Fragment consensus method for ultrasensitive detection of aberrant methylation

Abstract

Provided herein are methods related to detection of DNA methylation (e.g., the level of methylation at CpG dinucleotide clusters), as well as related therapeutic methods, uses, systems, and computer-readable storage media. These methods allow detection of abnormal DNA methylation patterns with low background and increased signal-to-background ratio, and may be useful, inter alia, for early detection or monitoring of cancer.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Patent Application No. 63 / 281,574, filed November 19, 2021, which is incorporated by reference herein in its entirety.

[0002] Provided herein are methods relating to the detection of methylation levels, as well as diagnostic, prognostic, monitoring, screening, and therapeutic methods, and related systems and computer readable storage media. [Background technology]

[0003] Aberrant methylation is widespread in cancer and can be detected in many different types of patient samples, including cell-free DNA (cfDNA) or circulating cell-free DNA (ccfDNA). Detection of patterns in rare cancers is a key challenge for many liquid biopsy applications, including detection and monitoring of minimal residual disease (MRD).

[0004] Some methylation patterns in cancer are associated with or predict the response to certain treatment regimens or disease management strategies. For example, in glioblastoma, promoter methylation of the gene MGMT is associated with better outcomes (Lalezari et al. (2013) Neuro Oncol 15:370-381). Methylation-based research may lead to the discovery of new predictive biomarkers to guide therapy or drug development. Many late-stage cancer patients have high levels of cancer signals in ccfDNA. However, some patients have lower levels of cancer signals in ccfDNA and may benefit from ultrasensitive detection of methylation levels. Furthermore, late-stage patients with the best response to treatment (chemotherapy, immunotherapy, targeted therapy, or some combination) have a dramatic decrease in cancer signal observed in serial ccfDNA samples just a few weeks after starting treatment (see, e.g., Davis, AA et al. (2020) Mol. Cancer Ther. 19:1486-1496; Hrebien, S. et al. (2019) Ann. Oncol. 30:945-952). Ultrasensitive detection of methylation levels can be useful, for example, to continuously monitor this subset of patients and detect recurrence as early as possible.

[0005] In early cancers, ccfDNA often contains cancer-derived molecules at frequencies of 1 in 1,000 to 1 in 100,000, presenting an obstacle to the application of many analytical methods. Similar challenges arise when using other sample types in which cancer DNA is present but in small amounts, including cell-free DNA from urine, cerebrospinal fluid, etc. Sensitive detection of this level of cancer signal will likely be necessary for the successful application of ccfDNA for the detection of MRD and blood-based monitoring of patients with early cancer.

[0006] Measurement of DNA methylation has been investigated as a method to detect cancer and distinguish tumor DNA from normal DNA, but existing methods have proven inadequate to enable ultrasensitive detection of cancer signals and improve analytical performance. Guo et al. (Nat. Genet. 2017 49:635-642) applied the concept of linkage disequilibrium to methylation and defined several read-based metrics to aid in cancer detection and clustering in tissue and ccfDNA samples. These include methyl haplotype load, a score that assesses sites that are contiguously methylated or contiguously unmethylated. Liu et al. (Ann. Oncol. 2020 31:745-759) defined the concept of methyl variants, i.e., a set of five consecutive CG dinucleotides that are frequently 0% or 100% methylated in at least one known cancer sample (tissue biopsy) in a dataset generated from a large cohort.

[0007] Therefore, there remains a need for improved methods and systems that provide robust and sensitive detection of aberrant methylation patterns in tumor DNA with low background signal and increased signal-to-background ratio compared to normal DNA.

[0008] All references cited herein, including patent applications and publications, are incorporated by reference in their entirety. Summary of the Invention

[0009] The present disclosure provides, inter alia, methods for detecting methylation levels (and changes therein) with extremely high sensitivity. These are based at least in part on the data disclosed herein demonstrating detection of cancer-associated methylation changes with extremely high sensitivity and dramatically increased signal-to-background ratios, allowing detection of very small amounts of nucleic acid with aberrant methylation in samples with overwhelming amounts of normal nucleic acid. These may find use, for example, in detecting methylation levels, and in detecting, monitoring, screening, diagnosing, and / or prognosing cancer, or response to cancer therapy.

[0010] In one aspect, provided herein is a method of detecting a methylation level (e.g., one or more of a methylation level or an unmethylation level) of a cluster of two or more CpG dinucleotides (e.g., in a sample from a subject), the method comprising obtaining a plurality of nucleic acid fragments from the sample; amplifying the plurality of nucleic acid fragments; sequencing the plurality of nucleic acid fragments with a sequencer to obtain a plurality of sequence reads, wherein at least a plurality of the amplified nucleic acid fragments have undergone cytosine conversion, the plurality of nucleic acid fragments corresponding to a genomic locus comprising the cluster of two or more CpG dinucleotides; and determining, by a processor, a consensus methylation pattern of the cluster, wherein the consensus methylation pattern is a methylation level of cytosine The method includes determining, based on the conversion, a cluster consensus fraction (CCF) representing each CpG dinucleotide in the cluster for which methylation is detected in at least one sequence read from the plurality of sequence reads; generating, by the processor, a cluster consensus fraction (CCF) for the cluster, the CCF representing a proportion of sequence reads corresponding to the cluster that exhibit a consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster; detecting one or more of a methylation level or a demethylation level of the cluster based on the CCF; and generating a genomic profile of the subject based at least in part on the detected methylation level, the detected demethylation level, or both.In one aspect, provided herein is a method of detecting a methylation level (e.g., one or more of a methylation level or an unmethylation level) of a cluster of two or more CpG dinucleotides (e.g., in a sample from a subject), the method comprising obtaining a plurality of nucleic acid fragments from the sample; amplifying the plurality of nucleic acid fragments; sequencing the plurality of amplified nucleic acid fragments with a sequencer to obtain a plurality of sequence reads, where at least a plurality of the amplified nucleic acid fragments have undergone cytosine conversion, and the plurality of nucleic acid fragments correspond to a genomic locus comprising the cluster of two or more CpG dinucleotides; and determining, by a processor, a consensus unmethylation pattern of the cluster, where the consensus unmethylation pattern represents each CpG dinucleotide in the cluster where no methylation was detected based on a cytosine conversion in at least one sequence read from the plurality of sequence reads; generating, by a processor, a cluster consensus fraction (CCF) for the cluster, where the CCF represents a proportion of sequence reads corresponding to the cluster that exhibit a consensus unmethylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster; detecting one or more of a methylation level or an unmethylation level of the cluster based on the CCF; and generating a genomic profile of the subject based on the detected methylation level, the detected unmethylation level, or both.

[0011] In some embodiments according to any of the embodiments described herein, the CCF is equal to or greater than a threshold or reference value, and the method further comprises detecting the presence of cancer nucleic acid in the plurality of nucleic acid fragments based at least in part on the CCF being equal to or greater than the threshold or reference value. In some embodiments according to any of the embodiments described herein, the CCF is less than a threshold or reference value, and the method further comprises detecting the absence of cancer nucleic acid in the plurality of nucleic acid fragments based at least in part on the CCF being less than the threshold or reference value. In some embodiments according to any of the embodiments described herein, the CCF is equal to or greater than a threshold or reference value, and the method further comprises detecting the absence of cancer nucleic acid in the plurality of nucleic acid fragments based at least in part on the CCF being less than the threshold or reference value. In some embodiments according to any of the embodiments described herein, the CCF is less than a threshold or reference value, and the method further comprises detecting the presence of cancer nucleic acid in the plurality of nucleic acid fragments based at least in part on the CCF being less than the threshold or reference value. In some embodiments, the method further comprises determining a consensus methylation pattern and a CCF for the plurality of clusters. In some embodiments, the plurality of clusters correspond to a plurality of genomic loci. In some embodiments, the method further comprises determining consensus methylation patterns and CCFs of more than 1,000 clusters, 10-100,000 clusters, or up to 1,000,000 clusters. In some embodiments, the plurality of sequence reads comprises 1-5 sequence reads, at least 100 sequence reads, or at least 1000 sequence reads corresponding to the clusters. In some embodiments, at least one CpG dinucleotide in the cluster is unmethylated in the consensus methylation pattern. In some embodiments, at least one CpG dinucleotide in the cluster is methylated in the consensus methylation pattern. In some embodiments, at least one cluster comprises two or more CpG dinucleotides. In some embodiments, each cluster comprises two or more CpG dinucleotides. In some embodiments, at least one cluster comprises five or more CpG dinucleotides.In some embodiments, each cluster comprises five or more CpG dinucleotides. In some embodiments, at least one cluster comprises six or more CpG dinucleotides. In some embodiments, all but one site in a cluster is unmethylated in a consensus methylation pattern. In some embodiments, all but two sites in a cluster are unmethylated in a consensus methylation pattern. In some embodiments, up to one site, up to two sites, up to 10% of the sites, up to 25% of the sites, more than 25% of the sites, more than 50% of the sites, or more than 75% of the sites in a cluster are methylated in a consensus methylation pattern. In some embodiments, all but one site in a cluster is methylated in a consensus methylation pattern. In some embodiments, all but two sites in a cluster are methylated in a consensus methylation pattern. In some embodiments, at most 1 site, at most 2 sites, at most 10% of the sites, at most 25% of the sites, more than 25% of the sites, more than 50% of the sites, or more than 75% of the sites in a cluster are unmethylated in the consensus methylation pattern.

[0012] In some embodiments according to any of the embodiments described herein, the plurality of sequence reads are obtained from whole genome methyl sequencing (WGMS) or next generation sequencing (NGS). In some embodiments, the plurality of sequence reads include paired-end sequence reads. In some embodiments, the consensus methylation pattern and CCF are determined based on the paired-end sequence reads corresponding to the cluster. In some embodiments, the plurality of sequence reads include unpaired sequence reads. In some embodiments, the method further comprises demultiplexing sequence reads from the plurality of sequence reads before determining the consensus methylation pattern and CCF. In some embodiments, the method further comprises performing a three-character alignment of sequence reads from the plurality of sequence reads against a reference genome before determining the consensus methylation pattern and CCF. In some embodiments, the method further comprises excluding sequencing reads from the plurality of sequencing reads that could not undergo cytosine conversion before determining the consensus methylation pattern and CCF. In some embodiments, the method further comprises excluding sequence reads having a base other than cytosine or thymine at the first position of at least one of the CpG dinucleotides before determining the consensus methylation pattern and CCF. In some embodiments, the method further comprises filtering out sequence reads having a base quality below a threshold base quality before determining the consensus methylation pattern and CCF. In some embodiments, the consensus methylation pattern and CCF are determined based on sequence reads that cover a plurality of CpG dinucleotides in the cluster. In some embodiments, the consensus methylation pattern and CCF are determined based on sequence reads that cover at least 50%, at least 90%, or all of the CpG dinucleotides in the cluster.

[0013] In some embodiments according to any of the embodiments described herein, the plurality of nucleic acid fragments have undergone cytosine conversion by bisulfite treatment, TET-assisted bisulfite treatment, TET-assisted pyridine borane treatment, oxidative bisulfite treatment, or APOBEC treatment. In some embodiments, the method further comprises treating the plurality of nucleic acids or nucleic acid fragments with bisulfite prior to providing the plurality of sequence reads. In some embodiments, the method further comprises treating the plurality of nucleic acids or nucleic acid fragments with TET-assisted bisulfite treatment, TET-assisted pyridine borane treatment, oxidative bisulfite treatment, or APOBEC treatment prior to providing the plurality of sequence reads. In some embodiments, the method further comprises subjecting the plurality of nucleic acids to fragmentation prior to providing the plurality of sequence reads. In some embodiments, the method further comprises selectively enriching the plurality of nucleic acids or nucleic acid fragments corresponding to genomic loci comprising a cluster of two or more CpG dinucleotides to generate an enriched sample prior to providing the plurality of sequence reads. In some embodiments, the method further comprises amplifying the plurality of nucleic acids or nucleic acid fragments by polymerase chain reaction (PCR) prior to providing the plurality of sequence reads. In some embodiments, the method further comprises isolating a plurality of nucleic acids from the sample prior to providing the plurality of sequence reads. In some embodiments, the sample comprises tumor cells and / or tumor nucleic acids. In some embodiments, the sample further comprises non-tumor cells and / or non-tumor nucleic acids. In some embodiments, the sample comprises a percentage of tumor nucleic acids that is less than 1%, less than 0.1%, and / or at least 0.01% of the total nucleic acids. In some embodiments, the sample comprises tumor cell-free DNA (cfDNA), circulating cell-free DNA (ccfDNA), or circulating tumor DNA (ctDNA). In some embodiments, the sample comprises a fluid, cell, or tissue. In some embodiments, the sample comprises blood or plasma. In some embodiments, the sample comprises a tumor biopsy or circulating tumor cells. In some embodiments, the sample is a tissue sample, and the method further comprises subjecting the plurality of nucleic acid molecules in the tissue to fragmentation to generate a plurality of nucleic acid fragments.In some embodiments, the method further comprises ligating one or more adaptors to one or more nucleic acid fragments from the plurality of nucleic acid fragments prior to amplifying the plurality of nucleic acid fragments.

[0014] In another aspect, provided herein is a method of detecting cancer in an individual, the method comprising detecting a methylation level or an unmethylation level in a sample comprising a plurality of nucleic acids obtained from the individual according to the method of any one of the above embodiments, wherein the methylation level or the unmethylation level detected in the sample identifies the individual as having cancer.

[0015] In another aspect, provided herein is a method of screening an individual suspected of having cancer, the method comprising detecting a methylation level or an unmethylation level in a sample comprising a plurality of nucleic acids obtained from the individual according to the method of any one of the above embodiments, wherein the methylation level or the unmethylation level detected in the sample identifies the individual as having an increased likelihood of having cancer.

[0016] In another aspect, provided herein is a method of determining a prognosis of an individual having cancer, the method comprising detecting a methylation level or an unmethylation level in a sample comprising a plurality of nucleic acids obtained from the individual according to the method of any one of the above embodiments, wherein the methylation level or the unmethylation level detected in the sample determines, at least in part, the prognosis of the individual.

[0017] In another aspect, provided herein is a method of predicting survival of an individual with cancer, the method comprising detecting a methylation level or an unmethylation level in a sample comprising a plurality of nucleic acids obtained from an individual with cancer according to any one of the methods of the above embodiments, wherein the methylation level or the unmethylation level detected in the sample at least partially predicts survival of the individual. In some embodiments, the methylation level detected in the sample is higher than a threshold or reference value, predicting decreased survival of the individual compared to survival of an individual whose methylation level in the sample is lower than the threshold or reference value.

[0018] In another aspect, provided herein is a method of predicting tumor burden in an individual having cancer, the method comprising detecting a methylation level or an unmethylation level in a sample comprising a plurality of nucleic acids obtained from the individual according to any one of the methods of the above embodiments, wherein the methylation level or the unmethylation level detected in the sample at least partially predicts the tumor burden of the individual. In some embodiments, the methylation level detected in the sample is higher than a threshold or reference value and predicts an increased tumor burden in the individual compared to the tumor burden of an individual whose methylation level in the sample is lower than the threshold or reference value.

[0019] In another aspect, provided herein is a method of predicting responsiveness of an individual having cancer to a treatment, the method comprising detecting a methylation level or an unmethylation level in a sample comprising a plurality of nucleic acids obtained from an individual having cancer according to the method of any one of the above embodiments, wherein the methylation level or the unmethylation level detected in the sample is used to at least partially predict the responsiveness of the individual to the treatment.

[0020] In another aspect, provided herein is a method of identifying an individual having cancer who may benefit from a treatment comprising an anthracycline-based chemotherapy, the method comprising detecting a methylation level or a demethylation level in a sample comprising a plurality of nucleic acids obtained from the individual according to any one of the methods of the above embodiments, the plurality of nucleic acids comprising one or more nucleic acids corresponding to the PITX2 locus, and methylation of the PITX2 locus detected in the sample identifies the individual as one who may benefit from a treatment comprising an anthracycline-based chemotherapy.

[0021] In another aspect, provided herein is a method of selecting a therapy for an individual having cancer, the method comprising detecting a methylation level or a demethylation level in a sample comprising a plurality of nucleic acids obtained from the individual according to any one of the methods of the above embodiments, the plurality of nucleic acids comprising one or more nucleic acids corresponding to the PITX2 locus, and methylation of the PITX2 locus detected in the sample identifies the individual as likely to benefit from treatment comprising anthracycline-based chemotherapy.

[0022] In another aspect, provided herein is a method of identifying one or more treatment options for an individual having cancer, the method comprising: (a) detecting a methylation level or a demethylation level in a sample comprising a plurality of nucleic acids obtained from the individual according to the method of any one of the above embodiments, wherein the plurality of nucleic acids comprises one or more nucleic acids corresponding to the PITX2 locus; and (b) generating a report comprising one or more treatment options identified for the individual based at least in part on the methylation of the PITX2 locus detected in the sample, wherein the one or more treatment options comprise anthracycline-based chemotherapy.

[0023] In another aspect, provided herein is a method of treating or delaying the progression of cancer, the method comprising: (a) detecting a methylation level or an unmethylation level in a sample comprising a plurality of nucleic acids obtained from an individual according to the method of any one of the above embodiments, wherein the plurality of nucleic acids comprises one or more nucleic acids corresponding to the PITX2 locus; and (b) administering a therapeutically effective amount of anthracycline-based chemotherapy to the individual.

[0024] In another aspect, provided herein is a method of identifying an individual having cancer who may benefit from a treatment comprising an alkylating agent, the method comprising detecting a methylation level or a demethylation level in a sample comprising a plurality of nucleic acids obtained from the individual according to any one of the methods of the above embodiments, the plurality of nucleic acids comprising one or more nucleic acids corresponding to the MGMT locus, and methylation of the MGMT locus detected in the sample identifies the individual as one who may benefit from a treatment comprising an alkylating agent.

[0025] In another aspect, provided herein is a method of selecting a therapy for an individual having cancer, the method comprising detecting a methylation level or a demethylation level in a sample comprising a plurality of nucleic acids obtained from the individual according to any one of the methods of the above embodiments, the plurality of nucleic acids comprising one or more nucleic acids corresponding to the MGMT locus, and methylation of the MGMT locus detected in the sample identifies the individual as likely to benefit from a treatment comprising an alkylating agent.

[0026] In another aspect, provided herein is a method of identifying one or more treatment options for an individual having cancer, the method comprising: (a) detecting a methylation level or a demethylation level in a sample comprising a plurality of nucleic acids obtained from the individual according to the method of any one of the above embodiments, wherein the plurality of nucleic acids comprises one or more nucleic acids corresponding to the MGMT locus; and (b) generating a report comprising one or more treatment options identified for the individual based at least in part on the methylation of the MGMT locus detected in the sample, wherein the one or more treatment options comprise an alkylating agent.

[0027] In another aspect, provided herein is a method of treating or delaying the progression of cancer, the method comprising: (a) detecting a methylation level or an unmethylation level in a sample comprising a plurality of nucleic acids obtained from an individual according to the method of any one of the above embodiments, wherein the plurality of nucleic acids comprises one or more nucleic acids corresponding to the MGMT locus; and (b) administering a therapeutically effective amount of an alkylating agent to the individual.

[0028] In another aspect, provided herein is a method of monitoring the response of an individual undergoing treatment for cancer, the method comprising: (a) administering the treatment to an individual having cancer; and (b) detecting a methylation level or an unmethylation level in a sample comprising a plurality of nucleic acids obtained from the individual after the treatment according to the method of any one of the above embodiments, wherein the methylation level or the unmethylation level detected in the sample is used to at least partially monitor the response to the treatment. In some embodiments, detection of a post-treatment methylation level that is below the pre-treatment methylation level or below a threshold or reference value indicates that the individual has responded to the treatment. In some embodiments, detection of a post-treatment methylation level that is equal to or below the pre-treatment methylation level or below a threshold or reference value indicates that the individual has responded to the treatment.

[0029] In another aspect, provided herein is a method of monitoring cancer in an individual, the method comprising: detecting a methylation level or an unmethylation level in a first sample comprising a plurality of nucleic acids obtained from the individual according to the method of any one of the embodiments above; detecting a methylation level or an unmethylation level in a second sample comprising a plurality of nucleic acids obtained from the individual according to the method of any one of the embodiments above, wherein the second sample is obtained from the individual after the first sample; and determining a difference in methylation levels between the first sample and the second sample, thereby monitoring cancer in the individual.

[0030] In another aspect, provided herein is a method of monitoring the response of an individual undergoing treatment for cancer, the method comprising: detecting a methylation level or an unmethylation level in a first sample comprising a plurality of nucleic acids obtained from the individual according to the method of any one of the embodiments above; and after obtaining the first sample from the individual, detecting a methylation level or an unmethylation level in a second sample comprising a plurality of nucleic acids obtained from the individual according to the method of any one of the embodiments above, wherein the second sample is obtained from the individual after administration of the treatment; and determining a difference in methylation levels between the first sample and the second sample, thereby monitoring the individual's response to the treatment.

[0031] In another aspect, provided herein is a method of detecting one or more methylation levels or unmethylation levels of a cluster of two or more CpG dinucleotides from a sample, the method comprising: obtaining a plurality of sequence reads from a plurality of nucleic acid fragments exhibiting cytosine conversions; determining, by a processor, a consensus methylation pattern of the cluster of two or more CpG dinucleotides at a locus, the consensus methylation pattern representing each CpG dinucleotide in the cluster for which methylation is detected; generating, by the processor, a cluster consensus fraction (CCF) of the cluster, the CCF representing a proportion of sequence reads corresponding to the cluster exhibiting the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster; and detecting, by the processor, one or more of the methylation level or unmethylation level of the cluster based on the CCF. In another aspect, provided herein is a method for detecting one or more of a methylation level or a demethylation level of a cluster of two or more CpG dinucleotides, the method comprising: sequencing a plurality of nucleic acid fragments by a sequencer to obtain a plurality of sequences; determining a consensus methylation pattern of the cluster by a processor, the consensus methylation pattern representing each CpG dinucleotide in the cluster where methylation is detected; generating a cluster consensus fraction (CCF) of the cluster by the processor, the CCF representing a proportion of sequence reads corresponding to the cluster that exhibit the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster, thereby detecting one or more of the methylation level or the demethylation level of the cluster; and detecting one or more of the methylation level or the demethylation level of the cluster based on the CCF by the processor. In some embodiments, the consensus methylation pattern represents each CpG dinucleotide in the cluster where methylation is detected based on a cytosine conversion in at least one sequence read from the plurality of sequence reads.In one aspect, provided herein is a method of detecting one or more of a methylation level or unmethylation level of a cluster of two or more CpG dinucleotides from a sample, the method comprising: obtaining a plurality of sequence reads from a plurality of nucleic acid fragments exhibiting cytosine conversion; determining, by a processor, a consensus unmethylation pattern of the cluster of two or more CpG dinucleotides at a locus, the consensus unmethylation pattern representing each CpG dinucleotide in the cluster where methylation was not detected; generating, by the processor, a cluster consensus fraction (CCF) of the cluster, the CCF representing a proportion of sequence reads corresponding to the cluster exhibiting the consensus unmethylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster; and detecting, by the processor, the one or more of a methylation level or unmethylation level of the cluster based on the CCF. In some embodiments, the CCF represents a proportion of sequence reads corresponding to the cluster exhibiting the consensus unmethylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster based on a cytosine conversion in at least one sequence read from the plurality of sequence reads.

[0032] In another aspect, a system is provided herein, the system comprising: one or more processors; and a memory configured to store one or more computer program instructions, the one or more computer program instructions, when executed by the one or more processors, are configured to: determine, using the one or more processors, a consensus methylation pattern of a cluster of two or more CpG dinucleotides at a genomic locus, the consensus methylation pattern representing each CpG dinucleotide in the cluster where methylation is detected in at least one sequence read from a plurality of sequence reads obtained from a plurality of nucleic acid fragments that underwent cytosine conversion; and generate, using the one or more processors, a cluster consensus fraction (CCF) of the cluster, the CCF representing a proportion of sequence reads corresponding to the cluster that exhibit the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster. In another aspect, a system is provided herein, the system comprising: one or more processors; and a memory configured to store one or more computer program instructions, the one or more computer program instructions, when executed by the one or more processors, are configured to: determine, using the one or more processors, a consensus unmethylation pattern of a cluster of two or more CpG dinucleotides at a genomic locus, the consensus unmethylation pattern representing each CpG dinucleotide in the cluster for which methylation was not detected in at least one sequence read from a plurality of sequence reads obtained from a plurality of nucleic acid fragments that underwent cytosine conversion; and generate, using the one or more processors, a cluster consensus fraction (CCF) of the cluster, the CCF representing a proportion of sequence reads corresponding to the cluster that exhibit the consensus unmethylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster.

[0033] In some embodiments according to any of the embodiments described herein, the CCF is equal to or greater than a threshold or reference value, and the one or more computer program instructions, when executed by the one or more processors, are further configured to use the one or more processors to detect the presence of cancer nucleic acid in the plurality of nucleic acid fragments based at least in part on the CCF being equal to or greater than the threshold or reference value. In some embodiments according to any of the embodiments described herein, the CCF is less than a threshold or reference value, and the one or more computer program instructions, when executed by the one or more processors, are further configured to use the one or more processors to detect the absence of cancer nucleic acid in the plurality of nucleic acid fragments based at least in part on the CCF being less than the threshold or reference value. In some embodiments according to any of the embodiments described herein, the CCF is equal to or greater than a threshold or reference value, and the one or more computer program instructions, when executed by the one or more processors, are further configured to use the one or more processors to detect the absence of cancer nucleic acid in the plurality of nucleic acid fragments based at least in part on the CCF being equal to or greater than the threshold or reference value. In some embodiments according to any of the embodiments described herein, the CCF is less than a threshold or reference value, and the one or more computer program instructions, when executed by the one or more processors, are further configured to: detect, using the one or more processors, the presence of cancer nucleic acid in the plurality of nucleic acid fragments based at least in part on the CCF being less than the threshold or reference value. In some embodiments, the one or more computer program instructions, when executed by the one or more processors, are further configured to: determine, using the one or more processors, a consensus methylation pattern of a plurality of clusters of two or more CpG dinucleotides; and generate, using the one or more processors, a cluster consensus fraction (CCF) for the plurality of clusters. In some embodiments, the plurality of clusters corresponds to a plurality of genomic loci.In some embodiments, the one or more computer program instructions, when executed by the one or more processors, are configured to: determine a consensus methylation pattern and generate a CCF of more than 1,000, 10-100,000, or up to 1,000,000 clusters. In some embodiments, the one or more computer program instructions, when executed by the one or more processors, are further configured to demultiplex sequence reads from the plurality of sequence reads using the one or more processors before determining a consensus methylation pattern and generating a CCF. In some embodiments, the one or more computer program instructions, when executed by the one or more processors, are further configured to perform a three-character alignment of sequence reads from the plurality of sequence reads against a reference genome using the one or more processors before determining a consensus methylation pattern and generating a CCF. In some embodiments, the one or more computer program instructions, when executed by the one or more processors, are further configured to exclude sequencing reads from the plurality of sequencing reads that could not undergo cytosine conversion using the one or more processors before determining a consensus methylation pattern and generating a CCF. In some embodiments, the one or more computer program instructions, when executed by the one or more processors, are further configured to: filter out, using the one or more processors, sequence reads having a base other than cytosine or thymine at a first position of at least one of the CpG dinucleotides prior to determining the consensus methylation pattern and generating the CCF. In some embodiments, the one or more computer program instructions, when executed by the one or more processors, are further configured to filter out, using the one or more processors, sequence reads having a base quality below a threshold base quality prior to determining the consensus methylation pattern and generating the CCF.

[0034] In another aspect, provided herein is a non-transitory computer readable storage medium comprising one or more programs executable by one or more computer processors for performing a method, the method comprising: obtaining a plurality of sequence reads from a plurality of nucleic acid fragments exhibiting cytosine conversion; determining, by the processor, a consensus methylation pattern of a cluster of two or more CpG dinucleotides at a genomic locus, the consensus methylation pattern representing each CpG dinucleotide in the cluster where methylation is detected; generating, using the one or more processors, a cluster consensus fraction (CCF) of the cluster, the CCF representing a proportion of sequence reads corresponding to the cluster exhibiting the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster; and detecting, by the processor, one or more of a methylation level or an unmethylation level of the cluster based on the CCF. In another aspect, provided herein is a non-transitory computer readable storage medium comprising one or more programs executable by one or more computer processors to perform a method, the method comprising: obtaining a plurality of sequence reads from a plurality of nucleic acid fragments exhibiting cytosine conversions; determining, by the one or more processors, a consensus unmethylation pattern of a cluster of two or more CpG dinucleotides at a locus, wherein the consensus unmethylation pattern represents each CpG dinucleotide in the cluster where methylation was not detected in at least one sequence read from the plurality of sequence reads; generating, by the one or more processors, a cluster consensus fraction (CCF) of the cluster, wherein the CCF represents a proportion of sequence reads corresponding to the cluster that exhibit the consensus unmethylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster; and detecting, by the processor, one or more of a methylation level or an unmethylation level of the cluster based on the CCF.

[0035] In some embodiments according to any of the embodiments described herein, the plurality of sequence reads are obtained from the plurality of nucleic acid fragments that have undergone cytosine conversion. In some embodiments, the CCF is equal to or greater than a threshold or reference value, and the method further comprises detecting, using one or more processors, the presence of cancer nucleic acid in the plurality of nucleic acid fragments, based at least in part on the CCF being equal to or greater than a threshold or reference value. In some embodiments, the CCF is equal to or greater than a threshold or reference value, and the method further comprises detecting, at least in part, the absence of cancer nucleic acid in the plurality of nucleic acid fragments, based at least in part on the CCF being less than a threshold or reference value. In some embodiments, the CCF is equal to or greater than a threshold or reference value, and the method further comprises detecting, using one or more processors, the absence of cancer nucleic acid in the plurality of nucleic acid fragments, based at least in part on the CCF being equal to or greater than a threshold or reference value. In some embodiments, the CCF is equal to or greater than a threshold or reference value, and the method further comprises detecting, at least in part, the presence of cancer nucleic acid in the plurality of nucleic acid fragments, based at least in part on the CCF being less than a threshold or reference value. In some embodiments, the method further comprises: determining, using one or more processors, a consensus methylation pattern for a plurality of clusters of two or more CpG dinucleotides; and generating, using one or more processors, a cluster consensus fraction (CCF) for the plurality of clusters. In some embodiments, the plurality of clusters corresponds to a plurality of genomic loci. In some embodiments, the method comprises determining a consensus methylation pattern and generating a CCF for more than 1,000 clusters, between 10 and 100,000 clusters, or up to 1,000,000 clusters. In some embodiments, the method comprises demultiplexing, using one or more processors, sequence reads from the plurality of sequence reads prior to determining the consensus methylation pattern and generating a CCF. In some embodiments, the method comprises performing a three-letter alignment of the sequence reads from the plurality of sequence reads to a reference genome using one or more processors prior to determining the consensus methylation pattern and generating a CCF.In some embodiments, the method includes, prior to determining the consensus methylation pattern and generating the CCF, filtering out sequencing reads from the plurality of sequencing reads that could not undergo cytosine conversion using one or more processors. In some embodiments, the method includes, prior to determining the consensus methylation pattern and generating the CCF, filtering out sequence reads having a base other than cytosine or thymine at a first position of at least one of the CpG dinucleotides using one or more processors. In some embodiments, the method includes, prior to determining the consensus methylation pattern and generating the CCF, filtering out sequence reads having a base quality below a threshold base quality using one or more processors.

[0036] In some embodiments according to any of the embodiments described herein, the plurality of sequence reads comprises 1-5 sequence reads, at least 100 sequence reads, or at least 1000 sequence reads corresponding to a cluster. In some embodiments, at least one CpG dinucleotide in the cluster is unmethylated in a consensus methylation pattern. In some embodiments, at least one CpG dinucleotide in the cluster is methylated in a consensus methylation pattern. In some embodiments, at least one cluster comprises two or more CpG dinucleotides. In some embodiments, each cluster comprises two or more CpG dinucleotides. In some embodiments, at least one cluster comprises five or more CpG dinucleotides. In some embodiments, each cluster comprises five or more CpG dinucleotides. In some embodiments, at least one cluster comprises six or more CpG dinucleotides. In some embodiments, all but one site in the cluster is unmethylated in a consensus methylation pattern. In some embodiments, all but two sites in the cluster are unmethylated in a consensus methylation pattern. In some embodiments, at most 1 site, at most 2 sites, at most 10% of the sites, at most 25% of the sites, more than 25% of the sites, more than 50% of the sites, or more than 75% of the sites in the cluster are methylated with a consensus methylation pattern. In some embodiments, the plurality of sequence reads are obtained from whole genome methyl sequencing (WGMS) or next generation sequencing (NGS). In some embodiments, the plurality of sequence reads include paired-end sequence reads. In some embodiments, the consensus methylation pattern and CCF are determined based on paired-end sequence reads corresponding to the cluster. In some embodiments, the plurality of sequence reads include unpaired sequence reads. In some embodiments, the consensus methylation pattern and CCF are determined and generated based on sequence reads covering multiple CpG dinucleotides in the cluster.In some embodiments, the consensus methylation pattern and CCF are determined based on sequence reads that cover at least 50%, at least 90%, or all of the CpG dinucleotides in the cluster. In some embodiments, the plurality of nucleic acid fragments have undergone cytosine conversion by bisulfite treatment, TET-assisted bisulfite treatment, TET-assisted pyridine borane treatment, oxidative bisulfite treatment, or APOBEC treatment.

[0037] It should be understood that one, some, or all of the features of the various embodiments described herein may be combined to form other embodiments of the present invention. These and other aspects of the present invention will become apparent to those skilled in the art. These and other embodiments of the present invention are further described in the following detailed description. [Brief description of the drawings]

[0038] [Figure 1A] FIG. 1 provides a schematic diagram of the average methylation fraction (AMF) approach for assessing DNA methylation. [Figure 1B] FIG. 1 provides a schematic of the Cluster Consensus Fraction (CCF) approach for assessing DNA methylation, according to some embodiments. [Diagram 2] 1 shows the design of a cell line panel to identify features used for whole genome methylation sequencing of healthy and TNBC cell lines. [Figure 3A] The results of CCF analysis of hypermethylated clusters in four cancer cell lines compared to negative controls are shown. [Figure 3B] Figure 2 shows the results of cluster consensus unmethylated fraction (CCUF) analysis of hypomethylated clusters in four cancer cell lines compared to negative controls. [Figure 4A-4C]Methylation analysis using the CCF (Figures 4A and 4B) and AMF (Figure 4C) approaches in mixtures of cancer and healthy cells is compared. CCF consistently yielded values ​​well above background for mixtures with a low percentage of cancer cells of 10-4, whereas using AMF, these mixtures had signals below background. [Diagram 5] Using the indicated mixtures of cancer and healthy cells (1%–0.01% cancer cells), we show the sensitivity (at 95% specificity) of methylation detection by CCF as a function of the number of clusters selected for analysis. [Figure 6] 4 shows that aberrant methylation was correlated with measurements in control samples. [Figure 7] A comparison of methylation rates obtained by AMF or majority methylation rate approaches from sequencing of TNBC cell lines or healthy cells (NA12878) is shown. [Figure 8] FIG. 1 shows a block diagram of an exemplary process for detecting methylation levels using CCF, according to some embodiments. [Figure 9] FIG. 1 shows a block diagram of an exemplary process for detecting cancer (e.g., tumor nucleic acid from a sample) using CCF, according to some embodiments. [Figure 10] 1 illustrates an exemplary system, according to some embodiments. [Figure 11] 1 illustrates an exemplary device, according to some embodiments. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0039] The present disclosure relates generally to detecting methylation levels of, for example, clusters of CpG dinucleotides.

[0040] Aberrant methylation is a hallmark of many cancers and can be detected in many different types of patient samples, including cell-free DNA (cfDNA) or circulating cell-free DNA (ccfDNA). Detection of methylation patterns from rare cancers is a key challenge in cancer screening and minimal residual disease (MRD) monitoring. The present disclosure describes, among other things, a method for detecting aberrant methylation (e.g., DNA methylation at CpG dinucleotide clusters), which effectively reduces background and increases signal-to-background ratio, thus enabling detection of very low frequency tumor DNA in otherwise normal DNA samples, and is useful for early detection and / or monitoring of cancer.

[0041] I. General Techniques The techniques and procedures described or referenced herein may generally be implemented using widely available methodologies such as those described in, for example, Sambrook et al., Molecular Cloning: A Laboratory Manual 3rd edition (2001) Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY; Current Protocols in Molecular Biology (FMA Usubel, et al. eds., (2003)); a series of Methods in Enzymology (Academic Press, Inc.): PCR 2: A Practical Approach (MJ MacPherson, B.D. Hames and G.R. Taylor eds. (1995)), Harlow and Lane, eds. (1988) Antibodies, A Laboratory Manual, and Animal Cell Culture (RI Freshney, ed. (1987)); Oligonucleotide Synthesis (MJ Gait, ed., 1984); Methods in Molecular Biology, Humana Press; Cell Biology: A Laboratory Manual (MJ Gait, ed., 1984); Notebook (JECellis, ed., 1998) Academic Press, Animal Cell Culture (RIFreshney), ed., 1987), Introduction to Cell and Tissue Culture (JP Mather and PE Roberts, 1998) Plenum Press, Cell and Tissue Culture: Laboratory Procedures (A. Doyle, JBGriffiths, and DG Newell, eds., 1993-8) J. Wiley and Sons, Handbook of Experimental Immunology (DM Weir and CC Blackwell, eds.), Gene Transfer Vectors for Mammalian Cells (JMMiller and MP Calos, eds., 1987), PCR: The Polymerase Chain Reaction, (Mullis et al., eds., 1994), Current Protocols in Immunology (JEColigan et al., eds., 1991), Short Protocols in Molecular Biology (Wiley and Sons, 1999), Immunobiology (CA Janeway and P. Travers, 1997), Antibodies (P. Finch, 1997), Antibodies: A Practical Approach (D. Catty., ed., IRL Press, 1988-1989), Monoclonal Antibodies: A Practical Approach (P. Shepherd and C. Dean, eds., Oxford University Press, 2000), Using Antibodies: A Laboratory Manual (E. Harlow and D. Lane (Cold Spring Harbor Laboratory Press, 1999), The These methods are well understood and commonly used using conventional methodology by those skilled in the art, such as those described in such publications as: Antibodies (M. Zanetti and JD Capra, eds., Harwood Academic Publishers, 1995), and Cancer: Principles and Practice of Oncology (VT DeVita et al., eds., J.B. Lippincott Company, 1993).

[0042] II. Definition As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Thus, for example, reference to "a molecule" optionally includes a combination of two or more such molecules, and so forth.

[0043] The term "about" as used herein refers to a normal error range for the respective value, which is readily known to a person skilled in the art. Reference herein to "about" a value or parameter includes (and describes) embodiments that are directed to the value or parameter itself.

[0044] It is understood that aspects and embodiments of the invention described herein include "comprising," "consisting of" and / or "consisting essentially of" aspects and embodiments.

[0045] The terms "cancer" and "cancerous" refer to or describe the physiological condition in mammals that is typically characterized by unregulated cell growth. This definition includes benign and malignant cancers.

[0046] The term "tumor," as used herein, refers to all neoplastic cell growth and proliferation, whether malignant or benign, and all pre-cancerous and cancerous cells and tissues. The terms "cancer," "cancerous," and "tumor" are not mutually exclusive when referred to herein.

[0047] "Polynucleotide" or "nucleic acid", as used interchangeably herein, refers to a polymer of nucleotides of any length, including DNA and RNA. The nucleotides can be deoxyribonucleotides, ribonucleotides, modified nucleotides or bases, and / or their analogs, or any substrate that can be incorporated into a polymer by a DNA or RNA polymerase or by a synthetic reaction. Thus, for example, polynucleotides as defined herein include, but are not limited to, single-stranded and double-stranded DNA, DNA including single-stranded and double-stranded regions, single-stranded and double-stranded RNA, RNA including single-stranded and double-stranded regions, hybrid molecules including DNA and RNA that may be single-stranded or may typically be double-stranded or include single-stranded and double-stranded regions. In addition, the term "polynucleotide" as used herein refers to triple-stranded regions that include RNA or DNA, or both RNA and DNA. The strands in such regions may be from the same molecule or from different molecules. A region may include all of one or more of the molecules, but more typically involves only some regions of the molecule. One of the molecules of the triple helix region is often an oligonucleotide. The term "polynucleotide" specifically includes cDNA.

[0048] A polynucleotide may comprise modified nucleotides, such as methylated nucleotides and their analogs. Modifications to the nucleotide structure, if present, may be imparted before or after assembly of the polymer. The sequence of nucleotides may be interrupted by non-nucleotide components. A polynucleotide may be further modified after synthesis, such as by conjugation with a label. Other types of modifications include, for example, substitution of one or more of the naturally occurring nucleotides with "caps", analogs, internucleotide modifications, such as those with uncharged linkages (e.g., methylphosphonates, phosphotriesters, phosphoamidates, carbamates, etc.) and those with charged linkages (e.g., phosphorothioates, phosphorodithioates, etc.), those with pendant moieties, such as proteins (e.g., nucleases, toxins, antibodies, signal peptides, poly-L-lysine, etc.), those with intercalators (e.g., acridine, psoralen, etc.), those containing chelators (e.g., metals, radioactive metals, boron, metal oxides, etc.), those containing alkylators, those with modified linkages (e.g., alpha anomeric nucleic acids), as well as unmodified forms of polynucleotides. Additionally, any of the hydroxyl groups normally present in the sugar may be replaced, for example, by phosphonate groups, phosphate groups, protected by standard protecting groups, or activated to prepare additional linkages to additional nucleotides, or conjugated to solid or semi-solid supports. The 5' and 3' terminal OH may be phosphorylated or substituted with amines or organic capping group moieties of 1-20 carbon atoms. Other hydroxyls may also be derivatized to standard protecting groups. Polynucleotides may also contain analogous forms of ribose or deoxyribose sugars commonly known in the art, including, for example, 2'-0-methyl-, 2'-0-allyl-, 2'-fluoro-, or 2'-azido-ribose, carbocyclic sugar analogs, a-anomeric sugars, epimeric sugars such as arabinose, xylose or lyxose, pyranose sugars, furanose sugars, sedoheptulose, acyclic analogs, and abasic nucleoside analogs such as methyl riboside.One or more phosphodiester linkages may be replaced by alternative linking groups. These alternative linking groups include, but are not limited to, embodiments in which the phosphate is replaced by P(0)S ("thioate"), P(S)S ("dithioate"), "(0)NR2 ("amidate"), P(0)R, P(0)OR', CO or CH2 ("formacetal"), where each R or R' is independently H or a substituted or unsubstituted alkyl (1-20C) optionally containing an ether (-0-) linkage, aryl, alkenyl, cycloalkyl, cycloalkenyl, or araldyl. Not all linkages in a polynucleotide need be identical. A polynucleotide may contain one or more different types of modifications and / or multiple modifications of the same type described herein. The preceding description applies to all polynucleotides referred to herein, including RNA and DNA.

[0049] "Oligonucleotide", as used herein, generally refers to a short, single-stranded polynucleotide, not necessarily less than about 250 nucleotides in length. Oligonucleotides may be synthetic. The terms "oligonucleotide" and "polynucleotide" are not mutually exclusive. The above description of polynucleotides is equally and fully applicable to oligonucleotides.

[0050] The term "detection" includes any means of detection, including direct detection and indirect detection.

[0051] "Amplification," as used herein, generally refers to the process of generating multiple copies of a desired sequence. "Multiple copies" means at least two copies. "Copy" does not necessarily mean perfect sequence complementarity or identity to the template sequence. For example, copies may contain nucleotide analogs such as deoxyinosine, deliberate sequence changes (e.g., sequence changes introduced by primers that contain sequences that are hybridizable to the template but are not complementary to the template), and / or sequence errors that occur during amplification.

[0052] The technique of "polymerase chain reaction" or "PCR" as used herein generally refers to a procedure in which a specific specimen of a minimal amount of nucleic acid, RNA, and / or DNA, is amplified, for example, as described in U.S. Pat. No. 4,683,195. Generally, sequence information from or beyond the ends of the region of interest must be available so that oligonucleotide primers can be designed that are identical or similar in sequence to opposite strands of the template to be amplified. The 5' terminal nucleotides of the two primers may coincide with the ends of the amplified material. PCR can be used to amplify specific RNA sequences, specific DNA sequences from total genomic DNA, cDNA transcribed from total cellular RNA, bacteriophage, or plasmid sequences, etc. See generally Mullis et al., Cold Spring Harbor Symp. Quant. Biol. 51:263 (1987) and Erlich, ed., PCR Technology (Stockton Press, NY, 1989). As used herein, PCR is considered to be one example, but not the only example, of a nucleic acid polymerase reaction method for amplifying a nucleic acid test sample that involves the use of known nucleic acids (DNA or RNA) as primers and a nucleic acid polymerase to amplify or generate a specific piece of nucleic acid, or a specific piece of nucleic acid that is complementary to a specific nucleic acid.

[0053] The term "diagnosis" is used herein to refer to the identification or classification of a molecular or pathological state, disease, or condition (e.g., cancer). For example, "diagnosis" can refer to the identification of a particular type of cancer. "Diagnosis" can also refer to the classification of a particular subtype of cancer, for example, by histopathological criteria, or by molecular features (e.g., a subtype characterized by the expression of one or a combination of biomarkers (e.g., a particular gene or a protein encoded by the gene described above), or by abnormal DNA methylation levels and / or patterns).

[0054] The term "aiding in diagnosis" is used herein to refer to a method of aiding in making a clinical decision regarding the presence of, or the nature of, a particular type of symptom or condition of a disease or disorder (e.g., cancer). For example, a method of aiding in the diagnosis of a disease or condition (e.g., cancer) may include measuring specific somatic mutations or DNA methylation levels and / or patterns in a biological sample from an individual.

[0055] The term "sample" as used herein refers to a composition obtained or derived from a subject and / or individual of interest that contains cells and / or other molecular entities to be characterized and / or identified, for example, based on physical, biochemical, chemical and / or physiological characteristics. For example, the phrase "disease sample" and variations thereof refer to any sample obtained from a subject of interest that is expected to contain or is known to contain cells and / or molecular entities to be characterized. Samples include, but are not limited to, tissue samples, primary or cultured cells or cell lines, cell supernatants, cell lysates, platelets, serum, plasma, vitreous fluid, lymphatic fluid, synovial fluid, follicular fluid, semen, amniotic fluid, milk, whole blood, plasma, serum, cells from blood, urine, cerebrospinal fluid, saliva, sputum, tears, sweat, mucus, tumor lysates, and tissue culture media, tissue extracts, e.g., homogenized tissue, tumor tissue, cell extracts, and combinations thereof. In some cases, the sample is a whole blood sample, a plasma sample, a serum sample, or a combination thereof. In some embodiments, the sample is from a tumor (e.g., a "tumor sample"), e.g., from a biopsy. In some embodiments, the sample is a formalin-fixed paraffin-embedded (FFPE) sample.

[0056] As used herein, "tumor cell" refers to any tumor cell present in a tumor or a sample thereof. Tumor cells can be distinguished from other cells that may be present in a tumor sample (e.g., stromal cells and tumor-infiltrating immune cells) using methods known in the art and / or described herein.

[0057] A "reference sample," "reference cell," "reference tissue," "control sample," "control cell," or "control tissue," as used herein, refers to a sample, cell, tissue, standard, or level used for comparison purposes.

[0058] "Correlate" or "correlating" refers, in any case, to comparing the performance and / or results of a first analysis or protocol to the performance and / or results of a second analysis or protocol. For example, the results of the first analysis or protocol may be used in carrying out the second protocol and / or to determine whether the second analysis or protocol should be performed. With respect to embodiments of a polypeptide analysis or protocol, the results of a polypeptide expression analysis or protocol may be used to determine whether a particular therapeutic regimen should be performed. With respect to embodiments of a polynucleotide analysis or protocol, the results of a polynucleotide expression analysis or protocol may be used to determine whether a particular therapeutic regimen should be performed.

[0059] "Individual response" or "response" can be assessed using any endpoint that indicates benefit to an individual, including, but not limited to, (1) inhibition to some extent of disease progression (e.g., cancer progression), including slowing or completely halting; (2) reduction in tumor size; (3) inhibition (i.e., reducing, slowing, or completely halting) of cancer cell invasion into adjacent surrounding organs and / or tissues; (4) inhibition (i.e., reducing, slowing, or completely halting) of metastasis; (5) alleviation to some extent of one or more symptoms associated with a disease or disorder (e.g., cancer); (6) increased or prolonged length of survival, including overall survival and progression-free survival; and / or (7) reduction in mortality at a given time point following treatment.

[0060] An "effective patient response" or a patient's "responsiveness" to a pharmaceutical treatment and like phrases refers to a clinical or therapeutic benefit conferred on a patient at risk for or suffering from a disease or disorder (e.g., cancer). In one embodiment, such benefit includes extending survival (including overall survival and / or progression-free survival), obtaining an objective response (including complete or partial response), or ameliorating the signs or symptoms of cancer.

[0061] "Effective amount" refers to the amount of a therapeutic agent to treat or prevent a disease or disorder in a mammal. In the case of cancer, a therapeutically effective amount of a therapeutic agent may reduce the number of cancer cells, reduce the size of a primary tumor, inhibit (i.e., slow to a certain extent, in one embodiment, stop) cancer cell invasion into surrounding organs, inhibit (i.e., slow to a certain extent, in one embodiment, stop) tumor metastasis, inhibit tumor growth to a certain extent, and / or alleviate to a certain extent one or more symptoms associated with the disorder. To the extent that the drug may prevent the growth and / or kill existing cancer cells, the drug may be cytostatic and / or cytotoxic. In the case of cancer therapy, in vivo efficacy may be measured, for example, by assessing the duration of survival, the time to disease progression (TTP), the response rate (e.g., CR or PR), the duration of response, and / or the quality of life.

[0062] The term "pharmaceutical formulation" refers to a preparation that is in a form that allows the biological activity of the active ingredients contained therein to be effective and does not contain additional components that are unacceptably toxic to a subject to which the formulation may be administered.

[0063] "Pharmaceutically acceptable carrier" refers to an ingredient in a pharmaceutical formulation, other than an active ingredient, that is non-toxic to a subject. Pharmaceutically acceptable carriers include, but are not limited to, buffers, excipients, stabilizers, or preservatives.

[0064] As used herein, "treatment" (and grammatical variations thereof, such as "treat" or "treating") refers to a clinical intervention that seeks to alter the natural course of the individual being treated, and may be performed either prophylactically or during the course of clinical pathology. Desirable effects of treatment include, but are not limited to, preventing the onset or recurrence of disease, alleviating symptoms, reducing any direct or indirect pathological consequences of the disease, preventing metastasis, reducing the rate of disease progression, ameliorating or alleviating the disease state, and remission or improved prognosis.

[0065] As used herein, the terms "individual," "patient," or "subject" are used interchangeably and refer to any single animal, e.g., mammals (including such non-human animals, e.g., dogs, cats, horses, rabbits, zoo animals, cows, pigs, sheep, and non-human primates), for which treatment is desired. In certain embodiments, a patient herein is a human.

[0066] As used herein, "administering" refers to a method of giving a dosage of a compound (e.g., an antagonist) or a pharmaceutical composition (e.g., a pharmaceutical composition including an antagonist) to a subject (e.g., a patient). Administration may be by any suitable means, including parenteral, intrapulmonary, and intranasal, and, if desired, for localized treatment, intralesional administration. Parenteral injections include, for example, intramuscular, intravenous, intraarterial, intraperitoneal, or subcutaneous administration. Administration may be by any suitable route, for example, by injection (e.g., intravenous or subcutaneous injection), depending in part on whether administration is temporary or chronic. Various dosing schedules are contemplated herein, including, but not limited to, single or multiple administrations over various time points, bolus administration, and pulse infusion.

[0067] The term "concurrently" is used herein to refer to the administration of two or more therapeutic agents, where at least a portion of the administration overlaps in time. Thus, concurrent administration includes dosing regimens where administration of one or more agents continues after administration of one or more other agents is discontinued.

[0068] The term "package insert" is used to refer to instructions typically included in the commercial packaging of a therapeutic product that contain information about the indications, use, dosage, administration, concomitant therapy, contraindications, and / or warnings regarding the use of such therapeutic product.

[0069] An "article of manufacture" is any product (e.g., package or container), or kit that contains at least one reagent, e.g., a pharmaceutical agent for the treatment of a disease or disorder (e.g., cancer) described herein, or a probe for specifically detecting a biomarker (e.g., DNA methylation). In certain embodiments, the product or kit is promoted, distributed, or sold as a unit for performing a method described herein.

[0070] The term "methylation" is used herein to refer to the presence of a methyl group at the C5 position of a cytosine nucleotide in a DNA nucleic acid (unless the context indicates otherwise). The term also includes cytosine nucleotides in which the methyl group has been further modified, such as 5-methylcytosine (5mC) as well as 5-hydroxymethylcytosine (5hmC). The term also includes DNA nucleic acids that have undergone a chemical or enzymatic conversion of the nucleotide, such as bisulfite conversion, which deaminates unmodified cytosine to uracil.

[0071] The term "aberrant methylation" is used herein to refer to a pattern of methylation that is not typically present in normal tissue. For example, the term can refer to increased methylation at a site that is not normally methylated in normal tissue, or decreased methylation at a site that is normally methylated in normal tissue. In some embodiments, nucleic acid derived from a cancer cell (e.g., cancer nucleic acid) is characterized by aberrant methylation when the pattern and / or amount of methylation at one or more genomic loci differs from that normally present at the corresponding locus / loci in a particular type of tissue.

[0072] The term "CpG dinucleotide" is used herein to refer to a region of two or more DNA bases in a 5'→3' direction in which a cytosine nucleotide is followed by a guanine nucleotide (e.g., 5'-C-phosphate-G-3'). In many genomes, CpG dinucleotides are commonly found in "clusters" or regions of DNA that contain multiple CpG dinucleotides (also called "CpG islands"). The majority of DNA methylation in many genomes occurs at CpG dinucleotides (wherein the cytosine is methylated or hydroxymethylated).

[0073] III. METHODS, SYSTEMS, AND DEVICES Certain aspects of the disclosure relate to methods of detecting a methylation level (e.g., of a cluster of two or more CpG dinucleotides). In some embodiments, the method includes obtaining a plurality of nucleic acid fragments from a sample (e.g., from a subject); amplifying the plurality of nucleic acid fragments; sequencing the plurality of amplified nucleic acid fragments by a sequencer to obtain a plurality of sequence reads, where at least a plurality of the amplified nucleic acid fragments have undergone cytosine conversion, where the plurality of nucleic acid fragments correspond to a genomic locus comprising the cluster of two or more CpG dinucleotides; and determining, by a processor, a consensus methylation pattern of the cluster, where the consensus unmethylation pattern is determined based on a cytosine conversion in at least one sequence read from the plurality of sequence reads. determining, by the processor, a cluster consensus fraction (CCF) for the cluster, the CCF representing a proportion of sequence reads corresponding to the cluster that exhibit a consensus unmethylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster; detecting one or more of a methylation level or a unmethylation level of the cluster based on the CCF; and generating a genomic profile of the subject based at least in part on the detected methylation level, the detected unmethylation level, or both.

[0074] In some embodiments, the method includes sequencing (e.g., by a sequencer) a plurality of nucleic acid fragments to obtain a plurality of sequence reads, where the plurality of nucleic acid fragments have undergone cytosine conversion, and the plurality of nucleic acid fragments correspond to a genomic locus comprising a cluster of two or more CpG dinucleotides; determining (e.g., by a processor) a consensus methylation pattern for the cluster, where the consensus methylation pattern represents each CpG dinucleotide in the cluster where methylation is detected in at least one sequence read from the plurality of sequence reads based on cytosine conversion; and generating (e.g., by a processor) a cluster consensus fraction (CCF) for the cluster, where the CCF represents a proportion of sequence reads corresponding to the cluster that exhibit the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster.

[0075] In other embodiments, the method includes: sequencing (e.g., by a sequencer) a plurality of nucleic acid fragments to obtain a plurality of sequence reads, where the plurality of nucleic acid fragments have undergone cytosine conversion, and the plurality of nucleic acid fragments correspond to a genomic locus that includes a cluster of two or more CpG dinucleotides; determining (e.g., by a processor) a consensus unmethylation pattern for the cluster, where the consensus unmethylation pattern represents each CpG dinucleotide in the cluster for which methylation was not detected in at least one sequence read from the plurality of sequence reads based on cytosine conversion; and generating (e.g., by a processor) a cluster consensus unmethylation rate (CCUF) for the cluster, where the CCUF represents a proportion of sequence reads corresponding to the cluster that exhibit the consensus unmethylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster. It will be understood by those skilled in the art that the methods disclosed herein for measuring methylation (e.g., CCMF) may be similarly applied to measuring unmethylated or unmethylated sites (e.g., CCUF). It will be understood that the cluster consensus methylation rate, the cluster consensus unmethylation rate, or both, may be generally referred to as the cluster consensus rate (CCF).

[0076] Another aspect of the present disclosure relates to a method of detecting cancer in an individual, the method comprising detecting a methylation level (e.g., of a cluster of two or more CpG dinucleotides) according to any one of the methods of the present disclosure. In some embodiments, the method comprises: sequencing (e.g., by a sequencer) a plurality of nucleic acid fragments to obtain a plurality of sequence reads, the plurality of nucleic acid fragments being obtained from a sample from the individual and subsequently subjected to cytosine conversion, the plurality of nucleic acid fragments corresponding to a genomic locus comprising a cluster of two or more CpG dinucleotides; determining (e.g., by a processor) a consensus methylation pattern of the cluster, the consensus methylation pattern representing each CpG dinucleotide in the cluster in which methylation is detected in at least one sequence read from the plurality of sequence reads based on cytosine conversion; and generating (e.g., by a processor) a cluster consensus fraction (CCF) of the cluster, the CCF representing a proportion of sequence reads corresponding to the cluster that exhibit the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster. In some embodiments, a CCF equal to or greater than the threshold or reference value indicates the presence of cancer in an individual, and the individual is identified as having cancer. In some embodiments, a CCF less than the threshold or reference value does not indicate the presence of cancer in an individual, and the individual is identified as not having cancer. In some embodiments, the method may find use, for example, in screening for cancer (e.g., new diagnoses in individuals not previously diagnosed with cancer or the same type of cancer), or in monitoring individuals for recurrence or minimal residual disease (e.g., in individuals previously diagnosed with cancer and who have achieved remission).

[0077] Another aspect of the present disclosure relates to a method of screening an individual suspected of having cancer, the method comprising detecting a methylation level (e.g., of a cluster of two or more CpG dinucleotides) according to any one of the methods of the present disclosure. In some embodiments, the method comprises: sequencing (e.g., by a sequencer) a plurality of nucleic acid fragments to obtain a plurality of sequence reads, the plurality of nucleic acid fragments being obtained from a sample from the individual and subsequently undergoing cytosine conversion, the plurality of nucleic acid fragments corresponding to a genomic locus comprising a cluster of two or more CpG dinucleotides; determining (e.g., by a processor) a consensus methylation pattern of the cluster, the consensus methylation pattern representing each CpG dinucleotide in the cluster in which methylation is detected in at least one sequence read from the plurality of sequence reads based on cytosine conversion; and generating (e.g., by a processor) a cluster consensus fraction (CCF) of the cluster, the CCF representing a proportion of sequence reads corresponding to the cluster that exhibit the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster. In some embodiments, a CCF equal to or greater than the threshold or reference value indicates the presence of cancer in the individual, and the individual is identified as likely to have cancer. In some embodiments, a CCF less than the threshold or reference value does not indicate the presence of cancer in the individual, and the individual is identified as not having cancer. In some embodiments, the method may find use, for example, in screening for cancer (e.g., new diagnoses in individuals not previously diagnosed with cancer or the same type of cancer), or in monitoring individuals for recurrence or minimal residual disease (e.g., in individuals previously diagnosed with cancer and who have achieved remission).

[0078] Another aspect of the present disclosure relates to a method of determining a prognosis of an individual having cancer, the method comprising detecting a methylation level (e.g., of a cluster of two or more CpG dinucleotides) according to any one of the methods of the present disclosure. In some embodiments, the method comprises: sequencing (e.g., by a sequencer) a plurality of nucleic acid fragments to obtain a plurality of sequence reads, the plurality of nucleic acid fragments being obtained from a sample from the individual and subsequently undergoing cytosine conversion, the plurality of nucleic acid fragments corresponding to a genomic locus comprising a cluster of two or more CpG dinucleotides; determining (e.g., by a processor) a consensus methylation pattern of the cluster, the consensus methylation pattern representing each CpG dinucleotide in the cluster in which methylation is detected in at least one sequence read from the plurality of sequence reads based on cytosine conversion; and generating (e.g., by a processor) a cluster consensus fraction (CCF) of the cluster, the CCF representing a proportion of sequence reads corresponding to the cluster that exhibit the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster. In some embodiments, a CCF equal to or greater than the threshold or reference value indicates the presence of cancer in the individual and at least partially determines the prognosis of the individual. In some embodiments, a CCF below the threshold or reference value does not indicate the presence of cancer in the individual and at least partially determines the prognosis of the individual. In some embodiments, a CCF above the threshold or reference value corresponds to a poorer prognosis of the individual compared to individuals having a CCF below the threshold or reference value.

[0079] Another aspect of the present disclosure relates to a method of predicting the prognosis of an individual having cancer, the method comprising detecting a methylation level (e.g., of a cluster of two or more CpG dinucleotides) according to any one of the methods of the present disclosure. In some embodiments, the method comprises: sequencing (e.g., by a sequencer) a plurality of nucleic acid fragments to obtain a plurality of sequence reads, the plurality of nucleic acid fragments being obtained from a sample from the individual and subsequently undergoing cytosine conversion, the plurality of nucleic acid fragments corresponding to a genomic locus comprising a cluster of two or more CpG dinucleotides; determining (e.g., by a processor) a consensus methylation pattern of the cluster, the consensus methylation pattern representing each CpG dinucleotide in the cluster in which methylation is detected in at least one sequence read from the plurality of sequence reads based on cytosine conversion; and generating (e.g., by a processor) a cluster consensus fraction (CCF) of the cluster, the CCF representing a proportion of sequence reads corresponding to the cluster that exhibit the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster. In some embodiments, a CCF equal to or greater than the threshold or reference value indicates the presence of cancer in the individual and at least partially predicts the survival of the individual. In some embodiments, a CCF below the threshold or reference value does not indicate the presence of cancer in the individual and at least partially predicts the survival of the individual. In some embodiments, a CCF equal to or greater than the threshold or reference value corresponds to a shorter survival of the individual compared to an individual having a CCF below the threshold or reference value. In some embodiments, the methylation level detected in the sample is higher than the threshold or reference value, predicting a decreased survival of the individual compared to the survival of an individual whose sample has a methylation level below the threshold or reference value.

[0080] Another aspect of the present disclosure relates to a method of predicting tumor burden in an individual having cancer, the method comprising detecting a methylation level (e.g., of a cluster of two or more CpG dinucleotides) according to any one of the methods of the present disclosure. In some embodiments, the method comprises: sequencing (e.g., by a sequencer) a plurality of nucleic acid fragments to obtain a plurality of sequence reads, the plurality of nucleic acid fragments being obtained from a sample from the individual and subsequently undergoing cytosine conversion, the plurality of nucleic acid fragments corresponding to a genomic locus comprising a cluster of two or more CpG dinucleotides; determining (e.g., by a processor) a consensus methylation pattern of the cluster, the consensus methylation pattern representing each CpG dinucleotide in the cluster in which methylation is detected in at least one sequence read from the plurality of sequence reads based on cytosine conversion; and generating (e.g., by a processor) a cluster consensus fraction (CCF) of the cluster, the CCF representing a proportion of sequence reads corresponding to the cluster that exhibit the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster. In some embodiments, a CCF equal to or greater than a threshold or reference value is predictive of higher tumor burden in the individual compared to a CCF below the threshold or reference value. In some embodiments, the methylation level detected in the sample is higher than the threshold or reference value and predicts increased tumor burden in the individual compared to the tumor burden of an individual whose sample has a methylation level below the threshold or reference value.

[0081] Another aspect of the present disclosure relates to a method of predicting responsiveness to treatment of an individual having cancer, the method comprising detecting a methylation level (e.g., of a cluster of two or more CpG dinucleotides) according to any one of the methods of the present disclosure. In some embodiments, the method comprises: sequencing (e.g., by a sequencer) a plurality of nucleic acid fragments to obtain a plurality of sequence reads, the plurality of nucleic acid fragments being obtained from a sample from the individual and subsequently undergoing cytosine conversion, the plurality of nucleic acid fragments corresponding to a genomic locus comprising a cluster of two or more CpG dinucleotides; determining (e.g., by a processor) a consensus methylation pattern of the cluster, the consensus methylation pattern representing each CpG dinucleotide in the cluster in which methylation is detected in at least one sequence read from the plurality of sequence reads based on cytosine conversion; and generating (e.g., by a processor) a cluster consensus fraction (CCF) of the cluster, the CCF representing a proportion of sequence reads corresponding to the cluster that exhibit the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster. In some embodiments, the methylation level detected in the sample is used to at least partially predict an individual's responsiveness to a treatment.

[0082] Another aspect of the present disclosure relates to a method of monitoring the response of an individual undergoing treatment for cancer, the method comprising administering the treatment to an individual having cancer and detecting methylation levels (e.g., of a cluster of two or more CpG dinucleotides) according to any one of the methods of the present disclosure. In some embodiments, the method includes: sequencing (e.g., by a sequencer) a plurality of nucleic acid fragments to obtain a plurality of sequence reads, the plurality of nucleic acid fragments being obtained from a sample from an individual and subsequently subjected to cytosine conversion, the plurality of nucleic acid fragments corresponding to a genomic locus comprising a cluster of two or more CpG dinucleotides; determining (e.g., by a processor) a consensus methylation pattern of the cluster, the consensus methylation pattern representing each CpG dinucleotide in the cluster in which methylation is detected in at least one sequence read from the plurality of sequence reads based on cytosine conversion; and generating (e.g., by a processor) a cluster consensus fraction (CCF) of the cluster, the CCF representing a proportion of sequence reads corresponding to the cluster that exhibit the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster. In some embodiments, the methylation level detected in the sample is used at least in part to monitor a response to a treatment. In some embodiments, detection of a post-treatment methylation level or CCF that is less than a pre-treatment methylation level or CCF or less than a threshold or reference value indicates that the individual has responded to the treatment. In some embodiments, detection of a post-treatment methylation level or CCF that is equal to or less than a pre-treatment methylation level or CCF, or below a threshold or reference value, indicates that the individual has responded to the treatment.

[0083] Another aspect of the present disclosure relates to a method of monitoring cancer in an individual, the method comprising: detecting a methylation level (e.g., of a cluster of two or more CpG dinucleotides) in a first sample obtained from the individual according to any one of the methods of the present disclosure; detecting a methylation level (e.g., of a cluster of two or more CpG dinucleotides) in a second sample obtained from the individual according to any one of the methods of the present disclosure; determining a difference in methylation; and determining a difference in methylation level or CCF between the first sample and the second sample. In some embodiments, the method includes sequencing (e.g., by a sequencer) a plurality of nucleic acid fragments to obtain a plurality of sequence reads, where the plurality of nucleic acid fragments is obtained from a first sample from the individual and has subsequently undergone cytosine conversion, and the plurality of nucleic acid fragments corresponds to a genomic locus comprising a cluster of two or more CpG dinucleotides; sequencing (e.g., by a sequencer) the plurality of nucleic acid fragments to obtain a second plurality of sequence reads, where the second plurality of nucleic acid fragments is obtained from a second sample from the individual and has subsequently undergone cytosine conversion, and the second plurality of nucleic acid fragments corresponds to a genomic locus comprising a cluster of two or more CpG dinucleotides; and determining (e.g., by a processor) a second consensus methylation pattern for the cluster, the second consensus methylation pattern representing each CpG dinucleotide in the cluster for which methylation is detected in at least one sequence from the second plurality of sequence reads based on cytosine conversion; generating (e.g., by a processor) a second cluster consensus fraction (CCF) for the cluster, the second CCF representing a proportion of sequence reads corresponding to the cluster that exhibit the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster; and comparing the first CCF and the second CCF. In some embodiments, the second CCF being greater than the first CCF indicates progression, spread, or proliferation of the cancer. In some embodiments, the second CCF being less than the first CCF indicates regression, response to treatment, or reduction of the cancer.In some embodiments, a second CCF equal to the first CCF indicates progression or lack of stability of the cancer.

[0084] Another aspect of the present disclosure relates to a method of monitoring the response of an individual undergoing treatment for cancer, the method comprising detecting a methylation level (e.g., of a cluster of two or more CpG dinucleotides) in a first sample obtained from the individual according to any one of the methods of the present disclosure, administering a treatment to the individual, and after administration of the treatment and the first sample, detecting a methylation level (e.g., of a cluster of two or more CpG dinucleotides) in a second sample obtained from the individual according to any one of the methods of the present disclosure, and determining a difference in methylation levels between the first sample and the second sample. In some embodiments, the method includes sequencing (e.g., by a sequencer) a plurality of nucleic acid fragments to obtain a plurality of sequence reads, where the plurality of nucleic acid fragments is obtained from a first sample from the individual and has subsequently undergone cytosine conversion, and the plurality of nucleic acid fragments corresponds to a genomic locus comprising a cluster of two or more CpG dinucleotides; sequencing (e.g., by a sequencer) the plurality of nucleic acid fragments to obtain a second plurality of sequence reads, where the second plurality of nucleic acid fragments is obtained from a second sample from the individual and has subsequently undergone cytosine conversion, and the second plurality of nucleic acid fragments corresponds to a genomic locus comprising a cluster of two or more CpG dinucleotides; and determining (e.g., by a processor) a second consensus methylation pattern for the cluster, the second consensus methylation pattern representing each CpG dinucleotide in the cluster for which methylation was detected in at least one sequence from the second plurality of sequence reads based on cytosine conversion; generating (e.g., by a processor) a second cluster consensus fraction (CCF) for the cluster, the second CCF representing a proportion of sequence reads corresponding to the cluster that exhibit the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster; and comparing the first CCF and the second CCF. In some embodiments, the second CCF being greater than the first CCF indicates a lack of response to the treatment. In some embodiments, the second CCF being less than the first CCF indicates a response to the treatment.In some embodiments, a second CCF equal to the first CCF indicates a partial or stable response to the treatment.

[0085] In some embodiments, the methods of the present disclosure further include detecting the presence of cancer nucleic acid in the plurality of nucleic acid fragments (e.g., if the CCF is equal to or greater than a threshold or reference value). In some embodiments, the detection of the cancer nucleic acid is based at least in part on the CCF being equal to or greater than a threshold or reference value. In some embodiments, the methods of the present disclosure further include detecting the presence of cancer in the sample (e.g., if the CCF is equal to or greater than a threshold or reference value).

[0086] In some embodiments, the method of the present disclosure further comprises detecting the absence of cancer nucleic acid in the plurality of nucleic acid fragments (e.g., when the CCF is less than a threshold or reference value). In some embodiments, detecting the absence of cancer nucleic acid is based at least in part on the CCF being less than a threshold or reference value. In some embodiments, the method of the present disclosure further comprises detecting the absence of cancer in the sample (e.g., when the CCF is less than a threshold or reference value). In some embodiments, the method of the present disclosure further comprises detecting the presence of normal or wild-type nucleic acid (e.g., nucleic acid as DNA having a normal or wild-type level and / or pattern of methylation) in the plurality of nucleic acid fragments (e.g., when the CCF is less than a threshold or reference value). In some embodiments, detecting the presence of normal or wild-type nucleic acid is based at least in part on the CCF being less than a threshold or reference value. In some embodiments, the method of the present disclosure further comprises detecting the presence of normal / wild-type cells or the presence of a methylation level / pattern in the sample (e.g., when the CCF is less than a threshold or reference value).

[0087] In some embodiments, the methods of the disclosure include determining a consensus methylation pattern and / or CCF for a plurality of clusters (e.g., of two or more CpG dinucleotides). In some embodiments, the clusters correspond to a plurality of genomic loci. In some embodiments, the methods of the disclosure include determining a consensus methylation pattern and / or CCF for more than 10 clusters (e.g., of two or more CpG dinucleotides), more than 50 clusters, more than 100 clusters, more than 200 clusters, more than 300 clusters, more than 400 clusters, more than 500 clusters, more than 600 clusters, more than 700 clusters, more than 800 clusters, more than 900 clusters, more than 1000 clusters, more than 2000 clusters, more than 3000 clusters, more than 4000 clusters, more than 5000 clusters, more than 6000 clusters, more than 7000 clusters, more than 8000 clusters, more than 9000 clusters, more than 10 ... determining the consensus methylation pattern and / or CCF of more than 20,000 clusters, more than 30,000 clusters, more than 40,000 clusters, more than 50,000 clusters, more than 60,000 clusters, more than 70,000 clusters, more than 80,000 clusters, more than 90,000 clusters, more than 100,000 clusters, more than 200,000 clusters, more than 300,000 clusters, more than 400,000 clusters, more than 500,000 clusters, more than 600,000 clusters, more than 700,000 clusters, more than 800,000 clusters, more than 900,000 clusters, or up to 1,000,000 clusters. In some embodiments, the methods of the disclosure include determining a consensus methylation pattern and / or CCF of 10-100,000 clusters, 100-100,000 clusters, 1000-100,000 clusters, 10,000-100,000 clusters, 10-100 clusters, 10-1000 clusters, 10-100 clusters, 10-1000 clusters, 10-10,000 clusters, or 10-1,000,000 clusters (e.g., of two or more CpG dinucleotides).In some embodiments, the methods of the present disclosure include methods for detecting 50, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000 , 70000, 80000, 90000, 100000, 200000, 300000, 400000, 500000, 600000, 700000, 800000, 900000, or 1000000 cluster upper limits, independently selected 900000, 800000, 700000, 600 In some embodiments, the method includes determining a consensus methylation pattern and / or CCF for a number of clusters (e.g., of two or more CpG dinucleotides) having a lower limit of 000, 500000, 400000, 300000, 200000, 100000, 90000, 80000, 70000, 60000, 50000, 40000, 30000, 20000, 10000, 9000, 8000, 7000, 6000, 5000, 4000, 3000, 2000, 1000, 900, 800, 700, 600, 500, 400, 300, 200, 100, 50, or 10 clusters (wherein the upper limit is greater than the lower limit).

[0088] In some embodiments, the plurality of sequence reads comprises at least 10, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 200, at least 300, at least 400, at least 500, at least 600, at least 700, at least 800, at least 900, at least 1000, at least 2000, at least 3000, at least 4000, or at least 5000 sequence reads corresponding to a cluster. In some embodiments, the plurality of sequence reads comprises 1-5, 1-10, 1-20, 1-30, 1-40, 1-50, 1-100, 10-100, 10-1000, 50-1000, or 100-1000 sequence reads corresponding to a cluster. In some embodiments, the plurality of sequence reads comprises a number of sequence reads corresponding to a cluster having an upper limit of 5000, 4000, 3000, 2000, 1000, 900, 800, 700, 600, 500, 400, 300, 200, 100, 90, 80, 70, 60, 50, 40, 30, 20, 10, or 5, and an independently selected lower limit of 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, or 5000 (wherein the upper limit is greater than the lower limit).

[0089] In some embodiments, at least one CpG dinucleotide in the cluster is unmethylated in a consensus methylation pattern. In some embodiments, at least one CpG dinucleotide in the cluster is methylated in a consensus methylation pattern. In some embodiments, at least one CpG dinucleotide in the cluster is unmethylated in a consensus unmethylation pattern. In some embodiments, at least one CpG dinucleotide in the cluster is methylated in a consensus unmethylation pattern.

[0090] In some embodiments, at least one cluster comprises 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, or 10 or more CpG dinucleotides. In some embodiments, each cluster comprises 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, or 10 or more CpG dinucleotides. In some embodiments, a cluster comprises 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, or 10 or more CpG dinucleotides within a certain number of bases, for example, within 300 bases, 250 bases, 200 bases, 150 bases, 125 bases, 100 bases, 90 bases, 80 bases, 70 bases, 60 bases, or 50 bases. In some embodiments, the cluster comprises 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, or 10 or more CpG dinucleotides within 80 bases.

[0091] In some embodiments, all but one, all but two, all but five, or all but ten sites in a cluster are unmethylated in a consensus methylation pattern. In some embodiments, all but two, all but five, or all but ten sites in a cluster are unmethylated in a consensus unmethylation pattern.

[0092] In some embodiments, up to 1 site, up to 2 sites, up to 3 sites, up to 4 sites, up to 5 sites, or up to 10 sites in a cluster are methylated in a consensus methylation pattern. In some embodiments, up to 1 site, up to 2 sites, up to 3 sites, up to 4 sites, up to 5 sites, or up to 10 sites in a cluster are methylated in a consensus unmethylation pattern. In some embodiments, up to 5%, up to 10%, up to 20%, up to 25%, up to 30%, up to 40%, up to 50%, or up to 75% of the sites in a cluster are methylated in a consensus methylation pattern. In some embodiments, up to 5%, up to 10%, up to 20%, up to 25%, up to 30%, up to 40%, up to 50%, or up to 75% of the sites in a cluster are methylated in a consensus unmethylation pattern. In some embodiments, more than 5%, 10%, 20%, 25%, 30%, 40%, 50%, or 75% of the sites in a cluster are methylated in a consensus methylation pattern, hi some embodiments, more than 5%, 10%, 20%, 25%, 30%, 40%, 50%, or 75% of the sites in a cluster are methylated in a consensus unmethylated pattern. In some embodiments, the percentage of sites in a cluster that are methylated in the consensus methylation pattern has an upper limit of 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% and an independently selected lower limit of 90%, 85%, 80%, 75%, 70%, 65%, 60%, 55%, 50%, 45%, 40%, 35%, 30%, 25%, 20%, 15%, 10%, 5%, or 1% (wherein the upper limit is greater than the lower limit).In some embodiments, the percentage of sites in a cluster that are methylated with a consensus unmethylation pattern has an upper limit of 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%, and an independently selected lower limit of 90%, 85%, 80%, 75%, 70%, 65%, 60%, 55%, 50%, 45%, 40%, 35%, 30%, 25%, 20%, 15%, 10%, 5%, or 1%, where the upper limit is greater than the lower limit. In some embodiments, at most 1 site, at most 2 sites, at most 3 sites, at most 4 sites, at most 5 sites, or at most 10 sites in a cluster are unmethylated with a consensus methylation pattern. In some embodiments, up to 1 site, up to 2 sites, up to 3 sites, up to 4 sites, up to 5 sites, or up to 10 sites in a cluster are not methylated in a consensus unmethylation pattern. In some embodiments, up to 5%, up to 10%, up to 20%, up to 25%, up to 30%, up to 40%, up to 50%, or up to 75% of the sites in a cluster are not methylated in a consensus methylation pattern. In some embodiments, up to 5%, up to 10%, up to 20%, up to 25%, up to 30%, up to 40%, up to 50%, or up to 75% of the sites in a cluster are not methylated in a consensus unmethylation pattern. In some embodiments, more than 5%, more than 10%, more than 20%, more than 25%, more than 30%, more than 40%, more than 50%, or more than 75% of the sites in a cluster are not methylated in a consensus methylation pattern. In some embodiments, more than 5%, more than 10%, more than 20%, more than 25%, more than 30%, more than 40%, more than 50%, or more than 75% of the sites in a cluster are unmethylated in a consensus unmethylation pattern.In some embodiments, the percentage of sites in a cluster that are unmethylated in the consensus methylation pattern has an upper limit of 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% and an independently selected lower limit of 90%, 85%, 80%, 75%, 70%, 65%, 60%, 55%, 50%, 45%, 40%, 35%, 30%, 25%, 20%, 15%, 10%, 5%, or 1% (wherein the upper limit is greater than the lower limit). In some embodiments, the percentage of sites in a cluster that are unmethylated in the consensus unmethylation pattern has an upper limit of 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% and an independently selected lower limit of 90%, 85%, 80%, 75%, 70%, 65%, 60%, 55%, 50%, 45%, 40%, 35%, 30%, 25%, 20%, 15%, 10%, 5%, or 1% (wherein the upper limit is greater than the lower limit).

[0093] In some embodiments, the consensus methylation pattern and / CCF is determined based on sequence reads covering a plurality of CpG dinucleotides in the cluster. In some embodiments, the consensus unmethylation pattern and / or CCUF is determined based on sequence reads covering a plurality of CpG dinucleotides in the cluster. In some embodiments, the consensus methylation pattern and / or CCMF is determined based on sequence reads covering at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, or at least 90% of the CpG dinucleotides in the cluster. In some embodiments, the consensus unmethylation pattern and / or CCUF is determined based on sequence reads covering at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, or at least 90% of the CpG dinucleotides in the cluster. In some embodiments, the consensus methylation pattern and / CCMF is determined based on sequence reads covering all CpG dinucleotides in the cluster. In some embodiments, the consensus unmethylation pattern and / or CCUF is determined based on sequence reads that cover all CpG dinucleotides in the cluster.

[0094] In some embodiments, the observed CCF (e.g., CCMF or CCUF) is compared to a threshold or reference value. In some embodiments, the threshold or reference value refers to a threshold or reference value used for comparison purposes. In some embodiments, the threshold or reference value is obtained by analyzing a wild-type or non-tumor sample, or nucleic acid, such as a control sample, a normal adjacent tumor (NAT), or any other non-cancerous sample from the same or different individual. In some embodiments, the threshold or reference value is obtained by analyzing (e.g., averaging or other types of statistical aggregation) values ​​obtained from multiple samples or individuals. In some embodiments, the threshold or reference value refers to an intermediate value obtained by analyzing one or more cancer or tumor tissues / cells / nucleic acids and one or more normal, wild-type, or non-tumor tissues / cells / nucleic acids, such that the threshold or reference value is indicative of cancer and includes values ​​obtained from one or more cancer or tumor cells / nucleic acids, or is indicative of normal tissues / cells / nucleic acids and includes values ​​obtained from one or more normal, wild-type, or non-tumor tissues / cells / nucleic acids.

[0095] As known in the art, methylation levels at certain genomic loci can predict response to certain treatments (e.g., predictive biomarkers and / or the presence of certain types of cancer). See, e.g., Locke, WJ et al. (2019) Front. Genet. 10:1150. For example, methylation of the MGMT locus (encoding O-6-methylguanine DNA methyltransferase) is believed to predict better response to alkylating agents such as temozolomide, and methylation of the PITX2 locus (encoding paired-like homeodomain 2 transcription factor) is believed to predict better response to anthracycline-based chemotherapy. Thus, in some embodiments, the methods of the present disclosure are used to detect methylation levels at certain genomic loci (e.g., certain types of cancer). In some embodiments, methylation of the MGMT locus is detected in glioblastoma. In some embodiments, methylation of the PITX2 locus is detected in breast cancer. In some embodiments, methylation of TWIST1, ONECUT2, OTX1, SOX1, and / or IRAK3 loci is detected in bladder cancer. In some embodiments, methylation of ASTN1, DLX1, ITGA4, RXFP3, SOX17, and / or ZNF671 loci is detected in cervical cancer. In some embodiments, methylation of FAM19A4 and / or hsa-mir124-2 loci is detected in cervical cancer. In some embodiments, methylation of NDRG4 and / or BMP3 loci is detected in colon cancer. In some embodiments, methylation of VIM locus is detected in colon cancer. In some embodiments, methylation of IKZF1 and / or BCAT1 locus is detected in colon cancer. In some embodiments, methylation of SEPT9 locus is detected in colon cancer or hepatocellular carcinoma. In some embodiments, methylation of SHOX2 and / or PTGER4 locus is detected in lung cancer. In some embodiments, methylation of the GSTP1, APC, and / or RASSF1 loci is detected in prostate cancer.Details of these genomic loci (e.g., human genomic loci) are known in the art, see, e.g., NCBI Gene ID No. 4255 for the human MGMT locus and NCBI Gene ID No. 5308 for the human PITX2 locus.

[0096] Other aspects of the present disclosure relate to a method of identifying an individual having cancer who may benefit from a treatment comprising an anthracycline-based chemotherapy, the method comprising detecting a methylation level (e.g., of a cluster of two or more CpG dinucleotides) according to any one of the methods of the present disclosure. In some embodiments, the method comprises: sequencing (e.g., by a sequencer) a plurality of nucleic acid fragments to obtain a plurality of sequence reads, the plurality of nucleic acid fragments being obtained from a sample from the individual and subsequently subjected to cytosine conversion, the plurality of nucleic acid fragments corresponding to a genomic locus comprising a cluster of two or more CpG dinucleotides; determining (e.g., by a processor) a consensus methylation pattern of the cluster, the consensus methylation pattern representing each CpG dinucleotide in the cluster in which methylation was detected in at least one sequence read from the plurality of sequence reads based on cytosine conversion; and generating (e.g., by a processor) a cluster consensus fraction (CCF) of the cluster, the CCF representing a proportion of sequence reads corresponding to the cluster that exhibit the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster. In some embodiments, the plurality of nucleic acids includes one or more nucleic acids corresponding to the PITX2 locus. In some embodiments, methylation of the PITX2 locus detected in the sample identifies the individual as likely to benefit from treatment, including anthracycline-based chemotherapy.

[0097] Another aspect of the present disclosure relates to a method of selecting a therapy for an individual having cancer, comprising detecting a methylation level (e.g., of a cluster of two or more CpG dinucleotides) according to any one of the methods of the present disclosure. In some embodiments, the method comprises: sequencing (e.g., by a sequencer) a plurality of nucleic acid fragments to obtain a plurality of sequence reads, the plurality of nucleic acid fragments being obtained from a sample from the individual and subsequently undergoing cytosine conversion, the plurality of nucleic acid fragments corresponding to a genomic locus comprising a cluster of two or more CpG dinucleotides; determining (e.g., by a processor) a consensus methylation pattern of the cluster, the consensus methylation pattern representing each CpG dinucleotide in the cluster in which methylation is detected in at least one sequence read from the plurality of sequence reads based on cytosine conversion; and generating (e.g., by a processor) a cluster consensus fraction (CCF) of the cluster, the CCF representing a proportion of sequence reads corresponding to the cluster that exhibit the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster. In some embodiments, the plurality of nucleic acids includes one or more nucleic acids corresponding to the PITX2 locus. In some embodiments, methylation of the PITX2 locus detected in the sample identifies the individual as likely to benefit from treatment, including anthracycline-based chemotherapy.

[0098] Other aspects of the present disclosure relate to a method of identifying one or more treatment options for an individual having cancer, the method comprising detecting a methylation level (e.g., of a cluster of two or more CpG dinucleotides) according to any one of the methods of the present disclosure. In some embodiments, the method comprises: sequencing (e.g., by a sequencer) a plurality of nucleic acid fragments to obtain a plurality of sequence reads, the plurality of nucleic acid fragments being obtained from a sample from the individual and subsequently subjected to cytosine conversion, the plurality of nucleic acid fragments corresponding to a genomic locus comprising a cluster of two or more CpG dinucleotides; determining (e.g., by a processor) a consensus methylation pattern of the cluster, the consensus methylation pattern representing each CpG dinucleotide in the cluster in which methylation was detected in at least one sequence read from the plurality of sequence reads based on cytosine conversion; and generating (e.g., by a processor) a cluster consensus fraction (CCF) of the cluster, the CCF representing a proportion of sequence reads corresponding to the cluster that exhibit the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster. In some embodiments, the plurality of nucleic acids includes one or more nucleic acids corresponding to the PITX2 locus. In some embodiments, the method further includes generating a report including one or more treatment options identified for the individual based at least in part on the methylation of the PITX2 locus detected in the sample. In some embodiments, the one or more treatment options include anthracycline-based chemotherapy.

[0099] Another aspect of the present disclosure relates to a method of treating or delaying the progression of cancer, the method comprising detecting a methylation level (e.g., of a cluster of two or more CpG dinucleotides) according to any one of the methods of the present disclosure and administering to an individual an effective amount of anthracycline-based chemotherapy. In some embodiments, detecting the methylation level includes: sequencing (e.g., by a sequencer) a plurality of nucleic acid fragments to obtain a plurality of sequence reads, the plurality of nucleic acid fragments being obtained from a sample from an individual and subsequently undergoing cytosine conversion, the plurality of nucleic acid fragments corresponding to a genomic locus including a cluster of two or more CpG dinucleotides; determining (e.g., by a processor) a consensus methylation pattern of the cluster, the consensus methylation pattern representing each CpG dinucleotide in the cluster in which methylation is detected in at least one sequence read from the plurality of sequence reads based on cytosine conversion; and generating (e.g., by a processor) a cluster consensus fraction (CCF) of the cluster, the CCF representing a proportion of sequence reads corresponding to the cluster that exhibit the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster. In some embodiments, the plurality of nucleic acids includes one or more nucleic acids corresponding to the PITX2 locus.

[0100] As known in the art, anthracycline-based chemotherapy is part of a class of drugs that act broadly by intercalating into DNA, inhibiting DNA / RNA synthesis, generating reactive oxygen species, and blocking the activity of topoisomerase II. Examples of anthracycline-based chemotherapy include, but are not limited to, doxorubicin (Adriamycin®, Rubex®), daunorubicin (Cerubidine®, Vyxeos®, daunomycin), epirubicin (Ellence®, Pharmorubicin®), idarubicin (Idamycin®), and mitoxantrone (Novantrone®).

[0101] Other aspects of the present disclosure relate to a method of identifying an individual having cancer who may benefit from a treatment comprising an alkylating agent, the method comprising detecting a methylation level (e.g., of a cluster of two or more CpG dinucleotides) according to any one of the methods of the present disclosure. In some embodiments, the method comprises: sequencing (e.g., by a sequencer) a plurality of nucleic acid fragments to obtain a plurality of sequence reads, the plurality of nucleic acid fragments being obtained from a sample from the individual and subsequently subjected to cytosine conversion, the plurality of nucleic acid fragments corresponding to a genomic locus comprising a cluster of two or more CpG dinucleotides; determining (e.g., by a processor) a consensus methylation pattern of the cluster, the consensus methylation pattern representing each CpG dinucleotide in the cluster in which methylation was detected in at least one sequence read from the plurality of sequence reads based on cytosine conversion; and generating (e.g., by a processor) a cluster consensus fraction (CCF) of the cluster, the CCF representing a proportion of sequence reads corresponding to the cluster that exhibit the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster. In some embodiments, the plurality of nucleic acids includes one or more nucleic acids corresponding to the MGMT locus. In some embodiments, the methylation of the MGMT locus detected in the sample identifies the individual as likely to benefit from a treatment that includes an alkylating agent.

[0102] Another aspect of the present disclosure relates to a method of selecting a therapy for an individual having cancer, comprising detecting a methylation level (e.g., of a cluster of two or more CpG dinucleotides) according to any one of the methods of the present disclosure. In some embodiments, the method comprises: sequencing (e.g., by a sequencer) a plurality of nucleic acid fragments to obtain a plurality of sequence reads, the plurality of nucleic acid fragments being obtained from a sample from the individual and subsequently undergoing cytosine conversion, the plurality of nucleic acid fragments corresponding to a genomic locus comprising a cluster of two or more CpG dinucleotides; determining (e.g., by a processor) a consensus methylation pattern of the cluster, the consensus methylation pattern representing each CpG dinucleotide in the cluster in which methylation is detected in at least one sequence read from the plurality of sequence reads based on cytosine conversion; and generating (e.g., by a processor) a cluster consensus fraction (CCF) of the cluster, the CCF representing a proportion of sequence reads corresponding to the cluster that exhibit the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster. In some embodiments, the plurality of nucleic acids includes one or more nucleic acids corresponding to the MGMT locus. In some embodiments, the methylation of the MGMT locus detected in the sample identifies the individual as likely to benefit from a treatment that includes an alkylating agent.

[0103] Other aspects of the present disclosure relate to a method of identifying one or more treatment options for an individual having cancer, the method comprising detecting a methylation level (e.g., of a cluster of two or more CpG dinucleotides) according to any one of the methods of the present disclosure. In some embodiments, the method comprises: sequencing (e.g., by a sequencer) a plurality of nucleic acid fragments to obtain a plurality of sequence reads, the plurality of nucleic acid fragments being obtained from a sample from the individual and subsequently subjected to cytosine conversion, the plurality of nucleic acid fragments corresponding to a genomic locus comprising a cluster of two or more CpG dinucleotides; determining (e.g., by a processor) a consensus methylation pattern of the cluster, the consensus methylation pattern representing each CpG dinucleotide in the cluster in which methylation was detected in at least one sequence read from the plurality of sequence reads based on cytosine conversion; and generating (e.g., by a processor) a cluster consensus fraction (CCF) of the cluster, the CCF representing a proportion of sequence reads corresponding to the cluster that exhibit the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster. In some embodiments, the plurality of nucleic acids includes one or more nucleic acids corresponding to the MGMT locus. In some embodiments, the method further includes generating a report including one or more treatment options identified for the individual based at least in part on the methylation of the MGMT locus detected in the sample. In some embodiments, the one or more treatment options include an alkylating agent.

[0104] Another aspect of the present disclosure relates to a method of treating or delaying the progression of cancer, the method comprising detecting a methylation level (e.g., of a cluster of two or more CpG dinucleotides) according to any one of the methods of the present disclosure, and administering to an individual an effective amount of an alkylating agent. In some embodiments, detecting the methylation level comprises: sequencing (e.g., by a sequencer) a plurality of nucleic acid fragments to obtain a plurality of sequence reads, the plurality of nucleic acid fragments being obtained from a sample from an individual and subsequently undergoing cytosine conversion, the plurality of nucleic acid fragments corresponding to a genomic locus comprising a cluster of two or more CpG dinucleotides; determining (e.g., by a processor) a consensus methylation pattern of the cluster, the consensus methylation pattern representing each CpG dinucleotide in the cluster in which methylation is detected in at least one sequence read from the plurality of sequence reads based on cytosine conversion; and generating (e.g., by a processor) a cluster consensus fraction (CCF) of the cluster, the CCF representing a proportion of sequence reads corresponding to the cluster that show the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster. In some embodiments, the plurality of nucleic acids comprises one or more nucleic acids corresponding to the MGMT locus.

[0105] As known in the art, alkylating agents refer to a broad group of chemicals that react with biomolecules to form covalent bonds either directly (SN1) or through reactive intermediates (SN2). Classes of alkylating agents include nitrogen mustards (e.g., mechlorethamine, mechlorethamine oxide hydrochloride, cyclophosphamide, colofosphamide, chromafazine, bendamustine, estramustine, ifosfamide, melphalan, nobembitine, phenesterine, prednimustine, trofosfamide, chlorambucil, uracil mustard), aziridines (e.g., benzodopa, carboquone, meturedopa, uredopa, thiotepa, mitomycin C, and diaziquone (AZQ)), epoxides (e.g., dianhydrogalactitol and dibromodalcitol), alkylsulfonates (e.g., busulfan, hepsulfam, , improsulfan, and piposulfan), nitrosoureas (e.g., carmustine, lomustine, chlorozotocin, semustine, or methyl-CCNU, numustine, ranimnustine, streptozocin, and fotemustine), triazenes / hydrazines (e.g., procarbazine, dacarbazine, or DTIC, methylazoxyprocarbazine, temozolomide), and methylamelamine / ethyleneimines (e.g., hexamethylmelamine, altretamine, triethylenemelamine, triethylenephosphoramide, triethylenethiophosphoramide, trimethylolmelamine, altretamine, and thiotepa)).

[0106] Methylation detection Certain aspects of the present disclosure relate to methods for detecting the methylation levels of a plurality of nucleic acid fragments (eg, DNA fragments) (eg, of a cluster of two or more CpG dinucleotides).

[0107] A CpG dinucleotide or site typically refers to a region of DNA where a cytosine nucleotide is located immediately adjacent to a guanine nucleotide in a linear sequence. "CpG" refers to cytosine and guanine separated by a phosphate (i.e., -C-phosphate-G-). Regions of DNA with a high frequency or concentration of CpG sites are known as "CpG islands." Many genes in the mammalian genome have CpG islands associated with the transcription start site (including the promoter) of the gene, which play an important role in controlling gene expression. See, for example, U.S. Patent Publication No. US2014 / 0357497. Aberrant methylation patterns are observed in many types of cancer. For example, in normal tissues, CpG islands are often unmethylated, but a subset of islands are methylated during carcinogenesis, cell development, and various disease states. Hypermethylation (i.e., increased methylation levels) of CpG sites within the promoter of a gene can lead to gene silencing (e.g., silencing of tumor suppressor genes), a feature seen, for example, in some human cancers.

[0108] In some embodiments, the plurality of nucleic acid fragments are subject to cytosine conversion. Commonly used methods for determining the level and / or pattern of DNA methylation require methylation state-dependent cytosine conversion to distinguish between methylated and unmethylated CpG dinucleotide sequences. For example, methylation of CpG dinucleotide sequences can be measured using cytosine conversion-based techniques that rely on methylation state-dependent chemical modification of CpG sequences in isolated genomic DNA or fragments thereof, followed by DNA sequence analysis. Chemical reagents that can distinguish between methylated and unmethylated CpG dinucleotide sequences include hydrazine and bisulfite treatments that cleave nucleic acids. Bisulfite treatment followed by alkaline hydrolysis specifically converts unmethylated cytosine to uracil, while 5-methylcytosine remains unmodified, as described in Olek A., Nucleic Acids Res. 24: 5064-6, 1996 or Frommer et al., Proc. Natl. Acad. Sci. USA 89: 1827-1831 (1992). Bisulfite-treated DNA can then be analyzed by conventional molecular techniques, such as PCR amplification, sequencing, and detection including oligonucleotide hybridization. See, for example, U.S. Patent No. 10,174,372.

[0109] Various methodologies for cytosine conversion are known in the art. In some embodiments, a plurality of nucleic acids or nucleic acid fragments of the present disclosure are subjected to cytosine conversion by bisulfite treatment, TET-assisted bisulfite treatment, TET-assisted pyridine borane treatment, oxidative bisulfite treatment, or APOBEC treatment, e.g., prior to sequencing, a consensus methylation or demethylation pattern is determined and a CCMF or CCUF is generated.

[0110] Thus, in some embodiments, the method of the present disclosure includes treating a plurality of nucleic acids or nucleic acid fragments of the present disclosure with bisulfite. Bisulfite sequencing is a method commonly used in the art to generate methylation data at single base resolution. Bisulfite conversion or treatment refers to a biochemical process for converting unmethylated cytosine residues to uracil or thymine residues (e.g., deamination to uracil followed by amplification as thymine during PCR), thereby preserving methylated cytosine residues (e.g., 5-methylcytosine (5mC) or 5-hydroxymethylcytosine (5hmC)). Reagents for converting cytosine to uracil are known to those skilled in the art and include bisulfite reagents such as sodium bisulfite, potassium bisulfite, ammonium bisulfite, magnesium bisulfite, sodium metabisulfite, potassium metabisulfite, ammonium metabisulfite, and magnesium metabisulfite.

[0111] In some embodiments, the method of the present disclosure includes treating multiple nucleic acids or nucleic acid fragments of the present disclosure with enzymatic digestion and bisulfite treatment. The principle of this method is that DNA fragmentation is not achieved by ultrasound, but by combined enzymatic digestion with multiple endonucleases (MseI, Tsp509I, NlaIII, and HpyCH4V). Here, the cleavage sites of restriction enzymes MseI, Tsp509I, NlaIII, and HpyCH4V are TTAA, AATT, CATG, and TGCA, respectively. See, for example, Smiraglia DJ, et al. Oncogene 2002;21:5414-5426. This is followed by, for example, bisulfite treatment as described herein.

[0112] Enzymatic methods for cytosine conversion are known, such as enzymatic methyl sequencing (EM-seq). Such an approach may be advantageous because bisulfite can damage and fragment DNA, resulting in DNA loss and potentially biased sequencing, and instead uses enzymes. For example, TET2 (Ten-Eleven Translocation (Tet) Family 2 Methylcytosine Dioxygenase) and T4-BGT (T4 phage β-glucosyltransferase) are used to convert 5mC and 5hmC to products that cannot be deaminated by APOBEC3A (Apolipoprotein B mRNA Editing Enzyme, Catalytic Polypeptide-Like 3A), and then deaminate the unmodified cytosine by converting it to uracil using APOBEC3A. See, for example, Vaisvila, R. et al. (2021) Genome Res. 31:1-10.

[0113] In some embodiments, the method of the present disclosure includes treating a plurality of nucleic acids or nucleic acid fragments of the present disclosure with TET-assisted bisulfite (e.g., TAB-seq). In the TAB-seq approach, β-glucosyltransferase (βGT) is used to convert 5hmC to β-glucosyl-5-hydroxymethylcytosine (5gmC), and a Tet enzyme (e.g., mTet1) is used to oxidize 5mC to 5-carboxylcytosine (5caC). The nucleic acid can then be treated with bisulfite. See, for example, Yu, M. et al. (2018) Methods Mol. Biol. 1708:645-663.

[0114] In some embodiments, the method of the present disclosure includes treating a plurality of nucleic acids or nucleic acid fragments of the present disclosure with TET-assisted pyridine borane (e.g., TAPS). In the TAPS approach, TET methylcytosine dioxygenase is used to oxidize 5mC and 5hmC to 5caC, which is then reduced to dihydrouracil (DHU) by pyridine borane. DHU is then converted to thymine during subsequent PCR. See, for example, Liu, Y. et al. (2019) Nat. Biotechnol. 37: 424-429.

[0115] In some embodiments, the method of the present disclosure includes treating a plurality of nucleic acids or nucleic acid fragments of the present disclosure with oxidative bisulfite (e.g., oxBS). In the oxBS approach, 5hmC is oxidized to 5-formylcytosine (5fC), which can be converted to uracil in the presence of bisulfite. The results of sequencing from bisulfite treatment and oxidative bisulfite treatment can then be used to infer 5hmC levels from 5mC levels. See, e.g., Booth, MJ et al. (2013) Nat. Protocols 8: 1841-1851. This approach can be extended to the genome-wide level with oxBS-seq. See, e.g., Kirschner, K. et al. (2018) Methods Mol. Biol. 1708: 665-678.

[0116] In some embodiments, the method of the present disclosure includes treating a plurality of nucleic acids or nucleic acid fragments of the present disclosure with APOBEC. Enzyme reagents that convert cytosine to uracil (i.e., cytosine deaminases) include those of the APOBEC family, such as APOBEC-seq or APOBEC3A. Members of the APOBEC family are cytidine deaminases that convert cytosine to uracil while preserving 5-methylcytosine (i.e., leaving 5-methylcytosine unchanged). Such enzymes are described in US2013 / 0244237 and WO2018 / 165366 and are commercially available (see, for example, NEBNext® Enzymatic Methyl-seq Kit, New England Biolabs). Non-limiting examples of APOBEC family proteins include APOBEC1, APOBEC2, APOBEC3A, APOBEC3B, APOBEC3C, APOBEC3D, APOBEC3F, APOBEC3G, APOBEC3H, APOBEC4, and activation-induced (cytidine) deaminase.

[0117] Sequencing In some embodiments, the multiple sequence reads of the present disclosure are obtained from whole genome methyl sequencing (WGMS) or next generation sequencing (NGS).

[0118] Various methods of WGMS are known in the art. Generally, these methods combine cytosine conversion (e.g., using the methods described above) with whole genome sequencing technology. For example, in some embodiments, WGMS includes bisulfite sequencing, whole genome bisulfite sequencing (WGBS), APOBEC-seq, methyl-CpG-binding domain (MBD) protein capture, methyl-DNA immunoprecipitation (MeDIP-seq), methylation-sensitive restriction enzyme sequencing (MSRE / MRE-Seq or Methyl-Seq), oxidative bisulfite sequencing (oxBS-Seq), reduced representation bisulfite sequencing (RRBS), or Tet-assisted bisulfite sequencing (TAB-Seq).

[0119] Some WGMS methods rely on library construction and adapter ligation followed by standard bisulfite conversion and sequencing (e.g., WGBS). Alternatively, bisulfite treatment may be performed before adapter ligation (see, e.g., Miura, F. et al. (2012) Nucleic Acids Res. 40: e136). More recent techniques use other cytosine conversion methods, such as enzymatic approaches, to mitigate damage to DNA caused by bisulfite, for example, the commercially available NEBNext® Enzymatic Methyl-seq Kit (New England Biolabs). Library amplification, quantification, and sequencing steps are usually performed after bisulfite conversion. In some embodiments, nucleic acids are extracted from the sample before WGMS. In some embodiments, nucleic acids are subjected to fragmentation, repair, and adapter ligation before WGMS. As previously mentioned, cytosine conversion can be performed before or after adapter ligation. In some embodiments, DNA repair is performed after cytosine conversion. PCR amplification (generally at least two cycles) is performed after cytosine conversion to convert uracil to thymine (previously generated by unmethylated cytosine) using a polymerase that can read uracil (excluding polymerases with proofreading and repair activity). In some embodiments, before sequencing, fragments of a desired length are enriched. In some embodiments, before sequencing, methylated sequences of nucleic acids are enriched, such as by immunoprecipitation using an antibody specific for 5mC, such as the MeDIP approach (see, e.g., Pomraning, KR et al. (2009) Methods 47:142-150).

[0120] NGS methods are known in the art and are described, for example, in Metzker, M. (2010) Nature Biotechnology Reviews 11:31-46. Platforms for next-generation sequencing include, for example, Genome Sequencer (GS) FLX System from Roche / 454, Genome Analyzer (GA) from Illumina / Solexa, HiSeq 2500, HiSeq 3000, HiSeq 4000 and NovaSeq 6000 sequencing systems from Illumina, Support Oligonucleotide Ligation Detection (SOLiD) system from Life / APG, G.007 system from Polonator, HeliScope Gene sequencing system from Helicos BioSciences, and PacBio RS system from Pacific Biosciences. NGS technology includes one or more steps such as template preparation, sequencing and imaging, and data analysis. Template preparation methods may include steps such as randomly degrading nucleic acids (e.g., genomic DNA) into smaller sizes to generate sequencing templates (e.g., fragment templates or mate pair templates). Spatially separated templates may be attached or immobilized to a solid surface or support, allowing a large number of sequencing reactions to be performed simultaneously. Types of templates that may be used in NGS reactions include, for example, clonal amplified templates derived from a single DNA molecule, and single DNA molecule templates. Exemplary sequencing and imaging steps for NGS include, for example, cyclic reversible termination (CRT), sequencing by ligation (SBL), single molecule addition (pyrosequencing), and real-time sequencing. After generating NGS reads, they can be aligned to a known reference sequence or assembled de novo. For example, identification of genetic variations such as single nucleotide polymorphisms and structural variants in a sample (e.g., a tumor sample) can be achieved by aligning NGS reads to a reference sequence (e.g., a wild-type sequence).Methods for sequence alignment in NGS are described, for example, in Trapnell C. and Salzberg SL Nature Biotech., 2009, 27:455-457. Examples of de novo assembly are described, for example, in Warren R. et al., Bioinformatics, 2007, 23:500-501, Butler J. et al., Genome Res., 2008, 18:810-820, and Zerbino DR and Birney E., Genome Res., 2008, 18:821-829. Sequence alignment or assembly can be performed using read data from one or more NGS platforms, for example, mixing Roche / 454 and Illumina / Solexa read data. In some embodiments, NGS can be performed according to methods described, for example, in Frampton, GM et al. (2013) Nat. Biotech. 31:1023-1031, and / or Montesion, M., et al., Cancer Discovery (2021) 11(2):282-92.

[0121] In some embodiments, the method further comprises subjecting the plurality of nucleic acids to fragmentation prior to sequencing the plurality of polynucleotides or providing the plurality of sequence reads. In the art, various DNA fragmentation techniques are used prior to NGS or WGMS approaches. In some embodiments, the nucleic acid is fragmented by nebulization, which uses compressed gas to mechanically shear the nucleic acid through a small opening. In some embodiments, the nucleic acid is fragmented by sonication, which uses ultrasound to shear the nucleic acid. In some embodiments, the nucleic acid is enzymatically fragmented, for example, using one or more enzymes to digest the nucleic acid into fragments. See, for example, NEBNext® dsDNA Fragmentase. It is a mixture of two enzymes, one that randomly generates dsDNA nicks and the other that recognizes the nick site and cuts the opposite strand, generating dsDNA breaks.

[0122] In some embodiments, the method further comprises selectively enriching a plurality of nucleic acids or nucleic acid fragments corresponding to genomic loci comprising a cluster of two or more CpG dinucleotides to generate an enriched sample prior to sequencing the plurality of polynucleotides or providing the plurality of sequence reads. For example, one or more baits or probes can be used to hybridize with a genomic locus of interest or a fragment thereof comprising, for example, a cluster of two or more CpG dinucleotides. See, e.g., Graham, BI et al. Twist Fast Hybridization targeted methylation sequencing: a tunable target enrichment solution for methylation detection [abstract]. Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021; 81 (13_Suppl): Abstract nr 2098.

[0123] In some embodiments, the method further comprises amplifying the plurality of nucleic acids or nucleic acid fragments by polymerase chain reaction (PCR) before sequencing the plurality of polynucleotides or providing the plurality of sequence reads. Various PCR techniques suitable for WGMS and NGS are known in the art. As described above, in some embodiments, after cytosine conversion, the plurality of nucleic acids or nucleic acid fragments are amplified by PCR, and PCR amplification is used to convert uracil or other cytosine conversion products to thymine. In some embodiments, PCR amplification is performed using deoxyribonucleotides that contain thymine.

[0124] In some embodiments, the method further comprises, prior to sequencing the plurality of polynucleotides or prior to providing a plurality of sequence reads, contacting the mixture of polynucleotides with a bait molecule under conditions suitable for hybridization, wherein the mixture comprises a plurality of polynucleotides capable of hybridizing to the bait molecule, and isolating the plurality of polynucleotides hybridized to the bait molecule, wherein the isolated plurality of polynucleotides hybridized to the bait molecule are sequenced by NGS.

[0125] In some embodiments, the plurality of sequence reads are obtained by performing sequencing on the nucleic acid captured by hybridization with the bait molecule. In some embodiments, the plurality of sequence reads are obtained by performing whole exome sequencing on the nucleic acid captured by hybridization with the bait molecule. In some embodiments, the plurality of sequence reads are obtained by performing next generation sequencing (NGS), whole exome sequencing, or methylation sequencing (e.g., WGMS) on the nucleic acid captured by hybridization with the bait molecule.

[0126] In some embodiments, a hybrid capture approach is used. Further details regarding this and other hybrid capture processes can be found in U.S. Patent No. 9,340,830, Frampton, GM et al. (2013) Nat. Biotech. 31: 1023-1031, and Montesion, M., et al., Cancer Discovery (2021) 11(2): 282-92. In some embodiments, the method further comprises obtaining a sample from an individual prior to contacting the mixture of polynucleotides with the bait molecule, the sample comprising tumor cells and / or tumor nucleic acid, and extracting the mixture of polynucleotides from the sample, the mixture of polynucleotides being from tumor cells and / or tumor nucleic acid. In some embodiments, the sample further comprises non-tumor cells.

[0127] In some embodiments, the plurality of sequence reads of the present disclosure comprises paired-end sequence reads. In some embodiments, a consensus methylation pattern and / or CCF is determined based on paired-end sequence reads corresponding to one or more clusters. In some embodiments, a consensus unmethylation pattern and / or CCUF is determined based on paired-end sequence reads corresponding to one or more clusters. In general, paired-end sequencing methodologies are described, for example, in WO2007 / 010252, WO2007 / 091077, and WO03 / 74734. This approach utilizes pairwise sequencing of double-stranded polynucleotide templates, resulting in sequential determination of nucleotide sequences in two distinct and separate regions of the polynucleotide template. The paired-end method allows for two linked or paired reads of sequence information to be obtained from each double-stranded template on a clustered array, rather than a single sequence read that can be obtained by other methods. Paired-end sequencing techniques can be specifically utilized with clustered arrays, which are typically formed by solid-phase amplification, as described, for example, in WO03 / 74734. The adaptor-tagged target polynucleotide duplexes are immobilized to a solid support at the 5'-end of each strand of each duplex, for example by bridge amplification as described above, to form a high-density cluster of double-stranded DNA. With the 5'-ends of both strands immobilized, a sequencing primer is then hybridized to the free 3'-end and sequencing by synthesis is performed. As described in WO2007 / 091077, an adapter sequence can be inserted between the target sequences to allow up to four reads from each duplex. This methodology can be further applied to cleave specific strands in a controlled manner, as described in WO2007 / 010252. As a result, the timing of the sequencing reads of each strand can be controlled, and the nucleotide sequence in two different and distinct regions on the complementary strand of a double-stranded template can be determined sequentially. See, for example, U.S. Pat. No. 10,174,372.

[0128] In some embodiments, the plurality of sequence reads comprises unpaired sequence reads.

[0129] In some embodiments, the method of the present disclosure further comprises demultiplexing sequence reads from the plurality of sequence reads before determining the consensus methylation pattern and CCF. In some embodiments, the method of the present disclosure further comprises aligning the sequence reads from the plurality of sequence reads to a reference genome (e.g., a human reference genome) before determining the consensus methylation pattern and CCF. In some embodiments, the alignment is a three-letter alignment to the human reference genome. In some embodiments, the method of the present disclosure further comprises excluding sequencing reads from the plurality of sequencing reads that could not undergo cytosine conversion before determining the consensus methylation pattern and CCF. In some embodiments, the method of the present disclosure further comprises excluding sequence reads having a base other than cytosine or thymine at the first position of at least one of the CpG dinucleotides before determining the consensus methylation pattern and CCF. For example, these may be due to sequencing errors or mutations (somatic or germline). In some embodiments, the method of the present disclosure further comprises excluding sequence reads having a base quality below a threshold base quality before determining the consensus methylation pattern and CCF. In some embodiments, a base call at a cytosine within a CpG dinucleotide is determined using two overlapping paired-end sequence reads.

[0130] Samples and cancer In some embodiments, the method of the present disclosure further comprises isolating a plurality of nucleic acids from the sample. In some embodiments, the nucleic acids are obtained, for example, from a sample that includes tumor cells and / or tumor nucleic acids. For example, the sample can include tumor cells, circulating tumor cells, tumor nucleic acids (e.g., tumor circulating tumor DNA, cfDNA, or cfRNA), part or all of a tumor biopsy, fluid, cells, tissue, mRNA, DNA, RNA, cell-free DNA, and / or cell-free RNA. In some embodiments, the sample is from a tumor biopsy or tumor specimen. In some embodiments, the sample further includes non-tumor cells and / or non-tumor nucleic acids. In some embodiments, the fluid includes blood, serum, plasma, saliva, semen, cerebrospinal fluid, amniotic fluid, peritoneal fluid, interstitial fluid, etc. In some embodiments, the sample further includes non-tumor cells and / or non-tumor cells.

[0131] In some embodiments, the sample comprises a percentage of tumor nucleic acid that is less than 1% of the total nucleic acid, less than 0.5% of the total nucleic acid, less than 0.1% of the total nucleic acid, or less than 0.05% of the total nucleic acid. In some embodiments, the sample comprises a percentage of tumor nucleic acid that is at least 0.01%, at least 0.05%, or at least 0.1% of the total nucleic acid. In some embodiments, the sample has an upper limit of 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, 0.9%, 0.8%, 0.7%, 0.6%, 0.5%, 0.4%, 0.3%, 0.2%, 0.1%, 0.09%, 0.08%, 0.07%, 0.06%, 0.05%, 0.04%, 0.03%, or 0.02% of the total nucleic acid, independently selected from 0.0001%, 0.0002%, 0.0003%, 0.0004%, 0.0005%, 0.0006%, 0.0007% of the total nucleic acid. , 0.0008%, 0.0009%, 0.001%, 0.002%, 0.003%, 0.004%, 0.005%, 0.006%, 0.007%, 0.008%, 0.009%, 0.01%, 0.02%, 0.03%, 0.04%, 0.05%, 0.06%, 0.07%, 0.08%, 0.09%, 0.1%, 0.2%, 0.3%, 0.4%, 0.5%, 0.6%, 0.7%, 0.8%, 0.9%, or 1% (wherein the upper limit is greater than the lower limit). Advantageously, as demonstrated herein, the methods of the present disclosure allow for robust and ultrasensitive detection of aberrant methylation levels in trace amounts of tumor nucleic acid among otherwise normal nucleic acid.

[0132] In some embodiments, the sample is or comprises a biological tissue or biological fluid. The sample may comprise compounds that are not naturally mixed with tissue in nature, such as preservatives, anticoagulants, buffers, fixatives, nutrients, antibiotics, etc. In one embodiment, the sample is preserved as a frozen sample or as a formaldehyde or paraformaldehyde fixed paraffin embedded (FFPE) tissue preparation. For example, the sample may be embedded in a matrix, such as an FFPE block or a frozen sample. In another embodiment, the sample is a blood or blood component sample. In yet another embodiment, the sample is a bone marrow aspirate sample. In another embodiment, the sample comprises cell-free DNA (cfDNA) or circulating cell-free DNA (ccfDNA), such as tumor cfDNA or tumor ccfDNA. Without wishing to be bound by theory, in some embodiments, cfDNA is believed to be DNA from apoptotic or necrotic cells. Typically, cfDNA is bound by proteins (e.g., histones) and protected by nucleases. cfDNA can be used as a biomarker for, for example, non-invasive prenatal testing (NIPT), organ transplantation, cardiomyopathy, microbiome, and cancer. In another embodiment, the sample comprises circulating tumor DNA (ctDNA). Without wishing to be bound by theory, ctDNA is cfDNA that has genetic or epigenetic changes (e.g., somatic changes or methylation signatures) that can distinguish between tumor cells and those derived from non-tumor cells. In another embodiment, the sample comprises circulating tumor cells (CTCs). Without wishing to be bound by theory, in some embodiments, CTCs are believed to be cells released into circulation from primary or metastatic tumors. In some embodiments, CTC apoptosis is the source of ctDNA in blood / lymph.

[0133] In some embodiments of any of the methods provided herein, the cancer is carcinoma, sarcoma, lymphoma, leukemia, myeloma, germ cell cancer, or blastoma. In some embodiments, the cancer is a solid tumor. In some embodiments, the cancer is a hematological malignancy. In some embodiments, the cancer is B-cell cancer, melanoma, breast cancer, lung cancer, bronchial cancer, colorectal cancer, prostate cancer, pancreatic cancer, gastric cancer, ovarian cancer, bladder cancer, brain cancer, central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, endometrial cancer, oral cancer, pharyngeal cancer, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small intestine cancer, appendix cancer, salivary gland cancer, thyroid cancer, adrenal cancer, osteosarcoma, chondrosarcoma, cancer of the blood tissue, gland cancer, Cancer, Inflammatory myofibroblastoma, Gastrointestinal stromal tumor (GIST), Colon cancer, Multiple myeloma (MM), Myelodysplastic syndrome (MDS), Myeloproliferative disorder (MPD), Acute lymphocytic leukemia (ALL), Acute myeloid leukemia (AML), Chronic myelocytic leukemia (CML), Chronic lymphocytic leukemia (CLL), Polycythemia Vera, Hodgkin's lymphoma, Non-Hodgkin's lymphoma (NHL), Soft tissue sarcoma, Fibrosarcoma, Myxosarcoma, Liposarcoma, Osteosarcoma, chordoma, angiosarcoma, endothelial sarcoma, lymphangiosarcoma, lymphangioendothelial sarcoma, synovium, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatocellular carcinoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, bladder carcinoma, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma , ependymoma, pineal cell tumor, glioblastoma, acoustic neuroblastoma, oligodendroglioma, meningioma, neuroblastoma, retinoblastoma, follicular lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, hepatocellular carcinoma, thyroid cancer, gastric cancer, head and neck cancer, small cell carcinoma, essential thrombocythemia, aplastic myeloid metaplasia, hypereosinophilic syndrome, systemic mastocytosis, familial eosinophilia, chronic eosinophilic leukemia, neuroendocrine carcinoma, or carcinoid tumor.

[0134] In some embodiments, the cancer is appendix adenocarcinoma, bladder adenocarcinoma, bladder urothelial (transitional cell) carcinoma, breast cancer not otherwise specified (NOS), breast cancer NOS, invasive ductal carcinoma (IDC), breast invasive lobular carcinoma (ILC), cervical squamous cell carcinoma (SCC), colon adenocarcinoma (CRC), esophageal adenocarcinoma, esophageal cancer NOS, esophageal squamous cell carcinoma (SCC), intraocular melanoma, gallbladder adenocarcinoma, gastroesophageal junction adenocarcinoma, intrahepatic cholangiocarcinoma, renal cancer NOS, liver hepatocellular carcinoma (HCC), lung cancer NOS, lung adenocarcinoma, lung large cell carcinoma, lung non-small cell lung cancer (NSCLC) NOS, small cell undifferentiated carcinoma of the lung, squamous cell carcinoma of the lung (SCC), ovarian cancer NOS, pancreatic cancer NOS, pancreatic ductal adenocarcinoma, pancreaticobiliary carcinoma, prostate cancer NOS, prostatic acinar adenocarcinoma, prostatic ductal adenocarcinoma, rectal adenocarcinoma (CRC), skin melanoma, small intestine adenocarcinoma, soft tissue sarcoma NOS, gastric adenocarcinoma NOS, adenocarcinoma of unknown primary source NOS, adenocarcinoma of unknown primary source, carcinoma of unknown primary source (CUP) NOS, neuroendocrine tumor of unknown primary source, squamous cell carcinoma of unknown primary source (SCC), or uterine endometrial adenocarcinoma NOS.

[0135] Software, Systems, and Devices In another aspect, a system is provided herein, the system including a memory configured to store one or more program instructions and one or more processors configured to execute the one or more program instructions. In another aspect, the one or more computer program instructions, when executed by the one or more processors, are configured to: determine, using the one or more processors, a consensus methylation pattern of a cluster of two or more CpG dinucleotides at a genomic locus, the consensus methylation pattern representing each CpG dinucleotide in the cluster in which methylation is detected in at least one sequence read from a plurality of sequence reads obtained from a plurality of nucleic acid fragments that have undergone cytosine conversion; and generate, using the one or more processors, a cluster consensus fraction (CCF) of the cluster, the CCF representing a proportion of sequence reads corresponding to the cluster that exhibit the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster. In some embodiments, if the CCF is equal to or greater than a threshold or reference value, the one or more computer program instructions are further configured to detect, using the one or more processors, the presence of cancer nucleic acid in the plurality of nucleic acid fragments, at least in part, based on the CCF being equal to or greater than a threshold or reference value. In some embodiments, if the CCF is less than a threshold or reference value, the one or more computer program instructions are further configured to detect, using one or more processors, the absence of cancer nucleic acid in the plurality of nucleic acid fragments, based at least in part on the CCF being less than the threshold or reference value. In some embodiments, the one or more computer program instructions are further configured to determine, using one or more processors, a consensus methylation pattern of a plurality of clusters of two or more CpG dinucleotides, and generate, using one or more processors, a cluster consensus fraction (CCF) of the plurality of clusters, e.g., according to any of the methods disclosed herein. In some aspects, a system is provided herein, the system comprising a memory and one or more processors.In some embodiments, the memory includes one or more programs for execution by the one or more processors, the one or more programs including instructions that, when executed by the one or more processors, cause the system to perform a method according to any of the embodiments described herein.

[0136] In another aspect, a temporary or non-transitory computer readable storage medium is provided herein. In some embodiments, the temporary or non-transitory computer readable storage medium comprises one or more programs executable by one or more computer processors to perform a method. In some embodiments, the method includes: determining, using one or more processors, a consensus methylation pattern of a cluster of two or more CpG dinucleotides at a genomic locus, the consensus methylation pattern representing each CpG dinucleotide in the cluster in which methylation is detected in at least one sequence read from a plurality of sequence reads obtained from a plurality of nucleic acid fragments that have undergone cytosine conversion; and generating, using one or more processors, a cluster consensus fraction (CCF) of the cluster, the CCF representing a proportion of sequence reads corresponding to the cluster that exhibit the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster. In some embodiments, if the CCF is equal to or greater than a threshold or reference value, the method further includes detecting, using one or more processors, the presence of cancer nucleic acid in the plurality of nucleic acid fragments based at least in part on the CCF being equal to or greater than a threshold or reference value. In some embodiments, if the CCF is equal to or greater than a threshold or reference value, the method further comprises detecting, using one or more processors, the absence of cancer nucleic acid in the plurality of nucleic acid fragments based at least in part on the CCF being equal to or greater than a threshold or reference value. In some embodiments, the method further comprises determining, using one or more processors, a consensus methylation pattern of a plurality of clusters of two or more CpG dinucleotides, e.g., according to any of the methods disclosed herein, and generating, using one or more processors, a cluster consensus fraction (CCF) of the plurality of clusters. In some aspects, a non-transitory computer readable storage medium is provided herein.In some embodiments, a non-transitory computer-readable storage medium includes one or more programs executed by one or more processors of a device, the one or more programs including instructions that, when executed by the one or more processors, cause the device to perform a method according to any of the embodiments described herein.

[0137] FIG. 11 illustrates an example of a computing system according to one embodiment. The device 1100 may be a host computer connected to a network. The device 1100 may be a client computer or a server. As shown in FIG. 11, the device 1100 may be any suitable type of microprocessor-based device, such as a personal computer, a workstation, a server, or a handheld computing device (a portable electronic device, e.g., a phone or tablet). The device may include, for example, a processor 1110, input devices 1120, output devices 1130, storage 1140, communication devices 1160, a power supply 1170, an operating system 1180, and a system bus 1190. The input devices 1120 and output devices 1130 may generally correspond to those described herein and may be connectable to or integrated with the computer.

[0138] The input device 1120 may be any suitable device that provides input, such as a touch screen, a keyboard or keypad, a mouse, or a voice recognition device. The output device 1130 may be any suitable device that provides output, such as a touch screen, a tactile device, or a speaker.

[0139] Storage 1140 can be any suitable device that provides storage (e.g., electrical, magnetic, or optical memory, including RAM (volatile and non-volatile), cache, hard drive, or removable storage disk). Communications device 1160 can include any suitable device that can send and receive signals over a network, such as a network interface chip or device. The components of a computer can be connected in any suitable manner, for example, via wired media (e.g., a physical bus, Ethernet, or any other wired transmission technology) or wirelessly (e.g., Bluetooth, Wi-Fi, or any other wireless technology). For example, in FIG. 11, the components are connected by a system bus 1190.

[0140] The detection module 1150 can be stored as executable instructions in the storage 1140 and executed by the processor 1110 and can include, for example, processes embodying functions of the present disclosure (e.g., embodied in the devices described above).

[0141] The detection module 1150 may also be stored and / or transferred in any non-transitory computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device (e.g., those described herein) and may fetch instructions associated with the software from and execute the instructions. In the context of the present disclosure, a computer-readable storage medium may be any medium, such as the storage 1140, that may include or store processes for use by or in connection with an instruction execution system, apparatus, or device. Examples of computer-readable storage media may include memory units, such as hard drives, flash drives, and distribution modules that operate as a single functional unit. Also, the various processes described herein may be embodied as modules configured to operate according to the above embodiments and techniques. Furthermore, while the processes may be shown and / or described separately, one skilled in the art will understand that the above processes may be routines or modules within other processes.

[0142] The detection module 1150 may also propagate in any transmission medium for use by or in connection with an instruction execution system, apparatus, or device (such as those mentioned above) and may fetch instructions associated with the software from and execute the instructions. In the context of this disclosure, a transmission medium may be any medium that may communicate, propagate, or transmit transmission programming for use by or in connection with an instruction execution system, apparatus, or device. A transmission-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, or infrared wired or wireless propagation media.

[0143] The device 1100 may be connected to a network (e.g., network 1004, shown in FIG. 10 and / or described below), which may be any suitable type of interconnected communications system. The network may implement any suitable communications protocol and may be protected by any suitable security protocol. The network may include any suitable arrangement of network links that may implement transmission and reception of network signals, such as wireless network connections (T1 or T3 lines), cable networks, DSL, or telephone lines.

[0144] The device 1100 may implement any operating system suitable for operating on a network (e.g., operating system 1180). The detection module 1150 may be written in any suitable programming language, such as C, C++, Java, or Python. In various embodiments, application software embodying functionality of the present disclosure may be deployed in different configurations (e.g., in a client / server arrangement, or via a web browser as a web-based application or web service). In some embodiments, the operating system 1180 is executed by one or more processors, such as the processor 1110.

[0145] The device 1100 may further include a power source 1170, which may be any suitable power source.

[0146] In some embodiments, detection module 1150 is a module for detecting LOH and / or tumor mutation burden of one or more HLA-I genes and includes a process embodying functionality of the present disclosure (e.g., a process as embodied in a device described herein).

[0147] 10 illustrates an example of a computing system according to one embodiment. In the system 1000, a device 1100 (e.g., as described above and illustrated in FIG. 11) is connected to a network 1004, which is also connected to a device 1006. In some embodiments, the device 1006 is a sequencer. Exemplary sequencers include, but are not limited to, the Genome Sequencer (GS) FLX System from Roche / 454, the Genome Analyzer (GA) from Illumina / Solexa, the HiSeq 2500, HiSeq 3000, HiSeq 4000, and NovaSeq 6000 sequencing systems from Illumina, the Support Oligonucleotide Ligation Detection (SOLiD) system from Life / APG, the G.007 system from Polonator, the HeliScope Gene sequencing system from Helicos BioSciences, or the PacBio RS system from Pacific Biosciences. The devices 1100 and 1006 can communicate using a suitable communication interface over a network 1004, such as, for example, a local area network (LAN), a virtual private network (VPN), or the Internet. In some embodiments, the network 1004 can be, for example, the Internet, an intranet, a virtual private network, a cloud network, a wired network, or a wireless network. The devices 1100 and 1006 can communicate partially or entirely over wireless or wired communications, such as Ethernet, IEEE 802.11b wireless, etc. Additionally, the devices 1100 and 1006 can communicate over a second network, such as, for example, a mobile / cellular network, using a suitable communication interface. The communication between the devices 1100 and 1006 can further include or communicate with various servers, such as a mail server, a mobile server, a media server, a telephone server, etc.In some embodiments, devices 1100 and 1006 may communicate directly (instead of or in addition to communicating via network 1004), e.g., via wireless or wired communication, such as Ethernet, IEEE 802.11b wireless, etc. In some embodiments, devices 1100 and 1006 communicate via communication 1008, which may be a direct connection or may occur over a network (e.g., network 1004).

[0148] One or all of the devices 1100 and 1006 generally include logic (e.g., http web server logic) or are programmed to format data accessed from local or remote databases or other sources of data and content to provide and / or receive information over the network 1004 in accordance with various embodiments described herein.

[0149] FIG. 8 illustrates an exemplary process 800 for detecting methylation levels (e.g., of a cluster of two or more CpG dinucleotides) according to some embodiments of the present disclosure. Process 800 is performed, for example, using one or more electronic devices implementing a software program. In some examples, process 800 is performed using a client-server system, and blocks of process 800 are divided in any manner between a server and a client device. In other examples, blocks of process 800 are divided between a server and multiple client devices. Thus, while portions of process 800 are described herein as being performed by a particular device of a client-server system, it should be understood that process 800 is not so limited. In some embodiments, steps performed may be performed across many systems, for example, in a cloud environment. In other examples, process 800 is performed using only a client device or only multiple client devices. In process 800, some blocks are optionally combined, the order of some blocks is optionally changed, and some blocks are optionally omitted. In some examples, additional steps can be performed in combination with process 800. Accordingly, the operations illustrated (and described in more detail below) are exemplary in nature and therefore should not be considered as limiting.

[0150] In block 802, a plurality of sequence reads of one or more nucleic acids are obtained by sequencing the plurality of nucleic acids or nucleic acid fragments. In some embodiments, the plurality of nucleic acids or nucleic acid fragments correspond to one or more genomic loci that contain a cluster of two or more CpG dinucleotides. In some embodiments, the sequence reads are obtained using a sequencer (e.g., as described herein or otherwise known in the art). Optionally, prior to obtaining the sequence reads, the plurality of nucleic acids or nucleic acid fragments are isolated from the sample, subjected to cytosine conversion (e.g., by bisulfite treatment, TET-assisted bisulfite treatment, TET-assisted pyridine borane treatment, oxidative bisulfite treatment, or APOBEC treatment), subjected to fragmentation, selectively enriching for genomic loci that contain clusters of CpG dinucleotides, and / or amplified by PCR. In block 804, the exemplary system (e.g., one or more electronic devices) determines a consensus methylation pattern of the clusters representing each CpG dinucleotide in the clusters in which methylation was detected in at least one sequence read. At block 806, the exemplary system (e.g., one or more electronic devices) generates a CCF for the cluster, which represents a proportion of sequence reads corresponding to the cluster that exhibit a consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster. Optionally, prior to determining the consensus methylation pattern and generating the CCF, the sequence reads are demultiplexed, aligned to a reference genome, and / or exclude (e.g., sequence reads that could not undergo cytosine conversion, sequence reads having a base other than cytosine or thymine at the first position of at least one of the CpG dinucleotides, or sequence reads having a base quality below a threshold base quality).

[0151] FIG. 9 illustrates an exemplary process 900 for detecting methylation levels (e.g., of a cluster of two or more CpG dinucleotides) according to some embodiments of the present disclosure. Process 900 is performed, for example, using one or more electronic devices implementing a software program. In some examples, process 900 is performed using a client-server system, and blocks of process 900 are divided in any manner between a server and a client device. In other examples, blocks of process 900 are divided between a server and multiple client devices. Thus, while portions of process 900 are described herein as being performed by a particular device of a client-server system, it should be understood that process 900 is not so limited. In some embodiments, steps performed may be performed across many systems, for example, in a cloud environment. In other examples, process 900 is performed using only a client device or only multiple client devices. In process 900, some blocks are optionally combined, some blocks are optionally reordered, and some blocks are optionally omitted. In some examples, additional steps can be performed in combination with process 900. Accordingly, the operations illustrated (and described in more detail below) are exemplary in nature and therefore should not be considered as limiting.

[0152] In block 902, a plurality of sequence reads of one or more nucleic acids are obtained by sequencing the plurality of nucleic acids or nucleic acid fragments. In some embodiments, the plurality of nucleic acids or nucleic acid fragments correspond to one or more genomic loci that contain a cluster of two or more CpG dinucleotides. In some embodiments, the sequence reads are obtained using a sequencer (e.g., as described herein or otherwise known in the art). Optionally, prior to obtaining the sequence reads, the plurality of nucleic acids or nucleic acid fragments are isolated from the sample, subjected to cytosine conversion (e.g., by bisulfite treatment, TET-assisted bisulfite treatment, TET-assisted pyridine borane treatment, oxidative bisulfite treatment, or APOBEC treatment), subjected to fragmentation, selectively enriching for genomic loci that contain clusters of CpG dinucleotides, and / or amplified by PCR. In block 904, the exemplary system (e.g., one or more electronic devices) determines a consensus methylation pattern of the clusters representing each CpG dinucleotide in the clusters in which methylation was detected in at least one sequence read. In block 906, the exemplary system (e.g., one or more electronic devices) generates a CCF of the cluster, which represents a proportion of sequence reads corresponding to the cluster that show a consensus methylation pattern among the total number of sequence reads from the plurality of sequence reads corresponding to the cluster. Optionally, before determining the consensus methylation pattern and generating the CCF, the sequence reads are demultiplexed, aligned to a reference genome, and / or excluded (e.g., sequence reads that could not undergo cytosine conversion, sequence reads that have a base other than cytosine or thymine in at least one first position of a CpG dinucleotide, or sequence reads that have a base quality below a threshold base quality). In block 908, the CCF is compared to a reference value or threshold. In block 910, if the CCF is equal to or greater than the reference value or threshold, cancer or abnormal methylation level is detected. In block 912, if the CCF is less than the reference value or threshold, cancer or abnormal methylation level is not detected, or normal or wild-type methylation level is detected.

[0153] report In some embodiments, the methods provided herein include generating a report and / or providing a report to a party, in some embodiments, the report includes one or more treatment options identified for the individual based at least in part on the methylation levels detected in a sample from the individual, e.g., as described herein.

[0154] In some embodiments, the one or more treatment options are based at least in part on the general amount of methylation detected.

[0155] In other embodiments, one or more treatment options are based at least in part on the methylation of one or more specific genomic loci. For example, in some embodiments, one or more treatment options are based at least in part on the methylation of the PITX2 locus or the MGMT locus. In some embodiments, methylation of the PITX2 locus detected in the sample identifies the individual as likely to benefit from a treatment that includes an anthracycline-based chemotherapy. In some embodiments, methylation of the MGMT locus detected in the sample identifies the individual as likely to benefit from a treatment that includes an alkylating agent.

[0156] In some embodiments, the report includes information regarding the role of methylation (e.g., generally or at a particular genomic locus, such as the PITX2 or MGMT locus) in a disease, such as cancer. Such information may include one or more of the following: information regarding the prognosis of the cancer, information regarding the resistance of the cancer to one or more treatments, information regarding possible or proposed treatment options (e.g., anti-cancer therapies provided herein, such as anthracycline-based chemotherapy in the case of methylation of the PITX2 locus, or alkylating agents in the case of methylation of the MGMT locus, according to the methods provided herein), or information regarding treatments to avoid. In some embodiments, the report includes information regarding the likely efficacy, tolerability, and / or recommendability of applying a treatment option (e.g., anti-cancer therapies provided herein, such as anthracycline-based chemotherapy in the case of methylation of the PITX2 locus, or alkylating agents in the case of methylation of the MGMT locus, according to the methods provided herein) to an individual with cancer. In some embodiments, the report includes information or recommendations regarding administration of a treatment (e.g., an anti-cancer therapy provided herein, such as anthracycline-based chemotherapy in the case of methylation of the PITX2 locus, or an alkylating agent in the case of methylation of the MGMT locus, according to the methods provided herein). In some embodiments, the information or recommendation includes the dosage and / or treatment regimen of the treatment (e.g., as monotherapy or in combination with other treatments, such as a second anti-cancer agent). In some embodiments, the report includes information or recommendations for at least 1, at least 2, at least 3, at least 4, at least 5, at least 6 at least 7, at least 8, at least 9, at least 10, or more treatments.

[0157] Also provided herein is a method for generating a report according to the present disclosure. In some embodiments, a report according to the present disclosure is generated by a method including one or more of the following steps: sequencing a plurality of nucleic acid fragments by a sequencer to obtain a plurality of sequence reads, where the plurality of nucleic acid fragments have undergone cytosine conversion, and the plurality of nucleic acid fragments correspond to a genomic locus that includes a cluster of two or more CpG dinucleotides; determining a consensus methylation pattern of the cluster by a processor, where the consensus methylation pattern represents each CpG dinucleotide in the cluster in which methylation is detected in at least one sequence read from the plurality of sequence reads based on cytosine conversion; generating a cluster consensus fraction (CCF) of the cluster by a processor, where the CCF represents a proportion of sequence reads corresponding to the cluster that show the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads corresponding to the cluster, thereby detecting a methylation level of the cluster; and generating a report, e.g., based at least in part on the CCF. In some embodiments, the method further includes obtaining a sample (e.g., a sample described herein) from an individual (e.g., an individual having cancer), isolating a nucleic acid or nucleic acid fragment from the sample, and / or subjecting the nucleic acid or nucleic acid fragment to cytosine conversion, e.g., according to any of the methods described herein.

[0158] In some embodiments, reports generated according to the methods provided herein may include information regarding methylation levels in the sample (e.g., generally or at specific genomic loci, such as the PITX2 locus or the MGMT locus), an identifier for the individual from whom the sample was obtained, information regarding the role of methylation in the disease (e.g., cancer), information regarding prognosis, resistance, or potential or proposed treatment options (e.g., anti-cancer therapies provided herein, e.g., anthracycline-based chemotherapy in the case of methylation of the PITX2 locus, or alkylating agents in the case of methylation of the MGMT locus, etc.), treatment options for the individual (e.g., anti-cancer therapies provided herein, e.g., anthracycline-based chemotherapy in the case of methylation of the PITX2 locus, or alkylating agents in the case of methylation of the MGMT locus, etc.), and / or In some embodiments, the report generated may include one or more of the following: information regarding the likely efficacy, tolerability, or appropriateness of administering a treatment (e.g., an anti-cancer therapy provided herein, e.g., an anthracycline-based chemotherapy in the case of PITX2 locus methylation, or an alkylating agent in the case of MGMT locus methylation), a recommendation or information regarding administration of a treatment (e.g., an anti-cancer therapy provided herein, e.g., an anthracycline-based chemotherapy in the case of PITX2 locus methylation, or an alkylating agent in the case of MGMT locus methylation), or a recommendation or information regarding a dosage or treatment regimen of a treatment, e.g., a treatment in combination with another treatment (e.g., a second anti-cancer therapy). In some embodiments, the report generated is a personalized cancer report.

[0159] A report according to the present disclosure may be in electronic, web-based, or paper form. The report may be provided to an individual or patient (e.g., an individual or patient with cancer), or to an individual or entity other than the individual or patient (e.g., other than an individual or patient with cancer), such as one or more of a caregiver, physician, oncologist, hospital, clinic, third party payer, insurance company, or government agency. In some embodiments, the report is provided or delivered to the individual or entity within about 1 day or more, about 7 days or more, about 14 days or more, about 21 days or more, about 30 days or more, about 45 days or more, or about 60 days or more from obtaining a sample from an individual (e.g., an individual with cancer). In some embodiments, the report is provided or delivered to the individual or entity within about 1 day or more, about 7 days or more, about 14 days or more, about 21 days or more, about 30 days or more, about 45 days or more, or about 60 days or more from detecting a methylation level in a sample obtained from an individual (e.g., an individual with cancer).

[0160] Immune checkpoint inhibitors and anti-cancer therapies Certain aspects of the present disclosure relate to immune checkpoint inhibitors (ICIs). As known in the art, checkpoint inhibitors target at least one immune checkpoint protein to alter the regulation of the immune response. Immune checkpoint proteins include, for example, CTLA4, PD-L1, PD-1, PD-L2, VISTA, B7-H2, B7-H3, B7-H4, B7-H6, 2B4, ICOS, HVEM, CEACAM, LAIR1, CD80, CD86, CD276, VTCN1, MHC class I, MHC class II, GALS, adenosine, TGFR, CSF1R, MICA / B, arginase, CD160, gp49B, PIR-B, KIR family receptors, TIM-1, TIM-3, TIM-4, LAG-3, BTLA, SIRPα (CD47), CD48, 2B4 (CD244), B7.1, B7.2, ILT-2, ILT-4, TIGIT, LAG-3, BTLA, IDO, OX40, and A2aR.In some embodiments, molecules involved in regulating immune checkpoints include, but are not limited to, PD-1 (CD279), PD-L1 (B7-H1, CD274), PD-L2 (B7-CD, CD273), CTLA-4 (CD152), HVEM, BTLA (CD272), killer cell immunoglobulin-like receptors (KIR), LAG-3 (CD223), TIM-3 (HAVCR2), CEACAM, CEACAM-1, CEACAM-3, CEACAM-5, GAL9, VISTA (PD-1H), TIGIT, LAIR1, CD160, 2B4, TGFRβ, A2AR, GITR (CD357), CD80 (B7-1), CD86 (B7-2), CD276 (B7-H3), VTCNI (B7-H4), MHC class I, MHC class II, GALS, adenosine, TGFR, B7-H1, OX40 (CD134), CD94 (KLRD1), CD137 (4-1BB), CD137L (4-1BBL), CD40, IDO, CSF1R, CD40L, CD47, CD70 (CD27L), CD226, HHLA2, ICOS (CD278), ICOSL (CD275), LIGHT (TNFSF14, CD258), NKG2a, NKG2d, OX40L (CD134L), PVR (NECL5, CD155), SIRPa, MICA / B, and / or arginase. In some embodiments, an immune checkpoint inhibitor (i.e., a checkpoint inhibitor) reduces the activity of a checkpoint protein that negatively regulates immune cell function, e.g., to enhance T cell activation and / or anti-cancer immune responses. In other embodiments, a checkpoint inhibitor increases the activity of a checkpoint protein that positively regulates immune cell function, e.g., to enhance T cell activation and / or anti-cancer immune responses. In some embodiments, a checkpoint inhibitor is an antibody.Examples of checkpoint inhibitors include, but are not limited to, PD-1 axis binding antagonists, PD-L1 axis binding antagonists (e.g., anti-PD-L1 antibodies, e.g., atezolizumab (MPDL3280A)), antagonists against co-inhibitory molecules (e.g., CTLA4 antagonists (e.g., anti-CTLA4 antibodies), TIM-3 antagonists (e.g., anti-TIM-3 antibodies), or LAG-3 antagonists (e.g., anti-LAG-3 antibodies)), or any combination thereof. In some embodiments, immune checkpoint inhibitors include drugs such as small molecules, recombinant forms of ligands or receptors, or antibodies (e.g., human antibodies) (see, e.g., International Patent Publication No. WO 2015 / 016718; Pardoll, Nat Rev Cancer, 12(4):252-64, 2012, both of which are incorporated herein by reference). In some embodiments, known inhibitors of immune checkpoint proteins or analogs thereof may be used, in particular chimeric, humanized, or human forms of antibodies.

[0161] In some embodiments according to any of the embodiments described herein, the ICI comprises a PD-1 antagonist / inhibitor or a PD-L1 antagonist / inhibitor.

[0162] In some embodiments, the checkpoint inhibitor is a PD-L1 axis binding antagonist (e.g., a PD-1 binding antagonist, a PD-L1 binding antagonist, or a PD-L2 binding antagonist). PD-1 (programmed death 1) is also referred to in the art as "programmed cell death 1", "PDCD1", "CD279", and "SLEB2". An exemplary human PD-1 is set forth in UniProtKB / Swiss-Prot Accession No. Q15116. PD-L1 (programmed death ligand 1) is also referred to in the art as "programmed cell death 1 ligand 1", "PDCD1 LG1", "CD274", "B7-H", and "PDL1". An exemplary human PD-L1 is set forth in UniProtKB / Swiss-Prot Accession No. Q9NZQ7.1. PD-L2 (Programmed Death Ligand 2) is also referred to in the art as "Programmed Cell Death 1 Ligand 2", "PDCD1 LG2", "CD273", "B7-DC", "Btdc", and "PDL2". An exemplary human PD-L2 is set forth in UniProtKB / Swiss-Prot Accession No. Q9BQ51. In some instances, PD-1, PD-L1, and PD-L2 are human PD-1, PD-L1, and PD-L2.

[0163] In some cases, the PD-1 binding antagonist / inhibitor is a molecule that inhibits the binding of PD-1 to its ligand binding partner. In certain embodiments, the PD-1 ligand binding partner is PD-L1 and / or PD-L2. In another example, the PD-L1 binding antagonist / inhibitor is a molecule that inhibits the binding of PD-L1 to its binding ligand. In certain embodiments, the PD-L1 binding partner is PD-1 and / or B7-1. In another example, the PD-L2 binding antagonist is a molecule that inhibits the binding of PD-L2 to its ligand binding partner. In certain embodiments, the PD-L2 binding ligand partner is PD-1. The antagonist may be an antibody, an antigen-binding fragment thereof, an immunoadhesin, a fusion protein, or an oligopeptide. In some embodiments, the PD-1 binding antagonist is a small molecule, a nucleic acid, a polypeptide (e.g., an antibody), a carbohydrate, a lipid, a metal, or a toxin.

[0164] In some instances, the PD-1 binding antagonist is an anti-PD-1 antibody (e.g., a human antibody, a humanized antibody, or a chimeric antibody), such as those described below. In some instances, the anti-PD-1 antibody is MDX-1106 (nivolumab), MK-3475 (pembrolizumab, Keytruda®) cemiplimab, dostallimab, MEDI-0680 (AMP-514), PDR001, REGN2810, MGA-012, JNJ-63723283, BI754091, or BGB-108. In other examples, the PD-1 binding antagonist is an immunoadhesin (e.g., an immunoadhesin that includes an extracellular or PD-1 binding portion of PD-L1 or PD-L2 fused to a constant region (e.g., an Fc region of an immunoglobulin sequence)). In some cases, the PD-1 binding antagonist is AMP-224. Other examples of anti-PD-1 antibodies include MEDI-0680 (AMP-514; AstraZeneca), PDR001 (CAS Registry Number 1859072-53-9; Novartis), REGN2810 (LIBTAYO® or cemiplimab-rwlc; Regeneron), BGB-108 (BeiGene), BGB-A317 (BeiGene), BI 754091, JS-001 (Shanghai Junshi), STI-A1110 (Sorrento), INCSHR-1210 (Incyte), PF-06801591 (Pfizer), TSR-042 (also known as ANB011; Tesaro / AnaptysBio), AM0001 (ARMO Biosciences), ENUM244C8 (Enumeral Biomedical Holdings), or ENUM388D4 (Enumeral Biomedical Holdings).In some embodiments, the PD-1 axis binding antagonist is tislelizumab (BGB-A317), BGB-108, STI-A1110, AM0001, BI754091, sintilimab (IBI308), cetrelimab (JNJ-63723283), toripalimab (JS-001), camrelizumab (SHR-1210, INCSHR-1210, HR-301210) , MEDI-0680 (AMP-514), MGA-012 (INCMGA0012), nivolumab (BMS-936558, MDX1106, ONO-4538), spartalizumab (PDR00l), pembrolizumab (MK-3475, SCH900475, Keytruda®), PF-06801591, cemiplimab (REGN-2810, REGEN2 810), dostallimab (TSR-042, ANB011), FITC-YT-16 (PD-1 binding peptide), APL-501 or CBT-501 or genolimuzumab (GB-226), AB-122, AK105, AMG404, BCD-100, F520, HLX10, HX008, JTX-4014, LZM009, Sym021, PSB205, AMP-2 24 (fusion protein targeting PD-1), CX-188 (PD-1 probody), AGEN-2034, GLS-010, budigalimab (ABBV-181), AK-103, BAT-1306, CS-1003, AM-0001, TILT-123, BH-2922, BH-2941, BH-2950, ​​ENUM-244C8, ENUM-388D4, HAB-21, H EISCOI11-003, IKT-202, MCLA-134, MT-17000, PEGMP-7, PRS-332, RXI-762, STI-1110, VXM-10, XmAb-23104, AK-112, HLX-20, SSI-361, AT-16201, SNA-01, AB122, PD1-PIK, PF-06936308, RG-7 769, CABPD-1 Abs, AK-123, MEDI-3387, MEDI-5771, 4H1128Z-E27, REMD-288, SG-001, BY-24.3, CB-201, IBI-319, ONCR-177, Max-1, CS-4100, JBI-426, CCC-0701, or CCX-4503, or derivatives thereof.

[0165] In some embodiments, the PD-L1 binding antagonist is a small molecule that inhibits PD-1. In some embodiments, the PD-L1 binding antagonist is a small molecule that inhibits PD-L1. In some embodiments, the PD-L1 binding antagonist is a small molecule that inhibits PD-L1 and VISTA or PD-L1 and TIM3. In some embodiments, the PD-L1 binding antagonist is CA-170 (also known as AUPM-170). In some embodiments, the PD-L1 binding antagonist is an anti-PD-L1 antibody. In some embodiments, the anti-PD-L1 antibody is capable of binding to human PD-L1 (e.g., human PD-L1 as set forth in UniProtKB / Swiss-Prot Accession No. Q9NZQ7.1) or a variant thereof. In some embodiments, the PD-L1 binding antagonist is a small molecule, a nucleic acid, a polypeptide (e.g., an antibody), a carbohydrate, a lipid, a metal, or a toxin.

[0166] In some cases, the PD-L1 binding antagonist is an anti-PD-L1 antibody, such as those described below. In some cases, the anti-PD-L1 antibody can inhibit the binding between PD-L1 and PD-1 and / or the binding between PD-L1 and B7-1. In some cases, the anti-PD-L1 antibody is a monoclonal antibody. In some cases, the anti-PD-L1 antibody is an antibody fragment selected from a Fab, Fab'-SH, Fv, scFv, or (Fab')2 fragment. In some cases, the anti-PD-L1 antibody is a humanized antibody. In some cases, the anti-PD-L1 antibody is a human antibody. In some cases, the anti-PD-L1 antibody is selected from YW243.55.S70, MPDL3280A (atezolizumab), MDX-1 105, MEDI4736 (durvalumab), or MSB0010718C (avelumab).In some embodiments, the PD-L1 axis binding antagonist is atezolizumab, avelumab, durvalumab (Imfinzi), BGB-A333, SHR-1316 (HTI-1088), CK-301, BMS-936559, embafolimab (KN035, ASC22), CS1001, MDX-1105 (BMS-936559), LY3300 054, STI-A1014, FAZ053, CX-072, INCB086550, GNS-1480, CA-170, CK-301, M-7824, HTI-1088(HT I-131, SHR-1316), MSB-2311, AK-106, AVA-004, BBI-801, CA-327, CBA-0710, CBT-502, FPT-155, IKT-201, IKT-703, 10-103, JS-003, KD-033, KY-1003, MCLA-145, MT-5050, SNA-02, BCD-135, AP L-502 (CBT-402 or TQB2450), IMC-001, KD-045, INBRX-105, KN-046, IMC-2102, IMC-2101, KD-005 , IMM-2502, 89Zr-CX-072, 89Zr-DFO-6E11, KY-1055, MEDI-1109, MT-5594, SL-279252, DSP-106, Gensci-047, REMD-290, N-809, PRS-344, FS-222, GEN-1046, BH-29xx, or FS-118, or derivatives thereof.

[0167] In some embodiments, the checkpoint inhibitor is an antagonist / inhibitor of CTLA4. In some embodiments, the checkpoint inhibitor is a small molecule antagonist of CTLA4. In some embodiments, the checkpoint inhibitor is an anti-CTLA4 antibody. CTLA4 is part of the CD28-B7 immunoglobulin superfamily of immune checkpoint molecules and acts to negatively regulate T cell activation (particularly CD28-dependent T cell responses). CTLA4 competes for binding to ligands common to CD28, such as CD80 (B7-1) and CD86 (B7-2), and binds these ligands with higher affinity than CD28. Blocking CTLA4 activity (e.g., using anti-CTLA4 antibodies) is believed to enhance CD28-mediated co-stimulation (leading to increased T cell activation / priming), affect T cell development, and / or deplete Tregs (e.g., intratumoral Tregs). In some embodiments, the CTLA4 antagonist is a small molecule, a nucleic acid, a polypeptide (e.g., an antibody), a carbohydrate, a lipid, a metal, or a toxin. In some embodiments, the CTLA-4 inhibitor comprises ipilimumab (IBI310, BMS-734016, MDX010, MDX-CTLA4, MEDI4736), tremelimumab (CP-675, CP-675, 206), APL-509, AGEN1884, CS1002, AGEN1181, abatacept (Orencia, BMS-188667, RG2077), BCD-145, ONC-392, ADU-1604, REGN4659, ADG116, KN044, KN046, or a derivative thereof.

[0168] In some embodiments, the anti-PD-1 antibody or antibody fragment is MDX-1106 (nivolumab), MK-3475 (pembrolizumab, Keytruda®) cemiplimab, dostallimab, MEDI-0680 (AMP-514), PDR001, REGN2810, MGA-012, JNJ-63723283, BI754091, BGB-108, BGB-A317, JS-001, STI-A1110, INCSHR-1210, PF-06801591, TSR-042, AM0001, ENUM244C8, or ENUM388D4. In some embodiments, the PD-1 binding antagonist is an anti-PD-1 immunoadhesin. In some embodiments, the anti-PD-1 immunoadhesin is AMP-224. In some embodiments, the anti-PD-L1 antibody or antibody fragment is YW243.55.S70, MPDL3280A (atezolizumab), MDX-1105, MEDI4736 (durvalumab), MSB0010718C (avelumab), LY3300054, STI-A1014, KN035, FAZ053, or CX-072.

[0169] In some embodiments, the immune checkpoint inhibitor comprises a LAG-3 inhibitor (e.g., an antibody, an antibody conjugate, or an antigen-binding fragment thereof). In some embodiments, the LAG-3 inhibitor comprises a small molecule, a nucleic acid, a polypeptide (e.g., an antibody), a carbohydrate, a lipid, a metal, or a toxin. In some embodiments, the LAG-3 inhibitor comprises a small molecule. In some embodiments, the LAG-3 inhibitor comprises a LAG-3 binding agent. In some embodiments, the LAG-3 inhibitor comprises an antibody, an antibody conjugate, or an antigen-binding fragment thereof. In some embodiments, the LAG-3 inhibitor comprises eftiragimob alfa (IMP321, IMP-321, EDDP-202, EOC-202), leratolimab (BMS-986016), GSK2831781 (IMP-731), LAG525 (IMP701), TSR-033, EVIP321 (soluble LAG-3 protein), BI754111, IMP761, REGN3767, MK-4280, MGD-013, XmAb22841, INCAGN-2385, ENUM-006, AVA-017, AM-0003, iOnctura anti-LAG-3 antibody, Arcus Biosciences LAG-3 antibody, Sym022, a derivative thereof, or an antibody that competes with any of the foregoing.

[0170] In some embodiments, the immune checkpoint inhibitor is monovalent and / or monospecific, hi some embodiments, the immune checkpoint inhibitor is multivalent and / or multispecific.

[0171] In some embodiments, immune checkpoint inhibitors may be administered in combination with immunomodulatory molecules or cytokines. An immunomodulatory profile is necessary to elicit an efficient immune response and balance the immunity of a subject. Examples of suitable immunomodulatory cytokines include, but are not limited to, interferons (e.g., IFNα, IFNβ, and IFNγ), interleukins (e.g., IL-1, IL-2, IL-3, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12, and IL-20), tumor necrosis factors (e.g., TNFα and TNFβ), erythropoietin (EPO), FLT-3 ligand, gIp10, TCA-3, MCP-1, MIF, MIP-1α, MIP-1β, Rantes, macrophage colony-stimulating factor (M-CSF), granulocyte colony-stimulating factor (G-CSF), or granulocyte-macrophage colony-stimulating factor (GM-CSF), and functional fragments thereof. In some embodiments, any immunomodulatory chemokine that binds to a chemokine receptor (i.e., a CXC, CC, C, or CX3C chemokine receptor) can be used in the context of the present disclosure. Examples of chemokines include, but are not limited to, MIP-3α (Lax), MIP-3β, Hcc-1, MPIF-1, MPIF-2, MCP-2, MCP-3, MCP-4, MCP-5, Eotaxin, Tarc, Elc, I309, IL-8, GCP-2 Groα, Gro-β, Nap-2, Ena-78, Ip-10, MIG, I-Tac, SDF-1, or BCA-1 (Blc), as well as functional fragments thereof. In some embodiments, an immunomodulatory molecule is included in any of the treatments provided herein.

[0172] In some embodiments, the methods provided herein include administering an immune checkpoint inhibitor to an individual (e.g., as described above). In some embodiments, the methods provided herein include selecting / identifying a treatment or one or more treatment options for an individual, where the treatment or one or more treatment options includes an immune checkpoint inhibitor (e.g., as described above). In some embodiments, the treatment or one or more treatment options further includes an additional anti-cancer therapy. In some embodiments, the additional anti-cancer therapy is an agent other than an ICI (e.g., as described below) or a second ICI (e.g., as described above).

[0173] In some embodiments, the anti-cancer therapy comprises a small molecule inhibitor, a chemotherapeutic agent, a cancer immunotherapy, an antibody, a cell therapy, a nucleic acid, surgery, radiation therapy, an anti-angiogenic therapy, an anti-DNA repair therapy, an anti-inflammatory therapy, an anti-neoplastic agent, a growth inhibitory agent, an anti-hormonal agent, a kinase inhibitor, a peptide, gene therapy, a vaccine, a platinum-based chemotherapeutic agent, an immunotherapy, a growth inhibitory agent, a cytotoxic agent, or any combination thereof.

[0174] In some embodiments, the anti-cancer therapy comprises chemotherapy. In some embodiments, the methods provided herein comprise administering chemotherapy to an individual in combination with another anti-cancer therapy, such as, for example, an immune checkpoint inhibitor. Examples of chemotherapeutic agents include alkylating agents (e.g., thiotepa and cyclophosphamide), alkylsulfonates (e.g., busulfan, improsulfan, piposulfan), aziridines (e.g., benzodopa, carboquone, meturedopa, and uredopa), ethylenimines and methylameramines (including altretamine, triethylenemelamine, triethylenephosphoramide, triethylenethiophosphoramide, and trimethylolmelamine), acetogenins (particularly bullatacin and blutacin), and the like. latacinone), camptothecins (including the synthetic analog topotecan), bryostatins, kallistatins, CC-1065 (including the synthetic analogs adozelesin, carzelesin, and bizelesin), cryptophycins (specifically cryptophycin 1 and cryptophycin 8), dolastatins, duocarmycins (including the synthetic analogs KW-2189 and CB1-TM1), erytherobin, pancratistatins, sarcodictins, spongiostatins, nitrogen mustards (e.g., chlorambucil, clorambucil, mafazine, chlorophosphamide, estramustine, ifosfamide, mechlorethamine, mechlorethamine oxide hydrochloride, melphalan, nobembitine, phenesterine, prednimustine, trofosfamide, and uracil mustard), nitrosoureas (carmustine, chlorozotocin, fotemustine, lomustine, nimustine, and ranimustine), antibiotics (e.g., enediyne antibiotics such as calicheamicin, especially calicheamicin gamma ll and calicheamicin omega ll). , dynemicins (including, for example, dynemicin A), bisphosphonates (e.g., clodronate), esperamicins, and neocarzinostatin chromophores and related chromoprotein enediyne antibiotic chromophores, aclacinomycin, actinomycin, autramycin, azaserine, bleomycin, cactinomycin, carabicin, carminomycin, carzinophilin, chromomycin, dactinomycin, daunorubicin, detorubicin, 6-diazo-5-oxo-L-norleucine,doxorubicin (including morpholinodoxorubicin, cyanomorpholinodoxorubicin, 2-pyrrolino-doxorubicin, and deoxydoxorubicin), epirubicin, esorubicin, idarubicin, marcelomycin, mitomycin (e.g., mitomycin C), mycophenolic acid, nogalamycin, olivomycin, peplomycin, potfilomycin, puromycin, keramycin, rodorubicin, streptonigrin, streptozocin, tubercidin, ubenimex, zinostatin, and zorubicin), antimetabolites (e.g., methotrexate ... trexate and 5-fluorouracil (5-FU)), folic acid analogs (e.g., denopterin, pteropterin, trimetrexate), purine analogs (e.g., fludarabine, 6-mercaptopurine, thiamiprine, thioguanine), pyrimidine analogs (e.g., ancitabine, azacitidine, 6-azauridine, carmofur, cytarabine, dideoxyuridine, doxifluridine, enocitabine, and floxuridine), androgens (e.g., calsterone, dromostanolone propionate, epithiostanol, mepitiostane, and tesulfamethoxazole, tetracycline ... antiadrenal drugs (e.g., mitotane and trilostane), folic acid supplements (e.g., folic acid), aceglatone, aldophosphamide glycosides, aminolevulinic acid, eniluracil, amsacrine, bestravcil, bisantrene, edatraxate, defoamin, demecolcine, diazicon, elformitin, elliptinium acetate, epothilone, etoglucide, gallium nitrate, hydroxyurea, lentinan, lonidain, maytansinoids (e.g., maytansine and ansamitocin), mitoguazone, mitoxantrone, mopidanmol, nifedipine, Traerin, pentostatin, phenamet, pirarubicin, losoxantrone, podophyllic acid, 2-ethylhydrazide, procarbazine, PSK polysaccharide complex, razoxane, rhizoxin, schizophyllan, spirogermanium, tenuazonic acid, triazicon, 2,2',2"-trichlorotriethylamine, trichothecenes (especially T-2 toxin, veracrine A, roridin A, and anguidin), urethane, vindesine, dacarbazine, mannomustine, mitobronitol, mitolactol, pipobroman, gacytosine, arabinoside ("Ara-C");Cyclophosphamide, taxoids (e.g., paclitaxel and docetaxel gemcitabine), 6-thioguanine, mercaptopurine, platinum coordination complexes (e.g., cisplatin, oxaliplatin, and carboplatin), vinblastine, platinum, etoposide (VP-16), ifosfamide, mitoxantrone, vincristine, vinorelbine, novantrone, teniposide, edatrexate, daunomycin, aminopterin, xeloda, ibandronate, irinotecan (e.g., CPT-1, l), topoisomerase inhibitors RFS2000, difluoromethylornithine (DMFO), retinoids (e.g., retinoic acid), capecitabine, carboplatin, procarbazine, plicomycin, gemcitabine, navelbine, farnesyl protein transferase inhibitors, transplatinum, and pharma- ceutically acceptable salts, acids, or derivatives of any of the above.

[0175] Some non-limiting examples of chemotherapeutic agents that can be combined with the anti-cancer therapies of the present disclosure, e.g., immune checkpoint inhibitors, are carboplatin (Paraplatin), cisplatin (Platinol, Platinol-AQ), cyclophosphamide (Cytoxan, Neosar), docetaxel (Taxotere), doxorubicin (Adriamycin), erlotinib (Tarceva), etoposide (VePesi), and cisplatin (Platinol-AQ). d), fluorouracil (5-FU), gemcitabine (Gemzar), imatinib mesylate (Gleevec), irinotecan (Camptosar), methotrexate (Folex, Mexate, Amethopterin), paclitaxel (Taxol, Abraxane), sorafinib (Nexavar), sunitinib (Sutent), topotecan (Hycamtin), vincristine (Oncovin, Vincasar PFS), and vinblastine (Velban).

[0176] In some embodiments, the anti-cancer therapy comprises a kinase inhibitor. In some embodiments, the methods provided herein include administering to an individual a kinase inhibitor in combination with another anti-cancer therapy, such as, for example, an immune checkpoint inhibitor. Examples of kinase inhibitors include those that target one or more receptor tyrosine kinases (e.g., BCR-ABL, B-Raf, EGFR, HER-2 / ErbB2, IGF-IR, PDGFR-a, PDGFR-β, cKit, Flt-4, Flt3, FGFR1, FGFR3, FGFR4, CSF1R, c-Met, RON, c-Ret, or ALK), those that target one or more cytoplasmic tyrosine kinases (e.g., c-SRC, c-YES, Abl, or JAK-2), those that target one or more serine / threonine kinases (e.g., ATM, Aurora A and B, CDK, mTOR, PKCi, PLK, b-Raf, S6K, or STK11 / LKB1), or those that target one or more lipid kinases (e.g., PI3K or SKI). Small molecule kinase inhibitors include PHA-739358, nilotinib, dasatinib, PD166326, NSC743411, lapatinib (GW-572016), canertinib (CI-1033), semaxinib (SU5416), vatalanib (PTK787 / ZK222584), sutent (SU11248), sorafenib (BAY43-9006), or leflunomide (SU101). Additional non-limiting examples of tyrosine kinase inhibitors include imatinib (Gleevec / Glivec) and gefitinib (Iressa).

[0177] In some embodiments, the anti-cancer therapy comprises an anti-angiogenic agent. In some embodiments, the methods provided herein comprise administering an anti-angiogenic agent to an individual in combination with another anti-cancer therapy, such as, for example, an immune checkpoint inhibitor. Angiogenesis inhibitors prevent the extensive growth of blood vessels (angiogenesis) that tumors need to survive. Non-limiting examples of angiogenesis mediating molecules or angiogenesis inhibitors that can be used in the methods of the present disclosure include soluble VEGF (e.g., VEGF isoforms (e.g., VEGF121 and VEGF165), VEGF receptors (e.g., VEGFR1, VEGFR2), and co-receptors (e.g., neuropilin-1 and neuropilin-2)), NRP-1, angiopoietin 2, TSP-1 and TSP-2, angiostatin and related molecules, endostatin, vasostatin, calreticulin, platelet factor-4, TIMPs and CDAI, Meth-1 and Meth-2, IFNα, IFN-β and IFN-γ, CXCL10, IL-4, IL-12, and IL-18, prothrombin (kringle domain- 2), antithrombin III fragment, prolactin, VEGI, SPARC, osteopontin, maspin, canstatin, proliferin-related protein, restin, and drugs (e.g., bevacizumab, itraconazole, carboxyamidotriazole, TNP-470, CM101, IFN-a, platelet factor-4, suramin, SU5416, thrombospondin, VEGFR antagonists, angiogenesis inhibitory steroids, and heparin), cartilage-derived angiogenesis inhibitor, matrix metalloproteinase inhibitor, 2-methoxyestradiol, tecogalan, tetrathiomolybdate, thalidomide, thrombospondin, prolactin νβ3 inhibitor, linomide, or tasquinimod. In some embodiments, known therapeutic candidates that may be used according to the methods of the present disclosure include naturally occurring angiogenesis inhibitors, including, but not limited to, angiostatin, endostatin, or platelet factor-4. In another embodiment, potential therapeutic agents that may be used according to the methods of the present disclosure include, but are not limited to, specific inhibitors of endothelial cell proliferation, such as TNP-470, thalidomide, and interleukin-12.Still other antiangiogenic agents that may be used according to the methods of the present disclosure include those that neutralize angiogenic molecules, including, but not limited to, antibodies against fibroblast growth factor, antibodies against vascular endothelial growth factor, antibodies against platelet-derived growth factor, or antibodies or other types of inhibitors of receptors for EGF, VEGF, or PDGF. In some embodiments, antiangiogenic agents that may be used according to the methods of the present disclosure include, but are not limited to, suramin and its analogs, and tecogalan. In other embodiments, antiangiogenic agents that may be used according to the methods of the present disclosure include, but are not limited to, agents that neutralize receptors for angiogenic factors or agents that disrupt vascular basement membranes and extracellular matrices, including, but are not limited to, metalloprotease inhibitors and angiogenic inhibitory steroids. Another group of antiangiogenic compounds that may be used according to the methods of the present disclosure includes, but is not limited to, anti-adhesion molecules, such as antibodies against integrin αvβ3. Still other antiangiogenic compounds or compositions that may be used according to the methods of the present disclosure include kinase inhibitors, thalidomide, itraconazole, carboxyamidotriazole, CM101, IFN-α, IL-12, SU5416, thrombospondin, cartilage-derived angiogenesis inhibitor, 2-methoxyestradiol, tetrathiomolybdate, thrombospondin, prolactin, and linomide. In one particular embodiment, an antiangiogenic compound that may be used according to the methods of the present disclosure is an antibody against VEGF, such as Avastin® / bevacizumab (Genentech).

[0178] In some embodiments, the anti-cancer therapy comprises an anti-DNA repair therapy. In some embodiments, the methods provided herein comprise administering an anti-DNA repair therapy to an individual in combination with another anti-cancer therapy, such as, for example, an immune checkpoint inhibitor. In some embodiments, the anti-DNA repair therapy is a PARP inhibitor (e.g., talazoparib, rucaparib, olaparib), a RAD51 inhibitor (e.g., RI-1), or an inhibitor of DNA damage response kinase (e.g., CHCK1 (e.g., AZD7762), ATM (e.g., KU-55933, KU-60019, NU7026, or VE-821), and ATR (e.g., NU7026)).

[0179] In some embodiments, the anti-cancer therapy includes a radiosensitizer. In some embodiments, the methods provided herein include administering a radiosensitizer to an individual, for example in combination with another anti-cancer therapy, such as an immune checkpoint inhibitor. Exemplary radiosensitizers include hypoxia radiosensitizers (e.g., misonidazole, metronidazole) and trans-sodium crocetinate, a compound that helps increase the diffusion of oxygen to hypoxic tumor tissue. The radiosensitizer can also be a DNA damage response inhibitor that interferes with base excision repair (BER), nucleotide excision repair (NER), mismatch repair (MMR), including homologous recombination (HR) and non-homologous end joining (NHEJ) and direct repair mechanisms. Single-strand break (SSB) repair mechanisms include the BER, NER, or MMR pathways, while double-strand break (DSB) repair mechanisms are composed of the HR and NHEJ pathways. Radiation causes DNA breaks that are lethal if not repaired. SSBs are repaired via a combination of BER, NER, and MMR mechanisms, using the intact DNA strand as a template. The primary pathway for SSB repair is BER, which utilizes a family of related enzymes called poly(ADP-ribose) polymerases (PARPs). Radiosensitizers can therefore include DNA damage response inhibitors, such as PARP inhibitors.

[0180] In some embodiments, the anti-cancer therapy includes an anti-inflammatory agent. In some embodiments, the methods provided herein include administering an anti-inflammatory agent to an individual in combination with another anti-cancer therapy, such as, for example, an immune checkpoint inhibitor. In some embodiments, the anti-inflammatory agent is an agent that blocks, inhibits, or reduces inflammation or signaling from an inflammatory signaling pathway. In some embodiments, the anti-inflammatory agent inhibits or reduces the activity of any one or more of the following: IL-1, IL-2, IL-3, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12, IL-13, IL-15, IL-18, IL-23, interferon (IFN) (e.g., IFNα, IFNβ, IFNγ, IFN-γ)-inducing factor (IGIF), transforming growth factor-β (IFN-β), IFN-β-inducing factor ... TGF-β), transforming growth factor-α (TGF-α), tumor necrosis factor (e.g., TNF-α, TNF-β, TNF-RI, TNF-RII), CD23, CD30, CD40L, EGF, G-CSF, GDNF, PDGF-BB, RANTES / CCL5, IKK, NF-κB, TLR2, TLR3, TLR4, TLR5, TLR6, TLR7, TLR8, TLR9, and / or their cognate receptors. In some embodiments, the anti-inflammatory agent is IL-1 or an IL-1 receptor antagonist, such as anakinra (Kineret®), rilonacept, or canakinumab. In some embodiments, the anti-inflammatory agent is an IL-6 or IL-6 receptor antagonist, such as an anti-IL-6 antibody or an anti-IL-6 receptor antibody (such as tocilizumab (ACTEMRA®), olokizumab, clazakizumab, sarilumab, sirumab, silkumab, siltuximab, or ALX-0061). In some embodiments, the anti-inflammatory agent is a TNF-α antagonist, such as an anti-TNFα antibody (such as infliximab (Remicade®), golimumab (Simponi®), adalimumab (Humira®), certolizumab pegol (Cimzia®), or etanercept). In some embodiments, the anti-inflammatory agent is a corticosteroid.Examples of corticosteroids include cortisone (hydrocortisone, hydrocortisone sodium phosphate, hydrocortisone sodium succinate, Ala-Cort®, Hydrocort Acetate®, hydrocortone phosphate Lanacort®, Solu-Cortef®), Decadron (dexamethasone, dexamethasone acetate, dexamethasone sodium phosphate, Dexasone®, Diodex®, Hexadrol®, Maxidex®), methylprednisolone (6-methylprednisolone, methylprednisolone acetate, methylprednisolone sodium succinate, Duralone®, Medralone®, Medrol®, M-Prednisol®, Solu-Medrol®), prednisolone (Delta-Cortef®, ORAPRED®, Pediapred®, Prezone®), and prednisone (Deltasone®, Liquid Pred®, Meticorten®, Orasone®), and bisphosphonates (e.g., pamidronate (Aredia®), and zoledronic acid (Zometac®)).

[0181] In some embodiments, the anti-cancer therapy includes an anti-hormonal agent. In some embodiments, the methods provided herein include administering an anti-hormonal agent to an individual in combination with another anti-cancer therapy, such as, for example, an immune checkpoint inhibitor. Anti-hormonal agents are agents that act to regulate or inhibit hormone action on tumors. Examples of antihormonal agents include antiestrogens and selective estrogen receptor modulators (SERMs), such as tamoxifen (including NOLVADEX® tamoxifen), raloxifene, droloxifene, 4-hydroxytamoxifen, trioxyphene, ketoxifene, LY117018, onapristone, and FARESTON®, toremifene, aromatase inhibitors (which inhibit the enzyme aromatase, which regulates estrogen production in the adrenal glands) (e.g., 4(5)-imidazole, aminoglutethimide, MEGACE® megestrol acetate, AROMASIN® exemestane, formestane, fadrozole, RIVISOR® vorozole, FEMARA® letrozole, and ARIMIDEX® (anastrozole)); Included are antiandrogens (e.g., flutamide, nilutamide, bicalutamide, leuprolide, goserelin), troxacitabine (a 1,3-dioxolane nucleoside cytosine analog); antisense oligonucleotides (particularly oligonucleotides that inhibit the expression of genes in signal transduction pathways involved in abnormal cell proliferation, such as, for example, PKC-α, Raf, H-Ras, and epidermal growth factor receptor (EGF-R)), vaccines such as gene therapy vaccines (e.g., ALLOVECTIN® vaccine, LEUVECTIN® vaccine, and VAXID® vaccine), PROLEUKIN® rIL-2, LURTOTECAN® topoisomerase 1 inhibitors, ABARELIX® rmRH, as well as pharma- ceutically acceptable salts, acids, or derivatives of any of the above.

[0182] In some embodiments, the anti-cancer therapy comprises an antimetabolite chemotherapeutic agent. In some embodiments, the methods provided herein comprise administering an antimetabolite chemotherapeutic agent to an individual in combination with another anti-cancer therapy, such as, for example, an immune checkpoint inhibitor. An antimetabolite chemotherapeutic agent is a drug that is structurally similar to a metabolite, but cannot be productively used in the body. Many antimetabolite chemotherapeutic agents interfere with the production of RNA or DNA. Examples of antimetabolite chemotherapeutic agents include gemcitabine (GEMZAR®), 5-fluorouracil (5-FU), capecitabine (XELODA™), 6-mercaptopurine, methotrexate, 6-thioguanine, pemetrexed, raltitrexed, arabinosylcytosine, ARA-C, cytarabine (CYTOSAR-U®), dacarbazine (DTIC-DOMED), azocytosine, deoxycytosine, pyridomidene, fludarabine (FLUDARA®), cladrabine, and 2-deoxy-D-glucose. In some embodiments, the antimetabolite chemotherapeutic agent is gemcitabine. Gemcitabine hydrochloride is sold by Eli Lilly under the trademark GEMZAR®.

[0183] In some embodiments, the anti-cancer therapy comprises a platinum-based chemotherapeutic agent. In some embodiments, the methods provided herein comprise administering to an individual a platinum-based chemotherapeutic agent in combination with another anti-cancer therapy, such as, for example, an immune checkpoint inhibitor. A platinum-based chemotherapeutic agent is a chemotherapeutic agent that comprises an organic compound that contains platinum as an integral part of the molecule. In some embodiments, the chemotherapeutic agent is a platinum agent. In some such embodiments, the platinum agent is selected from cisplatin, carboplatin, oxaliplatin, nedaplatin, triplatin tetranitrate, phenanthriplatin, picoplatin, or satraplatin.

[0184] In some embodiments, the anti-cancer therapy comprises a heat shock protein (HSP) inhibitor, a MYC inhibitor, an HDAC inhibitor, an immunotherapy, a neo-antigen, a vaccine, or a cell therapy. In some embodiments, the anti-cancer therapy comprises one or more of a chemotherapeutic drug, a VEGF inhibitor, an integrin β3 inhibitor, a statin, an EGFR inhibitor, an mTOR inhibitor, a PI3K inhibitor, a MAPK inhibitor, or a CDK4 / 6 inhibitor.

[0185] In some embodiments, the anti-cancer therapy includes a kinase inhibitor. In some embodiments, the methods provided herein include administering a kinase inhibitor to an individual in combination with another anti-cancer therapy, such as, for example, an immune checkpoint inhibitor. In some embodiments, the kinase inhibitor is crizotinib, alectinib, ceritinib, lorlatinib, brigutinib, ensartinib (X-396), repotrectinib (TPX-005), entrectinib (RXDX-101), AZD3463, CEP-37440, berizatinib (TSR-011), ASP3026, KRCA-0008, TQ-B3139, TPX-0131, or TAE684 (NVP-TAE684). Further examples of ALK kinase inhibitors that can be used in accordance with any of the methods provided herein are described in Examples 3-39 of WO2005016894, incorporated herein by reference.

[0186] In some embodiments, the anti-cancer therapy includes a heat shock protein (HSP) inhibitor. In some embodiments, the methods provided herein include administering an HSP inhibitor to an individual in combination with another anti-cancer therapy, such as, for example, an immune checkpoint inhibitor. In some embodiments, the HSP inhibitor is a pan-HSP inhibitor, such as KNK423. In some embodiments, the HSP inhibitor is an HSP70 inhibitor, such as cmHsp70.1, quercetin, VER155008, or 17-AAD. In some embodiments, the HSP inhibitor is an HSP90 inhibitor. In some embodiments, the HSP90 inhibitor is 17-AAD, Debio0932, ganetespib (STA-9090), retaspimycin hydrochloride (retaspimycin, IPI-504), AUY922, alvespimycin (KOS-1022, 17-DMAG), tanespimycin (KOS-953, 17-AAG), DS2248, or AT13387 (onarespib). In some embodiments, the HSP inhibitor is an HSP27 inhibitor, such as apatorsen (OGX-427).

[0187] In some embodiments, the anti-cancer therapy comprises a MYC inhibitor. In some embodiments, the methods provided herein include administering to an individual a MYC inhibitor in combination with another anti-cancer therapy, such as, for example, an immune checkpoint inhibitor. In some embodiments, the MYC inhibitor is MYCi361 (NUCC-0196361), MYCi975 (NUCC-0200975), Omomyc (dominant negative peptide), ZINC16293153 (Min9), 10058-F4, JKY-2-169, 7594-0035, or an inhibitor of MYC / MAX dimerization and / or MYC / MAX / DNA complex formation.

[0188] In some embodiments, the anti-cancer therapy comprises a histone deacetylase (HDAC) inhibitor. In some embodiments, the methods provided herein comprise administering to an individual an HDAC inhibitor in combination with another anti-cancer therapy, such as, for example, an immune checkpoint inhibitor. In some embodiments, the HDAC inhibitor is belinostat (PXD101, Beleodac®), SAHA (vorinostat, suberoylanilide hydroxamine, Zolinza®), panobinostat (LBH589, LAQ-824), ACY1215 (Rocilinostat), xinostat (JNJ-26481585), abexinostat (PCI-24781), pracinostat (SB939), gibinostat (ITF2357), resminostat (4SC-201), trichostatin A (TSA), MS-275 (echinostat), romidepsin (depsipeptide, FK228), MGCD0103 (mosetinostat), BML-210, CAY10603, Valproic acid, MC1568, CUDC-907, CI-994 (tacedinaline), Pivanex (AN-9), AR-42, chidamide (CS055, HBI-8000), CUDC-101, CHR-3996, MPT0E028, BRD8430, MRLB-223, apicidin, RGFP966, BG45, PCI-34051, C149 (NCC14 9), TMP269, Cpd2, T247, T326, LMK235, C1A, HPOB, Nextulastat A, Befexamac, CBHA, phenylbutyric acid, MC1568, SNDX275, Scriptaid, Merck60, PX089344, PX105684, PX117735, PX117792, PX117245, PX105844, compound 12 described in Li et al., Cold Spring Harb Perspect Med (2016) 6 (10): a026831, or PX117445.

[0189] In some embodiments, the anti-cancer therapy comprises a VEGF inhibitor. In some embodiments, the methods provided herein include administering to an individual a VEGF inhibitor in combination with another anti-cancer therapy, such as, for example, an immune checkpoint inhibitor. In some embodiments, the VEGF inhibitor is bevacizumab (Avastin®), BMS-690514, ramucirumab, pazopanib, sorafenib, sunitinib, golvatinib, vandetanib, cabozantinib, levantinib, axitinib, cediranib, tivozanib, lucitanib, semaxanib, nindentanib, regorafiniib, or aflibercept.

[0190] In some embodiments, the anti-cancer therapy comprises an integrin β3 inhibitor. In some embodiments, the methods provided herein include administering an integrin β3 inhibitor to an individual in combination with another anti-cancer therapy, such as, for example, an immune checkpoint inhibitor. In some embodiments, the integrin β3 inhibitor is anti-avb3 (clone LM609), cilengitide (EMD121974, NSC, 707544), siRNA, GLPG0187, MK-0429, CNTO95, TN-161, etaracizumab (MEDI-522), intetumumab (CNTO95) (anti-αV subunit antibody), avituzumab (EMD525797 / DI17E6) (anti-αV subunit antibody), JSM6427, SJ749, BCH-15046, SCH221153, or SC56631. In some embodiments, the anti-cancer therapy comprises an αIIbβ3 integrin inhibitor. In some embodiments, the methods provided herein include administering an αIIbβ3 integrin inhibitor to an individual in combination with another anti-cancer therapy, such as, for example, an immune checkpoint inhibitor. In some embodiments, the αIIbβ3 integrin inhibitor is abciximab, eptifibatide (Integrilin®), or tirofiban (Aggrastat®).

[0191] In some embodiments, the anti-cancer therapy includes a statin or statin-based drug. In some embodiments, the methods provided herein include administering a statin or statin-based drug to an individual in combination with another anti-cancer therapy, such as, for example, an immune checkpoint inhibitor. In some embodiments, the statin or statin-based drug is simvastatin, atorvastatin, fluvastatin, pitavastatin, pravastatin, rosuvastatin, or cerivastatin.

[0192] In some embodiments, the anti-cancer therapy comprises an mTOR inhibitor. In some embodiments, the methods provided herein include administering an mTOR inhibitor to an individual in combination with another anti-cancer therapy, such as, for example, an immune checkpoint inhibitor. In some embodiments, the mTOR inhibitor is temsirolimus (CCI-779), KU-006379, PP242, Torin 1, Torin 2, ICSN3250, Rapalink-1, CC-223, Sirolimus (rapamycin), Everolimus (RAD001), Ductosilibu (NVP-BEZ235), GSK2126458, WAY-001, WAY-600, WYE-687, WYE-354, SF1126, XL765, INK128 (MLN012), AZD8055, OSI027, AZD2014, or AP-23573.

[0193] In some embodiments, the anti-cancer therapy comprises a PI3K inhibitor. In some embodiments, the methods provided herein include administering a PI3K inhibitor to an individual in combination with another anti-cancer therapy, such as, for example, an immune checkpoint inhibitor. In some embodiments, the PI3K inhibitor is GSK2636771, buparlisib (BKM120), AZD8186, copanlisib (BAY80-6946), LY294002, PX-866, TGX115, TGX126, BEZ235, SF1126, idelalisib (GS-1101, CAL-101), pictilisib (GDC-094), GDC0032, IPI145, INK1117 (MLN1117), SAR260301, KIN-193 (AZD6482), duvelisib, GS-9820, GSK2636771, GDC-0980, AMG319, pazovanib, or alpelisib (BYL719, Piqray).

[0194] In some embodiments, the anti-cancer therapy comprises a MAPK inhibitor. In some embodiments, the methods provided herein include administering a MAPK inhibitor to an individual in combination with another anti-cancer therapy, such as, for example, an immune checkpoint inhibitor. In some embodiments, the MAPK inhibitor is SB203580, SKF-86002, BIRB-796, SC-409, RJW-67657, BIRB-796, VX-745, RO3201195, SB-242235, or MW181.

[0195] In some embodiments, the anti-cancer therapy includes a CDK4 / 6 inhibitor. In some embodiments, the methods provided herein include administering a CDK4 / 6 inhibitor to an individual in combination with another anti-cancer therapy, such as, for example, an immune checkpoint inhibitor. In some embodiments, the CDK4 / 6 inhibitor is ribociclib (Kisqali®, LEE011), palbociclib (PD0332991, Ibrance®), or abemaciclib (LY2835219).

[0196] In some embodiments, the anti-cancer therapy includes an EGFR inhibitor. In some embodiments, the methods provided herein include administering an EGFR inhibitor to an individual in combination with another anti-cancer therapy, such as, for example, an immune checkpoint inhibitor. In some embodiments, the EGFR inhibitor is cetuximab, panitumumab, lapatinib, gefitinib, vandetanib, dacomitinib, icotinib, osimertinib (AZD9291), afatanib, olmutinib, EGF816 (nazartinib), avitinib (AC0010), rociletinib (CO-1686), BMS-690514, YH5448, PF-06747775, ASP8273, PF299804, AP26113, or erlotinib. In some embodiments, the EGFR inhibitor is gefitinib or cetuximab.

[0197] In some embodiments, the anti-cancer therapy comprises a cancer immunotherapy, such as a cancer vaccine, a cell-based therapy, a T cell receptor (TCR)-based therapy, an adjuvant immunotherapy, a cytokine immunotherapy, and an oncolytic virus therapy. In some embodiments, the methods provided herein comprise administering to an individual a cancer immunotherapy, such as a cancer vaccine, a cell-based therapy, a T cell receptor (TCR)-based therapy, an adjuvant immunotherapy, a cytokine immunotherapy, and an oncolytic virus therapy, in combination with another anti-cancer therapy, such as, for example, an immune checkpoint inhibitor. In some embodiments, the cancer immunotherapy comprises a small molecule, a nucleic acid, a polypeptide, a carbohydrate, a toxin, a cell-based agent, or a cell-binding agent. Examples of cancer immunotherapies are described in more detail herein, but are not intended to be limiting. In some embodiments, the cancer immunotherapy activates one or more aspects of the immune system to attack cells (e.g., tumor cells) that express neo-antigens (e.g., neo-antigens expressed by the cancer of the present disclosure). The cancer immunotherapies of the present disclosure are contemplated for use as a monotherapy or in a combination approach including any combination or number of two or more, subject to medical judgment. Any of the cancer immunotherapies (optionally as a monotherapy or in combination with another cancer immunotherapy or other therapeutic agent described herein) can be used in any of the methods described herein.

[0198] In some embodiments, the cancer immunotherapy comprises a cancer vaccine. A variety of cancer vaccines have been tested using different approaches to promote immune responses against cancer (see, for example, Emens LA, Expert Opin Emerg Drugs 13(2):295-308(2008) and US20190367613). Approaches have been designed to enhance the response of B cells, T cells, or professional antigen-presenting cells against tumors. Exemplary types of cancer vaccines include, but are not limited to, DNA-based vaccines, RNA-based vaccines, viral transduction vaccines, peptide-based vaccines, dendritic cell vaccines, oncolytic viruses, whole tumor cell vaccines, tumor antigen vaccines, and the like. In some embodiments, the cancer vaccine can be prophylactic or therapeutic. In some embodiments, the cancer vaccine is formulated as a peptide-based vaccine, a nucleic acid-based vaccine, an antibody-based vaccine, or a cell-based vaccine. For example, vaccine compositions can include naked cDNA in cationic lipid formulations, lipopeptides (e.g., Vitiello, A. et al., J. Clin. Invest. 95:341, 1995), naked cDNA or peptides encapsulated in, for example, poly(DL-lactide-co-glycolide) ("PLG") microspheres (see, e.g., Eldridge, et al., Molec. Immunol. 28:287-294, 1991; Alonso et al., Vaccine 12:299-306, 1994; Jones et al., Vaccine 13:675-681, 1995), peptide compositions contained in immune stimulating complexes (ISCOMs) (see, e.g., Takahashi et al., Nature 344:873-875, 1990; Hu et al., Nature 344:873-875, 1990), and the like. al, Clin. Exp. Immunol. 113:235-243, 1998), or multiple antigen peptide systems (MAP) (see, e.g., Tam, JP, Proc. Natl Acad. Sci. USA 85:5409-5413, 1988; Tam, JP, J. Immunol. Methods 196:17-32, 1996).In some embodiments, the cancer vaccine is formulated as a peptide-based vaccine or a nucleic acid-based vaccine (wherein the nucleic acid encodes the polypeptide). In some embodiments, the cancer vaccine is formulated as an antibody-based vaccine. In some embodiments, the cancer vaccine is formulated as a cell-based vaccine. In some embodiments, the cancer vaccine is a peptide cancer vaccine, in some embodiments, a personalized peptide vaccine. In some embodiments, the cancer vaccine is a multivalent long peptide, multiple peptides, peptide mixtures, hybrid peptides, or peptide-pulsed dendritic cell vaccine (see, e.g., Yamada et al, Cancer Sci, 104:14-21), 2013). In some embodiments, such cancer vaccines enhance anti-cancer responses.

[0199] In some embodiments, the cancer vaccine comprises a polynucleotide encoding a neo-antigen (e.g., a neo-antigen expressed by a cancer of the present disclosure). In some embodiments, the cancer vaccine comprises DNA or RNA encoding a neo-antigen. In some embodiments, the cancer vaccine comprises a polynucleotide encoding a neo-antigen. In some embodiments, the cancer vaccine further comprises one or more additional antigens, neo-antigens, or other sequences that promote antigen presentation and / or immune response. In some embodiments, the polynucleotide is complexed with one or more additional agents, such as liposomes or lipoplexes. In some embodiments, the polynucleotide is taken up and translated by antigen presenting cells (APCs), which then present the neo-antigen via MHC class I on the APC cell surface.

[0200] In some embodiments, the cancer vaccine is selected from sipuleucel-T (Provenge®, Dendreon / Valeant Pharmaceuticals), which is approved for the treatment of asymptomatic or minimally symptomatic metastatic castration-resistant (hormone refractory) prostate cancer, and talimogene laherparepvec (Imlygic®, BioVex / Amgen, formerly known as T-VEC), which is a genetically modified oncolytic virus therapy approved for the treatment of unresectable cutaneous, subcutaneous, and lymph node lesions in melanoma. In some embodiments, the cancer vaccine is selected from the group consisting of pexastimogene devacirepvec (PexaVec / JX-594, SillaJen / formerly Jennerex Biotherapeutics), which is a thymidine kinase-(TK-) deficient vaccinia virus engineered to express GM-CSF for hepatocellular carcinoma (NCT02562755) and melanoma (NCT00429312), pelareorep (Reolysin®, OncolyticsBiotech) (a variant of a respiratory enteric orphan virus (reovirus) that does not replicate in cells in which RAS is not activated in many cancers, including colorectal cancer (NCT01622543), prostate cancer (NCT01619813), head and neck squamous cell carcinoma (NCT01166542), pancreatic adenocarcinoma (NCT00998322), and non-small cell lung cancer (NSCLC) (NCT00861627)); enadenotucirev (NG-348, PsiOxus, formerly known as ColoAdl) (ovarian cancer (NCT02028117), metastatic or adenovirus engineered to express full-length CD80 and an antibody fragment specific for the T-cell receptor CD3 protein in advanced epithelial tumors (e.g., colorectal cancer, bladder cancer, head and neck squamous cell carcinoma, and salivary gland cancer (NCT02636036)); ONCOS-102 (Targovax / formerly Oncos), an adenovirus engineered to express GM-CSF in melanoma (NCT03003676), and peritoneal disease, colorectal cancer, or ovarian cancer (NCT02963831); GL-ONC1 (GLV-1h68 / GLV-1h153, Genelux GmbH) (which are vaccinia viruses engineered to express β-galactosidase (β-gal) / β-glucoronidase or β-gal / human sodium iodide symporter (hNIS), respectively, studied in peritoneal carcinomatosis (NCT01443260), fallopian tube cancer, and ovarian cancer (NCT02759588)); or CG0070 (Cold Genesys) (which is an adenovirus engineered to express GM-CSF in bladder cancer (NCT02365818)), anti-gp100, STINGVAX, GVAX, DCVaxL, and oncolytic virotherapy such as DNX-2401. In some embodiments, the cancer vaccine is selected from JX-929 (SillaJen / formerly JennerexBiotherapeutics (a TK-deficient and vaccinia growth factor-deficient vaccinia virus engineered to express cytosine deaminase capable of converting the prodrug 5-fluorocytosine to the cytotoxic drug 5-fluorouracil), TGO1 and TG02 (Targovax / formerly Oncos) (peptide-based immunotherapeutics targeting difficult-to-treat RAS mutations), TILT-123 (TILT Biotherapeutics) (a modified adenovirus called Ad5 / 3-E2F-δ24-hTNFα-IRES-hIL20), VSV-GP (ViraTherapeutics) (an antigen-specific CD8 + The tumor antigen vaccine is selected from the group consisting of vesicular stomatitis virus (VSV) engineered to express the glycoprotein (GP) of lymphocytic choriomeningitis virus (LCMV), which can be further engineered to express an antigen designed to elicit a T cell response. In some embodiments, the cancer vaccine comprises a vector-based tumor antigen vaccine. A vector-based tumor antigen vaccine can be used as a method to provide a steady supply of antigens to stimulate an anti-tumor immune response. In some embodiments, a vector encoding a tumor antigen is injected into an individual (possibly with other inducer substances such as pro-inflammatory agents or GM-CSF) and is taken up by cells in vivo to produce the specific antigen, which then elicits the desired immune response. In some embodiments, a vector can be used to deliver more than one tumor antigen at a time to increase the immune response. Additionally, recombinant viral, bacterial, or yeast vectors can themselves elicit an immune response, which can also enhance the overall immune response.

[0201] In some embodiments, the cancer vaccine comprises a DNA-based vaccine. In some embodiments, a DNA-based vaccine can be used to stimulate an anti-tumor response. The ability to directly inject DNA encoding an antigen protein to induce a protective immune response has been demonstrated in a number of experimental systems. Vaccination by directly injecting DNA encoding an antigen protein to induce a protective immune response often provokes both cellular and humoral responses. Furthermore, reproducible immune responses to DNA encoding various antigens have been reported in mice that persist essentially throughout the life of the animal (see, e.g., Yankauckas et al. (1993) DNA Cell Biol., 12:771-776). In some embodiments, a plasmid (or other vector) DNA comprising a protein-encoding sequence operably linked to regulatory elements required for gene expression is administered to an individual (e.g., a human patient, a non-human mammal, etc.). In some embodiments, the cells of the individual take up the administered DNA and the coding sequence is expressed. In some embodiments, the antigen so produced becomes a target against which an immune response is directed.

[0202] In some embodiments, the cancer vaccine comprises an RNA-based vaccine. In some embodiments, the RNA-based vaccine can be used to stimulate anti-tumor responses. In some embodiments, the RNA-based vaccine comprises a self-replicating RNA molecule. In some embodiments, the self-replicating RNA molecule can be an RNA replicon derived from an alphavirus. Self-replicating RNA (or "SAM") molecules are well known in the art and can be generated, for example, by using replication elements derived from alphaviruses to replace structural viral proteins with nucleotide sequences that code for proteins of interest. Self-replicating RNA molecules are typically +-strand molecules that can be directly translated after delivery to cells, and this translation provides an RNA-dependent RNA polymerase that generates both antisense and sense transcripts from the delivered RNA. Thus, the delivered RNA leads to the production of multiple daughter RNAs. These daughter RNAs, as well as collinear subgenomic transcripts, can themselves be translated to provide in situ expression of the encoded polypeptide or can be transcribed to provide further transcripts of the same sense strand as the delivered RNA, which are translated to provide in situ expression of the antigen.

[0203] In some embodiments, the cancer immunotherapy comprises a cell-based therapy. In some embodiments, the cancer immunotherapy comprises a T cell-based therapy. In some embodiments, the cancer immunotherapy comprises an adoptive therapy (e.g., an adoptive T cell-based therapy). In some embodiments, the T cells are autologous or allogeneic to the recipient. In some embodiments, the T cells are CD8+ T cells. In some embodiments, the T cells are CD4+ T cells. Adoptive immunotherapy refers to a therapeutic approach for treating cancer or infectious diseases in which immune cells are administered to a host with the goal that the cells mediate direct or indirect specific immunity against (i.e., mount an immune response against) the cancer cells. In some embodiments, the immune response results in inhibition of growth and / or proliferation of tumor cells and / or metastatic cells, and in related embodiments, death and / or resorption of tumor cells. The immune cells can be derived from a different organism / host (exogenous immune cells) or can be cells obtained from the subject organism (autologous immune cells). In some embodiments, immune cells (e.g., autologous or allogeneic T cells (e.g., regulatory T cells, CD4+ T cells, CD8+ T cells, or γδ T cells), NK cells, invariant NK cells, or NKT cells) may be genetically engineered to express an antigen receptor, such as an engineered TCR and / or chimeric antigen receptor (CAR). For example, host cells (e.g., autologous or allogeneic T cells) are modified to express a T cell receptor (TCR) with antigen specificity for a cancer antigen. In some embodiments, NK cells are engineered to express a TCR. NK cells may be further engineered to express a CAR. Multiple CARs and / or TCRs, such as for different antigens, can be added to a single cell type, such as a T cell or NK cell. In some embodiments, the cells comprise one or more nucleic acids / expression constructs / vectors introduced via genetic engineering that encode one or more antigen receptors, and the genetically engineered products of such nucleic acids. In some embodiments, the nucleic acids are heterologous. That is, it is not normally present in a cell or a sample obtained from a cell, such as one obtained from another organism or cell, e.g., it is not normally found in the cell being manipulated and / or the organism from which such a cell is derived.In some embodiments, the nucleic acid is not naturally occurring, such as a nucleic acid not found in nature (e.g., a chimera). In some embodiments, the population of immune cells can be obtained from a subject in need of therapy or from a subject suffering from a disease associated with decreased activity of immune cells. Thus, the cells are autologous to the subject in need of therapy. In some embodiments, the population of immune cells can be obtained from a donor (e.g., a histocompatibility-matched donor). In some embodiments, the immune cell population can be harvested from peripheral blood, umbilical cord blood, bone marrow, spleen, or any other organ / tissue in which immune cells are present in the subject or donor. In some embodiments, the immune cells can be isolated from a pool of subjects and / or donors (e.g., pooled umbilical cord blood). In some embodiments, if the population of immune cells is obtained from a donor different from the subject, the donor can be allogeneic, so long as the obtained cells are subject-compatible in that they can be introduced into the subject. In some embodiments, the allogeneic donor cells may or may not be human leukocyte antigen (HLA)-compatible. In some embodiments, the allogeneic cells can be treated to reduce immunogenicity in order to be subject-compatible.

[0204] In some embodiments, cell-based treatments include T cell-based therapy, including autologous cells, e.g., tumor infiltrating lymphocytes (TILs); T cells activated ex vivo using autologous DCs, lymphocytes, artificial antigen presenting cells (APCs) or beads coated with T cell ligands and activating antibodies, or cells isolated by capturing target cell membranes; allogeneic cells that naturally express anti-host tumor T cell receptors (TCRs); and non-tumor specific autologous or allogeneic cells that have been genetically reprogrammed or "redirected" to express tumor-reactive TCRs or chimeric TCR molecules (known as "T bodies" and exhibiting antibody-like tumor recognition capabilities). Several approaches for the isolation, induction, engineering or modification, activation, and expansion of functional anti-tumor effector cells have been described in the past 20 years and can be used according to any of the methods provided herein. In some embodiments, the T cells are derived from blood, bone marrow, lymph, umbilical cord, or lymphoid organs. In some embodiments, the cells are human cells. In some embodiments, the cells are primary cells, such as cells isolated directly from a subject and / or cells isolated and frozen from a subject. In some embodiments, the cells are T cells or other cell types (e.g., total T cell populations, CD4 T cells, CD4+ ... + cells, CD8 + The methods include one or more subsets of cells, including, but not limited to, those derived from a particular cell type, such as a human or mouse model. In some embodiments, the cells may be allogeneic and / or autologous. In some embodiments, such as off-the-shelf technologies, the cells are pluripotent and / or multipotent, such as stem cells, such as induced pluripotent stem cells (iPSCs).

[0205] In some embodiments, the T cell-based therapy comprises chimeric antigen receptor (CAR)-T cell-based therapy. This approach involves engineering a CAR. The CAR specifically binds to an antigen of interest and contains one or more intracellular signaling domains for T cell activation. The CAR is then expressed on the surface of engineered T cells (CAR-T) and administered to a patient, resulting in a T cell-specific immune response against cancer cells expressing the antigen.

[0206] In some embodiments, T cell-based therapy involves T cells expressing recombinant T cell receptors (TCRs). This approach involves identifying a TCR that specifically binds to an antigen of interest. This is then used to replace the endogenous or natural TCR on the surface of engineered T cells. When administered to a patient, this results in a T cell-specific immune response against cancer cells expressing the antigen.

[0207] In some embodiments, the T cell-based therapy includes tumor infiltrating lymphocytes (TILs). For example, TILs can be isolated from a tumor or cancer of the present disclosure, and then isolated and expanded in vitro. Some or all of these TILs can specifically recognize antigens expressed by the tumor or cancer of the present disclosure. In some embodiments, the TILs are exposed to one or more neo-antigens (e.g., one neo-antigen) after being isolated in vitro. The TILs are then administered to the patient (optionally in combination with one or more cytokines or other immune stimulants).

[0208] In some embodiments, the cell-based therapy comprises natural killer (NK) cell-based therapy. Natural killer (NK) cells are a subpopulation of lymphocytes that have spontaneous cytotoxicity against various tumor cells, virus-infected cells, and some normal cells in bone marrow and thymus. NK cells are important effectors of early innate immune responses against transformed and virus-infected cells. NK cells can be detected by specific surface markers, such as CD16, CD56, and CD8 in humans. NK cells do not express T cell antigen receptors, pan-T marker CD3, or surface immunoglobulin B cell receptors. In some embodiments, NK cells are obtained from human peripheral blood mononuclear cells (PBMCs), unstimulated leukapheresis products (PBSCs), human embryonic stem cells (hESCs), induced pluripotent stem cells (iPSCs), bone marrow, or umbilical cord blood by methods well known in the art.

[0209] In some embodiments, the cell-based therapy includes dendritic cell (DC)-based therapy (e.g., dendritic cell vaccines). In some embodiments, the DC vaccine includes antigen-presenting cells capable of inducing specific T cell immunity, harvested from a patient or donor. In some embodiments, the DC vaccine can then be exposed to peptide antigens in vitro, for which T cells are generated in the patient. In some embodiments, the antigen-loaded dendritic cells are then injected back into the patient. In some embodiments, immunization can be repeated multiple times, if necessary. Methods for harvesting, expanding, and administering dendritic cells are known in the art (see, e.g., WO2019 / 178081). Dendritic cell vaccines (e.g., Sipuleucel-T, also known as APC8015 and PROVENGE®) are vaccines that involve administration of dendritic cells that function as APCs to present one or more cancer-specific antigens to the patient's immune system. In some embodiments, the dendritic cells are autologous or allogeneic to the recipient.

[0210] In some embodiments, the cancer immunotherapy comprises a TCR-based therapy. In some embodiments, the cancer immunotherapy comprises administration of one or more TCRs or TCR-based therapeutics that specifically bind to an antigen expressed by the cancer of the present disclosure. In some embodiments, the TCR-based therapeutic may further comprise a moiety that binds to an immune cell (e.g., a T cell), such as an antibody or antibody fragment that specifically binds to a T cell surface protein or receptor (e.g., an anti-CD3 antibody or antibody fragment).

[0211] In some embodiments, the immunotherapy comprises adjuvant immunotherapy, which comprises the use of one or more agents that activate components of the innate immune system, such as HILTONOL® (imiquimod), which targets the TLR7 pathway.

[0212] In some embodiments, immunotherapy includes cytokine immunotherapy. Cytokine immunotherapy involves the use of one or more cytokines that activate components of the immune system. Examples include, but are not limited to, aldesleukin (PROLEUKIN®, interleukin-2), interferon alpha-2a (ROFERON®-A), interferon alpha-2b (INTRON®-A), and PEG-interferon alpha-2b (PEGINTRON®).

[0213] In some embodiments, the immunotherapy comprises oncolytic virotherapy, in which genetically modified viruses are used to replicate in and kill cancer cells, releasing antigens that stimulate the immune response. In some embodiments, the replication-competent oncolytic virus expressing a tumor antigen comprises any naturally occurring (e.g., from a "field source") or modified replication-competent oncolytic virus. In some embodiments, the oncolytic virus, in addition to expressing a tumor antigen, can be modified to increase the selectivity of the virus for cancer cells. In some embodiments, the replication-competent oncolytic viruses include those from the Myoviridae, Siphoviridae, Podoviridae, Tesiviridae, Corticoviridae, Plasmaviridae, Liposthrixviridae, Fuselloviridae, Poxyiridae, Iridoviridae, Phycodnaviridae, Baculoviridae, Herpesviridae, Adnoviridae, Papovaviridae, Polydnaviridae, Inoviridae, Microviridae, Geminiviridae, Circoviridae, Parvoviridae, Hepadnaviridae, Retroviridae, Oncolytic viruses include, but are not limited to, members of the Cytoviridae, Reoviridae, Birnaviridae, Paramyxoviridae, Rhabdoviridae, Filoviridae, Orthomyxoviridae, Bunyaviridae, Arenaviridae, Leviviridae, Picornaviridae, Sequiviridae, Comoviridae, Potyviridae, Caliciviridae, Astroviridae, Nodaviridae, Tetraviridae, Tombusviridae, Coronaviridae, Graviviridae, Togaviridae, and Barnaviridae families. In some embodiments, replication-competent oncolytic viruses include adenoviruses, retroviruses, reoviruses, rhabdoviruses, Newcastle disease virus (NDV), polyomaviruses, vaccinia viruses (VacV), herpes simplex viruses, picornaviruses, coxsackieviruses, and parvoviruses. In some embodiments, replicative oncolytic vaccinia viruses expressing tumor antigens can be engineered to lack one or more functional genes to enhance the cancer selectivity of the virus.In some embodiments, the oncolytic vaccinia virus is engineered to lack thymidine kinase (TK) activity. In some embodiments, the oncolytic vaccinia virus can be engineered to lack vaccinia virus growth factor (VGF). In some embodiments, the oncolytic vaccinia virus can be engineered to lack both VGF and TK activity. In some embodiments, the oncolytic vaccinia virus can be engineered to lack one or more genes involved in evading the host interferon (IFN) response, such as E3L, K3L, B18R, or B8R. In some embodiments, the replicative oncolytic vaccinia virus is a Western Reserve, Copenhagen, Lister, or Wyeth strain and lacks a functional TK gene. In some embodiments, the oncolytic vaccinia virus is a Western Reserve, Copenhagen, Lister, or Wyeth strain that lacks a functional B18R and / or B8R gene. In some embodiments, a replicating oncolytic vaccinia virus expressing a tumor antigen can be administered locally or systemically to a subject, for example, via intratumoral, intraperitoneal, intravenous, intraarterial, intramuscular, intradermal, intracranial, subcutaneous, or intranasal administration.

[0214] In some embodiments, the anti-cancer therapy comprises a nucleic acid molecule, such as a dsRNA, siRNA, or shRNA. In some embodiments, the methods provided herein comprise administering to an individual a nucleic acid molecule, such as a dsRNA, siRNA, or shRNA, for example, in combination with another anti-cancer therapy. As is known in the art, dsRNA having a double-stranded structure is effective in inducing RNA interference (RNAi). In some embodiments, the anti-cancer therapy comprises a small interfering RNA molecule (siRNA). dsRNA and siRNA can be used to suppress gene expression in mammalian cells (e.g., human cells). In some embodiments, the dsRNA of the present disclosure comprises any of about 5 to about 10 base pairs, about 10 to about 12 base pairs, about 12 to about 15 base pairs, about 15 to about 20 base pairs, about 20 to 23 base pairs, about 23 to about 25 base pairs, about 25 to about 27 base pairs, or about 27 to about 30 base pairs. As known in the art, siRNA is a small dsRNA that optionally includes an overhang. In some embodiments, the double-stranded region of the siRNA is about 18-25 nucleotides (e.g., any of 18, 19, 20, 21, 22, 23, 24, or 25 nucleotides). siRNA can also be a short hairpin RNA (shRNA), such as one with an approximately 29 base pair stem and a 2-nucleotide 3' overhang. Methods for designing, optimizing, producing, and using dsRNA, siRNA, or shRNA are known in the art.

[0215] In some aspects, therapeutic formulations are provided herein that include an anti-cancer therapy provided herein (e.g., an immune checkpoint inhibitor and / or an additional anti-cancer therapy) and a pharma- ceutically acceptable carrier, excipient, or stabilizer. The formulations provided herein can contain more than one active compound, e.g., an anti-cancer therapy provided herein and one or more additional agents (e.g., anti-cancer agents).

[0216] Acceptable carriers, excipients, or stabilizers are nontoxic to recipients at the dosages and concentrations employed and include, for example, buffers such as phosphate, citrate, or other organic acids; antioxidants including ascorbic acid and methionine; preservatives, for example, octadecyldimethylbenzyl ammonium chloride, hexamethonium chloride, benzalkonium chloride, benzethonium chloride, phenol, butyl or benzyl alcohol, alkyl parabens, for example, methyl or propyl paraben, catechol, resorcinol, cyclohexanol, 3-pentanol, or m-cresol; low molecular weight polypeptides (e.g., less than about 10 residues), groups); proteins such as serum albumin, gelatin, or immunoglobulins; hydrophilic polymers such as polyvinylpyrrolidone; amino acids such as glycine, glutamine, asparagine, histidine, arginine, or lysine; monosaccharides, disaccharides, and other carbohydrates, including glucose, mannose, or dextrins; chelating agents such as EDTA; sugars such as sucrose, mannitol, trehalose, or sorbitol; salt-forming counterions such as sodium; metal complexes (e.g., Zn-protein complexes); surfactants, such as non-ionic surfactants; or polymers such as polyethylene glycol (PEG).

[0217] The active ingredient may be encapsulated in microcapsules. Such microcapsules can be prepared, for example, by coacervation techniques or by interfacial polymerization of, for example, hydroxymethylcellulose or gelatin-microcapsules, and poly(methyl methacrylate) microcapsules, respectively; in colloidal drug delivery systems (for example, liposomes, albumin microspheres, microemulsions, nanoparticles and nanocapsules); or in macroemulsions. Such techniques are known in the art.

[0218] Sustained release compositions may be prepared. Suitable examples of sustained release compositions include semipermeable matrices of solid hydrophobic polymers containing the anticancer therapy of the present disclosure. Such matrices may be in the form of shaped articles, such as membranes, or microcapsules. Examples of sustained release matrices include polyesters, hydrogels (e.g., poly(2-hydroxyethyl-methacrylate) or poly(vinyl alcohol)), polylactides, copolymers of L-glutamic acid and gamma-ethyl-L-glutamate, non-degradable ethylene-vinyl acetate, degradable lactic acid-glycolic acid copolymers, such as LUPRON DEPOT™ (injectable microspheres composed of lactic acid-glycolic acid copolymer and leuprolide acetate), and poly-D-(-)-3-hydroxybutyric acid.

[0219] The formulations provided herein may also contain more than one active compound, e.g., those with complementary activities that do not adversely affect each other. The type and effective amount of such medicaments will depend, for example, on the amount and type of active compound present in the formulation and the clinical parameters of the subject.

[0220] For general information on formulations, see, for example, Gilman et al. (eds.) The Pharmacological Bases of Therapeutics, 8th Ed., Pergamon Press, 1990, A. Gennaro (ed.), Remington's Pharmaceutical Sciences, 18th Edition, Mack Publishing Co., Pennsylvania, 1990, Avis et al. (eds.) Pharmaceutical Dosage Forms: Parenteral Medications Dekker, New York, 1993, Lieberman et al. (eds.) Pharmaceutical Dosage Forms: Tablets Dekker, New York, 1990, Lieberman et al. (eds.), Pharmaceutical Dosage Forms: Disperse Systems Dekker, New York, 1990, and Walters (ed.) Dermatological and Transdermal Formulations (Drugs and the Pharmaceutical Sciences), Vol 1 19, Marcel See Dekker, 2002.

[0221] The formulations to be used for in vivo administration are sterile, which is readily accomplished by filtration through sterile filtration membranes, or other methods known in the art.

[0222] In some embodiments, the immune checkpoint inhibitor is administered as a monotherapy.

[0223] In some embodiments, the immune checkpoint inhibitor is a first-selection immune checkpoint inhibitor. In some embodiments, the immune checkpoint inhibitor is a second-selection immune checkpoint inhibitor. In some embodiments, the immune checkpoint inhibitor is administered in combination with one or more additional anti-cancer therapies or treatments. In some embodiments, the one or more additional anti-cancer therapies or treatments include one or more anti-cancer therapies described herein. In some embodiments, the methods of the disclosure include administration of any combination of an immune checkpoint inhibitor and any of the anti-cancer therapies provided herein. In some embodiments, the additional anti-cancer therapy includes one or more of surgery, radiation therapy, chemotherapy, anti-angiogenic therapy, anti-DNA repair therapy, and anti-inflammatory therapy. In some embodiments, the additional anti-cancer therapy includes an anti-neoplastic agent, a chemotherapeutic agent, a growth inhibitor, an anti-angiogenic therapy, radiation therapy, a cytotoxic agent, or a combination thereof. In some embodiments, the immune checkpoint inhibitor may be administered in conjunction with a chemotherapy or chemotherapeutic agent. In some embodiments, the chemotherapy or chemotherapeutic agent is a platinum-based agent, including but not limited to cisplatin, carboplatin, oxaliplatin, and straplatin. In some embodiments, the immune checkpoint inhibitor may be administered in conjunction with radiation therapy.

[0224] IV. Illustrative Embodiments The following exemplary embodiments are representative of certain aspects of the present invention. Embodiment 1. A method for detecting one or more of a methylation level or a non-methylation level of a cluster of two or more CpG dinucleotides in a sample from a subject, comprising: obtaining a plurality of nucleic acid fragments from the sample; amplifying the plurality of nucleic acid fragments; sequencing, with a sequencer, a plurality of amplified nucleic acid fragments to obtain a plurality of sequence reads, wherein at least the plurality of amplified nucleic acid fragments have undergone cytosine conversion, and the plurality of nucleic acid fragments correspond to genomic loci comprising a cluster of two or more CpG dinucleotides; determining, by a processor, a consensus methylation pattern for the cluster, the consensus methylation pattern representing each CpG dinucleotide in the cluster for which methylation was detected based on the cytosine conversion in at least one sequence read from the plurality of sequence reads; generating, by the processor, a cluster consensus fraction (CCF) for the cluster, the CCF representing a proportion of sequence reads corresponding to the cluster that exhibit the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads that correspond to the cluster; detecting one or more of the methylation level or the unmethylation level of the cluster based on the CCF; generating a genomic profile of the subject based on the detected methylation level, the detected unmethylation level, or both. Embodiment 2. The method of embodiment 1, wherein the CCF is greater than or equal to a threshold or reference value, and the method further comprises detecting the presence of cancer nucleic acid in the plurality of nucleic acid fragments based, at least in part, on the CCF being greater than or equal to the threshold or reference value. Embodiment 3. The method of embodiment 1, wherein the CCF is less than a threshold or reference value, and the method further comprises detecting an absence of cancer nucleic acid in the plurality of nucleic acid fragments based, at least in part, on the CCF being less than the threshold or reference value. Embodiment 4. The method of any one of embodiments 1 to 3, comprising determining a consensus methylation pattern and a CCF of a plurality of clusters. Embodiment 5. The method of embodiment 4, wherein the plurality of clusters corresponds to a plurality of genomic loci. Embodiment 6. The method of embodiment 4 or 5, comprising determining the consensus methylation pattern and CCF of more than 1,000 clusters. The method of embodiment 7, comprising determining the consensus methylation pattern and CCF of 10 to 100,000 clusters. Embodiment 8. The method of any one of embodiments 1 to 7, comprising determining the consensus methylation pattern and CCF of up to 1,000,000 clusters. Embodiment 9. The method of any one of embodiments 1 to 8, wherein the plurality of sequence reads comprises at least 100 sequence reads corresponding to the cluster. Embodiment 10. The method of embodiment 9, wherein the plurality of sequence reads comprises at least 1000 sequence reads corresponding to the cluster. Embodiment 11. The method of any one of embodiments 1 to 8, wherein the plurality of sequence reads comprises 1 to 5 sequence reads corresponding to the cluster. Embodiment 12. The method of any one of embodiments 1 to 11, wherein at least one CpG dinucleotide in said cluster is unmethylated in said consensus methylation pattern. Embodiment 13. The method of any one of embodiments 1 to 12, wherein at least one CpG dinucleotide in said cluster is methylated in said consensus methylation pattern. Embodiment 14. The method of any one of embodiments 1 to 13, wherein at least one cluster comprises two or more CpG dinucleotides. Embodiment 15. The method of embodiment 14, wherein each cluster comprises two or more CpG dinucleotides. Embodiment 16 The method of any one of embodiments 1 to 13, wherein at least one cluster comprises five or more CpG dinucleotides. Embodiment 17. The method of embodiment 16, wherein each cluster comprises five or more CpG dinucleotides. Embodiment 18. The method of any one of embodiments 1 to 17, wherein at least one cluster comprises six or more CpG dinucleotides. Embodiment 19. The method of any one of embodiments 1 to 18, wherein all but one of the sites in the cluster is unmethylated in the consensus methylation pattern. Embodiment 20. The method of any one of embodiments 1 to 18, wherein all but two of the sites in the cluster are unmethylated in the consensus methylation pattern. Embodiment 21. The method of any one of embodiments 1 to 18, wherein at most one site in said cluster is methylated in said consensus methylation pattern. Embodiment 22. The method of any one of embodiments 1 to 18, wherein a maximum of two sites in said cluster are methylated in said consensus methylation pattern. Embodiment 23. The method of any one of embodiments 1 to 18, wherein up to 10% of the sites in said cluster are methylated in said consensus methylation pattern. Embodiment 24. The method of any one of embodiments 1 to 18, wherein up to 25% of the sites in the cluster are methylated with the consensus methylation pattern. Embodiment 25. The method of any one of embodiments 1 to 20, wherein more than 75% of the sites in said cluster are methylated in said consensus methylation pattern. Embodiment 26 The method of any one of embodiments 1 to 20, wherein more than 50% of the sites in said cluster are methylated in said consensus methylation pattern. Embodiment 27. The method of any one of embodiments 1 to 20, wherein more than 25% of the sites in said cluster are methylated in said consensus methylation pattern. Embodiment 28. The method of any one of embodiments 1 to 27, wherein the plurality of sequence reads are obtained from whole genome methyl sequencing (WGMS) or next generation sequencing (NGS). Embodiment 29. The method of any one of embodiments 1 to 28, wherein the plurality of sequence reads comprises paired-end sequence reads. Embodiment 30. The method of embodiment 29, wherein the consensus methylation pattern and CCF are determined based on paired-end sequence reads corresponding to the cluster. Embodiment 31. The method of any one of embodiments 1 to 28, wherein the plurality of sequence reads comprises unpaired sequence reads. Embodiment 32. The method of any one of embodiments 1 to 31, further comprising demultiplexing sequence reads from the plurality of sequence reads prior to determining the consensus methylation pattern and CCF. Embodiment 33. The method of any one of embodiments 1 to 32, further comprising performing a three-letter alignment of sequence reads from the plurality of sequence reads to a reference genome prior to determining the consensus methylation pattern and CCF. Embodiment 34. The method of any one of embodiments 1 to 33, further comprising excluding sequencing reads from the plurality of sequencing reads that were not able to undergo cytosine conversion prior to determining the consensus methylation pattern and CCF. Embodiment 35. The method of any one of embodiments 1 to 34, further comprising filtering out sequence reads having a base other than cytosine or thymine at the first position of at least one of said CpG dinucleotides prior to determining said consensus methylation pattern and CCF. Embodiment 36. The method of any one of embodiments 1 to 35, further comprising filtering out sequence reads having a base quality below a threshold base quality prior to determining the consensus methylation pattern and CCF. Embodiment 37. The method of any one of embodiments 1 to 36, wherein the consensus methylation pattern and CCF are determined based on sequence reads that cover multiple CpG dinucleotides in the cluster. Embodiment 38. The method of embodiment 37, wherein the consensus methylation pattern and CCF are determined based on sequence reads that cover at least 50% of the CpG dinucleotides in the cluster. Embodiment 39. The method of embodiment 37, wherein the consensus methylation pattern and CCF are determined based on sequence reads that cover at least 90% of the CpG dinucleotides in the cluster. Embodiment 40. The method of embodiment 37, wherein the consensus methylation pattern and CCF are determined based on sequence reads that cover all CpG dinucleotides in the cluster. Embodiment 41. The method of any one of embodiments 1 to 40, wherein the plurality of nucleic acid fragments have been subjected to cytosine conversion by bisulfite treatment. Embodiment 42. The method of any one of embodiments 1 to 40, wherein the plurality of nucleic acid fragments have been subjected to cytosine conversion by TET-assisted bisulfite treatment, TET-assisted pyridine borane treatment, oxidative bisulfite treatment, or APOBEC treatment. Embodiment 43. The method of any one of embodiments 1 to 40, further comprising treating the plurality of nucleic acids or nucleic acid fragments with bisulfite prior to providing the plurality of sequence reads. Embodiment 44. The method of any one of embodiments 1 to 40, further comprising treating the plurality of nucleic acids or nucleic acid fragments with TET-assisted bisulfite treatment, TET-assisted pyridine borane treatment, oxidative bisulfite treatment, or APOBEC treatment prior to providing the plurality of sequence reads. Embodiment 45. The method of any one of embodiments 1 to 44, further comprising subjecting the plurality of nucleic acids to fragmentation prior to providing the plurality of sequence reads. Embodiment 46. The method of any one of embodiments 1 to 45, further comprising selectively enriching a plurality of nucleic acids or nucleic acid fragments corresponding to genomic loci that comprise a cluster of two or more CpG dinucleotides to generate an enriched sample prior to providing the plurality of sequence reads. Embodiment 47. The method of any one of embodiments 1 to 46, wherein said amplification of said plurality of nucleic acids or nucleic acid fragments is carried out by polymerase chain reaction (PCR). Embodiment 48. The method of any one of embodiments 1 to 47, further comprising isolating the plurality of nucleic acids from the sample prior to providing the plurality of sequence reads. Embodiment 49. The method of embodiment 48, wherein the sample comprises tumor cells and / or tumor nucleic acid. Embodiment 50. The method of embodiment 49, wherein the sample further comprises non-tumor cells and / or non-tumor nucleic acids. Embodiment 51. The method of embodiment 50, wherein the sample contains a percentage of tumor nucleic acid that is less than 1% of total nucleic acid. Embodiment 52. The method of embodiment 50, wherein the sample contains a percentage of tumor nucleic acid that is less than 0.1% of total nucleic acid. Embodiment 53. The method of any one of embodiments 50 to 52, wherein the sample comprises a percentage of tumor nucleic acid that is at least 0.01% of total nucleic acid. Embodiment 54. The method of any one of embodiments 48 to 53, wherein the sample comprises tumor cell-free DNA (cfDNA), circulating cell-free DNA (ccfDNA), or circulating tumor DNA (ctDNA). Embodiment 55. The method of any one of embodiments 48 to 53, wherein the sample comprises a fluid, a cell, or a tissue. Embodiment 56 The method of embodiment 55, wherein the sample comprises blood or plasma. Embodiment 57. The method of any one of embodiments 48 to 53, wherein the sample comprises a tumor biopsy or circulating tumor cells. Embodiment 58. The method of any one of embodiments 1 to 57, wherein the sample is a tissue sample and the method further comprises subjecting a plurality of nucleic acid molecules in the tissue to fragmentation to generate the plurality of nucleic acid fragments. Embodiment 59. The method of embodiment 58, further comprising ligating one or more adaptors to one or more nucleic acid fragments from the plurality of nucleic acid fragments prior to amplifying the plurality of nucleic acid fragments. Embodiment 60. A method for detecting cancer in an individual, comprising detecting the methylation level or the unmethylation level in a sample comprising a plurality of nucleic acids obtained from the individual according to the method of any one of embodiments 1 to 59, wherein the individual is identified as having cancer based on the methylation level or the unmethylation level detected in the sample. Embodiment 61. A method for screening an individual suspected of having cancer, comprising detecting the methylation level or the unmethylation level in a sample containing multiple nucleic acids obtained from the individual according to the method of any one of embodiments 1 to 59, wherein the individual is identified as having a high probability of having cancer based on the methylation level or the unmethylation level detected in the sample. Embodiment 62. A method for determining the prognosis of an individual having cancer, comprising detecting the methylation level or the unmethylation level in a sample comprising a plurality of nucleic acids obtained from the individual according to the method of any one of embodiments 1 to 59, wherein the prognosis of the individual is determined, at least in part, by the methylation level or the unmethylation level detected in the sample. Embodiment 63. A method for predicting survival of an individual having cancer, comprising detecting the methylation level or the unmethylation level in a sample comprising a plurality of nucleic acids obtained from the individual according to the method of any one of embodiments 1 to 59, wherein the methylation level or the unmethylation level detected in the sample at least partially predicts the survival of the individual. Embodiment 64. The method of embodiment 63, wherein the methylation level detected in the sample is higher than a threshold or reference value, predicting a decreased survival of the individual compared to the survival of an individual whose methylation level in the sample is lower than the threshold or reference value. Embodiment 65. A method for predicting tumor burden in an individual having cancer, comprising detecting the methylation level or the unmethylation level in a sample comprising a plurality of nucleic acids obtained from the individual according to a method according to any one of embodiments 1 to 59, wherein the methylation level or the unmethylation level detected in the sample is at least partially predictive of the tumor burden in the individual. Embodiment 66. The method of embodiment 65, wherein the methylation level detected in the sample is higher than a threshold or reference value and predicts an increased tumor burden in the individual compared to the tumor burden of an individual whose methylation level in the sample is lower than the threshold or reference value. Embodiment 67. A method for predicting the responsiveness of an individual having cancer to a treatment, comprising detecting the methylation level or the unmethylation level in a sample comprising a plurality of nucleic acids obtained from the individual according to the method of any one of embodiments 1 to 59, wherein the methylation level or the unmethylation level detected in the sample is used to at least partially predict the responsiveness of the individual to the treatment. Embodiment 68. A method for identifying an individual having cancer who may benefit from a treatment comprising an anthracycline-based chemotherapy, comprising detecting the methylation level or the unmethylation level in a sample comprising a plurality of nucleic acids obtained from the individual according to the method of any one of embodiments 1 to 59, wherein the plurality of nucleic acids comprises one or more nucleic acids corresponding to the PITX2 locus, and wherein methylation of the PITX2 locus detected in the sample identifies the individual as one who may benefit from the treatment comprising an anthracycline-based chemotherapy. Embodiment 69. A method for selecting a therapy for an individual having cancer, comprising detecting the methylation level or the unmethylation level in a sample comprising a plurality of nucleic acids obtained from the individual according to the method of any one of embodiments 1 to 59, wherein the plurality of nucleic acids comprises one or more nucleic acids corresponding to the PITX2 locus, and wherein methylation of the PITX2 locus detected in the sample identifies the individual as likely to benefit from treatment comprising anthracycline-based chemotherapy. Embodiment 70. A method for identifying one or more treatment options for an individual with cancer, the method comprising: (a) detecting the methylation level or the unmethylation level in a sample comprising a plurality of nucleic acids obtained from the individual according to the method of any one of embodiments 1 to 59, wherein the plurality of nucleic acids comprises one or more nucleic acids corresponding to the PITX2 locus; (b) generating a report including one or more treatment options identified for the individual based at least in part on methylation of the PITX2 locus detected in the sample, wherein the one or more treatment options include anthracycline-based chemotherapy. Embodiment 71. A method for treating or slowing the progression of cancer, comprising: (a) detecting the methylation level or the unmethylation level in a sample comprising a plurality of nucleic acids obtained from the individual according to the method of any one of embodiments 1 to 59, wherein the plurality of nucleic acids comprises one or more nucleic acids corresponding to the PITX2 locus; (b) administering to the individual an effective amount of anthracycline-based chemotherapy. Embodiment 72. A method for identifying an individual having cancer who may benefit from a treatment comprising an alkylating agent, comprising detecting the methylation level or the unmethylation level in a sample comprising a plurality of nucleic acids obtained from the individual according to the method of any one of embodiments 1 to 59, wherein the plurality of nucleic acids comprises one or more nucleic acids corresponding to the MGMT locus, and wherein methylation of the MGMT locus detected in the sample identifies the individual as one who may benefit from the treatment comprising an alkylating agent. Embodiment 73. A method for selecting a therapy for an individual having cancer, comprising detecting the methylation level or the unmethylation level in a sample comprising a plurality of nucleic acids obtained from the individual according to the method of any one of embodiments 1 to 59, wherein the plurality of nucleic acids comprises one or more nucleic acids corresponding to the MGMT locus, and wherein methylation of the MGMT locus detected in the sample identifies the individual as likely to benefit from a treatment comprising an alkylating agent. Embodiment 74. A method for identifying one or more treatment options for an individual having cancer, the method comprising: (a) detecting the methylation level or the unmethylation level in a sample comprising a plurality of nucleic acids obtained from the individual according to the method of any one of embodiments 1 to 59, wherein the plurality of nucleic acids comprises one or more nucleic acids corresponding to the MGMT locus; (b) generating a report comprising one or more treatment options identified for the individual based at least in part on methylation of the MGMT locus detected in the sample, wherein the one or more treatment options comprise an alkylating agent. Embodiment 75. A method for treating or slowing the progression of cancer, comprising: (a) detecting the methylation level or the unmethylation level in a sample comprising a plurality of nucleic acids obtained from the individual according to the method of any one of embodiments 1 to 59, wherein the plurality of nucleic acids comprises one or more nucleic acids corresponding to the MGMT locus; (b) administering to the individual an effective amount of an alkylating agent. Embodiment 76. A method for monitoring the response of an individual undergoing treatment for cancer, comprising: (a) administering a treatment to an individual having cancer; (b) detecting the methylation level or the unmethylation level in a sample comprising a plurality of nucleic acids obtained from the individual after the treatment according to the method of any one of embodiments 1 to 59, wherein the methylation level or the unmethylation level detected in the sample is used, at least in part, to monitor a response to the treatment. Embodiment 77. The method of embodiment 76, wherein detection of a post-treatment methylation level that is below the pre-treatment methylation level or below a threshold or reference value indicates that the individual has responded to the treatment. Embodiment 78. The method of embodiment 76, wherein detection of a post-treatment methylation level that is equal to or less than the pre-treatment methylation level or below a threshold or reference value indicates that the individual has responded to the treatment. Embodiment 79. A method for monitoring cancer in an individual, comprising: (a) detecting the methylation level or the unmethylation level in a first sample comprising a plurality of nucleic acids obtained from the individual according to a method according to any one of embodiments 1 to 59; (b) detecting the methylation level or the unmethylation level in a second sample comprising a plurality of nucleic acids obtained from the individual according to the method of any one of embodiments 1 to 57, wherein the second sample is obtained from the individual after the first sample; (c) determining a difference in methylation levels between said first sample and said second sample, thereby monitoring said cancer in said individual. Embodiment 80. A method for monitoring the response of an individual undergoing treatment for cancer, comprising: (a) detecting the methylation level or the unmethylation level in a first sample comprising a plurality of nucleic acids obtained from the individual according to a method according to any one of embodiments 1 to 59; (b) administering a treatment to the individual after obtaining the first sample from the individual; and (c) detecting the methylation level or the unmethylation level in a second sample comprising a plurality of nucleic acids obtained from the individual according to the method of any one of embodiments 1 to 59, wherein the second sample is obtained from the individual after administration of the treatment; (d) determining a difference in methylation levels between said first sample and said second sample, thereby monitoring said individual's response to said treatment. Embodiment 81. A method for detecting one or more of the methylation or unmethylation levels of a cluster of two or more CpG dinucleotides from a sample, comprising: obtaining a plurality of sequence reads from a plurality of nucleic acid fragments that exhibit cytosine conversions; determining, by a processor, a consensus methylation pattern of a cluster of two or more CpG dinucleotides at a locus, the consensus methylation pattern representing each CpG dinucleotide in the cluster for which methylation is detected; generating, by the processor, a cluster consensus fraction (CCF) for the cluster, the CCF representing a proportion of sequence reads corresponding to the cluster that exhibit the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads that correspond to the cluster; detecting, by the processor, one or more of the methylation level or the unmethylation level of the cluster based on the CCF. Embodiment 82. A method for detecting one or more of the methylation or unmethylation levels of a cluster of two or more CpG dinucleotides, comprising: sequencing, with a sequencer, the plurality of nucleic acid fragments to obtain a plurality of sequence reads; determining, by a processor, a consensus methylation pattern for the cluster, the consensus methylation pattern representing each CpG dinucleotide in the cluster for which methylation is detected; generating, by a processor, a cluster consensus fraction (CCF) for the cluster, the CCF representing a proportion of sequence reads corresponding to the cluster that exhibit the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads that correspond to the cluster, thereby detecting one or more of the methylation level or the unmethylation level of the cluster; detecting, by the processor, one or more of the methylation level or the unmethylation level of the cluster based on the CCF. Embodiment 83. The method of embodiment 81 or 82, wherein the consensus methylation pattern represents each CpG dinucleotide in the cluster in which methylation is detected based on a cytosine conversion in at least one sequence read from the plurality of sequence reads. Embodiment 84. The method of any one of embodiments 81-83, wherein the CCF is greater than or equal to a threshold or reference value, and the method further comprises detecting the presence of cancer nucleic acid in the plurality of nucleic acid fragments based, at least in part, on the CCF being greater than or equal to the threshold or reference value. Embodiment 85. The method of any one of embodiments 81-83, wherein the CCF is less than a threshold or reference value, and the method further comprises detecting the absence of cancer nucleic acid in the plurality of nucleic acid fragments based, at least in part, on the CCF being less than the threshold or reference value. Embodiment 86. The method of any one of embodiments 81 to 85, comprising determining a consensus methylation pattern and CCF of a plurality of clusters. Embodiment 87. The method of embodiment 86, wherein the plurality of clusters corresponds to a plurality of genomic loci. Embodiment 88. The method of embodiment 86 or 87, comprising determining the consensus methylation pattern and CCF of more than 1,000 clusters. Embodiment 89. The method of embodiment 86 or 87, comprising determining the consensus methylation pattern and CCF of 10 to 100,000 clusters. Embodiment 90. The method of any one of embodiments 81 to 89, comprising determining the consensus methylation pattern and CCF of up to 1,000,000 clusters. Embodiment 91. The method of any one of embodiments 81 to 90, wherein the plurality of sequence reads comprises at least 100 sequence reads corresponding to the cluster. Embodiment 92. The method of embodiment 91, wherein the plurality of sequence reads comprises at least 1000 sequence reads corresponding to the cluster. Embodiment 93. The method of any one of embodiments 81 to 90, wherein the plurality of sequence reads comprises 1 to 5 sequence reads corresponding to the cluster. Embodiment 94. The method of any one of embodiments 81 to 93, wherein at least one CpG dinucleotide in said cluster is unmethylated in said consensus methylation pattern. Embodiment 95. The method of any one of embodiments 81 to 94, wherein at least one CpG dinucleotide in said cluster is methylated in said consensus methylation pattern. Embodiment 96. The method of any one of embodiments 81 to 95, wherein at least one cluster contains two or more CpG dinucleotides. Embodiment 97. The method of embodiment 96, wherein each cluster comprises two or more CpG dinucleotides. Embodiment 98. The method of any one of embodiments 81 to 95, wherein at least one cluster comprises five or more CpG dinucleotides. Embodiment 99. The method of embodiment 98, wherein each cluster comprises five or more CpG dinucleotides. Embodiment 100. The method of any one of embodiments 81 to 99, wherein at least one cluster comprises six or more CpG dinucleotides. Embodiment 101. The method of any one of embodiments 81 to 100, wherein all but one of the sites in said cluster is unmethylated in said consensus methylation pattern. Embodiment 102. The method of any one of embodiments 81 to 100, wherein all but two of the sites in the cluster are unmethylated in the consensus methylation pattern. Embodiment 103. The method of any one of embodiments 81 to 100, wherein at most one site in said cluster is methylated in said consensus methylation pattern. Embodiment 104. The method of any one of embodiments 81 to 100, wherein a maximum of two sites in said cluster are methylated in said consensus methylation pattern. Embodiment 105. The method of any one of embodiments 81 to 100, wherein up to 10% of the sites in the cluster are methylated in the consensus methylation pattern. Embodiment 106 The method of any one of embodiments 81 to 100, wherein up to 25% of the sites in the cluster are methylated in the consensus methylation pattern. Embodiment 107. The method of any one of embodiments 81 to 102, wherein more than 75% of the sites in said cluster are methylated in said consensus methylation pattern. Embodiment 108. The method of any one of embodiments 81 to 102, wherein more than 50% of the sites in said cluster are methylated in said consensus methylation pattern. Embodiment 109. The method of any one of embodiments 81 to 102, wherein more than 25% of the sites in said cluster are methylated in said consensus methylation pattern. Embodiment 110. The method of any one of embodiments 81 to 109, wherein the plurality of sequence reads are obtained from whole genome methyl sequencing (WGMS) or next generation sequencing (NGS). Embodiment 111. The method of any one of embodiments 81 to 110, wherein the plurality of sequence reads comprises paired-end sequence reads. Embodiment 112. The method of embodiment 111, wherein the consensus methylation pattern and CCF are determined based on paired-end sequence reads corresponding to the cluster. Embodiment 113. The method of any one of embodiments 81 to 110, wherein the plurality of sequence reads comprises unpaired sequence reads. Embodiment 114. The method of any one of embodiments 81 to 113, further comprising demultiplexing sequence reads from the plurality of sequence reads prior to determining the consensus methylation pattern and CCF. Embodiment 115. The method of any one of embodiments 81 to 114, further comprising performing a three-letter alignment of sequence reads from the plurality of sequence reads to a reference genome prior to determining the consensus methylation pattern and CCF. Embodiment 116. The method of any one of embodiments 81 to 115, further comprising excluding sequencing reads from the plurality of sequencing reads that were not able to undergo cytosine conversion prior to determining the consensus methylation pattern and CCF. Embodiment 117. The method of any one of embodiments 81 to 116, further comprising excluding sequence reads having a base other than cytosine or thymine at the first position of at least one of said CpG dinucleotides prior to determining said consensus methylation pattern and CCF. Embodiment 118. The method of any one of embodiments 81 to 117, further comprising filtering out sequence reads having a base quality below a threshold base quality prior to determining the consensus methylation pattern and CCF. Embodiment 119. The method of any one of embodiments 81 to 118, wherein the consensus methylation pattern and CCF are determined based on sequence reads covering multiple CpG dinucleotides in the cluster. Embodiment 120. The method of embodiment 119, wherein the consensus methylation pattern and CCF are determined based on sequence reads that cover at least 50% of the CpG dinucleotides in the cluster. Embodiment 121. The method of embodiment 119, wherein the consensus methylation pattern and CCF are determined based on sequence reads that cover at least 90% of the CpG dinucleotides in the cluster. Embodiment 122. The method of embodiment 119, wherein the consensus methylation pattern and CCF are determined based on sequence reads that cover all CpG dinucleotides in the cluster. Embodiment 123. The method of any one of embodiments 81 to 122, wherein the plurality of nucleic acid fragments have been subjected to cytosine conversion by bisulfite treatment. Embodiment 124. The method of any one of embodiments 81 to 122, wherein the plurality of nucleic acid fragments have been subjected to cytosine conversion by TET-assisted bisulfite treatment, TET-assisted pyridine borane treatment, oxidative bisulfite treatment, or APOBEC treatment. Embodiment 125. The method of any one of embodiments 81 to 122, further comprising treating the plurality of nucleic acids or nucleic acid fragments with bisulfite prior to obtaining the plurality of sequence reads. Embodiment 126. The method of any one of embodiments 81 to 122, further comprising treating the plurality of nucleic acids or nucleic acid fragments with TET-assisted bisulfite treatment, TET-assisted pyridine borane treatment, oxidative bisulfite treatment, or APOBEC treatment prior to obtaining the plurality of sequence reads. Embodiment 127. The method of any one of embodiments 81 to 126, further comprising subjecting the plurality of nucleic acids to fragmentation prior to obtaining the plurality of sequence reads. Embodiment 128. The method of any one of embodiments 81 to 127, further comprising selectively enriching a plurality of nucleic acids or nucleic acid fragments corresponding to genomic loci comprising a cluster of two or more CpG dinucleotides to generate an enriched sample prior to obtaining the plurality of sequence reads. Embodiment 129. The method of any one of embodiments 81 to 128, wherein said amplification of said plurality of nucleic acids or nucleic acid fragments is carried out by polymerase chain reaction (PCR). Embodiment 130. The method of any one of embodiments 81 to 129, further comprising isolating the plurality of nucleic acids from a sample prior to obtaining the plurality of sequence reads. Embodiment 131. The method of embodiment 130, wherein the sample comprises tumor cells and / or tumor nucleic acid. Embodiment 132. The method of embodiment 131, wherein the sample further comprises non-tumor cells and / or non-tumor nucleic acids. Embodiment 133. The method of embodiment 132, wherein the sample contains a percentage of tumor nucleic acid that is less than 1% of the total nucleic acid. Embodiment 134. The method of embodiment 132, wherein the sample comprises a percentage of tumor nucleic acid that is less than 0.1% of total nucleic acid. Embodiment 135. The method of any one of embodiments 132 to 134, wherein the sample comprises a percentage of tumor nucleic acid that is at least 0.01% of total nucleic acid. Embodiment 136. The method of any one of embodiments 130 to 135, wherein the sample comprises tumor cell-free DNA (cfDNA), circulating cell-free DNA (ccfDNA), or circulating tumor DNA (ctDNA). Embodiment 137. The method of any one of embodiments 130 to 135, wherein the sample comprises a fluid, a cell, or a tissue. Embodiment 138. The method of embodiment 137, wherein the sample comprises blood or plasma. Embodiment 139. The method of any one of embodiments 130 to 135, wherein the sample comprises a tumor biopsy or circulating tumor cells. Embodiment 140. The method of any one of embodiments 81 to 139, wherein the sample is a tissue sample and the method further comprises subjecting a plurality of nucleic acid molecules in the tissue to fragmentation to generate the plurality of nucleic acid fragments. Embodiment 141. The method of embodiment 140, further comprising ligating one or more adaptors to one or more nucleic acid fragments from the plurality of nucleic acid fragments prior to amplifying the plurality of nucleic acid fragments. Embodiment 142. A system comprising: one or more processors; and a memory configured to store one or more computer program instructions, which, when executed by the one or more processors, using the one or more processors to determine a consensus methylation pattern of a cluster of two or more CpG dinucleotides at a genomic locus, the consensus methylation pattern representing each CpG dinucleotide in the cluster that is methylated in at least one sequence read from a plurality of sequence reads obtained from a plurality of nucleic acid fragments that have been subjected to cytosine conversion; The system is configured to generate, using the one or more processors, a cluster consensus fraction (CCF) for the cluster, the CCF representing a proportion of sequence reads corresponding to the cluster that exhibit the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads that correspond to the cluster. Embodiment 143. The system described in embodiment 142, further configured to detect, using the one or more processors, the presence of cancer nucleic acid in the plurality of nucleic acid fragments based at least in part on the CCF being greater than or equal to a threshold or reference value, and when the one or more computer program instructions are executed by the one or more processors, the presence of cancer nucleic acid in the plurality of nucleic acid fragments based at least in part on the CCF being greater than or equal to the threshold or reference value. Embodiment 144. The system described in embodiment 142, further configured to, when the CCF is less than a threshold or reference value and the one or more computer program instructions are executed by the one or more processors, detect, using the one or more processors, the absence of cancer nucleic acid in the plurality of nucleic acid fragments based at least in part on the CCF being less than the threshold or reference value.

[0036] The one or more computer program instructions, when executed by the one or more processors, using the one or more processors to determine a consensus methylation pattern for a plurality of clusters of two or more CpG dinucleotides; A system described in any one of embodiments 142 to 144, further configured to generate a cluster consensus factor (CCF) for multiple clusters using the one or more processors. Embodiment 146. The system of embodiment 145, wherein the plurality of clusters corresponds to a plurality of genomic loci. Embodiment 147. The system described in embodiment 145 or 146, wherein the one or more computer program instructions, when executed by the one or more processors, are configured to determine consensus methylation patterns and generate CCFs of more than 1,000 clusters. Embodiment 148. A system described in embodiment 145 or 146, wherein the one or more computer program instructions, when executed by the one or more processors, are configured to determine consensus methylation patterns and generate CCFs of 10 to 100,000 clusters. Embodiment 149. A system described in embodiment 145 or 146, wherein the one or more computer program instructions, when executed by the one or more processors, are configured to determine consensus methylation patterns and generate CCFs for up to 1,000,000 clusters. Embodiment 150. A system described in any one of embodiments 142 to 149, wherein the plurality of sequence reads comprises at least 100 sequence reads corresponding to the cluster. Embodiment 151. The system of embodiment 150, wherein the plurality of sequence reads comprises at least 1,000 sequence reads corresponding to the cluster. Embodiment 152. A system described in any one of embodiments 142 to 149, wherein the plurality of sequence reads includes 1 to 5 sequence reads corresponding to the cluster. Embodiment 153. A system described in any one of embodiments 142 to 152, wherein at least one CpG dinucleotide in the cluster is unmethylated in the consensus methylation pattern. Embodiment 154. A system described in any one of embodiments 142 to 153, wherein at least one CpG dinucleotide in the cluster is methylated in the consensus methylation pattern. Embodiment 155. A system described in any one of embodiments 142 to 154, wherein at least one cluster contains two or more CpG dinucleotides. Embodiment 156. The system of embodiment 155, wherein each cluster comprises two or more CpG dinucleotides. Embodiment 157. A system described in any one of embodiments 142 to 154, wherein at least one cluster contains five or more CpG dinucleotides. Embodiment 158. The system of embodiment 157, wherein each cluster comprises five or more CpG dinucleotides. Embodiment 159. A system described in any one of embodiments 142 to 158, wherein at least one cluster contains six or more CpG dinucleotides. Embodiment 160. A system described in any one of embodiments 142 to 159, wherein all but one site in the cluster is unmethylated in the consensus methylation pattern. Embodiment 161. A system described in any one of embodiments 142 to 159, wherein all but two sites in the cluster are unmethylated in the consensus methylation pattern. Embodiment 162. A system described in any one of embodiments 142 to 159, wherein at most one site in the cluster is methylated in the consensus methylation pattern. Embodiment 163. A system described in any one of embodiments 142 to 159, wherein a maximum of two sites in the cluster are methylated in the consensus methylation pattern. Embodiment 164. A system described in any one of embodiments 142 to 159, wherein up to 10% of the sites in the cluster are methylated in the consensus methylation pattern. Embodiment 165. A system described in any one of embodiments 142 to 159, wherein up to 25% of the sites in the cluster are methylated in the consensus methylation pattern. Embodiment 166. A system described in any one of embodiments 142 to 161, wherein more than 75% of the sites in the cluster are methylated in the consensus methylation pattern. Embodiment 167. A system described in any one of embodiments 142 to 161, wherein more than 50% of the sites in the cluster are methylated in the consensus methylation pattern. Embodiment 168. A system described in any one of embodiments 142 to 161, wherein more than 25% of the sites in the cluster are methylated in the consensus methylation pattern. Embodiment 169. A system described in any one of embodiments 142 to 168, wherein the plurality of sequence reads are obtained from whole genome methylsequencing (WGMS) or next-generation sequencing (NGS). Embodiment 170. A system described in any one of embodiments 142 to 169, wherein the plurality of sequence reads comprises paired-end sequence reads. Embodiment 171. The system of embodiment 170, wherein the consensus methylation pattern and CCF are determined based on paired-end sequence reads corresponding to the cluster. Embodiment 172. A system described in any one of embodiments 142 to 169, wherein the plurality of sequence reads includes unpaired sequence reads. Embodiment 173. The system described in any one of embodiments 142 to 172, wherein the one or more computer program instructions, when executed by the one or more processors, are further configured to demultiplex sequence reads from the plurality of sequence reads using the one or more processors before determining the consensus methylation pattern and generating the CCF. Embodiment 174. The system described in any one of embodiments 142 to 173, wherein the one or more computer program instructions, when executed by the one or more processors, are further configured to perform, using the one or more processors, a three-character alignment of sequence reads from the plurality of sequence reads to a reference genome before determining the consensus methylation pattern and generating the CCF. Embodiment 175. A system described in any one of embodiments 142 to 174, wherein the one or more computer program instructions, when executed by the one or more processors, are further configured to exclude, using the one or more processors, sequencing reads from the plurality of sequencing reads that were not able to undergo cytosine conversion before determining the consensus methylation pattern and generating the CCF. Embodiment 176. The system described in any one of embodiments 142 to 175, wherein the one or more computer program instructions, when execute...

Claims

1. 1. A method for detecting one or more of the methylation level or unmethylation level of a cluster of two or more CpG dinucleotides in a sample from a subject, comprising: obtaining a plurality of nucleic acid fragments from the sample; amplifying the plurality of nucleic acid fragments; sequencing, with a sequencer, a plurality of amplified nucleic acid fragments to obtain a plurality of sequence reads, wherein at least the plurality of amplified nucleic acid fragments have undergone cytosine conversion, and the plurality of nucleic acid fragments correspond to genomic loci comprising clusters of two or more CpG dinucleotides; determining, by a processor, a consensus methylation pattern for the cluster, the consensus methylation pattern representing each CpG dinucleotide in the cluster for which methylation is detected based on the cytosine conversion in at least one sequence read from the plurality of sequence reads; generating, by a processor, a cluster consensus fraction (CCF) for the cluster, the CCF representing a proportion of sequence reads corresponding to the cluster that exhibit the consensus methylation pattern out of a total number of sequence reads from the plurality of sequence reads that correspond to the cluster; detecting one or more of the methylation level or the unmethylation level of the cluster based on the CCF; generating a genomic profile of the subject based on the detected methylation level, the detected unmethylation level, or both. (a) the CCF is equal to or greater than a threshold or reference value, and the method further comprises detecting the presence of cancer nucleic acids in the plurality of nucleic acid fragments based at least in part on the CCF being equal to or greater than the threshold or reference value; or (b) the CCF is less than a threshold or reference value, and the method further comprises detecting the absence of cancer nucleic acid in the plurality of nucleic acid fragments based, at least in part, on the CCF being less than the threshold or reference value. The method of claim 1.

3. determining a consensus methylation pattern and a CCF for the plurality of clusters; The plurality of clusters may correspond to a plurality of genomic loci. The method of claim 1.

4. i. at least one CpG dinucleotide in said cluster is unmethylated in said consensus methylation pattern, and / or ii. at least one CpG dinucleotide in the cluster is methylated in the consensus methylation pattern; and / or iii. at least one cluster comprises two or more CpG dinucleotides, or five or more CpG dinucleotides, and optionally at least one cluster comprises six or more CpG dinucleotides; The method of claim 1.

5. the plurality of sequence reads are obtained from whole genome methylsequencing (WGMS) or next generation sequencing (NGS); and / or the plurality of sequence reads comprises paired-end sequence reads or unpaired sequence reads; The method of claim 1.

6. Prior to determining the consensus methylation pattern and CCF, (a) demultiplexing sequence reads from the plurality of sequence reads; and / or (b) performing a three-letter alignment of sequence reads from the plurality of sequence reads to a reference genome; and / or (c) removing sequencing reads from the plurality of sequencing reads that failed to undergo cytosine conversion; and / or (d) filtering out sequence reads that have a base other than cytosine or thymine at the first position of at least one of said CpG dinucleotides; and / or (e) filtering out sequence reads having a base quality below a threshold base quality; The method of claim 1 further comprising:

7. the consensus methylation pattern and CCF are determined based on sequence reads that cover multiple CpG dinucleotides in the cluster; 2. The method of claim 1, wherein the consensus methylation pattern and CCF may be determined based on sequence reads that cover at least 50%, at least 90%, or all CpG dinucleotides in the cluster.

8. 2. The method of claim 1, wherein the plurality of nucleic acid fragments have undergone cytosine conversion by bisulfite treatment, TET-assisted bisulfite treatment, TET-assisted pyridine borane treatment, oxidative bisulfite treatment, or APOBEC treatment.

9. (a) further comprising subjecting the plurality of nucleic acids to fragmentation prior to providing said plurality of sequence reads; and / or (b) prior to providing said plurality of sequence reads, further comprising selectively enriching a plurality of nucleic acids or nucleic acid fragments corresponding to genomic loci comprising clusters of two or more CpG dinucleotides to generate an enriched sample; and / or (c) the amplification of the plurality of nucleic acids or nucleic acid fragments is performed by polymerase chain reaction (PCR); The method of claim 1.

10. further comprising isolating the plurality of nucleic acids from the sample prior to providing the plurality of sequence reads; The sample may comprise tumor cells and / or tumor nucleic acids; The sample may further comprise non-tumor cells and / or non-tumor nucleic acids, and further The sample may contain a proportion of tumor nucleic acid that is less than 1% or less than 0.1% and / or at least 0.01% of total nucleic acid. The method of claim 1.

11. The sample is (a) tumor cell-free DNA (cfDNA), circulating cell-free DNA (ccfDNA), or circulating tumor DNA (ctDNA); or (b) a fluid, cell, or tissue, which may include blood or plasma; or (c) tumor biopsy or circulating tumor cells The method of claim 10, comprising:

12. the sample is a tissue sample, and the method further comprises subjecting a plurality of nucleic acid molecules in the tissue to fragmentation to generate the plurality of nucleic acid fragments; and optionally further comprising ligating one or more adaptors to one or more nucleic acid fragments from the plurality of nucleic acid fragments prior to amplifying the plurality of nucleic acid fragments. The method of claim 1.

13. 1. A method for assisting in monitoring cancer in an individual, comprising: (a) detecting the methylation level or the unmethylation level in a first sample comprising a plurality of nucleic acids obtained from the individual according to the method of any one of claims 1 to 12; (b) detecting the methylation level or the unmethylation level in a second sample comprising a plurality of nucleic acids obtained from the individual according to the method of any one of claims 1 to 12, wherein the second sample is obtained from the individual after the first sample; (c) determining a difference in methylation levels between the first sample and the second sample, thereby monitoring the cancer in the individual.

14. A pharmaceutical for treating or delaying the progression of cancer in an individual, comprising a drug for anthracycline-based chemotherapy, (a) the methylation level or the unmethylation level in a sample comprising a plurality of nucleic acids obtained from the individual is detected according to the method of any one of claims 1 to 12, wherein the plurality of nucleic acids comprises one or more nucleic acids corresponding to the PITX2 locus; and (b) A medicament in which an effective amount of a drug for anthracycline-based chemotherapy is administered.

15. A pharmaceutical agent for treating or delaying the progression of cancer in an individual, comprising an alkylating agent, (a) the methylation level or the unmethylation level in a sample comprising a plurality of nucleic acids obtained from the individual is detected according to the method of any one of claims 1 to 12, wherein the plurality of nucleic acids comprises one or more nucleic acids corresponding to the MGMT locus; and (b) a medicament wherein an effective amount of an alkylating agent is administered to said individual.

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