EP4107732A1 - Procédés et systèmes de dosage de biopsie de liquide - Google Patents

Procédés et systèmes de dosage de biopsie de liquide

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Publication number
EP4107732A1
EP4107732A1 EP21711691.2A EP21711691A EP4107732A1 EP 4107732 A1 EP4107732 A1 EP 4107732A1 EP 21711691 A EP21711691 A EP 21711691A EP 4107732 A1 EP4107732 A1 EP 4107732A1
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EP
European Patent Office
Prior art keywords
bin
sequence
segment
level
measure
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Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
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EP21711691.2A
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German (de)
English (en)
Inventor
Robert Tell
Wei Zhu
Justin David Finkle
Christine LO
Terri M. Driessen
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Tempus Ai Inc
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Tempus Labs Inc
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Application filed by Tempus Labs Inc filed Critical Tempus Labs Inc
Publication of EP4107732A1 publication Critical patent/EP4107732A1/fr
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/10—Ploidy or copy number detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

  • the present disclosure relates generally to the use of cell-free DNA sequencing data to provide clinical support for personalized treatment of cancer.
  • 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 home 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.
  • Such intertumor or intratumor 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.
  • 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.
  • 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.
  • tissue collection can result in sample degradation and variable quality DNA.
  • This leads to inaccuracies in downstream assays and analysis, including next- generation sequencing (NGS) for the identification of biomarkers.
  • NGS next- generation sequencing
  • 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.
  • 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.
  • cfDNA Cell-free DNA
  • bodily fluids e.g., blood serum, plasma, urine, etc. Chan etal., 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.
  • genomic alterations in cancerous tissues can be identified from cfDNA isolated from cancer patients.
  • 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.
  • cell-free nucleic acids e.g., cfDNA
  • 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.
  • 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 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.
  • CNVs copy number variations
  • genomic targets e.g., biomarkers
  • CNVs are a form of genomic alteration with known relevance to cancer.
  • Conventional methodologies typically assign a genomic target to an integer copy number and/or one of three copy number states (e.g., amplified, neutral, or deleted) using a copy ratio cutoff above or below which an amplified or deleted status is called, respectively, or in which a neutral status is otherwise called.
  • Such methodologies make these assignments based on the fact that at a given tumor fraction and a known ploidy, the copy number in a segment is positively correlated with its copy ratio and thus the copy ratio can be mathematically converted to an integer copy number.
  • one conventional method ichorCNA utilizes software that estimates tumor fraction in circulating cfDNA from ultra-low-pass whole genome sequencing, which is then used to determine genomic alterations such as copy number alterations. See, Adalsteinsson et cil, Nat Commun., 8:1324 (2017).
  • VAFs somatic variant allele fractions
  • Non-focal copy number variations are identified (e.g., where an entire chromosome or a large portion of a chromosome is amplified or deleted).
  • Non-focal copy number variations are often difficult to interpret, as these large- scale copy number changes may represent real copy number variations or may be artifacts resulting from incorrect normalization due to low sample quality, capture failures, or other unknown issues during library preparation or sequencing. Because such large-scale copy number changes are unlikely to be associated with therapeutically actionable genomic alterations, the ability to differentiate between real and artifactual copy number variations is an important and unmet need in precision oncology applications.
  • ctFEs circulating tumor fraction estimates
  • ctFEs correlate with important clinical outcomes, and provide a minimally invasive method to monitor patients for response to therapy, disease relapse, and disease progression.
  • conventional methodologies used for determining ctFEs in liquid biopsy samples rely on low-pass, whole-genome sequencing, which cannot also be used for variant detection (see, for example, Adalsteinsson et al, “Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors,” (2017) Nature Communications Nov 6;8(1):1324, doi:10.1038/s41467-017-00965-y; and ichorCNA, the Broad Institute, available on the internet at github.com/broadinstitute/ichorCNA).
  • VAFs variant allele fractions
  • the systems and methods described herein reject or validate a focal copy number status annotation for a at a locus that is potentially actionable using precision oncology.
  • the present disclosure provides a method of validating a copy number variation in a test subject, at a computer system having one or more processors, and memory storing one or more programs for execution by the one or more processors.
  • the method comprises obtaining a first dataset that comprises a plurality of bin- level sequence ratios, each respective bin-level sequence ratio in the plurality of bin-level sequence ratios corresponding to a respective bin in a plurality of bins.
  • Each respective bin in the plurality of bins represents a corresponding region of a human reference genome
  • each respective bin-level sequence ratio in the plurality of bin-level sequence ratios is determined from a sequencing of a plurality of cell-free nucleic acids in a first liquid biopsy sample of the test subject and one or more reference samples.
  • the first dataset also comprises a plurality of segment-level sequence ratios, each respective segment-level sequence ratio in the plurality of segment-level sequence ratios corresponding to a segment in a plurality of segments.
  • Each respective segment in the plurality of segments represents a corresponding region of the human reference genome encompassing a subset of adjacent bins in the plurality of bins, and each respective segment- level sequence ratio in the plurality of segment-level sequence ratios is determined from a measure of central tendency of the plurality of bin-level sequence ratios corresponding to the subset of adjacent bins encompassed by the respective segment.
  • the first dataset further comprises a plurality of segment-level measures of dispersion, where each respective segment-level measure of dispersion in the plurality of segment-level measures of dispersion (i) corresponds to a respective segment in the plurality of segments and (ii) is determined using the plurality of bin-level sequence ratios corresponding to the subset of adjacent bins encompassed by the respective segment.
  • the method comprises validating a copy number status annotation of a respective segment in the plurality of segments that is annotated with a copy number variation by applying the first dataset to an algorithm having a plurality of filters.
  • a first filter in the plurality of filters is a measure of central tendency bin-level sequence ratio filter that is fired when a measure of central tendency of the plurality of bin-level sequence ratios corresponding to the subset of bins encompassed by the respective segment fails to satisfy one or more bin-level sequence ratio thresholds.
  • a second filter in the plurality of filters is a confidence filter that is fired when the segment-level measure of dispersion corresponding to the respective segment fails to satisfy a confidence threshold.
  • a third filter in the plurality of filters is a measure of central tendency-plus-deviation bin-level sequence ratio filter that is fired when a measure of central tendency of the plurality of bin-level sequence ratios corresponding to the subset of bins encompassed by the respective segment fails to satisfy one or more measure of central tendency-plus-deviation bin-level sequence ratio thresholds.
  • the one or more measure of central tendency -plus-deviation bin-level copy ratio thresholds are derived from (i) a measure of the bin-level sequence ratios corresponding to the plurality of bins that map to the same chromosome of the human reference genome as the respective segment, and (ii) a measure of dispersion across the bin-level sequence ratios corresponding to the plurality of bins that map to the respective chromosome.
  • the present disclosure provides a method for treating a patient with a cancer containing a copy number variation of a target gene.
  • the method comprises determining whether the patient has an aggressive form of cancer associated with a focal copy number variation of the target gene by obtaining a first biological sample of the cancer from the patient and performing copy number variation analysis on the first biological sample to identify the copy number status of the target gene in the cancer.
  • the copy number variation analysis generates a first dataset comprising a plurality of bin-level sequence ratios, each respective bin-level sequence ratio in the plurality of bin- level sequence ratios corresponding to a respective bin in a plurality of bins.
  • Each respective bin in the plurality of bins represents a corresponding region of a human reference genome, and each respective bin-level sequence ratio in the plurality of bin-level sequence ratios is determined from a sequencing of a plurality of nucleic acids in the first biological sample of the cancer from the patient and one or more reference samples.
  • the first dataset also comprises a plurality of segment-level sequence ratios, each respective segment-level sequence ratio in the plurality of segment-level sequence ratios corresponding to a segment in a plurality of segments.
  • Each respective segment in the plurality of segments represents a corresponding region of the human reference genome encompassing a subset of adjacent bins in the plurality of bins, and the plurality of segment- level sequence ratios is determined from a measure of central tendency of the plurality of bin- level sequence ratios corresponding to the subset of adjacent bins encompassed by the respective segment.
  • the first dataset further comprises a plurality of segment-level measures of dispersion, where each respective segment-level measure of dispersion in the plurality of segment-level measures of dispersion (i) corresponds to a respective segment in the plurality of segments and (ii) is determined using the plurality of bin-level sequence ratios corresponding to the subset of adjacent bins encompassed by the respective segment.
  • the method further comprises determining whether the copy number variation of the target gene is a focal copy number variation by applying the first dataset to an algorithm having a plurality of copy number variation filters.
  • a first therapy for the aggressive form of the cancer to the patient is administered, and when the patient does not have the aggressive form of cancer associated with focal copy number variation of the target gene, a second therapy for a less aggressive form of the cancer to the patient is administered.
  • the present disclosure solves this and other needs in the art by providing improved somatic variant identification methodology that better accounts for locus-specific and/or sample specific considerations to more accurately identify true somatic mutations in a liquid biopsy sample.
  • the variant filter methodologies described herein tune the specificity and sensitivity of variant count thresholds in a locus-specific fashion to achieve higher accuracy of true somatic variant calling in a liquid biopsy assay.
  • the present disclosure provides a method of validating a somatic sequence variant in a test subject having a cancer condition.
  • the method is performed at a computer system having one or more processors, and memory storing one or more programs for execution by the one or more processors.
  • the method includes obtaining, from a first sequencing reaction, a corresponding sequence of each cell-free DNA fragment in a first plurality of cell-free DNA fragments in a liquid biopsy sample of the test subject, thus obtaining a first plurality of sequence reads.
  • Each respective sequence read in the first plurality of sequence reads is aligned to a reference sequence for the species of the subject, thus identifying a variant allele fragment count for a candidate variant that maps to a locus in the reference sequence, and a locus fragment count for the locus encompassing the candidate variant.
  • the method further includes comparing the variant allele fragment count for the candidate variant against a dynamic variant count threshold for the locus in the reference sequence that the candidate variant maps to.
  • the dynamic variant count threshold is based upon a pre-test odds of a positive variant call for the locus based on the prevalence of variants in a genomic region that includes the locus from a first set of nucleic acids obtained from a cohort of subjects having the cancer condition.
  • the method then includes rejecting or validating the variant as a true somatic variant based upon the dynamic variant count threshold. For instance, when the variant allele fragment count for the candidate variant satisfies the dynamic variant count threshold for the locus, the presence of the somatic sequence variant in the test subject 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 presence of the somatic sequence variant in the test subject is rejected.
  • the present disclosure solves this and other needs in the art by providing methods and systems for estimating the circulating tumor fraction of a liquid biopsy sample from a targeted-panel sequencing reaction. For example, by fitting segment-level coverage ratios for on-target and off-target sequence reads distributed relatively uniformly along the genome to integer copy states across a range of simulated tumor fractions (e.g., using maximum likelihood estimation, for example, with an expectation-maximization algorithm), the systems and methods described herein can generate an accurate estimate of the circulating tumor fraction of a liquid biopsy sample. This is achieved, in some embodiments, by identifying the expected coverage ratios, given the fitted integer copy states, that best match the experimental coverage ratios. Such an accurate estimate of the circulating tumor fraction can be used in conjunction with on-target sequencing results to improve variant detection identification, as well as serve as an informative biomarker itself.
  • the present disclosure provides a method of estimating a circulating tumor fraction for a test subject from panel-enriched sequencing data for a plurality of sequences, at a computer system having one or more processors, and memory storing one or more programs for execution by the one or more processors.
  • the method includes obtaining, from a first panel-enriched sequencing reaction, a first plurality of sequences.
  • the plurality of sequences includes a corresponding sequence for each cell-free DNA fragment in a first plurality of cell-free DNA fragments obtained from a liquid biopsy sample from the test subject, wherein each respective cell-free DNA fragment in the first plurality of cell-free DNA fragments corresponds to a respective probe sequence in a plurality of probe sequences used to enrich cell-free DNA fragments in the liquid biopsy sample in the first panel-enriched sequencing reaction.
  • the first plurality of sequences also includes a corresponding sequence for each cell-free DNA fragment in a second plurality of cell-free DNA fragments obtained from the liquid biopsy sample, wherein each respective cell-free DNA fragment in the second plurality of DNA fragments does not correspond to any probe sequence in the plurality of probe sequences.
  • the method includes determining a plurality of bin-level coverage ratios from the plurality of sequences, each respective bin-level coverage ratio in the plurality of bin-level coverage ratios corresponding to a respective bin in a plurality of bins.
  • Each respective bin in the plurality of bins represents a corresponding region of a human reference genome.
  • Each respective bin-level sequence ratio in the plurality of bin-level sequence ratios is determined from a comparison of (i) a number of sequence reads in the plurality of sequences that map to the corresponding bin and (ii) a number of sequence reads from one or more reference samples that map to the corresponding bin.
  • the method further includes determining a plurality of segment-level coverage ratios by forming a plurality of segments by grouping respective subsets of adjacent bins in the plurality of bins based on a similarity between the respective coverage ratios of the subset of adjacent bins, and determining, for each respective segment in the plurality of segments, a segment-level coverage ratio based on the corresponding bin-level coverage ratios for each bin in the respective segment.
  • the method For each respective simulated circulating tumor fraction in a plurality of simulated circulating tumor fractions, the method includes fitting each respective segment in the plurality of segments to a respective integer copy state in a plurality of integer copy states, by identifying the respective integer copy state in the plurality of integer copy states that best matches the segment-level coverage ratio, thus generating, for each respective simulated circulating tumor fraction in the plurality of simulated tumor fractions, a respective set of integer copy states for the plurality of segments.
  • the method further includes determining the circulating tumor fraction for the test subject based on a comparison between the corresponding segment-level coverage ratios and integer copy states across the plurality of simulated circulated tumor fractions.
  • the comparison includes optimization of an error between corresponding segment-level coverage ratios and integer copy states across the plurality of simulated circulated tumor fractions.
  • the comparison includes finding two or more local optima for fit (e.g., local minima for an error between corresponding segment- level coverage ratios and integer copy states across the plurality of simulated circulated tumor fractions) and choosing the local optima (e.g., minima) that is most consistent with one or more alternative estimations of the tumor fraction.
  • FIGS 1A, IB, 1C1, 1D1, 1C2, 1D2, 1E2, 1F2, 1C3, and 1D3 collectively illustrate a block diagram of an example computing device for supporting clinical decisions in precision oncology using liquid biopsy assays (e.g ., by validating a copy number variation, validating a somatic sequence variant in a test subject having a cancer condition, estimating the circulating tumor fraction of a liquid biopsy sample based on on-target and off-target sequence reads from targeted-panel sequencing data etc.), in which dashed boxes represent optional portions of the method, in accordance with some embodiments of the present disclosure.
  • 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.
  • 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.
  • Figure 3 provides an example flow chart of processes and features for liquid biopsy sample collection and analysis for use in precision oncology, in which dashed boxes represent optional portions of the method, in accordance with some embodiments of the present disclosure.
  • Figures 4A, 4B, 4C, 4D, 4E, 4F1, 4F2, 4G1, 4G2, 4G3, and 4F3 collectively illustrate an example bioinformatics pipeline for precision oncology.
  • 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 the present 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.
  • Figure 4F1 provides a flow chart of a method for validating a copy number variation, in which dashed boxes represent optional portions of the method, in accordance with some embodiments of the present disclosure.
  • Figure 4F2 provides a flow chart of a method for validating a somatic sequence variant in a test subject having a cancer condition, in which dashed boxes represent optional portions of the method, in accordance with some embodiments of the present disclosure.
  • Figures 4G1, 4G2, and 4G3 illustrate a method of variant detection, in which dashed boxes represent optional portions of the method, in accordance with some embodiments of the present disclosure.
  • Figure 4F3 provides an overview of a method for estimating the circulating tumor fraction for a liquid biopsy sample, based on targeted panel sequencing data, in which dashed boxes represent optional portions of the method, in accordance with some embodiments of the present disclosure.
  • Figures 5A1, 5B1, 5C1, 5D1, and 5E1 collectively provide a flow chart of processes and features for validating a copy number variation in a test subject, in which dashed boxes represent optional portions of the method, in accordance with some embodiments of the present disclosure.
  • Figures 5A2 and 5B2 collectively provide a flow chart of processes and features for validating a somatic sequence variant in a test subject, in which dashed boxes represent optional portions of the method, in accordance with some embodiments of the present disclosure.
  • Figures 5A3 and 5B3 collectively provide a flow chart of processes and features for estimating the circulating tumor fraction of a liquid biopsy sample based on on-target and off-target sequence reads from a targeted-panel sequencing data, in which dashed boxes represent optional portions of the method, in accordance with some embodiments of the present disclosure.
  • Figures 6A1, 6B1, and 6C1 collectively provide a flow chart of processes and features for treating a patient with a cancer containing a copy number variation of a target gene, in which dashed boxes represent optional portions of the method, in accordance with some embodiments of the present disclosure.
  • Figure 6A2 illustrates a flow chart of a method for obtaining a distribution of variant detection sensitivities as a function of circulating variant allele fraction from a cohort of subjects, in accordance with some embodiments of the present disclosure.
  • Figures 6A3, 6B3, and 6C3 collectively illustrate a process for fitting segment- level coverage ratios to an integer copy number (6A3 and 6B3) and subsequently determining the error associated with the fit (6C3) at a particular simulated circulating tumor fraction, in accordance with some embodiments of the present disclosure.
  • Figures 7A1 and 7B1 illustrate a non-focal amplified segment and a focal amplified segment comprising the MYC gene, in accordance with some embodiments of the present disclosure.
  • Figure 7C1 illustrates a focal deleted segment comprising the BRCA2 gene, in accordance with some embodiments of the present disclosure.
  • Figures 7A2 and 7B2 collectively illustrate a method of inferring an effect of a sequence variant as a gain-of-function or a loss-of-function of a gene, in accordance with some embodiments of the present disclosure.
  • Figure 7A3 illustrates an overview of an experimental and analytical workflow used for validation of the performance of a method for estimating the circulating tumor fraction of a liquid biopsy sample based on on-target and off-target sequence reads from a targeted-panel sequencing data, in accordance with some embodiments of the present disclosure.
  • Figures 8A, 8B, 8C, and 8D collectively illustrate results of an inter-assay comparison between a liquid biopsy assay, a digital droplet polymerase chain reaction (ddPCR), and a solid-tumor biopsy assay, in accordance with various embodiments of the present disclosure.
  • ddPCR digital droplet polymerase chain reaction
  • Figures 9A, 9B, 9C, 9D, 9E, 9F, 9G, and 9H collectively illustrate results of a comparison between circulating tumor fraction estimate (ctFE) and variant allele fraction (VAF) using an Off-Target Tumor Estimation Routine (OTTER) method, in accordance with various embodiments of the present disclosure.
  • ctFE circulating tumor fraction estimate
  • VAF variant allele fraction
  • OOTTER Off-Target Tumor Estimation Routine
  • Figures 10A and 10B collectively illustrate results of evaluating ctFE and mutational landscape according to cancer type, in accordance with various embodiments of the present disclosure.
  • Figures 11A, 11B, and 11C collectively illustrate results of evaluating associations between ctFE and advanced disease states, in accordance with various embodiments of the present disclosure.
  • Figures 12A, 12B, and 12C collectively illustrate results of comparing ctFE with recent clinical response outcomes, in accordance with various embodiments of the present disclosure.
  • Figure 13 illustrates a first table describing sensitivity for all SNVs, indels, CNVs, and rearrangements targeted in reference samples, in accordance with various embodiments of the present disclosure.
  • Figure 14 illustrates a second table describing sensitivity for all SNVs, indels, CNVs, and rearrangements targeted in reference samples, in accordance with various embodiments of the present disclosure.
  • Figure 15 illustrates a third table describing comparisons between the presently disclosed liquid biopsy assay and a commercial liquid biopsy kit, in accordance with various embodiments of the present disclosure.
  • Figures 16A, 16B, and 16C collectively illustrate a fourth table describing variants detected by a liquid biopsy assay, in accordance with various embodiments of the present disclosure.
  • Figure 17 illustrates a fifth table describing dynamic filtering methodology to further reduced discordance, in accordance with various embodiments of the present disclosure.
  • Figure 18 illustrates a sixth table describing cancer groups included in clinical profiling analysis, in accordance with various embodiments of the present disclosure.
  • Figure 19 illustrates an example plot of the errors between corresponding segment-level coverage ratios and integer copy states determined across a plurality of simulated circulated tumor fractions ranging from about 0 to about 1, in accordance with some embodiments of the disclosure.
  • the methods and systems described herein utilize annotation and filtering that applies a statistical method to bin-level copy ratios, segment-level copy ratios and corresponding segment-level confidence intervals of binned and segmented sequence reads aligned to a reference genome.
  • the statistical method filters out segments with non-focal copy number variations, which are either non- actionable, e.g., in the case of a copy number variation spanning a significant portion of a chromosome, or artifactual, e.g., due to incorrect data normalization.
  • Figure 4F1 illustrates a workflow of a method 400-1 for validating copy number variation, e.g., to identify therapeutically actionable genomic alterations, in accordance with some embodiments of the present disclosure.
  • the methods described herein utilize conventional methodologies to putatively identify copy number variations, which are then validated using the methodologies described herein. For instance, in some embodiments, copy number variations (CNVs) are analyzed using a combination of an open-source tool, e.g., CNVkit, to putatively identify copy number variations, and a script, e.g., a Python script, to validate or reject the putative copy number variations, using the validation methodologies described herein. In other embodiments, the validation methodologies described herein are used to identify focal copy number variations independently of conventional bioinformatics tools, e.g., CNVkit.
  • CNVkit open-source tool
  • a script e.g., a Python script
  • the methods described herein include one or more data collection steps, in addition to data analysis and downstream steps.
  • the methods include collection of a liquid biopsy sample and, optionally, one or more matching biological samples from the subject (e.g., a matched cancerous and/or matched non- cancerous sample from the subject).
  • the methods include extraction of DNA from the liquid biopsy sample and, optionally, one or more matching biological samples from the subject (e.g., a matched cancerous and/or matched non-cancerous sample from the subject).
  • the methods include nucleic acid sequencing of DNA from the liquid biopsy sample and, optionally, one or more matching biological samples from the subject (e.g., a matched cancerous and/or matched non-cancerous sample from the subject).
  • nucleic acid sequencing results e.g., raw or collapsed sequence reads of DNA from a liquid biopsy sample and, optionally, one or more matching biological samples from the subject (e.g., a matched cancerous and/or matched non-cancerous sample from the subject), from which the statistics needed for focal CNV validation (e.g., bin-level sequence ratios, segment-level sequence ratios, and segment-level measures of dispersion) can be determined.
  • sequencing data 122 for a patient 121 is accessed and/or downloaded over network 105 by system 100.
  • genomic bin values e.g., bin counts or bin coverages
  • one or more matching biological samples from the subject e.g., a matched cancerous and/or matched non-cancerous sample from the subject
  • the statistics needed for focal CNV validation e.g., bin-level sequence ratios, segment- level sequence ratios, and segment-level measures of dispersion
  • genomic bin values 135-cf-bv for a patient 121 is accessed and/or downloaded over network 105 by system 100.
  • the methods described herein begin with obtaining the statistics needed for focal CNV validation (e.g., bin-level sequence ratios, segment-level sequence ratios, and segment-level measures of dispersion) for a sequencing of a liquid biopsy sample and, optionally, one or more matching biological samples from the subject (e.g., a matched cancerous and/or matched non-cancerous sample from the subject), e.g., as an output of a conventional bioinformatics tool (such as CNVkit).
  • a conventional bioinformatics tool such as CNVkit
  • bin-level sequence ratios 135-cf-br, segment-level sequence ratios 135- cf-sr, and segment-level measures of dispersion for a patient 121 is accessed and/or downloaded over network 105 by system 100.
  • the method includes obtaining a dataset including cell-free DNA sequencing data (Block 402-1), and determining the statistics needed for focal CNV validation (e.g., bin-level sequence ratios, segment-level sequence ratios, and segment-level measures of dispersion).
  • system 100 obtains sequencing data 122 (e.g., sequence reads 123 and/or aligned sequences 124) and applies a copy number segmentation algorithm 153-b (e.g., CNVkit) to the sequencing data.
  • sequencing data 122 e.g., sequence reads 123 and/or aligned sequences 12
  • a copy number segmentation algorithm 153-b e.g., CNVkit
  • sequence reads 123 obtained from the sequencing dataset 122 are aligned to a reference human construct (Block 404-1), generating a plurality of aligned reads 124 (Block 406-1). Aligned cfDNA sequence reads are then optionally processed (e.g., using normalization, filtering, and/or quality control) (Block 408- iy
  • a copy number segmentation algorithm 153-b is then used for genomic region binning, coverage calculation, bias correction, normalization to a reference pool, segmentation, and/or visualization (Block 410-1). For example, in some embodiments, aligned sequence reads are sorted into bins (e.g., on target bins 153-b-l-a and off-target bins 153-b-l-b) of pre-specified bin sizes (e.g., 100-150 base pairs) based on their genomic location using binning subroutine 153-b-l.
  • bins e.g., on target bins 153-b-l-a and off-target bins 153-b-l-b
  • pre-specified bin sizes e.g., 100-150 base pairs
  • binning subroutine 153-b-l reads in mapped sequences 124 and pre-selected bins (e.g., target bins 153-b-l-a and off-target bins 153-b-l-b for target panel sequencing analysis) and assigns respective sequences to the bins based on their mapping within the reference genome.
  • Bin values 135-bv e.g., liquid biopsy genomic bin values 135-cf-bv
  • Bin values 135-bv are optionally pre-processed, e.g., normalized, standardized, corrected, etc., as described in further detail herein.
  • Bin values 135-bv are then used to determine bin-level sequence ratios 135-br (e.g., liquid biopsy bin-level sequence ratios 135-cf-br).
  • a copy ratio subroutine 153- b-2 reads in bin values 135-bv and reference bin coverages 153-b-2-a determined for one or more reference samples (e.g., a matched non-cancerous sample of the subject or a an average from a plurality of non-cancerous reference samples), and compares bin values for corresponding bins, thereby generating bin-level sequence ratios 135-br.
  • bin-level sequence ratios 135-br are then used to group adjacent bins, having similar sequence ratios, into segments, e.g., using circular binary segmentation.
  • segmentation subroutine 153-a-3 reads in and applies a segmentation model (e.g., a circular binary segmentation model) to bin-level sequence ratios 135-br, thereby generating a plurality of genomic segments, each corresponding to one or more contiguous bins.
  • a segmentation model e.g., a circular binary segmentation model
  • Segment-level sequence ratios 135-sr e.g., liquid biopsy segment-level sequence ratios 135-cf-sr
  • segment-level measures of dispersion 135-sd e.g., liquid biopsy segment-level measures of dispersion 135-cf-sd
  • a statistics subroutine 153-a-4 which may be read out from the copy number segmentation algorithm 153-b, as illustrated in Figure 1D1, or may be separately implemented, e.g., by reading-in segment annotations (e.g., including bin assignments to each segment) generated by the segmentation subroutine 153-a-3 and bin-level sequence ratios 135-br from the copy ratio subroutine 153-b-2.
  • a copy number annotation subroutine 153-a-5 reads in one or both segment-level sequence ratios 135-sr (e.g., liquid biopsy segment-level sequence ratios 135- cf-sr) and segment-level measures of dispersion 135-sd, to provide copy number status annotations (e.g., amplified, neutral, or deleted) 135-cn (e.g., liquid biopsy copy numb annotations 135-cf-cn) for one or more of the identified segments.
  • segment-level sequence ratios 135-sr e.g., liquid biopsy segment-level sequence ratios 135- cf-sr
  • segment-level measures of dispersion 135-sd e.g., amplified, neutral, or deleted
  • 135-cn e.g., liquid biopsy copy numb annotations 135-cf-cn
  • the process above is also performed for a matched tumor tissue biopsy of the subject, e.g., thereby generating one or more tumor segment copy number annotations 135-t-cn.
  • the bin-level copy ratios, segment-level copy ratios and the corresponding segment-level confidence intervals statistics obtained from the copy number segmentation algorithm 153 are used as inputs for a focal amplification / deletion validation algorithm, to determine whether putative segment amplifications and/or deletions can be validated.
  • the copy number segmentation algorithm 153 applies a plurality of filters to statistics for one or more identified segment (. Block 412-1). In some embodiments, these filters include one or more of:
  • a bin-level measure of central tendency sequence ratio filter 153-a-l e.g., a median bin-level copy ratio filter (. Block 414-1);
  • a segment-level measure of dispersion confidence filter 153-a-2 e.g., a segment-level confidence interval filter ⁇ Block 416-1
  • a bin-level measure of central tendency plus deviation filter 153-a-3 e.g., a median- plus-median absolute deviation (MAD) bin-level copy ratio filter ⁇ Block 418-1
  • a segment-level sequence ratio filter 153-a-4 e.g., a segment-level copy ratio filter ⁇ Block 419-1).
  • the plurality of filters includes at least two of the above filters. In some embodiments, the plurality of filters includes at least three of the above filters. In some embodiments, the plurality of filters includes all four of the above filters.
  • the copy number status annotation ⁇ e.g., amplified, neutral, deleted) for each segment is validated or rejected if it passes or fails the plurality of copy number status annotation validation filters ⁇ Block 420-1). Specifically, when a filter in the plurality of filters is fired, the copy number annotation of the segment is rejected, and the copy number variation is determined to be a non-focal copy number variation. When no filter in the plurality of filters is fired, the copy number annotation of the segment is validated, and the copy number variation is determined to be a focal copy number variation ⁇ Block 422-1).
  • Validated copy number variations ⁇ e.g., focal amplifications and/or focal deletions of target genes
  • focal copy number variations can be matched to the appropriate therapies and/or clinical trials ⁇ Block 426-1).
  • a patient report indicating the validated copy number variations and any matched therapies and/or clinical trials can then be generated for use in precision oncology applications ⁇ Block 426-1).
  • Copy number variations are considered a biomarker for cancer diagnosis and certain copy number variations are targets of treatment.
  • a subset of copy number variations that can be investigated using the methods disclosed herein include amplifications in MET, EGFR, ERBB2, CD274, CCNE1, and MYC, and deletions in BRCA1 and BRCA2.
  • the method utilizes bin-level copy ratios, in addition to segment-level copy ratios, to validate the copy number variations of target genomic segments, thus allowing a highly sensitive characterization of local (both internal and external) changes in copy number to detect true copy number variations with greater accuracy.
  • the presently disclosed systems and methods enable an automatic and reliable way to detect actionable, focal copy number variations via a liquid biopsy assay that is not achieved by conventional methods and is considerably less invasive than a tissue biopsy.
  • the combination of liquid biopsy and copy number variation detection benefits physicians, clinicians, and medical institutions by providing a powerful tool for diagnosing cancer conditions and administering treatments.
  • the methods disclosed herein can be performed alone or alongside traditional solid tumor biopsy methods as a validation method for detecting copy number variations.
  • the annotation and filtering algorithm can be used to distinguish between actionable and non-actionable copy number variations of target biomarkers that are informative for precision oncology. For example, as reported in Example 2 (Identification of Focal Copy Number Variation; see Examples, below), when applied to two experimental samples both containing a conventionally obtained amplification status for the MYC gene, the method rejected the amplification in a first sample as anon-focal amplification, and validated the amplification in a second sample as a focal, and likely actionable, amplification.
  • the overall temporal and spatial computation complexity of simple global and local pairwise sequence alignment algorithms are quadratic in nature (e.g., second order problems), that increase rapidly as a function of the size of the nucleic acid sequences (n and m) being compared.
  • the temporal and spatial complexities of these sequence alignment algorithms can be estimated as O(mn), where O is the upper bound on the asymptotic growth rate of the algorithm, n is the number of bases in the first nucleic acid sequence, and m is the number of bases in the second nucleic acid sequence.
  • the method comprises dividing the plurality of aligned sequence reads into “bins” (e.g., regions of a predefined span of base pairs corresponding to a reference genome), determining the copy ratio of each bin by calculating the differential read depths between experimental and reference samples, and grouping subsets of adjacent bins with shared copy ratios into segments. Grouping bins into segments divides each chromosome into regions of equal copy number that minimizes noise in the data.
  • Such methods essentially perform a change-point or edge detection algorithm, which are either temporally limited or computationally intense. For example, in some embodiments, the segmentation is performed using circular binary segmentation.
  • Circular binary segmentation calculates a statistic for each genomic position, where the statistic comprises a likelihood ratio for the null hypothesis (no change in copy ratio at the respective position) against the alternative (one change in copy ratio at the respective position), and where the null hypothesis is rejected if the statistic is greater than a predefined distribution threshold.
  • the chromosome is assumed to be circularized, such that the calculation is performed recursively for each position (e.g., each bin) around the circumference of the circle to identify all change-points across the length of the chromosome.
  • a reference distribution is generated using a permutation approach, where the copy ratios for the plurality of bins are randomized (typically 10,000 times).
  • the number of permutations required to perform this recursive method contributes to a computationally intense procedure. See, for example, Olshen et al, Biostatistics 5, 4, 557- 572 (2004), doi:10.1093/biostatistics/kxh008, which is hereby incorporated herein by reference in its entirety.
  • the present disclosure provides various systems and methods that improve the computational elucidation of actionable genomic alterations from a liquid biopsy sample of a cancer patient. Specifically, the present disclosure improves a computer- implemented method for identifying focal copy number variations by validating copy number status annotations assigned to genomic segments.
  • the application of the plurality of filters to the bin-level copy ratios, segment-level copy ratios, and corresponding segment-level confidence intervals is iterated, on a computer system, over each segment in the plurality of segments, and in some embodiments requires calculations using the copy ratios of each bin in the plurality of bins for each chromosome, for each segment in the plurality of segments.
  • the methods disclosed herein are a computational process designed to solve a computational problem.
  • the methods and systems described herein provide an improvement to the abovementioned technical problem (e.g., performing complex computer- implemented methods for analyzing a plurality of sequence reads for detection and validation of copy number variations in human genetic targets).
  • the methods described herein therefore solve a problem in the computing art by improving upon conventional methods for identifying copy number variations for cancer diagnosis and treatment.
  • the application of a plurality of filters to the bin-level copy ratios, segment-level copy ratios, and corresponding segment-level confidence intervals provides a means for detecting true copy number variations for clinically relevant biomarkers and filtering out artifactual variations that are not therapeutically actionable, thus improving the accuracy and precision of genomic alteration detection in precision oncology.
  • the methods and systems described herein also improve precision oncology methods for assigning and/or administering treatment because of the improved accuracy of copy number variation detection.
  • the removal of non-therapeutically actionable, non-focal copy number variations reduces the risk of patients undergoing unnecessary or potentially harmful regimens due to misdiagnoses.
  • the present disclosure provides methods and systems that more accurately call somatic variants by adjusting the variant count threshold in a locus-by-locus fashion, e.g., by lowering the variant count threshold when there is an increased likelihood (orthogonal to the variant count in the sequencing reaction) that a variant at a particular locus is a true somatic variant and/or by raising the variant count threshold when there is an increased likelihood (orthogonal to the variant count in the sequencing reaction) that a variant at a particular locus is a result of a sequencing error, rather than a true somatic variant.
  • the methods and systems described herein employ a generalized application of Bayes’ Theorem through the likelihood ratio test that allows dynamic calibration of filtering threshold for diagnostic assays.
  • These thresholds are based on one or more of a sample-specific error rate, a methodology-specific sequencing error rate (e.g., from a pool of process matched healthy control samples), an estimate of the variant allele fraction for the variant being evaluated, and a historical likelihood that a variant would be present at a particular locus in a particular cancer (e.g., derived from an extensive cohort of human solid tumor tissue samples to inform probability models).
  • the dynamic variant filtering methodology described herein uses an application of Bayes theorem to dynamically tune a variant count threshold for calling a somatic variant at a particular genomic region based on the prevalence of similar mutations within that genomic regions in similar cancers. For instance, where there is a high prevalence of a somatic variant in a given gene for a particular cancer, ( e.g .
  • the dynamic filtering method accounts for this prior (e.g., the prior knowledge that BRCA mutations are commonly found in breast cancers) by setting a lower variant count threshold to call somatic variants in the BRCA1 gene for a breast cancer. That is, the dynamic filtering methodology requires less evidence in order to call a variant in the BRCA1 gene when the subject has breast cancer than when the subject has a different cancer that is not associated with a high prevalence of BRCA1 mutations.
  • the dynamic variant filtering methodology described herein uses an application of Bayes theorem to dynamically tune a variant count threshold for calling a somatic variant based on an estimated variant allele fraction for the variant being evaluated. That is, the dynamic filtering methodology takes into account the fact that in a sample having a lower tumor fraction, and therefore a lower variant allele fraction, a fewer number of sequences encompassing a somatic variant would be expected than in a sample having a higher tumor fraction, and therefore a higher variant allele fraction.
  • the sensitivity and specificity of the dynamic filter are tuned to account for the expectation that a higher percentage of variant sequences with low sequence counts (e.g., lower support) represent true somatic variants in a sample with a low tumor fraction than in a sample with a high tumor fraction, for which a higher percentage of variant sequences with low sequence counts represent sequencing errors.
  • the dynamic variant filtering methodology described herein used an application of Bayes theorem to dynamically tune a variant count threshold for calling a somatic variant at a particular genomic locus based on a historical sequencing error rate for the locus. That is, the dynamic filtering methodology takes into account the fact that at genomic loci that are more prone to sequencing errors, such as loci with short nucleotide repeat sequences (e.g., di-nucleotide or tri-nucleotide repeats), there is a higher likelihood that a particular variant is a product of a sequencing error, rather than a true somatic mutation, than at a locus that is not prone to sequencing errors.
  • short nucleotide repeat sequences e.g., di-nucleotide or tri-nucleotide repeats
  • the dynamic variant filtering methodology described herein used an application of Bayes theorem to dynamically tune a variant count threshold for calling a somatic variant at a particular genomic locus based on a reaction- specific sequencing error rate. That is, the dynamic filtering methodology takes into account the fact that in reactions with higher sequencing rates there is a higher likelihood that a particular variant is a product of a sequencing error, rather than a true somatic mutation.
  • the present disclosure provides improved systems and methods for precision oncology based on improved variant calling in liquid biopsy data.
  • the various improvements described herein e.g., improved variant detection at low circulating fractions, are embodied in an example liquid biopsy workflow described in Examples 2 and 3.
  • These examples describe an example liquid biopsy assay employing a 105-gene hybrid-capture next- generation sequencing (NGS) panel spanning 270 kb of the human genome, configured to detect targets in four variant classes, including single nucleotide variants (SNVs), insertions and/or deletions (indels), copy number variants (CNVs), and gene rearrangements.
  • NGS next- generation sequencing
  • the example liquid biopsy assay detected actionable variants with high accuracy in comparison to a commercial ctDNA NGS kit, commercial solid tumor biopsy-based assays, such as a solid tumor biopsy NGS tissue assay, and digital droplet PCR (ddPCR).
  • ddPCR digital droplet PCR
  • the present disclosure provides various systems and methods that improve the computational elucidation of actionable genomic alterations from a liquid biopsy sample of a cancer patient. Specifically, the present disclosure improves a method for identifying variants in ctDNA using a dynamic thresholding approach.
  • the disclosed methods and systems are necessarily computer-implemented due to their complexity and heavy computational requirements, and thus solve a problem in the computing art.
  • the methods and systems described herein provide an improvement to the abovementioned technical problem (e.g., performing complex computer- implemented methods for identifying variants in ctDNA using a dynamic thresholding approach).
  • the methods described herein therefore solve a problem in the computing art by improving upon conventional methods for identifying variants (e.g., actionable oncologic targets) for cancer diagnosis and treatment.
  • the application of Bayes’ Theorem through the likelihood ratio test provides a means for improving detection of true positive variants and reducing detection of false positive variants for clinically relevant biomarkers, thus improving the accuracy and precision of genomic alteration detection in precision oncology.
  • 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 removal of false positive variant detection reduces the risk of patients undergoing unnecessary or potentially harmful regimens due to misdiagnoses.
  • conventional liquid biopsy assays do not provide accurate determination of circulating tumor fraction estimates (ctFEs).
  • ctFEs circulating tumor fraction estimates
  • low-pass, whole-genome sequencing can be used to estimate tumor fractions
  • somatic variant sequences are poorly identified from low-pass, whole genome sequencing data, particularly from samples having low tumor fractions.
  • conventional liquid biopsy assays typically use targeted-panel sequencing in order to achieve higher sequence coverage required to identify somatic variants present at low levels within the sample.
  • targeted-panel sequencing data does not span a large enough portion of the genome to accurately estimate tumor fraction. Rather, tumor fraction estimates obtained using variant allele fractions (VAFs) in targeted-panel sequencing data are noisy, due to variant tissue source and capture bias.
  • VAFs variant allele fractions
  • the present disclosure provides methods and systems that do provide accurate determination of circulating tumor fraction estimates by using on-target and off-target sequence reads from targeted-panel sequencing data.
  • the methods and systems described herein fit experimental coverage ratios for segmented sequence reads across the genome to integer copy numbers across a range of simulated tumor fractions. These fitted copy numbers can then be used to determine the expected coverage ratio for the segment, at the given simulated tumor fraction. The aggregate difference between the experimental coverage ratios for all segments and the expected coverage ratios based on the fitted copy number at the given simulated tumor fraction is used as a measure of the accuracy of the fit.
  • the simulated tumor fraction is a good estimate of the actual tumor fraction of the sample.
  • the experimental coverage ratios do not closely match the expected coverage ratios, the simulated tumor fraction is a poor estimate of the actual tumor fraction of the sample.
  • the systems and methods described herein leverage data collected across a majority of the human genome, which allows for more accurate estimation of circulating tumor fraction than data that is limited to on-target probe regions.
  • this method allows for both accurate tumor fraction estimation and robust variant identification from a single, low-cost sequencing reaction.
  • two sequencing reactions would need to be performed; a low-pass whole genome sequencing reaction to generate data across the genome for estimating circulating tumor fraction and a targeted-panel sequencing reaction to generate sufficiently deep sequencing data to identify variants.
  • the systems and methods described herein can be used in conjunction with variant detection methods that rely on targeted panel sequencing, such as high-depth sequencing reactions.
  • the systems and methods described herein ensure that any variation detected in regions of the genome are representative of the reference genome. This approach reduces noise resulting from capture bias, which can result in unreliable circulating tumor fraction estimates.
  • the systems and methods described herein further improve the accuracy and reliability of circulating tumor fraction estimates.
  • the sequencing coverage of on-target and off-target sequence reads are used to determine a test coverage ratio for regions of the genome in a test liquid biopsy sample.
  • the test coverage ratio is compared to a set of expected coverage ratios obtained using assumptions for expected copy states and expected tumor fractions, which gives a distance (e.g., an error) of the test coverage ratio from the expected copy state.
  • a distance e.g., an error
  • An improved method for obtaining accurate circulating tumor fraction estimates provide several benefits to liquid biopsies.
  • more reliable ctFEs improves the classification accuracy of detected variants as somatic or germline variants (e.g., any variant detected at or below the ctFE can be classified as a somatic variant with high confidence).
  • accurate ctFEs can greatly improve the sensitivity of detection of clinically relevant copy number variations, including integer copy number calling.
  • ctFEs are used as biomarkers for tumor burden, metastases, disease progression, or treatment resistance. For example, ctFEs have been shown to correlate with tumor volumes and vary in response to treatment.
  • the methods and systems disclosed herein provide a sensitive, cost- effective, and minimally invasive method to monitor patients for response to therapy, disease burden, relapse, progression, and/or emerging resistance mutations, which can translate into better care for patients.
  • serial ctFE monitoring can predict objective measures of progression in at-risk individuals. Due to cost and convenience of sampling, the methods and systems disclosed herein can be applied at shorter time intervals than radiographic methods and can allow for more timely intervention in the case of disease progression.
  • the methods and systems disclosed herein provide benefits to clinicians by generating more accurate variant calls and/or informative ctFE biomarkers that can aid in the prediction of clinical outcomes in patients and/or the selection of appropriate treatment plans.
  • a validation of the performance of a method for on-target and off- target tumor estimation revealed a correlation between ctFEs and metastases and disease progression.
  • high ctFEs were found to (i) correlate well with estimates derived from low-pass, whole genome sequencing, (ii) be a highly specific predictor of metastases, (iii) be positively correlated with reported “progressive disease” and (iv) be negatively correlated with better clinical outcomes.
  • Figure 7A3 provides an overview of an experimental and analytical workflow used for validation of the off-target tumor estimation routine (OTTER).
  • the present disclosure provides various systems and methods that improve the computational elucidation of actionable genomic alterations from a liquid biopsy sample of a cancer patient. Specifically, the present disclosure improves upon the accuracy of circulating tumor fractions estimated from targeted-panel sequencing. Moreover, because the methods described herein eliminate the need to process data from two different sequencing reactions, the disclosure lowers the computational budget for accurately estimating circulating tumor fractions and identifying actionable variants. As described above, the disclosed methods and systems are necessarily computer-implemented due to their complexity and heavy computational requirements, and thus solve a problem in the computing art.
  • the methods and systems described herein provide an improvement to the abovementioned technical problem (e.g., performing complex computer- implemented methods for determining accurate circulating tumor fraction estimates).
  • the methods described herein therefore solve a problem in the computing art by improving upon conventional methods for determining tumor fraction estimates for cancer diagnosis, monitoring, and treatment.
  • a maximum likelihood estimation e.g., an expectation-maximization algorithm
  • the application of a maximum likelihood estimation to estimate genomic alterations using on-target and off-target sequence reads in liquid biopsy samples improves upon conventional approaches for precision oncology by providing highly reliable circulating tumor fraction estimates, while allowing concurrent variant detection in targeted panel sequencing of liquid biopsy samples. This in turn lowers the computational budget required for these processes, thereby improving the speed and lowering the power requirements of the computer.
  • the methods and systems described herein also improve precision oncology methods for assigning and/or administering treatment because of the improved accuracy of circulating tumor fraction estimations.
  • Accurate ctFEs can be reported as biomarkers and/or used in downstream analysis for identification of therapeutically actionable variants to be included in a clinical report for patient and/or clinician review. Additionally, ctFEs and any therapeutically actionable variants identified using ctFEs can be matched with appropriate therapies and/or clinical trials, allowing for more accurate assignment of treatments.
  • the improved accuracy of biomarker detection increases the chance of efficacy and reduces the risk of patients undergoing unnecessary or potentially harmful regimens due to misdiagnoses.
  • 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.
  • a subject is a male or female of any age (e.g., a man, a woman, or a child).
  • control 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.
  • a particular condition e.g., 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.
  • 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).
  • cancer 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.
  • 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.
  • a malignant tumor can have the capacity to metastasize to distant sites.
  • a cancer cell is a cell found within the abnormal mass of tissue whose growth is not coordinated with the growth of normal tissue.
  • a “tumor sample” refers to a biological sample obtained or derived from a tumor of a subject, as described herein.
  • 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
  • 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.).
  • 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.
  • personal habits e.g., smoking, drinking, diet
  • other pertinent medical conditions e.g., high blood pressure, dry skin, other diseases
  • current medications e.g., allergies, pertinent medical history, current side effects of cancer treatments and other medications, etc.
  • liquid biopsy sample refers to a liquid sample obtained from a subject that includes cell-free DNA.
  • liquid biopsy samples include, but are 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.
  • a liquid biopsy sample is a cell-free sample, e.g., a cell free blood sample.
  • a liquid biopsy sample is obtained from a subject with cancer.
  • 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.
  • a liquid biopsy is collected from a subject with a non-cancerous disorder, e.g., a cardiovascular disease.
  • a liquid biopsy is collected from a subject with an unknown status for anon-cancerous disorder, e.g., for use in determining a non-cancerous disorder status of the subject.
  • 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.
  • 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.
  • 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.
  • 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.
  • a normal mammalian genome e.g., a human genome
  • 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.
  • 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.
  • genomic alteration refers 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
  • SNV single nucleotide variant
  • MNV multi-nucle
  • 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.
  • 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.
  • a mutation in a germline of the subject e.g., which is found in substantially all ‘normal cells’ in the subject
  • 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.
  • 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.
  • 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.
  • a mutation that is present in the 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.
  • 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.
  • focal copy number variation As used herein, the terms “focal copy number variation,” “focal copy number alteration,” “focal copy number variant,” and the like interchangeably refer to a genomic variation, relative to a reference genome, in the copy number of a small genomic segment. Unless otherwise specified, a small genomic segment is less than 30 Mb. However, in some embodiments, a small genomic segment is less than 25 Mb, less than 20 Mb, less 15 Mb, less than 10 Mb, less than 5 Mb, less than 4 Mb, less than 3 Mb, less than 2 Mb, less than 1 Mb, or smaller. Generally, focal copy number variations range from several hundred bases to tens of Mb.
  • a focal copy number variation consists of one or a few exons of a gene or several genes.
  • focal copy number variations see, for example, Nord et ctl, Int. J. Cancer, 126, 1390-1402 (2010), which is hereby incorporated herein by reference in its entirety.
  • 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.
  • 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.
  • 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.
  • variant allele fraction 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).
  • 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.
  • UMI unique molecular indices
  • germline variants refers to genetic variants inherited from maternal and paternal DNA. Germline variants may be determined through a matched tumor-normal calling pipeline.
  • 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.
  • single nucleotide variant 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.”
  • a cytosine to thymine SNV may be denoted as “C>T.”
  • insertions and deletions refers to a variant resulting from the gain or loss of DNA base pairs within an analyzed region.
  • CNV copy number variation
  • 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.
  • 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.
  • 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 heterozygosity across 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.
  • 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.
  • a tumor is composed of various sub-clonal populations, each of which may have a different mutational status in a gene of interest.
  • 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.
  • 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).
  • 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.”
  • 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.
  • microsatellite instability refers to a genetic hypermutability condition associated with various cancers that results from impaired DNA mismatch repair (MMR) in a subject.
  • MMR DNA mismatch repair
  • 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.
  • MCS MMR Stable a cancer
  • MMR Stable 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.
  • MSE Microsatellite Equivocal
  • RNA e.g. , mRNA or miRNA
  • 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.
  • 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.
  • a gene product an RNA species, e.g., mRNA or miRNA, or protein molecule
  • RNA species e.g., mRNA or miRNA, or protein molecule
  • the genomic locus can be identified using a gene name, a chromosomal location, or any other genetic mapping metric.
  • the term “ratio” refers to any comparison of a first metric X, or a first mathematical transformation thereof X' (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 Y' (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 XJY, Y/X, log N (X/Y), logN(Y/X), X'/Y, Y/X', logN(X'/Y), or logN(Y/X'), X/Y', Y'/X, log N (X/Y'), logN(YVX) , X'/Y', Y'/X', logN(X
  • X is transformed to X' prior to ratio calculation by raising X by the power of two (X 2 ) and Y is transformed to Y' prior to ratio calculation by raising Y by the power of 3.2 (Y 3 - 2 ) and the ratio of X and Y is computed as log 2 (X'/Y').
  • 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.
  • 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.
  • 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
  • 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.
  • 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 e.g., aligning to a particular region of the exome
  • sequencing refers to any biochemical processes that may be used to determine the order of biological macromolecules such as nucleic acids or proteins.
  • 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.
  • 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.
  • sequence 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 from tens to hundreds of base pairs (bp).
  • 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.
  • 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
  • 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 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).
  • 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.
  • PCR polymerase chain reaction
  • 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.
  • 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.
  • 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, lOOx, 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 the actual sequencing depth for a particular locus.
  • 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.
  • 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.
  • Y may be expressed as a fraction or a decimal, because it refers to an average coverage across a plurality of loci.
  • 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.
  • different sequencing technologies provide different sequencing depths.
  • 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.
  • 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).
  • 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.
  • “broad sequencing” refers to sequencing/analysis of at least 0.1% of an exome or genome.
  • sequence ratio and “coverage ratio” interchangeably refer to any measurement of a number of units of a genomic sequence in a first one or more biological samples (e.g., a test and/or tumor sample) compared to the number of units of the respective genomic sequence in a second one or more biological samples (e.g., a reference and/or control sample).
  • a sequence ratio is a copy ratio, a log2- transformed copy ratio (e.g., log2 copy ratio), a coverage ratio, a base fraction, an allele fraction (e.g. , a variant allele fraction), and/or a tumor ploidy .
  • sequence ratio is a logN-transformed copy ratio, where N is any real number greater than 1.
  • 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.
  • targeted panel or “targeted gene panel” refers to a combination of probes for sequencing (e.g., by next-generation sequencing) nucleic acids present in a biological sample from a subject (e.g., a tumor sample, liquid biopsy sample, germbne 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.
  • a biological sample from a subject e.g., a tumor sample, liquid biopsy sample, germbne tissue sample, white blood cell sample, or tumor or tissue organoid sample.
  • 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 Table 1.
  • a targeted panel in addition to loci that are informative for precision oncology, includes one or more probes for sequencing one or more of a loci associated with a different medical condition, a loci used for internal control purposes, or a loci from a pathogenic organism (e.g., an oncogenic pathogen).
  • a pathogenic organism e.g., an oncogenic pathogen
  • 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.
  • 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.
  • a reference exome comprises sequences assigned to chromosomes.
  • 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).
  • NCBI National Center for Biotechnology Information
  • UCSC Santa Cruz
  • 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 multiple individuals.
  • 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.
  • a reference genome comprises sequences assigned to chromosomes.
  • Exemplary human reference genomes include but are not limited to NCBI build 34 (UCSC equivalent: hgl6), NCBI build 35 (UCSC equivalent: hgl7), NCBI build 36.1 (UCSC equivalent: hgl8), GRCh37 (UCSC equivalent: hgl9), and GRCh38 (UCSC equivalent: hg38).
  • UCSC equivalent: hgl6 NCBI build 34
  • hgl7 NCBI build 35
  • NCBI build 36.1 UCSC equivalent: hgl8
  • GRCh37 UCSC equivalent: hgl9
  • GRCh38 GRCh38
  • 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.
  • 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.
  • 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.
  • 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.
  • level of detection 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 genomic alteration based on sequencing data. For example, a minimal number of unique sequence reads to support identification of a sequence variant such as a SNV.
  • 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).
  • 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.
  • SAM Sequence Alignment Map
  • 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.
  • 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.
  • PPV Positive Predictive Value
  • an 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
  • An 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) at which 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.
  • the term “classification” can refer to any number(s) or other characters(s) that are associated with a particular property of a sample.
  • 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.
  • 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.
  • 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.
  • TNR true negative rate
  • 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.
  • 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 positive effect in a cancer patient that has the actionable variant than in a similarly situated cancer patient that does not have the actionable variant.
  • a genomic alteration e.g., a SNV, MNV, indel, rearrangement, copy number variation, or ploidy variation
  • another cancer metric derived from nucleic acid sequencing data e.g., a tumor mutational burden, MSI status, or tumor fraction
  • an EGFR mutation in exon 19/21 is an actionable variant.
  • 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.
  • 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.
  • a genomic alteration e.g., a SNV, MNV, indel, rearrangement, copy number variation, or ploidy variation
  • another cancer metric derived from nucleic acid sequencing data e.g., a tumor mutational burden, MSI status, or tumor fraction
  • 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.
  • a genomic alteration e.g., a SNV, MNV, indel, rearrangement, copy number variation, or ploidy variation
  • another cancer metric derived from nucleic acid sequencing data e.g., a tumor mutational burden, MSI status, or tumor fraction
  • 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.
  • a genomic alteration e.g., a SNV, MNV, indel, rearrangement, copy number variation, or ploidy variation
  • another cancer metric derived from nucleic acid sequencing data e.g., a tumor mutational burden, MSI status, or tumor fraction
  • 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.
  • 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 factors include 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.
  • 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.
  • 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.
  • 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.
  • Figures 1A, IB, 1C1, 1D1, 1C2, 1D2, 1E2, 1F2, 1C3, and lD3 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.
  • 1E2, 1F2, 1C3, and lD3 improves upon conventional methods for providing clinical support for personalized cancer therapy by validating copy number variations, thus identifying focal copy number variations for actionable treatment, validating a somatic sequence variant in a test subject having a cancer condition, and/or determining circulating tumor fraction estimates using on-target and off-target sequence reads.
  • FIG. 1 A 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.
  • 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;
  • 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;
  • a reporting module 180 for generating and transmitting reports that provide clinical support for personalized cancer therapy.
  • Figures 1A, IB, 1C1, 1D1, 1C2, 1D2, 1E2, 1F2, 1C3, and 1D3 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.
  • 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.
  • system 100 is represented as a single computer that includes all of the functionality for providing clinical support for personalized cancer therapy.
  • 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.
  • system 100 includes one or more computers.
  • 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.
  • FIG. 1A, IB, 1C1, 1D1, 1C2, 1D2, 1E2, 1F2, 1C3, and lD3 can be stored and/or executed on the various instances of a processing device and/or processing server/database in the distributed diagnostic environment 210 illustrated in Figure 2B (e.g ., processing devices 224, 234, 244, and 254, processing server 262, and database 264).
  • processing devices 224, 234, 244, and 254, processing server 262, and database 264 e.g ., processing devices 224, 234, 244, and 254, processing server 262, and database 264.
  • 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.
  • PC personal computer
  • PDA Personal Digital Assistant
  • the system comprises a virtual machine that includes a module for executing instructions for performing any one or more of the methodologies disclosed herein.
  • a virtual machine 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.
  • 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 IB, different sets of patient data may be stored in different databases or modules spread across one or more system memories.
  • data for patients 121-1 to 121-M e.g., cancer patients or patients being tested for cancer
  • sequencing data 122 from one or more sequencing reactions 122 -i 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.
  • 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.
  • sequence data that has been aligned to a reference construct, e.g., BAM file 124, is stored in test patient data store 120.
  • the test patient data store 120 includes feature data 125, e.g., that is useful for identifying clinical support for personalized cancer therapy.
  • 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.
  • 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.
  • 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.
  • test patient data store 120 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 the patient, or curated from other sources, such as fields from various testing records (e.g., genetic sequencing reports).
  • EMR electronic medical record
  • EHR electronic health record
  • the feature data 125 includes genomic features 131 for the patient.
  • 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 134 (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
  • 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 Figure 4 (e.g, Figures 4A-E, 4F1, 4F2, and 4F3).
  • the feature data 125 include genomic copy numbers 135 (e.g, 135-1 for Patient 1 121-1) variant allele fractions 133, and/or circulating tumor fraction estimates 131-i, as determined using the improved methods for analyzing copy number variations (CNVs) using the copy number variation analysis module 153, validating somatic sequence variants, and/or determining circulating tumor fraction estimates, and as described in further detail below with reference to Figures 1 and 4 (e.g, Figures 1C1, 1D1, 4F1; Figures 1C2, 1D2, and 4F2; and/or Figures 1C3, 1D3, and 4F3).
  • 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.
  • the one or more genomic features 131 include genomic copy numbers 135 comprising liquid biopsy genomic copy numbers 135-cf and optional tumor biopsy genomic copy numbers 135-t, in accordance with some embodiments of the present disclosure.
  • the liquid biopsy genomic copy numbers 135-cf are determined by a nucleic acid bioinformatics pipeline (e.g, as described in detail below with reference to Figures 4A-E and 4F1) using a plurality of sequence reads 123 obtained from a sequencing of cell-free nucleic acids from a liquid biopsy sample.
  • the liquid biopsy genomic copy numbers comprise plurality of copy number annotations (e.g, 135-cf-l, 135-cf-2,...
  • the optional tumor biopsy genomic copy numbers 135-t are determined by a nucleic acid bioinformatics pipeline using a plurality of sequence reads 123 obtained from a sequencing of nucleic acids from a tumor (e.g, tissue) biopsy.
  • the optional tumor biopsy genomic copy numbers comprise a plurality of optional copy number annotations (e.g, 135-1-t-l, 135-l-t-2,... , 135-1-t-O), where each copy number annotation corresponds to a genomic target (e.g., a gene or a region of a genome).
  • the feature data 125 further includes data 138 from other -omics fields of study.
  • -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, gly comics, cellomics, and organoidomics.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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
  • clinical reports 139-3 generated for the subject, e.g.,
  • 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.
  • clinical assessment data 139 is generated, modified, and/or validated by evaluation of feature data 125 by a clinician, e.g., an oncologist.
  • 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.
  • 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.
  • 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).
  • 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).
  • 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 pluralit
  • bioinformatics module 140 includes a sequence data processing module 141 that includes instructions for processing sequence reads, e.g., raw sequence reads 123 from one or more sequencing reactions 122-i, prior to analysis by the various feature extraction algorithms, as described in detail below.
  • sequence data processing module 141 includes one or more pre-processing algorithms 142 that prepare the data for analysis.
  • 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).
  • 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).
  • 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.
  • 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.
  • 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.
  • BWA Burrows-Wheeler Alignment tool
  • BWT Burrows- Wheeler Transform
  • 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.
  • 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 sequencing reaction. 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.
  • a sample-specific and/or molecule-specific sequence tag e.g., a UMI
  • 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.
  • 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.
  • diagnostic features e.g., genomic features 131
  • a feature extraction algorithm compare
  • feature extraction module 145 includes one or more variant identification modules that include instructions for various variant calling processes.
  • variants in the germline of the subject are identified, e.g., using a germline variant identification module 146.
  • variants in the cancer genome e.g., somatic variants
  • somatic variant identification module 150 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.
  • 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).
  • SNV single nucleotide variants
  • MNV multi-nucleotide variants
  • 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.
  • 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-l,...
  • a dynamic variant count threshold 146-d (e.g., 146-d-l) 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-l) for the candidate variant against the dynamic variant count threshold 146-d for the locus corresponding to the candidate variant.
  • the variant validation module 146-0 determines whether the candidate variant is validated or rejected as a somatic sequence variant based on the comparison.
  • 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.
  • 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).
  • the variant thresholding module 146-a takes as input one or more variant allele fractions 133 from the genomic features module 131.
  • the variant allele fractions 133 comprises a plurality of variant allele fractions obtained from tumor tissue biopsies 133-t (e.g.. 133-t-l, 133-t-2... , 133-t-O) for the cohort of subjects.
  • the variant allele fractions comprise a plurality of variant allele fractions obtained from liquid biopsy samples 133-cf (e.g., 133-cf-l, 133-cf-2..., 133- cf-N) for the cohort of subjects.
  • the circulating variant allele fraction is obtained by comparing the liquid biopsy variant allele fractions 133-cf to the tumor biopsy variant allele fraction 133-t.
  • variant allele fractions e.g., variant allele frequencies
  • 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, b-thalassemia and cystic fibrosis result from SNPs).
  • diseases e.g., sickle-cell anemia, b-thalassemia and cystic fibrosis result from SNPs).
  • 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
  • An MNP Multiple-nucleotide polymorphisms
  • 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 10 000 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.
  • SNP single nucleotide polymorphisms
  • 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. Fusion genes 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- AML 1 (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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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).
  • 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 p
  • the copy number variation analysis module 153 performs a method that validates a copy number annotation of a genomic segment in a test subject, in accordance with some embodiments of the present disclosure.
  • the method comprises obtaining an input data store 153-r (e.g., a dataset), where the input data store includes a bin-level sequence ratio data structure 153-r- 1 containing a plurality of bin-level sequence ratios; a segment-level sequence ratio data structure 153-r-2 containing a plurality of segment-level sequence ratios; and a segment-level dispersion measure data structure 153- r-3 containing a plurality of segment-level measures of dispersion.
  • an input data store 153-r e.g., a dataset
  • the input data store includes a bin-level sequence ratio data structure 153-r- 1 containing a plurality of bin-level sequence ratios; a segment-level sequence ratio data structure 153-r-2 containing a plurality of segment-level sequence ratios; and a segment-
  • the method further comprises passing the data in the input data store 153-r to an amplification/deletion filter construct 153-a, thus applying the dataset to a plurality of filters.
  • the amplification/deletion filter construct 153-a comprises a plurality of filters, including an optional measure of central tendency bin-level sequence ratio filter 153-a-l; an optional segment-level measure of dispersion confidence filter 153-a-2; an optional measure of central tendency-plus-deviation bin-level sequence ratio filter 153-a-3; and/or an optional segment- level sequence ratio filter 153-a-4.
  • the copy number variation analysis module further provides an output via the validation construct 153-o, where, when a filter in the amplification/deletion filter construct 153-a is fired, the copy number annotation of the genomic segment is rejected, and when no filter in the amplification/deletion filter construct 153-a is fired, the copy number annotation of the genomic segment is validated.
  • copy number annotations validated using the copy number variation analysis module 153 in the feature extraction module 145 are used to populate the plurality of genomic copy numbers 135 in the one or more genomic features 131 of the test patient data store 120.
  • feature extraction module 145 comprises a tumor fraction estimation module 145-tf.
  • the tumor fraction estimation module 145-tf comprises a sequence ratio data structure 145-tf-r including a plurality of sequence ratios (e.g., coverage ratios) obtained from a sequencing of a test liquid biopsy sample of a subject.
  • the sequence ratio data structure 145-tf-r includes the sequence ratios that are used as input to determine tumor fraction estimates for the test liquid biopsy sample.
  • the tumor fraction estimation module 145-tf also comprises a tumor purity algorithm construct 145-tf-a that executes, for example, a maximum likelihood estimation (e.g., an expectation- maximization algorithm) to calculate an estimate of the circulating tumor fraction.
  • the tumor purity algorithm construct 145-tf-a comprises an optional input data filtration construct 145- tf-k (e.g., for removing one or more inputs passed from the sequence ratio data structure based on a minimum probe threshold or a position on a sex chromosome) and a plurality of model parameters 145-tf-d (e.g., 145-tf-d-l, 145-tf-d-2,...) used for executing the algorithm.
  • model parameters include expected sequence ratios for a set of copy states at a given tumor purity; a distance (e.g. , an error) from a test sequence ratio to the closest expected sequence ratio at the given tumor purity; a minimum distance (e.g., a minimum error) from a test sequence ratio to the closest expected sequence ratio at the given tumor purity (e.g., an assigned test copy state selected from a minimal distance expected copy state); and/or a tumor purity score (e.g., a sum of weighted errors).
  • a distance e.g. , an error
  • a minimum distance e.g., a minimum error
  • a tumor purity score e.g., a sum of weighted errors
  • the tumor fraction estimation module 145-tf is used to obtain one or more circulating tumor fraction estimates 131-i that are included as feature data 125 in a test patient data store 120.
  • a plurality of circulating tumor fraction estimates is obtained from a test liquid biopsy sample of a subject 131-i-cf (e.g., 131-i-cf-l, 131 -i-cf-2... , 131-i-cf-N).
  • the plurality of circulating tumor fraction estimates is obtained from a single patient at different collection times.
  • 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.
  • feature analysis module 160 identifies actionable variants and characteristics 139-1 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.
  • analysis algorithms e.g., algorithms 162, 163, 164, and 165
  • 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.
  • a user e.g., a clinician
  • a medical professional e.g., an oncologist
  • 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.
  • 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.
  • 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.
  • LUT look-up-table
  • 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.
  • 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.
  • genomic alteration interpretation algorithm 161 determines whether a particular genomic feature 131 should be reported to a medical professional treating the cancer patient.
  • genomic features 131 e.g., genomic alterations and compound features
  • genomic features 131 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.
  • a genomic alteration interpretation algorithm 161 may classify 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.”
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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, naive 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.
  • supervised algorithms such as algorithms where the features/classifications in the data set are annotated
  • Apriori means clustering, principal component analysis, random forest, adaptive boosting
  • semi-supervised algorithms such as algorithms where an incomplete number of features/classifications in the data
  • 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.
  • 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 explicitly stated 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.
  • 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.
  • system 100 also includes a database of training data for use in training the one or more classifiers.
  • the classifier training module accesses a remote storage device hosting training data.
  • the training data includes a set of training features, including but not limited to, various types of the feature data 125 illustrated in Figure IB.
  • 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.
  • 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.
  • 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).
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the therapeutic curation algorithm then generates one or more clinical reports 139-3 for the patient.
  • 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.
  • feature analysis module 160 includes a recommendation validation module 167 that includes an interface allowing a clinician to review, modify, and approve a clinical report 139-3 prior to the report being sent to a medical professional, e.g., an oncologist, treating the patient.
  • 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.
  • 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.
  • such processes and features of the system are carried out by modules 118, 120, 140, 160, and/or 170, as illustrated in Figure 1A.
  • the systems described herein include instructions for determining and validating focal copy number variations that are improved compared to conventional methods for copy number analysis, instructions for validating somatic variants that are improved compared to conventional methods for somatic variant detection, and/or instructions for determining accurate circulating tumor fraction estimates that are improved compared to conventional methods for obtaining circulating tumor fraction estimates.
  • Figure 2B Distributed Diagnostic and Clinical Environment
  • 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.
  • the improved methods described herein for supporting clinical decisions in precision oncology using liquid biopsy assays 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 the results of the methods described herein, may be performed across a distributed diagnostic/clinical environment.
  • Figure 2B illustrates an example of a distributed diagnostic/clinical environment 210.
  • the distributed diagnostic/clinical environment is connected via communication network 105.
  • 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).
  • 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).
  • 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.
  • 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.
  • 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.
  • 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.
  • a processing server 262 and/or database 264 which may be located in yet another environment, e.g., processing/storage center 260.
  • processing/storage center 260 e.g., different portions of the systems and methods described herein are fulfilled by different processing devices located in different physical environments.
  • a method for providing clinical support for personalized cancer therapy e.g., with improved validation of copy number variations, improved validation of somatic sequence variants, and/or improved determination of circulating tumor fraction estimates, is performed across one or more environments, as illustrated in Figure 2B.
  • 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 bioinformatics module 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.
  • a medical professional e.g., an oncologist
  • 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.
  • the methods described herein improve this process, for example, by improving various stages within feature extraction 206, including validating copy number variations, validating somatic sequence variants, and/or determining circulating tumor fraction estimates.
  • 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).
  • personal data 126 corresponding to the patient and a record of the one or more biological samples obtained are entered into a data analysis platform, e.g., test patient data store 120.
  • the methods disclosed herein include obtaining one or more biological samples from one or more subjects, e.g., cancer patients.
  • the subject is a human, e.g., a human cancer patient.
  • one or more of the biological samples obtained from the patient are a biological liquid sample, also referred to as a liquid biopsy sample.
  • 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.
  • the liquid biopsy sample includes blood and/or saliva.
  • the liquid biopsy sample is peripheral blood.
  • blood samples are collected from patients in commercial blood collection containers, e.g., using a PAXgene® Blood DNA Tubes.
  • saliva samples are collected from patients in commercial saliva collection containers, e.g., using an Oragene® DNA Saliva Kit.
  • the liquid biopsy sample has a volume of from about 1 mL to about 50 mL.
  • 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.
  • Liquid biopsy samples include cell free nucleic acids, including cell-free DNA (cfDNA).
  • 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).
  • ctDNA circulating tumor DNA
  • germline e.g., healthy or non-cancerous
  • 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.
  • the ‘tumor burden’ of the subject refers to the percentage cfDNA that originated from cancerous cells.
  • 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.
  • 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.
  • bodily fluids such as blood, circulate throughout the body, the cfDNA population represents a sampling of many different tissue types from many different locations.
  • a liquid biopsy sample is separated into two different samples.
  • a blood sample is separated into a blood plasma sample, containing cfDNA, and a huffy coat preparation, containing white blood cells.
  • a plurality of liquid biopsy samples is obtained from a respective subject at intervals over a period of time (e.g., using serial testing).
  • 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.
  • 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.
  • 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.
  • 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
  • skin biopsies e.g., shave biopsy, punch biopsy, incisional biopsy, and excisional biopsy
  • surgical biopsies can be used to obtain samples of cancers affecting internal organs of a patient.
  • a solid tissue sample is a formalin-fixed tissue (FFT).
  • a solid tissue sample is a macro-dissected formalin fixed paraffin embedded (FFPE) tissue.
  • FFPE formalin fixed paraffin embedded
  • a solid tissue sample is a fresh frozen tissue sample.
  • 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.
  • 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.
  • the buccal swab DNA is used as a source of normal DNA in circulating heme malignancies.
  • 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).
  • 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.
  • 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.
  • a specimen e.g., a solid tumor biopsy
  • the sample is a solid tissue biopsy sample.
  • the tissue biopsy sample is a formalin-fixed tissue (FFT), e.g., a formalin-fixed paraffin-embedded (FFPE) tissue.
  • FFT formalin-fixed tissue
  • FFPE formalin-fixed paraffin-embedded
  • the tissue biopsy sample is an FFPE or FFT block.
  • 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).
  • pathology review e.g., by staining with immunohistochemistry stain for IHC review and/or with hematoxylin and eosin stain for H&E pathology review.
  • pathology review e.g., by staining with immunohistochemistry stain for IHC review and/or with hematoxylin and eosin stain for H&E pathology review.
  • pathology review e.g., by staining with immunohistochemistry stain for IHC review and/or with hematoxylin and eosin stain for H&E pathology review.
  • a liquid sample e.g . , blood
  • a slide e.g., by smearing
  • 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.
  • 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.
  • 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.
  • 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.
  • the solid tissue sample is insufficient for NGS testing (for example, the sample is too small or too degraded, the amount or quality of nucleic acids extracted from the sample does not result in quality NGS results that would result in reliable determination of variants and/or other genetic characteristics of the sample), and the physician or patient may decide to convert the solid tissue test that was ordered to a liquid biopsy test to be performed on a liquid biopsy sample collected from the same patient.
  • the resulting report and/or display of the results on a portal may include an “xF Conversion Badge” to distinguish any order that has been converted from solid tissue test to a liquid biopsy test (compared to, for example, a liquid biopsy test that was not initially ordered as a solid tissue test). This will allow a user to identify which orders have been converted by this process, and distinguish between orders that were intentionally placed for the liquid biopsy panel.
  • 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.
  • pathology data 128-1 includes features determined using machine learning algorithms to evaluate pathology data collected as described above.
  • 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).
  • imaging data 128-2 includes features determined using machine learning algorithms to evaluate imaging data collected as described above.
  • 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.
  • tissue organoid e.g., personal tumor organoid
  • tissue organoid e.g., personal tumor organoid
  • feature extractions 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.
  • 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.
  • 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.
  • 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).
  • FFPE formalin-fixed paraffin-embedded
  • 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.
  • RNA isolation e.g., genomic DNA isolation
  • organic extraction silica adsorption
  • anion exchange chromatography e.g., mRNA isolation
  • RNA isolation e.g., mRNA isolation
  • acid guanidinium thiocyanate-phenol-chloroform extraction see, for example, Chomczynski and Sacchi, 2006, Nat Protoc, l(2):581-85, which is hereby incorporated by reference herein
  • silica bead/glass fiber adsorption see, for example, Poeckh, T.
  • cfDNA is isolated from blood samples using commercially available reagents, including proteinase K, to generate a liquid solution of cfDNA.
  • isolated DNA molecules are mechanically sheared to an average length using an ultrasonicator (for example, a Covaris ultrasonicator).
  • 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.
  • 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.
  • Wet lab processing 204 then includes preparing a nucleic acid library from the isolated nucleic acids (e.g., cfDNA, DNA, and/or RNA).
  • DNA libraries e.g., gDNA and/or cfDNA libraries
  • 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.
  • the solid tissue sample is insufficient for NGS testing (for example, the sample is too small or too degraded, the amount or quality of nucleic acids extracted from the sample does not result in quality NGS results that would result in reliable determination of variants and/or other genetic characteristics of the sample), and the physician or patient may decide to convert the solid tissue test that was ordered to a liquid biopsy test to be performed on a liquid biopsy sample collected from the same patient.
  • the resulting report and/or display of the results on a portal may include an “xF Conversion Badge” to distinguish any order that has been converted from solid tissue test to a liquid biopsy test (compared to, for example, a liquid biopsy test that was not initially ordered as a solid tissue test). This will allow a user to identify which orders have been converted by this process, and distinguish between orders that were intentionally placed for the liquid biopsy panel.
  • adapters e.g., UDI adapters, such as Roche SeqCap dual end adapters, or UMI adapters such as full length or stubby Y adapters
  • 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.
  • 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.
  • a patient-specific index is also added to the nucleic acid molecules.
  • 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 sequences are described, for example, in Kivioja et al, Nat. Methods 9(l):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.
  • 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.
  • 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.
  • 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.
  • DNA libraries are amplified and purified using commercial reagents, (e.g., Axygen MAG PCR clean up beads).
  • 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.
  • 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 Alumina NovaSeq 6000) to a unique on-target depth selected by the user.
  • a next generation sequencer e.g., an Illumina HiSeq 4000 or an Alumina NovaSeq 6000
  • DNA library preparation is performed with an automated system, using a liquid handling robot (e.g., a SciClone NGSx).
  • a liquid handling robot e.g., a SciClone NGSx.
  • nucleic acids isolated from the biological sample 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.
  • kits are available for bisulfite-mediated conversion of methylated cytosines to uracils, for instance, the EZ DNA MethylationTM- Gold, EZ DNA MethylationTM-Direct, and EZ DNA MethylationTM-Lightning kit (available from Zymo Research Corp (Irvine, CA)).
  • kits are also available for enzymatic conversion of methylated cytosines to uracils, for example, the APOBEC-Seq kit (available from NEBiolabs, Ipswich, MA).
  • 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.
  • 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.
  • this allows for the processing of more samples per sequencing reaction.
  • 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.
  • 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.
  • one or more off-target nucleic acids will remain in the final sequencing pool.
  • 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.
  • 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. [0288] 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, this raises the possibility of pull-down bias and/or that small variations in the enrichment protocol will result in less consistent results.
  • 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 Ready Mix).
  • 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
  • amplified with commercially available reagents for example, the KAPA HiFi HotStart Ready Mix.
  • 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.
  • 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.
  • a fluorescent dye for example, PicoGreen pool quantification
  • a fluorescence microplate reader for example, PicoGreen pool quantification
  • standard spectrofluorometer standard spectrofluorometer
  • filter fluorometer filter fluorometer.
  • the DNA library preparation and/or capture is performed with an automated system, using a liquid handling robot (for example, a SciClone NGSx).
  • nucleic acid sequencing libraries are not target-enriched prior to sequencing, in order to obtain sequencing data on substantially all of the competent nucleic acids in the sequencing library.
  • nucleic acid sequencing libraries are not mixed, because of bandwidth limitations related to obtaining significant sequencing depth across an entire genome.
  • LWGS low pass whole genome sequencing
  • a plurality of nucleic acid probes 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.
  • a nucleic acid sample e.g., an isolated nucleic acid sample or a nucleic acid sequencing library
  • 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.
  • one or more of the target sequences and/or a property of one or more of the target sequences is used in a classifier trained to distinguish two or more cancer states.
  • 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,
  • the probe set includes probes targeting one or more of the genes listed in Table 1. In some embodiments, the probe set includes probes targeting at least 5 of the genes listed in Table 1. In some embodiments, the probe set includes probes targeting at least 10 of the genes listed in Table 1. In some embodiments, the probe set includes probes targeting at least 25 of the genes listed in Table 1. In some embodiments, the probe set includes probes targeting at least 50 of the genes listed in Table 1. In some embodiments, the probe set includes probes targeting at least 75 of the genes listed in Table 1. In some embodiments, the probe set includes probes targeting at least 100 of the genes listed in Table 1. In some embodiments, the probe set includes probes targeting all of the genes listed in Table 1. [0295] Table 1. An example panel of 105 genes that are informative for precision oncology.
  • 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 70 of the genes listed in List 1. In some embodiments, the probe set includes probes targeting all of the genes listed in List 1.
  • the probe set includes probes targeting one or more of the genes listed in List 2, provided below. In some embodiments, the probe set includes probes targeting at least 5 of the genes listed in List 2. In some embodiments, the probe set includes probes targeting at least 10 of the genes listed in List 2. In some embodiments, the probe set includes probes targeting at least 25 of the genes listed in List 2. In some embodiments, the probe set includes probes targeting at least 50 of the genes listed in List 2. In some embodiments, the probe set includes probes targeting at least 75 of the genes listed in List 2. In some embodiments, the probe set includes probes targeting at least 100 of the genes listed in List 2. In some embodiments, the probe set includes probes targeting all of the genes listed in List 2.
  • panels of genes including one or more genes from the following lists are used for analyzing specimens, sequencing, and/or identification.
  • panels of genes for analyzing specimens, sequencing, and/or identification include one or more genes from List 1 or List 2.
  • panels of genes for analyzing specimens, sequencing, and/or identification include one or more genes from:
  • CDK4 (12ql4.1), CDK6 (7q21.2), CDKN2A (9p21.3), CTNNB1 (3p22.1), DDR2 (lq23.3), EGFR (7pl 1.2), ERBB2 (17ql2), ESR1 (6q25.1-25.2), EZH2 (7q36.1), FBXW7 (4q31.3), FGFR1 (8pl 1.23), FGFR2 (10q26.13), FGFR3 (4pl6.3), GAT A3 (10pl4), GNA11 (19pl3.3), GNAQ (9q21.2), GNAS (20ql3.32), HNF1A (12q24.31), HRAS (llpl5.5), IDH1 (2q34), IDH2 (15q26.1), JAK2 (9p24.1), JAK3 (19pl3.11), KIT (4ql2), KRAS (12pl2.1), MAP2K1 (15q22.31), MAP2K2 (19pl3.3), MAPK1 (22qll.22), MA
  • [0300] List 2 ABL1, ACVR1B, AKT1, AKT2, AKT3, ALK, ALOX12B, AMERl (FAM123B), APC, AR, ARAF, ARFRP1, ARID 1 A, ASXL1, ATM, ATR, ATRX, AURKA, AURKB, AXIN1, AXL, BAP1, BARD1, BCL2, BCL2L1, BCL2L2, BCL6, BCOR, BCORL1, BRAF, BRCA1, BRCA2, BRD4, BRIP1, BTG1, BTG2, BTK, Cllorf30 (EMSY), C17orf39 (GID4), CALR, CARD11, CASP8, CBFB, CBL, CCND1, CCND2, CCND3, CCNE1, CD22, CD274 (PD-L1), CD70, CD79A, CD79B, CDC73, CDH1, CDK12, CDK4, CDK6, CDK8, CDKN1A, CDKN1B, CDKN2A, CDK
  • EGFR EGFR, EP300, EPHA3, EPHB1, EPHB4, ERBB2, ERBB3, ERBB4, ERCC4, ERG, ERRFIl, ESR1, EZH2, FAM46C, FANCA, FANCC, FANCG, FANCL, FAS, FBXW7, FGF10, FGF12, FGF14, FGF19, FGF23, FGF3, FGF4, FGF6, FGFR1, FGFR2, FGFR3, FGFR4, FH, FLCN, FLT1, FLT3, FOXL2, FUBP1, GABRA6, GAT A3, GATA4, GATA6, GNA11, GNA13, GNAQ, GNAS, GRM3, GSK3B, H3F3A, HDAC1, HGF, HNF1A, HRAS,
  • TIP ARP TIP ARP, TNFAIP3, TNFRSF14, TP53, TSC1, TSC2, TYR03, U2AF1, VEGFA, VHL, WHSC1, WT1, XPOl, XRCC2, ZNF217, and ZNF703.
  • probes for enrichment of nucleic acids include DNA, RNA, or a modified nucleic acid structure with a base sequence that is complementary to a locus of interest.
  • 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.
  • 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 locus of interest.
  • 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.
  • Targeted panels provide several benefits for nucleic acid sequencing.
  • 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).
  • the gene panel is a whole-exome panel that analyzes the exomes of a biological sample. In some embodiments, the gene panel is a whole-genome panel that analyzes the genome of a specimen. In some embodiments, the gene panel is optimized for use with liquid biopsy samples (e.g., to provide clinical decision support for solid tumors). See, for example, Table 1 above.
  • the probes include additional nucleic acid sequences that do not share any homology to the locus of interest.
  • the probes 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 el 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.
  • the probes also include primer nucleic acid sequences useful for amplifying the nucleic acid molecule of interest, e.g., using PCR.
  • 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.
  • the probes each include a non-nucleic acid affinity moiety covalently attached to nucleic acid molecule that is complementary to the locus of interest, for recovering the nucleic acid molecule of interest.
  • Non-limited examples of non-nucleic acid affinity moieties include biotin, digoxigenin, and dinitrophenol.
  • 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.
  • 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.
  • 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.
  • 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.
  • NGS next generation sequencing
  • sequencing-by synthesis technology Illumina
  • pyrosequencing 454 Life Sciences
  • Ion semiconductor technology Ion semiconductor technology
  • Single-molecule real-time sequencing Pacific Biosciences
  • sequencing by ligation SOLiD sequencing
  • nanopore sequencing Oxford Nanopore Technologies
  • paired-end sequencing paired-end sequencing.
  • massively parallel sequencing is performed using sequencing-by-synthesis with reversible dye terminators.
  • sequencing is performed using next generation sequencing technologies, such as short-read
  • next-generation sequencing produces millions of short reads (e.g., sequence reads) for each biological sample.
  • the plurality of sequence reads obtained by next-generation sequencing of cfDNA molecules are DNA sequence reads.
  • the sequence reads have an average length of at least fifty 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.
  • 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.
  • nucleic acids e.g., cfDNA, gDNA, and/or RNA
  • 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.
  • 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 Table 1).
  • panel-targeting sequencing is performed to an average on- target depth of at least 500x, at least 750x, at least lOOOx, at least 2500x, at least 500x, at least 10,000x, or greater depth.
  • samples are further assessed for uniformity above a sequencing depth threshold (e.g., 95% of all targeted base pairs at 300x sequencing depth).
  • the sequencing depth threshold is a minimum depth selected by a user or practitioner.
  • the sequence reads are obtained by a whole genome or whole exome sequencing methodology.
  • 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.
  • whole genome or whole exome sequencing is performed to an average sequencing depth of at least 3x, at least 5x, at least lOx, at least 15x, at least 20x, or greater.
  • 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.
  • a nucleic acid sample e.g., a cfDNA, gDNA, or mRNA sample
  • a cfDNA sample is evaluated using both targeted-panel sequencing and whole genome/whole exome sequencing (e.g., LPWGS).
  • the raw sequence reads resulting from the sequencing reaction are output from the sequencer in a native file format, e.g., a BCL file.
  • the native file is passed directly to a bioinformatics pipeline (e.g., variant analysis 206), components of which are described in detail below.
  • pre-processing is performed prior to passing the sequences to the bioinformatics platform.
  • 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).
  • the raw sequence reads are filtered to remove sequences that do not meet one or more quality thresholds.
  • 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.
  • one or more of these pre-processing activities is performed within the bioinformatics pipeline itself.
  • 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.
  • sequence reads in the corresponding FASTQ files may be matched, such that a liquid biopsy -normal analysis may be performed.
  • 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.
  • the results of paired-end sequencing of each isolated nucleic acid sample are contained in a split pair of FASTQ files, for efficiency.
  • 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.
  • 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.
  • workflow 200 illustrates obtaining a biological sample, extracting nucleic acids from the biological sample, and sequencing the isolated nucleic acids
  • sequencing data used in the improved systems and methods described herein e.g., which include improved methods for validating copy number variations, improved methods for validating a somatic sequence variant in a test subject having a cancer condition, and/or improved methods for determining accurate circulating tumor fraction estimates
  • 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.
  • a bioinformatics pipeline e.g., using bioinformatics module 140 of system 100.
  • An example overview for a bioinformatics pipeline is described below with respect to Figure 4 (e.g., Figure 4A-E, 4F1-3, and/or 4G1-3).
  • the present disclosure improves bioinformatics pipelines, like pipeline 206, by improving methods and systems for the validation of copy number variations, the validation of somatic sequence variants, and/or the determination of circulating tumor fraction estimates.
  • 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.
  • sequencing data 122 obtained from the wet lab processing 204 e.g., sequence reads 314.
  • 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.
  • the sequencing data is processed (e.g., using sequence data processing module 141) to prepare it for genomic feature identification 385.
  • the sequencing data is present in a native file format provided by the sequencer.
  • the system e.g., system 100
  • 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.
  • 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.
  • 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 such as AfterQC, Kraken, RNA-SeQC, FastQC, or another similar software program. For paired end reads, reads may be merged.
  • 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
  • FASTQ files from the liquid biopsy sample are analyzed in the ‘tumor-only’ mode. See, for example, Figure 4B.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the demultiplexing algorithm is part of the same software package as one or more pre-processing algorithms 142.
  • the bcl2fastq or bcl2fastq2 conversion software include instructions for both converting the native file format output from the sequencer and demultiplexing sequence reads 122 output from the reaction.
  • 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.
  • a reference sequence construct 158 e.g, a reference genome, reference exome, or other reference construct prepared for a particular targeted-panel sequencing reaction.
  • 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.
  • 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.
  • 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.
  • 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.,
  • 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 etal, 2013, “Benchmarking short sequence mapping tools,” BMC Bioinformatics 14: p. 184; and Flicek and Bimey, 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.
  • mapping tools methodology makes use of a hash table or a Burrows- Wheeler transform (BWT).
  • BWT Burrows- Wheeler transform
  • 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, hgl9, GRCh38, hg38, GRCh37, and/or other reference genomes developed by the Genome Reference Consortium.
  • 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.
  • 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, hgl9, 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.
  • a reference genome for example, hgl9, GRCh38, hg38, GRCh37, other reference genomes developed by the Genome Reference Consortium, etc.
  • 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.
  • the BAM files are sorted, and duplicate reads are marked for deletion, resulting in de-duplicated BAM files.
  • 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 (e.g., available on the internet at fulcrumgenomics.github.io/fgbio/).
  • fgbio tools e.g., available on the internet at fulcrumgenomics.github.io/fgbio/).
  • Bases with insufficient quality or significant disagreement among family members may be replaced by N's to represent a wildcard nucleotide type.
  • PHRED scores are then scaled based on initial base calling estimates combined across all family members.
  • 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.
  • 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.
  • liquid biopsy BAM file e.g., Liquid BAM 124- 1-i-cf
  • normal BAM file e.g., Germline BAM 124-1-i-g
  • 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.
  • the sequencing data is normalized, e.g. , to account for pull down, amplification, and/or sequencing bias (e.g., mappability, GC bias etc.).
  • sequencing bias e.g., mappability, GC bias etc.
  • SAM files generated after alignment are converted to BAM files 124.
  • 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
  • Figure 4 e.g., Figure 4A-E, 4F1-3, and/or 4G1-3
  • analyses performed using sequencing data from cfDNA of a cancer patient e.g., obtained from a liquid biopsy sample of the patient.
  • 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.
  • the methods described below include one or more features 204 of generating sequencing data, as illustrated in Figures 2A and 3.
  • Alignment files prepared as described above 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.).
  • genomic alterations e.g., SNVs/MNVs, indels, genomic rearrangements, copy number variations, etc.
  • characteristics of the patient’s cancer e.g., MSI status, TMB, tumor ploidy, HRD status, tumor fraction, tumor purity, methylation patterns, etc.
  • 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).
  • system 100 digests the contents of the native output file to populate feature data 125 in test patient data store 120.
  • the native output file serves as the record of these genomic features 131 in test patient data store 120.
  • system 100 can employ any combination of available variant calling software packages and internally developed variant identification algorithms.
  • the output of a particular algorithm of a variant calling software is further evaluated, e.g., to improve variant identification.
  • 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.
  • variants are identified indiscriminately and later classified as either germline or somatic, e.g., based on sequencing data, population data, or a combination thereof.
  • 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.
  • variants that are represented in at least 1% of the alleles in a population are annotated as germline and/or non-actionable.
  • 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.
  • sequencing data from a matched sample from the patient e.g. , a normal tissue sample
  • the detected genetic variants and genetic features are analyzed as a form of quality control.
  • 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.).
  • 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.
  • any combination of the modules and algorithms of feature extraction module 145 e.g., illustrated in Figure 1 A, can be used for a bioinformatics pipeline, and particularly for a bioinformatics pipeline for analyzing liquid biopsy samples.
  • 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.
  • an architecture includes at least 2, 3, 4, 5, 6, 7, 8, 9, 10, or more of the modules or variant calling algorithms shown in feature extraction module 145.
  • feature extraction modules and/or algorithms not illustrated in Figure 1A find use in the methods and systems described herein.
  • variant analysis of aligned sequence reads 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).
  • SNVs single nucleotide variants
  • MNVs multiple nucleotide variants
  • indels e.g., nucleotide additions and deletions
  • genomic rearrangements e.g., inversions, translocations, and gene fusions
  • 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.
  • VCF variant call format
  • software packages such as freebayes and pindel are used to call variants using sorted BAM files and reference BED files as the input.
  • 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.
  • raw VCF data is then normalized, e.g., by parsimony and left alignment.
  • 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.
  • Vcflib A C++ library for parsing and manipulating VCF files, GitHub, available on the internet at ai th ub. com/eka/vcfl ib (2012), the content of which is hereby incorporated by reference, in its entirety, for all purposes.
  • a normalization algorithm is included within the architecture of a broader variant identification software package.
  • 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.
  • an annotation algorithm is included within the architecture of a broader variant identification software package.
  • 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.
  • variants identified in the normal tissue sample inform annotation of the variants in the liquid biopsy sample.
  • that variant is annotated as a germline variant in the liquid biopsy sample.
  • the variant is annotated as a somatic variant when the variant otherwise satisfies any additional criteria placed on somatic variant calling, e.g., a threshold variant allele fraction (VAF) in the sample.
  • VAF threshold variant allele fraction
  • the annotation algorithm relies on other characteristics of the variant in order to annotate the origin of the variant. For instance, in some embodiments, the annotation algorithm evaluates the VAF of the variant in the sample, e.g., alone or in combination with additional characteristics of the sample, e.g., tumor fraction. Accordingly, in some embodiments, where the VAF is within a first range encompassing a value that corresponds to a 1 : 1 distribution of variant and reference alleles in the sample, the algorithm annotates the variant as a germline variant, because it is presumably represented in cfDNA originating from both normal and cancer tissues.
  • the algorithm annotates the variant as undeterminable, because there is not sufficient evidence to distinguish between the possibility that the variant arose as a result of an amplification or sequencing error and the possibility that the variant originated from a cancerous tissue.
  • the algorithm annotates the variant as a somatic variant.
  • the baseline variant threshold is a value from 0.01% VAF to 0.5% VAF. In some embodiments, the baseline variant threshold is a value from 0.05% VAF to 0.35% VAF. In some embodiments, the baseline variant threshold is a value from 0.1% VAF to 0.25% VAF.
  • the baseline variant threshold is about 0.01% VAF, 0.015% VAF, 0.02% VAF, 0.025% VAF, 0.03% VAF, 0.035% VAF, 0.04% VAF, 0.045% VAF, 0.05% VAF, 0.06% VAF, 0.07% VAF, 0.075% VAF, 0.08% VAF, 0.09% VAF, 0.1% VAF, 0.15% VAF, 0.2% VAF, 0.25% VAF, 0.3% VAF, 0.35% VAF,
  • the baseline variant threshold is different for variants located in a first region, e.g., a region identified as a mutational hotspot and/or having high genomic complexity, than for variants located in a second region, e.g., a region that is not identified as a mutational hotspot and/or having average genomic complexity.
  • the baseline variant threshold is a value from 0.01% to 0.25% for variants located in the first region and is a value from 0.1% to 0.5% for variants located in the second region.
  • the first region is a region of interest in the genome that may have been manually selected based on criteria (for example, selection may be based on a known likelihood that a region is associated with variants) and the second region is a region that did not meet the selection criteria.
  • the baseline variant threshold is a value from 0.01% to 0.5% for variants located in the first region and is a value from 1% to 5% for variants located in the second region.
  • the first region is a region of interest in the genome that may have been manually selected based on criteria (for example, selection may be based on a known likelihood that a region is associated with variants) and the second region is a region selected based on a second set of criteria.
  • a baseline variant threshold is influenced by the sequencing depth of the reaction, e.g., a locus-specific sequencing depth and/or an average sequencing depth (e.g., across a targeted panel and/or complete reference sequence construct).
  • the baseline variant threshold is dependent upon the type of variant being detected. For example, in some embodiments, different baseline variant thresholds are set for SNPs/MNVs than for indels and/or genomic rearrangements. For instance, while an apparent SNP may be introduced by amplification and/or sequencing errors, it is much less likely that a genomic rearrangement is introduced this way and, thus, a lower baseline variant threshold may be appropriate for genomic rearrangements than for SNPs/MNVs.
  • one or more additional criteria are required to be satisfied before a variant can be annotated as a somatic variant.
  • a threshold number of unique sequence reads encompassing the variant must be present to annotate the variant as somatic.
  • the threshold number of unique sequence reads is 2, 3, 4, 5, 7, 10, 12, 15, or greater.
  • the threshold number of unique sequence reads is only applied when certain conditions are met, e.g., when the variant allele is located in a region of a certain genomic complexity.
  • the certain genomic complexity is a low genomic complexity.
  • the certain genomic complexity is an average genomic complexity.
  • the certain genomic complexity is a high genomic complexity.
  • a threshold sequencing coverage e.g., a locus-specific and/or an average sequencing depth (e.g., across a targeted panel and/or complete reference sequence construct) must be satisfied to annotate the variant as somatic.
  • the threshold sequencing coverage is 50X, 100X, 150X, 200X, 250X, 300X, 350X, 400X or greater.
  • the variant is located in a microsatellite instable (MSI) region. In some embodiments, the variant is not located in a microsatellite instable (MSI) region. In some embodiments, the variant has sufficient signal-to-noise ratio.
  • bases contributing to the variant satisfy a threshold mapping quality to annotate the variant as somatic.
  • alignments contributing to the variant must satisfy a threshold alignment quality to annotate the variant as somatic.
  • a threshold value is determined for a variant detected in a somatic (cancer) sample by analyzing the threshold metric (for example, the baseline variant threshold is determined by analyzing VAF, or the threshold sequencing coverage is determined by analyzing coverage) associated with that variant in a group of germline (normal) samples that were each processed by the same sample processing and sequencing protocol as the somatic sample (process-matched). This may be used to ensure the variants are not caused by observed artifact generating processes.
  • the threshold value is set above the median base fraction of the threshold metric value associated with the variant in more than a specified percentage of process-matched germline samples, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more standard deviations above the median base fraction of the threshold metric value associated with 25%, 30, 40, 50, 60, 70, 75, or more of the processed-matched germline samples.
  • the threshold value is set to a value 5 standard deviations above the median base fraction of the threshold metric value associated with the variant in more than 50% of the process matched germline samples.
  • variants around homopolymer and multimer regions known to generate artifacts may be specifically filtered to avoid such artifacts.
  • strand specific filtering is performed in the direction of the read in order to minimize stranded artifacts.
  • variants that do not exceed the stranded minimum deviation for their specific locus within a known artifact generating region may be filtered to avoid artifacts.
  • Variants may be filtered using dynamic methods, such as through the application of Bayes’ Theorem through a likelihood ratio test.
  • the threshold is dynamically calibrated to account for variants with low support (e.g., due to low tumor fraction, low circulating tumor fraction, and/or low sequencing depths).
  • the dynamic threshold may be based on, for example, factors such as sample specific error rate, the error rate from a healthy reference pool (e.g., a pool of process matched healthy control samples for validation of variants detected in tumor samples), and information from internal human solid tumors (e.g., for validation of variants detected in liquid biopsy samples).
  • the dynamic filtering method employs a tri-nucleotide context-based Bayesian model. That is, in some embodiments, the threshold for filtering any particular putative variant is dynamically calibrated using a context-based Bayesian model that considers one or more of a sample-specific sequencing error rate, a process-matched control sequencing error rate, and/or a variant-specific frequency (e.g., determined from similar cancers). In this fashion, a minimum number of alternative alleles required to positively identify a true variant is determined for individual alleles and/or loci.
  • the dynamic threshold is selected from a Bayesian probability model, where the selection is based on one or more error rates and/or information from one or more baseline variant distributions.
  • the dynamic threshold is selected based on a variant detection specificity that is calculated using a distribution of variant detection sensitivities, where the distribution of variant detection sensitivities is a function of circulating variant allele fraction from a plurality of baseline and/or reference alleles (e.g., from a cohort of subjects).
  • Filtration of variants using a dynamic threshold is performed by comparing the number of unique sequence reads encompassing the variant (e.g., a variant allele fragment count for the variant) against the dynamic threshold.
  • the methods described herein include one or more data collection steps, in addition to data analysis and downstream steps.
  • the methods include collection of a liquid biopsy sample and, optionally, one or more matching biological samples from the subject (e.g., a matched cancerous and/or matched non-cancerous sample from the subject).
  • the methods include extraction of cfDNA from the liquid biopsy sample and, optionally, one or more matching biological samples from the subject (e.g., a matched cancerous and/or matched non-cancerous sample from the subject).
  • the methods include nucleic acid sequencing of cfDNA from the liquid biopsy sample and, optionally, one or more matching biological samples from the subject (e.g., a matched cancerous and/or matched non-cancerous sample from the subject).
  • nucleic acid sequencing results e.g., raw or collapsed sequence reads of cfDNA from a liquid biopsy sample and, optionally, one or more matching biological samples from the subject (e.g., a matched cancerous and/or matched non-cancerous sample from the subject), from which the statistics needed for somatic variant identification (e.g., variant allele count 133-ac and/or variant allele fraction 133-af) can be determined.
  • sequencing data 122 for a patient 121 is accessed and/or downloaded over network 105 by system 100.
  • the methods described herein begin with obtaining the genomic features needed for somatic variant identification (e.g., variant allele count 133-ac and/or variant allele fraction 133-af) for a sequencing of a liquid biopsy sample and, optionally, one or more matching biological samples from the subject (e.g., a matched cancerous and/or matched non-cancerous sample from the subject).
  • variant allele counts 133-cf-ac and/or variant allele fractions 133-cf-af for sequencing data 122 of patient 121 is accessed and/or downloaded over network 105 by system 100.
  • a dynamic variant filtering method is applied which uses an application of Bayes' Theorem through the likelihood ratio test.
  • the dynamic threshold is based on sample specific error rate, the error rate from a healthy reference pool, and from internal human solid tumors.
  • the specificity represents the minimum acceptable quantile of an error distribution (e.g., a BetaBinomial, Beta, and Poisson error distribution).
  • Specificity can then be plugged into the quantile error (e.g., BetaBinomial, Beta, or Poisson) function to derive the minimum number of alternative alleles that can be observed at a given depth to validate a candidate somatic variant.
  • the post-test odds are post-test probability / (1 - post-test probability).
  • the post-test probability is the probability of having a positive variant given Bayes Theorem.
  • the post-test-odds is pre-defmed.
  • the pre-test odds are pre-test probability / (1 - pre-test probability).
  • the pre-test probability is the probability of having a positive variant given the patient's cancer-type and the prevalence of variant alterations within a genomic region encompassing a candidate somatic sequence variant in a reference population having the same cancer type.
  • a pre-test-odds multiplier is applied to the pre-test odds for a resistance mutation that would develop and/or become more prominent within a heterogeneous population of cancer cells in response to therapeutic treatment.
  • the multiplier is applied to specific genomic regions (e.g., exon windows) containing the resistance mutation position.
  • the multiplier is only applied in specified cancer contexts.
  • a multiplier is applied to a pre-test odds for a genomic region containing a mutation that is resistant to at least one cancer therapy used to treat the type of cancer the subject has.
  • a multiplier will be applied to the pre-test odds for the genomic region encompassing the mutation if the subject has breast cancer, but not if the subject has brain cancer.
  • sensitivity is the fraction of variants detected by the liquid biopsy assay at a given variant allele fraction (e.g., 0.1%, 0.25%, 0.5%, etc.).
  • the pre-test probability is calculated using historical data for a set of reference subjects having the same type of cancer, e.g., from sequencing of solid tumor samples. In this fashion, it is possible to accurately assess the prevalence of specific variants within the population of advanced human tumors.
  • the set of reference subjects is at least 10 reference subjects.
  • the set of reference subjects is at least 50 reference subjects. In some embodiments, the set of reference subjects is at least 100 reference subjects. In some embodiments, the set of reference subjects is at least 500 reference subjects. In some embodiments, the set of reference subjects is at least 1000 reference subjects. In some embodiments, the set of reference subjects is at least 5000 reference subjects. In some embodiments, the set of reference subjects is at least 10000 reference subjects.
  • variant prevalence is calculated by indexing genomic regions (e.g., exons) in the reference sample and counting the number of variants in each genomic region (e.g., exon) for each cancer-type.
  • the number of patients who have at least one variant in the genomic region (e.g., the exon) / the number of patients equals the variant prevalence.
  • a default pan cancer cancer-type is used for a cancer where the number of patients in the reference is too low to calculate prevalence. Where no prevalence can be calculated, the mean variant prevalence across cancer-types is used.
  • pre-test-odds are not calculated each time an input sample is run. Rather, in some embodiments, it is read from a pre-existing file, which will be evaluated and regenerated if deemed necessary.
  • Resistance mutations have historically low prevalence and variant allele fraction and may incorrectly be filtered by the dynamic variant filtering method due to low pre-test-odds.
  • the resistance mutations develop in response to therapeutic treatment, and detecting resistance mutations early provides insights into the current treatment strategy.
  • Low variant allele frequency, low prevalence resistance mutations in historic solid tumor samples have been identified.
  • the high sensitivity of the liquid biopsy assay described herein permits the early detection of these resistance mutations in circulating DNA. Examples of such resistance mutations include PIK3CA p.E545K in breast cancer, EGFR p.T790M in non-small cell lung cancer, and AR p.H875Y for prostate cancer.
  • the average depth for each variant position is utilized from the reference pool (e.g., the reference pool used to determine the pre-test odds) depth, at a high minimum average depth (e.g., of 2500X).
  • the reference pool e.g., the reference pool used to determine the pre-test odds
  • a high minimum average depth e.g., of 2500X.
  • the number of alternate alleles required to achieve a 0.1% or 0.25% VAF were calculated.
  • the total alternate alleles and depth for each resistance mutation was input to the Dynamic Variant Filtering method, and multipliers were applied until those resistance mutations passed the filtering strategy.
  • the minimum multiplier required to pass resistance mutations is determined when the input sample alternate allele count is greater than the background alternate allele count (as outlined in Calculating Testing Sample Alt Allele Count and Calculating Background Alt Allele Count below).
  • the multiplier is selected based on the multiplier required to pass the variant at a low variant allele fraction (e.g. , 0.1 % VAF or 0.25% VAF).
  • a maximum value for the multiplier is applied, in order to prevent excessive artifacts from passing the filter. Large multipliers may permit false positive variants to pass the Dynamic Variant Filtering method, however, large multipliers are necessary to pass resistance mutations that have historically low prevalence.
  • the maximum multiplier is between 750 and 1500.
  • the maximum multiplier is between 900 and 1100. In some embodiments, the maximum multiplier is between 1000 and 1050.
  • the usage of the pre-test-odds-multiplier is limited by cancer-type context and genomic region (e.g., exon-window). In some embodiments, therefore, the multipliers will not be applied to all genomic regions (e.g., exon-windows) given a specified cancer-type, nor all cancer-types given a specific genomic region (e.g., exon- window).
  • the filtering method (the statistical method used for the Dynamic Variant Filtering method) is selected from a beta-binomial distribution model, a beta distribution model, and a Poisson distribution model.
  • the model is a beta-binomial model.
  • when applying a quantile beta-binomial distribution the sum of the input sample alternate reads is divided by the input sample sequencing depth at each variant position, and then multiplied by the reference pool depth (the sequencing depth at genomic positions for a pool of reference, e.g., healthy normal, controls).
  • the background variant allele count calculation takes into account the background error from a pool of reference (e.g., healthy normal subjects), the input sample error, and the prevalence of historical variants in the reference cancer subjects.
  • the pre- test-odds calculated for a specific genomic region (e.g., exon window) and cancer-type will yield a unique alpha for each variant, given that the variants do not fall in the same genomic region (e.g., exon window)).
  • the background posterior error incorporates a trinucleotide error average (e.g., a reaction-specific sequencing error rate), the reference pool error (e.g., a locus-specific, process-matched sequencing error rate; e.g., a sum of alternate reads for each position / depth from a pool of healthy normal controls), and a shrinkage weight parameter.
  • a trinucleotide error average e.g., a reaction-specific sequencing error rate
  • the reference pool error e.g., a locus-specific, process-matched sequencing error rate; e.g., a sum of alternate reads for each position / depth from a pool of healthy normal controls
  • shrinkage weight parameter e.g., a shrinkage weight parameter.
  • a reference pool error can be used in place of an input sample background average, for calculating the background posterior average error rate.
  • the background posterior average, the reference pool depth, and the alpha are used in calculating the input to the quantile beta- binomial function.
  • the alpha is used in calculating the mean value of the beta-binomial distribution, which equals 1 - alpha / 2.
  • the size of the quantile beta-binomial is the matrix of the reference pool depth.
  • the shape 1 parameter for the quantile beta-binomial function is the reference pool depth multiplied by the background posterior average error rate, and the shape 2 parameter of the quantile beta-binomial function is the shape 1 parameter subtracted from reference pool depth.
  • the output from the quantile BetaBinomial function is the minimum value a variant needs to be called. Any variant that has a normalized allele count below the quantile(BetaBinomial) output will be filtered due to the high background error observed at that position.
  • Figure 4F2 illustrates a flow chart of a method 400-2 for validating a somatic sequence variant in a test subject having a cancer condition, in accordance with some embodiments of the present disclosure.
  • the method includes obtaining (402-2) cell-free DNA sequencing data 122 from a sequencing reaction of a liquid biopsy sample of a test subject 121 (e.g., sequence reads 123-1-1-1,... ,123-1-1-K for sequence run 122-1-1 for aliquid biopsy sample from patient 121-1, as illustrated in Figure IB)
  • the obtaining includes a step of sequencing cell-free nucleic acids from a liquid biopsy sample. Example methods for sequencing cell-free nucleic acids are described herein.
  • Sequence reads 123 from the sequencing data 122 are then aligned (404-2) to a human reference sequence (e.g. , a human genome or a portion of a human genome, e.g. , 1 %, 5%, 10%, 15%, 20%, 25%, 30%, 40%, 50%, 60%, 75%, 90%, 95%, 99%, or more of the human genome, or to a map of a human reference genome or a set of human reference genomes, or a portion thereof), thereby generating a plurality of aligned reads 124.
  • a human reference sequence e.g. , a human genome or a portion of a human genome, e.g. , 1 %, 5%, 10%, 15%, 20%, 25%, 30%, 40%, 50%, 60%, 75%, 90%, 95%, 99%, or more of the human genome, or to a map of a human reference genome or a set of human reference genomes, or a portion thereof
  • the pre-aligned sequence reads 123 and/or aligned sequence reads 124 are pre- processed (408-2) using any of the methods disclosed above (e.g., normalization, bias correction, etc.).
  • device 100 obtains previously aligned sequence reads.
  • the aligned sequences reads 124 are then evaluated to identify mismatches with the reference construct (e.g., reference genome or set of reference genomes), thereby identifying one or more candidate somatic sequence variants 132-c at respective genomic loci.
  • the number of aligned sequence reads containing the sequence variant at the locus are determined, thereby defining a variant allele fragment count 132-c-ac (e.g., variant allele fragment count 132-c-l-ac as illustrated in Figure 1C2).
  • the number of aligned sequence reads containing the locus of the candidate variant allele are also determined, thereby defining a variant allele locus count 132-c-lc (e.g., variant allele locus count 132-c-l-lc as illustrated in Figure 1C2).
  • the variant allele fragment count 132-c-ac can be compared to the variant allele locus count 132-c-lc to determine a variant allele fraction 132-c-vf (e.g., variant allele fraction 132-c-l-vf as illustrated in Figure 1C2) for the candidate variant allele.
  • this measure represents a measure of the portion of sequence reads encompassing the nucleotide(s) that is altered in the candidate variant allele that include the candidate variant.
  • this measure can be used to define a sensitivity for the detection of the candidate variant based on a distribution of detection sensitivities corresponding to detection of a variant within a genomic region encompassing the locus in reference samples with defined variant allele fractions.
  • Method 400-2 then includes obtaining (412-2) a dynamic variant count threshold 191 for the candidate variant allele.
  • the dynamic variant count threshold is based upon a prevalence of sequence variations in a genomic region encompassing the locus of the candidate variant allele in cancer patients sharing one or more similarities with the test subject. For example, in some embodiments, this prevalence defines a pre-test odds that the test subject has a sequence variant within the genomic region encompassing the locus at which the candidate sequence variant is located. In some embodiments, this pre-test odds is used in an application of Bayes theorem to derive a minimal amount of support required of the sequencing reaction to validate the presence of the candidate sequence variant in a cancerous tissue of the subject at a desired confidence level. Information about Bayes theorem and Bayesian inference can be found, for instance, in Section 8.7 of Stuart, A. and Ord, K. (1994), Kendall's Advanced Theory of Statistics:
  • the prevalence of sequence variants in the genomic region encompassing the locus of the candidate variant allele is determined from a population of reference cancer subjects having the same type of cancer.
  • the population of reference cancer subjects is further defined by a matching personal characteristic, e.g., an age, gender, race, smoking status, or any other personal characteristic.
  • the population of reference subjects is further defined by a plurality of matching personal characteristics, e.g., at least 2, 3, 4, 5, 6, 7, 8, 9, 10, or more person characteristics, in addition to cancer type.
  • the prevalence of sequence variants is determined from variant prevalence training data 192, as illustrated in Figure IF.
  • the variant prevalence training data 192 includes data on the variants found in a cancerous tissue from a plurality of reference subjects 193.
  • training data 192 for reference subject 1 193-1 includes a cancer type 194-1 and a list of somatic sequence variants 195-1, including individual variants 196-1-1 . . . 196-1-S.
  • a genomic region encompassing the locus of the candidate sequence variant is defined (e.g., the exon of a gene in which a candidate sequence variant is detected).
  • sequence variant prevalence is predetermined and stored in a database, e.g., in non-persistent memory 111, or in an addressable remote server, as a look-up table.
  • system 100 determines a sequence variant prevalence for a genomic region and matching patient profile upon identification of a candidate sequence variant, e.g., by filtering variant prevalence training data 192 for the relevant genomic region and matching reference subjects.
  • the genomic region encompassing the candidate sequence variant is larger than a single nucleotide.
  • the genomic region includes at least 10 nucleotides, at least 50 nucleotides, at least 100 nucleotides, at least 250 nucleotides, at least 500 nucleotides, at least 1000 nucleotides, at least 2500 nucleotides, or more nucleotides.
  • the genomic region is no larger than 10,000 nucleotides, not larger than 7500 nucleotides, no larger than 5000 nucleotides, no larger than 2500 nucleotides, or fewer nucleotides.
  • the genomic region is from 10 nucleotides to 10,000 nucleotides. In some embodiments, the genomic region is from 25 nucleotides to 5000 nucleotides. In some embodiments, the genomic region is from 50 nucleotides to 2500 nucleotides.
  • the genomic region when the candidate sequence variant falls within a protein coding sequence, the genomic region is defined as the exon in which the candidate sequence variant is located. In some embodiments, the genomic region is defined as several adjacent exons, including the exon in which the candidate sequence variant is located. In some embodiments, when the candidate sequence variant falls within a protein coding sequence, the genomic region is defined as all exons of the gene in which the candidate sequence variant is located. In some embodiments, when the candidate sequence variant falls within a protein coding sequence, the genomic region is defined as the entire gene in which the candidate sequence variant is located. Similarly, in some embodiments, when the candidate sequence variant falls within an intronic sequence of a gene, the genomic region is defined as the entire intron in which the candidate sequence variant is located, or several adjacent introns including the intron in which the candidate sequence variant is located.
  • the genomic region encompassing the candidate sequence variant is a fixed window encompassing, e.g., surrounding, the candidate sequence variant.
  • the genomic region when the candidate sequence variant falls within anon- coding portion of the genome, the genomic region is defined as a fixed window surrounding the candidate sequence variant.
  • the genomic region when the sequence variant falls within a non-coding genetic element, e.g., a promoter, enhancer, etc., the genomic region is defined as the entirety of the genetic element.
  • the genomic region encompassing the candidate sequence variant is dependent upon the sequence context of the locus. For example, when the candidate sequence variant falls within a coding sequence, the exon or several adjacent exons defines the genomic region, but when the candidate sequence variant falls within a non-coding sequence, the genomic region is defined by a fixed window encompassing the candidate sequence variant.
  • the genomic region encompassing the candidate sequence variant is dependent upon a known or inferred effect of the sequence variant. For instance, as described in more detail below, in some embodiments, when the candidate sequence variant causes, or is inferred to cause, a partial or complete loss of function mutation in a gene, the genomic region is defined by all exons of the gene in which the candidate sequence variant is located. Similarly, as described in more detail below, in some embodiments, when the candidate sequence variant causes, or is inferred to cause, a gain of function mutation in a gene having one or more hotspots for gain of function mutations, the genomic region is defined as those exons of the gene encompassing the one or more hotspots.
  • the pre-test odds determined based on the historical prevalence data is multiplied by a pre-test-odds multiplier (e.g., as described above).
  • the Bayesian analysis is further informed by defining the specificity of variant detection based on an apparent variant allele fraction in the sample.
  • the variant allele fraction for the candidate sequence variant is determined by a comparison of the variant allele fragment count 132-c-ac to the variant allele locus count 132-c-lc (e.g., a ratio of the variant allele fragment count to the variant allele locus count), thereby determining a variant allele fraction 132-c-vf.
  • the variant allele fraction is then compared to a distribution of variant detection specificities established based on a set of training samples (e.g., sensitivity distribution training data) with known variant allele fractions.
  • nucleic acids from each of a plurality of training samples 181 having a known variant allele fraction 184 for one or more variant alleles 183 is sequenced according to a processed- matched sequencing reaction (e.g., using a substantially identical or identical sequencing reaction), and it is determined whether each sequence variant can be detected, e.g., defining a detection status 185 for each locus/variant 183.
  • a specificity of detection of variants having different variant allele fractions can be determined.
  • the specificity is determined on a locus-by -locus basis, such that the specificity of detection is specific for the genomic region or locus encompassing the candidate sequence variant.
  • the specificity is determined globally, e.g., not on a locus-by-locus basis.
  • a correlation can then be established between the measured detection specificity and the variant allele fraction (e.g., variant detection sensitivity distribution 186).
  • the correlation is a linear or non-linear fit between measured detection specificities and variant allele fractions.
  • the correlation is determined by binning specificities (e.g., in bins 187) as a function of ranges of variant allele fractions 188, and determining a measure of central tendency (e.g., a mean) for the specificities 189 in the bin.
  • the variant allele fraction 132-c-ac determined for the candidate sequence variant is then compared to the established correlation (e.g., variant detection sensitivity distribution 186) to define the specificity of detection for the candidate sequence variant.
  • the Bayesian analysis is further informed by accounting for the sequencing error rate for the variant allele and, accordingly, the probability that the candidate sequence variant is a product of a sequencing error, rather than a genomic variant.
  • a reaction-specific error rate e.g., a trinucleotide sequencing error rate
  • a locus-specific error rate is determined from historical sequencing errors at the genomic region, or specific locus, encompassing the candidate sequence variant.
  • both a reaction-specific sequencing error rate and a locus-specific error rate are used to define a variant count distribution (e.g., variant count distribution 190), representing the number of variant allele counts (e.g., variant allele fragment count 132-c-ac) necessary to validate the presence of the candidate variant sequence in the cancer of the subject at a defined detection sensitivity.
  • a beta binomial distribution is established based on the reaction-specific sequencing error rate and the locus-specific error rate.
  • Method 400-2 then includes applying (414-2) the dynamic variant count threshold (e.g., locus-specific dynamic variant count threshold 191) to the sequencing data, e.g., by determining whether the variant allele fragment count 132-c-ac for the candidate sequence variant satisfies the threshold, and validating the candidate sequence variant (e.g., creating a record 132-v of the validation) when the threshold is satisfied or rejecting the candidate sequence variant when the threshold is not satisfied.
  • the dynamic variant count threshold e.g., locus-specific dynamic variant count threshold 191
  • one or more additional filters relating to global sequencing metrics and/or locus-specific sequencing metrics (e.g., one or more of variant locus coverage filter(s) 463, variant allele fraction filter(s) 465, variant support mapping filter(s) 467, variant support sequencing quality filter(s) 469, and low complexity region filter(s) 471, as illustrated in Figure 1D2) must be satisfied before validating a candidate sequence variant.
  • locus coverage filter(s) 463 e.g., one or more of variant locus coverage filter(s) 463, variant allele fraction filter(s) 465, variant support mapping filter(s) 467, variant support sequencing quality filter(s) 469, and low complexity region filter(s) 471, as illustrated in Figure 1D2
  • one or more validated variant statuses 132-v are used to match (424-2) the subject with a targeted therapy and/or a clinical trial.
  • one or more validated variant statuses 132-v for one or more actionable variants 139-1-1, one or more matched therapies 139-1-2, and/or one or more matched clinical trials are used to generate (426-2) a patient report 139-1-3.
  • the patient report is transmitted to a medical professional treating the subject.
  • the patient is then administered (428-2) a personalized course of therapy, e.g., based on a matched therapy and/or clinical trial.
  • the methods of validating a candidate somatic sequence variant using a dynamic threshold described herein fall within the context of a larger variant detection method, e.g., as illustrated by method 450 illustrated in Figures 4G1-4G3.
  • the method includes obtaining (452) cfDNA sequence reads, as described herein, and aligning (454) those reads to a reference construct (e.g., a reference genome or mapped representation of several reference genomes), to generate aligned sequences 124 (e.g., a plurality of unique sequence reads).
  • a reference construct e.g., a reference genome or mapped representation of several reference genomes
  • putative somatic sequence variants are identified (456), e.g., those sequence variants having a variant allele fraction that is lower than expected for a germline sequence variant (which should be around 50% after accounting for an estimated circulating tumor fraction for the liquid biopsy sample), e.g., less than 30%, less than 20%, less than 10% etc.
  • One or more candidate somatic sequence variants are then validated by applying one or more filters. For instance, as described herein, a dynamic variant count threshold is determined (459) and then used to apply (460) a dynamic probabilistic variant count filter to sequencing data for the candidate somatic sequence variant. In some embodiments, the method also includes applying (462) a variant loci coverage filter.
  • the method also includes applying (464) a variant allele fraction filter. In some embodiments, the method also includes applying (466) a variant support mapping filter. In some embodiments, the method also includes applying (468) a variant support sequencing quality filter. In some embodiments, the method also includes applying (470) a low complexity region filter. When all selected candidate somatic sequence variants have been validated or rejected according to these filters (472), the process proceeds with a reporting function.
  • method 450 also includes validating (474) the sequencing data globally, using any of the metrics described herein.
  • the validation includes applying (476) a loci minimal coverage filter.
  • the validation includes applying (478) a loci central tendency coverage filter.
  • the validation includes applying (480) a total sequence read filter.
  • the validation includes applying (481) a sequence read quality filter.
  • the validation includes applying a sequencing control filter (482). The entire sequencing reaction is then validated or rejected (483) based on whether the sequencing data passes these global filters.
  • method 450 also includes validating (485) one or more germline mutations.
  • candidate germline sequence variants are identified (484), e.g., those sequence variants having a variant allele fraction that is higher than expected for a somatic sequence variant.
  • the validation includes applying (486) a germline-specific variant allele fraction filter.
  • the validation includes applying (487) a variant support mapping filter.
  • the validation includes applying (488) a variant support sequencing quality filter.
  • one or more validated variant statuses 132-v are used to match (490) the subject with a targeted therapy and/or a clinical trial.
  • one or more validated variant statuses 132-v for one or more actionable variants 139-1-1, one or more matched therapies 139-1-2, and/or one or more matched clinical trials are used to generate (492) a patient report 139-1-3.
  • the patient report is transmitted to a medical professional treating the subject.
  • the patient is then administered (494) a personalized course of therapy, e.g., based on a matched therapy and/or clinical trial.
  • all, or nearly all, of the aligned sequence reads are evaluated to identify candidate sequence variants (e.g., candidate somatic sequence variants and/or candidate germline sequence variants).
  • a subset of the aligned sequence reads is evaluated to identify candidate sequence variants.
  • targeted-panel sequencing reaction is used to generate sequencing data 122 and only sequence reads corresponding to the target panel (on-target reads) are evaluated to identify candidate sequence variants.
  • targeted-panel sequencing reaction is used to generate sequencing data 122 and a subset of sequence reads corresponding to a subset of the target panel are evaluated to identify candidate sequence variants.
  • a subset of the sequence reads corresponding to a subset of genes regardless of whether the sequencing reaction is a targeted-panel sequencing reaction, a whole exome sequencing reaction, or a whole genome sequencing reaction, are evaluated to identify candidate sequence variants.
  • a subset of sequence reads corresponding to a defined set of regions within the genome e.g., one or more genes, one or more introns, one or more exons, one or more subregion of an intron and/or exon associated with cancer etiology, etc., are evaluated to identify candidate sequence variants.
  • a subset of candidate sequence variants is further validated. For example, in some embodiments, only candidate sequence variants corresponding to the target panel (on-target reads) are validated. Similarly, in some embodiments, only candidate sequence variants corresponding to a subset of the target panel are validated. Likewise, in some embodiments, only candidate sequence variants corresponding to a subset of genes, regardless of whether the sequencing reaction is a targeted-panel sequencing reaction, a whole exome sequencing reaction, or a whole genome sequencing reaction, are validated.
  • candidate variants corresponding to a defined set of regions within the genome e.g., one or more genes, one or more introns, one or more exons, one or more subregion of an intron and/or exon associated with cancer etiology, etc. , are validated.
  • different sets of sequence variants are evaluated depending on the type of cancer being evaluated. That is, when the subject has a first type of cancer, candidate sequence variants in a first set of genomic loci are evaluated, typically associated with the etiology of the first type cancer and/or a particular course of actionable therapy for the first type cancer, and when the subject has a second type of cancer, candidate sequence variants in a second set of genomic loci are evaluated, typically associated with the etiology of the second type cancer and/or a particular course of actionable therapy for the second type of cancer.
  • selections may be applied at the level of initial sequence read evaluation (e.g., only sequence reads corresponding to a defined set of loci are evaluated to identify a candidate sequence variant) or the validation level (e.g., sequence reads corresponding to a larger set of loci are evaluated to identify candidate sequence variants, but only those candidates corresponding to a defined set are further validated).
  • candidate sequence variants that would result in an amino acid change in the amino acid sequence encoded by the gene are evaluated.
  • any candidate sequence variant resulting in an amino acid change are evaluated.
  • candidate sequence variants resulting in a defined amino acid change e.g., an amino acid change associated with cancer etiology and/or a particular actionable cancer therapy, are evaluated.
  • only a subset of validated sequence variants is included on a clinical report for the sample.
  • aligned sequence reads corresponding to all or a subset of genomic loci are evaluated to identify candidate sequence variants, all or a subset of identified candidate sequence variants are evaluated for validation, and only a subset of all possibly validated sequence variants are included on a clinical report generated for the sample.
  • the subject has breast cancer and candidate variants associated with at least one of the following genes and/or genetic loci are evaluated: ERBB2 (or a genetic locus including a chromosomal position of 17:37880220 and/or 17:37881064), EGFR (or a genetic locus including a chromosomal position of 7:55227926, 7:55242511, and/or 7:55249022), ESR1 (or a genetic locus including a chromosomal position of 6:152419922, 6:152419923 and/or 6:152419926), KRAS (or a genetic locus including a chromosomal position of 12:25380275, 12:25380276, 12:25380277, and/or 12:25380279), MAP2K1 (or a genetic locus including a chromosomal position of 15:66729162 and/or 15:66729163), MET (or a genetic locus including a chromoso
  • the subject has breast cancer and candidate variants associated with at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, or at least 8 of the genes listed above (or loci including the enumerated corresponding chromosomal positions) are evaluated. In some embodiments, the subject has breast cancer and candidate variants associated with any of the genes listed above (or loci including the enumerated corresponding chromosomal positions) are evaluated.
  • the subset of possible candidate sequence variants in the ERBB2 gene includes variants resulting in an amino acid change selected from L755*, L755S, L755W, T798I, T798K, and T798R.
  • the subset of possible candidate sequence variants in the EGFR gene includes variants resulting in an amino acid change selected from G465*,
  • the subset of possible candidate sequence variants in the ESR1 gene includes variants resulting in an amino acid change selected from Y537D,
  • the subset of possible candidate sequence variants in the KRAS gene includes variants resulting in an amino acid change selected from G60D, Q61H, Q61Q, Q61L, Q61P, Q61R, Q61*, Q61E, and Q61K.
  • the subset of possible candidate sequence variants in the MAP2K1 gene includes variants resulting in an amino acid change selected from P124A, P124S, P124T, P124R, P124L, P124Q.
  • the subset of possible candidate sequence variants in the MET gene includes variants resulting in an amino acid change selected from FI 2001,
  • the subset of possible candidate sequence variants in the MTOR gene includes variants resulting in an amino acid change selected from A2034E, A2034G, A2034V, F2108F, F2108I, F2108L, and F2108V.
  • the subset of possible candidate sequence variants in the MTOR gene includes variants resulting in an amino acid change selected from A2034E, A2034G, A2034V, F2108F, F2108I, F2108L, and F2108V.
  • the subset of possible candidate sequence variants in the NTRK1 gene includes variants resulting in an amino acid change selected from G595R, G595W, F646I, F646L, F646V, D679A, D679G, and D679V.
  • the subset of possible candidate sequence variants in the PIK3CA gene includes variants resulting in an amino acid change selected from E542K, E545*, E545K, E545Q, E545A, E545G, E545V, E545D, E545E, H1047D, H1047Y, H1047N, H1047L, H1047P, H1047R.
  • the subject has non-small cell lung cancer and candidate variants associated with at least one of the following genes and/or genetic loci are evaluated: ALK (or a genetic locus including a chromosomal position of 2:29443613, 2:29443631, 2:29443695, 2:29443697, 2:29445213, and/or 2:29445258), B2M (or a genetic locus including a chromosomal position of 15:45003745), BRAF (or a genetic locus including a chromosomal position of 7:140453135, 7:140453136, and/or 7:140453137), EGFR (or a genetic locus including a chromosomal position of 7:55227926, 7:55241704, 7:55241705, 7:55241706, 7:55242469, 7:55242511, 7:55249022, 7:55249071, 7:
  • ALK or a genetic locus including
  • the subject has non-small cell lung cancer and candidate variants associated with at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 of the genes listed above (or loci including the enumerated corresponding chromosomal positions) are evaluated. In some embodiments, the subject has non-small cell lung cancer and candidate variants associated with any of the genes listed above (or loci including the enumerated corresponding chromosomal positions) are evaluated.
  • the subset of possible candidate sequence variants in the ALK gene includes variants resulting in an amino acid change selected from G1202*, G1202R, L1196L, L1196M, LI 196V, F1174F, F1174L, FI 1741, FI 174V, I1171N, I1171S, I1171T, C1156F, C1156S, and C1156Y.
  • the subset of possible candidate sequence variants in the BRAF gene includes variants resulting in an amino acid change selected from V600*, V600A, V600E, V600G, V600L, and V600M.
  • the subset of possible candidate sequence variants in the EGFR gene includes variants resulting in an amino acid change selected from G465*, G465R, L718L, L718M, L718V, L718P, L718Q, L718R, L747I, L747L, L747V, D761H, D761N, D761Y, V774L, V774M, T790K, T790M, T790R, C797G, C797R, C797S, C797F, C797Y, C797*, C797C, C797W, L798F, L798I, L798V, L858P, L858Q, and L858R.
  • the subset of possible candidate sequence variants in the ERBB2 gene includes variants resulting in an amino acid change selected from L755*, L755S, and L755W.
  • the subset of possible candidate sequence variants in the KRAS gene includes variants resulting in an amino acid change selected from A146T, D119N, Q61H, Q61Q, Q61L, Q61P, Q61R, Q61*, Q61E, Q61K, G60V, Q22K, G13G, G13A, G13V, G13D, G13C, G13R, G13S, G12G, G12A, G12V, G12D, G12C, G12R, and G12S.
  • the subset of possible candidate sequence variants in the MAP2K1 gene includes variants resulting in an amino acid change selected from PI 24 A, P124S, P124T, P124R, P124L, and P124Q.
  • the subset of possible candidate sequence variants in the MET gene includes variants resulting in an amino acid change selected from F1200I, F1200L, F1200V, Y1230D, Y1230H, and Y1230N.
  • the subset of possible candidate sequence variants in the NTRK1 gene includes variants resulting in an amino acid change selected from G595R, G595W, F646I, F646L, F646V, D679A, D679G, and D679V.
  • the subset of possible candidate sequence variants in the PIK3CA gene includes variants resulting in an amino acid change selected from E545*, E545K, E545Q, E545A, E545G, E545V, E545D, E545E, M1043V, H1047D, H1047Y, H1047N, H1047L, H1047P, and H1047R.
  • the subset of possible candidate sequence variants in the STK11 gene includes variants resulting in an amino acid change selected from El 20*, D194Y, S216F, and E223*, as well as nucleotide substitution c.465-2A>T.
  • the subject has prostate cancer and candidate variants associated with at least one of the following genes and/or genetic loci are evaluated: AR (or a genetic locus including a chromosomal position of X: 66766292, X: 66931463, X:66931504, X:66937370, X:66937371, X: 66937372, X: 66943543, X:66943549, and/or X: 66943552), EGFR (or a genetic locus including a chromosomal position of 7:55227926, 7:55242511, and/or 7:55249022), ERBB2 (or a genetic locus including a chromosomal position of 17:37880220), KRAS (or a genetic locus including a chromosomal position of 12:25380275, 12:25380276, and/or 12:25380277), MAP2K1 (or
  • the subject has prostate cancer and candidate variants associated with at least 2, at least 3, at least 4, at least 5, at least 6, or at least 7 of the genes listed above (or loci including the enumerated corresponding chromosomal positions) are evaluated. In some embodiments, the subject has prostate cancer and candidate variants associated with any of the genes listed above (or loci including the enumerated corresponding chromosomal positions) are evaluated.
  • the subset of possible candidate sequence variants in the AR gene includes variants resulting in an amino acid change selected from W435L, L702H, L702P, L702R, V716M, W742G, W742R, W742*, W742L, W742S, W742C, H875Y, F877L, T878A, T878P, and T878S.
  • the subset of possible candidate sequence variants in the EGFR gene includes variants resulting in an amino acid change selected from G465*, G465R, D761H, D761N, D761Y, V774L, and V774M.
  • the subset of possible candidate sequence variants in the ERBB2 gene includes variants resulting in an amino acid change selected from L755*, L755S, and L755W.
  • the subset of possible candidate sequence variants in the KRAS gene includes variants resulting in an amino acid change selected from Q61H, Q61Q, Q61L, Q61P, Q61R, Q61*, Q61E, and Q61K.
  • the subset of possible candidate sequence variants in the MAP2K1 gene includes variants resulting in an amino acid change selected from PI 24 A, P124S, P124T, P124R, P124L, and P124Q.
  • the subset of possible candidate sequence variants in the MET gene includes variants resulting in an amino acid change selected from F 12001, F1200L, FI 200V, Y1230D, Y1230H, and Y1230N.
  • the subset of possible candidate sequence variants in the NTRK1 gene includes variants resulting in an amino acid change selected from G595R, G595W, F646I, F646L, F646V, D679A, D679G, and D679V.
  • the subset of possible candidate sequence variants in the PIK3CA gene includes variants resulting in an amino acid change selected from H1047D, H1047Y, H1047N, H1047L, H1047P, and H1047R.
  • the cancer condition is any type of cancer (for example, pan cancer) and the somatic variants validated by this method include variants associated with any of the following genes: EGFR (or a genetic locus including a chromosomal position of 7:55227926, 7:55242511, and/or 7:55249022), ERBB2 (or a genetic locus including a chromosomal position of 17:37880220), KRAS (or a genetic locus including a chromosomal position of 12:25380275, 12:25380276, and/or 12:25380277), MAP2K1 (or a genetic locus including a chromosomal position of 15:66729162 and/or 15:66729163), MET (or a genetic locus including a chromosomal position of 7:116422117 and/or 7:116423413), NTRK1 (or a genetic locus including a chromosomal position of 1:156
  • the subject has any cancer (e.g., pan cancer) and candidate variants associated with at least 2, at least 3, at least 4, at least 5, at least 6, or at least 7 of the genes listed above (or loci including the enumerated corresponding chromosomal positions) are evaluated.
  • the subject has any cancer (e.g., pan cancer) and candidate variants associated with any of the genes listed above (or loci including the enumerated corresponding chromosomal positions) are evaluated.
  • the subset of possible candidate sequence variants in the EGFR gene includes variants resulting in an amino acid change selected from G465*, G465R, D761H, D761N, D761Y, V774L, and V774M.
  • the subset of possible candidate sequence variants in the ERBB2 gene includes variants resulting in an amino acid change selected from L755*, L755S, and L755W.
  • the subset of possible candidate sequence variants in the KRAS gene includes variants resulting in an amino acid change selected from Q61H, Q61Q, Q61L, Q61P, Q61R, Q61*, Q61E, and Q61K.
  • the subset of possible candidate sequence variants in the MAP2K1 gene includes variants resulting in an amino acid change selected from P124A, P124S, P124T, P124R, P124L, and P124Q.
  • the subset of possible candidate sequence variants in the MET gene includes variants resulting in an amino acid change selected from F1200I, F1200L, F1200V, Y1230D, Y1230H, and Y1230N.
  • the subset of possible candidate sequence variants in the NTRK1 gene includes variants resulting in an amino acid change selected from G595R, G595W, F646I, F646L, F646V, D679A, D679G, and D679V.
  • the subset of possible candidate sequence variants in the PIK3CA gene includes variants resulting in an amino acid change selected from H1047D, H1047Y, H1047N, H1047L, H1047P, and H1047R.
  • the subject has a tumor of unknown origin or a cancer of unknown primary and candidate variants associated with at least one of the following genes and/or genetic loci are evaluated: EGFR (or a genetic locus including a chromosomal position of 7:55227926, 7:55242511, and/or 7:55249022), ERBB2 (or a genetic locus including a chromosomal position of 17:37880220), KRAS (or a genetic locus including a chromosomal position of 12:25380275, 12:25380276, 12:25380277, and/or 12:25398255), MAP2K1 (or a genetic locus including a chromosomal position of 15:66729162 and/or 15:66729163), MET (or a genetic locus including a chromosomal position of 7:116422117 and/or 7:116423413), NRAS (or a genetic locus including a genetic locus including a chromos
  • the subject has any cancer (e.g., pan cancer) and candidate variants associated with at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, or at least 8 of the genes listed above (or loci including the enumerated corresponding chromosomal positions) are evaluated.
  • the subject has any cancer (e.g., pan cancer) and candidate variants associated with any of the genes listed above (or loci including the enumerated corresponding chromosomal positions) are evaluated.
  • the subset of possible candidate sequence variants in the EGFR gene includes variants resulting in an amino acid change selected from G465*, G465R, D761H, D761N, D761Y, V774L, and V774M.
  • the subset of possible candidate sequence variants in the ERBB2 gene includes variants resulting in an amino acid change selected from L755*, L755S, and L755W.
  • the subset of possible candidate sequence variants in the KRAS gene includes variants resulting in an amino acid change selected from Q61H, Q61Q, Q61L, Q61P, Q61R, Q61*, Q61E, Q61K, and Q22K.
  • the subset of possible candidate sequence variants in the MAP2K1 gene includes variants resulting in an amino acid change selected from PI 24 A, P124S, P124T, P124R, P124L, and P124Q.
  • the subset of possible candidate sequence variants in the MET gene includes variants resulting in an amino acid change selected from F1200I, F1200L, F1200V, Y1230D, Y1230H, and Y1230N.
  • the subset of possible candidate sequence variants in the NRAS gene includes variants resulting in an amino acid change of G12S.
  • the subset of possible candidate sequence variants in the NTRK1 gene is evaluated and/or reported.
  • the subset of possible candidate sequence variants in the NTRK1 gene includes variants resulting in an amino acid change selected from G595R, G595W, F646I, F646L, F646V, D679A, D679G, and D679V.
  • the subset of possible candidate sequence variants in the PIK3CA gene includes variants resulting in an amino acid change selected from C420R, H1047D, H1047Y, H1047N, H1047L, H1047P, and H1047R.
  • the cancer condition is acute myeloid leukemia, adrenal cancer, b cell lymphoma, basal cell carcinoma, biliary cancer, bladder cancer, brain cancer, breast cancer, cervical cancer, chromophobe renal cell carcinoma, clear cell renal cell carcinoma, colorectal cancer, confirm at path review (cancer type unconfirmed), endocrine tumor, endometrial cancer, esophageal cancer, gastric cancer, gastrointestinal stromal tumor, glioblastoma, head and neck cancer, head and neck squamous cell carcinoma, heme other, high-grade glioma, kidney cancer, liver cancer, low grade glioma, medulloblastoma, melanoma, meningioma, mesothelioma, multiple myeloma, neuroblastoma, non-clear cell renal cell carcinoma, non-small cell lung cancer, oropharyngeal cancer, ovarian cancer, pan cancer, pancreatic cancer
  • certain variants pre-identified on a whitelist may be rescued, e.g., not filtered out, when they fail to pass selective filters, e.g., MSI/SN, a Bayesian filtering method, and/or a coverage, VAF or region-based filter.
  • filters e.g., MSI/SN, a Bayesian filtering method, and/or a coverage, VAF or region-based filter.
  • the rationale for whitelisting a variant is to apply less stringent filtering criteria to such a variant so that it can be reviewed and/or reported.
  • one or more variant on the whitelist is a common pathogenic variant, e.g., with high clinical relevance. In this fashion, when a variant on the whitelist fails to pass certain filters, it will be rescued and not filtered out.
  • MSI/SN refers to a variant filter for filtering out potential artifactual variants based on the MSI (microsatellite instable) and SN (signal-to-noise ratio) values calculated by the variant caller VarDict. See, for example, VarDict documentation, available on the internet at github. com/AstraZeneca-NGS/V arDictJava.
  • one or more locus and/or genomic region is blacklisted, preventing somatic variant annotation for variants identified at the locus or region.
  • the variant has a length of 120, 100, 80, 60, 40, 20, 10, 5 or less base pairs.
  • any combination of the additional criteria, as well as additional criteria not listed above, may be applied to the variant calling process. Again, in some embodiments, different criteria are applied to the annotation of different types of variants.
  • liquid biopsy assays are used to detect variant alterations present at low circulating fractions in the patient’s blood. In such circumstances, it may be warranted to lower the requirements for positively identifying a variant. That is, in some embodiments, low levels of support may be sufficient to call a variant, dependent upon the reason for using the liquid biopsy assay.
  • SNV/INDEL detection is accomplished using VarDict (available on the internet at 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.
  • genomic rearrangements e.g., inversions, translocations, and gene fusions
  • genomic rearrangements are detected following de-multiplexing by aligning tumor FASTQ files against a human reference genome using a local alignment algorithm, such as BWA.
  • 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.
  • 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.
  • 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.
  • gene rearrangements are detected using the SpeedSeq analysis pipeline.
  • FASTQ files are aligned to hgl9 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.
  • putative fusion variants supported by less than a minimum number of unique sequence reads are filtered.
  • the minimum number of unique sequence reads is 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, or 20 unique sequence reads.
  • the analysis of aligned sequence reads includes determination of variant allele fractions (133) for one or more of the variant alleles 132 identified as described above.
  • 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.
  • 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.
  • 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.
  • 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 can be 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.
  • the analysis of aligned sequence reads includes determination of methylation states 132 for one or more loci in the genome of the patient.
  • 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.
  • the analysis of aligned sequence reads includes determination of the copy number 135 for one or more locus, using a copy number variation analysis module 153.
  • Figure 4F1 illustrates a workflow of an exemplary method 400-1 for validating copy number variation to be used in generating clinical reports to support clinical decision making in precision oncology, in accordance with some embodiments of the present disclosure. More specifically, method 400-1 describes a bioinformatics pipeline for extraction and identification of genomic copy number variation (e.g., a method for feature extraction 206), in accordance with some embodiments of the present disclosure.
  • the method comprises obtaining a dataset of cell-free DNA sequencing data.
  • the sequencing data can be obtained using any of the methods and/or embodiments disclosed herein, including any of the implementations for wet lab processing 204.
  • 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.
  • comparison between a tumor sample and a pool of process-matched normal controls is used.
  • Pre-processing and/or alignment can be applied to the cfDNA sequencing data, as described in detail above.
  • sequence reads obtained from the cfDNA sequencing data are aligned to a reference human construct, thus generating a plurality of aligned reads 406-1.
  • the method further comprises optionally processing the aligned cfDNA sequence reads by, for example, normalization, filtering, and/or quality control, as described in detail above.
  • the method further comprises obtaining for validation one or more copy number status annotations (e.g., amplified, neutral, deleted).
  • the copy number status annotations are obtained via copy number analysis.
  • CNVs copy number variants
  • CNVkit is used for genomic region binning, coverage calculation, bias correction, normalization to a reference pool, segmentation and visualization.
  • the log2 ratios between the tumor sample and a pool of process matched healthy samples from the CNVkit output are then annotated and filtered using statistical models whereby the amplification status (amplified or not-amplified) of each gene is predicted and non-focal amplifications are removed.
  • copy number variations are analyzed using a combination of an open-source tool, such as CNVkit, and an annotation/filtering algorithm, e.g. , implemented via a python script.
  • CNVkit is used initially to perform genomic region binning, coverage calculation, bias correction, normalization to a reference pool, segmentation and, optionally, visualization.
  • the bin-level copy ratios and segment-level copy ratios, in addition to their corresponding confidence intervals, from the CNVkit output are then used in the annotation and filtering where the copy number state (amplified, neutral, deleted) of each segment and bin are determined and non-focal amplifications/deletions are filtered out based on a set of acceptance criteria.
  • CCNE1, and MYC genes are analyzed.
  • deletions in the BRCA1 and BRCA2 genes are analyzed.
  • the methods described herein is not limited to only these reportable genes.
  • CNV analysis is performed using a tumor BAM file, a target region BED file, a pool of process matched normal samples, and inputs for initial reference pool construction.
  • Inputs for initial reference pool construction include one or more of normal BAM files, a human reference genome file, mappable regions of the genome, and a blacklist that contains recurrent problematic areas of the genome.
  • CNVkit utilizes both targeted captured sequencing reads and non-specifically captured off-target reads to infer copy number information.
  • the targeted genomic regions specified in the probe target BED file are divided to target bins with an average size of, e.g., 100 base pairs, which can be specified by the user.
  • the genomic regions between the target regions e.g., excluding regions that cannot be mapped reliably, are automatically divided into off-target (also referred to as anti-target) bins with an average size of, e.g., 150 kbp, which again can be specified by the user.
  • Raw log2-transformed depths are then calculated from the alignments in the input BAM file and written to two tab-delimited .cnn files, one for each of the target and off-target bins.
  • a pooled reference is constructed from a panel of process matched normal samples.
  • the raw log2 depths of target and off-target bins in each normal sample are computed as described above, and then each are median-centered and corrected for bias including GC content, genome sequence repetitiveness, target size, and/or spacing.
  • the corrected target and off-target log2 depths are combined, and a weighted average and spread are calculated as Tukey’s biweight location and midvariance in each bin. These values are written to a tab delimited reference .cnn file, which is used to normalize an input tumor sample as follows.
  • the raw log2 depths of an input sample are median-centered and bias-corrected as described in the reference construction.
  • the corrected log2 depth of each bin is then subtracted by the corresponding log2 depth in the reference file, resulting in the log2 copy ratios (also referred to as copy ratios or log2 ratios) between the input tumor sample and the reference pool.
  • log2 copy ratios also referred to as copy ratios or log2 ratios
  • the copy ratios are then segmented, e.g., via a circular binary segmentation (CBS) algorithm or another suitable segmentation algorithm, whereby adjacent bins are grouped to larger genomic regions (segments) of equal copy number.
  • CBS circular binary segmentation
  • the segment’s copy ratio is calculated as the weighted mean of all bins within the segment.
  • the confidence interval of the segment mean is estimated by bootstrapping the bin-level copy ratios within the segment.
  • the segments’ genomic ranges, copy ratios and confidence intervals are written to a tab- delimited .cns file.
  • copy number analysis includes application of a circular binary segmentation algorithm and selection of segments with highly differential log2 ratios between the cancer sample and its comparator (e.g., a matched normal or normal pool).
  • 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.
  • the integer copy number of a genomic segment in a cancer sample is used to assign a copy number status annotation to the genomic segment (e.g., amplified, neutral, deleted) based on a comparison with the integer copy number of a corresponding genomic segment in a reference pool.
  • Validation filters Referring again to Block 410-1, the annotation/filtering algorithm is subsequently applied to the bin-level copy ratios and segment-level copy ratios, in addition to their corresponding confidence intervals, obtained from the CNVkit output.
  • the annotation/filtering algorithm comprises a plurality of filters for validation of copy number status annotations 412-1, including an optional median bin-level copy ratio filter 414- 1; an optional segment-level confidence interval filter 416-1; an optional median-plus-median absolute deviation (MAD) bin-level copy ratio filter 418-1; and/or an optional segment-level copy ratio filter.
  • the method further comprises validating or rejecting a copy number variation as a focal copy number variation based on the plurality of copy number status annotation validation filters. Specifically, when a filter in the plurality of filters is fired, the copy number annotation of the segment is rejected, and the copy number variation is determined to be a non-focal copy number variation. When no filter in the plurality of filters is fired, the copy number annotation of the segment is validated 422-1 and the copy number variation is determined to be a focal copy number variation.
  • the extracted features can then be used for variant analysis 208 and clinical report generation (e.g., as described in further detail below with reference to Figure 2A).
  • the method further comprises matching therapies and/or clinical trials based on the status (e.g., validated or rejected) of the respective copy number annotation.
  • the method further comprises generating a patient report indicating the CNV status, in addition to matched therapies and/or clinical trials based on the CNV status.
  • MSI Microsatellite Instability
  • analysis of aligned sequence reads includes analysis of the microsatellite instability status 137 of a cancer, using a microsatellite instability analysis module 154.
  • an MSI classification algorithm classifies a cancer into three categories: microsatellite instability -high (MSI-H), microsatellite stable (MSS), or microsatellite equivocal (MSE).
  • MSI-H microsatellite instability -high
  • MSS microsatellite stable
  • MSE microsatellite equivocal
  • Microsatellite instability is a clinically actionable genomic indication for cancer immunotherapy.
  • MSI-H microsatellite instability -high tumors
  • MMR DNA mismatch repair
  • 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.
  • 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.
  • 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-Smimov 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.
  • 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.
  • 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.
  • a baseline threshold may be established to call the patient as MSS.
  • the baseline threshold is no more than 40%, or no more than 35% probability, 30% probability, 25% probability, 20% probability, or less.
  • the patient is identified as MSE.
  • 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.
  • TLB Tumor Mutational Burden
  • the analysis of aligned sequence reads includes determination of a mutation burden for the cancer (e.g., a tumor mutational burden 136), using a tumor mutational burden analysis module 155.
  • a tumor mutational burden is a measure of the mutations in a cancer per unit of the patient’s genome.
  • 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.
  • a tumor mutational burden refers to only a set of possible mutations, e.g., one or more of SNVs, MNVs, indels, or genomic rearrangements.
  • 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.
  • 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.
  • a tumor mutational burden 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.
  • 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, lOOx, 250x, 500x, or greater.
  • 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.
  • 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.
  • 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.
  • analysis of aligned sequence reads includes analysis of whether the cancer is homologous recombination deficient (HRD status 137-3), using a homologous recombination pathway analysis module 157.
  • HR homologous recombination
  • homologous recombination 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.
  • PARP poly (ADP-ribose) polymerase
  • 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.
  • 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.
  • 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.
  • the analysis of aligned sequence reads 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).
  • a non- cancerous tissue e.g., a germline or hematopoietic tissue.
  • FACETS (Shen R, Seshan VE, Nucleic Acids Res., 44(16):el31 (2016)) is designed to estimate tumor fraction from sequencing data of solid tumor samples.
  • 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 typically small size of a targeted panel used for liquid biopsy sequencing.
  • packages such as PureCN and FACETS perform poorly at low tumor fractions and with sequencing data generated using small targeted-panels.
  • circulating tumor fraction is estimated from a targeted- panel sequencing reaction of a liquid biopsy sample using an off-target read methodology, e.g., as described herein with reference to Figures 4 and 5 (e.g., Figures 4F3, 5A3-5B3). Briefly, a circulating tumor fraction estimate is determined from reads in the target captured regions, as well as off-target reads uniformly distributed across the human reference genome.
  • Segments having similar copy ratios are fit to integer copy states, e.g., via an expectation-maximization algorithm using the sum of squared error of the segment log2 ratios (normalized to genomic interval size) to expected ratios given a putative copy state and tumor fraction.
  • a measure of fit between corresponding segment-level coverage ratios and assigned integer copy states across the plurality of simulated circulating tumor fractions is then used to select the simulated circulating tumor fraction to be used as the circulating tumor fraction for the liquid biopsy sample.
  • error minimization is used to identify the simulated tumor fraction providing the best fit to the data.
  • circulating tumor fraction is estimated from a targeted- panel sequencing reaction of a liquid biopsy sample using an off-target read methodology, e.g., as described herein with reference to Figures 4 and 5 (e.g., Figures 4F3, 5A3-5B3). Briefly, a circulating tumor fraction estimate is determined from reads in the target captured regions, as well as off-target reads uniformly distributed across the human reference genome.
  • Segments having similar copy ratios are fit to integer copy states, e.g., via an expectation-maximization algorithm using the sum of squared error of the segment log2 ratios (normalized to genomic interval size) to expected ratios given a putative copy state and tumor fraction.
  • expectation maximization algorithms see, for example, Sundberg, Rolf (1974). "Maximum likelihood theory for incomplete data from an exponential family”. Scandinavian Journal of Statistics. 1 (2): 49-58, the content of which is hereby incorporated by reference in its entirety.
  • a measure of fit between corresponding segment-level coverage ratios and assigned integer copy states across the plurality of simulated circulating tumor fractions is then used to select the simulated circulating tumor fraction to be used as the circulating tumor fraction for the liquid biopsy sample.
  • error minimization is used to identify the simulated tumor fraction providing the best fit to the data.
  • a measure of fit between corresponding segment-level coverage ratios and assigned integer copy states across the plurality of simulated circulating tumor fractions provides a number of local optima (e.g., local minima for an error minimization model or local maxima for a fix maximization model) for the best fit between the segment-level coverage ratios and assigned integer copy states.
  • a second estimate of circulating tumor fraction is used to select the local optima (e.g., the local minima in best agreement with the second estimate of circulating tumor fraction) to be used as the circulating tumor fraction for the liquid biopsy sample.
  • multiple local optima can be disambiguated based on a difference between somatic and germline variant allele fractions.
  • the assumption is that the variant allele fraction (VAF) of germline variants that exhibit loss of heterozygosity (LOH) will increase or decrease by the amount approximately equal to half of the tumor purity (e.g. , the circulating tumor fraction for a liquid biopsy sample).
  • VAF variant allele fraction
  • LH heterozygosity
  • the VAFnormai is unknown. In some embodiments, the VAFnormai is assumed to be 50%.
  • minima e.g., minima
  • the off-target read methodology ctFE peaks corresponding to all the local optima (e.g., minima) are identified and the one closest to the ctFE estimated by LOH delta is chosen as the most likely global optima (e.g., minima).
  • these methods are used in combination with the off-target tumor estimate method described herein.
  • 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.
  • 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.
  • a probabilistic tumor fraction estimation model in the “methods” section.
  • 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.
  • Tiancheng H. et al Journal of Clinical Oncology 37:15 suppl, el3053-el3053 (2019), the content of which is disclosed herein for its description of a Maximum Likelihood tumor fraction estimation model.
  • a statistic for somatic variant allele fractions determined for the liquid biopsy sample is used as an estimate for the circulating tumor fraction of the liquid biopsy sample.
  • a measure of central tendency e.g., a mean or median
  • a lowest (minimum) variant allele fraction determined for the liquid biopsy sample is used as an estimate of circulating tumor fraction.
  • a highest (maximum) variant allele fraction determined for the liquid biopsy sample is used as an estimate of circulating tumor fraction.
  • a range defined by two or more of these statistics is used to limit the range of simulated tumor fraction analysis via the off-target read methodology described herein.
  • lower and upper bounds of the simulated tumor fraction analysis are defined by the minimum variant allele fraction and the maximum variant allele fraction determined for a liquid biopsy sample, respectively.
  • the range is further expanded, e.g., on either or both the lower and upper bounds.
  • the lower bound of a simulated tumor fraction analysis is defined as 0.5-times the minimum variant allele fraction, 0.75-times the minimum variant allele fraction, 0.9-times the minimum variant allele fraction, 1.1 -times the minimum variant allele fraction, 1.25-times the minimum variant allele fraction, 1.5-times the minimum variant allele fraction, or a similar multiple of the minimum variant allele fraction determined for the liquid biopsy sample.
  • the upper bound of a simulated tumor fraction analysis is defined as 2.5-times the maximum variant allele fraction, 2-times the maximum variant allele fraction, 1.75-times the maximum variant allele fraction, 1.5-times the maximum variant allele fraction, 1.25-times the maximum variant allele fraction, 1.1 -times the maximum variant allele fraction, 0.9-times the maximum variant allele fraction, or a similar multiple of the maximum variant allele fraction determined for the liquid biopsy sample.
  • circulating tumor fraction is estimated based on a distribution of the lengths of cfDNA in the liquid biopsy sample.
  • sequence reads are binned according to their position within the genome, e.g., as described elsewhere herein. For each bin, the length of each fragment is determined. Each fragment is then classified as belonging to one of a plurality of classes, e.g., one of two classes corresponding to a population of short fragments and a population of long fragments.
  • the classification is performed using a static length threshold, e.g., that is the same across all the bins.
  • the classification is performed using a dynamic length threshold.
  • a dynamic length threshold is determined by comparing the distribution of fragment lengths in liquid biopsy samples from reference subjects that do not have cancer to the distribution of fragment lengths in liquid biopsy samples from reference subjects that have cancer, in a positional fashion.
  • the comparison is done over windows spanning entire chromosomes, e.g., each chromosome defines a comparison window over which a dynamic length threshold is determined.
  • the comparison is done over a window spanning a single bin, e.g., each bin defines a comparison window over which a dynamic length threshold is determined.
  • the bin determination may be made according to various genomic features.
  • the comparison window may be based on a chromosome by chromosome basis, or a chromosomal arm by chromosomal arm basis.
  • the comparison window is based on a gene level basis.
  • the comparison window is a fixed size, such as 1 KB, 5 KB, 10 KB, 25 kB, 50kB, lOOkB, 25 KB, 500 KB, 1 MB, 2 MB, 3 MB, or more.
  • the reference subjects having cancer used to determine the dynamic fragment length is matched to the cancer type of the subject whose liquid biopsy sample is being evaluated.
  • a model trained to estimate circulating tumor fraction based on fragment length distribution data across the genome is applied to the binned data to generate an estimate of the circulating tumor fraction for the liquid biopsy sample.
  • a comparison of (i) the population of short fractions and (ii) the population of long fragments is made for each bin, e.g., a fraction of the number of short fragments to the number of long fragments in each bin is determined and used as an input for the model.
  • the model is a probabilistic model (e.g., an application of Bayes theorem), a deep learning model (e.g., a neural network, such as a convolutional neural network), or an admixture model.
  • a probabilistic model e.g., an application of Bayes theorem
  • a deep learning model e.g., a neural network, such as a convolutional neural network
  • an admixture model e.g., a convolutional neural network
  • two or more of the circulating tumor estimation models described herein are used to generate respective tumor fraction estimates, which are combined to form a final tumor fraction estimate.
  • a measure of central tendency e.g., a mean
  • a tumor fraction estimate derived from a plurality of estimation models e.g., a measure of central tendency for several tumor fraction estimates is used to identify the nearest local optima (e.g., minima) obtained from the tumor fraction estimation methods described above, and further herein.
  • a positive sensitivity control sample is processed and sequenced along with one or more clinical samples.
  • 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.
  • the control includes a pool of controls.
  • a quality control analysis requires that read metrics of variants present in the control sample fall within acceptable criteria.
  • a quality control requires approval by a pathologist before the results are reported.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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 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, 9 million, or more unique sequence reads.
  • 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.
  • a measure of central tendency e.g., a mean or median
  • 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.
  • 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.
  • 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.
  • 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).
  • 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 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.
  • 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.
  • a deamination or OxoG Q-score threshold will be selected based on the sequencing technology used.
  • the criterion is implemented as a minimum deamination or OxoG Q-score threshold of at least 10, 20, 30, 40, 50, 60, 70, 80, 90, or higher.
  • 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. el al, Am. J. Hum. Genet., 91:839-48 (2012).
  • 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.
  • 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-defmed single nucleotide polymorphisms (SNPs) in two samples.
  • SNPs single nucleotide polymorphisms
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the criterion is implemented as a minimum PCR duplication rate threshold of at least 91%, 92% ,93%, 94%, 95%, 96%, 97%, 98%, 99%, or higher.
  • 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.
  • the quality control system includes methods that fail samples for reporting if various criteria are met.
  • the system includes methods that allow for more manual review if a sample does meet the criteria established for automatic fail.
  • 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.
  • 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.
  • the criterion is implemented as a maximum on-target rate threshold of no more than 30%, 40%, 50%, 60%, 70%, or greater.
  • 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.
  • 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.
  • 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.
  • 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 sequence reads.
  • 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.
  • 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 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the criterion is implemented as falling outside of a range of acceptable unique read depth, e.g., falling outside of a unique 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).
  • 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.
  • 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.
  • 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.
  • 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,
  • 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.
  • 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. el al, Am. J. Hum. Genet., 91:839-48 (2012).
  • 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.
  • 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-defmed single nucleotide polymorphisms (SNPs) in two samples.
  • SNPs single nucleotide polymorphisms
  • 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 el03.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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 for final review and can be released for downstream processing at the discretion of the laboratory medical director or designee.
  • the disclosure provides a method for validating a copy number variation (e.g., identifying a true focal copy number variation) in a test subject, by applying one or more filters to segmented copy ratio data from a sequencing assay performed on a liquid biopsy sample from the subject.
  • the method includes obtaining, from a first sequencing reaction, a corresponding sequence of each cell-free DNA fragment in a first plurality of cell-free DNA fragments in a liquid biopsy sample of the test subject, thereby obtaining a first plurality of sequence reads, e.g., a plurality of de-dupbcated sequence reads, where each sequence read correspond to a unique cell-free DNA fragment from the sample.
  • the first plurality of sequence reads includes at least 1000 sequence reads. In some embodiments, the first plurality of sequence reads includes at least 10,000 sequence reads. In some embodiments, the first plurality of sequence reads includes at least 100,000 sequence reads. In some embodiments, the first plurality of sequence reads includes at least 200,000, 300,000, 400,000, 500,000, 750,000, 1,000,000, 2,500,000, 5,000,000 sequence reads, or more.
  • the method then includes aligning each respective sequence read in the first plurality of sequence reads to a reference sequence for the species of the subject.
  • the reference sequence is a reference genome, e.g., a reference human genome.
  • a reference genome has several blacklisted regions, such that the reference genome covers only about 75%, 80%, 85%, 90%, 95%, 98%, 99%, 99.5%, or 99.9% of the entire genome for the species of the subject.
  • the reference sequence for the subject covers at least 10% of the entire genome for the species of the subject, or at least 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, or more of the entire genome for the species of the subject.
  • the reference sequence for the subject represents a partial or whole exome for the species of the subject.
  • the reference sequence for the subject covers at least 10% of the exome for the species of the subject, or at least 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 98%, 99%, 99.9%, or 100% of the exome for the species of the subject.
  • the reference sequence covers a plurality of loci that constitute a panel of genomic loci, e.g., a panel of genes used in a panel-enriched sequencing reaction.
  • An example of genes useful for precision oncology, e.g., which may be targeted with such a panel are shown in Table 1.
  • the reference sequence for the subject covers at least 100 kb of the genome for the species of the subject. In other embodiments, the reference sequence for the subject covers at least 250 kb, 500 kb, 750 kb, 1 Mb, 2 Mb, 5 Mb, 10 Mb, 25 Mb, 50 Mb, 100 Mb, 250 Mb, or more of the genome for the species of the subject.
  • the reference sequence can be a sequence for a single locus, e.g., a single exon, gene, etc.) within the genome for the species of the subject.
  • the method then includes determining several metrics for the sequencing data.
  • the metrics include a plurality of bin-level sequence ratios, each respective bin-level sequence ratio in the plurality of bin-level sequence ratios corresponding to a respective bin in a plurality of bins.
  • the plurality of bins includes at least 10, 25, 50, 100, 250, 500, 1000, 2500, 5000, 10,000, 25,000, 50,000, or more bins distributed across the reference sequence (e.g., the genome) for the species of the subject.
  • the bins are distributed relatively uniformly across the reference sequence, e.g., such that the each encompasses a similar number of bases, e.g, about 0.5 kb,
  • Each respective bin in the plurality of bins represents a corresponding region of a reference sequence (e.g., genome) for the species of the subject.
  • the bins are distributed relatively uniformly across the reference sequence, e.g., such that the each encompasses a similar number of bases, e.g., about 0.5 kb, 1 kb, 2 kb, 5 kb, 10 kb, 25 kb, 50 kb, 100 kb or more bases.
  • Each respective bin-level sequence ratio in the plurality of bin-level sequence ratios is determined from a comparison of the first plurality of sequence reads to sequence reads from one or more reference samples.
  • the one or more reference sample is a process- matched reference sample. That is, in some embodiments the one or more reference samples are prepared for sequencing using the same methodology as used to prepare the sample from the test subject. Similarly, in some embodiments, the one or more reference samples are sequenced using the same sequencing methodology as used to sequence the sample from the test subject. In this fashion, internal biases for particular regions or sequences are controlled for in the reference samples.
  • the metrics include a plurality of segment-level sequence ratios, each respective segment-level sequence ratio in the plurality of segment-level sequence ratios corresponding to a segment in a plurality of segments.
  • Each respective segment in the plurality of segments represents a corresponding region of the reference genome for the species of the subject encompassing a subset of adjacent bins in the plurality of bins.
  • Each respective segment-level sequence ratio in the plurality of segment-level sequence ratios is determined from a measure of central tendency of the plurality of bin-level sequence ratios corresponding to the subset of adjacent bins encompassed by the respective segment.
  • bins adjacent to each other in the reference sequence are grouped together to form segments of the reference sequence (e.g., genome) having similar sequence ratios and, therefore, presumably the same copy number in the cancerous tissue of the subject.
  • the metrics include a plurality of segment-level measures of dispersion.
  • Each respective segment-level measure of dispersion in the plurality of segment-level measures of dispersion corresponding to a respective segment in the plurality of segments.
  • Each respective segment-level measure of dispersion in the plurality of segment-level measures of dispersion is determined using the plurality of bin-level sequence ratios corresponding to the subset of adjacent bins encompassed by the respective segment. That is, a measure of the dispersion of the individual bin-level sequence ratio that make up a segment is determined.
  • the method then includes validating a copy number status annotation (e.g., determining whether a copy number variation is a focal amplification or deletion) of a respective segment in the plurality of segments that is annotated with a copy number variation by applying the first dataset to an algorithm having one or more criteria filters.
  • the copy number status annotation of the respective segment e.g., whether or not a segment represents a focal amplification or focal deletion
  • the one or more filters includes a measure of central tendency bin-level sequence ratio filter that is fired when a measure of central tendency of the plurality of bin-level sequence ratios corresponding to the subset of bins encompassed by the respective segment fails to satisfy one or more bin-level sequence ratio thresholds.
  • the one or more filters includes a confidence filter that is fired when the segment-level measure of dispersion corresponding to the respective segment fails to satisfy a confidence threshold.
  • the one or more filters includes a measure of central tendency-plus-deviation bin-level sequence ratio filter that is fired when a measure of central tendency of the plurality of bin-level sequence ratios corresponding to the subset of bins encompassed by the respective segment fails to satisfy one or more measure of central tendency-plus-deviation bin-level sequence ratio thresholds.
  • the one or more measure of central tendency-plus-deviation bin-level copy ratio thresholds are derived from (i) a measure of central tendency of the bin-level sequence ratios corresponding to the plurality of bins that map to the same chromosome of the reference genome for the species of the subject as the respective segment, and (ii) a measure of dispersion across the bin-level sequence ratios corresponding to the plurality of bins that map to the respective chromosome.
  • Method 500-1 This general method is described with further, optional details below, with reference to Method 500-1.
  • the present disclosure provides a method for validating a copy number variation in a test subject.
  • the method includes obtaining a first dataset that comprises a plurality of bin-level sequence ratios, each respective bin-level sequence ratio in the plurality of bin-level sequence ratios corresponding to a respective bin in a plurality of bins.
  • Each respective bin in the plurality of bins represents a corresponding region of a human reference genome, and each respective bin-level sequence ratio in the plurality of bin- level sequence ratios is determined from a sequencing of a plurality of cell-free nucleic acids in a first liquid biopsy sample of the test subject and one or more reference samples.
  • the plurality of bin-level sequence ratios comprises 2 or more bin-level sequence ratios, 3 or more bin-level sequence ratios, 4 or more bin-level sequence ratios, 5 or more bin-level sequence ratios, 6 or more bin-level sequence ratios, 7 or more bin-level sequence ratios, 8 or more bin-level sequence ratios, 100 or more bin-level sequence ratios, 1000 or more bin-level sequence ratios, 1500 or more bin-level sequence ratios, 2000 or more bin-level sequence ratios, 2500 or more bin-level sequence ratios, 3000 or more bin-level sequence ratios, 3500 or more bin-level sequence ratios, 4000 or more bin-level sequence ratios, 4500 or more bin-level sequence ratios, 5000 or more bin-level sequence ratios, 5500 or more bin-level sequence ratios, 6000 or more bin-level sequence ratios, 6500 or more bin-level sequence ratios, 7000 or more bin-level sequence ratios, 7500 or more bin-level sequence ratios, 8000 or more bin-level sequence
  • the plurality of bin-level sequence ratios consists of between 100 and 100,000 bin-level sequence ratios.
  • the test subject is a patient in a clinical trial.
  • the test subject is a patient with a cancer.
  • the cancer is a solid tumor cancer.
  • the cancer is Ovarian Cancer, Cervical Cancer, Uveal Melanoma, Colorectal Cancer, Chromophobe Renal Cell Carcinoma, Liver Cancer, Endocrine Tumor, Oropharyngeal Cancer, Retinoblastoma, Biliary Cancer, Adrenal cancer, Neural, Neuroblastoma, Basal Cell Carcinoma, Brain Cancer, Breast Cancer, Melanoma, Non-Clear Cell Renal Cell Carcinoma, Glioblastoma, Glioma, Tumor of Unknown Origin, Kidney Cancer, Gastrointestinal Stromal Tumor, Medulloblastoma, Bladder Cancer, Gastric Cancer, Bone Cancer, Non-Small Cell Lung Cancer, Thy
  • the liquid biopsy sample is a liquid biopsy sample.
  • the liquid biopsy sample is blood.
  • the liquid biopsy sample comprises blood, whole blood, peripheral blood, plasma, serum, or lymph of the test subject.
  • the liquid biopsy sample is any of the embodiments described above (see, Definitions: Liquid Biopsy and/or Example Methods: Figure 2A: Example Workflow for Precision Oncology).
  • the method further comprises obtaining the liquid biopsy sample from a sample repository or database (e.g., BioIVT, TSC Biosample Repository, BioLINCC, etc.).
  • the liquid biopsy sample is obtained from the test subject at least 1 hour, at least 2 hours, at least 12 hours, at least 1 day, at least 2 days, at least 1 week, at least 1 month, or at least 1 year prior to processing and/or sequencing the liquid biopsy sample.
  • the liquid biopsy sample is fresh, frozen, dried, and/or fixed.
  • the liquid biopsy sample is processed and/or sequenced at least 1 day, at least 2 days, at least 1 week, at least 1 month, or at least 1 year prior to obtaining the first dataset.
  • the sequencing data for the liquid biopsy sample are obtained from a data repository (e.g., GenBank, NCBI Assembly, DNA DataBank of Japan, European Nucleotide Archive, European Variation Archive, etc.).
  • the term “concurrent” as it relates to assays refers to a period of time between zero and ninety days.
  • concurrent tests using different biological samples from the same subject e.g., two or more of a liquid biopsy sample, cancerous tissue — such as a solid tumor sample or blood sample for a blood-based cancer — and a non-cancerous sample
  • a period of time e.g., the biological samples are collected within the period of time
  • concurrent tests using different biological samples from the same subject are performed within a period of time (e.g., the biological samples are collected within the period of time) of from 0 days to 60 days.
  • concurrent tests using different biological samples from the same subject are performed within a period of time (e.g., the biological samples are collected within the period of time) of from 0 days to 30 days.
  • concurrent tests using different biological samples from the same subject are performed within a period of time (e.g., the biological samples are collected within the period of time) of from 0 days to 21 days.
  • concurrent tests using different biological samples from the same subject are performed within a period of time (e.g., the biological samples are collected within the period of time) of from 0 days to 14 days.
  • concurrent tests using different biological samples from the same subject are performed within a period of time (e.g., the biological samples are collected within the period of time) of from 0 days to 7 days.
  • concurrent tests using different biological samples from the same subject are performed within a period of time (e.g., the biological samples are collected within the period of time) of from 0 days to 3 days.
  • a liquid biopsy assay may be used concurrently with a solid tumor assay to return more comprehensive information about a patient’s variants.
  • a blood specimen and a solid tumor specimen may be sent to a laboratory for evaluation.
  • the solid tumor specimen may be analyzed using a bioinformatics pipeline to produce a solid tumor result.
  • a solid tumor assay is described, for instance, in U.S. Patent Application No. 16/657,804.
  • the cancer type of the solid tumor may include, for example, non small cell lung cancer, colorectal cancer, or breast cancer.
  • Alterations identified in the tumor/matched normal result may include, for example, EGFR+ for non small cell lung cancer; HER2+ for breast cancer; or KRAS G12C for several cancers.
  • the blood specimen may be divided into a first portion and a second portion.
  • the first portion of the blood specimen and the solid tumor specimen may be analyzed using a bioinformatics pipeline to produce a tumor/matched normal result.
  • the second portion of the blood specimen may be analyzed using a bioinformatics pipeline to produce a liquid biopsy result.
  • the blood specimen may be analyzed using at least an improvement in somatic variant identification, e.g., as described herein in the section entitled “Systems and Methods for Improved Validation of Somatic Sequence Variants” and/or “Variant Identification.”
  • the blood specimen may be analyzed using an improvement in focal copy number identification, e.g., as described herein in the section entitled “Systems and Methods for Improved Validation of Copy Number Variation” and/or “Copy Number Variation.”
  • the blood specimen may be analyzed using an improvement in circulating tumor fraction determination, e.g., as described above in the section entitled “Systems and Methods for Improved Circulating Tumor Fraction Estimates” and/or “Circulating Tumor Fraction.”
  • Therapies may be identified for further consideration in response to receiving the tumor or tumor/matched normal result along with the liquid biopsy result. For example, if the results overall indicate that the patient has HER2+ breast cancer, neratinib may be identified along with the test results for further consideration by the ordering clinician.
  • the solid tumor or tumor/matched normal assay may be ordered concurrently; their results may be delivered concurrently; and they may be analyzed concurrently.
  • the liquid biopsy sample corresponds to a matched tumor sample (e.g., a solid tumor sample obtained from the test subject).
  • the method further comprises obtaining a second dataset that is determined from a sequencing of a plurality of cell-free nucleic acids in a matched tumor sample of the test subject.
  • the matched tumor sample is obtained from the test subject concurrently with the liquid biopsy sample.
  • the matched tumor sample is obtained from the test subject at a different time point from the obtaining the liquid biopsy sample.
  • the matched tumor sample is any of the embodiments described above (see, Example Methods: Figure 2A: Example Workflow for Precision Oncology).
  • the method further comprises obtaining the matched tumor sample from a sample repository or database (e.g., BioIVT, TSC Biosample Repository, BioLINCC, etc.).
  • a sample repository or database e.g., BioIVT, TSC Biosample Repository, BioLINCC, etc.
  • the matched tumor sample is obtained from the test subject at least 1 hour, at least 2 hours, at least 12 hours, at least 1 day, at least 2 days, at least 1 week, at least 1 month, or at least 1 year prior to obtaining the liquid biopsy sample.
  • the matched tumor sample is fresh, frozen, dried, and/or fixed.
  • the matched tumor sample is processed and/or sequenced at least 1 day, at least 2 days, at least 1 week, at least 1 month, or at least 1 year prior to obtaining the second dataset.
  • the sequencing data for the plurality of nucleic acids in the matched tumor sample are obtained from a data repository (e.g., GenBank, NCBI Assembly, DNA DataBank of Japan, European Nucleotide Archive, European Variation Archive, etc.).
  • a data repository e.g., GenBank, NCBI Assembly, DNA DataBank of Japan, European Nucleotide Archive, European Variation Archive, etc.
  • the one or more reference samples are non-cancerous samples.
  • the one or more reference samples is a matched normal sample (e.g., a normal sample obtained from the test subject).
  • the matched normal sample is obtained from the test subject concurrently with the liquid biopsy sample.
  • the matched normal sample is obtained from the test subject at a different time point from the obtaining the liquid biopsy sample.
  • the matched normal sample is any of the embodiments described above (see, Example Methods: Figure 2A: Example Workflow for Precision Oncology).
  • the one or more reference samples comprise a pool of normal (e.g., non-cancerous) samples obtained from a plurality of control subjects (e.g., healthy subjects).
  • the method further comprises obtaining the one or more reference samples from a sample repository or database (e.g., BioIVT, TSC Biosample Repository, BioLINCC, etc.).
  • the one or more reference samples include liquid biopsy samples comprising a plurality of cell-free nucleic acids and/or solid tissue samples comprising a plurality of nucleic acids.
  • the one or more reference samples are processed and/or sequenced at least 1 day, at least 2 days, at least 1 week, at least 1 month, or at least 1 year prior to obtaining the first dataset.
  • the sequencing data for the one or more reference samples are obtained from a data repository (e.g., GenBank, NCBI Assembly, DNA DataBank of Japan, European Nucleotide Archive, European Variation Archive, etc.).
  • a data repository e.g., GenBank, NCBI Assembly, DNA DataBank of Japan, European Nucleotide Archive, European Variation Archive, etc.
  • the cell-free nucleic acids e.g., in the first liquid biopsy sample of the test subject and the one or more reference samples
  • the method further comprises isolating the plurality of cell-free nucleic acids from the liquid biopsy sample of the test subject prior to the sequencing.
  • the sequencing is multiplexed sequencing.
  • the sequencing is short-read sequencing or long-read sequencing.
  • the sequencing is a panel-enriched sequencing reaction.
  • the sequencing reaction is performed at a read depth of 100X or more, 250X or more, 500X or more, 1000X or more, 2500X or more, 5000X or more, IO,OOOC or more, 20,000X or more, or 30,000X or more.
  • the sequencing panel comprises 1 or more, 10 or more, 20 or more, 50 or more, 100 or more, 150 or more, 200 or more, 300 or more, 500 or more, or 1000 or more genes.
  • the sequencing panel comprises one or more genes listed in Table 1.
  • the sequencing panel includes at least 2, 3, 4, 5, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100, or all of the genes listed in Table 1.
  • the sequencing panel comprises one or more genes selected from the group consisting of MET, EGFR, ERBB2, CD274, CCNE1, MYC, BRCA1 and BRCA2.
  • the sequencing panel includes at least 2, 3, 4, 5, 6, 7, or all 8 of MET, EGFR, ERBB2, CD274, CCNE1, MYC, BRCA1 and BRCA2.
  • the sequencing reaction is a whole exome sequencing reaction.
  • the sequencing reaction is a whole genome sequencing reaction. In some such embodiments, the sequencing reaction is performed at an average read depth of 10X or more, 15X or more, 20X or more, 25X or more, 30X or more, 40X or more, or 50X or more. In some embodiments, certain regions of the genome are blacklisted from the analysis of a whole genome sequencing reaction, e.g., centromeres, telomeres, highly repeated sequences, and the like, for which accurate sequencing results are difficult to obtain.
  • the obtaining the first dataset further comprises aligning a plurality of sequence reads, obtained from a sequencing of the plurality of cell-free nucleic acids in the first liquid biopsy sample of the test subject, to the human reference genome.
  • each respective bin in the plurality of bins has two or more, three or more, five or more, ten or more, fifteen or more, twenty or more, fifty or more, one hundred or more, five hundred or more, one thousand or more, ten thousand or more, or 100,000 or more sequence reads in the plurality of sequence reads mapping onto the portion of the reference genome corresponding to the respective bin, where each such sequence read uniquely represents a different molecule in the plurality of cell-free nucleic acids in the liquid biopsy sample.
  • the plurality of cell- free nucleic acids in the liquid biopsy sample are sequenced with a sequencing methodology that makes use of unique molecular identifier (UMIs) for each cell-free nucleic acid in the liquid biopsy sample and each sequence read in the plurality of sequence reads has a unique UMI.
  • UMIs unique molecular identifier
  • sequence reads with the same UMI are bagged (collapsed) into a single sequence read bearing the UMI.
  • the sequencing of the plurality of cell-free nucleic acids in the first liquid biopsy sample of the test subject is performed at a central laboratory or sequencing facility.
  • the obtaining the first dataset comprises accessing one or more sequencing datasets and/or one or more auxiliary files, in electronic form, through a cloud-based interface.
  • a first dataset can be obtained by performing a bioinformatics pipeline using tumor BAM files, normal BAM files, a human reference genome file, a target region BED file, a list of mappable regions of the genome, and/or a blacklist of recurrent problematic areas of the genome.
  • the obtaining the first dataset comprises accessing the first dataset, in electronic form, through a cloud-based interface.
  • a first dataset can comprise one or more outputs from a bioinformatics pipeline (e.g., CNVkit outputs “ cns” and/or “ cnr”).
  • Example Methods Figure 2A: Example Workflow for Precision Oncology). Additional methods and embodiments for performing the presently disclosed methods at a distributed diagnostic and clinical environment are described in detail above (see, Example Methods: Figure 2B: Distributed Diagnostic and Clinical Environment). Other embodiments and/or any combinations, substitutions, additions or deletions thereof are possible, as will be apparent to one skilled in the art. [0542] Bins and Sequence Ratios.
  • the methods and systems described herein bin sequences (e.g., sequence reads) across one or more regions of a genome to evaluate the copy number at one or more locations of the genome in a tissue of a subject. In this fashion, a count of the number of sequences generated for a test sample that map to the region of the genome corresponding to the bin, or a measure of depth of coverage across the region of the genome corresponding to the bin, are determined.
  • sequences e.g., sequence reads
  • bin values e.g., copy number or count number
  • reference values for the same corresponding bins, to evaluate how the genomic copy number of the genome corresponding to the test sample differs from that of a reference, which can be a single sample or an average of a plurality of samples.
  • a comparison of bin values for the test sample to these reference values can reveal copy number differences having biological significance for the diagnosis and/or treatment of cancer in the test subject.
  • each bin in a plurality of bins corresponds to a contiguous and non overlapping region, of any size, of a reference genome (e.g., a reference human genome or equivalent construct).
  • a reference genome e.g., a reference human genome or equivalent construct.
  • each bin in a plurality of bins is at least 50 base pairs (bp), at least 100 bp, at least 150 bp, at least 200 bp, at least 300 bp, at least 400 bp, at least 500 bp, at least 750 bp, at least 1 kilobase pairs (kb), at least 2.5 kb, at least 5 kb, at least 10 kb, at least 25 kb, or more.
  • each bin in a plurality of bins is less than 250 kb, less than 100 kb, less than 50 kb, less than 25 kb, less than 10 kb, less than 5 kb, less than 2.5 kb, or less.
  • the average bin size of each bin in the plurality of bins is from 50 bp to 25 kb, from 50 bp to 5 kb, from 50 bp to 1 kb, from 50 bp to 500 bp, or within any other range starting no lower than 25 bp and ending no higher than 350 kb.
  • bins encompassing on-target reads (those sequence reads corresponding to fragments bound by an enrichment probe) will have smaller sizes than bins encompassing off-target reads (those sequence reads corresponding to fragments not bound by an enrichment probe).
  • the size of each bin depends on whether it is an on-target bin or an off-target bin.
  • bin size also varies bin to bin, e.g., such that the number of reads per bin is similar (e.g., within 25% or less of each other). For example, CNVkit automatically adjusts each bin's size so that the number of reads per bin is roughly consistent.
  • on-target bins have an average size of about 100 bp. In some embodiments, on-target bins have an average size of from 25 to 500 bp. In some embodiments, on-target bins have an average size of from 25 to 250 bp. In some embodiments, on-target bins have an average size of from 50 to 250 bp. In some embodiments, on-target bins have an average size of from 50 to 150 bp. A smaller size could be used if a higher resolution (for segmentation and subsequent CNV calling) is desired, but the bins may be noisier since they would contain fewer reads. Thus, the optimal bin size may depend on sequencing depth and sensitivity requirements.
  • off-target bins have an average size of at least 1 kb. In some embodiments, off-target bins have an average size of at least 5 kb. In some embodiments, off-target bins have an average size of from 5 kb to 350 kb. In some embodiments, off-target bins have an average size of from 10 kb to 250 kb. The size of off- target bins may depend on both the on-target and off-target sequencing depths of a sequencing reaction.
  • each bin has a defined start nucleotide and a defined ending nucleotide in the reference genome for the species of subject.
  • each bin comprises a start and end position that indicates its location in the human reference genome.
  • each bin corresponds to (i) a first subset of bins that map to the same position of the human reference genome as a locus in a targeted sequencing panel (e.g., target bins), or (ii) a second subset of bins that map to an off-target portion of a reference genome that is not represented in the targeted sequencing panel (e.g., off-target bins).
  • each bin in the first subset of bins represents a different gene, open reading frame, or genetic feature (e.g., promoter of a gene, enhancer of a gene, repressor of a gene) in a reference genome.
  • each bin in a plurality of bins is approximately the same size (e.g., spans about the same number of base pairs in the reference genome as every other bin).
  • the bin size specified by a user such that the number of bins is dependent upon the size of the region over which the plurality of bins span.
  • the number of bins spanning a region is specified by a user, such that the size of each respective bin is dependent upon the size of the region over which the plurality of bins span.
  • each bin in a plurality of bins is not the same size.
  • each bin size is determined based on the number of sequences falling with the bins in one or more reference samples, e.g., to normalize for an expected number of sequence reads mapping to each bin.
  • bins in a first subset of bins spanning regions of the genome corresponding to the enrichment panel are smaller than a second subset of bins spanning regions of the genome that do not correspond to the enrichment panel (e.g, bins corresponding to off-target reads of the sequencing reaction).
  • the plurality of bins covers at least 1 Mb of a reference genome for the species of the subject (e.g, the human genome). In some embodiments, the plurality of bins covers at least 2.5 Mb, at least 5 Mb, at least 10 Mb, at least 25 Mb, at least 50 Mb, at least 100 Mb, at least 250 Mb, at least 500 Mb, at least 1000 Mb, at least 2000 Mb, at least 3000 Mb, or more of the reference genome. In some embodiments, the plurality of bins covers at least 25% of a reference genome for the species of the subject (e.g, the human genome). In some embodiments, the plurality of bins covers at least 50%, at least 75%, at least 90%, at least 95%, at least 98%, at least 99%, or more of the reference genome.
  • a plurality of sequence reads are obtained from a sequencing of nucleic acids (e.g, in the liquid biopsy sample and/or in the one or more reference samples), and the obtained sequences, e.g, collapsed (de-dupbcated) sequence reads, are assigned to respective bins corresponding to the region of the genome that the sequence reads map to.
  • the sequencing data is pre-processed to correct biases or errors using one or more methods such as normalization, correction of GC biases, correction of biases due to PCR over-amplification, etc., prior to binning.
  • the bin values processed to correct for biases or errors e.g. , by normalization, standardization, etc.
  • a median bin value across a plurality of bin values for a sample is obtained, and each respective bin value in the plurality of bin values is divided by this median value, assuring that the bin values for the respective training subject are centered on a known value (e.g., on zero):
  • bv L the bin value of bin i in the plurality of bin values for the sample
  • bv * the normalized bin value of bin i in the plurality of bin values for the sample upon this first normalization
  • median ⁇ bv j ) the median bin value across the plurality of unnormalized bin values for the sample.
  • some other measure of central tendency is used, such as an arithmetic mean, weighted mean, midrange, midhinge, trimean, Winsorized mean, mean, or mode across the plurality of bin values of the sample.
  • the un-normalized bin values (counts) bv L are GC normalized.
  • the normalized bin values b v * are GC normalized.
  • GC counts of respective sequence reads in the plurality of sequence reads of each sample in the plurality of reference samples are binned.
  • a curve describing the conditional mean fragment count per GC value is estimated by such binning (Yoon et a ⁇ , 2009, Genome Research 19(9): 1586), or, alternatively, by assuming smoothness (Boeva et ctl, 2011, Bioinformatics 27(2), p.
  • the resulting GC curve determines a predicted count for each bin based on the bin's GC. These predictions can be used directly to normalize the original signal ⁇ e.g., bv * , bv t , or bv ** ). As a non-limiting example, in the case of binning and direct normalization, for each respective G+C percentage in the set ⁇ 0%, 1%, 2%, 3%,...
  • the value mGC, the median value of b n ** o ⁇ all bins across the plurality of training subjects having this respective G+C percentage is determined and subtracted from the normalized bin values bv ** of those bins having the respective G+C percentage to form GC normalized bin values bv *** .
  • some other form of measure of central tendency of bv ** of all bins across the plurality of training subjects having this respective G+C percentage is used, such as an arithmetic mean, weighted mean, midrange, midhinge, trimean, Winsorized mean, mean, or mode.
  • a correction curve is determined using a locally weighted scatterplot smoothing model (e.g., LOESS, LOWESS, etc.). See, for example, Benjamini and Speed, 2012, Nucleic Acids Research 40(10): e72; and Alkan et ctl, 2009, Nat Genet 41:1061-7.
  • the GC bias curve is determined by LOESS regression of count by GC (e.g., using the ‘loess’ R package) on a random sampling (or exhaustive sampling) of bins from the plurality of training subjects.
  • the GC bias curve is determined by LOESS regression of count by GC (e.g., using the ‘loess’ R package), or some other form of curve fitting, on a random sampling of bins from a cohort of reference samples that were sequenced using the same sequencing techniques used to sequence the test sample.
  • the bin counts are normalized using principal component analysis (PCA) to remove higher-order artifacts for a population-based (e.g., healthy subjects) correction.
  • PCA principal component analysis
  • Such normalization can be in addition to or instead of any of the above-identified normalization techniques.
  • a data matrix comprising LOESS normalized bin counts bv *** from young healthy subjects in the plurality of training subjects (or another cohort that was sequenced in the same manner as the plurality of training subjects) is used and the data matrix is transformed into principal component space thereby obtaining the top N number of principal components across the training set.
  • the top 2, the top 3, the top 4, the top 5, the top 6, the top 7, the top 8, the top 9 or the top 10 are used to build a linear regression model:
  • a linear regression model is fit between its normalized bin counts ⁇ bv * , ... , bv *** ⁇ and the top principal components from the training set.
  • the residuals of this model serve as final normalized bin values ⁇ bvTM ** , ... , bv TM** ⁇ for the respective sample.
  • the top principal components represent noise commonly seen in reference samples, and therefore removing such noise (in the form of the top principal components derived from the healthy cohort) from the bin values bv ⁇ ** can effectively improve normalization. See Zhao el a ⁇ , 2015, Clinical Chemistry 61(4), pp. 608-616 for further disclosure on PCA normalization of sequence reads using a health population. Regarding the above normalization, it will be appreciated that all variables are standardized ( e.g ., by subtracting their means and dividing by their standard deviations) when necessary.
  • any form of representation of the number of nucleic sequence reads mapping to a given bin i can constitute a “bin value” and that such a bin value can be in un-normalized form (e.g., bv ; ) or normalized form (e.g., bv * , bvTM, bvTM * , bv ** TM, etc).
  • a bin count or read depth is determined for each bin.
  • the read depth is the average number of times that the corresponding region of the human reference genome spanned by the respective bin is represented in the plurality of sequence reads obtained from the sequencing reaction.
  • the read depths for each respective bin, in the plurality of bins are determined by binning sequence reads obtained for the plurality of cell-free nucleic acids in a panel-enriched sequencing reaction.
  • the panel-enriched sequencing reaction is an ultra-high depth sequencing, where each locus in the plurality of loci in the targeted sequencing panel is sequenced at an average coverage of at least lOOOx, at least 2500x, or at least 5000x.
  • read depths are obtained from targeted captured sequencing reads (e.g., target bins) and non-specifically captured off-target reads (e.g., off-target bins).
  • the bin values e.g.
  • each respective bin-level sequence ratio in the plurality of bin-level sequence ratios is derived from a comparison of (a) a read depth for the corresponding bin in the plurality of bins, determined from a sequencing of a plurality of cell- free nucleic acids in a liquid biopsy sample of the test subject, to (b) a measure of central tendency of read depths for the corresponding bin, across one or more reference samples (or simply the read depth of a single reference sample in the case where only one reference sample is used).
  • a sequence ratio for a respective bin is a comparison of the read depths between the test sample and one or more reference samples, e.g., a pool of reference samples.
  • the one or more reference sample is a single sample, two or more samples, five or more samples, or 100 or more samples.
  • the (a) read depth and the (b) read depths are determined by binning sequence reads from one or more panel -enriched sequencing reactions, and the plurality of bin-level sequence ratios comprises (i) a first sub-plurality of bin-level sequence ratios corresponding to bins that map to the same position of the human reference genome as an enriched locus in the panel-enriched sequencing reaction; and (ii) a second sub-plurality of bin-level sequence ratios corresponding to bins that do not map to the same position of the human reference genome as any enriched locus in the panel-enriched sequencing reaction.
  • the bin-level sequence ratios for target bins and the bin-level sequence ratios for off-target bins are separately determined.
  • the (a) read depth and the (b) read depths are log2- transformed (e.g., log2 read depths).
  • the ratio of the (a) read depth (X) and the (b) measure of central tendency of the read depths (Y) is taken as XJY, Y/X, logN(X/Y), logN(Y/X), X'/Y, Y/X', log N (X'/Y), or log N (Y/X'), X/Y', Y'/X, log N (X/Y'), logN(YTX) , X'/Y', Y'/X', logN(X'/Y'), or logN(Y'/X'), 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.
  • the (a) read depth and the (b) read depths are centered and corrected. In some such embodiments, the (a) read depth and the (b) read depths are median- centered.
  • the correcting comprises correcting for bias (e.g., GC content, genome sequence repetitiveness, target size and/or spacing).
  • the method further comprises, for each sample, centering and correcting the plurality of read depths corresponding to the plurality of bins, across all target and off-target bins in the sample.
  • the (b) measure of central tendency of read depths for the corresponding bin, across the one or more reference samples is an arithmetic mean, a weighted mean, a midrange, a midhinge, a trimean, a Winsorized mean, a mean, a median, or a mode.
  • the (b) measure of central tendency of read depths for the corresponding bin, across the one or more reference samples is Tukey’s biweight location.
  • the method further comprises determining the spread of the (b) read depths for the corresponding bin, across the one or more reference samples.
  • the spread is a measure of dispersion including, but not limited to, a range, a standard deviation, a standard error, and/or a confidence interval.
  • the spread is a midvariance.
  • each bin-level sequence ratio in the plurality of bin-level sequence ratios is a copy ratio.
  • the centered and corrected log2 read depth of each bin in the test sample is subtracted by the log2 read depth of the corresponding bin in the one or more reference samples (e.g., the reference pool). This generates a log2 copy ratio between the test sample and the one or more reference samples.
  • the one or more reference samples includes one or more test samples comprising less than a threshold number of copy number variations.
  • the one or more reference samples includes one or more test samples comprising one or more copy number variations, where each copy number variation occurs less than a threshold number of times in the one or more of test samples.
  • the threshold number of copy number variations is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more than 10.
  • the threshold number of occurrences for each of the one or more copy number variations is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more than 10.
  • the one or more reference samples includes one or more process-matched normal samples.
  • the one or more process-matched normal samples are not pooled (e.g ., the read depths are not averaged across the one or more matched normal samples), and each test sample is normalized against its process-matched normal sample.
  • each test sample is normalized using one or more fixed values for normalization (e.g., a specified log2 depth correction value for each bin in the tumor sample).
  • a fixed value for log2 depth correction is a neutral copy number (e.g., log2 1.0).
  • the method further comprises removing (e.g., filtering), from the plurality of bins, each bin that fails to satisfy one or more filtering criteria.
  • the one or more filtering criteria comprises a threshold reference log2 read depth.
  • each bin that has a reference log2 read depth below a threshold value is removed from the plurality of bins.
  • the threshold reference log2 read depth is less than 5, less than 1, less than 0, less than -1, less than -2, less than -3, less than -4, less than -5, less than -6, less than -7, less than -8, less than -9, or less than -10.
  • the threshold reference log2 read depth is between 0 and -10.
  • the one or more filtering criteria comprises a threshold test log2 read depth.
  • each bin that has a test log2 read depth below a threshold value is removed from the plurality of bins.
  • the threshold test log2 read depth is less than 10, less than 5, less than 1, less than 0, less than -1, less than -2, less than -3, less than -4, less than -5, less than -6, less than -7, less than -8, less than -9, or less than -10.
  • the threshold test log2 read depth is between 5 and -5.
  • the one or more filtering criteria comprises a proximity of a test log2 read depth to a blacklist value. For example, in some embodiments, each bin that has a test log2 read depth that is within a specified range around a blacklist value is removed from the plurality of bins. In some embodiments, the blacklist value is 0, and the specified range is +/- 1 or less (e.g., each bin that has a test log2 read depth between -1 and 1 is removed from the plurality of bins).
  • the blacklist value is 0, and the specified range is +/- 0.9 or less, +/- 0.8 or less, +/- 0.7 or less, +/- 0.6 or less, +/- 0.5 or less, +/- 0.4 or less, +/- 0.3 or less, +/- 0.2 or less, or +/- 0.1 or less. In some embodiments, the specified range is greater than +/- 1.
  • the one or more filtering criteria comprises a distance of a test log2 read depth from a whitelist value. For example, in some such embodiments, each bin that has a test log2 read depth that is outside of a specified range around a whitelist value is removed from the plurality of bins.
  • the whitelist value is a measure of central tendency of the test log2 read depths for a subset of bins in the plurality of bins.
  • the measure of central tendency can be a mean, a median, or a mode.
  • the subset of bins is 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10 bins including the respective bin.
  • the subset of bins is 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10 contiguous bins including the respective bin.
  • the measure of central tendency of the test log2 read depths for a subset of bins can be a local average of log2 read depths, where the local average of log2 read depths is determined by calculating a rolling average for the subset of bins including the respective bin.
  • the specified range around the whitelist value is at least +1- 1 (e.g.
  • each bin that has a test log2 read depth between that has a difference of 1 or greater from the rolling average is removed from the plurality of bins).
  • the specified range is at least +/- 2, at least +/- 3, at least +/- 4, or at least +/- 5. In some embodiments, the specified range is less than +/- 1.
  • the one or more filtering criteria comprises a threshold spread (e.g., a standard deviation, a standard error, and/or a confidence interval) of reference log2 read depths, for the respective bin, across all samples in the one or more reference samples. For example, in some such embodiments, each bin that has a spread of read depths greater than a threshold value is removed from the plurality of bins. [0578] In some embodiments, each bin in the plurality of bins is assigned a weight, and the one or more filtering criteria comprises a threshold weight.
  • a threshold spread e.g., a standard deviation, a standard error, and/or a confidence interval
  • the weight is determined based on one or more of: a size of the bin (e.g., the number of base pairs in the respective bin); a deviation (e.g., distance) from 0 of the log2 read depth for the respective bin in the pooled reference; and/or the spread of log2 read depths for the respective bin in the pooled reference. For example, in some embodiments, each bin with a weight of 0 is removed from the plurality of bins.
  • the first dataset further comprises a plurality of segment-level sequence ratios, each respective segment-level sequence ratio in the plurality of segment-level sequence ratios corresponding to a segment in a plurality of segments.
  • Each respective segment in the plurality of segments represents a corresponding region of the human reference genome encompassing a subset of adjacent bins in the plurality of bins, and each respective segment-level sequence ratio in the plurality of segment-level sequence ratios is determined from a measure of central tendency of the plurality of bin-level sequence ratios corresponding to the subset of adjacent bins encompassed by the respective segment.
  • the first dataset further comprises a plurality of segment-level measures of dispersion, each respective segment-level measure of dispersion in the plurality of segment- level measures of dispersion (i) corresponding to a respective segment in the plurality of segments and (ii) determined using the plurality of bin-level sequence ratios corresponding to the subset of adjacent bins encompassed by the respective segment.
  • one or more respective segments in the plurality of segments that represents a corresponding region of the human reference genome encodes a target gene.
  • the target gene is MET, EGFR, ERBB2, CD274, CCNE1, MYC, BRCA1 or BRCA2.
  • the target gene is any of the genes listed in Table 1.
  • the method further comprises, for each respective segment in the plurality of segments that represents a corresponding region of the human reference genome, grouping the respective subset of adjacent bins in the plurality of bins based on a similarity between the respective sequence ratios of the subset of adjacent bins.
  • the grouping is performed using circular binary segmentation (CBS).
  • Circular binary segmentation groups bins into larger segments that divide each chromosome into regions comprising equal sequence ratios (e.g., copy number or copy ratio). This is generally performed by calculated a statistic for each genomic position, where the statistic comprises a likelihood ratio for the null hypothesis (no change in sequence ratio at the respective position) against the alternative (one change in sequence ratio at the respective position), and where the null hypothesis is rejected if the statistic is greater than a predefined distribution threshold.
  • the chromosome is assumed to be circularized, such that the calculation is performed recursively for each position (e.g., each bin) around the circumference of the circle to identify all change-points across the length of the chromosome. See, for example, Olshen et al, Biostatistics 5, 4, 557-572 (2004), doi: 10.1093/biostatistics/kxh008, which is hereby incorporated herein by reference in its entirety.
  • the grouping (e.g., segmentation) is performed using a Fused Lasso algorithm, a wavelet-based algorithm (e.g., HaarSeg), and/or a Hidden Markov Model.
  • the grouping is performed using a 3-state Hidden Markov Model, a 5-state Hidden Markov Model, and/or a 3-state Hidden Markov Model with fixed amplitude for the loss, neutral, and gain states.
  • the grouping is performed by dividing a respective chromosome into a plurality of predefined regions (e.g., chromosome arms) are calculating the sequence ratios for each predefined region using a measure of central tendency of the sequence ratios of all bins within the predefined region (e.g., a weighted mean of the log2 copy ratios of all bins within each chromosome arm).
  • predefined regions e.g., chromosome arms
  • the segment-level sequence ratio is then calculated, for each segment, as a measure of central tendency for the one or more bins grouped together by the segmentation.
  • the measure of central tendency of the plurality of bin- level sequence ratios corresponding to the subset of bins encompassed by the respective segment is an arithmetic mean, a weighted mean, a midrange, a midhinge, a trimean, a Winsorized mean, a mean, a median, or a mode.
  • the measure of central tendency of the plurality of bin-level sequence ratios is a weighted mean.
  • a segment-level copy ratio can be calculated as the weighted mean of the plurality of copy ratios for all bins grouped within the segment.
  • the segmentation further comprises obtaining a measure of dispersion based on the sequence ratios (e.g., copy ratios) for each bin in the subset of adjacent bins.
  • each respective segment-level measure of dispersion in the plurality of segment-level measures of dispersion is a confidence interval, a standard deviation, a standard error, a variance, or a range.
  • each respective segment-level measure of dispersion in the plurality of segment-level measures of dispersion is a confidence interval
  • determining each respective segment-level measure of dispersion in the plurality of segment-level measures of dispersion comprises bootstrapping the plurality of bin-level sequence ratios corresponding to the subset of bins encompassed by the respective segment.
  • determining segment-level measures of dispersion is performed using normal distributions, binomial distributions, and/or statistical models for estimation as will be apparent to one skilled in the art.
  • the present disclosure provides systems and methods for validating a copy number variation in a test subject, such as a copy number status annotation assigned to a genomic segment.
  • a respective segment in the plurality of segments is annotated with a copy number status annotation when the corresponding segment-level sequence ratio satisfies one or more segment-level sequence ratio thresholds.
  • a copy number status annotation is a qualitative status.
  • a copy number status annotation is selected from the group consisting of “amplified”, “deleted”, or “neutral”.
  • the annotation can comprise, when the segment-level sequence ratio is a positive number, marking the segment as “amplified”; when the segment-level sequence ratio is a negative number, marking the segment as “deleted”; and when the segment-level sequence ratio is zero or within a specified range around zero, marking the segment as “neutral”.
  • the annotation can comprise, when the segment-level sequence ratio is greater than a first threshold, marking the segment as “amplified”; when the segment-level sequence ratio is less than a second threshold, marking the segment as “deleted”; and when the segment-level sequence ratio is between the first and the second thresholds, marking the segment as “neutral”.
  • the one or more segment- level sequence ratio thresholds are one or more segment-level copy ratio thresholds, where the copy number status annotation is “amplified” if the segment-level copy ratio is greater than 0.03, “deleted” if the segment-level copy ratio is less than -0.5, or “neutral” if between - 0.5 and 0.03.
  • a copy number status annotation is a quantitative status (e.g., an integer copy number).
  • the annotation comprises, for each segment, rounding the segment-level sequence ratio to the nearest integer and assigning an absolute copy number based on one or more integer segment-level sequence ratio thresholds. For example, in some embodiments, segment-level copy numbers can be estimated based on positive correlations with segment-level copy ratios. In some embodiments, the annotation further comprises, for each segment, determining whether the segment-level sequence ratio falls within a specified range in a plurality of specified ranges, and assigning an absolute copy number (e.g., an integer copy number) based on the specified range.
  • an absolute copy number e.g., an integer copy number
  • the annotation further comprises rescaling the segment- level sequence ratio based on one or more scaling factors (e.g., tumor fraction, B-allele frequency, known ploidy, and/or point estimates (mean, median, maximum, etc.) of somatic variant allele frequencies).
  • scaling factors e.g., tumor fraction, B-allele frequency, known ploidy, and/or point estimates (mean, median, maximum, etc.) of somatic variant allele frequencies.
  • a segment-level copy ratio can be divided by a tumor fraction estimate for the test subject or the biological sample, thus estimating the copy ratio that would be expected in a pure tumor sample.
  • the method further comprises removing (e.g., filtering) from the plurality of segments each respective segment that fails to satisfy one or more filtering criteria.
  • the one or more filtering criteria comprises a threshold absolute copy number, where each segment that is annotated with an absolute copy number lower than the threshold is removed from the plurality of segments.
  • the threshold absolute copy number is 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10 copies.
  • the one or more filtering criteria comprises one or more threshold values for a measure of dispersion, where (i) each segment in the plurality of segments is annotated with an absolute copy number; (ii) for a subset of adjacent segments, the measure of dispersion is calculated using the absolute copy number of each segment in the subset of adjacent segments; and (iii) the removing from the plurality of segments each respective segment that fails to satisfy the one or more filtering criteria comprises removing the each segment in the subset of adjacent segments.
  • the measure of dispersion for a group of adjacent segments fails to satisfy a filtering criterion, then all of the segments used to calculate the measure of dispersion are removed from the plurality of segments.
  • the measure of dispersion is a confidence interval
  • the filtering criterion is inclusion of zero.
  • genomic region binning, coverage calculation, bias correction, normalization to a reference pool, segmentation, visualization and annotation are performed using any methods and/or software, or any embodiments, combinations, substitutions, additions, and/or deletions thereof as will be apparent to one skilled in the art.
  • the method further comprises validating a copy number status annotation of a respective segment in the plurality of segments that is annotated with a copy number variation by applying the first dataset to an algorithm having a plurality of filters.
  • the plurality of filters comprises (1) a measure of central tendency bin-level sequence ratio filter that is fired when a measure of central tendency of the plurality of bin-level sequence ratios corresponding to the subset of bins encompassed by the respective segment fails to satisfy one or more bin-level sequence ratio thresholds.
  • the measure of central tendency of the plurality of bin- level sequence ratios corresponding to the subset of bins encompassed by the respective segment fails to satisfy one or more bin-level sequence ratio thresholds when the measure of central tendency is lower than a bin-level sequence ratio amplification threshold.
  • a bin-level sequence ratio amplification threshold is between -0.5 and 5, between -0.1 and 3, between -0.047 and 1.6, or between 0 and 0.5. In some embodiments, a bin-level sequence ratio amplification threshold is lower than 0.3.
  • the measure of central tendency of the plurality of bin-level sequence ratios corresponding to the subset of bins encompassed by the respective segment fails to satisfy one or more bin-level sequence ratio thresholds when the measure of central tendency is higher than a bin-level sequence ratio deletion threshold.
  • a bin-level sequence ratio deletion threshold is between -5 and 0.5, between -2 and 0, between -1 and -0.2, or between -0.75 and -0.25.
  • the measure of central tendency of the plurality of bin- level sequence ratios corresponding to the subset of bins encompassed by the respective segment is an arithmetic mean, a weighted mean, a midrange, a midhinge, a trimean, a Winsorized mean, a mean, a median or a mode of the bin-level sequence ratios for all the respective bins encompassed by the respective segment.
  • the measure of central tendency of the plurality of bin-level sequence ratios is a median.
  • the measure of central tendency of the plurality of bin-level sequence ratios in the (1) a measure of central tendency bin-level sequence ratio filter is different from the measure of central tendency of the plurality of bin-level sequence ratios used to determine the segment-level sequence ratio (e.g., where the (1) filter is a median copy ratio filter for all the bins in the segment, and the segment-level sequence ratio is calculated from a weighted mean of the bins in the segment).
  • the plurality of filters further comprises (2) a confidence filter that is fired when the segment-level measure of dispersion (e.g., confidence interval) corresponding to the respective segment fails to satisfy a confidence threshold.
  • a confidence filter that is fired when the segment-level measure of dispersion (e.g., confidence interval) corresponding to the respective segment fails to satisfy a confidence threshold.
  • the segment-level measure of dispersion (e.g., confidence interval) corresponding to the respective segment fails to satisfy a confidence threshold (e.g., for amplification) when the lower bound of the measure of dispersion is lower than the confidence threshold.
  • the segment-level measure of dispersion (e.g., confidence interval) corresponding to the respective segment fails to satisfy a confidence threshold (e.g., for deletion) when the upper bound of the measure of dispersion is higher than the confidence threshold.
  • the confidence threshold is a measure of central tendency of the segment-level sequence ratios corresponding to all other segments that map to the same chromosome of the human reference genome as the respective segment (e.g., all other segments excluding the respective segment, if the segment is located at an end of the chromosome).
  • the confidence threshold comprises a measure of central tendency of the segment-level sequence ratios corresponding to all preceding segments that map to the same chromosome of the human reference genome as the respective segment, and the measure of central tendency of the segment-level sequence ratios corresponding to all subsequent segments that map to the same chromosome of the human reference genome as the respective segment (e.g., all preceding segments and all following segments, if the respective segment is not located at an end of the chromosome).
  • the (2) confidence filter tests the upper or lower bound of the measure of dispersion (e.g., confidence interval) against two independent confidence thresholds (e.g., one preceding measure of central tendency, and one following measure of central tendency), where the bound of the measure of dispersion must satisfy both confidence thresholds in order to pass the filter.
  • the two independent confidence thresholds have different values.
  • the measure of central tendency of the segment-level sequence ratios in the (2) confidence filter is an arithmetic mean, a weighted mean, a midrange, a midhinge, a trimean, a Winsorized mean, a mean, a median or a mode.
  • the plurality of filters further comprises (3) a measure of central tendency-plus-deviation bin-level sequence ratio filter that is fired when a measure of central tendency of the plurality of bin-level sequence ratios corresponding to the subset of bins encompassed by the respective segment fails to satisfy one or more measure of central tendency-plus-deviation bin-level sequence ratio thresholds.
  • the one or more measure of central tendency-plus-deviation bin-level copy ratio thresholds are derived from (i) a measure of central tendency of the bin-level sequence ratios corresponding to the plurality of bins that map to the same chromosome of the human reference genome as the respective segment, and (ii) a measure of dispersion across the bin-level sequence ratios corresponding to the plurality of bins that map to the respective chromosome.
  • the (i) measure of central tendency is calculated using all of the bins that map to the respective chromosome, including the bins encompassed by the respective segment under investigation.
  • the measure of dispersion across the bin-level sequence ratios in the (3) measure of central tendency-plus-deviation filter is a variance, standard deviation, or interquartile range across the bin-level copy ratios.
  • the measure of dispersion is a median of a plurality of absolute deviations, where each absolute deviation corresponds to a bin in the plurality of bins that map to the chromosome and is calculated by subtracting the “chromosome sequence ratio” (e.g., the median of all bin-level sequence ratios for the plurality of bins in the chromosome) from each bin’s sequence ratio.
  • the one or more measures of central tendency-plus- deviation bin-level sequence ratio thresholds is a sum of (i) a measure of central tendency value of the bin-level sequence ratios corresponding to the plurality of bins that map to the same chromosome (e.g., the “chromosome sequence ratio”), and (ii) the measure of central tendency value of a plurality of absolute dispersions, where each absolute dispersion is determined using a comparison (e.g., a subtraction) between each bin-level sequence ratio corresponding to each bin in the plurality of bins that map to the same chromosome as the respective segment, and the measure of central tendency value of the bin-level sequence ratios measured in (i).
  • a comparison e.g., a subtraction
  • the measure of central tendency of the plurality of bin-level sequence ratios corresponding to the subset of bins encompassed by the respective segment fails to satisfy the one or more measure of central tendency -plus-deviation bin-level sequence ratio thresholds when the measure of central tendency of the plurality of bin-level sequence ratios corresponding to the subset of bins encompassed by the respective segment is lower than the one or more measure of central tendency-plus-deviation bin-level sequence ratio thresholds.
  • a segment annotated with an amplification status will pass the (3) filter if the median copy ratio of all bins encompassed in the segment is equal to or higher than the median plus the median absolute deviation (MAD) of all bins’ copy ratios on the same chromosome.
  • MAD median absolute deviation
  • the one or more measure of central tendency -plus- deviation bin-level sequence ratio thresholds comprises (i) a measure of central tendency value of the bin-level sequence ratios corresponding to the plurality of bins that map to the same chromosome (e.g., the “chromosome sequence ratio”), minus (ii) the measure of central tendency value of a plurality of absolute dispersions, where each absolute dispersion is determined using a comparison (e.g., a subtraction) between each bin-level sequence ratio corresponding to each bin in the plurality of bins that map to the same chromosome as the respective segment, and the measure of central tendency value of the bin- level sequence ratios measured in (i).
  • the measure of central tendency of the plurality of bin- level sequence ratios corresponding to the subset of bins encompassed by the respective segment fails to satisfy the one or more measure of central tendency-plus-deviation bin-level sequence ratio thresholds when the measure of central tendency of the plurality of bin-level sequence ratios corresponding to the subset of bins encompassed by the respective segment is higher than the one or more measure of central tendency-plus-deviation bin-level sequence ratio thresholds.
  • the one or more measure of central tendency-plus- deviation bin-level sequence ratio thresholds is the measure of central tendency value of the bin-level sequence ratios corresponding to the plurality of bins that map to the same chromosome, minus the measure of central tendency value of the plurality of absolute dispersions multiplied by a factor k.
  • k is between 0.1 and 0.95, between 0.3 and 0.9, between 0.5 and 0.85, between 0.65 and 0.8, or between 0.73 and 0.77.
  • a segment annotated with a deletion status will pass the (3) filter if the median copy ratio of all bins encompassed in the segment is less than or equal to the median minus 0.75 of the median absolute deviation (MAD) of all bins’ copy ratios on the same chromosome.
  • MAD median absolute deviation
  • the plurality of filters further comprises (4) a segment-level sequence ratio filter that is fired when the segment-level sequence ratio corresponding to the respective segment fails to satisfy one or more segment- level sequence ratio thresholds.
  • the segment-level sequence ratio corresponding to the respective segment fails to satisfy one or more segment-level sequence ratio thresholds when the segment-level sequence ratio is lower than a segment-level sequence ratio amplification threshold.
  • a segment-level sequence ratio amplification threshold is between -0.5 and 5, between -0.1 and 3, between -0.047 and 1.6, or between 0 and 0.5.
  • the segment-level sequence ratio corresponding to the respective segment fails to satisfy one or more segment-level sequence ratio thresholds when the segment-level sequence ratio is higher than a segment-level sequence ratio deletion threshold.
  • a segment-level sequence ratio deletion threshold is between -5 and 0.5, between -2 and 0, between -1 and -0.2, or between -0.75 and -0.25.
  • a segment annotated with an amplification status will pass the (4) segment-level sequence ratio filter if the segment’s copy ratio is greater than 0.03, and a segment annotated with a deletion status will pass the (4) segment- level sequence ratio filter if the segment’s copy ratio is less than -0.5.
  • the amplification and/or deletion thresholds are specified by the user or practitioner. In some embodiments, the amplification and/or deletion thresholds are optimized for improved specificity and sensitivity for one or more test samples.
  • the threshold for the (4) segment-level sequence ratio filter is determined by (i) estimating a circulating tumor fraction for the liquid biopsy sample, and (ii) calculating an expected log2 copy ratio for a high copy gain or deletion, where the expected log2 copy ratio is used as the threshold.
  • a high copy gain is at least 4 copies. In some embodiments, a high copy gain is at least 5 copies. In some embodiments, a high copy gain is at least 6 copies. In some embodiments, a high copy gain is at least 7 copies. In some embodiments, a high copy gain is at least 8 copies. In some embodiments, a high copy gain is at least 9 copies. In some embodiments, a high copy gain is at least 10 copies.
  • an additional filter is used that filters out candidate segments that are longer than threshold length.
  • the threshold length is determined empirically. In some embodiments, the threshold length is at least 15 Mb. In some embodiments, the threshold length is at least 20 Mb. In some embodiments, the threshold length is at least 25 Mb. In some embodiments, the threshold length is at least 30 Mb. In some embodiments, the threshold length is at least 35 Mb. In some embodiments, the threshold length is no more than 50 Mb. In some embodiments, the threshold length is no more than 40 Mb. In some embodiments, the threshold length is no more than 30 Mb. In some embodiments, the threshold length is from 15 Mb to 50 Mb.
  • one or more of the validation filters disclosed herein are optionally included in the plurality of validation filters applied to the first dataset.
  • the plurality of validation filters comprises less than one, less than two, less than three, or less than four of the validation filters described in the present disclosure.
  • any one or more of the validation filters described herein can include any modifications, substitutions, additions and/or combinations thereof, as will be apparent to one skilled in the art.
  • the method further comprises, when a filter in the plurality of filters is fired, the copy number status annotation of the respective segment is rejected; and when no filter in the plurality of filters is fired, the copy number status annotation of the respective segment is validated.
  • validation of an amplification status requires satisfaction of each filter in a plurality of amplification filters
  • validation of a deletion status requires satisfaction of each filter in a plurality of deletion filters.
  • all filters in the plurality of filters applied to the first dataset must be appropriate for the type of copy number status annotation to be validated.
  • the method further comprises validating an amplification status of a respective segment in the plurality of segments, by applying the first dataset to an algorithm having a plurality of filters.
  • the plurality of filters comprises (1) a measure of central tendency bin-level sequence ratio filter that is fired when a measure of central tendency of the plurality of bin-level sequence ratios corresponding to the subset of bins encompassed by the respective segment is lower than a bin-level sequence ratio amplification threshold; (2) a confidence filter that is fired when the lower bound of the segment-level measure of dispersion corresponding to the respective segment is lower than the confidence threshold; and (3) a measure of central tendency -plus - deviation bin-level sequence ratio filter that is fired when a measure of central tendency of the plurality of bin-level sequence ratios corresponding to the subset of bins encompassed by the respective segment is lower than the measure of central tendency-plus-deviation bin-level sequence ratio threshold.
  • the method further comprises validating a deletion status of a respective segment in the plurality of segments, by applying the first dataset to an algorithm having a plurality of filters.
  • the plurality of filters comprises (1) a measure of central tendency bin-level sequence ratio filter that is fired when a measure of central tendency of the plurality of bin-level sequence ratios corresponding to the subset of bins encompassed by the respective segment is higher than a bin-level sequence ratio deletion threshold; (2) a confidence filter that is fired when the upper bound of the segment-level measure of dispersion corresponding to the respective segment is higher than the confidence threshold; and (3) a measure of central tendency-plus-deviation bin-level sequence ratio filter that is fired when a measure of central tendency of the plurality of bin- level sequence ratios corresponding to the subset of bins encompassed by the respective segment is higher than the measure of central tendency -plus-deviation bin-level sequence ratio threshold.
  • the plurality of filters comprises a plurality of amplification filters and a plurality of deletion filters.
  • the copy number status annotation is “neutral”, and the validating the copy number status annotation comprises firing at least one filter in the plurality of amplification filters and firing at least one filter in the plurality of deletion filters.
  • a segment is flagged as ambiguous if less than a threshold number of filters is fired. For example, in some embodiments, a segment is flagged as ambiguous if less than 4, less than 3, or less than 2 filters are fired.
  • a validated copy number variation for a segment is assigned to the segment and to each bin encompassed by the respective segment.
  • the method further comprises, after the validating, applying the validated copy number variation of the respective segment to a diagnostic assay.
  • the method further comprises treating a patient with a cancer containing a copy number variation of a target gene by determining whether the copy number variation of the target gene is a focal copy number variation by validating the copy number variation in the patient, thus determining whether the patient has an aggressive form of the cancer associated with a focal copy number variation of the target gene.
  • the method further comprises, when the patient has the aggressive form of cancer associated with focal copy number variation of the target gene, administering a first therapy for the aggressive form of the cancer to the patient, and when the patient does not have the aggressive form of cancer associated with focal copy number variation of the target gene, administering a second therapy for a less aggressive form of the cancer to the patient.
  • the first therapy is selected from Table 2.
  • the first therapy is trastuzumab, lapatinib, or crizotinib.
  • the method further comprises generating a report (e.g. , for use by a physician) comprising the validated copy number status of the respective segment for the biological sample of the respective test subject.
  • the generated report further comprises matched therapies (e.g., treatments and/or clinical trials) based on the copy number status of the respective segment.
  • the method further comprises disease screening and/or monitoring over a plurality of time points.
  • the method is used for monitoring disease progression and/or recurrence after treatment, for assessing the efficacy of a treatment, and/or for performing comparative studies using liquid biopsy samples and matched solid tissue samples.
  • the method further comprises obtaining a second dataset that comprises a plurality of bin-level sequence ratios, each respective bin- level sequence ratio in the plurality of bin-level sequence ratios corresponding to a respective bin in a plurality of bins, where each respective bin in the plurality of bins represents a corresponding region of a human reference genome, and each respective bin-level sequence ratio in the plurality of bin-level sequence ratios is determined from a sequencing of a plurality of cell-free nucleic acids in a second liquid biopsy sample of the test subject and one or more reference samples.
  • the second dataset further comprises a plurality of segment-level sequence ratios, each respective segment-level sequence ratio in the plurality of segment- level sequence ratios corresponding to a segment in a plurality of segments, where each respective segment in the plurality of segments represents a corresponding region of the human reference genome encompassing a subset of adjacent bins in the plurality of bins, and each respective segment-level sequence ratio in the plurality of segment-level sequence ratios is determined from a measure of central tendency of the plurality of bin-level sequence ratios corresponding to the subset of adjacent bins encompassed by the respective segment.
  • the second dataset also includes a plurality of segment-level measures of dispersion, each respective segment-level measure of dispersion in the plurality of segment-level measures of dispersion (i) corresponding to a respective segment in the plurality of segments and (ii) determined using the plurality of bin-level sequence ratios corresponding to the subset of adjacent bins encompassed by the respective segment.
  • the method further includes validating a copy number status annotation of a respective segment in the plurality of segments that is annotated with a copy number variation by applying the second dataset to an algorithm having a plurality of filters.
  • the plurality of filters can include any of the filters disclosed herein.
  • the first liquid biopsy sample is obtained at a first time point and the second liquid biopsy sample of the test subject is obtained at a second time point.
  • the second time point is at least 1 day, at least 1 week, at least 1 month, at least 2 months, at least 3 months, at least 6 months, or at least 1 year after the first time point.
  • one or more liquid biopsy assays described herein may be used to analyze specimens from a patient taken over the course of the patient’s treatment.
  • a blood specimen may be obtained periodically and/or upon indication of response to therapy, disease relapse, and/or disease progression.
  • the one or more liquid biopsy assays may be used on a specimen collected from the patient each month, every two months, every three months, every four months, every five months, every 6-12 months, and so forth.
  • the longitudinal use of liquid biopsy assays may be used to track clonal evolution to identify resistance mutations.
  • the longitudinal use of liquid biopsy assays may be used to track evolution of mutations, such as EGFR or APC mutations.
  • longitudinal use of liquid biopsy assays may be used to detect emerging therapy resistance mechanisms.
  • longitudinal use of liquid biopsy assays may be used to detect AR gene alterations.
  • longitudinal use of liquid biopsy assays may be used to detect WNT pathway alterations in mCRPC associated with resistance to enzalutimide and abiraterone.
  • longitudinal use of liquid biopsy assays may be used to detect ER mutations, such as ER mutations associated with resistance to endocrine therapy in breast cancer.
  • longitudinal use of liquid biopsy assays may be used to detect EGFR mutations responsible for anti-EGFR therapy resistance (e.g., T790M) in NSCLC.
  • longitudinal use of liquid biopsy assays may be used to detect KRAS, NRAS, MET, ERBB2, FLT3, or EGFR mutations associated with primary or acquired resistance to EGFR inhibitors in colorectal cancer.
  • longitudinal use of liquid biopsy assays may be used to assess gene alterations from tumor cells shed by primary tumor and metastatic sites.
  • the one or more blood specimens may be collected from the patient in a home-based environment.
  • the blood specimens may be collected by a mobile phlebotomist.
  • a first blood specimen, a second blood specimen, and a third blood specimen may be collected from a patient during the course of treatment.
  • the first blood specimen may be analyzed using at least an improvement in somatic variant identification, e.g., as described herein in the section entitled “Systems and Methods for Improved Validation of Somatic Sequence Variants” and/or “Variant Identification”
  • the second blood specimen may be analyzed using at least an improvement in somatic variant identification, e.g., as described herein in the section entitled “Systems and Methods for Improved Validation of Somatic Sequence Variants” and/or “Variant Identification”
  • the third blood specimen may be analyzed using at least an improvement in somatic variant identification, e.g., as described herein in the section entitled “Systems and Methods for Improved Validation of Somatic Sequence Variants” and/or “Variant Identification.”
  • the first blood specimen may be analyzed using at least an improvement in focal copy number identification, e.g., as described herein in the section entitled “Systems and Methods for Improved Validation of Copy Number Variation” and/or
  • the first blood specimen may be analyzed using at least an improvement in circulating tumor fraction determination, e.g., as described herein in the section entitled “Systems and Methods for Improved Circulating Tumor Fraction Estimates” and/or “Circulating Tumor Fraction”
  • the second blood specimen may be analyzed using at least an improvement in circulating tumor fraction determination, e.g., as described herein in the section entitled “Systems and Methods for Improved Circulating Tumor Fraction Estimates” and/or “Circulating Tumor Fraction”
  • the third blood specimen may be analyzed using using at least an improvement in circulating tumor fraction determination, e.g., as described herein in the section entitled “Systems and Methods for Improved Circulating Tumor Fraction Estimates” and/or “Circulating Tumor Fraction.”
  • Block 600-1 the present disclosure also provides a method for treating a patient with a cancer containing a copy number variation of a target gene.
  • the method comprises determining whether the patient has an aggressive form of cancer associated with a focal copy number variation of the target gene.
  • a focal copy number variation of a target gene can be associated with, for example, recurrence, high-grade forms of a cancer, aggressive forms of a cancer, tumor growth, and/or other aberrations. See, for example, Nord et al, Int. J. Cancer, 126, 1390- 1402 (2010), which is hereby incorporated herein by reference in its entirety.
  • the target gene is any of the embodiments described above.
  • the target gene is any of the genes listed in Table 1.
  • the target gene is MET, EGFR, ERBB2, CD274, CCNE1, MYC, BRCA1 or BRCA2.
  • the method further comprises obtaining a first biological sample of the cancer from the patient.
  • the biological sample is a liquid biopsy sample or a solid tissue biological sample.
  • the biological sample is a liquid biopsy sample or a tumor biopsy sample.
  • the biological sample comprises (e.g., is obtained, prepared, sequenced, and/or analyzed by) any of the methods and/or embodiments described above, or any modifications, substitutions, and/or combinations thereof as will be apparent to one skilled in the art.
  • the method further comprises performing copy number variation analysis on the first biological sample to identify the copy number status of the target gene in the cancer, where the copy number variation analysis generates a first dataset.
  • the first dataset includes a plurality of bin-level sequence ratios, each respective bin-level sequence ratio in the plurality of bin-level sequence ratios corresponding to a respective bin in a plurality of bins, where each respective bin in the plurality of bins represents a corresponding region of a human reference genome, and each respective bin- level sequence ratio in the plurality of bin-level sequence ratios is determined from a sequencing of a plurality of nucleic acids in the first biological sample of the cancer from the patient and one or more reference samples.
  • the first dataset also includes a plurality of segment-level sequence ratios, each respective segment-level sequence ratio in the plurality of segment-level sequence ratios corresponding to a segment in a plurality of segments, where each respective segment in the plurality of segments represents a corresponding region of the human reference genome encompassing a subset of adjacent bins in the plurality of bins, and the plurality of segment- level sequence ratios is determined from a measure of central tendency of the plurality of bin- level sequence ratios corresponding to the subset of adjacent bins encompassed by the respective segment.
  • the first dataset further comprises a plurality of segment-level measures of dispersion, each respective segment-level measure of dispersion in the plurality of segment- level measures of dispersion (i) corresponding to a respective segment in the plurality of segments and (ii) determined using the plurality of bin-level sequence ratios corresponding to the subset of adjacent bins encompassed by the respective segment.
  • Methods for obtaining the first dataset including binning, segmenting, calculating sequence ratios and measures of dispersion, normalizing and/or preprocessing, can comprise any of the methods and/or embodiments described above, or any modifications, substitutions, and/or combinations thereof as will be apparent to one skilled in the art.
  • the method further comprises determining whether the copy number variation of the target gene is a focal copy number variation by applying the first dataset to an algorithm having a plurality of copy number variation filters.
  • the plurality of copy number variation filters comprises a measure of central tendency bin-level sequence ratio filter that is fired when a measure of central tendency of the plurality of bin-level sequence ratios corresponding to the subset of bins encompassed by the respective segment fails to satisfy one or more bin-level sequence ratio thresholds, thus determining that the copy number variation of the target gene is not a focal copy number variation when fired.
  • the plurality of copy number variation filters further comprises a confidence filter that is fired when the segment-level measure of dispersion corresponding to the respective segment fails to satisfy a confidence threshold, thus determining that the copy number variation of the target gene is not a focal copy number variation when fired.
  • the plurality of copy number variation filters further comprises a measure of central tendency-plus-deviation bin-level sequence ratio filter that is fired when a measure of central tendency of the plurality of bin- level sequence ratios corresponding to the subset of bins encompassed by the respective segment fails to satisfy one or more measure of central tendency-plus-deviation bin-level sequence ratio thresholds.
  • the one or more measure of central tendency -plus-deviation bin- level copy ratio thresholds are derived from (i) a measure of the bin-level sequence ratios corresponding to the plurality of bins that map to the same chromosome of the human reference genome as the respective segment, and (ii) a measure of dispersion across the bin- level sequence ratios corresponding to the plurality of bins that map to the respective chromosome.
  • the method further comprises determining that the copy number variation of the target gene is not a focal copy number variation when fired.
  • the plurality of copy number variation filters further comprises a segment-level sequence ratio filter that is fired when the segment-level sequence ratio corresponding to the respective segment fails to satisfy one or more segment-level sequence ratio thresholds, thus determining that the copy number variation of the target gene is not a focal copy number variation when fired.
  • the plurality of copy number variation filters comprises any of the methods and/or embodiments described above, or any modifications, substitutions, and/or combinations thereof as will be apparent to one skilled in the art.
  • the method further comprises, when the patient has the aggressive form of cancer associated with focal copy number variation of the target gene, administering a first therapy for the aggressive form of the cancer to the patient, and when the patient does not have the aggressive form of cancer associated with focal copy number variation of the target gene, administering a second therapy for a less aggressive form of the cancer to the patient.
  • the first therapy is selected from Table 2.
  • the first therapy is trastuzumab, lapatinib, or crizotinib.
  • the method further comprises generating a report (e.g., for use by a physician) comprising the copy number status of the target gene.
  • the generated report further comprises matched therapies (e.g., treatments and/or clinical trials) based on the copy number status of the respective segment.
  • the present disclosure also provides a computer system comprising one or more processors and a non-transitory computer-readable medium including computer-executable instructions that, when executed by the one or more processors, cause the processors to perform any of the methods and embodiments disclosed herein.
  • the present disclosure also provides a non-transitory computer-readable storage medium having stored thereon program code instructions that, when executed by a processor, cause the processor to perform any of the methods and embodiments disclosed herein.
  • the methods described herein include generating a clinical report 139-3 (e.g., a patient report), providing clinical support for personalized cancer therapy, and/or using the information curated from sequencing of a liquid biopsy sample, as described above.
  • the report is provided to a patient, physician, medical personnel, or researcher in a digital copy (for example, a JSON object, a pdf file, or an image on a website or portal), a hard copy (for example, printed on paper or another tangible medium).
  • a report object such as a JSON object, can be used for further processing and/or display.
  • information from the report object can be used to prepare a clinical laboratory report for return to an ordering physician.
  • the report is presented as text, as audio (for example, recorded or streaming), as images, or in another format and/or any combination thereof.
  • the report includes information related to the specific characteristics of the patient’s cancer, e.g., detected genetic variants, epigenetic abnormalities, associated oncogenic pathogenic infections, and/or pathology abnormalities. In some embodiments, other characteristics of a patient’s sample and/or clinical records are also included in the report.
  • the clinical report includes information on clinical variants, e.g., one or more of copy number variants (e.g., for actionable genes CCNE1, CD274(PD-L1), EGFR, ERBB2(HER2), MET, MYC, BRCA1, and/or BRCA2), fusions, translocations, and/or rearrangements (e.g., in actionable genes ALK, ROS1, RET, NTRK1, FGFR2, FGFR3, NTRK2 and/or NTRK3), pathogenic single nucleotide polymorphisms, insertion-deletions (e.g., somatic/tumor and/or germline/normal), therapy biomarkers, microsatellite instability status, and/or tumor mutational burden.
  • copy number variants e.g., for actionable genes CCNE1, CD274(PD-L1), EGFR, ERBB2(HER2), MET, MYC, BRCA1, and/or BRCA2
  • the solid tissue sample is insufficient for NGS testing (for example, the sample is too small or too degraded, the amount or quality of nucleic acids extracted from the sample does not result in quality NGS results that would result in reliable determination of variants and/or other genetic characteristics of the sample), and the physician or patient may decide to convert the solid tissue test that was ordered to a liquid biopsy test to be performed on a liquid biopsy sample collected from the same patient.
  • the resulting report and/or display of the results on a portal may include an “xF Conversion Badge” to distinguish any order that has been converted from solid tissue test to a liquid biopsy test (compared to, for example, a liquid biopsy test that was not initially ordered as a solid tissue test). This will allow a user to identify which orders have been converted by this process, and distinguish between orders that were intentionally placed for the liquid biopsy panel.
  • a report may include and/or compare the results of multiple liquid biopsy tests and/or solid tumor tests (for example, multiple tests associated with the same patient).
  • the results of multiple liquid biopsy tests and/or solid tumor tests may be displayed on a portal in a variety of configurations that may be selected and/or customized by the viewer. The tests may have been performed at different times, and the samples on which the tests were performed may have been collected at different times.
  • Clinical and/or molecular data associated with a patient may be aggregated and made available via the portal. Any portion of the report data may be available for download (for example, as a CSV file) by the physician and/or patient.
  • the data may include data related to genetic variants, RNA expression levels, immunotherapy markers (including MSI and TMB), RNA fusions, etc.
  • results associated with more than one test may be aggregated into a single file for downloading.
  • Bayes Theorem through the likelihood ratio test for diagnostic assays that allows dynamic calibration of filtering thresholds for somatic sequence variant detection in a patient, in accordance with some embodiments of the present disclosure. These thresholds are based on sample specific error rate, error rate from a pool of process matched healthy control samples, and/or a cohort of human solid tumors to inform our probability models.
  • odds (post- test) is the post-test odds of a variant being positive given the application of Bayes Theorem
  • odds(pre- test) is the pre-test odds of a positive given the cancer type of the patient
  • sensitivity is the sensitivity bin nearest that measured for the assay at a proposed circulating variant fraction
  • specificity is a term to be solved for, denoting the level of uncertainty that is acceptable given some fixed value of odds(post- test).
  • beta binomial is a beta binomial distribution defined by specified parameters (alpha, beta, Pr), and
  • Min(AO) is the minimum number of alternate alleles observed for a given sample.
  • pre-test probability which is related to odds(pre- test) is defined as: odds(p)
  • GAF gain of function
  • LEF loss of function
  • the present disclosure provides a method for validating a somatic sequence variant in a test subject having a cancer condition, at a computer system having one or more processors, and memory storing one or more programs for execution by the one or more processors.
  • the method comprises obtaining, from a first sequencing reaction, a corresponding sequence of each cell-free DNA fragment in a first plurality of cell-free DNA fragments in a liquid biopsy sample of the test subject, thus obtaining a first plurality of sequence reads, e.g., a plurality of de-duplicated sequence reads, where each sequence read correspond to a unique cell-free DNA fragment from the sample.
  • a first sequencing reaction e.g., a plurality of de-duplicated sequence reads
  • the first plurality of sequence reads includes at least 1000 sequence reads. In some embodiments, the first plurality of sequence reads includes at least 10,000 sequence reads. In some embodiments, the first plurality of sequence reads includes at least 100,000 sequence reads. In some embodiments, the first plurality of sequence reads includes at least 200,000, 300,000, 400,000, 500,000, 750,000, 1,000,000, 2,500,000, 5,000,000 sequence reads, or more.
  • the liquid biopsy sample is blood.
  • the liquid biopsy sample comprises blood, whole blood, peripheral blood, plasma, serum, or lymph of the test subject.
  • the cancer condition is a particular type and stage of cancer (e.g., stage 2 lung cancer).
  • stage 2 lung cancer e.g., stage 2 lung cancer.
  • the variant filtering methods described herein are superior to filtering methods that simply account for the tumor fraction of a sample. This is achieved, in part, by accounting for the types of mutations found in a particular type of cancer, which improves the quality of the pre-odds probability of finding a particular type of variant (e.g., a variant within a particular genomic region) in a sample from a subject with a known type of cancer.
  • the pre-odds probabilities are based on as specific of a cancer type as possible, e.g., accounting for one or more of a type of cancer, an origin of the cancer, the stage of the cancer, any previously known genomic variants in the cancer (e.g., whether a breast cancer subject is BRCA1 or BRCA2 positive), a personal characteristic of the subject, e.g., age, gender, race, smoking status, alcohol consumption status, etc.), any pathology classification of the cancer, etc.
  • a level of specificity for which a subject’s cancer should be specified when matching the cancer to a training cohort. For instance, when an insufficient number of training samples from matching samples are available for calculation of pre-test odds, the specificity of the cancer classification should be reduced in order to provide a large enough sample of training data to provide meaningful prior information.
  • test subject the liquid biopsy sample, the cancer condition, and/or methods and systems for obtaining, accessioning, storing, processing, preparing and/or analyzing thereof, comprise any of the embodiments as described above in the present disclosure with reference to Figures 2-4.
  • the first sequencing reaction is a panel-enriched sequencing reaction.
  • the first sequencing reaction is a panel-enriched sequencing reaction of a first plurality of enriched loci, and each respective locus in the plurality of enriched loci are sequenced at an average unique sequence depth of at least 250x.
  • each respective locus in the plurality of enriched loci are sequenced at an average unique sequence depth of at least lOOOx.
  • the first plurality of sequence reads is obtained from ultra-high depth sequencing (e.g., where each locus in a plurality of loci are sequenced at an average coverage of at least lOOOx, at least 2500x, or at least 5000x).
  • Example genes that are informative for precision oncology, e.g., when implemented in a liquid biopsy-based assay, are shown in Table 1.
  • a panel-enriched sequencing reaction described herein uses a probe set that includes at least 5, 10, 15, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, 100, or all 105 of the genes listed in Table 1.
  • the first sequencing reaction is a whole genome sequencing reaction, and the average sequencing depth of the reaction across the genome is at least 5x, lOx, 15x, 20x, 25x, 30x, 40x, 50x, or higher.
  • the first plurality of sequence reads includes at least 50,000 sequence reads, at least 100,000 sequence reads, at least 250,000 sequence reads, at least 500,000 sequence reads, at least 1,000,000 sequence reads, at least 5,000,000 sequence reads, or more.
  • the first sequencing reaction and/or the first plurality of sequence reads includes any of the embodiments as described above in the present disclosure.
  • methods and systems for nucleic acid extraction, library preparation, capture and hybridization, pooling, sequencing, aligning, normalization and/or other sequence read processing comprise any of the embodiments as described above in the present disclosure with reference to Figures 2-4.
  • the method further comprises aligning each respective sequence read in the first plurality of sequence reads to a reference sequence for the species of the subject thus identifying (i) a variant allele fragment count for a candidate variant, where the candidate variant maps to a locus in the reference sequence, and (ii) a locus fragment count for the locus encompassing the candidate variant.
  • the variant allele fragment count refers to a unique number of sequence reads in the test subject that encompass the candidate variant.
  • the locus fragment count refers to the number of sequence reads in the test subject that map to the respective locus encompassing the candidate variant.
  • the reference sequence is a reference genome, e.g., a reference human genome.
  • a reference genome has several blacklisted regions, such that the reference genome covers only about 75%, 80%, 85%, 90%, 95%, 98%, 99%, 99.5%, or 99.9% of the entire genome for the species of the subject.
  • the reference sequence for the subject covers at least 10% of the entire genome for the species of the subject, or at least 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, or more of the entire genome for the species of the subject.
  • the reference sequence for the subject represents a partial or whole exome for the species of the subject.
  • the reference sequence for the subject covers at least 10% of the exome for the species of the subject, or at least 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 98%, 99%, 99.9%, or 100% of the exome for the species of the subject.
  • the reference sequence covers a plurality of loci that constitute a panel of genomic loci, e.g., a panel of genes used in a panel-enriched sequencing reaction. An example of genes useful for precision oncology, e.g., which may be targeted with such a panel, are shown in Table 1. Accordingly, in some embodiments, the reference sequence for the subject covers at least 100 kb of the genome for the species of the subject.
  • the reference sequence for the subject covers at least 250 kb, 500 kb, 750 kb, 1 Mb, 2 Mb, 5 Mb, 10 Mb, 25 Mb, 50 Mb, 100 Mb, 250 Mb, or more of the genome for the species of the subject.
  • the reference sequence can be a sequence for a single locus, e.g., a single exon, gene, etc.) within the genome for the species of the subject.
  • the method further comprises comparing the variant allele fragment count for the candidate variant against a dynamic variant count threshold for the locus in the reference sequence that the candidate variant maps to.
  • the dynamic variant count threshold is based upon a pre-test odds of a positive variant call for the locus based upon the prevalence of variants in a genomic region that includes the locus from a first set of nucleic acids obtained from a cohort of subjects having the cancer condition.
  • the dynamic variant count threshold is determined based on the number of sequence variants that map to the respective locus, obtained from a sequencing of nucleic acids from a cohort of subjects having the cancer condition (e.g., a baseline variant threshold).
  • the cohort of subjects having the cancer condition are matched to at least one personal characteristic of the test subject (e.g., age, gender, race, smoking status, average alcohol consumption, other underlying medical conditions, etc.).
  • the dynamic variant count threshold is also based upon a sequencing error rate for the sequencing reaction.
  • the sequencing error rate for the sequencing reaction is a trinucleotide sequencing error rate.
  • the dynamic variant count threshold is also based upon a background sequencing error rate determined for the locus.

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Abstract

L'invention concerne des procédés, des systèmes et un logiciel permettant de valider une variation d'un nombre de copies, de valider une variante de séquence somatique, et/ou de déterminer des estimations de fractions tumorales circulantes à l'aide de lectures de séquences sur cible et hors cible chez un sujet de test. Une annotation d'état de nombre de copies d'un segment génomique est validée par application d'un premier ensemble de données à une pluralité de filtres comprenant un filtre de mesure de rapport de séquence de niveau binaire de tendance centrale, un filtre de confiance, et un filtre de mesure de rapport de séquence de niveau binaire de tendance centrale-plus-écart. Une variante de séquence somatique est validée par comparaison d'un compte de fragments d'allèle variant d'une variante de séquence somatique candidate d'un locus respectif, par rapport à un seuil de compte de variantes dynamiques du locus dans une séquence de référence respective. Une fraction tumorale circulante est estimée sur la base d'une mesure d'ajustement entre des rapports de segment génomique-couverture de niveau et des états de copie entière sur une pluralité de fractions tumorales en circulation simulées.
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US20240076744A1 (en) 2021-01-21 2024-03-07 Tempus Labs, Inc. METHODS AND SYSTEMS FOR mRNA BOUNDARY ANALYSIS IN NEXT GENERATION SEQUENCING
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