the art. Accordingly, in some embodiments, a metric representing loss of heterozygosityacross the entire genome of a tissue of a subject is represented as a single value, e.g., a percentage or fraction of the genome. In some cases, a tumor is composed of various sub- clonal populations, each of which may have a different degree of loss of heterozygosity across their respective genomes. Accordingly, in some embodiments, loss of heterozygosity across the entire genome of a cancerous tissue refers to an average loss of heterozygosity across a heterogeneous tumor population. As used herein, when referring to a metric for loss of heterozygosity in a particular gene, e.g., a DNA repair protein such as a protein involved in the homologous DNA recombination pathway (e.g., BRCA1 or BRCA2), loss of heterozygosity refers to complete or partial loss of one copy of the gene encoding the protein in the genome of the tissue and / or a mutation in one copy of the gene that prevents translation of a full-length gene product, e.g., a frameshift or truncating (creating a premature stop codon in the gene) mutation in the gene of interest. In some cases, a tumor is composed of various sub-clonal populations, each of which may have a different mutational status in a gene of interest. Accordingly, in some embodiments, loss of heterozygosity for a particular gene of interest is represented by an average value for loss of heterozygosity for the gene across all sequenced sub-clonal populations of the cancerous tissue. In other embodiments, loss of heterozygosity for a particular gene of interest is represented by a count of the number of unique incidences of loss of heterozygosity in the gene of interest across all sequenced sub- clonal populations of the cancerous tissue (e.g., the number of unique frame-shift and / or truncating mutations in the gene identified in the sequencing data).

[0117] As used herein, the term “microsatellites” refers to short, repeated sequences of DNA. The smallest nucleotide repeated unit of a microsatellite is referred to as the “repeated unit” or “repeat unit.” In some embodiments, the stability of a microsatellite locus is evaluated by comparing some metric of the distribution of the number of repeated units at a microsatellite locus to a reference number or distribution.

[0118] As used herein, the term “microsatellite instability” or “MSI” refers to a genetic hypermutability condition associated with various cancers that results from impaired DNA mismatch repair (MMR) in a subject. Among other phenotypes, MSI causes changes in the size of microsatellite loci, e.g., a change in the number of repeated units at microsatellite loci, during DNA replication. Accordingly, the size of microsatellite repeats is varied in MSI cancers as compared to the size of the corresponding microsatellite repeats in the germline of a cancer subject. The term “Microsatellite Instability-High” or “MSI-H” refers to a state of a cancer (e.g., a tumor) that has a significant MMR defect, resulting in microsatellite loci withsignificantly different lengths than the corresponding microsatellite loci in normal cells of the same individual. The term “Microsatellite Stable” or “MSS” refers to a state of a cancer (e.g., a tumor) without significant MMR defects, such that there is no significant difference between the lengths of the microsatellite loci in cancerous cells and the lengths of the corresponding microsatellite loci in normal (e.g., non-cancerous) cells in the same individual. The term “Microsatellite Equivocal” or “MSE” refers to a state of a cancer (e.g., a tumor) having an intermediate microsatellite length phenotype, that cannot be clearly classified as MSI-H or MSS based on statistical cutoffs used to define those two categories.

[0119] As used herein, the term “gene product” refers to an RNA (e.g., mRNA or miRNA) or protein molecule transcribed or translated from a particular genomic locus, e.g., a particular gene. The genomic locus can be identified using a gene name, a chromosomal location, or any other genetic mapping metric.

[0120] As used herein, the term “ratio” refers to any comparison of a first metric X, or a first mathematical transformation thereof Xʹ (e.g., measurement of a number of units of a genomic sequence in a first one or more biological samples or a first mathematical transformation thereof) to another metric Y or a second mathematical transformation thereof Yʹ (e.g., the number of units of a respective genomic sequence in a second one or more biological samples or a second mathematical transformation thereof) expressed as X / Y, Y / X, logN(X / Y), logN(Y / X), Xʹ / Y, Y / Xʹ, logN(Xʹ / Y), or logN(Y / Xʹ), X / Yʹ, Yʹ / X, logN(X / Yʹ), logN(Yʹ / X) , Xʹ / Yʹ, Yʹ / Xʹ, logN(Xʹ / Yʹ), or logN(Yʹ / Xʹ), where N is any real number greater than 1 and where example mathematical transformations of X and Y include, but are not limited to. raising X or Y to a power Z, multiplying X or Y by a constant Q, where Z and Q are any real numbers, and / or taking an M based logarithm of X and / or Y, where M is a real number greater than 1. In one non-limiting example, X is transformed to Xʹ prior to ratio calculation by raising X by the power of two (X2) and Y is transformed to Yʹ prior to ratio calculation by raising Y by the power of 3.2 (Y3.2) and the ratio of X and Y is computed as log2(Xʹ / Yʹ).

[0121] As used herein, the terms “expression level,” “abundance level,” or simply “abundance” refers to an amount of a gene product, (an RNA species, e.g., mRNA or miRNA, or protein molecule) transcribed or translated by a cell, or an average amount of a gene product transcribed or translated across multiple cells. When referring to mRNA or protein expression, the term generally refers to the amount of any RNA or protein species corresponding to a particular genomic locus, e.g., a particular gene. However, in someembodiments, an expression level can refer to the amount of a particular isoform of an mRNA or protein corresponding to a particular gene that gives rise to multiple mRNA or protein isoforms. The genomic locus can be identified using a gene name, a chromosomal location, or any other genetic mapping metric.

[0122] As used herein, the term “relative abundance” refers to a ratio of a first amount of a compound measured in a sample, e.g., a gene product (an RNA species, e.g., mRNA or miRNA, or protein molecule) or nucleic acid fragments having a particular characteristic (e.g., aligning to a particular locus or encompassing a particular allele), to a second amount of a compound measured in a second sample. In some embodiments, relative abundance refers to a ratio of an amount of species of a compound to a total amount of the compound in the same sample. For instance, a ratio of the amount of mRNA transcripts encoding a particular gene in a sample (e.g., aligning to a particular region of the exome) to the total amount of mRNA transcripts in the sample. In other embodiments, relative abundance refers to a ratio of an amount of a compound or species of a compound in a first sample to an amount of the compound of the species of the compound in a second sample. For instance, a ratio of a normalized amount of mRNA transcripts encoding a particular gene in a first sample to a normalized amount of mRNA transcripts encoding the particular gene in a second and / or reference sample.

[0123] As used herein, the terms “sequencing,” “sequence determination,” and the like refer to any biochemical processes that may be used to determine the order of biological macromolecules such as nucleic acids or proteins. For example, sequencing data can include all or a portion of the nucleotide bases in a nucleic acid molecule such as an mRNA transcript or a genomic locus.

[0124] As used herein, the term “genetic sequence” refers to a recordation of a series of nucleotides present in a subject’s RNA or DNA as determined by sequencing of nucleic acids from the subject.

[0125] As used herein, the term “sequence reads” or “reads” refers to nucleotide sequences produced by any nucleic acid sequencing process described herein or known in the art. Reads can be generated from one end of nucleic acid fragments (“single-end reads”) or from both ends of nucleic acid fragments (e.g., paired-end reads, double-end reads). The length of the sequence read is often associated with the particular sequencing technology. High-throughput methods, for example, provide sequence reads that can vary in size fromtens to hundreds of base pairs (bp). In some embodiments, the sequence reads are of a mean, median or average length of about 15 bp to 900 bp long (e.g., about 20 bp, about 25 bp, about 30 bp, about 35 bp, about 40 bp, about 45 bp, about 50 bp, about 55 bp, about 60 bp, about 65 bp, about 70 bp, about 75 bp, about 80 bp, about 85 bp, about 90 bp, about 95 bp, about 100 bp, about 110 bp, about 120 bp, about 130, about 140 bp, about 150 bp, about 200 bp, about 250 bp, about 300 bp, about 350 bp, about 400 bp, about 450 bp, or about 500 bp. In some embodiments, the sequence reads are of a mean, median or average length of about 1000 bp, 2000 bp, 5000 bp, 10,000 bp, or 50,000 bp or more. Nanopore® sequencing, for example, can provide sequence reads that can vary in size from tens to hundreds to thousands of base pairs. Illumina® parallel sequencing, for example, can provide sequence reads that do not vary as much, for example, most of the sequence reads can be smaller than 200 bp. A sequence read (or sequencing read) can refer to sequence information corresponding to a nucleic acid molecule (e.g., a string of nucleotides). For example, a sequence read can correspond to a string of nucleotides (e.g., about 20 to about 150) from part of a nucleic acid fragment, can correspond to a string of nucleotides at one or both ends of a nucleic acid fragment, or can correspond to nucleotides of the entire nucleic acid fragment. A sequence read can be obtained in a variety of ways, e.g., using sequencing techniques or using probes, e.g., in hybridization arrays or capture probes, or amplification techniques, such as the polymerase chain reaction (PCR) or linear amplification using a single primer or isothermal amplification.

[0126] As used herein, the term “read segment” refers to any form of nucleotide sequence read including the raw sequence reads obtained directly from a nucleic acid sequencing technique or from a sequence derived therefrom, e.g., an aligned sequence read, a collapsed sequence read, or a stitched sequence read.

[0127] As used herein, the term “read count” refers to the total number of nucleic acid reads generated, which may or may not be equivalent to the number of nucleic acid molecules generated, during a nucleic acid sequencing reaction.

[0128] As used herein, the term “read-depth,” “sequencing depth,” or “depth” can refer to a total number of unique nucleic acid fragments encompassing a particular locus or region of the genome of a subject that are sequenced in a particular sequencing reaction. Sequencing depth can be expressed as “Yx”, e.g., 50x, 100x, etc., where “Y” refers to the number of unique nucleic acid fragments encompassing a particular locus that are sequenced in a sequencing reaction. In such a case, Y is necessarily an integer, because it represents theactual sequencing depth for a particular locus. Alternatively, read-depth, sequencing depth, or depth can refer to a measure of central tendency (e.g., a mean or mode) of the number of unique nucleic acid fragments that encompass one of a plurality of loci or regions of the genome of a subject that are sequenced in a particular sequencing reaction. For example, in some embodiments, sequencing depth refers to the average depth of every locus across an arm of a chromosome, a targeted sequencing panel, an exome, or an entire genome. In such case, Y may be expressed as a fraction or a decimal, because it refers to an average coverage across a plurality of loci. When a mean depth is recited, the actual depth for any particular locus may be different than the overall recited depth. Metrics can be determined that provide a range of sequencing depths in which a defined percentage of the total number of loci fall. For instance, a range of sequencing depths within which 90% or 95%, or 99% of the loci fall. As understood by the skilled artisan, different sequencing technologies provide different sequencing depths. For instance, low-pass whole genome sequencing can refer to technologies that provide a sequencing depth of less than 5x, less than 4x, less than 3x, or less than 2x, e.g., from about 0.5x to about 3x.

[0129] As used herein, the term “sequencing breadth” refers to what fraction of a particular reference exome (e.g., human reference exome), a particular reference genome (e.g., human reference genome), or part of the exome or genome has been analyzed. Sequencing breadth can be expressed as a fraction, a decimal, or a percentage, and is generally calculated as (the number of loci analyzed / the total number of loci in a reference exome or reference genome). The denominator of the fraction can be a repeat-masked genome, and thus 100% can correspond to all of the reference genome minus the masked parts. A repeat-masked exome or genome can refer to an exome or genome in which sequence repeats are masked (e.g., sequence reads align to unmasked portions of the exome or genome). In some embodiments, any part of an exome or genome can be masked and, thus, sequencing breadth can be evaluated for any desired portion of a reference exome or genome. In some embodiments, “broad sequencing” refers to sequencing / analysis of at least 0.1% of an exome or genome.

[0130] As used herein, the term “sequencing probe” refers to a molecule that binds to a nucleic acid with affinity that is based on the expected nucleotide sequence of the RNA or DNA present at that locus.

[0131] As used herein, the term “targeted panel” or “targeted gene panel” refers to a combination of probes for sequencing (e.g., by next-generation sequencing) nucleic acidspresent in a biological sample from a subject (e.g., a tumor sample, liquid biopsy sample, germline tissue sample, white blood cell sample, or tumor or tissue organoid sample), selected to map to one or more loci of interest on one or more chromosomes. An example set of loci / genes useful for precision oncology, e.g., via solid or liquid biopsy assay, that can be analyzed using a targeted panel is described in Lists 1-6. In some embodiments, in addition to loci that are informative for precision oncology, a targeted panel includes one or more probes for sequencing one or more of a locus associated with a different medical condition, a locus used for internal control purposes, or a locus from a pathogenic organism (e.g., an oncogenic pathogen).

[0132] As used herein, the term, “reference exome” refers to any sequenced or otherwise characterized exome, whether partial or complete, of any tissue from any organism or pathogen that may be used to reference identified sequences from a subject. Typically, a reference exome will be derived from a subject of the same species as the subject whose sequences are being evaluated. Example reference exomes used for human subjects as well as many other organisms are provided in the on-line genome browser hosted by the National Center for Biotechnology Information (“NCBI”). An “exome” refers to the complete transcriptional profile of an organism or pathogen, expressed in nucleic acid sequences. As used herein, a reference sequence or reference exome often is an assembled or partially assembled exomic sequence from an individual or multiple individuals. In some embodiments, a reference exome is an assembled or partially assembled exomic sequence from one or more human individuals. The reference exome can be viewed as a representative example of a species’ set of expressed genes. In some embodiments, a reference exome comprises sequences assigned to chromosomes.

[0133] As used herein, the term “reference genome” refers to any sequenced or otherwise characterized genome, whether partial or complete, of any organism or pathogen that may be used to reference identified sequences from a subject. Typically, a reference genome will be derived from a subject of the same species as the subject whose sequences are being evaluated. Exemplary reference genomes used for human subjects as well as many other organisms are provided in the on-line genome browser hosted by the National Center for Biotechnology Information (“NCBI”) or the University of California, Santa Cruz (UCSC). A “genome” refers to the complete genetic information of an organism or pathogen, expressed in nucleic acid sequences. As used herein, a reference sequence or reference genome often is an assembled or partially assembled genomic sequence from an individual or multipleindividuals. In some embodiments, a reference genome is an assembled or partially assembled genomic sequence from one or more human individuals. The reference genome can be viewed as a representative example of a species’ set of genes. In some embodiments, a reference genome comprises sequences assigned to chromosomes. Exemplary human reference genomes include but are not limited to NCBI build 34 (UCSC equivalent: hg16), NCBI build 35 (UCSC equivalent: hg17), NCBI build 36.1 (UCSC equivalent: hg18), GRCh37 (UCSC equivalent: hg19), and GRCh38 (UCSC equivalent: hg38). For a haploid genome, there can be only one nucleotide at each locus. For a diploid genome, heterozygous loci can be identified; each heterozygous locus can have two alleles, where either allele can allow a match for alignment to the locus.

[0134] As used herein, the term “bioinformatics pipeline” refers to a series of processing stages used to determine characteristics of a subject’s genome or exome based on sequencing data of the subject’s genome or exome. A bioinformatics pipeline may be used to determine characteristics of a germline genome or exome of a subject and / or a cancer genome or exome of a subject. In some embodiments, the pipeline extracts information related to genomic alterations in the cancer genome of a subject, which is useful for guiding clinical decisions for precision oncology, from sequencing results of a biological sample, e.g., a tumor sample, liquid biopsy sample, reference normal sample, etc., from the subject. Certain processing stages in a bioinformatics may be ‘connected,’ meaning that the results of a first respective processing stage are informative and / or essential for execution of a second, downstream processing stage. For instance, in some embodiments, a bioinformatics pipeline includes a first respective processing stage for identifying genomic alterations that are unique to the cancer genome of a subject and a second respective processing stage that uses the quantity and / or identity of the identified genomic alterations to determine a metric that is informative for precision oncology, e.g., a tumor mutational burden. In some embodiments, the bioinformatics pipeline includes a reporting stage that generates a report of relevant and / or actionable information identified by upstream stages of the pipeline, which may or may not further include recommendations for aiding clinical therapy decisions.

[0135] As used herein, the term “limit of detection” or “LOD” refers to the minimal quantity of a feature that can be identified with a particular level of confidence. Accordingly, level of detection can be used to describe an amount of a substance that must be present in order for a particular assay to reliably detect the substance. A level of detection can also be used to describe a level of support needed for an algorithm to reliably identify a genomicalteration based on sequencing data. For example, a minimal number of unique sequence reads to support identification of a sequence variant such as a SNV.

[0136] As used herein, the term “BAM File” or “Binary file containing Alignment Maps” refers to a file storing sequencing data aligned to a reference sequence (e.g., a reference genome or exome). In some embodiments, a BAM file is a compressed binary version of a SAM (Sequence Alignment Map) file that includes, for each of a plurality of unique sequence reads, an identifier for the sequence read, information about the nucleotide sequence, information about the alignment of the sequence to a reference sequence, and optionally metrics relating to the quality of the sequence read and / or the quality of the sequence alignment. While BAM files generally relate to files having a particular format, for simplicity they are used herein to simply refer to a file, of any format, containing information about a sequence alignment, unless specifically stated otherwise.

[0137] As used herein, the term “measure of central tendency” refers to a central or representative value for a distribution of values. Non-limiting examples of measures of central tendency include an arithmetic mean, weighted mean, midrange, midhinge, trimean, geometric mean, geometric median, Winsorized mean, median, and mode of the distribution of values.

[0138] As used herein, the term “Positive Predictive Value” or “PPV” means the likelihood that a variant is properly called given that a variant has been called by an assay. PPV can be expressed as (number of true positives) / (number of false positives + number of true positives).

[0139] As used herein, the term “assay” refers to a technique for determining a property of a substance, e.g., a nucleic acid, a protein, a cell, a tissue, or an organ. An assay (e.g., a first assay or a second assay) can comprise a technique for determining the copy number variation of nucleic acids in a sample, the methylation status of nucleic acids in a sample, the fragment size distribution of nucleic acids in a sample, the mutational status of nucleic acids in a sample, or the fragmentation pattern of nucleic acids in a sample. Any assay known to a person having ordinary skill in the art can be used to detect any of the properties of nucleic acids mentioned herein. Properties of a nucleic acids can include a sequence, genomic identity, copy number, methylation state at one or more nucleotide positions, size of the nucleic acid, presence or absence of a mutation in the nucleic acid at one or more nucleotide positions, and pattern of fragmentation of a nucleic acid (e.g., the nucleotide position(s) atwhich a nucleic acid fragments). An assay or method can have a particular sensitivity and / or specificity, and their relative usefulness as a diagnostic tool can be measured using ROC- AUC statistics.

[0140] As used herein, the term “classification” can refer to any number(s) or other characters(s) that are associated with a particular property of a sample. For example, in some embodiments, the term “classification” can refer to a type of cancer in a subject, a stage of cancer in a subject, a prognosis for a cancer in a subject, a tumor load, a presence of tumor metastasis in a subject, and the like. The classification can be binary (e.g., positive or negative) or have more levels of classification (e.g., a scale from 1 to 10 or 0 to 1). The terms “cutoff” and “threshold” can refer to predetermined numbers used in an operation. For example, a cutoff size can refer to a size above which fragments are excluded. A threshold value can be a value above or below which a particular classification applies. Either of these terms can be used in either of these contexts.

[0141] As used herein, the term “sensitivity” or “true positive rate” (TPR) refers to the number of true positives divided by the sum of the number of true positives and false negatives. Sensitivity can characterize the ability of an assay or method to correctly identify a proportion of the population that truly has a condition. For example, sensitivity can characterize the ability of a method to correctly identify the number of subjects within a population having cancer. In another example, sensitivity can characterize the ability of a method to correctly identify the one or more markers indicative of cancer.

[0142] As used herein, the term “specificity” or “true negative rate” (TNR) refers to the number of true negatives divided by the sum of the number of true negatives and false positives. Specificity can characterize the ability of an assay or method to correctly identify a proportion of the population that truly does not have a condition. For example, specificity can characterize the ability of a method to correctly identify the number of subjects within a population not having cancer. In another example, specificity characterizes the ability of a method to correctly identify one or more markers indicative of cancer.

[0143] As used herein, an “actionable genomic alteration” or “actionable variant” refers to a genomic alteration (e.g., a SNV, MNV, indel, rearrangement, copy number variation, or ploidy variation), or value of another cancer metric derived from nucleic acid sequencing data (e.g., a tumor mutational burden, MSI status, or tumor fraction), that is known or believed to be associated with a therapeutic course of action that is more likely to produce a positiveeffect in a cancer patient that has the actionable variant than in a similarly situated cancer patient that does not have the actionable variant. For instance, administration of EGFR inhibitors (e.g., afatinib, erlotinib, gefitinib) is more effective for treating non-small cell lung cancer in patients with an EGFR mutation in exons 19 / 21 than for treating non-small cell lung cancer in patients that do not have an EGFR mutation in exons 19 / 21. Accordingly, an EGFR mutation in exon 19 / 21 is an actionable variant. In some instances, an actionable variant is only associated with an improved treatment outcome in one or a group of specific cancer types. In other instances, an actionable variant is associated with an improved treatment outcome in substantially all cancer types.

[0144] As used herein, a “variant of uncertain significance” or “VUS” refers to a genomic alteration (e.g., a SNV, MNV, indel, rearrangement, copy number variation, or ploidy variation), or value of another cancer metric derived from nucleic acid sequencing data (e.g., a tumor mutational burden, MSI status, or tumor fraction), whose impact on disease development / progression is unknown.

[0145] As used herein, a “benign variant” or “likely benign variant” refers to a genomic alteration (e.g., a SNV, MNV, indel, rearrangement, copy number variation, or ploidy variation), or value of another cancer metric derived from nucleic acid sequencing data (e.g., a tumor mutational burden, MSI status, or tumor fraction), that is known or believed to not contribute to disease development / progression.

[0146] As used herein, a “pathogenic variant” or “likely pathogenic variant” refers to a genomic alteration (e.g., a SNV, MNV, indel, rearrangement, copy number variation, or ploidy variation), or value of another cancer metric derived from nucleic acid sequencing data (e.g., a tumor mutational burden, MSI status, or tumor fraction), that is known or believed to contribute to disease development / progression.

[0147] As used herein, an “effective amount” or “therapeutically effective amount” is an amount sufficient to affect a beneficial or desired clinical result upon treatment. An effective amount can be administered to a subject in one or more doses. In terms of treatment, an effective amount is an amount that is sufficient to palliate, ameliorate, stabilize, reverse or slow the progression of the disease, or otherwise reduce the pathological consequences of the disease. The effective amount is generally determined by the physician on a case-by-case basis and is within the skill of one in the art. Several factors are typically taken into account when determining an appropriate dosage to achieve an effective amount. These factorsinclude age, sex and weight of the subject, the condition being treated, the severity of the condition and the form and effective concentration of the therapeutic agent being administered.

[0148] The terminology used in the present disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the description of the invention and the appended claims, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and / or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “comprises” and / or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. Furthermore, to the extent that the terms “including,” “includes,” “having,” “has,” “with,” or variants thereof are used in either the detailed description and / or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising.”

[0149] As used herein, the term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” may be construed to mean “upon determining” or “in response to determining” or “upon detecting [the stated condition or event]” or “in response to detecting [the stated condition or event],” depending on the context.

[0150] It will also be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first subject could be termed a second subject, and, similarly, a second subject could be termed a first subject, without departing from the scope of the present disclosure. The first subject and the second subject are both subjects, but they are not the same subject. Furthermore, the terms “subject,” “user,” and “patient” are used interchangeably herein.

[0151] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the presentdisclosure, including example systems, methods, techniques, instruction sequences, and computing machine program products that embody illustrative implementations. However, the illustrative discussions below are not intended to be exhaustive or to limit the implementations to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The features described herein are not limited by the illustrated ordering of acts or events, as some acts can occur in different orders and / or concurrently with other acts or events.

[0152] The implementations provided herein are chosen and described in order to best explain the principles and their practical applications, to thereby enable others skilled in the art to best utilize the various embodiments with various modifications as are suited to the particular use contemplated. In some instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments. In other instances, it will be apparent to one of ordinary skill in the art that the present disclosure may be practiced without one or more of the specific details.

[0153] It will be appreciated that, in the development of any such actual implementation, numerous implementation-specific decisions are made in order to achieve the designer’s specific goals, such as compliance with use case- and business-related constraints, and that these specific goals will vary from one implementation to another and from one designer to another. Moreover, it will be appreciated that though such a design effort might be complex and time-consuming, it will nevertheless be a routine undertaking of engineering for those of ordering skill in the art having the benefit of the present disclosure. Example System Embodiments

[0154] Now that an overview of some aspects of the present disclosure and some definitions used in the present disclosure have been provided, details of an exemplary system for providing clinical support for personalized cancer therapy using a liquid biopsy assay are now described in conjunction with Figures 1A-B. Figures 1A-B collectively illustrate the topology of an example system for providing clinical support for personalized cancer therapy using a liquid biopsy assay, in accordance with some embodiments of the present disclosure. Advantageously, the example system illustrated in Figures 1A-B improves upon conventional methods for providing clinical support for personalized cancer therapy by validating a somatic sequence variant in a test subject having a cancer condition.

[0155] Figure 1A is a block diagram illustrating a system in accordance with some implementations. The device 100 in some implementations includes one or more processing units CPU(s) 102 (also referred to as processors), one or more network interfaces 104, a user interface 106, e.g., including a display 108 and / or an input 110 (e.g., a mouse, touchpad, keyboard, etc.), a non-persistent memory 111, a persistent memory 112, and one or more communication buses 114 for interconnecting these components. The one or more communication buses 114 optionally include circuitry (sometimes called a chipset) that interconnects and controls communications between system components. The non-persistent memory 111 typically includes high-speed random access memory, such as DRAM, SRAM, DDR RAM, ROM, EEPROM, flash memory, whereas the persistent memory 112 typically includes CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid state storage devices. The persistent memory 112 optionally includes one or more storage devices remotely located from the CPU(s) 102. The persistent memory 112, and the non-volatile memory device(s) within the non-persistent memory 112, comprise non- transitory computer readable storage medium. In some implementations, the non-persistent memory 111 or alternatively the non-transitory computer readable storage medium stores the following programs, modules and data structures, or a subset thereof, sometimes in conjunction with the persistent memory 112: • an operating system 116, which includes procedures for handling various basic system services and for performing hardware dependent tasks; • a network communication module (or instructions) 118 for connecting the system 100 with other devices and / or a communication network 105; • a test patient data store 120 for storing one or more collections of features from patients (e.g., subjects); • a bioinformatics module 140 for processing sequencing data and extracting features from sequencing data, e.g., from liquid biopsy sequencing assays; • a feature analysis module 160 for evaluating patient features, e.g., genomic alterations, compound genomic features, and clinical features; and • a reporting module 180 for generating and transmitting reports that provide clinical support for personalized cancer therapy.

[0156] Although Figures 1A-B depict a “system 100,” the figures are intended more as a functional description of the various features that may be present in computer systems than as a structural schematic of the implementations described herein. In practice, and as recognized by those of ordinary skill in the art, items shown separately could be combined and some items could be separated. Moreover, although Figure 1 depicts certain data and modules in non-persistent memory 111, some or all of these data and modules may be in persistent memory 112. For example, in various implementations, one or more of the above identified elements are stored in one or more of the previously mentioned memory devices and correspond to a set of instructions for performing a function described above. The above identified modules, data, or programs (e.g., sets of instructions) need not be implemented as separate software programs, procedures, datasets, or modules, and thus various subsets of these modules and data may be combined or otherwise re-arranged in various implementations.

[0157] In some implementations, the non-persistent memory 111 optionally stores a subset of the modules and data structures identified above. Furthermore, in some embodiments, the memory stores additional modules and data structures not described above. In some embodiments, one or more of the above-identified elements is stored in a computer system, other than that of system 100, that is addressable by system 100 so that system 100 may retrieve all or a portion of such data when needed.

[0158] For purposes of illustration in Figure 1A, system 100 is represented as a single computer that includes all of the functionality for providing clinical support for personalized cancer therapy. However, while a single machine is illustrated, the term “system” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

[0159] For example, in some embodiments, system 100 includes one or more computers. In some embodiments, the functionality for providing clinical support for personalized cancer therapy is spread across any number of networked computers and / or resides on each of several networked computers and / or is hosted on one or more virtual machines at a remote location accessible across the communications network 105. For example, different portions of the various modules and data stores illustrated in Figures 1A-B can be stored and / or executed on the various instances of a processing device and / or processing server / database inthe distributed diagnostic environment 210 illustrated in Figure 2B (e.g., processing devices 224, 234, 244, and 254, processing server 262, and database 264).

[0160] The system may operate in the capacity of a server or a client machine in client- server network environment, as a peer machine in a peer-to-peer (or distributed) network environment, or as a server or a client machine in a cloud computing infrastructure or environment. The system may be a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a server, a network router, a switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine.

[0161] In another implementation, the system comprises a virtual machine that includes a module for executing instructions for performing any one or more of the methodologies disclosed herein. In computing, a virtual machine (VM) is an emulation of a computer system that is based on computer architectures and provides functionality of a physical computer. Some such implementations may involve specialized hardware, software, or a combination of hardware and software.

[0162] One of skill in the art will appreciate that any of a wide array of different computer topologies are used for the application and all such topologies are within the scope of the present disclosure. Test Patient Data Store (120)

[0163] Referring to Figure 1B, in some embodiments, the system (e.g., system 100) includes a patient data store 120 that stores data for patients 121-1 to 121-M (e.g., cancer patients or patients being tested for cancer) including one or more sequencing data 122, feature data 125, and clinical assessments 139. These data are used and / or generated by the various processes stored in the bioinformatics module 140 and feature analysis module 160 of system 100, to ultimately generate a report providing clinical support for personalized cancer therapy of a patient. While the feature scope of patient data 121 across all patients may be informationally dense, an individual patient’s feature set may be sparsely populated across the entirety of the collective feature scope of all features across all patients. That is to say, the data stored for one patient may include a different set of features that the data stored for another patient. Further, while illustrated as a single data construct in Figure 1B, different sets of patient data may be stored in different databases or modules spread across one or more system memories.

[0164] In some embodiments, sequencing data 122 from one or more sequencing reactions 122-i, including a plurality of sequence reads 123-i-1 to 123-i-K, is stored in the test patient data store 120. The data store may include different sets of sequencing data from a single subject, corresponding to different samples from the patient, e.g., a tumor sample, liquid biopsy sample, tumor organoid derived from a patient tumor, and / or a normal sample, and / or to samples acquired at different times, e.g., while monitoring the progression, regression, remission, and / or recurrence of a cancer in a subject. The sequence reads may be in any suitable file format, e.g., BCL, FASTA, FASTQ, etc. In some embodiments, sequencing data 122 is accessed by a sequencing data processing module 141, which performs various pre-processing, genome alignment, and demultiplexing operations, as described in detail below with reference to bioinformatics module 140. In some embodiments, sequence data that has been aligned to a reference construct, e.g., BAM file 124, is stored in test patient data store 120.

[0165] In some embodiments, the test patient data store 120 includes feature data 125, e.g., that is useful for identifying clinical support for personalized cancer therapy. In some embodiments, the feature data 125 includes personal characteristics 126 of the patient, such as patient name, date of birth, gender, ethnicity, physical address, smoking status, alcohol consumption characteristic, anthropomorphic data, etc.

[0166] In some embodiments, the feature data 125 includes medical history data 127 for the patient, such as cancer diagnosis information (e.g., date of initial diagnosis, date of metastatic diagnosis, cancer staging, tumor characterization, tissue of origin, previous treatments and outcomes, adverse effects of therapy, therapy group history, clinical trial history, previous and current medications, surgical history, etc.), previous or current symptoms, previous or current therapies, previous treatment outcomes, previous disease diagnoses, diabetes status, diagnoses of depression, diagnoses of other physical or mental maladies, and family medical history. In some embodiments, the feature data 125 includes clinical features 128, such as pathology data 128-1, medical imaging data 128-2, and tissue culture and / or tissue organoid culture data 128-3.

[0167] In some embodiments, yet other clinical features, such as previous laboratory testing results, are stored in the test patient data store 120. Medical history data 127 and clinical features may be collected from various sources, including at intake directly from the patient, from an electronic medical record (EMR) or electronic health record (EHR) for thepatient, or curated from other sources, such as fields from various testing records (e.g., genetic sequencing reports).

[0168] In some embodiments, the feature data 125 includes genomic features 131 for the patient. Non-limiting examples of genomic features include allelic states 132 (e.g., the identity of alleles at one or more loci, support for wild type or variant alleles at one or more loci, support for SNVs / MNVs at one or more loci, support for indels at one or more loci, and / or support for gene rearrangements at one or more loci), allelic fractions 133 (e.g., ratios of variant to reference alleles (or vice versa), methylation states 132 (e.g., a distribution of methylation patterns at one or more loci and / or support for aberrant methylation patterns at one or more loci), genomic copy numbers 135 (e.g., a copy number value at one or more loci and / or support for an aberrant (increased or decreased) copy number at one or more loci), tumor mutational burden 136 (e.g., a measure of the number of mutations in the cancer genome of the subject), and microsatellite instability status 137 (e.g., a measure of the repeated unit length at one or more microsatellite loci and / or a classification of the MSI status for the patient’s cancer). In some embodiments, one or more of the genomic features 131 are determined by a nucleic acid bioinformatics pipeline, e.g., as described in detail below with reference to Figures 4A-4E. In particular, in some embodiments, the feature data 125 include variant allele fractions 133, as determined using improved methods for validating somatic sequence variants. In some embodiments, one or more of the genomic features 131 are obtained from an external testing source, e.g., not connected to the bioinformatics pipeline as described below.

[0169] In some embodiments, the feature data 125 further includes data 138 from other -omics fields of study. Non-limiting examples of -omics fields of study that may yield feature data useful for providing clinical support for personalized cancer therapy include transcriptomics, epigenomics, proteomics, metabolomics, metabonomics, microbiomics, lipidomics, glycomics, cellomics, and organoidomics.

[0170] In some embodiments, yet other features may include features derived from machine learning approaches, e.g., based at least in part on evaluation of any relevant molecular or clinical features, considered alone or in combination, not limited to those listed above. For instance, in some embodiments, one or more latent features learned from evaluation of cancer patient training datasets improve the diagnostic and prognostic power of the various analysis algorithms in the feature analysis module 160.

[0171] The skilled artisan will know of other types of features useful for providing clinical support for personalized cancer therapy. The listing of features above is merely representative and should not be construed to be limiting.

[0172] In some embodiments, a test patient data store 120 includes clinical assessment data 139 for patients, e.g., based on the feature data 125 collected for the subject. In some embodiments, the clinical assessment data 139 includes a catalogue of actionable variants and characteristics 139-1 (e.g., genomic alterations and compound metrics based on genomic features known or believed to be targetable by one or more specific cancer therapies), matched therapies 139-2 (e.g., the therapies known or believed to be particularly beneficial for treatment of subjects having actionable variants), and / or clinical reports 139-3 generated for the subject, e.g., based on identified actionable variants and characteristics 139-1 and / or matched therapies 139-2.

[0173] In some embodiments, clinical assessment data 139 is generated by analysis of feature data 125 using the various algorithms of feature analysis module 160, as described in further detail below. In some embodiments, clinical assessment data 139 is generated, modified, and / or validated by evaluation of feature data 125 by a clinician, e.g., an oncologist. For instance, in some embodiments, a clinician (e.g., at clinical environment 220) uses feature analysis module 160, or accesses test patient data store 120 directly, to evaluate feature data 125 to make recommendations for personalized cancer treatment of a patient. Similarly, in some embodiments, a clinician (e.g., at clinical environment 220) reviews recommendations determined using feature analysis module 160 and approves, rejects, or modifies the recommendations, e.g., prior to the recommendations being sent to a medical professional treating the cancer patient. Bioinformatics Module (140)

[0174] Referring again to Figure 1A, the system (e.g., system 100) includes a bioinformatics module 140 that includes a feature extraction module 145 and optional ancillary data processing constructs, such as a sequence data processing module 141 and / or one or more reference sequence constructs 158 (e.g., a reference genome, exome, or targeted- panel construct that includes reference sequences for a plurality of loci targeted by a sequencing panel).

[0175] In some embodiments, bioinformatics module 140 includes a sequence data processing module 141 that includes instructions for processing sequence reads, e.g., rawsequence reads 123 from one or more sequencing reactions 122, prior to analysis by the various feature extraction algorithms, as described in detail below. In some embodiments, sequence data processing module 141 includes one or more pre-processing algorithms 142 that prepare the data for analysis. In some embodiments, the pre-processing algorithms 142 include instructions for converting the file format of the sequence reads from the output of the sequencer (e.g., a BCL file format) into a file format compatible with downstream analysis of the sequences (e.g., a FASTQ or FASTA file format). In some embodiments, the pre-processing algorithms 142 include instructions for evaluating the quality of the sequence reads (e.g., by interrogating quality metrics like Phred score, base-calling error probabilities, Quality (Q) scores, and the like) and / or removing sequence reads that do not satisfy a threshold quality (e.g., an inferred base call accuracy of at least 80%, at least 90%, at least 95%, at least 99%, at least 99.5%, at least 99.9%, or higher). In some embodiments, the pre- processing algorithms 142 include instructions for filtering the sequence reads for one or more properties, e.g., removing sequences failing to satisfy a lower or upper size threshold or removing duplicate sequence reads.

[0176] In some embodiments, sequence data processing module 141 includes one or more alignment algorithms 143, for aligning pre-processed sequence reads 123 to a reference sequence construct 158, e.g., a reference genome, exome, or targeted-panel construct. Many algorithms for aligning sequencing data to a reference construct are known in the art, for example, BWA, Blat, SHRiMP, LastZ, and MAQ. One example of a sequence read alignment package is the Burrows-Wheeler Alignment tool (BWA), which uses a Burrows- Wheeler Transform (BWT) to align short sequence reads against a large reference construct, allowing for mismatches and gaps. Li and Durbin, Bioinformatics, 25(14):1754-60 (2009), the content of which is incorporated herein by reference, in its entirety, for all purposes. Sequence read alignment packages import raw or pre-processed sequence reads 122, e.g., in BCL, FASTA, or FASTQ file formats, and output aligned sequence reads 124, e.g., in SAM or BAM file formats.

[0177] In some embodiments, sequence data processing module 141 includes one or more demultiplexing algorithms 144, for dividing sequence read or sequence alignment files generated from sequencing reactions of pooled nucleic acids into separate sequence read or sequence alignment files, each of which corresponds to a different source of nucleic acids in the nucleic acid sequencing pool. For instance, because of the cost of sequencing reactions, it is common practice to pool nucleic acids from a plurality of samples into a single sequencingreaction. The nucleic acids from each sample are tagged with a sample-specific and / or molecule-specific sequence tag (e.g., a UMI), which is sequenced along with the molecule. In some embodiments, demultiplexing algorithms 144 sort these sequence tags in the sequence read or sequence alignment files to demultiplex the sequencing data into separate files for each of the samples included in the sequencing reaction.

[0178] Bioinformatics module 140 includes a feature extraction module 145, which includes instructions for identifying diagnostic features, e.g., genomic features 131, from sequencing data 122 of biological samples from a subject, e.g., one or more of a solid tumor sample, a liquid biopsy sample, or a normal tissue (e.g., control) sample. For instance, in some embodiments, a feature extraction algorithm compares the identity of one or more nucleotides at a locus from the sequencing data 122 to the identity of the nucleotides at that locus in a reference sequence construct (e.g., a reference genome, exome, or targeted-panel construct) to determine whether the subject has a variant at that locus. In some embodiments, a feature extraction algorithm evaluates data other than the raw sequence, to identify a genomic alteration in the subject, e.g., an allelic ratio, a relative copy number, a repeat unit distribution, etc.

[0179] For instance, in some embodiments, feature extraction module 145 includes one or more variant identification modules that include instructions for various variant calling processes. In some embodiments, variants in the germline of the subject are identified, e.g., using a germline variant identification module 146. In some embodiments, variants in the cancer genome, e.g., somatic variants, are identified, e.g., using a somatic variant identification module 150. While separate germline and somatic variant identification modules are illustrated in Figure 1A, in some embodiments they are integrated into a single module. In some embodiments, the variant identification module includes instructions for identifying one or more of nucleotide variants (e.g., single nucleotide variants (SNV) and multi-nucleotide variants (MNV)) using one or more SNV / MNV calling algorithms (e.g., algorithms 147 and / or 151), indels (e.g., insertions or deletions of nucleotides) using one or more indel calling algorithms (e.g., algorithms 148 and / or 152), and genomic rearrangements (e.g., inversions, translocation, and fusions of nucleotide sequences) using one or more genomic rearrangement calling algorithms (e.g., algorithms 149 and / or 153).

[0180] For example, in some embodiments, feature extraction module 145 comprises, in the variant identification module 146, a variant thresholding module 146-a, a sequence variant data store 146-r, and a variant validation module 146-o. In some such embodiments,the sequence variant data store 146-r comprises one or more candidate variants for a test subject identified by aligning to a reference sequence a plurality of sequence reads obtained from sequencing a liquid biopsy sample of the test subject, the one or more candidate variants corresponding to a respective one or more loci in the reference sequence. The plurality of sequence reads aligned to the reference sequence is used to identify a variant allele fragment count for each candidate variant. The sequence variant data store 146-r further comprises, in some embodiments, a plurality of variants from a first set of nucleic acids obtained from a cohort of subjects (e.g., from a tumor tissue biopsy for each subject in a baseline cohort of subjects). The variant thresholding module 146-a performs a function for each candidate variant in the one or more candidate variants where, for each corresponding locus 146-b (e.g., 146-b-1,…, 146-b-P), a dynamic variant count threshold 146-d (e.g., 146-d-1) is obtained based on a pre-test odds of a positive variant call for the locus, based on the prevalence of variants in the genomic region that includes the locus, using the plurality of variants for the baseline cohort. The variant thresholding module 146-a compares the variant allele fragment count 146-c (e.g., 146-c-1) for the candidate variant against the dynamic variant count threshold 146-d for the locus corresponding to the candidate variant. In some embodiments, the variant validation module 146-o determines whether the candidate variant is validated or rejected as a somatic sequence variant based on the comparison. For example, when the variant allele fragment count for the candidate variant satisfies the dynamic variant count threshold for the locus, the somatic sequence variant is validated, and when the variant allele fragment count for the candidate variant does not satisfy the dynamic variant count threshold for the locus, the somatic sequence variant is rejected.

[0181] In some embodiments, the dynamic variant count threshold is determined based on a distribution of variant detection sensitivities as a function of circulating variant allele fraction from the cohort of subjects (e.g., the baseline cohort). For example, in some such embodiments, the variant thresholding module 146-a takes as input one or more variant allele fractions 133 from the genomic features module 131. In some such embodiments, the variant allele fractions 133 comprises a plurality of variant allele fractions obtained from tumor tissue biopsies 133-t (e.g., 133-t-1, 133-t-2…, 133-t-O) for the cohort of subjects. In some embodiments, the variant allele fractions comprise a plurality of variant allele fractions obtained from liquid biopsy samples 133-cf (e.g., 133-cf-1, 133-cf-2…, 133-cf-N) for the cohort of subjects. In some embodiments, the circulating variant allele fraction is obtained bycomparing the liquid biopsy variant allele fractions 133-cf to the tumor biopsy variant allele fraction 133-t.

[0182] Additional embodiments for using variant allele fractions (e.g., variant allele frequencies) to identify somatic variants are detailed below (see, Example Methods: Variant Identification).

[0183] A SNV / MNV algorithm 147 may identify a substitution of a single nucleotide that occurs at a specific position in the genome. For example, at a specific base position, or locus, in the human genome, the C nucleotide may appear in most individuals, but in a minority of individuals, the position is occupied by an A. This means that there is a SNP at this specific position and the two possible nucleotide variations, C or A, are said to be alleles for this position. SNPs underlie differences in human susceptibility to a wide range of diseases (e.g. – sickle-cell anemia, β-thalassemia and cystic fibrosis result from SNPs). The severity of illness and the way the body responds to treatments are also manifestations of genetic variations. For example, a single-base mutation in the APOE (apolipoprotein E) gene is associated with a lower risk for Alzheimer's disease. A single-nucleotide variant (SNV) is a variation in a single nucleotide without any limitations of frequency and may arise in somatic cells. A somatic single-nucleotide variation (e.g., caused by cancer) may also be called a single-nucleotide alteration. An MNP (Multiple-nucleotide polymorphisms) module may identify the substitution of consecutive nucleotides at a specific position in the genome.

[0184] An indel calling algorithm 148 may identify an insertion or deletion of bases in the genome of an organism classified among small genetic variations. While indels usually measure from 1 to 10000 base pairs in length, a microindel is defined as an indel that results in a net change of 1 to 50 nucleotides. Indels can be contrasted with a SNP or point mutation. An indel inserts and / or deletes nucleotides from a sequence, while a point mutation is a form of substitution that replaces one of the nucleotides without changing the overall number in the DNA. Indels, being insertions and / or deletions, can be used as genetic markers in natural populations, especially in phylogenetic studies. Indel frequency tends to be markedly lower than that of single nucleotide polymorphisms (SNP), except near highly repetitive regions, including homopolymers and microsatellites.

[0185] A genomic rearrangement algorithm 149 may identify hybrid genes formed from two previously separate genes. It can occur as a result of translocation, interstitial deletion, or chromosomal inversion. Gene fusion can play an important role in tumorigenesis. Fusiongenes can contribute to tumor formation because fusion genes can produce much more active abnormal protein than non-fusion genes. Often, fusion genes are oncogenes that cause cancer; these include BCR-ABL, TEL-AML1 (ALL with t(12 ; 21)), AML1-ETO (M2 AML with t(8 ; 21)), and TMPRSS2-ERG with an interstitial deletion on chromosome 21, often occurring in prostate cancer. In the case of TMPRSS2-ERG, by disrupting androgen receptor (AR) signaling and inhibiting AR expression by oncogenic ETS transcription factor, the fusion product regulates prostate cancer. Most fusion genes are found from hematological cancers, sarcomas, and prostate cancer. BCAM-AKT2 is a fusion gene that is specific and unique to high-grade serous ovarian cancer. Oncogenic fusion genes may lead to a gene product with a new or different function from the two fusion partners. Alternatively, a proto- oncogene is fused to a strong promoter, and thereby the oncogenic function is set to function by an upregulation caused by the strong promoter of the upstream fusion partner. The latter is common in lymphomas, where oncogenes are juxtaposed to the promoters of the immunoglobulin genes. Oncogenic fusion transcripts may also be caused by trans-splicing or read-through events. Since chromosomal translocations play such a significant role in neoplasia, a specialized database of chromosomal aberrations and gene fusions in cancer has been created. This database is called Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer.

[0186] In some embodiments, feature extraction module 145 includes instructions for identifying one or more complex genomic alterations (e.g., features that incorporate more than a change in the primary sequence of the genome) in the cancer genome of the subject. For instance, in some embodiments, feature extraction module 145 includes modules for identifying one or more of copy number variation (e.g., copy number variation analysis module 153), microsatellite instability status (e.g., microsatellite instability analysis module 154), tumor mutational burden (e.g., tumor mutational burden analysis module 155), tumor ploidy (e.g., tumor ploidy analysis module 156), and homologous recombination pathway deficiencies (e.g., homologous recombination pathway analysis module 157). Feature Analysis Module (160)

[0187] Referring again to Figure 1A, the system (e.g., system 100) includes a feature analysis module 160 that includes one or more genomic alteration interpretation algorithms 161, one or more optional clinical data analysis algorithms 165, an optional therapeutic curation algorithm 165, and an optional recommendation validation module 167. In some embodiments, feature analysis module 160 identifies actionable variants and characteristics139-1 and corresponding matched therapies 139-2 and / or clinical trials using one or more analysis algorithms (e.g., algorithms 162, 163, 164, and 165) to evaluate feature data 125. The identified actionable variants and characteristics 139-1 and corresponding matched therapies 139-2, which are optionally stored in test patient data store 120, are then curated by feature analysis module 160 to generate a clinical report 139-3, which is optionally validated by a user, e.g., a clinician, before being transmitted to a medical professional, e.g., an oncologist, treating the patient.

[0188] In some embodiments, the genomic alteration interpretation algorithms 161 include instructions for evaluating the effect that one or more genomic features 131 of the subject, e.g., as identified by feature extraction module 145, have on the characteristics of the patient’s cancer and / or whether one or more targeted cancer therapies may improve the clinical outcome for the patient. For example, in some embodiments, one or more genomic variant analysis algorithms 163 evaluate various genomic features 131 by querying a database, e.g., a look-up-table (“LUT”) of actionable genomic alterations, targeted therapies associated with the actionable genomic alterations, and any other conditions that should be met before administering the targeted therapy to a subject having the actionable genomic alteration. For instance, evidence suggests that depatuxizumab mafodotin (an anti-EGFR mAb conjugated to monomethyl auristatin F) has improved efficacy for the treatment of recurrent glioblastomas having EGFR focal amplifications. van den Bent M. et al., Cancer Chemother Pharmacol., 80(6):1209-17 (2017). Accordingly, the actionable genomic alteration LUT would have an entry for the focal amplification of the EGFR gene indicating that depatuxizumab mafodotin is a targeted therapy for glioblastomas (e.g., recurrent glioblastomas) having a focal gene amplification. In some instances, the LUT may also include counter indications for the associated targeted therapy, e.g., adverse drug interactions or personal characteristics that are counter-indicated for administration of the particular targeted therapy.

[0189] In some embodiments, a genomic alteration interpretation algorithm 161 determines whether a particular genomic feature 131 should be reported to a medical professional treating the cancer patient. In some embodiments, genomic features 131 (e.g., genomic alterations and compound features) are reported when there is clinical evidence that the feature significantly impacts the biology of the cancer, impacts the prognosis for the cancer, and / or impacts pharmacogenomics, e.g., by indicating or counter-indicating particular therapeutic approaches. For instance, a genomic alteration interpretation algorithm 161 mayclassify a particular CNV feature 135 as “Reportable,” e.g., meaning that the CNV has been identified as influencing the character of the cancer, the overall disease state, and / or pharmacogenomics, as “Not Reportable,” e.g., meaning that the CNV has not been identified as influencing the character of the cancer, the overall disease state, and / or pharmacogenomics, as “No Evidence,” e.g., meaning that no evidence exists supporting that the CNV is “Reportable” or “Not Reportable,” or as “Conflicting Evidence,” e.g., meaning that evidence exists supporting both that the CNV is “Reportable” and that the CNV is “Not Reportable.”

[0190] In some embodiments, the genomic alteration interpretation algorithms 161 include one or more pathogenic variant analysis algorithms 162, which evaluate various genomic features to identify the presence of an oncogenic pathogen associated with the patient’s cancer and / or targeted therapies associated with an oncogenic pathogen infection in the cancer. For instance, RNA expression patterns of some cancers are associated with the presence of an oncogenic pathogen that is helping to drive the cancer. See, for example, U.S. Patent Application Serial No.16 / 802,126, filed February 26, 2020, the content of which is hereby incorporated by reference, in its entirety, for all purposes. In some instances, the recommended therapy for the cancer is different when the cancer is associated with the oncogenic pathogen infection than when it is not. Accordingly, in some embodiments, e.g., where feature data 125 includes RNA abundance data for the cancer of the patient, one or more pathogenic variant analysis algorithms 162 evaluate the RNA abundance data for the patient’s cancer to determine whether a signature exists in the data that indicates the presence of the oncogenic pathogen in the cancer. Similarly, in some embodiments, bioinformatics module 140 includes an algorithm that searches for the presence of pathogenic nucleic acid sequences in sequencing data 122. See, for example, U.S. Provisional Patent Application Serial No.62 / 978,067, filed February 18, 2020, the content of which is hereby incorporated by reference, in its entirety, for all purposes. Accordingly, in some embodiments, one or more pathogenic variant analysis algorithms 162 evaluates whether the presence of an oncogenic pathogen in a subject is associated with an actionable therapy for the infection. In some embodiments, system 100 queries a database, e.g., a look-up-table (“LUT”), of actionable oncogenic pathogen infections, targeted therapies associated with the actionable infections, and any other conditions that should be met before administering the targeted therapy to a subject that is infected with the oncogenic pathogen. In some instances, the LUT may also include counter indications for the associated targeted therapy, e.g., adverse druginteractions or personal characteristics that are counter-indicated for administration of the particular targeted therapy.

[0191] In some embodiments, the genomic alteration interpretation algorithms 161 include one or more multi-feature analysis algorithms 164 that evaluate a plurality of features to classify a cancer with respect to the effects of one or more targeted therapies. For instance, in some embodiments, feature analysis module 160 includes one or more classifiers trained against feature data, one or more clinical therapies, and their associated clinical outcomes for a plurality of training subjects to classify cancers based on their predicted clinical outcomes following one or more therapies.

[0192] In some embodiments, the classifier is implemented as an artificial intelligence engine and may include gradient boosting models, random forest models, neural networks (NN), regression models, Naive Bayes models, and / or machine learning algorithms (MLA). An MLA or a NN may be trained from a training data set that includes one or more features 125, including personal characteristics 126, medical history 127, clinical features 128, genomic features 131, and / or other -omic features 138. MLAs include supervised algorithms (such as algorithms where the features / classifications in the data set are annotated) using linear regression, logistic regression, decision trees, classification and regression trees, naïve Bayes, nearest neighbor clustering; unsupervised algorithms (such as algorithms where no features / classification in the data set are annotated) using Apriori, means clustering, principal component analysis, random forest, adaptive boosting; and semi-supervised algorithms (such as algorithms where an incomplete number of features / classifications in the data set are annotated) using generative approach (such as a mixture of Gaussian distributions, mixture of multinomial distributions, hidden Markov models), low density separation, graph-based approaches (such as mincut, harmonic function, manifold regularization), heuristic approaches, or support vector machines.

[0193] NNs include conditional random fields, convolutional neural networks, attention based neural networks, deep learning, long short term memory networks, or other neural models where the training data set includes a plurality of tumor samples, RNA expression data for each sample, and pathology reports covering imaging data for each sample.

[0194] While MLA and neural networks identify distinct approaches to machine learning, the terms may be used interchangeably herein. Thus, a mention of MLA may include a corresponding NN or a mention of NN may include a corresponding MLA unless explicitlystated otherwise. Training may include providing optimized datasets, labeling these traits as they occur in patient records, and training the MLA to predict or classify based on new inputs. Artificial NNs are efficient computing models which have shown their strengths in solving hard problems in artificial intelligence. They have also been shown to be universal approximators, that is, they can represent a wide variety of functions when given appropriate parameters.

[0195] In some embodiments, system 100 includes a classifier training module that includes instructions for training one or more untrained or partially trained classifiers based on feature data from a training dataset. In some embodiments, system 100 also includes a database of training data for use in training the one or more classifiers. In other embodiments, the classifier training module accesses a remote storage device hosting training data. In some embodiments, the training data includes a set of training features, including but not limited to, various types of the feature data 125 illustrated in Figure 1B. In some embodiments, the classifier training module uses patient data 121, e.g., when test patient data store 120 also stores a record of treatments administered to the patient and patient outcomes following therapy.

[0196] In some embodiments, feature analysis module 160 includes one or more clinical data analysis algorithms 165, which evaluate clinical features 128 of a cancer to identify targeted therapies which may benefit the subject. For example, in some embodiments, e.g., where feature data 125 includes pathology data 128-1, one or more clinical data analysis algorithms 165 evaluate the data to determine whether an actionable therapy is indicated based on the histopathology of a tumor biopsy from the subject, e.g., which is indicative of a particular cancer type and / or stage of cancer. In some embodiments, system 100 queries a database, e.g., a look-up-table (“LUT”), of actionable clinical features (e.g., pathology features), targeted therapies associated with the actionable features, and any other conditions that should be met before administering the targeted therapy to a subject associated with the actionable clinical features 128 (e.g., pathology features 128-1). In some embodiments, system 100 evaluates the clinical features 128 (e.g., pathology features 128-1) directly to determine whether the patient’s cancer is sensitive to a particular therapeutic agent. Further details on example methods, systems, and algorithms for classifying cancer and identifying targeted therapies based on clinical data, such as pathology data 128-1, imaging data 138-2, and / or tissue culture / organoid data 128-3 are discussed, for example, in U.S. Patent Application No.16 / 830,186, filed on March 25, 2020, U.S. Patent Application No.16 / 789,363, filed on Feb.12, 2020, and U.S. Provisional Application No.63 / 007,874, filed on April 9, 2020, the contents of which are hereby incorporated by reference, in their entireties, for all purposes.

[0197] In some embodiments, feature analysis module 160 includes a clinical trials module that evaluates test patient data 121 to determine whether the patient is eligible for inclusion in a clinical trial for a cancer therapy, e.g., a clinical trial that is currently recruiting patients, a clinical trial that has not yet begun recruiting patients, and / or an ongoing clinical trial that may recruit additional patients in the future. In some embodiments, a clinical trial module evaluates test patient data 121 to determine whether the results of a clinical trial are relevant for the patient, e.g., the results of an ongoing clinical trial and / or the results of a completed clinical trial. For instance, in some embodiments, system 100 queries a database, e.g., a look-up-table (“LUT”) of clinical trials, e.g., active and / or completed clinical trials, and compares patient data 121 with inclusion criteria for the clinical trials, stored in the database, to identify clinical trials with inclusion criteria that closely match and / or exactly match the patient’s data 121. In some embodiments, a record of matching clinical trials, e.g., those clinical trials that the patient may be eligible for and / or that may inform personalized treatment decisions for the patient, are stored in clinical assessment database 139.

[0198] In some embodiments, feature analysis module 160 includes a therapeutic curation algorithm 166 that assembles actionable variants and characteristics 139-1, matched therapies 139-2, and / or relevant clinical trials identified for the patient, as described above. In some embodiments, a therapeutic curation algorithm 166 evaluates certain criteria related to which actionable variants and characteristics 139-1, matched therapies 139-2, and / or relevant clinical trials should be reported and / or whether certain matched therapies, considered alone or in combination, may be counter-indicated for the patient, e.g., based on personal characteristics 126 of the patient and / or known drug-drug interactions. In some embodiments, the therapeutic curation algorithm then generates one or more clinical reports 139-3 for the patient. In some embodiments, the therapeutic curation algorithm generates a first clinical report 139-3-1 that is to be reported to a medical professional treating the patient and a second clinical report 139-3-2 that will not be communicated to the medical professional but may be used to improve various algorithms within the system.

[0199] In some embodiments, feature analysis module 160 includes a recommendation validation module 167, that includes an interface allowing a clinician to review, modify, andapprove a clinical report 139-3 prior to the report being sent to a medical professional, e.g., an oncologist, treating the patient.

[0200] In some embodiments, each of the one or more feature collections, sequencing modules, bioinformatics modules (including, e.g., alteration module(s), structural variant calling and data processing modules), classification modules and outcome modules are communicatively coupled to a data bus to transfer data between each module for processing and / or storage. In some alternative embodiments, each of the feature collection, alteration module(s), structural variant and feature store are communicatively coupled to each other for independent communication without sharing the data bus.

[0201] Further details on systems and exemplary embodiments of modules and feature collections are discussed in PCT Application PCT / US19 / 69149, titled “A METHOD AND PROCESS FOR PREDICTING AND ANALYZING PATIENT COHORT RESPONSE, PROGRESSION, AND SURVIVAL,” filed December 31, 2019, which is hereby incorporated herein by reference in its entirety. Example Methods

[0202] Now that details of a system 100 for providing clinical support for personalized cancer therapy, e.g., with improved validation of somatic sequence variants, have been disclosed, details regarding processes and features of the system, in accordance with various embodiments of the present disclosure, are disclosed below. Specifically, example processes are described below with reference to Figures 2A, 3, 4A-E, and 5A-B. In some embodiments, such processes and features of the system are carried out by modules 118, 120, 140, 160, and / or 170, as illustrated in Figure 1A. Referring to these methods, the systems described herein (e.g., system 100) include instructions for validating somatic variants that are improved compared to conventional methods for somatic variant detection. Figure 2B: Distributed Diagnostic and Clinical Environment

[0203] In some aspects, the methods described herein for providing clinical support for personalized cancer therapy are performed across a distributed diagnostic / clinical environment, e.g., as illustrated in Figure 2B. However, in some embodiments, the improved methods described herein for validating somatic sequence variants are performed at a single location, e.g., at a single computing system or environment, although ancillary procedures supporting the methods described herein, and / or procedures that make further use of theresults of the methods described herein, may be performed across a distributed diagnostic / clinical environment.

[0204] Figure 2B illustrates an example of a distributed diagnostic / clinical environment 210. In some embodiments, the distributed diagnostic / clinical environment is connected via communication network 105. In some embodiments, one or more biological samples, e.g., one or more liquid biopsy samples, solid tumor biopsy, normal tissue samples, and / or control samples, are collected from a subject in clinical environment 220, e.g., a doctor’s office, hospital, or medical clinic, or at a home health care environment (not depicted). Advantageously, while solid tumor samples should be collected within a clinical setting, liquid biopsy samples can be acquired in a less invasive fashion and are more easily collected outside of a traditional clinical setting. In some embodiments, one or more biological samples, or portions thereof, are processed within the clinical environment 220 where collection occurred, using a processing device 224, e.g., a nucleic acid sequencer for obtaining sequencing data, a microscope for obtaining pathology data, a mass spectrometer for obtaining proteomic data, etc. In some embodiments, one or more biological samples, or portions thereof are sent to one or more external environments, e.g., sequencing lab 230, pathology lab 240, and / or molecular biology lab 250, each of which includes a processing device 234, 244, and 254, respectively, to generate biological data 121 for the subject. Each environment includes a communications device 222, 232, 242, and 252, respectively, for communicating biological data 121 about the subject to a processing server 262 and / or database 264, which may be located in yet another environment, e.g., processing / storage center 260. Thus, in some embodiments, different portions of the systems and methods described herein are fulfilled by different processing devices located in different physical environments.

[0205] Accordingly, in some embodiments, a method for providing clinical support for personalized cancer therapy, e.g., with improved validation of somatic sequence variants, is performed across one or more environments, as illustrated in Figure 2B. For instance, in some such embodiments, a liquid biopsy sample is collected at clinical environment 220 or in a home healthcare environment. The sample, or a portion thereof, is sent to sequencing lab 230 where raw sequence reads 123 of nucleic acids in the sample are generated by sequencer 234. The raw sequencing data 123 is communicated, e.g., from communications device 232, to database 264 at processing / storage center 260, where processing server 262 extracts features from the sequence reads by executing one or more of the processes in bioinformaticsmodule 140, thereby generating genomic features 131 for the sample. Processing server 262 may then analyze the identified features by executing one or more of the processes in feature analysis module 160, thereby generating clinical assessment 139, including a clinical report 139-3. A clinician may access clinical report 139-3, e.g., at processing / storage center 260 or through communications network 105, via recommendation validation module 167. After final approval, clinical report 139-3 is transmitted to a medical professional, e.g., an oncologist, at clinical environment 220, who uses the report to support clinical decision making for personalized treatment of the patient’s cancer. Figure 2A: Example Workflow for Precision Oncology

[0206] Figure 2A is a flowchart of an example workflow 200 for collecting and analyzing data in order to generate a clinical report 139 to support clinical decision making in precision oncology. Advantageously, the methods described herein improve this process, for example, by improving various stages within feature extraction 206, including validation of somatic sequence variants.

[0207] Briefly, the workflow begins with patient intake and sample collection 201, where one or more liquid biopsy samples, one or more tumor biopsy, and one or more normal and / or control tissue samples are collected from the patient (e.g., at a clinical environment 220 or home healthcare environment, as illustrated in Figure 2B). In some embodiments, personal data 126 corresponding to the patient and a record of the one or more biological samples obtained (e.g., patient identifiers, patient clinical data, sample type, sample identifiers, cancer conditions, etc.) are entered into a data analysis platform, e.g., test patient data store 120. Accordingly, in some embodiments, the methods disclosed herein include obtaining one or more biological samples from one or more subjects, e.g., cancer patients. In some embodiments, the subject is a human, e.g., a human cancer patient.

[0208] In some embodiments, one or more of the biological samples obtained from the patient are a biological liquid sample, also referred to as a liquid biopsy sample. In some embodiments, one or more of the biological samples obtained from the patient are selected from blood, plasma, serum, urine, vaginal fluid, fluid from a hydrocele (e.g., of the testis), vaginal flushing fluids, pleural fluid, ascitic fluid, cerebrospinal fluid, saliva, sweat, tears, sputum, bronchoalveolar lavage fluid, discharge fluid from the nipple, aspiration fluid from different parts of the body (e.g., thyroid, breast), etc. In some embodiments, the liquid biopsy sample includes blood and / or saliva. In some embodiments, the liquid biopsy sample isperipheral blood. In some embodiments, blood samples are collected from patients in commercial blood collection containers, e.g., using a PAXgene® Blood DNA Tubes. In some embodiments, saliva samples are collected from patients in commercial saliva collection containers, e.g., using an Oragene® DNA Saliva Kit.

[0209] In some embodiments, the liquid biopsy sample has a volume of from about 1 mL to about 50 mL. For example, in some embodiments, the liquid biopsy sample has a volume of about 1 mL, about 2 mL, about 3 mL, about 4 mL, about 5 mL, about 6 mL, about 7 mL, about 8 mL, about 9 mL, about 10 mL, about 11 mL, about 12 mL, about 13 mL, about 14 mL, about 15 mL, about 16 mL, about 17 mL, about 18 mL, about 19 mL, about 20 mL, or greater.

[0210] Liquid biopsy samples include cell free nucleic acids, including cell-free DNA (cfDNA). As described above, cfDNA isolated from cancer patients includes DNA originating from cancerous cells, also referred to as circulating tumor DNA (ctDNA), cfDNA originating from germline (e.g., healthy or non-cancerous) cells, and cfDNA originating from hematopoietic cells (e.g., white blood cells). The relative proportions of cancerous and non- cancerous cfDNA present in a liquid biopsy sample varies depending on the characteristics (e.g., the type, stage, lineage, genomic profile, etc.) of the patient’s cancer. As used herein, the ‘tumor burden’ of the subject refers to the percentage cfDNA that originated from cancerous cells.

[0211] As described herein, cfDNA is a particularly useful source of biological data for various implementations of the methods and systems described herein, because it is readily obtained from various body fluids. Advantageously, use of bodily fluids facilitates serial monitoring because of the ease of collection, as these fluids are collectable by non-invasive or minimally invasive methodologies. This is in contrast to methods that rely upon solid tissue samples, such as biopsies, which often times require invasive surgical procedures. Further, because bodily fluids, such as blood, circulate throughout the body, the cfDNA population represents a sampling of many different tissue types from many different locations.

[0212] In some embodiments, a liquid biopsy sample is separated into two different samples. For example, in some embodiments, a blood sample is separated into a blood plasma sample, containing cfDNA, and a buffy coat preparation, containing white blood cells.

[0213] In some embodiments, a plurality of liquid biopsy samples is obtained from a respective subject at intervals over a period of time (e.g., using serial testing). For example, in some such embodiments, the time between obtaining liquid biopsy samples from a respective subject is at least 1 day, at least 2 days, at least 1 week, at least 2 weeks, at least 1 month, at least 2 months, at least 3 months, at least 4 months, at least 6 months, or at least 1 year.

[0214] In some embodiments, one or more biological samples collected from the patient is a solid tissue sample, e.g., a solid tumor sample or a solid normal tissue sample. Methods for obtaining solid tissue samples, e.g., of cancerous and / or normal tissue are known in the art and are dependent upon the type of tissue being sampled. For example, bone marrow biopsies and isolation of circulating tumor cells can be used to obtain samples of blood cancers, endoscopic biopsies can be used to obtain samples of cancers of the digestive tract, bladder, and lungs, needle biopsies (e.g., fine-needle aspiration, core needle aspiration, vacuum-assisted biopsy, and image-guided biopsy, can be used to obtain samples of subdermal tumors, skin biopsies, e.g., shave biopsy, punch biopsy, incisional biopsy, and excisional biopsy, can be used to obtain samples of dermal cancers, and surgical biopsies can be used to obtain samples of cancers affecting internal organs of a patient. In some embodiments, a solid tissue sample is a formalin-fixed tissue (FFT). In some embodiments, a solid tissue sample is a macro-dissected formalin fixed paraffin embedded (FFPE) tissue. In some embodiments, a solid tissue sample is a fresh frozen tissue sample.

[0215] In some embodiments, a dedicated normal sample is collected from the patient, for co-processing with a liquid biopsy sample. Generally, the normal sample is of a non- cancerous tissue, and can be collected using any tissue collection means described above. In some embodiments, buccal cells collected from the inside of a patient’s cheeks are used as a normal sample. Buccal cells can be collected by placing an absorbent material, e.g., a swab, in the subject’s mouth and rubbing it against their cheek, e.g., for at least 15 second or for at least 30 seconds. The swab is then removed from the patient’s mouth and inserted into a tube, such that the tip of the tube is submerged into a liquid that serves to extract the buccal cells off of the absorbent material. An example of buccal cell recovery and collection devices is provided in U.S. Patent No.9,138,205, the content of which is hereby incorporated by reference, in its entirety, for all purposes. In some embodiments, the buccal swab DNA is used as a source of normal DNA in circulating heme malignancies.

[0216] The biological samples collected from the patient are, optionally, sent to various analytical environments (e.g., sequencing lab 230, pathology lab 240, and / or molecular biology lab 250) for processing (e.g., data collection) and / or analysis (e.g., feature extraction). Wet lab processing 204 may include cataloguing samples (e.g., accessioning), examining clinical features of one or more samples (e.g., pathology review), and nucleic acid sequence analysis (e.g., extraction, library prep, capture + hybridize, pooling, and sequencing). In some embodiments, the workflow includes clinical analysis of one or more biological samples collected from the subject, e.g., at a pathology lab 240 and / or a molecular and cellular biology lab 250, to generate clinical features such as pathology features 128-3, imaging data 128-3, and / or tissue culture / organoid data 128-3.

[0217] In some embodiments, the pathology data 128-1 collected during clinical evaluation includes visual features identified by a pathologist’s inspection of a specimen (e.g., a solid tumor biopsy), e.g., of stained H&E or IHC slides. In some embodiments, the sample is a solid tissue biopsy sample. In some embodiments, the tissue biopsy sample is a formalin-fixed tissue (FFT), e.g., a formalin-fixed paraffin-embedded (FFPE) tissue. In some embodiments, the tissue biopsy sample is an FFPE or FFT block. In some embodiments, the tissue biopsy sample is a fresh-frozen tissue biopsy. The tissue biopsy sample can be prepared in thin sections (e.g., by cutting and / or affixing to a slide), to facilitate pathology review (e.g., by staining with immunohistochemistry stain for IHC review and / or with hematoxylin and eosin stain for H&E pathology review). For instance, analysis of slides for H&E staining or IHC staining may reveal features such as tumor infiltration, programmed death-ligand 1 (PD-L1) status, human leukocyte antigen (HLA) status, or other immunological features.

[0218] In some embodiments, a liquid sample (e.g., blood) collected from the patient (e.g., in EDTA-containing collection tubes) is prepared on a slide (e.g., by smearing) for pathology review. In some embodiments, macrodissected FFPE tissue sections, which may be mounted on a histopathology slide, from solid tissue samples (e.g., tumor or normal tissue) are analyzed by pathologists. In some embodiments, tumor samples are evaluated to determine, e.g., the tumor purity of the sample, the percent tumor cellularity as a ratio of tumor to normal nuclei, etc. For each section, background tissue may be excluded or removed such that the section meets a tumor purity threshold, e.g., where at least 20% of the nuclei in the section are tumor nuclei, or where at least 25%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or more of the nuclei in the section are tumor nuclei.

[0219] In some embodiments, pathology data 128-1 is extracted, in addition to or instead of visual inspection, using computational approaches to digital pathology, e.g., providing morphometric features extracted from digital images of stained tissue samples. A review of digital pathology methods is provided in Bera, K. et al., Nat. Rev. Clin. Oncol., 16:703-15 (2019), the content of which is hereby incorporated by reference, in its entirety, for all purposes. In some embodiments, pathology data 128-1 includes features determined using machine learning algorithms to evaluate pathology data collected as described above.

[0220] Further details on methods, systems, and algorithms for using pathology data to classify cancer and identify targeted therapies are discussed, for example, in are discussed, for example, in U.S. Patent Application No.16 / 830,186, filed on March 25, 2020, and U.S. Provisional Application No.63 / 007,874, filed on April 9, 2020, the contents of which are hereby incorporated by reference, in their entireties, for all purposes.

[0221] In some embodiments, imaging data 128-2 collected during clinical evaluation includes features identified by review of in-vitro and / or in-vivo imaging results (e.g., of a tumor site), for example a size of a tumor, tumor size differentials over time (such as during treatment or during other periods of change). In some embodiments, imaging data 128-2 includes features determined using machine learning algorithms to evaluate imaging data collected as described above.

[0222] Further details on methods, systems, and algorithms for using medical imaging to classify cancer and identify targeted therapies are discussed, for example, in are discussed, for example, in U.S. Patent Application No.16 / 830,186, filed on March 25, 2020, and U.S. Provisional Application No.63 / 007,874, filed on April 9, 2020, the contents of which are hereby incorporated by reference, in their entireties, for all purposes.

[0223] In some embodiments, tissue culture / organoid data 128-3 collected during clinical evaluation includes features identified by evaluation of cultured tissue from the subject. For instance, in some embodiments, tissue samples obtained from the patients (e.g., tumor tissue, normal tissue, or both) are cultured (e.g., in liquid culture, solid-phase culture, and / or organoid culture) and various features, such as cell morphology, growth characteristics, genomic alterations, and / or drug sensitivity, are evaluated. In some embodiments, tissue culture / organoid data 128-3 includes features determined using machine learning algorithms to evaluate tissue culture / organoid data collected as described above. Examples of tissue organoid (e.g., personal tumor organoid) culturing and featureextractions thereof are described in U.S. Provisional Application Serial No.62 / 924,621, filed on October 22, 2019, and U.S. Patent Application Serial No.16 / 693,117, filed on November 22, 2019, the contents of which are hereby incorporated by reference, in their entireties, for all purposes.

[0224] Nucleic acid sequencing of one or more samples collected from the subject is performed, e.g., at sequencing lab 230, during wet lab processing 204. An example workflow for nucleic acid sequencing is illustrated in Figure 3. In some embodiments, the one or more biological samples obtained at the sequencing lab 230 are accessioned (302), to track the sample and data through the sequencing process.

[0225] Next, nucleic acids, e.g., RNA and / or DNA are extracted (304) from the one or more biological samples. Methods for isolating nucleic acids from biological samples are known in the art and are dependent upon the type of nucleic acid being isolated (e.g., cfDNA, DNA, and / or RNA) and the type of sample from which the nucleic acids are being isolated (e.g., liquid biopsy samples, white blood cell buffy coat preparations, formalin-fixed paraffin- embedded (FFPE) solid tissue samples, and fresh frozen solid tissue samples). The selection of any particular nucleic acid isolation technique for use in conjunction with the embodiments described herein is well within the skill of the person having ordinary skill in the art, who will consider the sample type, the state of the sample, the type of nucleic acid being sequenced, and the sequencing technology being used.

[0226] For instance, many techniques for DNA isolation, e.g., genomic DNA isolation, from a tissue sample are known in the art, such as organic extraction, silica adsorption, and anion exchange chromatography. Likewise, many techniques for RNA isolation, e.g., mRNA isolation, from a tissue sample are known in the art. For example, acid guanidinium thiocyanate-phenol-chloroform extraction (see, for example, Chomczynski and Sacchi, 2006, Nat Protoc, 1(2):581-85, which is hereby incorporated by reference herein), and silica bead / glass fiber adsorption (see, for example, Poeckh, T. et al., 2008, Anal Biochem., 373(2):253-62, which is hereby incorporated by reference herein). The selection of any particular DNA or RNA isolation technique for use in conjunction with the embodiments described herein is well within the skill of the person having ordinary skill in the art, who will consider the tissue type, the state of the tissue, e.g., fresh, frozen, formalin-fixed, paraffin- embedded (FFPE), and the type of nucleic acid analysis that is to be performed.

[0227] In some embodiments where the biological sample is a liquid biopsy sample, e.g., a blood or blood plasma sample, cfDNA is isolated from blood samples using commercially available reagents, including proteinase K, to generate a liquid solution of cfDNA.

[0228] In some embodiments, isolated DNA molecules are mechanically sheared to an average length using an ultrasonicator (for example, a Covaris ultrasonicator). In some embodiments, isolated nucleic acid molecules are analyzed to determine their fragment size, e.g., through gel electrophoresis techniques and / or the use of a device such as a LabChip GX Touch. The skilled artisan will know of an appropriate range of fragment sizes, based on the sequencing technique being employed, as different sequencing techniques have differing fragment size requirements for robust sequencing. In some embodiments, quality control testing is performed on the extracted nucleic acids (e.g., DNA and / or RNA), e.g., to assess the nucleic acid concentration and / or fragment size. For example, sizing of DNA fragments provides valuable information used for downstream processing, such as determining whether DNA fragments require additional shearing prior to sequencing.

[0229] Wet lab processing 204 then includes preparing a nucleic acid library from the isolated nucleic acids (e.g., cfDNA, DNA, and / or RNA). For example, in some embodiments, DNA libraries (e.g., gDNA and / or cfDNA libraries) are prepared from isolated DNA from the one or more biological samples. In some embodiments, the DNA libraries are prepared using a commercial library preparation kit, e.g., the KAPA Hyper Prep Kit, a New England Biolabs (NEB) kit, or a similar kit.

[0230] In some embodiments, during library preparation, adapters (e.g., UDI adapters, such as Roche SeqCap dual end adapters, or UMI adapters such as full length or stubby Y adapters) are ligated onto the nucleic acid molecules. In some embodiments, the adapters include unique molecular identifiers (UMIs), which are short nucleic acid sequences (e.g., 3- 10 base pairs) that are added to ends of DNA fragments during adapter ligation. In some embodiments, UMIs are degenerate base pairs that serve as a unique tag that can be used to identify sequence reads originating from a specific DNA fragment. In some embodiments, e.g., when multiplex sequencing will be used to sequence DNA from a plurality of samples (e.g., from the same or different subjects) in a single sequencing reaction, a patient-specific index is also added to the nucleic acid molecules. In some embodiments, the patient specific index is a short nucleic acid sequence (e.g., 3-20 nucleotides) that are added to ends of DNA fragments during library construction, that serve as a unique tag that can be used to identify sequence reads originating from a specific patient sample. Examples of identifier sequencesare described, for example, in Kivioja et al., Nat. Methods 9(1):72-74 (2011) and Islam et al., Nat. Methods 11(2):163-66 (2014), the contents of which are hereby incorporated by reference, in their entireties, for all purposes.

[0231] In some embodiments, an adapter includes a PCR primer landing site, designed for efficient binding of a PCR or second-strand synthesis primer used during the sequencing reaction. In some embodiments, an adapter includes an anchor binding site, to facilitate binding of the DNA molecule to anchor oligonucleotide molecules on a sequencer flow cell, serving as a seed for the sequencing process by providing a starting point for the sequencing reaction. During PCR amplification following adapter ligation, the UMIs, patient indexes, and binding sites are replicated along with the attached DNA fragment. This provides a way to identify sequence reads that came from the same original fragment in downstream analysis.

[0232] In some embodiments, DNA libraries are amplified and purified using commercial reagents, (e.g., Axygen MAG PCR clean up beads). In some such embodiments, the concentration and / or quantity of the DNA molecules are then quantified using a fluorescent dye and a fluorescence microplate reader, standard spectrofluorometer, or filter fluorometer. In some embodiments, library amplification is performed on a device (e.g., an Illumina C- Bot2) and the resulting flow cell containing amplified target-captured DNA libraries is sequenced on a next generation sequencer (e.g., an Illumina HiSeq 4000 or an Illumina NovaSeq 6000) to a unique on-target depth selected by the user. In some embodiments, DNA library preparation is performed with an automated system, using a liquid handling robot (e.g., a SciClone NGSx).

[0233] In some embodiments, where feature data 125 includes methylation states 132 for one or more genomic locations, nucleic acids isolated from the biological sample (e.g., cfDNA) are treated to convert unmethylated cytosines to uracils, e.g., prior to generating the sequencing library. Accordingly, when the nucleic acids are sequenced, all cytosines called in the sequencing reaction were necessarily methylated, since the unmethylated cytosines were converted to uracils and accordingly would have been called as thymidines, rather than cytosines, in the sequencing reaction. Commercial kits are available for bisulfite-mediated conversion of methylated cytosines to uracils, for instance, the EZ DNA MethylationTM- Gold, EZ DNA Methylation™-Direct, and EZ DNA Methylation™-Lightning kit (available from Zymo Research Corp (Irvine, CA)). Commercial kits are also available for enzymatic conversion of methylated cytosines to uracils, for example, the APOBEC-Seq kit (available from NEBiolabs, Ipswich, MA).

[0234] In some embodiments, wet lab processing 204 includes pooling (308) DNA molecules from a plurality of libraries, corresponding to different samples from the same and / or different patients, to forming a sequencing pool of DNA libraries. When the pool of DNA libraries is sequenced, the resulting sequence reads correspond to nucleic acids isolated from multiple samples. The sequence reads can be separated into different sequence read files, corresponding to the various samples represented in the sequencing read based on the unique identifiers present in the added nucleic acid fragments. In this fashion, a single sequencing reaction can generate sequence reads from multiple samples. Advantageously, this allows for the processing of more samples per sequencing reaction.

[0235] In some embodiments, wet lab processing 204 includes enriching (310) a sequencing library, or pool of sequencing libraries, for target nucleic acids, e.g., nucleic acids encompassing loci that are informative for precision oncology and / or used as internal controls for the sequencing or bioinformatics processes. In some embodiments, enrichment is achieved by hybridizing target nucleic acids in the sequencing library to probes that hybridize to the target sequences, and then isolating the captured nucleic acids away from off-target nucleic acids that are not bound by the capture probes.

[0236] Advantageously, enriching for target sequences prior to sequencing nucleic acids significantly reduces the costs and time associated with sequencing, facilitates multiplex sequencing by allowing multiple samples to be mixed together for a single sequencing reaction, and significantly reduces the computation burden of aligning the resulting sequence reads, as a result of significantly reducing the total amount of nucleic acids analyzed from each sample.

[0237] In some embodiments, the enrichment is performed prior to pooling multiple nucleic acid sequencing libraries. However, in other embodiments, the enrichment is performed after pooling nucleic acid sequencing libraries, which has the advantage of reducing the number of enrichment assays that have to be performed.

[0238] In some embodiments, the enrichment is performed prior to generating a nucleic acid sequencing library. This has the advantage that fewer reagents are needed to perform both the enrichment (because there are fewer target sequences at this point, prior to library amplification) and the library production (because there are fewer nucleic acid molecules to tag and amplify after the enrichment). However, in addition to the issues raised above in theBackground and Introduction sections, this raises the possibility of pull-down bias and / or that small variations in the enrichment protocol will result in less consistent results.

[0239] In some embodiments, nucleic acid libraries are pooled (two or more DNA libraries may be mixed to create a pool) and treated with reagents to reduce off-target capture, for example Human COT-1 and / or IDT xGen Universal Blockers. Pools may be dried in a vacufuge and resuspended. DNA libraries or pools may be hybridized to a probe set (for example, a probe set specific to a panel that includes loci from at least 100, 600, 1,000, 10,000, etc. of the 19,000 known human genes) and amplified with commercially available reagents (for example, the KAPA HiFi HotStart ReadyMix). For example, in some embodiments, a pool is incubated in an incubator, PCR machine, water bath, or other temperature-modulating device to allow probes to hybridize. Pools may then be mixed with Streptavidin-coated beads or another means for capturing hybridized DNA-probe molecules, such as DNA molecules representing exons of the human genome and / or genes selected for a genetic panel.

[0240] Pools may be amplified and purified more than once using commercially available reagents, for example, the KAPA HiFi Library Amplification kit and Axygen MAG PCR clean up beads, respectively. The pools or DNA libraries may be analyzed to determine the concentration or quantity of DNA molecules, for example by using a fluorescent dye (for example, PicoGreen pool quantification) and a fluorescence microplate reader, standard spectrofluorometer, or filter fluorometer. In one example, the DNA library preparation and / or capture is performed with an automated system, using a liquid handling robot (for example, a SciClone NGSx).

[0241] In some embodiments, a plurality of nucleic acid probes (e.g., a probe set) is used to enrich one or more target sequences in a nucleic acid sample (e.g., an isolated nucleic acid sample or a nucleic acid sequencing library), e.g., where one or more target sequences is informative for precision oncology. For instance, in some embodiments, one or more of the target sequences encompasses a locus that is associated with an actionable allele. That is, variations of the target sequence are associated with targeted therapeutic approaches. In some embodiments, one or more of the target sequences and / or a property of one or more of the target sequences is used in a model, e.g., a machine learning model, trained to distinguish between two or more cancer states. For example, in some embodiments, sequence and / or structural variants identified in one or more of the target sequences are used to estimate a blood tumor molecular burden bTMB for a patient. Similarly, in some embodiments, thenumber of repeated sequence elements in one or more microsatellite target sequences are used to determine the microsatellite stability of a sample. Similarly, in some embodiments, the copy number of one or more target sequences are used to estimate a circulating tumor fraction of a sample.

[0242] In some embodiments, the probe set includes probes targeting one or more gene loci, e.g., exon or intron loci. In some embodiments, the probe set includes probes targeting one or more loci not encoding a protein, e.g., regulatory loci, miRNA loci, and other non- coding loci, e.g., that have been found to be associated with cancer. In some embodiments, the plurality of loci includes at least 25, 50, 100, 150, 200, 250, 300, 350, 400, 500, 750, 1000, 2500, 5000, or more human genomic loci.

[0243] In some embodiments, the probe sets described herein target a plurality of genomic loci. In some embodiments, the probe set includes probes targeting at least 10 genomic loci. In some embodiments, the probe set includes probes targeting at least 25 genomic loci. In some embodiments, the probe set includes probes targeting at least 50 genomic loci. In some embodiments, the probe set includes probes targeting at least 100 genomic loci. In some embodiments, the probe set includes probes targeting at least 200 genomic loci. In some embodiments, the probe set includes probes targeting at least 300 genomic loci. In some embodiments, the probe set includes probes targeting at least 400 genomic loci. In some embodiments, the probe set includes probes targeting at least 500 genomic loci. In some embodiments, the probe set includes probes targeting at least 750 genomic loci. In some embodiments, the probe set includes probes targeting at least 1000 genomic loci. In some embodiments, the probe set includes probes targeting at least 2500 genomic loci. In some embodiments, the probe set includes probes targeting at least 5000 genomic loci.

[0244] In some embodiments, the probe set includes probes targeting not more than 10,000 genomic loci. In some embodiments, the probe set includes probes targeting not more than 5000 genomic loci. In some embodiments, the probe set includes probes targeting not more than 2500 genomic loci. In some embodiments, the probe set includes probes targeting not more than 1000 genomic loci. In some embodiments, the probe set includes probes targeting not more than 750 genomic loci.

[0245] In some embodiments, the probe set includes probes targeting from 10 to 10,000 genomic loci. In some embodiments, the probe set includes probes targeting from 10 to 5000genomic loci. In some embodiments, the probe set includes probes targeting from 10 to 1000 genomic loci. In some embodiments, the probe set includes probes targeting from 10 to 750 genomic loci. In some embodiments, the probe set includes probes targeting from 50 to 10,000 genomic loci. In some embodiments, the probe set includes probes targeting from 50 to 5000 genomic loci. In some embodiments, the probe set includes probes targeting from 50 to 1000 genomic loci. In some embodiments, the probe set includes probes targeting from 50 to 750 genomic loci. In some embodiments, the probe set includes probes targeting from 100 to 10,000 genomic loci. In some embodiments, the probe set includes probes targeting from 100 to 5000 genomic loci. In some embodiments, the probe set includes probes targeting from 100 to 1000 genomic loci. In some embodiments, the probe set includes probes targeting from 100 to 750 genomic loci. In some embodiments, the probe set includes probes targeting from 250 to 10,000 genomic loci. In some embodiments, the probe set includes probes targeting from 250 to 5000 genomic loci. In some embodiments, the probe set includes probes targeting from 250 to 1000 genomic loci. In some embodiments, the probe set includes probes targeting from 250 to 750 genomic loci. In some embodiments, the probe set includes probes targeting from 500 to 10,000 genomic loci. In some embodiments, the probe set includes probes targeting from 500 to 5000 genomic loci. In some embodiments, the probe set includes probes targeting from 500 to 1000 genomic loci. In some embodiments, the probe set includes probes targeting from 500 to 750 genomic loci.

[0246] In some embodiments, the probe sets described herein target a plurality of genes and / or associated non-coding regions (e.g., promoters, introns, etc.) whose sequences can be analyzed, e.g., to identify sequence and / or structural variants, to inform clinical treatment of cancers. In some embodiments, the probe set includes probes targeting all or a portion of the coding sequence (CDS) of at least 10 genes. In some embodiments, the probe set includes probes targeting all or a portion of the coding sequence (CDS) of at least 25 genes. In some embodiments, the probe set includes probes targeting all or a portion of the coding sequence (CDS) of at least 50 genes. In some embodiments, the probe set includes probes targeting all or a portion of the coding sequence (CDS) of at least 100 genes. In some embodiments, the probe set includes probes targeting all or a portion of the coding sequence (CDS) of at least 200 genes. In some embodiments, the probe set includes probes targeting all or a portion of the coding sequence (CDS) of at least 300 genes. In some embodiments, the probe set includes probes targeting all or a portion of the coding sequence (CDS) of at least 400 genes. In some embodiments, the probe set includes probes targeting all or a portion of the codingsequence (CDS) of at least 500 genes. In some embodiments, the probe set includes probes targeting all or a portion of the coding sequence (CDS) of at least 750 genes. In some embodiments, the probe set includes probes targeting all or a portion of the coding sequence (CDS) of at least 1000 genes. In some embodiments, the probe set includes probes targeting all or a portion of the coding sequence (CDS) of at least 2500 genes. In some embodiments, the probe set includes probes targeting all or a portion of the coding sequence (CDS) of at least 5000 genes.

[0247] In some embodiments, the probe set includes probes targeting all or a portion of the coding sequence (CDS) of not more than 10,000 genes. In some embodiments, the probe set includes probes targeting all or a portion of the CDS of not more than 5000 genes. In some embodiments, the probe set includes probes targeting all or a portion of the CDS of not more than 2500 genes. In some embodiments, the probe set includes probes targeting all or a portion of the CDS of not more than 1000 genes. In some embodiments, the probe set includes probes targeting all or a portion of the CDS of not more than 750 genes.

[0248] In some embodiments, the probe set includes probes targeting the CDS of from 10 to 10,000 genes. In some embodiments, the probe set includes probes targeting the CDS of from 10 to 5000 genes. In some embodiments, the probe set includes probes targeting the CDS of from 10 to 1000 genes. In some embodiments, the probe set includes probes targeting the CDS of from 10 to 750 genes. In some embodiments, the probe set includes probes targeting the CDS of from 50 to 10,000 genes. In some embodiments, the probe set includes probes targeting the CDS of from 50 to 5000 genes. In some embodiments, the probe set includes probes targeting the CDS of from 50 to 1000 genes. In some embodiments, the probe set includes probes targeting the CDS of from 50 to 750 genes. In some embodiments, the probe set includes probes targeting the CDS of from 100 to 10,000 genes. In some embodiments, the probe set includes probes targeting the CDS of from 100 to 5000 genes. In some embodiments, the probe set includes probes targeting the CDS of from 100 to 1000 genes. In some embodiments, the probe set includes probes targeting the CDS of from 100 to 750 genes. In some embodiments, the probe set includes probes targeting the CDS of from 250 to 10,000 genes. In some embodiments, the probe set includes probes targeting the CDS of from 250 to 5000 genes. In some embodiments, the probe set includes probes targeting the CDS of from 250 to 1000 genes. In some embodiments, the probe set includes probes targeting the CDS of from 250 to 750 genes. In some embodiments, the probe set includes probes targeting the CDS of from 500 to 10,000 genes. In someembodiments, the probe set includes probes targeting the CDS of from 500 to 5000 genes. In some embodiments, the probe set includes probes targeting the CDS of from 500 to 1000 genes. In some embodiments, the probe set includes probes targeting the CDS of from 500 to 750 genes.

[0249] In some embodiments, the probe set includes probes targeting one or more of the genes listed in List 1, provided below. In some embodiments, the probe set includes probes targeting at least 5 of the genes listed in List 1. In some embodiments, the probe set includes probes targeting at least 10 of the genes listed in List 1. In some embodiments, the probe set includes probes targeting at least 25 of the genes listed in List 1. In some embodiments, the probe set includes probes targeting at least 50 of the genes listed in List 1. In some embodiments, the probe set includes probes targeting at least 75 of the genes listed in List 1. In some embodiments, the probe set includes probes targeting at least 100, at least 150, at least 200, at least 250, at least 300, at least 350, at least 400, or at least 450 of the genes listed in List 1. In some embodiments, the probe set includes probes targeting all the genes listed in List 1.

[0250] List 1 (523 genes): ABCC3, ABL1, ABL2, ABRAXAS1, ACVR1, ACVR1B, AJUBA, AKT1, AKT2, AKT3, ALK, ALOX12B, AMER1, APC, APLNR, AR, ARAF, ARFRP1, ARID1A, ARID1B, ARID2, ASNS, ASXL1, ATM, ATR, ATRX, AURKA, AURKB, AURKC, AXIN1, AXIN2, AXL, B2M, BAP1, BARD1, BAX, BCL2, BCL2L1, BCL2L11, BCL2L2, BCL6, BCLAF1, BCOR, BCORL1, BCR, BIRC3, BLM, BMPR1A, BRAF, BRCA1, BRCA2, BRD4, BRIP1, BTG1, BTG2, BTK, CALR, CARD11, CARM1, CASP8, CBFB, CBL, CCND1, CCND2, CCND3, CCNE1, CD22, CD274, CD70, CD74, CD79A, CD79B, CDC73, CDH1, CDK12, CDK4, CDK6, CDK8, CDK9, CDKN1A, CDKN1B, CDKN2A, CDKN2B, CDKN2C, CEBPA, CHD4, CHEK1, CHEK2, CIC, CKS1B, CREBBP, CRKL, CSF1R, CSF3R, CTC1, CTCF, CTLA4, CTNNA1, CTNNB1, CUL3, CUL4A, CUX1, CXCR4, CYLD, CYP17A1, CYSLTR2, DAXX, DDB2, DDR1, DDR2, DDX3X, DDX41, DEPTOR, DICER1, DIS3, DNMT1, DNMT3A, DOT1L, DPYD, EBF1, EED, EEF2, EGFR, EGLN1, EIF1AX, ELF3, EMSY, EP300, EPCAM, EPHA2, EPHA3, EPHB1, EPHB4, ERBB2, ERBB3, ERBB4, ERCC2, ERCC3, ERCC4, ERCC6, ERG, ERRFI1, ESR1, ETNK1, ETV1, ETV4, ETV5, ETV6, EWSR1, EZH2, EZR, FAM46C, FANCA, FANCC, FANCD2, FANCE, FANCG, FANCI, FANCL, FANCM, FAS, FAT1, FBXW7, FCGR2A, FCGR3A, FGF10, FGF12, FGF14, FGF19, FGF23, FGF3, FGF4, FGF6, FGFR1, FGFR2, FGFR3, FGFR4, FH, FHIT, FLCN, FLT1, FLT3, FLT4, FOLH1,FOXA1, FOXL2, FOXO1, FOXO3, FOXP1, FRS2, FUBP1, GABRA6, GALNT12, GATA1, GATA3, GATA4, GATA6, GID4, GLI2, GNA11, GNA13, GNAQ, GNAS, GPC3, GPS2, GREM1, GRIN2A, GRM3, GSK3B, GSTP1, H3F3A, HAVCR2, HDAC1, HDAC2, HGF, HIF1A, HIST1H3B, HLA-B, HNF1A, HNF1B, HOXB13, HRAS, HSD3B1, HSP90AA1, HSPH1, ID3, IDH1, IDH2, IFNA21, IFNAR1, IFNAR2, IFNG, IFNGR1, IFNGR2, IFNW1, IGF1, IGF1R, IKBKE, IKZF1, IL10RA, IL32, IL6R, IL7R, IMPDH1, ING1, INPP4B, INSR, IRF1, IRF2, IRF4, IRS2, JAK1, JAK2, JAK3, JUN, KAT6A, KDM5A, KDM5C, KDM5D, KDM6A, KDR, KEAP1, KEL, KIT, KLF4, KLHL6, KLLN, KMT2A, KMT2C, KMT2D, KRAS, LATS1, LCK, LMO1, LRP1B, LTK, LYN, LZTR1, MAF, MALT1, MAP2K1, MAP2K2, MAP2K4, MAP3K1, MAP3K13, MAP3K21, MAP3K7, MAPK1, MAPK3, MAX, MC1R, MCL1, MDM2, MDM4, MED12, MEF2B, MEN1, MERTK, MET, MITF, MKNK1, MLH1, MLH3, MPL, MRE11, MS4A1, MSH2, MSH3, MSH6, MST1R, MTAP, MTHFR, MTOR, MUC16, MUTYH, MYB, MYC, MYCL, MYCN, MYD88, NBN, NCOA2, NCOR1, NF1, NF2, NFE2L2, NFKBIA, NKX2-1, NOTCH1, NOTCH2, NOTCH3, NOTCH4, NPM1, NQO1, NRAS, NRG1, NSD1, NSD2, NSD3, NT5C2, NTRK1, NTRK2, NTRK3, NUTM1, P2RY8, PAK1, PALB2, PALLD, PARP1, PARP2, PARP3, PAX5, PBRM1, PDCD1, PDCD1LG2, PDGFRA, PDGFRB, PDK1, PHGDH, PHLPP1, PHLPP2, PIAS4, PIK3C2B, PIK3C2G, PIK3CA, PIK3CB, PIK3CD, PIK3CG, PIK3R1, PIK3R2, PIM1, PLCG1, PLCG2, PMS1, PMS2, POLA1, POLD1, POLE, POLQ, POT1, PPARG, PPM1D, PPP2R1A, PPP2R2A, PPP6C, PRDM1, PREX2, PRKACA, PRKAR1A, PRKCI, PRKN, PTCH1, PTEN, PTK2, PTPN11, PTPN13, PTPRD, PTPRO, PTPRT, QKI, RAC1, RAD21, RAD50, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54L, RAF1, RARA, RASA1, RB1, RBM10, RECQL4, REL, RET, RHEB, RHOA, RICTOR, RIT1, RNF43, ROS1, RPS6KB1, RPTOR, RRM1, RSF1, RSPO2, RUNX1, RXRA, SDC4, SDHA, SDHAF2, SDHB, SDHC, SDHD, SETBP1, SETD2, SF3B1, SGK1, SIRPA, SLC34A2, SLC9A3R1, SLFN11, SLIT2, SMAD2, SMAD3, SMAD4, SMARCA2, SMARCA4, SMARCB1, SMC1A, SMC3, SMO, SNCAIP, SOCS1, SOS1, SOX2, SOX9, SPEN, SPOP, SRC, SRSF2, STAG2, STAT3, STAT5B, STAT6, STK11, SUFU, SUZ12, SYK, TBX3, TCF7L2, TEK, TERC, TERT, TET2, TFEB, TGFB1, TGFBR1, TGFBR2, TIGIT, TIPARP, TMEM127, TMPRSS2, TNFAIP3, TNFRSF14, TNFRSF17, TOP1, TOP2A, TP53, TP53BP1, TP63, TRAF3, TRAF7, TSC1, TSC2, TSHR, TYMS, TYRO3, U2AF1, UGT1A1, VEGFA, VHL, VSIR, WEE1, WNK1, WRN, WT1, XBP1, XPA, XPC, XPO1, XRCC1, XRCC2, YEATS4, ZFHX3, ZMYM3, ZNF217, ZNF703, ZNF750, ZNRF3, and ZRSR2.

[0251] In some embodiments, the probe sets described herein include a first subset of probes targeting a first subset of genes, which are used at a first average concentration in a hybridization reaction, and a second subset of probes targeting a second subset of genes, which are used at a second concentration that is from five to eight times greater than the first average concentration. In some embodiments, the first subset of genes includes at least 10 of the genes listed in List 1 and the second subset of genes includes at least 10 of the genes listed in List 1. In some embodiments, the first subset of genes includes at least 25 of the genes listed in List 1 and the second subset of genes includes at least 25 of the genes listed in List 1. In some embodiments, the first subset of genes includes at least 50 of the genes listed in List 1 and the second subset of genes includes at least 50 of the genes listed in List 1. In some embodiments, the first subset of genes includes at least 75 of the genes listed in List 1 and the second subset of genes includes at least 75 of the genes listed in List 1. In some embodiments, the first subset of genes includes at least 100 of the genes listed in List 1 and the second subset of genes includes at least 100 of the genes listed in List 1.

[0252] In some embodiments, where a first subset of probes targeting a first subset of genes, which are used at a first average concentration in a hybridization reaction, and a second subset of probes targeting a second subset of genes, which are used at a second concentration that is from five to eight times greater than the first average concentration, the first subset of genes includes at least 10 of the genes listed in List 2 and the second subset of genes includes at least 10 of the genes listed in List 3. In some such embodiments, the first subset of genes includes at least 25 of the genes listed in List 2 and the second subset of genes includes at least 25 of the genes listed in List 3. In some such embodiments, the first subset of genes includes at least 50 of the genes listed in List 2 and the second subset of genes includes at least 50 of the genes listed in List 3. In some such embodiments, the first subset of genes includes at least 75 of the genes listed in List 2 and the second subset of genes includes at least 75 of the genes listed in List 3. In some such embodiments, the first subset of genes includes at least 100 of the genes listed in List 2 and the second subset of genes includes at least 100 of the genes listed in List 3. In some such embodiments, the first subset of genes includes at least 200 of the genes listed in List 2 and the second subset of genes includes at least 100 of the genes listed in List 3. In some such embodiments, the first subset of genes includes at least 300 of the genes listed in List 2 and the second subset of genes includes at least 100 of the genes listed in List 3. In some such embodiments, the first subset of genes includes at least 400 of the genes listed in List 2 and the second subset ofgenes includes at least 100 of the genes listed in List 3. In some such embodiments, the first subset of genes includes all of the genes listed in List 2 and the second subset of genes includes all of the genes listed in List 3.

[0253] List 2 (409 genes): ABCC3, ABL2, ABRAXAS1, ACVR1, ACVR1B, AJUBA, AKT3, ALOX12B, AMER1, APLNR, ARFRP1, ARID1B, ARID2, ASNS, ASXL1, ATRX, AURKA, AURKB, AURKC, AXIN1, AXIN2, AXL, BARD1, BAX, BCL2, BCL2L1, BCL2L11, BCL2L2, BCL6, BCLAF1, BCOR, BCORL1, BCR, BIRC3, BLM, BMPR1A, BRD4, BRIP1, BTG1, BTG2, CALR, CARD11, CARM1, CASP8, CBFB, CBL, CD22, CD70, CD74, CD79A, CD79B, CDC73, CDK8, CDK9, CDKN1A, CDKN1B, CDKN2B, CDKN2C, CEBPA, CHD4, CHEK1, CIC, CKS1B, CREBBP, CSF1R, CSF3R, CTC1, CTCF, CTLA4, CTNNA1, CUL3, CUL4A, CUX1, CXCR4, CYLD, CYP17A1, CYSLTR2, DAXX, DDB2, DDR1, DDX3X, DDX41, DEPTOR, DICER1, DIS3, DNMT1, DNMT3A, DOT1L, DPYD, EBF1, EED, EEF2, EGLN1, EIF1AX, ELF3, EMSY, EP300, EPCAM, EPHA2, EPHA3, EPHB1, EPHB4, ERBB4, ERCC2, ERCC3, ERCC4, ERCC6, ERG, ETNK1, ETV1, ETV4, ETV5, ETV6, EWSR1, EZR, FAM46C, FANCA, FANCC, FANCD2, FANCE, FANCG, FANCI, FANCL, FANCM, FAS, FAT1, FCGR2A, FCGR3A, FGF10, FGF12, FGF14, FGF19, FGF23, FGF3, FGF4, FGF6, FH, FHIT, FLCN, FLT1, FLT4, FOLH1, FOXA1, FOXO1, FOXO3, FOXP1, FRS2, FUBP1, GABRA6, GALNT12, GATA1, GATA4, GATA6, GID4, GLI2, GNA13, GPC3, GPS2, GREM1, GRIN2A, GRM3, GSK3B, GSTP1, H3F3A, HAVCR2, HDAC1, HDAC2, HGF, HIF1A, HIST1H3B, HLA-B, HNF1B, HOXB13, HSD3B1, HSP90AA1, HSPH1, ID3, IFNA21, IFNAR1, IFNAR2, IFNG, IFNGR1, IFNGR2, IFNW1, IGF1, IGF1R, IKBKE, IKZF1, IL10RA, IL32, IL6R, IL7R, IMPDH1, ING1, INPP4B, INSR, IRF1, IRF2, IRF4, IRS2, JUN, KAT6A, KDM5A, KDM5C, KDM5D, KDM6A, KEL, KLF4, KLHL6, KLLN, KMT2C, KMT2D, LATS1, LCK, LMO1, LRP1B, LTK, LYN, LZTR1, MAF, MALT1, MAP2K4, MAP3K1, MAP3K13, MAP3K21, MAP3K7, MAX, MC1R, MCL1, MDM4, MED12, MEF2B, MEN1, MERTK, MITF, MKNK1, MLH3, MRE11, MS4A1, MST1R, MTAP, MTHFR, MUC16, MUTYH, MYB, MYCL, NBN, NCOA2, NCOR1, NFKBIA, NKX2-1, NOTCH2, NOTCH3, NOTCH4, NQO1, NRG1, NSD1, NSD2, NSD3, NT5C2, NUTM1, P2RY8, PAK1, PALLD, PARP1, PARP2, PARP3, PAX5, PDCD1, PDK1, PHGDH, PHLPP1, PHLPP2, PIAS4, PIK3C2B, PIK3C2G, PIK3CB, PIK3CD, PIK3CG, PIK3R2, PIM1, PLCG1, PLCG2, PMS1, POLA1, POLD1, POLE, POLQ, POT1, PPARG, PPM1D, PPP2R1A, PPP2R2A, PPP6C, PRDM1, PREX2, PRKACA, PRKAR1A, PRKCI, PRKN, PTK2, PTPN13, PTPRD, PTPRO,PTPRT, QKI, RAC1, RAD21, RAD50, RAD51, RAD51B, RAD51D, RAD52, RAD54L, RARA, RASA1, RBM10, RECQL4, REL, RICTOR, RPS6KB1, RPTOR, RRM1, RSF1, RSPO2, RUNX1, RXRA, SDC4, SDHAF2, SDHB, SDHC, SDHD, SETBP1, SETD2, SF3B1, SGK1, SIRPA, SLC34A2, SLC9A3R1, SLFN11, SLIT2, SMAD2, SMAD3, SMARCA2, SMARCA4, SMARCB1, SMC1A, SMC3, SNCAIP, SOCS1, SOS1, SOX2, SOX9, SPEN, SRC, SRSF2, STAG2, STAT3, STAT5B, STAT6, SUFU, SUZ12, SYK, TBX3, TCF7L2, TEK, TERC, TET2, TFEB, TGFB1, TGFBR1, TGFBR2, TIGIT, TIPARP, TMEM127, TMPRSS2, TNFAIP3, TNFRSF14, TNFRSF17, TOP1, TOP2A, TP53BP1, TP63, TRAF3, TRAF7, TSHR, TYMS, TYRO3, U2AF1, UGT1A1, VSIR, WEE1, WNK1, WRN, WT1, XBP1, XPA, XPC, XPO1, XRCC1, XRCC2, YEATS4, ZFHX3, ZMYM3, ZNF217, ZNF703, ZNF750, ZNRF3, and ZRSR2.

[0254] List 3 (114 genes): ABL1, AKT1, AKT2, ALK, APC, AR, ARAF, ARID1A, ATM, ATR, B2M, BAP1, BRAF, BRCA1, BRCA2, BTK, CCND1, CCND2, CCND3, CCNE1, CD274, CDH1, CDK12, CDK4, CDK6, CDKN2A, CHEK2, CRKL, CTNNB1, DDR2, EGFR, ERBB2, ERBB3, ERRFI1, ESR1, EZH2, FBXW7, FGFR1, FGFR2, FGFR3, FGFR4, FLT3, FOXL2, GATA3, GNA11, GNAQ, GNAS, HNF1A, HRAS, IDH1, IDH2, JAK1, JAK2, JAK3, KDR, KEAP1, KIT, KMT2A, KRAS, MAP2K1, MAP2K2, MAPK1, MAPK3, MDM2, MET, MLH1, MPL, MSH2, MSH3, MSH6, MTOR, MYC, MYCN, MYD88, NF1, NF2, NFE2L2, NOTCH1, NPM1, NRAS, NTRK1, NTRK2, NTRK3, PALB2, PBRM1, PDCD1LG2, PDGFRA, PDGFRB, PIK3CA, PIK3R1, PMS2, PTCH1, PTEN, PTPN11, RAD51C, RAF1, RB1, RET, RHEB, RHOA, RIT1, RNF43, ROS1, SDHA, SMAD4, SMO, SPOP, STK11, TERT, TP53, TSC1, TSC2, VEGFA, and VHL.

[0255] In some embodiments, in addition to including probes targeting some or all of the CDS, the probe set includes probes directed to all of some of the introns of select genes, e.g., genes in which sequence and / or structural variants are known to be associated with a disease or disorder such as cancer. In some embodiments, the probe set includes probes directed to some or all of the introns of a gene listed in list 4. In some embodiments, the probe set includes probes directed to some or all of the introns of at least 5 genes listed in list 4. In some embodiments, the probe set includes probes directed to some or all of the introns of at least 10 genes listed in list 4. In some embodiments, the probe set includes probes directed to some or all of the introns of all of the genes listed in list 4. In some embodiments, probes directed to an intron of a gene are used in a hybridization reaction at an enhanced concentration, e.g., at a concentration of from five to eight times a base concentration atwhich probes directed to ‘non-enhanced’ genes are used in the reaction. In other embodiments, probes directed to an intron of a gene are used in a hybridization reaction at a base concentration, e.g., a non-enhanced concentration. In some embodiments, probes directed to an intron of some genes are used in a hybridization reaction at an enhanced concentration, e.g., from five to eight times a base concentration, and probes directed to an intron in other genes are used in the hybridization reaction at a base concentration.

[0256] List 4: ALK, BRAF, EGFR, FGFR1, FGFR2, FGFR3, NTRK1, NTRK2, NTRK3, RET, and ROS1.

[0257] In some embodiments, in addition to including probes targeting some or all of the CDS, the probe set includes probes directed to a promoter region of a gene. In some embodiments, the probe set includes probes directed to the TERT gene. In some embodiments, probes directed to a promoter region of a gene are used in a hybridization reaction at an enhanced concentration, e.g., at a concentration of from five to eight times a base concentration at which probes directed to ‘non-enhanced’ genes are used in the reaction. In other embodiments, probes directed to the promoter region of a gene are used in a hybridization reaction at a base concentration, e.g., a non-enhanced concentration. In some embodiments, probes directed to a promoter region of some genes are used in a hybridization reaction at an enhanced concentration, e.g., from five to eight times a base concentration, and probes directed to a promoter region in other genes are used in the hybridization reaction at a base concentration.

[0258] In some embodiments, the sequences generated from one or more target reads are evaluated for gene fusions, e.g., in addition to being evaluated for SNVs and / or MNVs and / or other variants. In some embodiments, the probe set includes probes targeting at least 5 genes for which the sequences will be evaluated for gene fusions. In some embodiments, the probe set includes probes targeting at least 10 genes for which the sequences will be evaluated for gene fusions. In some embodiments, the probe set includes probes directed to at least 5 of the genes listed in list 5, for which the sequences will be evaluated for gene fusions. In some embodiments, the probe set includes probes directed to all of the genes listed in list 5, for which the sequences will be evaluated for gene fusions.

[0259] List 5: ALK, BRAF, FGFR1, FGFR2, FGFR3, NTRK1, NTRK2, NTRK3, RET, and ROS1.

[0260] In some embodiments, the sequences generated from one or more target reads are evaluated for local copy number variations, in addition to being evaluated for SNVs and / or MNVs and / or other variants. In some embodiments, the probe set includes probes targeting at least 5 genes for which the sequences will be evaluated for local copy number variations. In some embodiments, the probe set includes probes targeting at least 10 genes for which the sequences will be evaluated for local copy number variations. In some embodiments, the probe set includes probes directed to at least 5 of the genes listed in list 6, for which the sequences will be evaluated for local copy number variations. In some embodiments, the probe set includes probes directed to all of the genes listed in list 6, for which the sequences will be evaluated for local copy number variations.

[0261] List 6: BRCA1, BRCA2, CCNE1, CD274, EGFR, ERBB2, MDM2, MET, and MYC.

[0262] Generally, probes for enrichment of nucleic acids (e.g., cfDNA obtained from a liquid biopsy sample) include DNA, RNA, or a modified nucleic acid structure with a base sequence that is complementary to a locus of interest. For instance, a probe designed to hybridize to a locus in a cfDNA molecule can contain a sequence that is complementary to either strand, because the cfDNA molecules are double stranded. In some embodiments, each probe in the plurality of probes includes a nucleic acid sequence that is identical or complementary to at least 10, at least 11, at least 12, at least 13, at least 14, or at least 15 consecutive bases of a loci of interest. In some embodiments, each probe in the plurality of probes includes a nucleic acid sequence that is identical or complementary to at least 20, 25, 30, 40, 50, 75, 100, 150, 200, or more consecutive bases of a locus of interest.

[0263] Targeted panels provide several benefits for nucleic acid sequencing. For example, in some embodiments, algorithms for discriminating between, e.g., a first and second cancer condition can be trained on smaller, more informative data sets (e.g., fewer genes), which leads to more computationally efficient training of classifiers that discriminate between the first and second cancer states. Such improvements in computational efficiency, owing to the reduced size of the discriminating gene set, can advantageously either be used to speed up classifier training or be used to improve the performance of such classifiers (e.g., through more extensive training of the classifier).

[0264] In some embodiments, the probes include additional nucleic acid sequences that do not share any homology to the loci of interest. For example, in some embodiments, theprobes also include nucleic acid sequences containing an identifier sequence, e.g., a unique molecular identifier (UMI), e.g., that is unique to a particular sample or subject. Examples of identifier sequences are described, for example, in Kivioja et al., 2011, Nat. Methods 9(1), pp.72-74 and Islam et al., 2014, Nat. Methods 11(2), pp.163-66, which are incorporated by reference herein. Similarly, in some embodiments, the probes also include primer nucleic acid sequences useful for amplifying the nucleic acid molecule of interest, e.g., using PCR. In some embodiments, the probes also include a capture sequence designed to hybridize to an anti-capture sequence for recovering the nucleic acid molecule of interest from the sample.

[0265] Likewise, in some embodiments, the probes each include a non-nucleic acid affinity moiety covalently attached to a nucleic acid molecule that is complementary to the loci of interest, for recovering the nucleic acid molecule of interest. Non-limited examples of non-nucleic acid affinity moieties include biotin, digoxigenin, and dinitrophenol. In some embodiments, the probe is attached to a solid-state surface or particle, e.g., a dipstick or magnetic bead, for recovering the nucleic acid of interest. In some embodiments, the methods described herein include amplifying the nucleic acids that bound to the probe set prior to further analysis, e.g., sequencing. Methods for amplifying nucleic acids, e.g., by PCR, are well known in the art.

[0266] Sequence reads are then generated (312) from the sequencing library or pool of sequencing libraries. Sequencing data may be acquired by any methodology known in the art. For example, next generation sequencing (NGS) techniques such as sequencing-by- synthesis technology (Illumina), pyrosequencing (454 Life Sciences), ion semiconductor technology (Ion Torrent sequencing), single-molecule real-time sequencing (Pacific Biosciences), sequencing by ligation (SOLiD sequencing), nanopore sequencing (Oxford Nanopore Technologies), or paired-end sequencing. In some embodiments, massively parallel sequencing is performed using sequencing-by-synthesis with reversible dye terminators. In some embodiments, sequencing is performed using next generation sequencing technologies, such as short-read technologies. In other embodiments, long-read sequencing or another sequencing method known in the art is used.

[0267] Next-generation sequencing produces millions of short reads (e.g., sequence reads) for each biological sample. Accordingly, in some embodiments, the plurality of sequence reads obtained by next-generation sequencing of cfDNA molecules are DNA sequence reads. In some embodiments, the sequence reads have an average length of at leastfifty nucleotides. In other embodiments, the sequence reads have an average length of at least 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, or more nucleotides.

[0268] In some embodiments, sequencing is performed after enriching for nucleic acids (e.g., cfDNA, gDNA, and / or RNA) encompassing a plurality of predetermined target sequences, e.g., human genes and / or non-coding sequences associated with cancer. Advantageously, sequencing a nucleic acid sample that has been enriched for target nucleic acids, rather than all nucleic acids isolated from a biological sample, significantly reduces the average time and cost of the sequencing reaction. Accordingly, in some embodiments, the methods described herein include obtaining a plurality of sequence reads of nucleic acids that have been hybridized to a probe set for hybrid-capture enrichment (e.g., of one or more genes listed in Lists 1-6).

[0269] In some embodiments, panel-targeting sequencing is performed to an average on- target depth of at least 500x, at least 750x, at least 1000x, at least 2500x, at least 500x, at least 10,000x, or greater depth. In some embodiments, samples are further assessed for uniformity above a sequencing depth threshold (e.g., 95% of all targeted base pairs at 300x sequencing depth). In some embodiments, the sequencing depth threshold is a minimum depth selected by a user or practitioner.

[0270] In some embodiments, the sequence reads are obtained by a whole genome or whole exome sequencing methodology. In some such embodiments, whole exome capture is performed with an automated system, using a liquid handling robot (for example, a SciClone NGSx). Whole genome sequencing, and to some extent whole exome sequencing, is typically performed at lower sequencing depth than smaller target-panel sequencing reactions, because many more loci are being sequenced. For example, in some embodiments, whole genome or whole exome sequencing is performed to an average sequencing depth of at least 3x, at least 5x, at least 10x, at least 15x, at least 20x, or greater. In some embodiments, low-pass whole genome sequencing (LPWGS) techniques are used for whole genome or whole exome sequencing. LPWGS is typically performed to an average sequencing depth of about 0.25x to about 5x, more typically to an average sequencing depth of about 0.5x to about 3x.

[0271] Because of the differences in the sequencing methodologies, data obtained from targeted-panel sequencing is better suited for certain analyses than data obtained from whole genome / whole exome sequencing, and vice versa. For instance, because of the highersequencing depth achieved by targeted-panel sequencing, the resulting sequence data is better suited for the identification of variant alleles present at low allelic fractions in the sample, e.g., less than 20%. By contrast, data generated from whole genome / whole exome sequencing is better suited for the estimation of genome-wide metrics, such as tumor mutational burden, because the entire genome is better represented in the sequencing data. Accordingly, in some embodiments, a nucleic acid sample, e.g., a cfDNA, gDNA, or mRNA sample, is evaluated using both targeted-panel sequencing and whole genome / whole exome sequencing (e.g., LPWGS).

[0272] In some embodiments, the raw sequence reads resulting from the sequencing reaction are output from the sequencer in a native file format, e.g., a BCL file. In some embodiments, the native file is passed directly to a bioinformatics pipeline (e.g., variant analysis 206), components of which are described in detail below. In other embodiments, pre-processing is performed prior to passing the sequences to the bioinformatics platform. For instance, in some embodiments, the format of the sequence read file is converted from the native file format (e.g., BCL) to a file format compatible with one or more algorithms used in the bioinformatics pipeline (e.g., FASTQ or FASTA). In some embodiments, the raw sequence reads are filtered to remove sequences that do not meet one or more quality thresholds. In some embodiments, raw sequence reads generated from the same unique nucleic acid molecule in the sequencing read are collapsed into a single sequence read representing the molecule, e.g., using UMIs as described above. In some embodiments, one or more of these pre-processing activities is performed within the bioinformatics pipeline itself.

[0273] In one example, a sequencer may generate a BCL file. A BCL file may include raw image data of a plurality of patient specimens which are sequenced. BCL image data is an image of the flow cell across each cycle during sequencing. A cycle may be implemented by illuminating a patient specimen with a specific wavelength of electromagnetic radiation, generating a plurality of images which may be processed into base calls via BCL to FASTQ processing algorithms which identify which base pairs are present at each cycle. The resulting FASTQ file includes the entirety of reads for each patient specimen paired with a quality metric, e.g., in a range from 0 to 64 where a 64 is the best quality and a 0 is the worst quality. In embodiments where both a liquid biopsy sample and a normal tissue sample are sequenced, sequence reads in the corresponding FASTQ files may be matched, such that a liquid biopsy-normal analysis may be performed.

[0274] FASTQ format is a text-based format for storing both a biological sequence, such as a nucleotide sequence, and its corresponding quality scores. These FASTQ files are analyzed to determine what genetic variants or copy number changes are present in the sample. Each FASTQ file contains reads that may be paired-end or single reads and may be short-reads or long-reads, where each read represents one detected sequence of nucleotides in a nucleic acid molecule that was isolated from the patient sample or a copy of the nucleic acid molecule, detected by the sequencer. Each read in the FASTQ file is also associated with a quality rating. The quality rating may reflect the likelihood that an error occurred during the sequencing procedure that affected the associated read. In some embodiments, the results of paired-end sequencing of each isolated nucleic acid sample are contained in a split pair of FASTQ files, for efficiency. Thus, in some embodiments, forward (Read 1) and reverse (Read 2) sequences of each isolated nucleic acid sample are stored separately but in the same order and under the same identifier.

[0275] In various embodiments, the bioinformatics pipeline may filter FASTQ data from the corresponding sequence data file for each respective biological sample. Such filtering may include correcting or masking sequencer errors and removing (trimming) low quality sequences or bases, adapter sequences, contaminations, chimeric reads, overrepresented sequences, biases caused by library preparation, amplification, or capture, and other errors.

[0276] While workflow 200 illustrates obtaining a biological sample, extracting nucleic acids from the biological sample, and sequencing the isolated nucleic acids, in some embodiments, sequencing data used in the improved systems and methods described herein (e.g., which include improved methods for validating a somatic sequence variant in a test subject having a cancer condition) is obtained by receiving previously generated sequence reads, in electronic form.

[0277] Referring again to Figure 2A, nucleic acid sequencing data 122 generated from the one or more patient samples is then evaluated (e.g., via variant analysis 206) in a bioinformatics pipeline, e.g., using bioinformatics module 140 of system 100, to identify genomic alterations and other metrics in the cancer genome of the patient. An example overview for a bioinformatics pipeline is described below with respect to Figures 4A-4E. Advantageously, in some embodiments, the present disclosure improves bioinformatics pipelines, like pipeline 206, by improving methods and systems of validating somatic sequence variants.

[0278] Figure 4A illustrates an example bioinformatics pipeline 206 (e.g., as used for feature extraction in the workflows illustrated in Figures 2A and 3) for providing clinical support for precision oncology. As shown in Figure 4A, sequencing data 122 obtained from the wet lab processing 204 (e.g., sequence reads 314) is input into the pipeline.

[0279] In various embodiments, the bioinformatics pipeline includes a circulating tumor DNA (ctDNA) pipeline for analyzing liquid biopsy samples. The pipeline may detect SNVs, INDELs, copy number amplifications / deletions and genomic rearrangements (for example, fusions). The pipeline may employ unique molecular index (UMI)-based consensus base calling as a method of error suppression as well as a Bayesian tri-nucleotide context-based position level error suppression. In various embodiments, it is able to detect variants having a 0.1%, 0.15%, 0.2%, 0.25%, 0.3%, 0.4%, or 0.5% variant allele fraction.

[0280] In some embodiments, the sequencing data is processed (e.g., using sequence data processing module 141) to prepare it for genomic feature identification 385. For instance, in some embodiments as described above, the sequencing data is present in a native file format provided by the sequencer. Accordingly, in some embodiments, the system (e.g., system 100) applies a pre-processing algorithm 142 to convert the file format (318) to one that is recognized by one or more upstream processing algorithms. For example, BCL file outputs from a sequencer can be converted to a FASTQ file format using the bcl2fastq or bcl2fastq2 conversion software (Illumina®). FASTQ format is a text-based format for storing both a biological sequence, such as nucleotide sequence, and its corresponding quality scores. These FASTQ files are analyzed to determine what genetic variants, copy number changes, etc., are present in the sample.

[0281] In some embodiments, other preprocessing functions are performed, e.g., filtering sequence reads 122 based on a desired quality, e.g., size and / or quality of the base calling. In some embodiments, quality control checks are performed to ensure the data is sufficient for variant calling. For instance, entire reads, individual nucleotides, or multiple nucleotides that are likely to have errors may be discarded based on the quality rating associated with the read in the FASTQ file, the known error rate of the sequencer, and / or a comparison between each nucleotide in the read and one or more nucleotides in other reads that has been aligned to the same location in the reference genome. Filtering may be done in part or in its entirety by various software tools, for example, a software tool such as Skewer. See, Jiang, H. et al., BMC Bioinformatics 15(182):1-12 (2014). FASTQ files may be analyzed for rapid assessment of quality control and reads, for example, by a sequencing data QC software suchas AfterQC, Kraken, RNA-SeQC, FastQC, or another similar software program. For paired end reads, reads may be merged.

[0282] In some embodiments, when both a liquid biopsy sample and a normal tissue sample from the patient are sequenced, two FASTQ output files are generated, one for the liquid biopsy sample and one for the normal tissue sample. A ‘matched’ (e.g., panel-specific) workflow is run to jointly analyze the liquid biopsy-normal matched FASTQ files. When a matched normal sample is not available from the patient, FASTQ files from the liquid biopsy sample are analyzed in the ‘tumor-only’ mode. See, for example, Figure 4B. If two or more patient samples are processed simultaneously on the same sequencer flow cell, e.g., a liquid biopsy sample and a normal tissue sample, a difference in the sequence of the adapters used for each patient sample barcodes nucleic acids extracted from both samples, to associate each read with the correct patient sample and facilitate assignment to the correct FASTQ file.

[0283] For efficiency, in some embodiments, the results of paired-end sequencing of each isolate are contained in a split pair of FASTQ files. Forward (Read 1) and reverse (Read 2) sequences of each tumor and normal isolate are stored separately but in the same order and under the same identifier. See, for example, Figure 4C. In various embodiments, the bioinformatics pipeline may filter FASTQ data from each isolate. Such filtering may include correcting or masking sequencer errors and removing (trimming) low quality sequences or bases, adapter sequences, contaminations, chimeric reads, overrepresented sequences, biases caused by library preparation, amplification, or capture, and other errors. See, for example, Figure 4D.

[0284] Similarly, in some embodiments, sequencing (312) is performed on a pool of nucleic acid sequencing libraries prepared from different biological samples, e.g., from the same or different patients. Accordingly, in some embodiments, the system demultiplexes (320) the data (e.g., using demultiplexing algorithm 144) to separate sequence reads into separate files for each sequencing library included in the sequencing pool, e.g., based on UMI or patient identifier sequences added to the nucleic acid fragments during sequencing library preparation, as described above. In some embodiments, the demultiplexing algorithm is part of the same software package as one or more pre-processing algorithms 142. For instance, the bcl2fastq or bcl2fastq2 conversion software (Illumina®) include instructions for both converting the native file format output from the sequencer and demultiplexing sequence reads 122 output from the reaction.

[0285] The sequence reads are then aligned (322), e.g., using an alignment algorithm 143, to a reference sequence construct 158, e.g., a reference genome, reference exome, or other reference construct prepared for a particular targeted-panel sequencing reaction. For example, in some embodiments, individual sequence reads 123, in electronic form (e.g., in FASTQ files), are aligned against a reference sequence construct for the species of the subject (e.g., a reference human genome) by identifying a sequence in a region of the reference sequence construct that best matches the sequence of nucleotides in the sequence read. In some embodiments, the sequence reads are aligned to a reference exome or reference genome using known methods in the art to determine alignment position information. The alignment position information may indicate a beginning position and an end position of a region in the reference genome that corresponds to a beginning nucleotide base and end nucleotide base of a given sequence read. Alignment position information may also include sequence read length, which can be determined from the beginning position and end position. A region in the reference genome may be associated with a gene or a segment of a gene. Any of a variety of alignment tools can be used for this task.

[0286] For instance, local sequence alignment algorithms compare subsequences of different lengths in the query sequence (e.g., sequence read) to subsequences in the subject sequence (e.g., reference construct) to create the best alignment for each portion of the query sequence. In contrast, global sequence alignment algorithms align the entirety of the sequences, e.g., end to end. Examples of local sequence alignment algorithms include the Smith-Waterman algorithm (see, for example, Smith and Waterman, J Mol. Biol., 147(1):195-97 (1981), which is incorporated herein by reference), Lalign (see, for example, Huang and Miller, Adv. Appl. Math, 12:337-57 (1991), which is incorporated by reference herein), and PatternHunter (see, for example, Ma B. et al., Bioinformatics, 18(3):440-45 (2002), which is incorporated by reference herein).

[0287] In some embodiments, the read mapping process starts by building an index of either the reference genome or the reads, which is then used to retrieve the set of positions in the reference sequence where the reads are more likely to align. Once this subset of possible mapping locations has been identified, alignment is performed in these candidate regions with slower and more sensitive algorithms. See, for example, Hatem et al., 2013, “Benchmarking short sequence mapping tools,” BMC Bioinformatics 14: p.184; and Flicek and Birney, 2009, “Sense from sequence reads: methods for alignment and assembly,” Nat Methods 6(Suppl.11), S6-S12, each of which is hereby incorporated by reference. In someembodiments, the mapping tools methodology makes use of a hash table or a Burrows– Wheeler transform (BWT). See, for example, Li and Homer, 2010, “A survey of sequence alignment algorithms for next-generation sequencing,” Brief Bioinformatics 11, pp.473-483, which is hereby incorporated by reference.

[0288] Other software programs designed to align reads include, for example, Novoalign (Novocraft, Inc.), Bowtie, Burrows Wheeler Aligner (BWA), and / or programs that use a Smith-Waterman algorithm. Candidate reference genomes include, for example, hg19, GRCh38, hg38, GRCh37, and / or other reference genomes developed by the Genome Reference Consortium. In some embodiments, the alignment generates a SAM file, which stores the locations of the start and end of each read according to coordinates in the reference genome and the coverage (number of reads) for each nucleotide in the reference genome.

[0289] For example, in some embodiments, each read of a FASTQ file is aligned to a location in the human genome having a sequence that best matches the sequence of nucleotides in the read. There are many software programs designed to align reads, for example, Novoalign (Novocraft, Inc.), Bowtie, Burrows Wheeler Aligner (BWA), programs that use a Smith-Waterman algorithm, etc. Alignment may be directed using a reference genome (for example, hg19, GRCh38, hg38, GRCh37, other reference genomes developed by the Genome Reference Consortium, etc.) by comparing the nucleotide sequences in each read with portions of the nucleotide sequence in the reference genome to determine the portion of the reference genome sequence that is most likely to correspond to the sequence in the read. In some embodiments, one or more SAM files are generated for the alignment, which store the locations of the start and end of each read according to coordinates in the reference genome and the coverage (number of reads) for each nucleotide in the reference genome. The SAM files may be converted to BAM files. In some embodiments, the BAM files are sorted, and duplicate reads are marked for deletion, resulting in de-duplicated BAM files.

[0290] In some embodiments, adapter-trimmed FASTQ files are aligned to the 19th edition of the human reference genome build (HG19) using Burrows-Wheeler Aligner (BWA, Li and Durbin, Bioinformatics, 25(14):1754-60 (2009)). Following alignment, reads are grouped by alignment position and UMI family and collapsed into consensus sequences, for example, using fgbio tools (fulcrumgenomics.github.io / fgbio / ). Bases with insufficient quality or significant disagreement among family members (for example, when it is uncertain whether the base is an adenine, cytosine, guanine, etc.) may be replaced by N's to represent a wildcard nucleotide type. PHRED scores are then scaled based on initial base callingestimates combined across all family members. Following single-strand consensus generation, duplex consensus sequences are generated by comparing the forward and reverse oriented PCR products with mirrored UMI sequences. In various embodiments, a consensus can be generated across read pairs. Otherwise, single-strand consensus calls will be used. Following consensus calling, filtering is performed to remove low-quality consensus fragments. The consensus fragments are then re-aligned to the human reference genome using BWA. A BAM output file is generated after the re-alignment, then sorted by alignment position, and indexed.

[0291] In some embodiments, where both a liquid biopsy sample and a normal tissue sample are analyzed, this process produces a liquid biopsy BAM file (e.g., Liquid BAM 124- 1-i-cf) and a normal BAM file (e.g., Germline BAM 124-1-i-g), as illustrated in Figure 4A. In various embodiments, BAM files may be analyzed to detect genetic variants and other genetic features, including single nucleotide variants (SNVs), copy number variants (CNVs), gene rearrangements, etc.

[0292] In some embodiments, the sequencing data is normalized, e.g., to account for pull- down, amplification, and / or sequencing bias (e.g., mappability, GC bias etc.). See, for example, Schwartz et al., PLoS ONE 6(1):e16685 (2011) and Benjamini and Speed, Nucleic Acids Research 40(10):e72 (2012), the contents of which are hereby incorporated by reference, in their entireties, for all purposes.

[0293] In some embodiments, SAM files generated after alignment are converted to BAM files 124. Thus, after preprocessing sequencing data generated for a pooled sequencing reaction, BAM files are generated for each of the sequencing libraries present in the master sequencing pools. For example, as illustrated in Figure 4A, separate BAM files are generated for each of three samples acquired from subject 1 at time i (e.g., tumor BAM 124-1-i-t corresponding to alignments of sequence reads of nucleic acids isolated from a solid tumor sample from subject 1, Liquid BAM 124-1-i-cf corresponding to alignments of sequence reads of nucleic acids isolated from a liquid biopsy sample from subject 1, and Germline BAM 124-1-i-g corresponding to alignments of sequence reads of nucleic acids isolated from a normal tissue sample from subject 1), and one or more samples acquired from one or more additional subjects at time j (e.g., Tumor BAM 124-2-j-t corresponding to alignments of sequence reads of nucleic acids isolated from a solid tumor sample from subject 2). In some embodiments, BAM files are sorted, and duplicate reads are marked for deletion, resulting inde-duplicated BAM files. For example, tools like SamBAMBA mark and filter duplicate alignments in the sorted BAM files.

[0294] Many of the embodiments described below, in conjunction with Figures 4A-4E, relate to analyses performed using sequencing data from cfDNA of a cancer patient, e.g., obtained from a liquid biopsy sample of the patient. Generally, these embodiments are independent and, thus, not reliant upon any particular sequencing data generation methods, e.g., sample preparation, sequencing, and / or data pre-processing methodologies. However, in some embodiments, the methods described below include one or more features 204 of generating sequencing data, as illustrated in Figures 2A and 3.

[0295] Alignment files prepared as described above (e.g., BAM files 124) are then passed to a feature extraction module 145, where the sequences are analyzed (324) to identify genomic alterations (e.g., SNVs / MNVs, indels, genomic rearrangements, copy number variations, etc.) and / or determine various characteristics of the patient’s cancer (e.g., MSI status, TMB, tumor ploidy, HRD status, tumor fraction, tumor purity, methylation patterns, etc.). Many software packages for identifying genomic alterations are known in the art, for example, freebayes, PolyBayse, samtools, GATK, pindel, SAMtools, Breakdancer, Cortex, Crest, Delly, Gridss, Hydra, Lumpy, Manta, and Socrates. For a review of many of these variant calling packages see, for example, Cameron, D.L. et al., Nat. Commun., 10(3240):1- 11 (2019), the content of which is hereby incorporated by reference, in its entirety, for all purposes. Generally, these software packages identify variants in sorted SAM or BAM files 124, relative to one or more reference sequence constructs 158. The software packages then output a file e.g., a raw VCF (variant call format), listing the variants (e.g., genomic features 131) called and identifying their location relevant to the reference sequence construct (e.g., where the sequence of the sample nucleic acids differ from the corresponding sequence in the reference construct). In some embodiments, system 100 digests the contents of the native output file to populate feature data 125 in test patient data store 120. In other embodiments, the native output file serves as the record of these genomic features 131 in test patient data store 120.

[0296] Generally, the systems described herein can employ any combination of available variant calling software packages and internally developed variant identification algorithms. In some embodiments, the output of a particular algorithm of a variant calling software is further evaluated, e.g., to improve variant identification. Accordingly, in some embodiments,system 100 employs an available variant calling software package to perform some of all of the functionality of one or more of the algorithms shown in feature extraction module 145.

[0297] In some embodiments, as illustrated in Figure 1A, separate algorithms (or the same algorithm implemented using different parameters) are applied to identify variants unique to the cancer genome of the patient and variants existing in the germline of the subject. In other embodiments, variants are identified indiscriminately and later classified as either germline or somatic, e.g., based on sequencing data, population data, or a combination thereof. In some embodiments, variants are classified as germline variants, and / or non- actionable variants, when they are represented in the population above a threshold level, e.g., as determined using a population database such as ExAC or gnomAD. For instance, in some embodiments, variants that are represented in at least 1% of the alleles in a population are annotated as germline and / or non-actionable. In other embodiments, variants that are represented in at least 2%, at least 3%, at least 4%, at least 5%, at least 7.5%, at least 10%, or more of the alleles in a population are annotated as germline and / or non-actionable. In some embodiments, sequencing data from a matched sample from the patient, e.g., a normal tissue sample, is used to annotate variants identified in a cancerous sample from the subject. That is, variants that are present in both the cancerous sample and the normal sample represent those variants that were in the germline prior to the patient developing cancer and can be annotated as germline variants.

[0298] In various aspects, the detected genetic variants and genetic features are analyzed as a form of quality control. For example, a pattern of detected genetic variants or features may indicate an issue related to the sample, sequencing procedure, and / or bioinformatics pipeline (e.g., example, contamination of the sample, mislabeling of the sample, a change in reagents, a change in the sequencing procedure and / or bioinformatics pipeline, etc.).

[0299] Figure 4E illustrates an example workflow for genomic feature identification (324). This particular workflow is only an example of one possible collection and arrangement of algorithms for feature extraction from sequencing data 124. Generally, any combination of the modules and algorithms of feature extraction module 145, e.g., illustrated in Figure 1A, can be used for a bioinformatics pipeline, and particularly for a bioinformatics pipeline for analyzing liquid biopsy samples. For instance, in some embodiments, an architecture useful for the methods and systems described herein includes at least one of the modules or variant calling algorithms shown in feature extraction module 145. In some embodiments, an architecture includes at least 2, 3, 4, 5, 6, 7, 8, 9, 10, or more of the modulesor variant calling algorithms shown in feature extraction module 145. Further, in some embodiments, feature extraction modules and / or algorithms not illustrated in Figure 1A find use in the methods and systems described herein. Variant Identification

[0300] In some embodiments, variant analysis of aligned sequence reads, e.g., in SAM or BAM format, includes identification of single nucleotide variants (SNVs), multiple nucleotide variants (MNVs), indels (e.g., nucleotide additions and deletions), and / or genomic rearrangements (e.g., inversions, translocations, and gene fusions) using variant identification module 146, e.g., which includes a SNV / MNV calling algorithm (e.g., SNV / MNV calling algorithm 147), an indel calling algorithm (e.g., indel calling algorithm 148), and / or one or more genomic rearrangement calling algorithms (e.g., genomic rearrangement calling algorithm 149). An overview of an example method for variant identification is shown in Figure 4E. Essentially, the module first identifies a difference between the sequence of an aligned sequence read 124 and the reference sequence to which the sequence read is aligned (e.g., an SNV / MNV, an indel, or a genomic rearrangement) and makes a record of the variant, e.g., in a variant call format (VCF) file. For instance, software packages such as freebayes and pindel are used to call variants using sorted BAM files and reference BED files as the input. For a review of variant calling packages see, for example, Cameron, D.L. et al., Nat. Commun., 10(3240):1-11 (2019). A raw VCF file (variant call format) file is output, showing the locations where the nucleotide base in the sample is not the same as the nucleotide base in that position in the reference sequence construct.

[0301] In some embodiments, as illustrated in Figure 4E, raw VCF data is then normalized, e.g., by parsimony and left alignment. For example, software packages such as vcfbreakmulti and vt are used to normalize multi-nucleotide polymorphic variants in the raw VCF file and a variant normalized VCF file is output. See, for example, E. Garrison, “Vcflib: A C++ library for parsing and manipulating VCF files, GitHub, available on the internet at github.com / ekg / vcflib (2012), the content of which is hereby incorporated by reference, in its entirety, for all purposes. In some embodiments, a normalization algorithm is included within the architecture of a broader variant identification software package.

[0302] An algorithm is then used to annotate the variants in the (e.g., normalized) VCF file, e.g., determines the source of the variation, e.g., whether the variant is from the germline of the subject (e.g., a germline variant), a cancerous tissue (e.g., a somatic variant), a sequencing error, or of an undeterminable source. In some embodiments, an annotationalgorithm is included within the architecture of a broader variant identification software package. However, in some embodiments, an external annotation algorithm is applied to (e.g., normalized) VCF data obtained from a conventional variant identification software package. The choice to use a particular annotation algorithm is well within the purview of the skilled artisan, and in some embodiments is based upon the data being annotated.

[0303] Various methods of variant identification are contemplated for use in the present disclosure.

[0304] In some embodiments, SNV / INDEL detection is accomplished using VarDict (github.com / AstraZeneca-NGS / VarDictJava). Both SNVs and INDELs are called and then sorted, deduplicated, normalized and annotated. The annotation uses SnpEff to add transcript information, 1000 genomes minor allele frequencies, COSMIC reference names and counts, ExAC allele frequencies, and Kaviar population allele frequencies. The annotated variants are then classified as germline, somatic, or uncertain using a Bayesian model based on prior expectations informed by databases of germline and cancer variants. In some embodiments, uncertain variants are treated as somatic for filtering and reporting purposes.

[0305] In some embodiments, genomic rearrangements (e.g., inversions, translocations, and gene fusions) are detected following de-multiplexing by aligning tumor FASTQ files against a human reference genome using a local alignment algorithm, such as BWA. In some embodiments, DNA reads are sorted, and duplicates may be marked with a software, for example, SAMBlaster. Discordant and split reads may be further identified and separated. These data may be read into a software, for example, LUMPY, for structural variant detection. In some embodiments, structural alterations are grouped by type, recurrence, and presence and stored within a database and displayed through a fusion viewer software tool. The fusion viewer software tool may reference a database, for example, Ensembl, to determine the gene and proximal exons surrounding the breakpoint for any possible transcript generated across the breakpoint. The fusion viewer tool may then place the breakpoint 5’ or 3’ to the subsequent exon in the direction of transcription. For inversions, this orientation may be reversed for the inverted gene. After positioning of the breakpoint, the translated amino acid sequences may be generated for both genes in the chimeric protein, and a plot may be generated containing the remaining functional domains for each protein, as returned from a database, for example, Uniprot.

[0306] For instance, in an example implementation, gene rearrangements are detected using the SpeedSeq analysis pipeline. Chiang et al., 2015, “SpeedSeq: ultra-fast personal genome analysis and interpretation,” Nat Methods, (12), pg.966. Briefly, FASTQ files are aligned to hg19 using BWA. Split reads mapped to multiple positions and read pairs mapped to discordant positions are identified and separated, then utilized to detect gene rearrangements by LUMPY. Layer et al., 2014, “LUMPY: a probabilistic framework for structural variant discovery,” Genome Biol, (15), pg.84. Fusions can then be filtered according to the number of supporting reads.

[0307] In some embodiments, putative fusion variants supported by fewer than a minimum number of unique sequence reads are filtered. In some embodiments, the minimum number of unique sequence reads is 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, or 20 unique sequence reads.

[0308] Additional methods and embodiments for variant identification are possible, as disclosed, for example, in PCT Patent Application No. PCT / US21 / 18622, entitled “METHODS AND SYSTEMS FOR A LIQUID BIOPSY ASSAY,” filed February 18, 2021, which is hereby incorporated herein by reference in its entirety. Allelic Fraction Determination

[0309] In some embodiments, the analysis of aligned sequence reads, e.g., in SAM or BAM format, includes determination of variant allele fractions (133) for one or more of the variant alleles 132 identified as described above. In some embodiments, a variant allele fraction module 151 tallies the instances that each allele is represented by a unique sequence read encompassing the variant locus of interest, generating a count for each allele represented at that locus. In some embodiments, these tallies are used to determine the ratio of the variant allele, e.g., an allele other than the most prevalent allele in the subject’s population for a respective locus, to a reference allele. This variant allele fraction 133 can be used in several places in the feature extraction 206 workflow. For instance, in some embodiments, a variant allele fraction is used during annotations of identified variants, e.g., when determining whether the allele originated from a germline cell or a somatic cell. In other instances, a variant allele fraction is used in a process for estimating a tumor fraction for a liquid biopsy sample or a tumor purity for a solid tumor fraction. For instance, variant allele fractions for a plurality of somatic alleles can be used to estimate the percentage of sequence reads originating from one copy of a cancerous chromosome. Assuming a 100% tumor purity and that each cancer cell caries one copy of the variant allele, the overall purity of the tumor canbe estimated. This estimate, of course, can be further corrected based on other information extracted from the sequencing data, such as copy number alterations, tumor ploidy aberrations, tumor heterozygosity, etc. Methylation Determination

[0310] In some embodiments, where nucleic acid sequencing library was processed by bi- sulfite treatment or enzymatic methyl-cytosine conversion, as described above, the analysis of aligned sequence reads, e.g., in SAM or BAM format, includes determination of methylation states 132 for one or more loci in the genome of the patient. In some embodiments, methylation sequencing data is aligned to a reference sequence construct 158 in a different fashion than non-methylation sequencing, because non-methylated cytosines are converted to uracils, and the resulting uracils are ultimately sequenced as thymines, whereas methylated cytosine are not converted and sequenced as cytosine. Different approaches, therefore, have to be used to align these modified sequences to a reference sequence construct, such as seeding alignments with shorter regions of identity or converting all cytosines to thymidines in the sequencing data and then aligning the data to reference sequence constructs for both the plus and minus strand of the sequence construct. For review of these approaches, see Zhou Q. et al., BMC Bioinformatics, 20(47):1-11 (2019), the content of which is hereby incorporated by reference, in its entirety, for all purposes. Algorithms for calling methylated bases are known in the art. For example, Bismark is able to distinguish between cytosines in CpG, CHG, and CHH contexts. Krueger F. and Andrews SR, Bioinformatics, 27(11):1571-71 (2011), the content of which is hereby incorporated by reference, in its entirety, for all purposes. Copy Number Variation Analysis:

[0311] In some embodiments, the analysis of aligned sequence reads, e.g., in SAM or BAM format, includes determination of the copy number 135 for one or more locus, using a copy number variation analysis module 153. In some embodiments, where both a liquid biopsy sample and a normal tissue sample of the patient are analyzed, de-duplicated BAM files and a VCF generated from the variant calling pipeline are used to compute read depth and variation in heterozygous germline SNVs between sequencing reads for each sample. By contrast, in some embodiments, where only a liquid biopsy sample is being analyzed, comparison between a tumor sample and a pool of process-matched normal controls is used. In some embodiments, copy number analysis includes application of a circular binary segmentation algorithm and selection of segments with highly differential log2 ratios betweenthe cancer sample and its comparator (e.g., a matched normal or normal pool). In some embodiments, approximate integer copy number is assessed from a combination of differential coverage in segmented regions and an estimate of stromal admixture (for example, tumor purity, or the portion of a sample that is cancerous vs. non-cancerous, such as a tumor fraction for a liquid biopsy sample) is generated by analysis of heterozygous germline SNVs.

[0312] Additional methods and embodiments for copy number variation analysis are possible, as disclosed, for example, in PCT Patent Application No. PCT / US21 / 18622, entitled “METHODS AND SYSTEMS FOR A LIQUID BIOPSY ASSAY,” filed February 18, 2021, which is hereby incorporated herein by reference in its entirety. Microsatellite Instability (MSI):

[0313] In some embodiments, analysis of aligned sequence reads, e.g., in SAM or BAM format, includes analysis of the microsatellite instability status 137 of a cancer, using a microsatellite instability analysis module 154. In some embodiments, an MSI classification algorithm classifies a cancer into three categories: microsatellite instability-high (MSI-H), microsatellite stable (MSS), or microsatellite equivocal (MSE). Microsatellite instability is a clinically actionable genomic indication for cancer immunotherapy. In microsatellite instability-high (MSI-H) tumors, defects in DNA mismatch repair (MMR) can cause a hypermutated phenotype where alterations accumulate in the repetitive microsatellite regions of DNA. MSI detection is conventionally performed by subjecting tumor tissue (“solid biopsy”) to clinical next-generation sequencing or specific assays, such as MMR IHC or MSI PCR.

[0314] For example, microsatellite instability status can be assessed by determining the number of repeating units present at a plurality of microsatellite loci, e.g., 5, 10, 15, 20, 25, 30, 40, 50, 75, 100, 250, 500, 750, 1000, 2500, 5000, or more loci. In some embodiments, only reads encompassing a microsatellite locus that include a significant number of flanking nucleotides on both ends, e.g., at least 5, 10, 15, or more nucleotides flanking each end, are used for the analysis in order to avoid using reads that do not completely cover the locus. In some embodiments, a minimal number of reads, e.g., at least 5, 10, 20, 30, 40, 50, or more reads have to meet this criteria in order to use a particular microsatellite locus, in order to ensure the accuracy of the determination given the high incidence of polymerase slipping during replication of these repeated sequences.

[0315] In some embodiments, each locus is tested individually for instability, e.g., as measured by a change or variance in the number of nucleotide base repeats, e.g., in cancer- derived nucleotide sequences relative to a normal sample or standard, for example, using the Kolmogorov-Smirnov test. For example, if p ≤ 0.05, the locus is considered unstable. The proportion of unstable microsatellite loci may be fed into a logistic regression classifier trained on samples from various cancer types, especially cancer types which have clinically determined MSI statuses, for example, colorectal and endometrial cohorts. For MSI testing where only a liquid biopsy sample is analyzed, the mean and variance for the number of repeats may be calculated for each microsatellite locus. A vector containing the mean and variance data may be put into a classifier (e.g., a support vector machine classification algorithm) trained to provide a probability that the patient is MSI-H, which may be compared to a threshold value. In some embodiments, the threshold value for calling the patient as MSI-H is at least 60% probability, or at least 65% probability, 70% probability, 75% probability, 80% probability, or greater. In some embodiments, a baseline threshold may be established to call the patient as MSS. In some embodiments, the baseline threshold is no more than 40%, or no more than 35% probability, 30% probability, 25% probability, 20% probability, or less. In some embodiments, when the output of the classifier falls within the range between the MSI-H and MSS thresholds, the patient is identified as MSE.

[0316] Other methods for determining the MSI status of a subject are known in the art. For example, in some embodiments, microsatellite instability analysis module 154 employs an MSI evaluation methods described in U.S. Provisional Patent Application Serial No. 62 / 881,845, filed August 1, 2019, or U.S. Provisional Application Serial No.62 / 931,600, filed November 6, 2019, the contents of which are hereby incorporated by reference, in their entireties, for all purposes. Tumor Mutational Burden (TMB):

[0317] In some embodiments, the analysis of aligned sequence reads, e.g., in SAM or BAM format, includes determination of a mutation burden for the cancer (e.g., a tumor mutational burden 136), using a tumor mutational burden analysis module 155. Generally, a tumor mutational burden is a measure of the mutations in a cancer per unit of the patient’s genome. For example, a tumor mutational burden may be expressed as a measure of central tendency (e.g., an average) of the number of somatic variants per million base pairs in the genome. In some embodiments, a tumor mutational burden refers to only a set of possible mutations, e.g., one or more of SNVs, MNVs, indels, or genomic rearrangements. In someembodiments, a tumor mutational burden refers to only a subset of one or more types of possible mutations, e.g., non-synonymous mutations, meaning those mutations that alter the amino acid sequence of an encoded protein. In other embodiments, for example, a tumor mutational burden refers to the number of one or more types of mutations that occur in protein coding sequences, e.g., regardless of whether they change the amino acid sequence of the encoded protein.

[0318] As an example, in some embodiments, a tumor mutational burden (TMB) is calculated by dividing the number of mutations (e.g., all variants or non-synonymous variants) identified in the sequencing data (e.g., as represented in a VCF file) by the size (e.g., in megabases) of a capture probe panel used for targeted sequencing. In some embodiments, a variant is included in tumor mutation burden calculation only when certain criteria are met. For instance, in some embodiments, a threshold sequence coverage for the locus associated with the variant must be met before the variant is included in the calculation, e.g., at least 25x, 50x, 75x, 100x, 250x, 500x, or greater. Similarly, in some embodiments, a minimum number of unique sequence reads encompassing the variant allele must be identified in the sequencing data, e.g., at least 4, 5, 6, 7, 8, 9, 10, or more unique sequence reads. In some embodiments, a threshold variant allelic fraction threshold must be satisfied before the variant is included in the calculation, e.g., at least 0.01%, 0.1%, 0.25%, 0.5%, 0.75%, 1%, 1.5%, 2%, 2.5%, 3%, 4%, 5%, or greater. In some embodiments, an inclusion criteria may be different for different types of variants and / or different variants of the same type. For instance, a variant detected in a mutation hotspot within the genome may face less rigorous criteria than a variant detected in a more stable locus within the genome.

[0319] In some embodiments, the analysis of aligned sequence reads includes determination of a blood tumor mutational burden (bTMB) for the cancer. In some embodiments, the determination of bTMB comprises performing a method that includes identifying a plurality of variants in a liquid biological sample of a subject (e.g., a liquid biopsy sample). In some such embodiments, the liquid biological sample of the subject is blood or a sample derived therefrom. Identification of the plurality of variants can comprise any of the methods for variant identification disclosed herein (see, e.g., the sections entitled “Variant Identification,” “Allelic Fraction Determination,” “Methylation Determination,” “Copy Number Variation Analysis,” “Microsatellite Instability (MSI),” and / or “Homologous Recombination Status (HRD)”).

[0320] In some embodiments, the method for determination of bTMB further includes applying, to the plurality of identified variants, at least a first variant filter that removes one or more variant types from the plurality of identified variants.

[0321] In some embodiments, the at least a first variant filter comprises a first germline variant filter that removes germline variants from the plurality of identified variants. In some embodiments, the at least a first variant filter comprises a first synonymous variant filter that removes synonymous variants from the plurality of identified variants. Accordingly, in some implementations, the applying the at least the first variant filter to the plurality of identified variants thereby obtains a plurality of filtered variants (e.g., after the removing of germline variants and / or synonymous variants). For instance, in some embodiments, the plurality of filtered variants includes non-synonymous SNVs, MNVs, INDELs, and / or translocation variants.

[0322] The method further includes normalizing the plurality of filtered variants based on a plurality of nucleotide sequences corresponding to target nucleic acids in the liquid biological sample. For instance, in some embodiments, the target nucleic acids in the liquid biological sample are obtained from a targeted panel used for liquid biopsy sequencing, and the normalizing the plurality of filtered variants comprises dividing the number of filtered variants (e.g., non-synonymous SNVs, MNVs, INDELs, and / or translocation variants) by the size of the targeted panel. In some implementations, the size of the targeted panel is a number of targeted genomic regions (e.g., genes) encompassed by the targeted panel. In some implementations, the size of the target panel is a number of base pairs spanned by a plurality of targeted genomic regions (e.g., genes) encompassed by the targeted panel. In some implementations, the size of the targeted panel is a measure of central tendency (e.g., a mean, median, mode, etc.) of a number of targeted genomic regions (e.g., genes) encompassed by one or more targeted panels and / or of a number of base pairs spanned by a plurality of targeted genomic regions (e.g., genes) encompassed by one or more targeted panels.

[0323] In some embodiments, the plurality of filtered variants includes one or more of non-synonymous SNVs, MNVs, INDELs, and / or translocation variants. In some embodiments, the plurality of filtered variants includes synonymous somatic variants (e.g., synonymous SNVs, MNVs, INDELs, and / or translocation variants). In some embodiments, the plurality of filtered variants includes germline variants. In some embodiments, the plurality of filtered variants includes one or more non-synonymous variants selected from thegroup consisting of non-synonymous SNVs, MNVs, INDELs, and translocation variants, and one or more synonymous variants selected from the group consisting of synonymous SNVs, MNVs, INDELs, and / or translocation variants. In some embodiments, the plurality of filtered variants does not include germline variants. In some embodiments, the plurality of filtered variants does not include synonymous variants. In some embodiments, the plurality of filtered variants does not include one or more of synonymous SNVs, synonymous MNVs, synonymous INDELs, and / or synonymous translocation variants. In some embodiments, the plurality of filtered variants does not include one or more of non-synonymous SNVs, non- synonymous MNVs, non-synonymous INDELs, and / or non-synonymous translocation variants.

[0324] An example method of calculating bTMB is described below in Example 10. Other methods for calculating tumor mutation burden and / or blood tumor mutational burden in liquid biopsy samples and / or solid tissue samples are known in the art. See, for example, Fenizia F. et al., Transl Lung Cancer Res., 7(6):668-77 (2018) and Georgiadis A et al., Clin. Cancer Res., 25(23):7024-34 (2019), the disclosures of which are hereby incorporated by reference, in their entireties, for all purposes. Homologous Recombination Status (HRD):

[0325] In some embodiments, analysis of aligned sequence reads, e.g., in SAM or BAM format, includes analysis of whether the cancer is homologous recombination deficient (HRD status 137-3), using a homologous recombination pathway analysis module 157.

[0326] Homologous recombination (HR) is a normal, highly conserved DNA repair process that enables the exchange of genetic information between identical or closely related DNA molecules. It is most widely used by cells to accurately repair harmful breaks (e.g., damage) that occur on both strands of DNA. DNA damage may occur from exogenous (external) sources like UV light, radiation, or chemical damage; or from endogenous (internal) sources like errors in DNA replication or other cellular processes that create DNA damage. Double strand breaks are a type of DNA damage. Using poly (ADP-ribose) polymerase (PARP) inhibitors in patients with HRD compromises two pathways of DNA repair, resulting in cell death (apoptosis). The efficacy of PARP inhibitors is improved not only in ovarian cancers displaying germline or somatic BRCA mutations, but also in cancers in which HRD is caused by other underlying etiologies.

[0327] In some embodiments, HRD status can be determined by inputting features correlated with HRD status into a classifier trained to distinguish between cancers with homologous recombination pathway deficiencies and cancers without homologous recombination pathway deficiencies. For example, in some embodiments, the features include one or more of (i) a heterozygosity status for a first plurality of DNA damage repair genes in the genome of the cancerous tissue of the subject, (ii) a measure of the loss of heterozygosity across the genome of the cancerous tissue of the subject, (iii) a measure of variant alleles detected in a second plurality of DNA damage repair genes in the genome of the cancerous tissue of the subject, and (iv) a measure of variant alleles detected in the second plurality of DNA damage repair genes in the genome of the non-cancerous tissue of the subject. In some embodiments, all four of the features described above are used as features in an HRD classifier. More details about HRD classifiers using these and other features are described in U.S. Patent Application Serial No.16 / 789,363, filed February 12, 2020, the content of which is hereby incorporated by reference, in its entirety, for all purposes. Circulating Tumor Fraction:

[0328] In some embodiments, the analysis of aligned sequence reads, e.g., in SAM or BAM format, includes estimation of a circulating tumor fraction for the liquid biopsy sample. Tumor fraction or circulating tumor fraction is the fraction of cell free nucleic acid molecules in the sample that originates from a cancerous tissue of the subject, rather than from a non- cancerous tissue (e.g., a germline or hematopoietic tissue). Several open-source analysis packages have modules for calculating tumor fraction from solid tumor samples. For instance, PureCN (Riester, M., et al., Source Code Biol Med, 11:13 (2016)) is designed to estimate tumor purity from targeted short-read sequencing data of solid tumor samples. Similarly, FACETS (Shen R, Seshan VE, Nucleic Acids Res., 44(16):e131 (2016)) is designed to estimate tumor fraction from sequencing data of solid tumor samples. However, estimating tumor fraction from a liquid biopsy sample is more difficult because of the, generally, lower tumor fraction relative to a solid tumor sample and typic small size of a targeted panel used for liquid biopsy sequencing. Indeed, packages such as PureCN and FACETS perform poorly at low tumor fractions and with sequencing data generated using small targeted-panels.

[0329] Various methods can be used to estimate circulating tumor fractions. In some embodiments, these methods are used in combination with off-target tumor estimate method described, for instance, in PCT Patent Application No. PCT / US21 / 18622, entitled“METHODS AND SYSTEMS FOR A LIQUID BIOPSY ASSAY,” filed February 18, 2021, which is hereby incorporated herein by reference in its entirety. For example, in some embodiments, one or more of these methodologies is used to generate an estimate of tumor fraction, which is then used to identify the nearest local optima (e.g., minima) obtained from the tumor fraction estimation methods described above, and further herein. For example, the ichorCNA package applies a probabilistic model to normalized read coverages from ultra-low pass whole genome sequencing data of cell-free DNA to estimate tumor fraction in the liquid biopsy sample. For more information, see, Adalsteinsson, V.A. et al., Nat Commun 8:1324 (2017), the content of which is disclosed herein for its description of a probabilistic tumor fraction estimation model in the “methods” section. Similarly, Tiancheng H. et al., describe a Maximum Likelihood model based on the copy number of an allele in the sample and variant allele frequency in paired-control samples. For more information, see, Tiancheng H. et al., Journal of Clinical Oncology 37:15 suppl, e13053-e13053 (2019), the content of which is disclosed herein for its description of a Maximum Likelihood tumor fraction estimation model. Additional methods and embodiments for estimating circulating tumor fractions are possible, as disclosed, for example, in PCT Patent Application No. PCT / US21 / 18622, entitled “METHODS AND SYSTEMS FOR A LIQUID BIOPSY ASSAY,” filed February 18, 2021, which is hereby incorporated herein by reference in its entirety. Quality Control

[0330] In some embodiments, a positive sensitivity control sample is processed and sequenced along with one or more clinical samples. In some embodiments, the control sample is included in at least one flow cell of a multi-flow cell reaction and is processed and sequenced each time a set of samples is sequenced or periodically throughout the course of a plurality of sets of samples. In some embodiments, the control includes a pool of controls. In some embodiments, a quality control analysis requires that read metrics of variants present in the control sample fall within acceptable criteria. In some embodiments, a quality control requires approval by a pathologist before the results are reported.

[0331] In some embodiments, the quality control system includes methods that pass samples for reporting if various criteria are met. Similarly, in some embodiments, the system includes methods that allow for more manual review if a sample does not meet the criteria established for automatic pass. In some embodiments, the criteria for pass of panel sequencing results include one or more of the following:• A criterion for the on-target rate of the sequencing reaction, defined as a comparison (e.g., a ratio) of (i) the number of sequenced nucleotides or reads falling within the targeted panel region of a genome and (ii) the number of sequenced nucleotides or reads falling outside of the targeted panel region of the genome. Generally, an on- target rate threshold will be selected based on the sequencing technology used, the size of the targeted panel, and the expected number of sequence reads generated by the combination of the technology and targeted panel used. For example, in some embodiments where next generation sequencing-by-synthesis technology is used, the criterion is implemented as a minimum on-target rate threshold of at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, or greater. In some embodiments, the on-target rate criteria is implemented as a range of acceptable on-target rates, e.g., requiring that the on-target rate for a reaction is from 30% to 70%, from 30% to 80%, from 40% to 70%, from 40% to 80%, and the like. • A criterion for the number of total reads generated by the sequencing reaction, including both unique sequence reads and non-unique sequence reads. Generally, a total read number threshold will be selected based on the sequencing technology used, the size of the targeted panel, and the expected number of sequence reads generated by the combination of the technology and targeted panel used. For example, in some embodiments where next generation sequencing-by-synthesis technology is used, the criterion is implemented as a minimum number of total reads threshold of at least 100 million, 110 million, 120 million, 130 million, 140 million, 150 million, 160 million, 170 million, 180 million, 190 million, 200 million, or more total sequence reads. In some embodiments, the criterion is implemented as a range of acceptable number of total reads, e.g., requiring that the sequencing reaction generate from 50 million to 300 million total sequence reads, from 100 million to 300 million sequence reads, from 100 million to 200 million sequence reads, and the like. • A criterion for the number of unique reads generated by the sequencing reaction. Generally, a unique read number threshold will be selected based on the sequencing technology used, the size of the targeted panel, and the expected number of sequence reads generated by the combination of the technology and targeted panel used. For example, in some embodiments where next generation sequencing-by-synthesis technology is used, the criterion is implemented as a minimum number of total reads threshold of at least 3 million, 4 million, 5 million, 6 million, 7 million, 8 million, 9million, or more unique sequence reads. In some embodiments, the criterion is implemented as a range of acceptable number of unique reads, e.g., requiring that the sequencing reaction generate from 2 million to 10 million total sequence reads, from 3 million to 9 million sequence reads, from 3 million to 9 million sequence reads, and the like. • A criterion for unique read depth across the panel, defined as a measure of central tendency (e.g., a mean or median) for a distribution of the number of unique reads in the sequencing reaction encompassing the genomic regions targeted by each probe. For instance, in some embodiments, an average unique read depth is calculated for each targeted region defined in a target region BED file, using a first calculation of the number of reads mapped to the region multiplied by the read length, divided by the length of the region, if the length of the region is longer than the read length, or otherwise using a second calculation of the number of reads falling within the region multiplied by the read length. The median of unique read depth across the panel is then calculated as the median of those average unique read depths of all targeted regions. In some embodiments, the resolution as to how depth is calculated is increased or decreased, e.g., in cases where it is necessary or desirable to calculate depth for each base, or for a single gene. Generally, a unique read depth threshold will be selected based on the sequencing technology used, the size of the targeted panel, and the expected number of sequence reads generated by the combination of the technology and targeted panel used. For example, in some embodiments where next generation sequencing-by-synthesis technology is used, the criterion is implemented as a minimum unique read depth threshold of at least 1500, 1750, 2000, 2250, 2500, 2750, 3000, 3250, 3500, or higher unique read depth. In some embodiments, the criterion is implemented as a range of acceptable unique read depth, e.g., requiring that the sequencing reaction generate a unique read depth of from 1000 to 4000, from 1500 to 4000, from 1500 to 4000, and the like. • A criterion for the unique read depth of a lowest percentile across the panel, defined as a measure of central tendency (e.g., a mean or median) for a distribution of the number of unique reads in the sequencing reaction encompassing the genomic regions targeted by each probe that fall within the lowest percentile of genomic regions by read depth (e.g., the first, second, third, fourth, fifth, tenth, fifteenth, twentieth, twenty-fifth, or similar percentile). Generally, a unique read depth at a lowestpercentile threshold will be selected based on the sequencing technology used, the size of the targeted panel, the lowest percentile selected, and the expected number of sequence reads generated by the combination of the technology and targeted panel used. For example, in some embodiments where next generation sequencing-by- synthesis technology is used, the criterion is implemented as a minimum unique read depth threshold at the fifth percentile of at least 500, 750, 1000, 1250, 1500, 1750, 2000, 2250, 2500, or higher unique read depth. In some embodiments, the criterion is implemented as a range of acceptable unique read depth at the fifth percentile, e.g., requiring that the sequencing reaction generate a unique read depth at the fifth percentile of from 250 to 3000, from 500 to 3000, from 500 to 2500, and the like. • A criterion for the deamination or OxoG Q-score of a sequencing reaction, defined as a Q-score for the occurrence of artifacts arising from template oxidation / deamination. Generally, a deamination or OxoG Q-score threshold will be selected based on the sequencing technology used. For example, in some embodiments where next generation sequencing-by-synthesis technology is used, the criterion is implemented as a minimum deamination or OxoG Q-score threshold of at least 10, 20, 30, 40, 5,0 6,070, 80, 90, or higher. In some embodiments, the criterion is implemented as a range of acceptable deamination or OxoG Q-scores, e.g., from 10 to 100, from 10 to 90, and the like. • A criterion for the estimated contamination fraction is of a sequencing reaction, defined as an estimate of the fraction of template fragments in the sample being sequenced arising from contamination of the sample, commonly expressed as a decimal, e.g., where 1% contamination is expressed as 0.01. An example method for estimating contamination in a sequencing method is described in Jun G. et al., Am. J. Hum. Genet., 91:839-48 (2012). For example, in some embodiments, the criterion is implemented as a maximum contamination fraction threshold of no more than 0.001, 0.0015, 0.002, 0.0025, 0.003, 0.0035, 0.004. In some embodiments, the criterion is implemented as a range of acceptable contamination fractions, e.g., from 0.0005 to 0.005, from 0.0005 to 0.004, from 0.001 to 0.004, and the like. • A criterion for the fingerprint correlation score of a sequencing reaction, defined as a Pearson correlation coefficient calculated between the variant allele fractions of a set of pre-defined single nucleotide polymorphisms (SNPs) in two samples. An example method for determining a fingerprint correlation score is described in Sejoon L. et al.,Nucleic Acids Research, Volume 45, Issue 11, 20 June 2017, Page e103, the content of which is incorporated herein by reference, in its entirety, for all purposes. For example, in some embodiments, the criterion is implemented as a minimum fingerprint correlation score threshold of at least 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, or higher. In some embodiments, the criterion is implemented as a range of acceptable fingerprint correlation scores, e.g., from 0.1 to 0.9, from 0.2 to 0.9, from 0.3 to 0.9, and the like. • A criterion for the raw coverage of a minimum percentage of the genomic regions targeted by a probe, defined as a minimum number of unique reads in the sequencing reaction encompassing each of a minimum percentage (e.g., at least 80%, 85%, 90%, 95%, 98%, 99%, 99.5%, 99.9%, and the like) of the genomic regions targeted by the probe panel. In some embodiments, the term "unique read depth" is used to distinguish deduplicated reads from raw reads that may contain multiple reads sequenced from the same original DNA molecule via PCR. Generally, a raw coverage of a minimum percentage of the genomic regions targeted by a probe threshold will be selected based on the sequencing technology used, the size of the targeted panel, the minimum percentage selected, and the expected number of sequence reads generated by the combination of the technology and targeted panel used. For example, in some embodiments where next generation sequencing-by- synthesis technology is used, the criterion is implemented as a raw coverage of 95% of the genomic regions targeted by a probe threshold of at least 500, 750, 1000, 1250, 1500, 1750, 2000, 2250, 2500, or higher unique read depth. In some embodiments, the criterion is implemented as a range of acceptable unique read depth for 95% of the genomic regions targeted by a probe, e.g., requiring that the sequencing reaction generate a unique read depth for 95% of the targeted regions of from 250 to 3000, from 500 to 3000, from 500 to 2500, and the like. • A criterion for the PCR duplication rate of a sequencing reaction, defined as the percentage of sequence reads that arise from the same template molecule as at least one other sequence read generated by the reaction. Generally, a PCR duplication rate threshold will be selected based on the sequencing technology used, the size of the targeted panel, and the expected number of sequence reads generated by the combination of the technology and targeted panel used. For example, in some embodiments where next generation sequencing-by-synthesis technology is used, thecriterion is implemented as a minimum PCR duplication rate threshold of at least 91%, 92% ,93%, 94%, 95%, 96%, 97%, 98%, 99%, or higher. In some embodiments, the criterion is implemented as a range of acceptable PCR duplication rates, e.g., of from 90% to 100%, from 90% to 99%, from 91% to 99%, and the like.

[0332] Similarly, in some embodiments, the quality control system includes methods that fail samples for reporting if various criteria are met. In some embodiments, the system includes methods that allow for more manual review if a sample does meet the criteria established for automatic fail. In some embodiments, the criteria for failing panel sequencing results include one or more of the following: • A criterion for the on-target rate of the sequencing reaction, defined as a comparison (e.g., a ratio) of (i) the number of sequenced nucleotides or reads falling within the targeted panel region of a genome and (ii) the number of sequenced nucleotides or reads falling outside of the targeted panel region of the genome. Generally, an on- target rate threshold will be selected based on the sequencing technology used, the size of the targeted panel, and the expected number of sequence reads generated by the combination of the technology and targeted panel used. For example, in some embodiments where next generation sequencing-by-synthesis technology is used, the criterion is implemented as a maximum on-target rate threshold of no more than 30%, 40%, 50%, 60%, 70%, or greater. That is, the criterion for failing the sample is satisfied when the on-target rate for the sequencing reaction is below the maximum on-target rate threshold. In some embodiments, the on-target rate criteria is implemented as not falling within a range of acceptable on-target rates, e.g., falling outside of an on-target rate for a reaction of from 30% to 70%, from 30% to 80%, from 40% to 70%, from 40% to 80%, and the like. • A criterion for the number of total reads generated by the sequencing reaction, including both unique sequence reads and non-unique sequence reads. Generally, a total read number threshold will be selected based on the sequencing technology used, the size of the targeted panel, and the expected number of sequence reads generated by the combination of the technology and targeted panel used. For example, in some embodiments where next generation sequencing-by-synthesis technology is used, the criterion is implemented as a maximum number of total reads threshold of no more than 100 million, 110 million, 120 million, 130 million, 140 million, 150 million, 160 million, 170 million, 180 million, 190 million, 200 million, or more total sequencereads. That is, the criterion for failing the sample is satisfied when the number of total reads for the sequencing reaction is below the maximum total read threshold. In some embodiments, the criterion is implemented as not falling within a range of acceptable number of total reads, e.g., falling outside of a range of from 50 million to 300 million total sequence reads, from 100 million to 300 million sequence reads, from 100 million to 200 million sequence reads, and the like. • A criterion for the number of unique reads generated by the sequencing reaction. Generally, a unique read number threshold will be selected based on the sequencing technology used, the size of the targeted panel, and the expected number of sequence reads generated by the combination of the technology and targeted panel used. For example, in some embodiments where next generation sequencing-by-synthesis technology is used, the criterion is implemented as a maximum number of total reads threshold of no more than 3 million, 4 million, 5 million, 6 million, 7 million, 8 million, 9 million, or more unique sequence reads. That is, the criterion for failing the sample is satisfied when the number of unique reads for the sequencing reaction is below the maximum total read threshold. In some embodiments, the criterion is implemented as not falling within a range of acceptable number of unique reads, e.g., falling outside of a range of from 2 million to 10 million total sequence reads, from 3 million to 9 million sequence reads, from 3 million to 9 million sequence reads, and the like. • A criterion for unique read depth across the panel, defined as a measure of central tendency (e.g., a mean or median) for a distribution of the number of unique reads in the sequencing reaction encompassing the genomic regions targeted by each probe. Generally, a unique read depth threshold will be selected based on the sequencing technology used, the size of the targeted panel, and the expected number of sequence reads generated by the combination of the technology and targeted panel used. For example, in some embodiments where next generation sequencing-by-synthesis technology is used, the criterion is implemented as a maximum unique read depth threshold of no more than 1500, 1750, 2000, 2250, 2500, 2750, 3000, 3250, 3500, or higher unique read depth. That is, the criterion for failing the sample is satisfied when the unique read depth across the panel for the sequencing reaction is below the maximum total read threshold. In some embodiments, the criterion is implemented as falling outside of a range of acceptable unique read depth, e.g., falling outside of aunique read depth range of from 1000 to 4000, from 1500 to 4000, from 1500 to 4000, and the like. • A criterion for the unique read depth of a lowest percentile across the panel, defined as a measure of central tendency (e.g., a mean or median) for a distribution of the number of unique reads in the sequencing reaction encompassing the genomic regions targeted by each probe that fall within the lowest percentile of genomic regions by read depth (e.g., the first, second, third, fourth, fifth, tenth, fifteenth, twentieth, twenty-fifth, or similar percentile). Generally, a unique read depth at a lowest percentile threshold will be selected based on the sequencing technology used, the size of the targeted panel, the lowest percentile selected, and the expected number of sequence reads generated by the combination of the technology and targeted panel used. For example, in some embodiments where next generation sequencing-by- synthesis technology is used, the criterion is implemented as a maximum unique read depth threshold at the fifth percentile of no more than 500, 750, 1000, 1250, 1500, 1750, 2000, 2250, 2500, or higher unique read depth. That is, the criterion for failing the sample is satisfied when the unique read depth at a lowest percentile threshold for the sequencing reaction is below the maximum unique read depth at a lowest percentile threshold. In some embodiments, the criterion is implemented as falling outside of a range of acceptable unique read depth at the fifth percentile, e.g., falling outside of a unique read depth at the fifth percentile range of from 250 to 3000, from 500 to 3000, from 500 to 2500, and the like. • A criterion for the deamination or OxoG Q-score of a sequencing reaction, defined as a Q-score for the occurrence of artifacts arising from template oxidation / deamination. Generally, a deamination or OxoG Q-score threshold will be selected based on the sequencing technology used. For example, in some embodiments where next generation sequencing-by-synthesis technology is used, the criterion is implemented as a maximum deamination or OxoG Q-score threshold of no more than 10, 20, 30, 40, 5,06,070, 80, 90, or higher. That is, the criterion for failing the sample is satisfied when the deamination or OxoG Q-score for the sequencing reaction is below the maximum deamination or OxoG Q-score threshold. In some embodiments, the criterion is implemented as falling outside of a range of acceptable deamination or OxoG Q-scores, e.g., falling outside of a deamination or OxoG Q-score range of from 10 to 100, from 10 to 90, and the like.• A criterion for the estimated contamination fraction is of a sequencing reaction, defined as an estimate of the fraction of template fragments in the sample being sequenced arising from contamination of the sample, commonly expressed as a decimal, e.g., where 1% contamination is expressed as 0.01. An example method for estimating contamination in a sequencing method is described in Jun G. et al., Am. J. Hum. Genet., 91:839-48 (2012). For example, in some embodiments, the criterion is implemented as a minimum contamination fraction threshold of at least 0.001, 0.0015, 0.002, 0.0025, 0.003, 0.0035, 0.004. That is, the criterion for failing the sample is satisfied when the contamination fraction for the sequencing reaction is above the minimum contamination fraction threshold. In some embodiments, the criterion is implemented as falling outside of a range of acceptable contamination fractions, e.g., falling outside of a contamination fraction range of from 0.0005 to 0.005, from 0.0005 to 0.004, from 0.001 to 0.004, and the like. • A criterion for the fingerprint correlation score of a sequencing reaction, defined as a Pearson correlation coefficient calculated between the variant allele fractions of a set of pre-defined single nucleotide polymorphisms (SNPs) in two samples. An example method for determining a fingerprint correlation score is described in Sejoon L. et al., Nucleic Acids Research, Volume 45, Issue 11, 20 June 2017, Page e103. For example, in some embodiments, the criterion is implemented as a maximum fingerprint correlation score threshold of no more than 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, or higher. That is, the criterion for failing the sample is satisfied when the fingerprint correlation score for the sequencing reaction is below the maximum fingerprint correlation score threshold. In some embodiments, the criterion is implemented as falling outside of a range of acceptable fingerprint correlation scores, e.g., falling outside of a fingerprint correlation range of from 0.1 to 0.9, from 0.2 to 0.9, from 0.3 to 0.9, and the like. • A criterion for the raw coverage of a minimum percentage of the genomic regions targeted by a probe, defined as a minimum number of unique reads in the sequencing reaction encompassing each of a minimum percentage (e.g., at least 80%, 85%, 90%, 95%, 98%, 99%, 99.5%, 99.9%, and the like) of the genomic regions targeted by the probe panel. Generally, a raw coverage of a minimum percentage of the genomic regions targeted by a probe threshold will be selected based on the sequencing technology used, the size of the targeted panel, the minimum percentage selected, andthe expected number of sequence reads generated by the combination of the technology and targeted panel used. For example, in some embodiments where next generation sequencing-by-synthesis technology is used, the criterion is implemented as a raw coverage of 95% of the genomic regions targeted by a probe threshold of no more than 500, 750, 1000, 1250, 1500, 1750, 2000, 2250, 2500, or higher unique read depth. That is, the criterion for failing the sample is satisfied when the raw coverage of a minimum percentage of the genomic regions targeted by a probe for the sequencing reaction is below the maximum raw coverage of a minimum percentage of the genomic regions targeted by a probe threshold. In some embodiments, the criterion is implemented as falling outside of a range of acceptable unique read depth for 95% of the genomic regions targeted by a probe, e.g., requiring that the sequencing reaction generate a unique read depth for 95% of the targeted regions falling outside of a range of from 250 to 3000, from 500 to 3000, from 500 to 2500, and the like. • A criterion for the PCR duplication rate of a sequencing reaction, defined as the percentage of sequence reads that arise from the same template molecule as at least one other sequence read generated by the reaction. Generally, a PCR duplication rate threshold will be selected based on the sequencing technology used, the size of the targeted panel, and the expected number of sequence reads generated by the combination of the technology and targeted panel used. For example, in some embodiments where next generation sequencing-by-synthesis technology is used, the criterion is implemented as a maximum PCR duplication rate threshold of at least 91%, 92% ,93%, 94%, 95%, 96%, 97%, 98%, 99%, or higher. That is, the criterion for failing the sample is satisfied when the PCR duplication rate for the sequencing reaction is below the maximum PCR duplication rate threshold. In some embodiments, the criterion is implemented as falling outside of a range of acceptable PCR duplication rates, e.g., of from 90% to 100%, from 90% to 99%, from 91% to 99%, and the like.

[0333] Thresholds for the auto-pass and auto-fail criteria may be established with reference to one another but are not necessarily set at the same level. For instance, in some embodiments, samples with a metric that falls between auto-pass and auto-fail criteria may be routed for manual review by a qualified bioinformatics scientist. Samples that are failed either automatically or by manual review may be routed to medical and laboratory teams forfinal review and can be released for downstream processing at the discretion of the laboratory medical director or designee. Improved Liquid Biopsy Probe Sets:

[0334] One aspect of the present disclosure provides a composition for enriching target nucleic acids (e.g., prior to nucleic acid sequencing, as described above and illustrated in Figures 3 and 5A-B), the composition comprising a probe set and a plurality of nucleic acids. The probe set comprises a first set of polynucleotide probes (e.g., non-enhanced probes) collectively targeting a first plurality of genomic regions (e.g., at an average coverage of from 0.75X to 1.25X), the first set of polynucleotide probes comprising a first plurality of polynucleotide probe species. Each respective polynucleotide probe species in the first plurality of polynucleotide probe species targets a respective genomic region in the first plurality of genomic regions, and the polynucleotide probe species in the first plurality of polynucleotide probe species are present in the composition at a first average molar concentration.

[0335] The probe set further comprises a second set of polynucleotide probes (e.g., enhanced probes) collectively targeting a second plurality of genomic regions (e.g., at an average coverage of from 0.75X to 1.25X), the second set of polynucleotide probes comprising a second plurality of polynucleotide probe species. Each respective polynucleotide probe species in the second plurality of polynucleotide probe species targets a respective genomic region in the second plurality of genomic regions, the polynucleotide probe species in the second plurality of polynucleotide probe species are present in the composition at a second average molar concentration, and the second average molar concentration is from five to eight times greater than the first average concentration. In alternative embodiments, each probe species in the first and second set of probes has a proportion of probes that are conjugated to affinity moieties, and the proportion is adjusted such that the second set of probes achieves higher (enhanced) coverage than the first set of probes. In still other embodiments, differential coverage (for example, two or more probe sets where each probe set achieves a different coverage) is achieved by adjusting both the concentration and the affinity moiety proportion of any of the probe species in a probe set. See, for example, U.S. Patent Application No.17 / 323,986, entitled “SYSTEMS AND METHODS FOR NEXT GENERATION SEQUENCING UNIFORM PROBE DESIGN,” filed May 18, 2021, which is hereby incorporated herein by reference in its entirety.

[0336] The plurality of nucleic acids in the composition comprises cell-free nucleic acids from a first biological sample of a first subject, or nucleic acids prepared therefrom.

[0337] In some embodiments, each respective polynucleotide probe species (e.g., all polynucleotide probes that target the same genomic region) in the first plurality of polynucleotide probe species aligns to a different genomic region of a reference genome for the species of the subject. For instance, in some embodiments, the first set of polynucleotide probes tile (e.g., overlapping or non-overlapping tiling) a genomic region, such as a gene. Thus, the polynucleotide probes in the probe set bind to different subsequences of the genomic region.

[0338] In some embodiments, each respective polynucleotide probe species in the probe set targets a respective different genomic region from any other polynucleotide probe species in the probe set. In some embodiments, each respective polynucleotide probe species in the first plurality of polynucleotide probe species targets a respective different genomic region from any other polynucleotide probe species in the first plurality of polynucleotide probe species. In some embodiments, each respective polynucleotide probe species in the second plurality of polynucleotide probe species targets a respective different genomic region from any other polynucleotide probe species in the second plurality of polynucleotide probe species. In some embodiments, each respective polynucleotide probe species in one of the first plurality of polynucleotide probe species and the second plurality of polynucleotide probe species targets a respective different genomic region from any polynucleotide probe species in the other of the first plurality of polynucleotide probe species and the second plurality of polynucleotide probe species.

[0339] As used herein, a “polynucleotide probe species” refers to all polynucleotide probes in a composition that align to the same or substantially the same genomic sequence (e.g., the first 150 nucleotides of a particular exon of a gene). Generally, all probes of a particular polynucleotide probe species will have the same nucleotide sequence. However, in some embodiments, a particular probe of polynucleotide probe species may have one or a small number of nucleotide variations relative to other probes within the polynucleotide probe species. For instance, in some embodiments, different probes of a first polynucleotide probe species may include either an A or a G (or any other combination of bases) at a particular position (e.g., nucleotide 78 of the probe). Regardless, two probes that differ by one or a small number of nucleotide variants still belong to the same polynucleotide probe species because they align to the same position in the genome. Similarly, it can beenvisioned that, in some embodiments, a probe in a particular polynucleotide probe species may be one or a small number of nucleotides longer or shorter than other probes in the particular polynucleotide probe species. Similarly, it can be envisioned that, in some embodiments, a probe in a particular polynucleotide probe species may be shifted by one or a small number of nucleotides relative to the sequence of other probes in the particular polynucleotide probe species. For instance, in some embodiments, a first probe of a particular polynucleotide probe species may align to nucleotides 1-150 of an exon, while a second probe of the particular polynucleotide probe species may align to nucleotides 3-152 of the same exon. Regardless, two probes that are shifted by two nucleotides still belong to the same polynucleotide probe species because they align to the essentially the same position in the genome.

[0340] In some embodiments, the probe species described herein have a length of at least 25 nucleotides (nt), at least 30 nt, at least 40 nt, at least 50 nt, at least 60 nt, at least 70 nt, at least 75 nt, at least 80 nt, at least 90 nt, at least 100 nt, at least 110 nt, at least 120 nt, at least 125 nt, at least 130 nt, at least 140 nt, at least 150 nt, at least 175 nt, or at least 200 nt. In some embodiments, the probe species described herein have a length of no more than 1000 nt, no more than 750 nt, no more than 500 nt, no more than 400 nt, no more than 300 nt, no more than 250 nt, no more than 225 nt, no more than 200 nt, no more than 175 nt, or more than 150 nt. In some embodiments, the probe species described herein have a length of between 25 nt and 1000 nt. In some embodiments, the probe species described herein have a length of between 50 nt and 500 nt. In some embodiments, the probe species described herein have a length of between 75 nt and 250 nt. In some embodiments, the probe species described herein have a length of between 100 nt and 200 nt. In some embodiments, the probe species described herein have a length of between 100 nt and 150 nt.

[0341] In some embodiments, a probe species described herein hybridizes to least 25 nt, at least 30 nt, at least 40 nt, at least 50 nt, at least 60 nt, at least 70 nt, at least 75 nt, at least 80 nt, at least 90 nt, at least 100 nt, at least 110 nt, at least 120 nt, at least 125 nt, at least 130 nt, at least 140 nt, at least 150 nt, at least 175 nt, or at least 200 nt of a target genomic region. In some embodiments, a probe species described herein hybridizes to no more than 1000 nt, no more than 750 nt, no more than 500 nt, no more than 400 nt, no more than 300 nt, no more than 250 nt, no more than 225 nt, no more than 200 nt, no more than 175 nt, or more than 150 nt of a genomic region. In some embodiments, a probe species described herein hybridizes to from 25 nt to 1000 nt, from 50 nt to 500 nt, from 75 nt to 250 nt, from 100 nt to 200 nt, orfrom 100 nt to 150 nt of a target genomic region. In some embodiments, the entirety of a probe species described herein hybridizes to a target genomic region. In some embodiments, a probe sequence described herein includes one or more additional sequences that don’t bind to a target genomic region, e.g., a primer site, molecular barcode, affinity capture sequence, etc.

[0342] In some embodiments, a probe set described herein collectively hybridizes to at least 0.1 megabase (Mb), at least 0.2 Mb, at least 0.4 Mb, at least 0.5 Mb, at least 0.8 Mb, at least 1 Mb, at least 2 Mb, at least 3 Mb, at least 4 Mb, at least 5 Mb, at least 8 Mb, at least 10 Mb, at least 20 Mb, at least 30 Mb, at least 40 Mb, at least 50 Mb, at least 75 Mb, or at least 100 Mb of a species’ genome, e.g., the human genome. In some embodiments, a probe set described herein collectively hybridizes to no more than 50 Mb, no more than 20 Mb, no more than 10 Mb, no more than 8 Mb, no more than 5 Mb, no more than 3 Mb, no more than 1 Mb, or no more than 0.5 Mb of a species’ genome, e.g., the human genome. In some embodiments, In some embodiments, a probe set described herein collectively hybridizes to from 0.2 to 1 Mb, from 0.5 to 2 Mb, from 1 to 3 Mb, from 2 to 10 Mb, from 4 to 20 Mb, or from 5 to 50 Mb of a species’ genome, e.g., the human genome.

[0343] Non-enhanced probes.

[0344] In some embodiments, the number of genomic regions in the first plurality of genomic regions comprises at least 5, at least 10, at least 20, at least 30, at least 40, at least 50, at least 80, at least 100, at least 200, at least 300, at least 350, at least 400, at least 500, at least 800, at least 900, at least 1000, at least 2000, at least 5000, at least 10,000, at least 50,000, at least 100,000, or more genomic regions. In some embodiments, the first plurality of genomic regions comprises no more than 500,000, no more than 100,000, no more than 50,000, no more than 10,000, no more than 5000, no more than 2000, no more than 1000, no more than 800, no more than 500, no more than 400, no more than 300, no more than 200, no more than 100, no more than 80, no more than 50, no more than 25, or fewer genomic regions. In some embodiments, ...

Claims

WHAT IS CLAIMED IS:

1. A composition for enriching target nucleic acids, the composition comprising a probe set and a plurality of nucleic acids, wherein: the probe set comprises: a first set of polynucleotide probes collectively targeting a first plurality of genomic regions at an average coverage of from 0.75X to 1.25X, the first set of polynucleotide probes comprising a first plurality of polynucleotide probe species, wherein: each respective polynucleotide probe species in the first plurality of polynucleotide probe species targets a respective genomic region in the first plurality of genomic regions, and the polynucleotide probe species in the first plurality of polynucleotide probe species are present in the composition at a first average molar concentration; a second set of polynucleotide probes collectively targeting a second plurality of genomic regions at an average coverage of from 0.75X to 1.25X, the second set of polynucleotide probes comprising a second plurality of polynucleotide probe species, wherein: each respective polynucleotide probe species in the second plurality of polynucleotide probe species targets a respective genomic region in the second plurality of genomic regions, the polynucleotide probe species in the second plurality of polynucleotide probe species are present in the composition at a second average molar concentration, and the second average molar concentration is from five to eight times greater than the first average concentration; and the plurality of nucleic acids comprises cell-free nucleic acids from a first biological sample of a first subject, or nucleic acids prepared therefrom.

2. The composition of claim 1, wherein the probe set further comprises a third set of polynucleotide probes collectively targeting a third plurality of genomic regions at an average coverage of at least 1.5X, the third set of polynucleotide probes comprising a third plurality of polynucleotide probe species, wherein:each respective polynucleotide probe species in the third plurality of polynucleotide probe species targets a respective genomic region in the third plurality of genomic regions, the polynucleotide probe species in the third plurality of polynucleotide probe species are present in the composition at a third average molar concentration, and the third average molar concentration is from five to eight times greater than the first average concentration.

3. The composition of claim 2, wherein the third plurality of genomic regions comprises coding sequences for the BRCA1 and BRCA2 genes.

4. The composition of claim 3, wherein the third plurality of genomic regions comprises at least a portion of at least 5 exons, at least 10 exons, at least 15 exons, at least 20 exons, at least 25 exons, at least 30 exons, or at least 40 exons selected from the exons listed in Figure 55.

5. The composition of claim 3, wherein the third plurality of genomic regions comprises at least a portion of each of the exons listed in Figure 55.

6. The composition according to any one of claims 2-5, wherein the third plurality of genomic regions comprises at least a portion of introns 2, 16, 17, 19, 20, and 22 of the BRCA1 gene.

7. The composition according to any one of claims 2-6, wherein the third plurality of genomic regions comprises at least a portion of intron 20 of the BRCA2 gene.

8. The composition according to any one of claims 2-7, wherein the third plurality of probe species is at least 50 probe species, at least 100 probe species, at least 250 probe species, at least 500 probe species, at least 1000 probe species, or at least 2500 probe species.

9. The composition according to any one of claims 2-8, wherein each respective polynucleotide probe species in the third plurality of polynucleotide probe species is present in the composition at from 1.5 fmol to 3 fmol.

10. The composition according to any one of claims 2-8, wherein each respective polynucleotide probe species in the third plurality of polynucleotide probe species is present in the composition at from 3 fmol to 5 fmol.

11. The composition according to any one of claims 2-10, wherein the third plurality of probe species collectively target the third plurality of genomic regions at an average coverage of from 1.75X to 2.25X.

12. The composition according to any one of claims 1-11, wherein the probe set further comprises a fourth set of polynucleotide probes collectively targeting a plurality of viral sequences.

13. The composition of claim 12, wherein the plurality of viral sequences comprises sequences from the genome of at least four different viruses.

14. The composition of claim 12 or 13, wherein the plurality of viral sequences comprises sequences from the genome of at least human papillomavirus (HPV) type 16, HPV type 18, HPV type 33, human gammaherpesvirus 4 (HHV4), and Merkel cell polyomavirus isolate R17b.

15. The composition according to any one of claims 12-14, wherein the plurality of viral sequences comprises at least 5 viral genomic regions, at least 10 viral genomic regions, at least 25 viral genomic regions, at least 50 viral genomic regions, or at least 75 viral genomic regions selected from the viral genomic regions listed in Figures 29A-29B.

16. The composition according to any one of claims 12-14, wherein the plurality of viral sequences comprises each of the viral genomic regions listed in Figures 29A-29B.

17. The composition according to any one of claims 12-16, wherein the fourth set of polynucleotides collectively targets the plurality of viral sequences at an average coverage of from 0.75X to 1.25X.

18. The composition according to any one of claims 12-17, wherein the polynucleotide probe species in the fourth plurality of polynucleotide probe species are present in thecomposition at a fourth average molar concentration that is equal to the first average concentration.

19. The composition according to any one of claims 1-18, wherein the first plurality of genomic regions comprises at least a portion of the coding sequences for at least 5 genes, at least 10 genes, at least 25 genes, at least 50 genes, at least 100 genes, at least 200 genes, at least 300 genes, at least 400 genes, or at least 500 genes.

20. The composition according to any one of claims 1-19, wherein the first plurality of genomic regions comprises at least a portion of the coding sequences for at least 5 genes, at least 10 genes, at least 25 genes, at least 50 genes, at least 100 genes, at least 200 genes, at least 300 genes, or at least 400 genes selected from the genes listed in Figures 58A-58BF.

21. The composition according to any one of claims 1-19, wherein the first plurality of genomic regions comprises at least a portion of the coding sequences for each of the genes listed in Figures 58A-58BF.

22. The composition according to any one of claims 1-21, wherein the first plurality of genomic regions comprises at least a portion of at least 10 exons, at least 25 exons, at least 50 exons, at least 100 exons, at least 200 exons, at least 300 exons, at least 400 exons, at least 500 exons, at least 750 exons, or at least 1000 exons selected from the exons listed in Figures 58A-58BF.

23. The composition according to any one of claims 1-21, wherein the first plurality of genomic regions comprises at least a portion of each of the exons listed in Figures 58A-58BF.

24. The composition according to any one of claims 1-23, wherein the first plurality of genomic regions comprises at least a portion of an intron for a gene selected from the genes listed in Figures 58A-58BF.

25. The composition according to any one of claims 1-23, wherein the first plurality of genomic regions comprises at least a portion of an intron for each of the genes UGT1A1, EWSR1 and TMPRSS2.

26. The composition according to any one of claims 1-23, wherein the first plurality of genomic regions comprises at least a portion of an intron listed in Figures 58A-58BF.

27. The composition according to any one of claims 1-23, wherein the first plurality of genomic regions comprises at least a portion of each intron listed in Figures 58A-58BF.

28. The composition according to any one of claims 1-27, wherein the first plurality of probe species is at least 100 probe species, at least 250 probe species, at least 500 probe species, at least 1000 probe species, at least 2500 probe species, or at least 5000 probe species.

29. The composition according to any one of claims 1-28, wherein each respective polynucleotide probe species in the first plurality of polynucleotide probe species is present in the composition at from 200 amol to 600 amol.

30. The composition according to any one of claims 1-28, wherein each respective polynucleotide probe species in the first plurality of polynucleotide probe species is present in the composition at from 500 amol to 1 fmol.

31. The composition according to any one of claims 1-30, wherein the second plurality of genomic regions comprises at least a portion of the coding sequences for at least 5 genes, at least 10 genes, at least 25 genes, at least 50 genes, at least 100 genes, at least 200 genes, at least 300 genes, at least 400 genes, or at least 500 genes.

32. The composition according to any one of claims 1-31, wherein the second plurality of genomic regions comprises at least a portion of the coding sequences for at least 5 genes, at least 10 genes, at least 25 genes, at least 50 genes, at least 75 genes, or at least 100 genes selected from the genes listed in Figures 57A-57Y.

33. The composition according to any one of claims 1-31, wherein the second plurality of genomic regions comprises at least a portion of the coding sequences for each of the genes listed in Figures 57A-57Y.

34. The composition according to any one of claims 1-33, wherein the second plurality of genomic regions comprises at least a portion of at least 10 exons, at least 25 exons, at least 50exons, at least 100 exons, at least 200 exons, at least 300 exons, at least 400 exons, or at least 500 exons selected from the exons listed in Figures 57A-57Y.

35. The composition according to any one of claims 1-33, wherein the second plurality of genomic regions comprises at least a portion of each of the exons listed in Figures 58A-58BF.

36. The composition according to any one of claims 1-35, wherein the second plurality of genomic regions comprises at least a portion of an intron for a gene selected from the genes listed in Figures 57A-57Y.

37. The composition according to any one of claims 1-35, wherein the second plurality of genomic regions comprises at least a portion of an intron listed in Figures 57A-57Y.

38. The composition according to any one of claims 1-35, wherein the first plurality of genomic regions comprises at least a portion of each intron listed in Figures 57A-57Y.

39. The composition according to any one of claims 1-35, wherein the second plurality of genomic regions comprises at least a portion of an intron for at least 1, at least 2, at least 3, at least 4, at least 5, or at least 10 genes selected from the list of genes consisting of ABL1, ALK, BRAF, EGFR, ERBB3, FGFR1, FGFR2, FGFR3, FLT3, MYD88, NTRK1, NTRK2, NTRK3, RET, and ROS1.

40. The composition according to any one of claims 1-35, wherein the second plurality of genomic regions comprises at least a portion of an intron for each of the genes ABL1, ALK, BRAF, EGFR, ERBB3, FGFR1, FGFR2, FGFR3, FLT3, MYD88, NTRK1, NTRK2, NTRK3, RET, and ROS1.

41. The composition according to any one of claims 1-40, wherein the second plurality of genomic regions comprises at least a portion of the promoter region for the TERT gene.

42. The composition according to any one of claims 1-41, wherein the second plurality of probe species is at least 100 probe species, at least 250 probe species, at least 500 probe species, at least 1000 probe species, at least 2500 probe species, or at least 5000 probe species.

43. The composition according to any one of claims 1-42, wherein each respective polynucleotide probe species in the second plurality of polynucleotide probe species is present in the composition at from 1.5 fmol to 3 fmol.

44. The composition according to any one of claims 1-42, wherein each respective polynucleotide probe species in the second plurality of polynucleotide probe species is present in the composition at from 3 fmol to 5 fmol.

45. The composition of any one of claims 1-44, wherein the probe set further comprises a fifth set of polynucleotide probes collectively targeting a plurality of insertion-deletion (indel) sites, the fifth set of polynucleotide probes comprising a fifth plurality of polynucleotide probe species, wherein: each respective polynucleotide probe species in the fifth plurality of polynucleotide probe species comprises a respective nucleic acid sequence that hybridizes to a corresponding variant nucleic acid sequence for a respective indel site in the plurality of indel sites.

46. The composition of any one of claims 1-44, wherein the probe set further comprises a fifth set of polynucleotide probes collectively targeting a plurality of indel sites, the fifth set of polynucleotide probes comprising a fifth plurality of polynucleotide probe species, wherein: each respective polynucleotide probe species in the fifth plurality of polynucleotide probe species comprises a respective nucleic acid sequence that hybridizes to a corresponding genomic region, in a fifth plurality of genomic regions, that is positioned a threshold distance away from a respective indel site in the plurality of indel sites.

47. The composition of claim 46, wherein the threshold distance is from 1 to 50 nucleotides away from the respective indel site.

48. The composition of any one of claims 45-47, wherein all or a portion of the fifth set of polynucleotide probes targets a portion of the first plurality of genomic regions.

49. The composition of any one of claims 45-48, wherein all or a portion of the fifth set of polynucleotide probes targets a portion of the second plurality of genomic regions.

50. The composition of any one of claims 45-47 and 49, wherein no portion of the fifth set of polynucleotide probes targets any portion of the first plurality of genomic regions.

51. The composition of any one of claims 45-48 and 50, wherein no portion of the fifth set of polynucleotide probes targets any portion of the first plurality of genomic regions.

52. The composition of any one of claims 45-51, wherein each respective polynucleotide probe species in the fifth plurality of polynucleotide probe species is present in the composition at the first average molar concentration.

53. The composition of any one of claims 45-51, wherein each respective polynucleotide probe species in the fifth plurality of polynucleotide probe species is present in the composition at a fifth average molar concentration, and wherein the fifth average molar concentration is from five to eight times greater than the first average molar concentration.

54. The composition of any one of claims 45-51, wherein each respective polynucleotide probe species in a first subset of the fifth plurality of polynucleotide probe species is present in the composition at the first average molar concentration and each respective polynucleotide probe species is a second subset of the fifth plurality of polynucleotide probe species is present in the composition at a fifth average molar concentration, and wherein the fifth average molar concentration is from five to eight times greater than the first average molar concentration.

55. The composition of any one of claims 45-54, wherein the probe set further comprises a sixth set of polynucleotide probes collectively targeting the plurality of indel sites, the sixth set of polynucleotide probes comprising a sixth plurality of polynucleotide probe species, wherein: each respective polynucleotide probe species in the sixth plurality of polynucleotide probe species comprises a respective nucleic acid sequence that hybridizes to a corresponding wildtype nucleic acid sequence for a respective indel site in the plurality of indel sites.

56. The composition of claim 55, wherein each respective polynucleotide probe species in the sixth plurality of polynucleotide probe species is present at the first average molar concentration.

57. The composition of claim 55, wherein each respective polynucleotide probe species in the sixth plurality of polynucleotide probe species is present in the composition at a sixth average molar concentration that is from five to eight times greater than the first average molar concentration.

58. The composition of claim 55, wherein each respective polynucleotide probe species in a first subset of the sixth plurality of polynucleotide probe species is present in the composition at the first average molar concentration and each respective polynucleotide probe species is a second subset of the sixth plurality of polynucleotide probe species is present in the composition at a fifth average molar concentration, and wherein the fifth average molar concentration is from five to eight times greater than the first average molar concentration.

59. The composition of claim 56, wherein: for each respective polynucleotide probe species in the first plurality of polynucleotide probe species, a corresponding first proportion of the polynucleotide probes of the respective polynucleotide probe species comprises a non-nucleotidic capture moiety; for each respective polynucleotide probe species in the sixth plurality of polynucleotide probe species, a corresponding second proportion of the polynucleotide probes of the respective polynucleotide probe species comprises a non-nucleotidic capture moiety; and the second proportion is from five to eight times greater than the first proportion.

60. The composition of claim 59, wherein the capture moiety is biotin.

61. The composition of any one of claims 45-60, wherein the plurality of indel sites comprises one or more indel sites selected from Table 10.

62. The composition of any one of claims 45-60, wherein the plurality of indel sites comprises at least 5, at least 10, at least 20, at least 30, or at least 40 indel sites selected from Table 10.

63. The composition of any one of claims 45-60, wherein the plurality of indel sites comprises all of the indel sites selected from Table 10.

64. The composition of any one of claims 45-63, wherein each respective indel site in the plurality of indel sites is at least 8 nucleotides long.

65. The composition of any one of claims 45-63, wherein each respective indel site in the plurality of indel sites is at least 10, at least 20, or at least 30 nucleotides long.

66. The composition according to any one of claims 1-65, wherein the probe set further comprises a seventh set of polynucleotide probes collectively targeting a plurality of genomic regions associated with a clinically relevant copy number variation (CNV).

67. The composition of claim 66, wherein the plurality of genomic regions associated with a clinically relevant CNV comprises at least 50 genomic regions, at least 100 genomic regions, at least 250 genomic regions, at least 500 genomic regions, at least 1000 genomic regions, or at least 1500 genomic regions selected from the genomic regions listed in Figures 56A-56X.

68. The composition of claim 66, wherein the plurality of genomic regions associated with a clinically relevant CNV comprises each of the genomic regions listed in Figures 56A- 56X.

69. The composition according to any one of claims 1-68, wherein the probe set further comprises an eighth set of polynucleotide probes collectively targeting a plurality of genomic regions associated with resistance to immune oncology therapy.

70. The composition of claim 69, wherein the plurality of genomic regions associated with resistance to immune oncology therapy comprises at least 5 genomic regions, at least 10 genomic regions, at least 25 genomic regions, at least 50 genomic regions, or at least 75 genomic regions selected from the genomic regions listed in Figure 20.

71. The composition of claim 69, wherein the plurality of genomic regions associated with resistance to immune oncology therapy comprises each of the genomic regions listed in Figure 20.

72. The composition according to any one of claims 1-71, wherein the probe set further comprises a ninth set of polynucleotide probes collectively targeting a plurality of microsatellite regions.

73. The composition of claim 72, wherein the plurality of microsatellite regions comprises at least 10 microsatellite regions, at least 25 microsatellite regions, at least 50 microsatellite regions, at least 100 microsatellite regions, at least 150 microsatellite regions, or at least 200 microsatellite regions selected from the microsatellite regions listed in Figures 21A-21E.

74. The composition of claim 72, wherein the plurality of microsatellite regions comprises each of the microsatellite regions listed in Figures 21A-21E.

75. The composition according to any one of claims 1-74, wherein the probe set further comprises a tenth set of polynucleotide probes collectively targeting a plurality of genomic regions associated with resistance to androgen receptor therapy.

76. The composition of claim 75, wherein the plurality of genomic regions associated with resistance to androgen receptor therapy comprises at least 5 genomic regions, at least 10 genomic regions, at least 25 genomic regions, at least 50 genomic regions, or at least 75 genomic regions selected from the genomic regions listed in Figure 54.

77. The composition of claim 75, wherein the plurality of genomic regions associated with resistance to androgen receptor therapy comprises each of the genomic regions listed in Figure 54.

78. The composition according to any one of claims 1-77, wherein the plurality of nucleic acids is present in the composition at a molar concentration that is from 100 to 200,000 times greater than the first average concentration.

79. The composition according to any one of claims 1-77, wherein the plurality of nucleic acids is present in the composition at a molar concentration that is from 100 to 5000 times greater than the first average concentration.

80. The composition according to any one of claims 1-79, wherein the plurality of nucleic acids is present in the composition at from 2.5 ng to 100 ng, from 5 ng to 50 ng, or 10 ng to 30 ng.

81. The composition according to any one of claims 1-80, wherein the plurality of nucleic acids further comprises cell-free nucleic acids from a second biological sample of a second subject or nucleic acid prepared therefrom.

82. The composition according to any one of claims 1-81, wherein: for each respective polynucleotide probe species in the first plurality of polynucleotide probe species, a first portion of the polynucleotide probes of the respective polynucleotide probe species comprises a non-nucleotidic capture moiety; and for each respective polynucleotide probe species in the second plurality of polynucleotide probe species, a first portion of the polynucleotide probes of the respective polynucleotide probe species comprises the non-nucleotidic capture moiety.

83. The composition of claim 82, wherein the capture moiety is biotin.

84. The composition of claim 82 or 83, wherein: each respective polynucleotide probe in the first set of polynucleotide probes comprises a non-nucleotidic capture moiety; and each respective polynucleotide probe in the second set of polynucleotide probes comprises the non-nucleotidic capture moiety.

85. The composition of any one of claims 1-84, wherein each respective polynucleotide probe species in the first plurality of polynucleotide probe species comprises a respective nucleic acid sequence of from 50 nucleotides to 250 nucleotides that targets the respective genomic region in the first plurality of genomic regions, and each respective polynucleotide probe species in the second plurality of polynucleotide probe species comprises a respective nucleic acid sequence of from 50 nucleotides to 250 nucleotides that targets the respective genomic region in the second plurality of genomic regions.

86. The composition of any one of claims 1-85, wherein the first plurality of genomic regions collectively targeted by the first set of polynucleotide probes encompasses from 1 megabase pair (Mbp) to 5 megabase pairs (Mbp).

87. The composition of any one of claims 1-86, wherein the second plurality of genomic regions collectively targeted by the second set of polynucleotide probes encompasses from 200 kilobase pairs (Kbp) to 800 kilobase pairs (Kbp).

88. The composition of any one of claims 1-87, wherein the first plurality of genomic regions collectively targeted by the first set of polynucleotide probes and the second plurality of genomic regions collectively targeted by the second set of polynucleotide probes collectively encompasses a total of from 1 megabase pair (Mbp) to 7 megabase pairs (Mbp).

89. A method for enriching target nucleic acids, the method comprising contacting, in a composition, a plurality of nucleic acids comprising the target nucleic acids with a probe set under hybridizing conditions, wherein: the probe set comprises: a first set of polynucleotide probes collectively targeting a first plurality of genomic regions at an average coverage of from 0.75X to 1.25X, the first set of polynucleotide probes comprising a first plurality of polynucleotide probe species, wherein: each respective polynucleotide probe species in the first plurality of polynucleotide probe species targets a respective genomic region in the first plurality of genomic regions, and the polynucleotide probe species in the first plurality of polynucleotide probe species are present in the composition at a first average molar concentration; a second set of polynucleotide probes collectively targeting a second plurality of genomic regions at an average coverage of from 0.75X to 1.25X, the second set of polynucleotide probes comprising a second plurality of polynucleotide probe species, wherein: each respective polynucleotide probe species in the second plurality of polynucleotide probe species targets a respective genomic region in the second plurality of genomic regions,the polynucleotide probe species in the second plurality of polynucleotide probe species are present in the composition at a second average molar concentration, and the second average molar concentration is from five to eight times greater than the first average concentration; and the plurality of nucleic acids comprises cell-free nucleic acids from a first biological sample of a first subject, or nucleic acids prepared therefrom.

90. The method of claim 89, wherein the probe set further comprises a third set of polynucleotide probes collectively targeting a third plurality of genomic regions at an average coverage of at least 1.5X, the third set of polynucleotide probes comprising a third plurality of polynucleotide probe species, wherein: each respective polynucleotide probe species in the third plurality of polynucleotide probe species targets a respective genomic region in the third plurality of genomic regions, the polynucleotide probe species in the third plurality of polynucleotide probe species are present in the composition at a third average molar concentration, and the third average molar concentration is from five to eight times greater than the first average concentration.

91. The method of claim 90, wherein the third plurality of genomic regions comprises coding sequences for the BRCA1 and BRCA2 genes.

92. The method of claim 91, wherein the third plurality of genomic regions comprises at least a portion of at least 5 exons, at least 10 exons, at least 15 exons, at least 20 exons, at least 25 exons, at least 30 exons, or at least 40 exons selected from the exons listed in Figure 55.

93. The method of claim 91, wherein the third plurality of genomic regions comprises at least a portion of each of the exons listed in Figure 55.

94. The method according to any one of claims 90-93, wherein the third plurality of genomic regions comprises at least a portion of introns 2, 16, 17, 19, 20, and 22 of the BRCA1 gene.

95. The method according to any one of claims 90-94, wherein the third plurality of genomic regions comprises at least a portion of intron 20 of the BRCA2 gene.

96. The method according to any one of claims 90-95, wherein the third plurality of probe species is at least 50 probe species, at least 100 probe species, at least 250 probe species, at least 500 probe species, at least 1000 probe species, or at least 2500 probe species.

97. The method according to any one of claims 90-96, wherein each respective polynucleotide probe species in the third plurality of polynucleotide probe species is present in the composition at from 1.5 fmol to 3 fmol.

98. The method according to any one of claims 90-96, wherein each respective polynucleotide probe species in the third plurality of polynucleotide probe species is present in the composition at from 3 fmol to 5 fmol.

99. The method according to any one of claims 90-98, wherein the third plurality of probe species collectively target the third plurality of genomic regions at an average coverage of from 1.75X to 2.25X.

100. The method according to any one of claims 89-99, wherein the probe set further comprises a fourth set of polynucleotide probes collectively targeting a plurality of viral sequences.

101. The method of claim 100, wherein the plurality of viral sequences comprises sequences from the genome of at least four different viruses.

102. The method of claim 100 or 101, wherein the plurality of viral sequences comprises sequences from the genome of at least human papillomavirus (HPV) type 16, HPV type 18, HPV type 33, human gammaherpesvirus 4 (HHV4), and Merkel cell polyomavirus isolate R17b.

103. The method according to any one of claims 100-102, wherein the plurality of viral sequences comprises at least 5 viral genomic regions, at least 10 viral genomic regions, at least 25 viral genomic regions, at least 50 viral genomic regions, or at least 75 viral genomic regions selected from the viral genomic regions listed in Figures 29A-29B.

104. The method according to any one of claims 100-102, wherein the plurality of viral sequences comprises each of the viral genomic regions listed in Figures 29A-29B.

105. The method according to any one of claims 100-102, wherein the fourth set of polynucleotides collectively targets the plurality of viral sequences at an average coverage of from 0.75X to 1.25X.

106. The method according to any one of claims 100-105, wherein the polynucleotide probe species in the fourth plurality of polynucleotide probe species is present in the composition at a fourth average molar concentration that is equal to the first average concentration.

107. The method according to any one of claims 89-106, wherein the first plurality of genomic regions comprises at least a portion of the coding sequences for at least 5 genes, at least 10 genes, at least 25 genes, at least 50 genes, at least 100 genes, at least 200 genes, at least 300 genes, at least 400 genes, or at least 500 genes.

108. The method according to any one of claims 89-107, wherein the first plurality of genomic regions comprises at least a portion of the coding sequences for at least 5 genes, at least 10 genes, at least 25 genes, at least 50 genes, at least 100 genes, at least 200 genes, at least 300 genes, or at least 400 genes selected from the genes listed in Figures 58A-58BF.

109. The method according to any one of claims 89-107, wherein the first plurality of genomic regions comprises at least a portion of the coding sequences for each of the genes listed in Figures 58A-58BF.

110. The method according to any one of claims 89-109, wherein the first plurality of genomic regions comprises at least a portion of at least 10 exons, at least 25 exons, at least 50 exons, at least 100 exons, at least 200 exons, at least 300 exons, at least 400 exons, at least500 exons, at least 750 exons, or at least 1000 exons selected from the exons listed in Figures 58A-58BF.

111. The method according to any one of claims 89-109, wherein the first plurality of genomic regions comprises at least a portion of each of the exons listed in Figures 58A-58BF.

112. The method according to any one of claims 89-111, wherein the first plurality of genomic regions comprises at least a portion of an intron for a gene selected from the genes listed in Figures 58A-58BF.

113. The method according to any one of claims 89-111, wherein the first plurality of genomic regions comprises at least a portion of an intron listed in Figures 58A-58BF.

114. The method according to any one of claims 89-111, wherein the first plurality of genomic regions comprises at least a portion of each intron listed in Figures 58A-58BF.

115. The method according to any one of claims 89-109, wherein the first plurality of genomic regions comprises at least a portion of an intron for each of the genes UGT1A1, EWSR1 and TMPRSS2.

116. The method according to any one of claims 89-115, wherein the first plurality of probe species is at least 100 probe species, at least 250 probe species, at least 500 probe species, at least 1000 probe species, at least 2500 probe species, or at least 5000 probe species.

117. The method according to any one of claims 89-116, wherein each respective polynucleotide probe species in the first plurality of polynucleotide probe species is present in the composition at from 200 amol to 600 amol.

118. The method according to any one of claims 89-116, wherein each respective polynucleotide probe species in the first plurality of polynucleotide probe species is present in the composition at from 500 amol to 1 fmol.

119. The method according to any one of claims 89-118, wherein the second plurality of genomic regions comprises at least a portion of the coding sequences for at least 5 genes, atleast 10 genes, at least 25 genes, at least 50 genes, at least 100 genes, at least 200 genes, at least 300 genes, at least 400 genes, or at least 500 genes.

120. The method according to any one of claims 89-119, wherein the second plurality of genomic regions comprises at least a portion of the coding sequences for at least 5 genes, at least 10 genes, at least 25 genes, at least 50 genes, at least 75 genes, or at least 100 genes selected from the genes listed in Figures 57A-57Y.

121. The method according to any one of claims 89-119, wherein the second plurality of genomic regions comprises at least a portion of the coding sequences for each of the genes listed in Figures 57A-57Y.

122. The method according to any one of claims 89-121, wherein the second plurality of genomic regions comprises at least a portion of at least 10 exons, at least 25 exons, at least 50 exons, at least 100 exons, at least 200 exons, at least 300 exons, at least 400 exons, or at least 500 exons selected from the exons listed in Figures 57A-57Y.

123. The method according to any one of claims 89-121, wherein the second plurality of genomic regions comprises at least a portion of each of the exons listed in Figures 58A-58BF.

124. The method according to any one of claims 89-123, wherein the second plurality of genomic regions comprises at least a portion of an intron for a gene selected from the genes listed in Figures 57A-57Y.

125. The method according to any one of claims 89-123, wherein the second plurality of genomic regions comprises at least a portion of an intron listed in Figures 57A-57Y.

126. The method according to any one of claims 89-123, wherein the second plurality of genomic regions comprises at least a portion of each intron listed in Figures 57A-57Y.

127. The method according to any one of claims 89-123, wherein the second plurality of genomic regions comprises at least a portion of an intron for at least 1, at least 2, at least 3, at least 4, at least 5, or at least 10 genes selected from the list of genes consisting of ABL1, ALK, BRAF, EGFR, ERBB3, FGFR1, FGFR2, FGFR3, FLT3, MYD88, NTRK1, NTRK2, NTRK3, RET, and ROS1.

128. The method according to any one of claims 89-123, wherein the second plurality of genomic regions comprises at least a portion of an intron for each of the genes ABL1, ALK, BRAF, EGFR, ERBB3, FGFR1, FGFR2, FGFR3, FLT3, MYD88, NTRK1, NTRK2, NTRK3, RET, and ROS1.

129. The method according to any one of claims 89-128, wherein the second plurality of genomic regions comprises at least a portion of the promoter region for the TERT gene.

130. The method according to any one of claims 89-129, wherein the second plurality of probe species is at least 100 probe species, at least 250 probe species, at least 500 probe species, at least 1000 probe species, at least 2500 probe species, or at least 5000 probe species.

131. The method according to any one of claims 89-130, wherein each respective polynucleotide probe species in the second plurality of polynucleotide probe species is present in the composition at from 1.5 fmol to 3 fmol.

132. The method according to any one of claims 89-130, wherein each respective polynucleotide probe species in the second plurality of polynucleotide probe species is present in the composition at from 3 fmol to 5 fmol.

133. The method according to any one of claims 89-132, wherein the probe set further comprises a fifth set of polynucleotide probes collectively targeting a plurality of insertion- deletion (indel) sites, the fifth set of polynucleotide probes comprising a fifth plurality of polynucleotide probe species, wherein: each respective polynucleotide probe species in the fifth plurality of polynucleotide probe species comprises a respective nucleic acid sequence that hybridizes to a corresponding variant nucleic acid sequence for a respective indel site in the plurality of indel sites.

134. The method according to any one of claims 89-132, wherein the probe set further comprises a fifth set of polynucleotide probes collectively targeting a plurality of indel sites, the fifth set of polynucleotide probes comprising a fifth plurality of polynucleotide probe species, wherein: each respective polynucleotide probe species in the fifth plurality of polynucleotideprobe species comprises a respective nucleic acid sequence that hybridizes to a corresponding genomic region, in a fifth plurality of genomic regions, that is positioned a threshold distance away from a respective indel site in the plurality of indel sites.

135. The method of claim 134, wherein the threshold distance is from 1 to 50 nucleotides away from the respective indel site.

136. The method according to any one of claims 133-135, wherein all or a portion of the fifth set of polynucleotide probes targets a portion of the first plurality of genomic regions.

137. The method according to any one of claims 133-136, wherein all or a portion of the fifth set of polynucleotide probes targets a portion of the second plurality of genomic regions.

138. The method according to any one of claims 133-135 and 137, wherein no portion of the fifth set of polynucleotide probes targets any portion of the first plurality of genomic regions.

139. The method according to any one of claims 133-136 and 138, wherein no portion of the fifth set of polynucleotide probes targets any portion of the first plurality of genomic regions.

140. The method according to any one of claims 133-139, wherein each respective polynucleotide probe species in the fifth plurality of polynucleotide probe species is present in the composition at the first average molar concentration.

141. The method according to any one of claims 133-139, wherein each respective polynucleotide probe species in the fifth plurality of polynucleotide probe species is present in the composition at a fifth average molar concentration, and wherein the fifth average molar concentration is from five to eight times greater than the first average molar concentration.

142. The method according to any one of claims 133-139, wherein each respective polynucleotide probe species in a first subset of the fifth plurality of polynucleotide probe species is present in the composition at the first average molar concentration and each respective polynucleotide probe species is a second subset of the fifth plurality of polynucleotide probe species is present in the composition at a fifth average molarconcentration, and wherein the fifth average molar concentration is from five to eight times greater than the first average molar concentration.

143. The method according to any one of claims 133-142, wherein the probe set further comprises a sixth set of polynucleotide probes collectively targeting the plurality of indel sites, the sixth set of polynucleotide probes comprising a sixth plurality of polynucleotide probe species, wherein: each respective polynucleotide probe species in the sixth plurality of polynucleotide probe species comprises a respective nucleic acid sequence that hybridizes to a corresponding wildtype nucleic acid sequence for a respective indel site in the plurality of indel sites.

144. The method of claim 143, wherein each respective polynucleotide probe species in the sixth plurality of polynucleotide probe species is present at the first average molar concentration.

145. The method of claim 143, wherein each respective polynucleotide probe species in the sixth plurality of polynucleotide probe species is present in the composition at a sixth average molar concentration that is from five to eight times greater than the first average molar concentration.

146. The method of claim 143, wherein each respective polynucleotide probe species in a first subset of the sixth plurality of polynucleotide probe species is present in the composition at the first average molar concentration and each respective polynucleotide probe species is a second subset of the sixth plurality of polynucleotide probe species is present in the composition at a fifth average molar concentration, and wherein the fifth average molar concentration is from five to eight times greater than the first average molar concentration.

147. The method of claim 144, wherein: for each respective polynucleotide probe species in the first plurality of polynucleotide probe species, a corresponding first proportion of the polynucleotide probes of the respective polynucleotide probe species comprises a non-nucleotidic capture moiety; for each respective polynucleotide probe species in the sixth plurality of polynucleotide probe species, a corresponding second proportion of the polynucleotide probes of the respective polynucleotide probe species comprises a non-nucleotidic capturemoiety; and the second proportion is from five to eight times greater than the first proportion.

148. The method of claim 147, wherein the capture moiety is biotin.

149. The method according to any one of claims 133-148, wherein the plurality of indel sites comprises one or more indel sites selected from Table 10.

150. The method according to any one of claims 133-148, wherein the plurality of indel sites comprises at least 5, at least 10, at least 20, at least 30, or at least 40 indel sites selected from Table 10.

151. The method according to any one of claims 133-148, wherein the plurality of indel sites comprises all of the indel sites selected from Table 10.

152. The method according to any one of claims 133-151, wherein each respective indel site in the plurality of indel sites is at least 8 nucleotides long.

153. The method according to any one of claims 133-151, wherein each respective indel site in the plurality of indel sites is at least 10, at least 20, or at least 30 nucleotides long.

154. The method according to any one of claims 89-153, wherein the probe set further comprises a seventh set of polynucleotide probes collectively targeting a plurality of genomic regions associated with a clinically relevant copy number variation (CNV).

155. The method of claim 154, wherein the plurality of genomic regions associated with a clinically relevant CNV comprises at least 50 genomic regions, at least 100 genomic regions, at least 250 genomic regions, at least 500 genomic regions, at least 1000 genomic regions, or at least 1500 genomic regions selected from the genomic regions listed in Figures 56A-56X.

156. The method of claim 154, wherein the plurality of genomic regions associated with a clinically relevant CNV comprises each of the genomic regions listed in Figures 56A-56X.

157. The method according to any one of claims 89-156, wherein the probe set further comprises an eighth set of polynucleotide probes collectively targeting a plurality of genomic regions associated with resistance to immune oncology therapy.

158. The method of claim 157, wherein the plurality of genomic regions associated with resistance to immune oncology therapy comprises at least 5 genomic regions, at least 10 genomic regions, at least 25 genomic regions, at least 50 genomic regions, or at least 75 genomic regions selected from the genomic regions listed in Figure 20.

159. The method of claim 157, wherein the plurality of genomic regions associated with resistance to immune oncology therapy comprises each of the genomic regions listed in Figure 20.

160. The method according to any one of claims 89-159, wherein the probe set further comprises a ninth set of polynucleotide probes collectively targeting a plurality of microsatellite regions.

161. The method of claim 160, wherein the plurality of microsatellite regions comprises at least 10 microsatellite regions, at least 25 microsatellite regions, at least 50 microsatellite regions, at least 100 microsatellite regions, at least 150 microsatellite regions, or at least 200 microsatellite regions selected from the microsatellite regions listed in Figures 21A-21E.

162. The method of claim 160, wherein the plurality of microsatellite regions comprises each of the microsatellite regions listed in Figures 21A-21E.

163. The method according to any one of claims 89-162, wherein the probe set further comprises a tenth set of polynucleotide probes collectively targeting a plurality of genomic regions associated with resistance to androgen receptor therapy.

164. The method of claim 163, wherein the plurality of genomic regions associated with resistance to androgen receptor therapy comprises at least 5 genomic regions, at least 10 genomic regions, at least 25 genomic regions, at least 50 genomic regions, or at least 75 genomic regions selected from the genomic regions listed in Figure 54.

165. The method of claim 163, wherein the plurality of genomic regions associated with resistance to androgen receptor therapy comprises each of the genomic regions listed in Figure 54.

166. The method according to any one of claims 89-165, wherein the plurality of nucleic acids is present in the composition at a molar concentration that is from 100 to 200,000 times greater than the first average concentration.

167. The method according to any one of claims 89-165, wherein the plurality of nucleic acids is present in the composition at a molar concentration that is from 100 to 5000 times greater than the first average concentration.

168. The method according to any one of claims 89-167, wherein the plurality of nucleic acids is present in the composition at from 2.5 ng to 100 ng, from 5 ng to 50 ng, or 10 ng to 30 ng.

169. The method according to any one of claims 89-168, wherein the plurality of nucleic acids further comprises cell-free nucleic acids from a second biological sample of a second subject or nucleic acid prepared therefrom.

170. The method according to any one of claims 89-169, wherein: for each respective polynucleotide probe species in the first plurality of polynucleotide probe species, a first portion of the polynucleotide probes of the respective polynucleotide probe species comprises a non-nucleotidic capture moiety; and for each respective polynucleotide probe species in the second plurality of polynucleotide probe species, a first portion of the polynucleotide probes of the respective polynucleotide probe species comprises the non-nucleotidic capture moiety.

171. The method of claim 170, wherein the capture moiety is biotin.

172. The method of claim 170 or 171, wherein: each respective polynucleotide probe in the first set of polynucleotide probes comprises a non-nucleotidic capture moiety; and each respective polynucleotide probe in the second set of polynucleotide probescomprises the non-nucleotidic capture moiety.

173. The method according to any one of claims 89-172, wherein each respective polynucleotide probe species in the first plurality of polynucleotide probe species comprises a respective nucleic acid sequence of from 50 nucleotides to 250 nucleotides that targets the respective genomic region in the first plurality of genomic regions, and each respective polynucleotide probe species in the second plurality of polynucleotide probe species comprises a respective nucleic acid sequence of from 50 nucleotides to 250 nucleotides that targets the respective genomic region in the second plurality of genomic regions.

174. The method according to any one of claims 89-173, wherein the first plurality of genomic regions collectively targeted by the first set of polynucleotide probes encompasses from 1 megabase pair (Mbp) to 5 megabase pairs (Mbp).

175. The method according to any one of claims 89-174, wherein the second plurality of genomic regions collectively targeted by the second set of polynucleotide probes encompasses from 200 kilobase pairs (Kbp) to 800 kilobase pairs (Kbp).

176. The method according to any one of claims 89-175, wherein the first plurality of genomic regions collectively targeted by the first set of polynucleotide probes and the second plurality of genomic regions collectively targeted by the second set of polynucleotide probes collectively encompasses a total of from 1 megabase pair (Mbp) to 7 megabase pairs (Mbp).

177. The method according to any one of claims 89-176, further comprising: recovering respective nucleic acids in the plurality of nucleic acids that hybridize to a respective nucleic acid probe in the plurality of nucleic acid probes; and sequencing the recovered nucleic acids to obtain a plurality of sequence reads for the cell free nucleic acids from the first biological sample.

178. The method of claim 177, wherein: the first plurality of genomic regions are sequenced at an average coverage of at least 750X.

179. The method of claim 177 or 178, wherein:the second plurality of genomic regions are sequenced at an average coverage of at least 2000X.

180. The method according to any one of claims 177-179, wherein: the third plurality of genomic regions are sequenced at an average coverage of at least 2000X.

181. The method according to any one of claims 178-180, wherein the method is for characterizing a cancerous state of the subject, the method further comprising associating the plurality of sequence reads for the cell free nucleic acids from the first biological sample with a respective cancerous state in a plurality of cancerous states.

182. The method of claim 181, wherein the associating the plurality of sequence reads for the cell free nucleic acids from the first biological sample with the respective cancerous state comprises identifying one or more single nucleotide variants (SNVs) or multiple nucleotide variants (MNVs) that are associated with the respective cancerous state in the plurality of sequence reads.

183. The method of claim 182, wherein the one or more SNVs or MNVs comprise an SNV or MNV in a genomic region targeted by the first set of polynucleotide probes.

184. The method of claim 182 or 183, wherein the one or more SNVs or MNVs comprise an SNV or MNV in a genomic region targeted by the second set of polynucleotide probes.

185. The method according to any one of claims 181-184, wherein the associating the plurality of sequence reads for the cell free nucleic acids from the first biological sample with the respective cancerous state comprises identifying one or more indels that are associated with the respective cancerous state in the plurality of sequence reads.

186. The method of claim 185, wherein the one or more indels comprise an indel in a genomic region targeted by the first set of polynucleotide probes.

187. The method of claim 185 or 186, wherein the one or more indels comprise an indel in a genomic region targeted by the second set of polynucleotide probes.

188. The method of any one of claims 185-187, wherein the one or more indels comprise an indel in a genomic region targeted by the fifth set of polynucleotide probes.

189. The method according to any one of claims 181-188, wherein the associating the plurality of sequence reads for the cell free nucleic acids from the first biological sample with the respective cancerous state comprises identifying one or more mutations in a BRCA1 or BRCA2 gene that are associated with the respective cancerous state in the plurality of sequence reads.

190. The method of claim 189, wherein the one or more mutations comprise a mutation in a genomic region targeted by the third set of polynucleotide probes.

191. The method according to any one of claims 181-190, wherein the associating the plurality of sequence reads for the cell free nucleic acids from the first biological sample with the respective cancerous state comprises identifying the presence of one or more viral sequences associated with the respective cancerous state in the plurality of sequence reads.

192. The method of claim 191, wherein the one or more viral sequences comprise a viral sequence from the genome of a virus selected from the group consisting of human papillomavirus (HPV) type 16, HPV type 18, HPV type 33, human gammaherpesvirus 4 (HHV4), and Merkel cell polyomavirus isolate R17b.

193. The method of claim 191 or 192, wherein the one or more viral sequences comprise a viral sequence targeted by the fourth set of polynucleotide probes.

194. The method according to any one of claims 181-193, wherein the associating the plurality of sequence reads for the cell free nucleic acids from the first biological sample with the respective cancerous state comprises determining a microsatellite stability status of the subject from the plurality of sequence reads.

195. The method of claim 194, wherein the microsatellite stability status is determined from sequence reads of genomic regions targeted by the ninth set of polynucleotide probes.

196. The method according to any one of claims 181-195, wherein the associating the plurality of sequence reads for the cell free nucleic acids from the first biological sample withthe respective cancerous state comprises identifying one or more genomic rearrangements associated with the respective cancerous state in the plurality of sequence reads.

197. The method of claim 196, wherein the one or more genomic rearrangements comprise a genomic rearrangement in a genomic region targeted by the first set of polynucleotide probes.

198. The method of claim 196 or 197, wherein the one or more genomic rearrangements comprise a genomic rearrangement in a genomic region targeted by the second set of polynucleotide probes.

199. The method according to any one of claims 181-198, wherein the associating the plurality of sequence reads for the cell free nucleic acids from the first biological sample with the respective cancerous state comprises identifying one or more copy number variations (CNV) associated with the respective cancerous state in the plurality of sequence reads.

200. The method of claim 199, wherein the one or more genomic copy number variations comprise a copy number variation in a genomic region targeted by the seventh set of polynucleotide probes.

201. The method according to any one of claims 181-200, wherein the associating the plurality of sequence reads for the cell free nucleic acids from the first biological sample with the respective cancerous state comprises determining a blood tumor molecular burden (bTMB) that is associated with the respective cancerous state in the plurality of sequence reads.

202. The method of claim 201, wherein the bTMB status is determined from sequence reads of genomic regions targeted by the first set of polynucleotide probes.

203. The method of claim 201 or 202, wherein the bTMB status is determined from sequence reads of genomic regions targeted by the second set of polynucleotide probes.

204. The method according to any one of claims 181-203, wherein the associating the plurality of sequence reads for the cell free nucleic acids from the first biological sample withthe respective cancerous state comprises identifying one or more gene fusions associated with the respective cancerous state in the plurality of sequence reads.

205. The method of claim 204, wherein the one or more gene fusions comprise a gene fusion in a genomic region targeted by the first set of polynucleotide probes.

206. The method of claim 204 or 205, wherein the one or more gene fusions comprise a gene fusion in a genomic region targeted by the second set of polynucleotide probes.

207. The method according to any one of claims 181-206, wherein the associating the plurality of sequence reads for the cell free nucleic acids from the first biological sample with the respective cancerous state comprises determining a circulating tumor fraction (cTF) that is associated with the respective cancerous state in the plurality of sequence reads.

208. The method of claim 207, wherein the cTF is determined using sequence reads of genomic regions targeted by the first set of polynucleotide probes.

209. The method of claim 207 or 208, wherein the cTF is determined using sequence reads of genomic regions targeted by the second set of polynucleotide probes.

210. The method according to any one of claims 181-209, wherein the associating the plurality of sequence reads for the cell free nucleic acids from the first biological sample with the respective cancerous state comprises identifying one or more mutations associated with resistance to immune oncology therapy in the plurality of sequence reads.

211. The method of claim 210, wherein the one or more mutations associated with resistance to immune oncology therapy comprise a mutation associated with resistance to immune oncology therapy in a genomic region targeted by the eighth set of polynucleotide probes.

212. The method according to any one of claims 181-211, wherein the associating the plurality of sequence reads for the cell free nucleic acids from the first biological sample with the respective cancerous state comprises identifying one or more mutations associated with resistance to androgen receptor therapy in the plurality of sequence reads.

213. The method of claim 212, wherein the one or more mutations associated with resistance to androgen receptor therapy comprise a mutation associated with resistance to androgen receptor therapy in a genomic region targeted by the tenth set of polynucleotide probes.

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