Methods and systems for molecular disease assessment through analysis of circulating tumor DNA

Abstract

The present disclosure provides a method for evaluating a tumor status (for example, progression, regression, recurrence, etc.) in a subject. In one embodiment, the method for evaluating a tumor status (for example, progression, regression, recurrence, etc.) of a subject can include: based on the first and second WGS data of the cfDNA molecules of the subject at different time points, (i) determine a first and second plurality of CNAs and (ii) a first and second plurality of fragment lengths; process the first and second plurality of CNAs to determine CNA profile changes; compare the first and second plurality of fragment lengths to determine fragment length profile changes; determine the first or second tumor proportion of the subject at the first or second time point at least partially based on the CNA profile changes and fragment length profile changes; and detect the tumor status of the subject at least partially based on the first or second tumor proportion.

Description

[Technical Field]

[0001] Cross-reference of related applications This application claims priority to U.S. Provisional Patent Application No. 62 / 953,368, filed on 24 December 2019, and U.S. Provisional Patent Application No. 62 / 993,564, filed on 23 March 2020, the contents of which are incorporated herein by reference in their entirety.

[0002] Request for a sequence listing in an ASCII text file The contents of the following application in ASCII text file are incorporated herein by reference in their entirety: Sequence listing in computer-readable format (CRF) (filename: 197102004840SEQLIST.TXT, date recorded: December 18, 2020, size: 34KB). [Background technology]

[0003] Tumor progression generally refers to a situation where a patient has a tumor that has progressed in severity (e.g., tumor volume, tumor size, cancer stage). For example, tumor progression in a patient may indicate that the patient's tumor is not responding to the cancer treatment regimen. Conversely, non-progression in a patient may indicate that the patient's tumor is responding to the cancer treatment regimen. In addition, a patient's tumor progression or non-progression status may indicate the patient's prognosis for cancer treatment. [Overview of the Initiative] [Means for solving the problem]

[0004] The method and system are provided for evaluating the tumor status (e.g., progression, regression, recurrence, etc.) of a subject, such as a patient with cancer, by analyzing a fluid sample of the subject (e.g., a blood sample). Tumor progression or non-progression may be evaluated and / or monitored by analyzing tumor DNA (e.g., from cell-free DNA) from the subject's sample. The subject's tumor progression or non-progression status may indicate diagnostic, prognostic, or treatment options for the subject with cancer.

[0005] In one embodiment, the present disclosure is a method for evaluating tumor progression in a subject having cancer, comprising: obtaining first whole-genome sequencing (WGS) data of a first plurality of cell-free DNA (cfDNA) molecules, wherein the first plurality of cfDNA molecules are obtained from or derived from a first bodily fluid sample of the subject at a first time point, the first time point being before a therapeutic agent configured to treat cancer is administered to the subject; processing the first WGS data to determine (i) a first plurality of copy number anomalies (CNAs) in the first plurality of cfDNA molecules, and (ii) a first plurality of fragment lengths of the first plurality of cfDNA molecules; and obtaining second whole-genome sequencing (WGS) data of a second plurality of cell-free DNA (cfDNA) molecules, wherein the second plurality of cfDNA molecules are obtained from a second bodily fluid sample of the subject at a second time point. A method is provided which includes obtaining, or derived therefrom, a second time point in time being after the subject has been administered a therapeutic agent, and processing the second WGS data to determine (iii) a second set of copy number anomalies (CNAs) in a second set of cfDNA molecules, and (iv) a second set of fragment lengths in a second set of cfDNA molecules; processing the first set of CNAs with the second set of CNAs to determine changes in the CNA profile; processing the first set of fragment lengths with the second set of fragment lengths to determine changes in the fragment length profile; determining, at least in part, a first tumor percentage of the subject at the first time point or a second tumor percentage of the subject at the second time point based on the changes in the CNA profile and the changes in the fragment length profile; and at least in part, detecting tumor progression in the subject based on the first or second tumor percentage.

[0006] In one embodiment, the present disclosure provides a method for evaluating the tumor status (e.g., tumor progression, non-progression, regression, or recurrence) of a subject having cancer, comprising: obtaining first whole-genome sequencing (WGS) data of a first plurality of cell-free DNA (cfDNA) molecules, wherein the first plurality of cfDNA molecules are obtained from or derived from a first bodily fluid sample of the subject at a first time point, the first time point being before a therapeutic agent configured to treat cancer is administered to the subject; determining, based on the first WGS data, (i) a first plurality of copy number anomalies (CNAs) in the first plurality of cfDNA molecules, and (ii) a first plurality of fragment lengths of the first plurality of cfDNA molecules; and obtaining second whole-genome sequencing (WGS) data of a second plurality of cell-free DNA (cfDNA) molecules, wherein the second plurality of cfDNA molecules are obtained from a second bodily fluid sample of the subject at a second time point. A method is provided which includes obtaining, based on the second WGS data, (iii) a second set of copy number anomalies (CNAs) in a second set of cfDNA molecules, and (iv) a second set of fragment lengths in the second set of cfDNA molecules; comparing the first set of CNAs with the second set of CNAs to determine a change in the CNA profile; determining a change in the fragment length profile based on the first set of fragment lengths and the second set of fragment lengths; determining, at least partially, the first tumor percentage of the subject at the first time point or the second tumor percentage of the subject at the second time point based on the change in the CNA profile and the change in the fragment length profile; and at least partially, detecting the tumor status of the subject (e.g., tumor progression, non-progression, regression, or recurrence) based on the first or second tumor percentage.

[0007] In one embodiment, the present disclosure is a method for treating cancer in a subject, comprising obtaining first whole-genome sequencing (WGS) data of a first group of cell-free DNA (cfDNA) molecules, wherein the first group of cfDNA molecules are obtained from or derived from a first bodily fluid sample of the subject at a first time point, the first time point being before a therapeutic agent configured to treat cancer is administered to the subject, and based on the first WGS data, (i) first group of copy number abnormalities (C) in the first group of cfDNA molecules (ii) determining the lengths of first multiple fragments of first multiple cfDNA molecules and obtaining second whole-genome sequencing (WGS) data of second multiple cell-free DNA (cfDNA) molecules, wherein the second multiple cfDNA molecules are obtained from or derived from a second bodily fluid sample of the subject at a second time point, the second time point being after the subject has been administered a therapeutic agent, and based on the second WGS data, (iii) determining second multiple copy number anomalies (CNAs) in the second multiple cfDNA molecules, A method is provided comprising (iv) determining the lengths of a second set of fragments of a second set of cfDNA molecules; comparing a first set of CNAs with a second set of CNAs to determine a change in the CNA profile; determining a change in the fragment length profile based on the first set of fragment lengths and the second set of fragment lengths; at least in part, determining the first tumor percentage of the subject at a first time point or the second tumor percentage of the subject at a second time point based on the change in the CNA profile and the change in the fragment length profile; at least in part, detecting the tumor status of the subject (e.g., tumor progression, non-progression, regression, or recurrence) based on the first or second tumor percentage; and administering a therapeutically effective dose of treatment (e.g., surgery, chemotherapy, radiotherapy, targeted therapy, immunotherapy, cell therapy, antihormone agents, antimetabolites, kinase inhibitors, methyltransferase inhibitors, peptides, gene therapy, vaccines, platinum-based chemotherapy agents, antibodies, or checkpoint inhibitors) to treat the cancer of the subject based on the detected tumor status.In some embodiments, the detected tumor condition includes tumor progression, and the method comprises administering a second treatment to the patient, prior to administration, the patient has been treated with a first treatment for cancer (the first and second treatments are different).

[0008] In some embodiments, the first or second bodily fluid sample is selected from the group consisting of blood, serum, plasma, vitreous humor, sputum, urine, tears, sweat, saliva, semen, mucosal exudate, mucus, cerebrospinal fluid, cerebrospinal fluid (CSF), pleural fluid, ascites, amniotic fluid, and lymph. In some embodiments, obtaining the first WGS data involves sequencing a first set of cfDNA molecules to produce a first set of sequenced reads, or obtaining the second WGS data involves sequencing a second set of cfDNA molecules to produce a second set of sequenced reads. In some embodiments, sequencing is performed by nanopore sequencing, end-to-end (Sanger) sequencing, synthetic sequencing (e.g., Illumina or Solexa sequencing), single-molecule real-time sequencing, ultra-parallel signature sequencing, Polony sequencing, 454 pyrosequencing, combinatorial probe anchor synthesis, ligation sequencing (SOLiD sequencing), or GenapSys sequencing. In some embodiments, sequencing includes whole-genome bisulfite sequencing (WGBS), whole-genome enzymatic methyl-seq, whole-exome sequencing, whole-epigenome sequencing, methylation arrays, reduced-expression bisulfite sequencing (RRBS-Seq), TET-assisted pyridineborane sequencing (TAPS), TET-assisted bisulfite sequencing (TAB-Seq), APOBEC-bound epigenetic sequencing (ACE-seq), oxidative bisulfite sequencing (oxBS-Seq), pull-down or methylated DNA immunoprecipitation sequencing, or cytosine 5-hydroxymethylation sequencing (e.g., via Bluestar).

[0009] In some embodiments, sequencing is performed to a depth of approximately 40 or fewer iterations. In some embodiments, sequencing is performed to a depth of approximately 30 or fewer iterations. In some embodiments, sequencing is performed to a depth of approximately 25 or fewer iterations. In some embodiments, sequencing is performed to a depth of approximately 20 or fewer iterations. In some embodiments, sequencing is performed to a depth of approximately 12 or fewer iterations. In some embodiments, sequencing is performed to a depth of approximately 10 or fewer iterations. In some embodiments, sequencing is performed to a depth of approximately 8 or fewer iterations. In some embodiments, sequencing is performed to a depth of approximately 6 or fewer iterations. In some embodiments, sequencing is performed to a depth of approximately 5 or fewer iterations, approximately 4 or fewer iterations, approximately 3 or fewer iterations, approximately 2 or fewer iterations, or approximately 1 or fewer iterations.

[0010] In some embodiments, the method further includes aligning a first or second plurality of sequencing reads with a reference genome to thereby generate a plurality of aligned sequencing reads. In some embodiments, the method further includes enriching a first or second plurality of cfDNA molecules for a plurality of genomic regions. In some embodiments, enrichment includes amplifying a first or second plurality of cfDNA molecules. In some embodiments, amplification includes selective amplification. In some embodiments, amplification includes universal amplification. In some embodiments, enrichment includes selective isolation of at least a portion of a first or second plurality of cfDNA molecules. In some embodiments, selective isolation of at least a portion of a first or second plurality of cfDNA molecules includes using a plurality of probes, each of which has sequence complementarity with at least a portion of a genomic region among a plurality of genomic regions. In some embodiments, at least a portion includes tumor marker loci. In some embodiments, at least a portion includes a plurality of tumor marker loci. In some embodiments, multiple tumor marker loci include one or more loci with copy number variations (e.g., CNA loci such as MET, EGFR, and BRCA2, as well as whole-arm CNAs of chromosomes 1 and 8). Such CNA loci can be found using databases such as The Cancer Genome Atlas (TCGA) and Catalogue of Somatic Mutations in Cancer (COSMIC).

[0011] In some embodiments, determining a first plurality of CNAs includes determining quantitative measurements of CNAs in each of a plurality of genomic regions of a first plurality of sequencing reads, and determining a second plurality of CNAs includes determining quantitative measurements of CNAs in each of a plurality of genomic regions of a second plurality of sequencing reads. In some embodiments, the method further includes modifying the first plurality of CNAs or the second plurality of CNAs for GC content and / or mappability trend. In some embodiments, the modification includes using statistical modeling analysis. In some embodiments, the modification includes using LOESS regression or Bayesian models. In some embodiments, the plurality of genomic regions include non-overlapping genomic regions of a reference genome having a predetermined size. In some embodiments, the predetermined size is about 50 kilobases (kb), about 100 kb, about 200 kb, about 500 kb, about 1 megabase (Mb), about 2 Mb, about 5 Mb, or about 10 Mb.

[0012] In some embodiments, the multiple genomic regions include at least about 1,000 distinct genomic regions. In some embodiments, the multiple genomic regions include at least about 2,000 distinct genomic regions. In some embodiments, the multiple genomic regions include at least about 3,000 distinct genomic regions, at least about 4,000 distinct genomic regions, at least about 5,000 distinct genomic regions, at least about 6,000 distinct genomic regions, at least about 7,000 distinct genomic regions, at least about 8,000 distinct genomic regions, at least about 9,000 distinct genomic regions, at least about 10,000 distinct genomic regions, at least about 15,000 distinct genomic regions, at least about 20,000 distinct genomic regions, at least about 25,000 distinct genomic regions, at least about 30,000 Including 00 distinct genomic regions, at least approximately 35,000 distinct genomic regions, at least approximately 40,000 distinct genomic regions, at least approximately 45,000 distinct genomic regions, at least approximately 50,000 distinct genomic regions, at least approximately 100,000 distinct genomic regions, at least approximately 150,000 distinct genomic regions, at least approximately 200,000 distinct genomic regions, at least approximately 250,000 distinct genomic regions, at least approximately 300,000 distinct genomic regions, at least approximately 400,000 distinct genomic regions, or at least approximately 500,000 distinct genomic regions.

[0013] In some embodiments, determining CNA profile changes involves processing a first plurality of CNAs and a second plurality of CNAs with a plurality of baseline CNA values, the plurality of baseline CNA values ​​being obtained from or derived from additional cfDNA molecules of additional subjects. In some embodiments, the additional subjects include one or more subjects without cancer (e.g., subjects unaffected by cancer or subjects without a cancer diagnosis). In some embodiments, the additional subjects include one or more subjects without tumor progression. In some embodiments, the plurality of baseline CNA values ​​are obtained using additional physiotherapy samples of subjects obtained at one or more subsequent time points after the first time point.

[0014] In some embodiments, the method further includes excluding subsets of a first plurality of CNAs and a second plurality of CNAs that satisfy a predetermined criterion. In some embodiments, a given CNA value from the first plurality of CNAs or a second plurality of CNAs is excluded if the difference between a given CNA value and a corresponding criterion CNA value is about one standard deviation or less. In some embodiments, the method further includes excluding a given CNA value from the first plurality of CNAs or a second plurality of CNAs if the difference between a given CNA value and a corresponding criterion CNA value is about two standard deviations or less. In some embodiments, the method further includes excluding a given CNA value from the first plurality of CNAs or a second plurality of CNAs if the difference between a given CNA value and a corresponding criterion CNA value is about three standard deviations or less. In some embodiments, the method further includes excluding a given CNA value from a first or second set of CNA values ​​based on Spearman's rank correlation between a given CNA value and the corresponding local mean fragment length or local mean methylation. In some embodiments, the method further includes excluding a given CNA value from a first or second set of CNA values ​​if Spearman's rank correlation coefficient (Spearman's rho) is less than -0.1 (for example, indicating no significant negative correlation between local mean fragment length and local tumor copy number). This allows for verification of whether there is a negative correlation between CAN and fragment length or methylation using Pearson correlation or some other type of correlation statistics.

[0015] In some embodiments, the method further includes normalizing a first or second set of fragment lengths based on library or genomic location. In some embodiments, the method further includes determining that the tumor status (e.g., tumor progression, non-progression, regression, or recurrence) includes tumor progression of the subject if the first or second tumor percentage is greater than 1, greater than 1.1, greater than 1.2, greater than 1.3, greater than 1.4, greater than 1.5, greater than 1.6, greater than 1.7, greater than 1.8, greater than 1.9, greater than 2, greater than 3, greater than 4, or greater than 5. In some embodiments, the method further includes detecting the major molecular response (MMR) of the subject if the first or second tumor percentage is less than 0.01, less than 0.05, less than 0.1, less than 0.2, less than 0.3, less than 0.4, or less than 0.5.

[0016] In some embodiments, the method further includes detecting the tumor status of the subject (e.g., progression, non-progression, regression, or recurrence of the tumor) with a sensitivity of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, or at least about 94%. In some embodiments, the method further includes detecting the tumor status of the subject (e.g., progression, non-progression, regression, or recurrence of the tumor) with a sensitivity of at least about 95%, at least about 96%, at least about 97%, or at least about 98%. In some embodiments, the method further includes detecting the tumor status of the subject (e.g., progression, non-progression, regression, or recurrence of the tumor) with a sensitivity of at least about 99%.

[0017] In some embodiments, the method further includes detecting the tumor status of the subject (e.g., progression, non-progression, regression, or recurrence of the tumor) with specificity of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, or at least about 94%. In some embodiments, the method further includes detecting the tumor status of the subject (e.g., progression, non-progression, regression, or recurrence of the tumor) with specificity of at least about 95%, at least about 96%, at least about 97%, or at least about 98%. In some embodiments, the method further includes detecting the tumor status of the subject (e.g., progression, non-progression, regression, or recurrence of the tumor) with specificity of at least about 99%.

[0018] In some embodiments, the method further includes detecting the target tumor status (e.g., tumor progression, non-progression, regression, or recurrence) with positive predictive values ​​(PPV) of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, or at least about 94%. In some embodiments, the method further includes detecting the target tumor status (e.g., tumor progression, non-progression, regression, or recurrence) with positive predictive values ​​(PPV) of at least about 95%, at least about 96%, at least about 97%, or at least about 98%. In some embodiments, the method further includes detecting the target tumor status (e.g., tumor progression, non-progression, regression, or recurrence) with positive predictive values ​​(PPV) of at least about 99%.

[0019] In some embodiments, the method further includes detecting the target tumor status (e.g., tumor progression, non-progression, regression, or recurrence) with a negative predictive value (NPV) of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, or at least about 94%. In some embodiments, the method further includes detecting the target tumor status (e.g., tumor progression, non-progression, regression, or recurrence) with a negative predictive value (NPV) of at least about 95%, at least about 96%, at least about 97%, or at least about 98%. In some embodiments, the method further includes detecting the target tumor status (e.g., tumor progression, non-progression, regression, or recurrence) with a negative predictive value (NPV) of at least about 99%.

[0020] In some embodiments, the method further includes detecting the target tumor status (e.g., tumor progression, non-progression, regression, or recurrence) with an area under the curve (AUC) of at least about 0.50, at least about 0.55, at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.85, at least about 0.90, at least about 0.91, at least about 0.92, at least about 0.93, or at least about 0.94. In some embodiments, the method further includes detecting the target tumor status (e.g., tumor progression, non-progression, regression, or recurrence) with an area under the curve (AUC) of at least about 0.95, at least about 0.96, at least about 0.97, or at least about 0.98. In some embodiments, the method further includes detecting the target tumor status (e.g., tumor progression, non-progression, regression, or recurrence) with an area under the curve (AUC) of at least about 0.99.

[0021] In some embodiments, the method further comprises determining non - progression of the subject's tumor when tumor progression is not detected. In some embodiments, the method further comprises administering a therapeutically effective dose of treatment to treat the subject's cancer based on the determined tumor state of the subject (e.g., tumor progression, non - progression, regression, or recurrence). In some embodiments, the treatment comprises treatment by surgery, chemotherapy, radiation therapy, targeted therapy, immunotherapy, cell therapy, anti - hormonal agents, antimetabolite chemotherapy agents, kinase inhibitors, methyltransferase inhibitors, peptides, gene therapy, vaccines, platinum - based chemotherapy agents, antibodies, or checkpoint inhibitors. In some embodiments, the detected tumor state indicates tumor progression, non - progression, regression, or recurrence. In some embodiments, the first and second WGS data are obtained by a sequencing device or a computer processor (e.g., comprising one or more programs for executing instructions based on the methods of the present disclosure).

[0022] In another aspect, the Disclosure relates to a computer system for evaluating tumor progression in a subject having cancer, comprising: a database configured to store (i) first whole-genome sequencing (WGS) data of a first plurality of cell-free DNA (cfDNA) molecules, wherein the first plurality of cfDNA molecules are obtained from or derived from a first bodily fluid sample of the subject at a first time point, the first time point being before a therapeutic agent configured to treat cancer is administered to the subject; and (ii) second whole-genome sequencing (WGS) data of a second plurality of cell-free DNA (cfDNA) molecules, wherein the second plurality of cfDNA molecules are obtained from or derived from a second bodily fluid sample of the subject at a second time point, the second time point being after the therapeutic agent is administered to the subject; and one or more computer processors operably coupled to the database, wherein the one or more computer processors operate individually or collectively. A computer system is provided, which includes one or more computer processors programmed to process first WGS data to determine (i) first multiple copy number anomalies (CNAs) in a first multiple cfDNA molecule, and (ii) first multiple fragment lengths of the first multiple cfDNA molecule; process second WGS data to determine (iii) second multiple copy number anomalies (CNAs) in a second multiple cfDNA molecule, and (iv) second multiple fragment lengths of the second multiple cfDNA molecule; process the first multiple CNAs with the second multiple CNAs to determine CNA profile changes; process the first multiple fragment lengths with the second multiple fragment lengths to determine fragment length profile changes; determine, at least partially, a first tumor percentage of the subject at a first time point or a second tumor percentage of the subject at a second time point based on the CNA profile changes and fragment length profile changes; and at least partially, detect tumor progression of the subject based on the first or second tumor percentage.

[0023] In another aspect, the present disclosure provides a computer system for evaluating the tumor status (e.g., tumor progression, non - progression, regression, or recurrence) of a subject having cancer, comprising: (i) first whole - genome sequencing (WGS) data of a first plurality of cell - free DNA (cfDNA) molecules, wherein the first plurality of cfDNA molecules are obtained from or derived from a first body fluid sample of the subject at a first time point, and the first time point is before a therapeutic agent configured to treat cancer is administered to the subject; and (ii) second whole - genome sequencing (WGS) data of a second plurality of cell - free DNA (cfDNA) molecules, wherein the second plurality of cfDNA molecules are obtained from or derived from a second body fluid sample of the subject at a second time point, and the second time point is after the therapeutic agent is administered to the subject, a database configured to store the second whole - genome sequencing (WGS) data; and one or more computer processors operably coupled to the database, wherein the one or more computer processors, individually or collectively, based on the first WGS data, determine (i) a first plurality of copy number aberrations (CNA) in the first plurality of cfDNA molecules, and (ii) a first plurality of fragment lengths of the first plurality of cfDNA molecules, based on the second WGS data, determine (iii) a second plurality of copy number aberrations (CNA) in the second plurality of cfDNA molecules, and (iv) a second plurality of fragment lengths of the second plurality of cfDNA molecules, compare the first plurality of CNA with the second plurality of CNA to determine a CNA profile change, determine a fragment - length profile change based on the first plurality of fragment lengths and the second plurality of fragment lengths, at least partially based on the CNA profile change and the fragment - length profile change, determine a first tumor fraction of the subject at the first time point or a second tumor fraction of the subject at the second time point, and at least partially based on the first tumor fraction or the second tumor fraction, detect the tumor status of the subject.

[0024] In one embodiment, the Disclosure includes machine-executable instructions that, when executed by one or more computer processors on a non-temporary computer-readable medium, carry out a method for evaluating tumor progression in a subject having cancer, the method comprising: obtaining first whole-genome sequencing (WGS) data of a first plurality of cell-free DNA (cfDNA) molecules, wherein the first plurality of cfDNA molecules are obtained from or derived from a first bodily fluid sample of the subject at a first time point, the first time point being before a therapeutic agent configured to treat cancer is administered to the subject; processing the first WGS data to determine (i) a first plurality of copy number anomalies (CNAs) in the first plurality of cfDNA molecules, and (ii) a first plurality of fragment lengths of the first plurality of cfDNA molecules; and obtaining second whole-genome sequencing (WGS) data of a second plurality of cell-free DNA (cfDNA) molecules, wherein the second plurality of cfDNA molecules A non-temporary computer-readable medium is provided, which includes obtaining, processing the second WGS data, which is obtained from or derived from a second body fluid sample of the subject at a second time point, the second time point being after the subject has been administered a therapeutic agent; determining (iii) a second set of copy number anomalies (CNAs) in the second set of cfDNA molecules, and (iv) a second set of fragment lengths of the second set of cfDNA molecules; processing the first set of CNAs with the second set of CNAs to determine changes in the CNA profile; processing the first set of fragment lengths with the second set of fragment lengths to determine changes in the fragment length profile; determining, at least partially, the first tumor percentage of the subject at the first time point or the second tumor percentage of the subject at the second time point based on the changes in the CNA profile and the changes in the fragment length profile; and at least partially, detecting tumor progression in the subject based on the first or second tumor percentage.

[0025] In one embodiment, the Disclosure includes a machine-executable instruction in a non-temporal computer-readable medium, which, when executed by one or more computer processors, carries out a method for evaluating the tumor status of a subject having cancer (e.g., tumor progression, non-progression, regression, or recurrence), wherein the method comprises: obtaining first whole-genome sequencing (WGS) data of a first plurality of cell-free DNA (cfDNA) molecules, the first plurality of cfDNA molecules being obtained from or derived from a first bodily fluid sample of the subject at a first time point, the first time point being before a therapeutic agent configured to treat cancer is administered to the subject; obtaining; determining, based on the first WGS data, (i) first plurality of copy number anomalies (CNAs) in the first plurality of cfDNA molecules, and (ii) first plurality of fragment lengths of the first plurality of cfDNA molecules; and obtaining second whole-genome sequencing (WGS) data of a second plurality of cell-free DNA (cfDNA) molecules, the second plurality of A non-temporary computer-readable medium is provided, which includes obtaining, based on the second WGS data, (iii) determining a second set of copy number anomalies (CNAs) in the second set of CNAs

[0026] In another aspect, the Disclosure provides a method for evaluating tumor progression in a subject with cancer, comprising: obtaining first methylation sequencing (MS) data of a first group of cell-free DNA (cfDNA) molecules across a region of the genome, wherein the first group of cfDNA molecules are obtained from or derived from a fluid sample of the subject at a first time point, and the first time point is before the subject is administered a therapeutic agent configured to treat cancer; processing the first MS data to determine the mean methylation percentage for each of one or more CpG islands within the region of the genome, thereby obtaining a first mean methylation percentage profile; and obtaining second MS data of a second group of cell-free DNA (cfDNA) molecules across a region of the genome, wherein the second group of cfDNA molecules are obtained from a second fluid sample of the subject at a second time point. A method is provided which includes: obtaining, or deriving from, a second time point in the subject after the subject has been administered a therapeutic agent; processing the second MS data to determine a methylation profile for each of one or more CpG islands within a region of the genome, thereby obtaining a second mean methylation percentage profile; processing the first mean methylation percentage profile across one or more CpG islands and the second mean methylation percentage profile across one or more CpG islands to determine a methylation percentage profile; determining, at least partially, a first tumor percentage of the subject at the first time point or a second tumor percentage of the subject at the second time point based on the respective methylation percentage profiles; and at least partially, detecting tumor progression of the subject based on the first or second tumor percentage. In some embodiments, additional time points after the second time point may be collected and analyzed to detect tumor progression occurring at subsequent time points.

[0027] In another aspect, the Disclosure provides a method for evaluating the tumor status (e.g., tumor progression, non-progression, regression, or recurrence) of a subject with cancer, comprising: obtaining first methylation sequencing (MS) data of a first plurality of cell-free DNA (cfDNA) molecules across a region of the genome, wherein the first plurality of cfDNA molecules are obtained from or derived from a fluid sample of the subject at a first time point, and the first time point is before the subject is administered a therapeutic agent configured to treat cancer; determining the mean methylation percentage for each of one or more CpG islands within the region of the genome based on the first MS data, thereby obtaining a first mean methylation percentage profile; and obtaining second MS data of a second plurality of cell-free DNA (cfDNA) molecules across a region of the genome, wherein the second plurality of cfDNA molecules are obtained from the subject at a second time point. A method is provided which includes: obtaining a second body fluid sample obtained from or derived therefrom, where the second time point is after the subject has been administered a therapeutic agent; determining the mean methylation rate for each of one or more CpG islands in a region of the genome based on the second MS data, thereby obtaining a second mean methylation rate profile; determining a methylation rate profile by comparing a first mean methylation rate profile across one or more CpG islands with the second mean methylation rate profile across one or more CpG islands; determining, at least partially, the first tumor rate of the subject at the first time point or the second tumor rate of the subject at the second time point based on the respective methylation rate profiles; and at least partially, detecting the tumor status of the subject based on the first or second tumor rate. In some embodiments, additional time points after the second time point may be collected and analyzed to detect tumor progression occurring at subsequent time points.

[0028] In another aspect, the Disclosure relates to a method for treating cancer in a subject, comprising: obtaining first methylation sequencing (MS) data of a first plurality of cell-free DNA (cfDNA) molecules across a region of the genome, wherein the first plurality of cfDNA molecules are obtained from or derived from a bodily fluid sample of the subject at a first time point, the first time point being before the subject is administered a therapeutic agent configured to treat cancer; determining the mean methylation ratio for each of one or more CpG islands within the region of the genome based on the first MS data, thereby obtaining a first mean methylation ratio profile; and obtaining second MS data of a second plurality of cell-free DNA (cfDNA) molecules across a region of the genome, wherein the second plurality of cfDNA molecules are obtained from or derived from a second bodily fluid sample of the subject at a second time point, the second time point being after the subject is administered a therapeutic agent; and determining the mean methylation ratio for each of one or more CpG islands within the region of the genome based on the second MS data. A method is provided which includes: determining the mean methylation rate of a subject and thereby obtaining a second mean methylation rate profile; determining a methylation rate profile by comparing a first mean methylation rate profile across one or more CpG islands with a second mean methylation rate profile across one or more CpG islands; determining, at least partially, a first tumor rate of the subject at a first time point or a second tumor rate of the subject at a second time point based on the respective methylation rate profiles; detecting the tumor status of the subject at least partially based on the first or second tumor rate; and administering a therapeutically effective dose of treatment (e.g., surgery, chemotherapy, radiotherapy, targeted therapy, immunotherapy, cell therapy, antihormone agents, antimetabolites, kinase inhibitors, methyltransferase inhibitors, peptides, gene therapy, vaccines, platinum-based chemotherapy agents, antibodies, or checkpoint inhibitors) to treat the cancer of the subject based on the detected tumor status of the subject.In some embodiments, the detected tumor condition includes tumor progression, and the method comprises administering a second treatment to the patient, prior to administration, the patient has been treated with a first treatment for cancer (the first and second treatments are different).

[0029] In some embodiments, the first or second bodily fluid sample is selected from the group consisting of blood, serum, plasma, vitreous humor, sputum, urine, tears, sweat, saliva, semen, mucosal discharge, mucus, cerebrospinal fluid, cerebrospinal fluid (CSF), pleural fluid, ascites, amniotic fluid, and lymph. In some embodiments, obtaining first MS data involves performing methylation sequencing of a first set of cfDNA molecules to generate a first set of sequencing reads, or obtaining second MGS data involves performing methylation sequencing of a second set of cfDNA molecules to generate a second set of sequencing reads. In some embodiments, methylation sequencing includes whole-genome bisulfite sequencing. In some embodiments, methylation sequencing includes whole-genome enzymatic methyl-seq. In some embodiments, methylation sequencing includes oxidative bisulfite sequencing, TET-assisted pyridineborane sequencing (TAPS), TET-assisted bisulfite sequencing (TABS), oxidative bisulfite sequencing (oxBS-Seq), APOBEC-bound epigenetic sequencing (ACE-seq), methylated DNA immunoprecipitation (MeDIP) sequencing, hydroxymethylated DNA immunoprecipitation (hMeDIP) sequencing, methylation array analysis, reduced-expression bisulfite sequencing (RRBS-Seq), or cytosine 5-hydroxymethylation sequencing.

[0030] In some embodiments, methylation sequencing is performed to a depth of about 40 or fewer iterations. In some embodiments, methylation sequencing is performed to a depth of about 30 or fewer iterations. In some embodiments, methylation sequencing is performed to a depth of about 25 or fewer iterations. In some embodiments, methylation sequencing is performed to a depth of about 20 or fewer iterations. In some embodiments, methylation sequencing is performed to a depth of about 12 or fewer iterations. In some embodiments, methylation sequencing is performed to a depth of about 10 or fewer iterations. In some embodiments, methylation sequencing is performed to a depth of about 8 or fewer iterations. In some embodiments, methylation sequencing is performed to a depth of about 6 or fewer iterations. In some embodiments, methylation sequencing is performed to a depth of about 5 or fewer iterations, about 4 or fewer iterations, about 3 or fewer iterations, about 2 or fewer iterations, or about 1 or fewer iterations.

[0031] In some embodiments, the method further includes aligning a first or second plurality of sequencing reads with a reference genome (e.g., simultaneously with a C-to-T converted version of the reference genome) to thereby generate a plurality of aligned sequencing reads. In some embodiments, the method further includes enriching a first or second plurality of cfDNA molecules for a region of the genome. In some embodiments, enrichment includes amplifying a first or second plurality of cfDNA molecules. In some embodiments, amplification includes selective amplification. In some embodiments, amplification includes universal amplification. In some embodiments, enrichment includes selective isolation of at least a portion of the first or second plurality of cfDNA molecules. In some embodiments, selective isolation of at least a portion of the first or second plurality of cfDNA molecules includes using a plurality of probes, each of which has sequence complementarity with at least a portion of a region of the genome. In some embodiments, at least a portion includes tumor marker loci. In some embodiments, at least a portion includes a plurality of tumor marker loci. In some embodiments, multiple tumor marker loci include one or more loci selected from The Cancer Genome Atlas (TCGA) or Catalogue of Somatic Mutations in Cancer (COSMIC).

[0032] In some embodiments, a region of the genome includes one or more of the following: CpG islands, CpG Shores, patient-specific partial methylation domains, general partial methylation domains, promoters, gene bodies, equally spaced bins throughout the genome, and transposable elements. In some embodiments, a region of the genome includes multiple non-overlapping regions of the genome. In some embodiments, the multiple non-overlapping regions of the genome have predetermined sizes. In some embodiments, the predetermined sizes are about 50 kilobases (kb), about 100 kb, about 200 kb, about 500 kb, about 1 megabase (Mb), about 2 Mb, about 5 Mb, or about 10 Mb. In some embodiments, a region of the genome includes one or more MAGE (melanoma-associated antigen) genes, e.g., human MAGE genes. In some embodiments, a region of the genome includes one or more promoters corresponding to one or more MAGE (melanoma-associated antigen) genes, e.g., human MAGE genes.

[0033] In some embodiments, the multiple non-overlapping regions of the genome include at least about 1,000 distinct regions. In some embodiments, the multiple non-overlapping regions of the genome include at least about 2,000 distinct regions. In some embodiments, the multiple non-overlapping regions of the genome include at least about 3,000 distinct regions, at least about 4,000 distinct regions, at least about 5,000 distinct regions, at least about 6,000 distinct regions, at least about 7,000 distinct regions, at least about 8,000 distinct regions, at least about 9,000 distinct regions, at least about 10,000 distinct regions, at least about 15,000 distinct regions, at least about 20,000 distinct regions, at least about 25,000 distinct regions, and at least about 3 Including 0,000 distinct regions, at least approximately 35,000 distinct regions, at least approximately 40,000 distinct regions, at least approximately 45,000 distinct regions, at least approximately 50,000 distinct regions, at least approximately 100,000 distinct regions, at least approximately 150,000 distinct regions, at least approximately 200,000 distinct regions, at least approximately 250,000 distinct regions, at least approximately 300,000 distinct regions, at least approximately 400,000 distinct regions, or at least approximately 500,000 distinct regions.

[0034] In some embodiments, determining a first or second tumor percentage involves processing a methylation percentage profile with one or more baseline methylation percentage profiles, where one or more baseline methylation percentage profiles are obtained from or derived from additional cfDNA molecules of additional subjects. In some embodiments, the additional subjects include one or more subjects having cancer. In some embodiments, the additional subjects include one or more subjects not having cancer. In some embodiments, the additional subjects include one or more subjects with tumor progression. In some embodiments, the additional subjects include one or more subjects not with tumor progression. In some embodiments, one or more baseline methylation percentage profiles are obtained using additional methylation percentage profiles of subjects obtained at one or more subsequent time points after the first time point.

[0035] In some embodiments, the method further includes detecting that the tumor status (e.g., tumor progression, non-progression, regression, or recurrence) includes tumor progression of the subject if the first tumor percentage or the second tumor percentage is greater than 1, greater than 1.1, greater than 1.2, greater than 1.3, greater than 1.4, greater than 1.5, greater than 1.6, greater than 1.7, greater than 1.8, greater than 1.9, greater than 2, greater than 3, greater than 4, or greater than 5. In some embodiments, the method further includes detecting the major molecular response (MMR) of the subject if the first tumor percentage or the second tumor percentage is less than 0.01, less than 0.05, less than 0.1, less than 0.2, less than 0.3, less than 0.4, or less than 0.5. In some embodiments, the method further includes detecting tumor progression of the subject if the first tumor percentage or the second tumor percentage is statistically significant greater than 1, greater than 1.1, greater than 1.2, greater than 1.3, greater than 1.4, greater than 1.5, greater than 1.6, greater than 1.7, greater than 1.8, greater than 1.9, greater than 2, greater than 3, greater than 4, or greater than 5. In some embodiments, the method further includes detecting the major molecular response (MMR) of the subject if the first tumor percentage or the second tumor percentage is statistically significant less than 0.01, less than 0.05, less than 0.1, less than 0.2, less than 0.3, less than 0.4, or less than 0.5.

[0036] In some embodiments, the method further includes detecting the tumor status of the subject (e.g., progression, non-progression, regression, or recurrence of the tumor) with a sensitivity of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, or at least about 94%. In some embodiments, the method further includes detecting the tumor status of the subject (e.g., progression, non-progression, regression, or recurrence of the tumor) with a sensitivity of at least about 95%, at least about 96%, at least about 97%, or at least about 98%. In some embodiments, the method further includes detecting the tumor status of the subject (e.g., progression, non-progression, regression, or recurrence of the tumor) with a sensitivity of at least about 99%.

[0037] In some embodiments, the method further includes detecting the tumor status of the subject (e.g., progression, non-progression, regression, or recurrence of the tumor) with specificity of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, or at least about 94%. In some embodiments, the method further includes detecting the tumor status of the subject (e.g., progression, non-progression, regression, or recurrence of the tumor) with specificity of at least about 95%, at least about 96%, at least about 97%, or at least about 98%. In some embodiments, the method further includes detecting the tumor status of the subject (e.g., progression, non-progression, regression, or recurrence of the tumor) with specificity of at least about 99%.

[0038] In some embodiments, the method further includes detecting the target tumor status (e.g., tumor progression, non-progression, regression, or recurrence) with positive predictive values ​​(PPV) of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, or at least about 94%. In some embodiments, the method further includes detecting the target tumor status (e.g., tumor progression, non-progression, regression, or recurrence) with positive predictive values ​​(PPV) of at least about 95%, at least about 96%, at least about 97%, or at least about 98%. In some embodiments, the method further includes detecting the target tumor status (e.g., tumor progression, non-progression, regression, or recurrence) with positive predictive values ​​(PPV) of at least about 99%.

[0039] In some embodiments, the method further includes detecting the target tumor status (e.g., tumor progression, non-progression, regression, or recurrence) with a negative predictive value (NPV) of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, or at least about 94%. In some embodiments, the method further includes detecting the target tumor status (e.g., tumor progression, non-progression, regression, or recurrence) with a negative predictive value (NPV) of at least about 95%, at least about 96%, at least about 97%, or at least about 98%. In some embodiments, the method further includes detecting the target tumor status (e.g., tumor progression, non-progression, regression, or recurrence) with a negative predictive value (NPV) of at least about 99%.

[0040] In some embodiments, the method further includes detecting the target tumor status (e.g., tumor progression, non-progression, regression, or recurrence) with an area under the curve (AUC) of at least about 0.50, at least about 0.55, at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.85, at least about 0.90, at least about 0.91, at least about 0.92, at least about 0.93, or at least about 0.94. In some embodiments, the method further includes detecting the target tumor status (e.g., tumor progression, non-progression, regression, or recurrence) with an area under the curve (AUC) of at least about 0.95, at least about 0.96, at least about 0.97, or at least about 0.98. In some embodiments, the method further includes detecting the target tumor status (e.g., tumor progression, non-progression, regression, or recurrence) with an area under the curve (AUC) of at least about 0.99.

[0041] In some embodiments, the method further includes determining whether the target tumor is non-progressive if no tumor progression is detected. In some embodiments, the method further includes administering a therapeutically effective dose of a second therapeutic agent to treat the target cancer based on the determined tumor status of the target (e.g., tumor progression, non-progression, regression, or recurrence). In some embodiments, the second therapy includes surgery, chemotherapy, radiotherapy, targeted therapy, immunotherapy, cell therapy, antihormone agents, antimetabolitic chemotherapeutic agents, kinase inhibitors, methyltransferase inhibitors, peptides, gene therapy, vaccines, platinum-based chemotherapeutic agents, antibodies, or checkpoint inhibitors. In some embodiments, the first and second cfDNA molecules are derived from the target immune cells. In some embodiments, the detected tumor status indicates tumor progression, non-progression, regression, or recurrence. In some embodiments, the first and second MS data are obtained by a sequencing device or computer processor (e.g., including one or more programs for executing instructions based on the method of this disclosure).

[0042] In some embodiments according to any of the embodiments described herein, the subject has brain cancer, bladder cancer, breast cancer, cervical cancer, colorectal cancer, endometrial cancer, esophageal cancer, stomach cancer, kidney cancer, hepatobiliary tract cancer, leukemia, liver cancer, lung cancer, lymphoma, ovarian cancer, pancreatic cancer, prostate cancer, skin cancer, stomach cancer, thyroid cancer, or urinary tract cancer.

[0043] In another aspect, the Disclosure relates to a computer system for evaluating tumor progression in a subject having cancer, comprising: a database configured to store (i) first methylation sequencing (MS) data of a first plurality of cell-free DNA (cfDNA) molecules across a region of the genome, wherein the first plurality of cfDNA molecules are obtained from or derived from a first fluid sample of the subject at a first time point, the first time point being before the subject is administered a therapeutic agent configured to treat cancer; and (ii) second MS data of a second plurality of cell-free DNA (cfDNA) molecules across a region of the genome, wherein the second plurality of cfDNA molecules are obtained from or derived from a second fluid sample of the subject at a second time point, the second time point being after the subject is administered a therapeutic agent; and one or more computer processors operably coupled to the database, wherein the one or more computer processors operate individually or collectively. A computer system is provided, which includes one or more computer processors programmed to process first MS data to determine the mean methylation rate for each of one or more CpG islands within a region of the genome, thereby obtaining a first mean methylation rate profile; process second MS data to determine the mean methylation rate for each of one or more CpG islands within a region of the genome, thereby obtaining a second mean methylation rate profile; process the first mean methylation rate profile and the second mean methylation rate profile across one or more CpG islands to determine the methylation rate profiles; at least partially, determine the first tumor rate of the subject at a first time point or the second tumor rate of the subject at a second time point based on each methylation rate profile; and at least partially, detect tumor progression of the subject based on the first or second tumor rate.

[0044] In another aspect, the present disclosure relates to a computer system for evaluating the tumor status (e.g., tumor progression, non-progression, regression, or recurrence) of a subject having cancer, comprising: a database configured to store (i) first methylation sequencing (MS) data of a first plurality of cell-free DNA (cfDNA) molecules across a region of the genome, wherein the first plurality of cfDNA molecules are obtained from or derived from a first fluid sample of the subject at a first time point, the first time point being before the subject is administered a therapeutic agent configured to treat cancer; and (ii) second MS data of a second plurality of cell-free DNA (cfDNA) molecules across a region of the genome, wherein the second plurality of cfDNA molecules are obtained from or derived from a second fluid sample of the subject at a second time point, the second time point being after the subject is administered a therapeutic agent; and one or more computer processors operably coupled to the database, wherein the one or more computer processors A computer system is provided, which includes one or more computer processors programmed to individually or collectively determine the mean methylation rate for each of one or more CpG islands in a region of the genome based on first MS data, thereby obtaining a first mean methylation rate profile; determine the mean methylation rate for each of one or more CpG islands in a region of the genome based on second MS data, thereby obtaining a second mean methylation rate profile; determine a methylation rate profile by comparing the first mean methylation rate profile across one or more CpG islands with the second mean methylation rate profile across one or more CpG islands; determine, at least partially, the first tumor rate of the subject at a first time point or the second tumor rate of the subject at a second time point based on each methylation rate profile; and at least partially, detect the tumor status of the subject based on the first or second tumor rate.

[0045] In another aspect, the Disclosure includes machine-executable instructions in a non-temporary computer-readable medium that, when executed by one or more computer processors, carry out a method for evaluating tumor progression in a subject having cancer, the method comprising: obtaining first methylation sequencing (MS) data of a first plurality of cell-free DNA (cfDNA) molecules across a region of the genome, wherein the first plurality of cfDNA molecules are obtained from or derived from a fluid sample of the subject at a first time point, and the first time point is before the subject is administered a therapeutic agent configured to treat cancer; processing the first MS data to determine the mean methylation rate for each of one or more CpG islands within the region of the genome, thereby obtaining a first mean methylation rate profile; and obtaining second MS data of a second plurality of cell-free DNA (cfDNA) molecules across a region of the genome, wherein the second plurality of cfDNA A non-temporal, computer-readable medium is provided, comprising: obtaining a molecule obtained from or derived from a second bodily fluid sample of the subject at a second time point, where the second time point is after the subject has been administered a therapeutic agent; processing the second MS data to determine a methylation profile for each of one or more CpG islands within a region of the genome, thereby obtaining a second mean methylation percentage profile; processing a first mean methylation percentage profile across one or more CpG islands and a second mean methylation percentage profile across one or more CpG islands to determine a methylation percentage profile; determining, at least partially, a first tumor percentage of the subject at a first time point or a second tumor percentage of the subject at a second time point based on the respective methylation percentage profiles; and at least partially, detecting tumor progression of the subject based on the first or second tumor percentage.

[0046] In another aspect, the disclosure includes a machine-executable instruction in a non-temporary computer-readable medium, which, when executed by one or more computer processors, carries out a method for evaluating the tumor status of a subject having cancer (e.g., tumor progression, non-progression, regression, or recurrence), wherein the method involves obtaining first methylation sequencing (MS) data of a first plurality of cell-free DNA (cfDNA) molecules across a region of the genome, wherein the first plurality of cfDNA molecules are obtained from or derived from a fluid sample of the subject at a first time point, and the first time point is before the subject is administered a therapeutic agent configured to treat cancer; determining the mean methylation percentage for each of one or more CpG islands within the region of the genome based on the first MS data, thereby obtaining a first mean methylation percentage profile; and obtaining second MS data of a second plurality of cell-free DNA (cfDNA) molecules across a region of the genome. A non-temporary computer-readable medium is provided, comprising: obtaining a second set of cfDNA molecules obtained from or derived from a second bodily fluid sample of the subject at a second time point, where the second time point is after the subject has been administered a therapeutic agent; determining the mean methylation rate for each of one or more CpG islands within a region of the genome based on the second MS data, thereby obtaining a second mean methylation rate profile; determining a methylation rate profile by comparing a first mean methylation rate profile across one or more CpG islands with the second mean methylation rate profile across one or more CpG islands; determining, at least partially, the first tumor rate of the subject at a first time point or a second tumor rate of the subject at a second time point based on the respective methylation rate profiles; and at least partially, detecting the tumor status of the subject based on the first or second tumor rate.

[0047] In some embodiments, the detected tumor progression is based at least in part on one or more statistical modeling analyses of the respective methylation percentage profiles. In some embodiments, one or more statistical modeling analyses include linear regression, simple regression, binary regression, Bayesian linear regression, Bayesian modeling, multinomial regression, Gaussian process regression, Gaussian modeling, binary regression, logistic regression, or nonlinear regression. In some embodiments, one or more statistical modeling analyses compare the detected tumor progression with MS data from samples with known tumor percentages, MS data from pure tumor samples, or MS data from healthy samples.

[0048] In another aspect, the present disclosure provides a method for evaluating the tumor status (e.g., tumor progression, non-progression, regression, or recurrence) of a subject having cancer, comprising: obtaining first methylation sequencing (MS) data of a first plurality of cell-free DNA (cfDNA) molecules across a region of the genome, wherein the first plurality of cfDNA molecules are obtained from or derived from a first fluid sample of the subject at a first time point, and the first time point is before the subject is administered a therapeutic agent configured to treat cancer; determining a methylation profile for each of one or more loci in the genome based on the first MS data (e.g., one or more CpG islands or non-CpG methylated loci within a region of the genome), thereby obtaining a first methylation profile; and obtaining second MS data of a second plurality of cell-free DNA (cfDNA) molecules across a region of the genome, wherein the second plurality A method is provided comprising: obtaining a cfDNA molecule obtained from or derived from a second bodily fluid sample of the subject at a second time point, the second time point being after the subject has been administered a therapeutic agent; determining a methylation profile for each of one or more loci in the genome based on the second MS data (e.g., one or more CpG islands or non-CpG methylated loci within a genomic region), thereby obtaining a second methylation profile; comparing a first methylation profile across one or more loci with a second methylation profile across one or more loci; determining, at least partially, a first tumor percentage of the subject at the first time point or a second tumor percentage of the subject at the second time point based on the respective methylation percentage profiles; and at least partially, detecting the tumor status of the subject based on the first or second tumor percentage. In some embodiments, additional time points after the second time point may be collected and analyzed to detect tumor progression occurring at subsequent time points.

[0049] In another aspect, the Disclosure relates to a method for treating cancer in a subject, comprising obtaining first methylation sequencing (MS) data of a first group of cell-free DNA (cfDNA) molecules across a region of the genome, wherein the first group of cfDNA molecules are obtained from or derived from a bodily fluid sample of the subject at a first time point, and the first time point is before the subject is administered a therapeutic agent configured to treat cancer, and based on the first MS data, based on the first MS data, for each of one or more loci of the genome The method involves determining the methylation profile of (e.g., one or more CpG islands or non-CpG methylated loci within a genomic region) and thereby obtaining a first methylation profile, and obtaining second MS data of a second set of cell-free DNA (cfDNA) molecules across a genomic region, wherein the second set of cfDNA molecules are obtained from or derived from a second fluid sample of the subject at a second time point, and the second time point is after the subject has been administered a therapeutic agent, and based on the second MS data, A method is provided which includes: determining a methylation profile for each of one or more loci of a nomu (e.g., one or more CpG islands or non-CpG methylated loci within a genomic region) thereby obtaining a second methylation profile; comparing a first methylation profile across one or more loci with the second methylation profile across one or more loci; determining, at least partially, a first tumor proportion of the subject at a first time point or a second tumor proportion of the subject at a second time point based on the respective methylation proportion profiles; detecting a tumor status of the subject at least partially, based on the first or second tumor proportion; and administering a therapeutically effective dose of treatment (e.g., surgery, chemotherapy, radiotherapy, targeted therapy, immunotherapy, cell therapy, antihormone agents, antimetabolites, kinase inhibitors, methyltransferase inhibitors, peptides, gene therapy, vaccines, platinum-based chemotherapy agents, antibodies, or checkpoint inhibitors) to treat the cancer of the subject based on the detected tumor status.In some embodiments, the detected tumor condition includes tumor progression, and the method comprises administering a second treatment to the patient, prior to administration, the patient has been treated with a first treatment for cancer (the first and second treatments are different).

[0050] In another aspect, the Disclosure relates to a method for evaluating the tumor status (e.g., tumor progression, non-progression, regression, or recurrence) of a subject having cancer, comprising obtaining first whole-genome sequencing (WGS) data of a first plurality of cell-free DNA (cfDNA) molecules, wherein the first plurality of cfDNA molecules are obtained from or derived from a first bodily fluid sample of the subject at a first time point, the first time point being before a therapeutic agent configured to treat cancer is administered to the subject, and based on the first WGS data, (i) the first plurality of cfDNA molecules (ii) determining a first set of multiple copy number anomalies (CNAs), and (ii) determining a first set of multiple fragment lengths of a first set of multiple cfDNA molecules, and obtaining first methylation sequencing (MS) data of a first set of multiple cell-free DNA (cfDNA) molecules across a region of the genome, wherein the first set of multiple cfDNA molecules are obtained from or derived from a body fluid sample of the subject at a first time point, and determining the mean methylation rate for each of one or more CpG islands within the region of the genome based on the first MS data, thereby determining the first mean Obtaining a methylation ratio profile and obtaining second whole-genome sequencing (WGS) data for a second set of cell-free DNA (cfDNA) molecules, wherein the second set of cfDNA molecules is obtained from or derived from a second body fluid sample of the subject at a second time point, and the second time point is after the subject has been administered a therapeutic agent; and, based on the second WGS data, determining (iii) a second set of copy number anomalies (CNAs) in the second set of cfDNA molecules, and (iv) a second set of fragment lengths for the second set of cfDNA molecules. The method involves obtaining second MS data of a second set of cell-free DNA (cfDNA) molecules across a region of the genome, wherein the second set of cfDNA molecules are obtained from or derived from a body fluid sample of the subject at a second time point; determining the mean methylation rate for each of one or more CpG islands within the region of the genome based on the second MS data, thereby obtaining a second mean methylation rate profile; and comparing the first set of CNAs with the second set of CNAs to determine changes in the CNA profile.A method is provided that includes: determining changes in the fragment length profile based on a first set of fragment lengths and a second set of fragment lengths; determining a methylation ratio profile by comparing a first mean methylation ratio profile across one or more CpG islands with a second mean methylation ratio profile across one or more CpG islands; determining, at least partially, a first tumor percentage of the subject at a first time point or a second tumor percentage of the subject at a second time point based on changes in the CNA profile, changes in the fragment length profile, and their respective methylation ratio profiles; and at least partially, detecting the tumor status of the subject based on the first or second tumor percentage.

[0051] In another aspect, the Disclosure relates to a method for treating cancer in a subject, comprising obtaining first whole-genome sequencing (WGS) data of a first plurality of cell-free DNA (cfDNA) molecules, wherein the first plurality of cfDNA molecules are obtained from or derived from a first bodily fluid sample of the subject at a first time point, the first time point being before a therapeutic agent configured to treat cancer is administered to the subject, and based on the first WGS data, (i) first plurality of copy number anomalies (CNAs) in the first plurality of cfDNA molecules, and (ii) Determining the first multiple fragment lengths of one multiple cfDNA molecules, obtaining first methylation sequencing (MS) data of the first multiple cell-free DNA (cfDNA) molecules across a region of the genome, wherein the first multiple cfDNA molecules are obtained from or derived from a subject fluid sample at a first time point, determining the mean methylation ratio for each of one or more CpG islands within the region of the genome based on the first MS data, thereby obtaining a first mean methylation ratio profile, and second multiple Obtaining second whole-genome sequencing (WGS) data of cell-free DNA (cfDNA) molecules, wherein the second set of cfDNA molecules are obtained from or derived from a second body fluid sample of the subject at a second time point, the second time point being after the subject has been administered a therapeutic agent; and, based on the second WGS data, determining (iii) a second set of copy number anomalies (CNAs) in the second set of cfDNA molecules, and (iv) a second set of fragment lengths of the second set of cfDNA molecules, and the second set of cell-free DNs across the entire genomic region. Obtaining second MS data of A(cfDNA) molecules, wherein a second set of cfDNA molecules are obtained from or derived from a subject body fluid sample at a second time point; determining the mean methylation rate for each of one or more CpG islands within a genomic region based on the second MS data, thereby obtaining a second mean methylation rate profile; comparing a first set of CNAs with a second set of CNAs to determine changes in the CNA profile; and determining the first set of fragment lengths and the second set of fragment lengths.A method is provided which includes determining a change in the fragment length profile; determining a methylation rate profile by comparing a first mean methylation rate profile across one or more CpG islands with a second mean methylation rate profile across one or more CpG islands; determining, at least partially, a first tumor rate of the subject at a first time point or a second tumor rate of the subject at a second time point based on the change in the CNA profile, the change in the fragment length profile, and the respective methylation rate profiles; detecting the tumor status of the subject at least partially based on the first or second tumor rate; and administering a therapeutically effective dose of treatment (e.g., surgery, chemotherapy, radiotherapy, targeted therapy, immunotherapy, cell therapy, antihormone agents, antimetabolites, kinase inhibitors, methyltransferase inhibitors, peptides, gene therapy, vaccines, platinum-based chemotherapy agents, antibodies, or checkpoint inhibitors) to treat the cancer of the subject based on the detected tumor status. In some embodiments, the detected tumor condition includes tumor progression, and the method includes administering a second treatment to the patient, prior to administration, the patient having been treated with a first treatment for cancer (the first and second treatments are different).

[0052] In another aspect, the Disclosure relates to a method for evaluating the tumor status (e.g., tumor progression, non-progression, regression, or recurrence) of a subject having cancer, comprising obtaining first whole-genome sequencing (WGS) data of a first plurality of cell-free DNA (cfDNA) molecules, wherein the first plurality of cfDNA molecules are obtained from or derived from a first bodily fluid sample of the subject at a first time point, the first time point being before a therapeutic agent configured to treat cancer is administered to the subject, and based on the first WGS data, (i) in the first plurality of cfDNA molecules (ii) determining a first set of multiple copy number anomalies (CNAs), and (ii) determining a first set of multiple fragment lengths of a first set of multiple cfDNA molecules, and obtaining first methylation sequencing (MS) data of a first set of multiple cell-free DNA (cfDNA) molecules across a region of the genome, wherein the first set of multiple cfDNA molecules are obtained from or derived from a body fluid sample of the subject at a first time point, and determining a methylation profile for each of one or more gene loci in the genome based on the first MS data, thereby determining the first methylation profile The procedure involves obtaining and obtaining second whole-genome sequencing (WGS) data for a second set of cell-free DNA (cfDNA) molecules, wherein the second set of cfDNA molecules are obtained from or derived from a second body fluid sample of the subject at a second time point, and the second time point is after the subject has been administered a therapeutic agent; obtaining and, based on the second WGS data, determining (iii) a second set of copy number anomalies (CNAs) in the second set of cfDNA molecules, and (iv) a second set of fragment lengths for the second set of cfDNA molecules, across the entire region of the genome. The method involves obtaining second MS data of a second set of cell-free DNA (cfDNA) molecules, wherein the second set of cfDNA molecules are obtained from or derived from a subject body fluid sample at a second time point; determining the methylation profile for each of one or more loci in the genome based on the second MS data, thereby obtaining a second methylation profile; comparing the first set of CNAs with the second set of CNAs to determine changes in the CNA profile; and determining the first set of fragment lengths and the second set of fragment lengths.A method is provided that includes determining changes in the fragment length profile, comparing a first methylation profile across one or more loci with a second methylation profile across one or more loci, determining, at least partially, a first tumor percentage of a subject at a first time point or a second tumor percentage of a subject at a second time point based on changes in the CNA profile, changes in the fragment length profile, and their respective methylation percentage profiles, and at least partially detecting the tumor status of a subject based on the first or second tumor percentage.

[0053] In another aspect, the Disclosure relates to a method for treating cancer in a subject, comprising obtaining first whole-genome sequencing (WGS) data of a first plurality of cell-free DNA (cfDNA) molecules, wherein the first plurality of cfDNA molecules are obtained from or derived from a first bodily fluid sample of the subject at a first time point, the first time point being before a therapeutic agent configured to treat cancer is administered to the subject, and based on the first WGS data, (i) first plurality of copy number anomalies (CNAs) in the first plurality of cfDNA molecules, and (ii) Determining the lengths of a first set of multiple fragments of a set of cfDNA molecules, and obtaining first methylation sequencing (MS) data of the first set of multiple cell-free DNA (cfDNA) molecules across a region of the genome, wherein the first set of multiple cfDNA molecules are obtained from or derived from a body fluid sample of the subject at a first time point, and determining a methylation profile for each of one or more gene loci in the genome based on the first MS data, thereby obtaining a first methylation profile, and second set of multiple cell-free DNA (cfD Obtaining second whole-genome sequencing (WGS) data of the NA) molecule, wherein the second set of cfDNA molecules are obtained from or derived from a second body fluid sample of the subject at a second time point, the second time point being after the subject has been administered the therapeutic agent; and, based on the second WGS data, determining (iii) second set of copy number anomalies (CNAs) in the second set of cfDNA molecules, and (iv) second set of fragment lengths of the second set of cfDNA molecules, and the second set of cell-free DNA (cfDNA) molecules across the entire genomic region. Obtaining second MS data, wherein a second set of cfDNA molecules are obtained from or derived from a subject fluid sample at a second time point; determining the methylation profile for each of one or more loci of the genome based on the second MS data, thereby obtaining a second methylation profile; comparing the first set of CNAs with the second set of CNAs to determine changes in the CNA profile; and determining changes in the fragment length profile based on the first set of fragment lengths and the second set of fragment lengths.A method is provided comprising: comparing a first methylation profile across one or more gene loci with a second methylation profile across one or more gene loci; determining, at least partially, a first tumor percentage of a subject at a first time point or a second tumor percentage of a subject at a second time point based on changes in the CNA profile, changes in the fragment length profile, and the respective methylation percentage profiles; detecting a tumor state of a subject based at least partially on the first or second tumor percentage; and administering a therapeutically effective dose of treatment (e.g., surgery, chemotherapy, radiotherapy, targeted therapy, immunotherapy, cell therapy, antihormone agents, antimetabolites, kinase inhibitors, methyltransferase inhibitors, peptides, gene therapy, vaccines, platinum-based chemotherapy agents, antibodies, or checkpoint inhibitors) to treat the cancer of the subject based on the detected tumor state. In some embodiments, the detected tumor state includes tumor progression, and the method comprises administering the second treatment to the patient, prior to administration, the patient has been treated with the first treatment for cancer (the first and second treatments are different).

[0054] In some embodiments, the first and second methylation profiles include 5-hydroxymethylcytosine state, 5-methylcytosine state, enriched methylation assessment, central methylation level, mode methylation level, maximum methylation level, or minimum methylation level. In some embodiments, the first or second bodily fluid sample is selected from the group consisting of blood, serum, plasma, vitreous humor, sputum, urine, tears, sweat, saliva, semen, mucosal exudate, mucus, cerebrospinal fluid, cerebrospinal fluid (CSF), pleural fluid, ascites, amniotic fluid, and lymph. In some embodiments, obtaining first MS data includes performing methylation sequencing of a first set of cfDNA molecules to generate a first set of sequencing reads, or obtaining second MGS data includes performing methylation sequencing of a second set of cfDNA molecules to generate a second set of sequencing reads. In some embodiments, methylation sequencing includes whole-genome bisulfite sequencing. In some embodiments, methylation sequencing includes whole-genome enzymatic methyl-seq. In some embodiments, methylation sequencing includes oxidative bisulfite sequencing, TET-assisted pyridineborane sequencing (TAPS), TET-assisted bisulfite sequencing (TABS), oxidative bisulfite sequencing (oxBS-Seq), APOBEC-bound epigenetic sequencing (ACE-seq), methylated DNA immunoprecipitation (MeDIP) sequencing, hydroxymethylated DNA immunoprecipitation (hMeDIP) sequencing, methylation array analysis, reduced-expression bisulfite sequencing (RRBS-Seq), or cytosine 5-hydroxymethylation sequencing.

[0055] In some embodiments, methylation sequencing is performed to a depth of about 40 or fewer iterations. In some embodiments, methylation sequencing is performed to a depth of about 30 or fewer iterations. In some embodiments, methylation sequencing is performed to a depth of about 25 or fewer iterations. In some embodiments, methylation sequencing is performed to a depth of about 20 or fewer iterations. In some embodiments, methylation sequencing is performed to a depth of about 12 or fewer iterations. In some embodiments, methylation sequencing is performed to a depth of about 10 or fewer iterations. In some embodiments, methylation sequencing is performed to a depth of about 8 or fewer iterations. In some embodiments, methylation sequencing is performed to a depth of about 6 or fewer iterations. In some embodiments, methylation sequencing is performed to a depth of about 5 or fewer iterations, about 4 or fewer iterations, about 3 or fewer iterations, about 2 or fewer iterations, or about 1 or fewer iterations.

[0056] In some embodiments, the method further includes aligning a first or second plurality of sequencing reads with a reference genome (e.g., simultaneously with a C-to-T converted version of the reference genome) to thereby generate a plurality of aligned sequencing reads. In some embodiments, the method further includes enriching a first or second plurality of cfDNA molecules for a region of the genome. In some embodiments, enrichment includes amplifying a first or second plurality of cfDNA molecules. In some embodiments, amplification includes selective amplification. In some embodiments, amplification includes universal amplification. In some embodiments, enrichment includes selective isolation of at least a portion of the first or second plurality of cfDNA molecules. In some embodiments, selective isolation of at least a portion of the first or second plurality of cfDNA molecules includes using a plurality of probes, each of which has sequence complementarity with at least a portion of a region of the genome. In some embodiments, at least a portion includes tumor marker loci. In some embodiments, at least a portion includes a plurality of tumor marker loci. In some embodiments, multiple tumor marker loci include one or more loci selected from The Cancer Genome Atlas (TCGA) or Catalogue of Somatic Mutations in Cancer (COSMIC).

[0057] In some embodiments, a genomic locus or region includes one or more of the following: CpG islands, CpG Shores, patient-specific partial methylation domains, general partial methylation domains, promoters, gene bodies, equally spaced bins throughout the genome, and transposable elements. In some embodiments, a genomic region includes multiple non-overlapping regions of the genome. In some embodiments, the multiple non-overlapping regions of the genome have predetermined sizes. In some embodiments, the predetermined sizes are about 50 kilobases (kb), about 100 kb, about 200 kb, about 500 kb, about 1 megabase (Mb), about 2 Mb, about 5 Mb, or about 10 Mb.

[0058] In some embodiments, the multiple non-overlapping regions of the genome include at least about 1,000 distinct regions. In some embodiments, the multiple non-overlapping regions of the genome include at least about 2,000 distinct regions. In some embodiments, the multiple non-overlapping regions of the genome include at least about 3,000 distinct regions, at least about 4,000 distinct regions, at least about 5,000 distinct regions, at least about 6,000 distinct regions, at least about 7,000 distinct regions, at least about 8,000 distinct regions, at least about 9,000 distinct regions, at least about 10,000 distinct regions, at least about 15,000 distinct regions, at least about 20,000 distinct regions, at least about 25,000 distinct regions, and at least about 3 Including 0,000 distinct regions, at least approximately 35,000 distinct regions, at least approximately 40,000 distinct regions, at least approximately 45,000 distinct regions, at least approximately 50,000 distinct regions, at least approximately 100,000 distinct regions, at least approximately 150,000 distinct regions, at least approximately 200,000 distinct regions, at least approximately 250,000 distinct regions, at least approximately 300,000 distinct regions, at least approximately 400,000 distinct regions, or at least approximately 500,000 distinct regions.

[0059] In some embodiments, determining a first or second tumor percentage involves processing a methylation percentage profile with one or more baseline methylation percentage profiles, where one or more baseline methylation percentage profiles are obtained from or derived from additional cfDNA molecules of additional subjects. In some embodiments, the additional subjects include one or more subjects having cancer. In some embodiments, the additional subjects include one or more subjects not having cancer. In some embodiments, the additional subjects include one or more subjects with tumor progression. In some embodiments, the additional subjects include one or more subjects not with tumor progression. In some embodiments, one or more baseline methylation percentage profiles are obtained using additional methylation percentage profiles of subjects obtained at one or more subsequent time points after the first time point.

[0060] In some embodiments, the method further includes detecting that the tumor status (e.g., tumor progression, non-progression, regression, or recurrence) includes tumor progression of the subject if the first tumor percentage or the second tumor percentage is greater than 1, greater than 1.1, greater than 1.2, greater than 1.3, greater than 1.4, greater than 1.5, greater than 1.6, greater than 1.7, greater than 1.8, greater than 1.9, greater than 2, greater than 3, greater than 4, or greater than 5. In some embodiments, the method further includes detecting the major molecular response (MMR) of the subject if the first tumor percentage or the second tumor percentage is less than 0.01, less than 0.05, less than 0.1, less than 0.2, less than 0.3, less than 0.4, or less than 0.5. In some embodiments, the method further includes detecting that the tumor status (e.g., tumor progression, non-progression, regression, or recurrence) includes tumor progression of the subject if the first tumor percentage or the second tumor percentage is statistically significant greater than 1, greater than 1.1, greater than 1.2, greater than 1.3, greater than 1.4, greater than 1.5, greater than 1.6, greater than 1.7, greater than 1.8, greater than 1.9, greater than 2, greater than 3, greater than 4, or greater than 5. In some embodiments, the method further includes detecting the major molecular response (MMR) of the subject if the first tumor percentage or the second tumor percentage is statistically significant less than 0.01, less than 0.05, less than 0.1, less than 0.2, less than 0.3, less than 0.4, or less than 0.5.

[0061] In some embodiments, the method further includes detecting the tumor status of the subject (e.g., progression, non-progression, regression, or recurrence of the tumor) with a sensitivity of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, or at least about 94%. In some embodiments, the method further includes detecting the tumor status of the subject (e.g., progression, non-progression, regression, or recurrence of the tumor) with a sensitivity of at least about 95%, at least about 96%, at least about 97%, or at least about 98%. In some embodiments, the method further includes detecting the tumor status of the subject (e.g., progression, non-progression, regression, or recurrence of the tumor) with a sensitivity of at least about 99%.

[0062] In some embodiments, the method further includes detecting the tumor status of the subject (e.g., progression, non-progression, regression, or recurrence of the tumor) with specificity of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, or at least about 94%. In some embodiments, the method further includes detecting the tumor status of the subject (e.g., progression, non-progression, regression, or recurrence of the tumor) with specificity of at least about 95%, at least about 96%, at least about 97%, or at least about 98%. In some embodiments, the method further includes detecting the tumor status of the subject (e.g., progression, non-progression, regression, or recurrence of the tumor) with specificity of at least about 99%.

[0063] In some embodiments, the method further includes detecting the target tumor status (e.g., tumor progression, non-progression, regression, or recurrence) with positive predictive values ​​(PPV) of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, or at least about 94%. In some embodiments, the method further includes detecting the target tumor status (e.g., tumor progression, non-progression, regression, or recurrence) with positive predictive values ​​(PPV) of at least about 95%, at least about 96%, at least about 97%, or at least about 98%. In some embodiments, the method further includes detecting the target tumor status (e.g., tumor progression, non-progression, regression, or recurrence) with positive predictive values ​​(PPV) of at least about 99%.

[0064] In some embodiments, the method further includes detecting the target tumor status (e.g., tumor progression, non-progression, regression, or recurrence) with a negative predictive value (NPV) of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, or at least about 94%. In some embodiments, the method further includes detecting the target tumor status (e.g., tumor progression, non-progression, regression, or recurrence) with a negative predictive value (NPV) of at least about 95%, at least about 96%, at least about 97%, or at least about 98%. In some embodiments, the method further includes detecting the target tumor status (e.g., tumor progression, non-progression, regression, or recurrence) with a negative predictive value (NPV) of at least about 99%.

[0065] In some embodiments, the method further includes detecting the target tumor status (e.g., tumor progression, non-progression, regression, or recurrence) with an area under the curve (AUC) of at least about 0.50, at least about 0.55, at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.85, at least about 0.90, at least about 0.91, at least about 0.92, at least about 0.93, or at least about 0.94. In some embodiments, the method further includes detecting the target tumor status (e.g., tumor progression, non-progression, regression, or recurrence) with an area under the curve (AUC) of at least about 0.95, at least about 0.96, at least about 0.97, or at least about 0.98. In some embodiments, the method further includes detecting the target tumor status (e.g., tumor progression, non-progression, regression, or recurrence) with an area under the curve (AUC) of at least about 0.99.

[0066] In some embodiments, the method further includes determining whether the target tumor is non-progressive if no tumor progression is detected. In some embodiments, the method further includes administering a therapeutically effective dose of a second therapeutic agent to treat the target cancer based on the determined tumor status of the target (e.g., tumor progression, non-progression, regression, or recurrence). In some embodiments, the second therapy includes surgery, chemotherapy, radiotherapy, targeted therapy, immunotherapy, cell therapy, antihormone agents, antimetabolitic chemotherapeutic agents, kinase inhibitors, methyltransferase inhibitors, peptides, gene therapy, vaccines, platinum-based chemotherapeutic agents, antibodies, or checkpoint inhibitors. In some embodiments, the first and second cfDNA molecules are derived from the target immune cells. In some embodiments, the detected tumor status indicates tumor progression, non-progression, regression, or recurrence. In some embodiments, the first and second MS data are obtained by a sequencing device or computer processor (e.g., including one or more programs for executing instructions based on the method of this disclosure).

[0067] In some embodiments according to any of the embodiments described herein, the subject has brain cancer, bladder cancer, breast cancer, cervical cancer, colorectal cancer, endometrial cancer, esophageal cancer, stomach cancer, kidney cancer, hepatobiliary tract cancer, leukemia, liver cancer, lung cancer, lymphoma, ovarian cancer, pancreatic cancer, prostate cancer, skin cancer, stomach cancer, thyroid cancer, or urinary tract cancer.

[0068] Another aspect of the present disclosure provides a non-temporary computer-readable medium which, when executed by one or more computer processors, includes machine-executable instructions that, when executed, carry out any of the methods described above or elsewhere in this specification.

[0069] Another aspect of this disclosure provides a system comprising one or more computer processors and computer memory coupled thereto. The computer memory contains machine-executable code that, when executed by one or more computer processors, carries out any of the methods described above or elsewhere in this specification.

[0070] Additional aspects and advantages of the Disclosure will be readily apparent to those skilled in the art from the following detailed description, and only exemplary embodiments of the Disclosure are shown and described. As is obvious, other different embodiments of the Disclosure are possible, and some of their details can be modified in various obvious ways without departing from the Disclosure. Accordingly, the drawings and description should be considered exemplary in nature and not restrictive.

[0071] All publications, patents, and patent applications described herein are incorporated by reference to the same extent that each individual publication, patent, or patent application is specifically and individually indicated as being incorporated by reference. To the extent that any publications and patents or patent applications incorporated by reference conflict with any disclosures contained in the specification, the specification is intended to supersede and / or take precedence over any such conflicting material. In embodiments of the present invention, for example, the following items are provided. (Item 1) A method for evaluating the tumor status of a subject with cancer, Obtaining first whole-genome sequencing (WGS) data of a first group of cell-free DNA (cfDNA) molecules, wherein the first group of cfDNA molecules are obtained from or derived from a first bodily fluid sample of the subject at a first time point, and the first time point is before the therapeutic agent configured to treat the cancer is administered to the subject. Based on the first WGS data, (i) a first set of copy number anomalies (CNAs) in the first set of cfDNA molecules, and (ii) a first set of fragment lengths in the first set of cfDNA molecules are determined. Obtaining second whole-genome sequencing (WGS) data of a second group of cell-free DNA (cfDNA) molecules, wherein the second group of cfDNA molecules are obtained from or derived from a second bodily fluid sample of the subject at a second time point, the second time point being after the subject has been administered the therapeutic agent. Based on the second WGS data, (iii) a second set of copy number anomalies (CNAs) in the second set of cfDNA molecules, and (iv) a second set of fragment lengths in the second set of cfDNA molecules, The first set of CNAs is compared with the second set of CNAs to determine the change in the CNA profile, Based on the first plurality of fragment lengths and the second plurality of fragment lengths, the change in the fragment length profile is determined, At least in part, the first tumor percentage of the subject at the first time point or the second tumor percentage of the subject at the second time point is determined based on the CNA profile change and the fragment length profile change, A method comprising detecting the tumor status of the subject based at least in part on the first tumor percentage or the second tumor percentage. (Item 2) The method according to item 1, wherein the first or second body fluid sample is selected from the group consisting of blood, serum, plasma, vitreous humor, sputum, urine, tears, sweat, saliva, semen, mucosal discharge, mucus, cerebrospinal fluid, cerebrospinal fluid (CSF), pleural fluid, ascites, amniotic fluid, and lymph. (Item 3) The method according to item 1, wherein obtaining the first WGS data includes sequencing the first plurality of cfDNA molecules to generate a first plurality of sequencing reads, or obtaining the second WGS data includes sequencing the second plurality of cfDNA molecules to generate a second plurality of sequencing reads. (Item 4) The method according to item 3, wherein the sequence determination is performed at a depth of approximately 25 or fewer iterations. (Item 5) The method according to item 3, wherein the sequence determination is performed at a depth of approximately 10 times or less. (Item 6) The method according to item 3, wherein the sequence determination is performed at a depth of approximately 8 times or less. (Item 7) The method according to item 3, wherein the sequence determination is performed at a depth of approximately 6 times or less. (Item 8) The method according to item 3, further comprising aligning the first or second sequence readings with a reference genome to thereby generate a plurality of aligned sequence readings. (Item 9) The method according to item 1, further comprising enriching the first or second plurality of cfDNA molecules with respect to a plurality of genomic regions. (Item 10) The method according to item 9, wherein the enrichment comprises amplifying the first or second plurality of cfDNA molecules. (Item 11) The method according to item 10, wherein the amplification includes selective amplification. (Item 12) The method according to item 10, wherein the amplification includes universal amplification. (Item 13) The method according to item 9, wherein the enrichment comprises selectively isolating at least a portion of the first or second plurality of cfDNA molecules. (Item 14) The method according to item 13, wherein selective isolation of at least a portion of the first or second plurality of cfDNA molecules comprises using a plurality of probes, each of which probes is sequence-complementary to at least a portion of a genomic region among the plurality of genomic regions. (Item 15) The method according to item 13, wherein at least a portion of the above includes a tumor marker gene locus. (Item 16) The method according to item 15, wherein at least a portion of the above includes multiple tumor marker gene loci. (Item 17) The aforementioned multiple tumor marker gene loci are identified in The Cancer Genome Atlas (TCGA) or Catalogue of Somatic Mutations The method described in item 16, comprising one or more gene loci selected from in cancer (COSMIC). (Item 18) The method according to item 3, wherein determining the first plurality of CNAs includes determining a quantitative measure of CNAs in each of the plurality of genomic regions of the first plurality of sequencing reads, and determining the second plurality of CNAs includes determining a quantitative measure of CNAs in each of the plurality of genomic regions of the second plurality of sequencing reads. (Item 19) The method according to item 18, further comprising modifying the first or second CNAs with respect to GC content and / or mappability trend. (Item 20) The method described in item 19, wherein the aforementioned modification includes using statistical modeling analysis. (Item 21) The method described in item 20, wherein the statistical modeling analysis includes LOESS regression or Bayesian modeling. (Item 22) The method according to item 18, wherein the plurality of genomic regions include non-overlapping genomic regions of a reference genome having a predetermined size. (Item 23) The method according to item 22, wherein the predetermined size is approximately 50 kilobases (kb), approximately 100 kb, approximately 200 kb, approximately 500 kb, approximately 1 megabase (Mb), approximately 2 Mb, approximately 5 Mb, or approximately 10 Mb. (Item 24) The method according to item 18, wherein the plurality of genomic regions comprises at least about 1,000 distinct genomic regions. (Item 25) The claim states that the aforementioned multiple genomic regions include at least approximately 2,000 distinct genomic regions. The method described in item 24. (Item 26) The method according to item 1, wherein determining the CNA profile change comprises comparing the first plurality of CNAs and the second plurality of CNAs with a plurality of reference CNA values, the plurality of reference CNA values ​​being obtained from or derived from additional cfDNA molecules of an additional subject. (Item 27) The method according to item 26, wherein the aforementioned additional subjects include one or more subjects who do not have cancer. (Item 28) The method according to item 26, wherein the aforementioned additional subjects include one or more subjects who do not have tumor progression. (Item 29) The method according to item 26, wherein the plurality of reference CNA values ​​are obtained using additional body fluid samples of the subject obtained at one or more subsequent time points after the first time point. (Item 30) The method according to item 1, further comprising excluding a subset of the first and second CNAs that satisfy predetermined criteria. (Item 31) The method according to item 30, further comprising excluding the given CNA value from the first plurality of CNAs or the second plurality of CNAs if the difference between a given CNA value and a corresponding reference CNA value includes a difference of about 1 standard deviation or less. (Item 32) The method according to item 31, further comprising excluding the given CNA value from the first plurality of CNAs or the second plurality of CNAs if the difference between a given CNA value and a corresponding reference CNA value includes a difference of about 2 standard deviations or less. (Item 33) The method according to item 31, further comprising excluding the given CNA value from the first plurality of CNAs or the second plurality of CNAs if the difference between a given CNA value and a corresponding reference CNA value includes a difference of about 3 standard deviations or less. (Item 34) The method according to item 30, further comprising excluding the given CNA value from the first plurality of CNAs or the second plurality of CNA values ​​based on Spearman's rank correlation between a given CNA value and the corresponding local mean fragment length. (Item 35) The method of item 34, further comprising excluding a given CNA value from the first group of CNAs or the second group of CNA values ​​if the Spearman rank correlation coefficient (Spearman's rho) is less than -0.1. (Item 36) The method according to item 1, further comprising normalizing the first or second multiple fragment lengths based on library or genomic location. (Item 37) The method according to item 1, further comprising detecting that the tumor condition includes tumor progression of the subject if the first tumor percentage or the second tumor percentage is greater than 1, greater than 1.1, greater than 1.2, greater than 1.3, greater than 1.4, greater than 1.5, greater than 1.6, greater than 1.7, greater than 1.8, greater than 1.9, greater than 2, greater than 3, greater than 4, or greater than 5. (Item 38) The method according to item 1, further comprising detecting the major molecular response (MMR) of the subject when the first tumor percentage or the second tumor percentage is less than 0.01, less than 0.05, less than 0.1, less than 0.2, less than 0.3, less than 0.4, or less than 0.5. (Item 39) The method according to any one of items 1 to 38, further comprising detecting the tumor condition of the subject with a sensitivity of at least about 50%. (Item 40) The method according to item 39, further comprising detecting the tumor condition of the subject with a sensitivity of at least about 70%. (Item 41) The method according to item 40, further comprising detecting the tumor condition of the subject with a sensitivity of at least about 90%. (Item 42) The method according to any one of items 1 to 41, further comprising detecting the tumor condition of the subject with a specificity of at least about 50%. (Item 43) The method according to item 42, further comprising detecting the tumor condition of the subject with a specificity of at least about 70%. (Item 44) The method according to item 43, further comprising detecting the tumor condition of the subject with a specificity of at least about 90%. (Item 45) The method according to item 44, further comprising detecting the tumor condition of the subject with a specificity of at least about 98%. (Item 46) The method according to any one of items 1 to 45, further comprising detecting the tumor condition of the subject with a positive predictive value (PPV) of at least about 50%. (Item 47) The method according to item 46, further comprising detecting the tumor condition of the subject with a positive predictive value (PPV) of at least about 70%. (Item 48) The method according to item 47, further comprising detecting the tumor condition of the subject with a positive predictive value (PPV) of at least approximately 90%. (Item 49) The method according to any one of items 1 to 48, further comprising detecting the tumor condition of the subject with a negative predictive value (NPV) of at least approximately 50%. (Item 50) The method according to item 49, further comprising detecting the tumor condition of the subject with a negative predictive value (NPV) of at least approximately 70%. (Item 51) The method according to item 50, further comprising detecting the tumor condition of the subject with a negative predictive value (NPV) of at least approximately 90%. (Item 52) The method according to any one of items 1 to 51, further comprising detecting the tumor condition of the subject with an area under the curve (AUC) of at least about 0.60. (Item 53) The method according to item 52, further comprising detecting the tumor condition of the subject with an area under the curve (AUC) of at least about 0.75. (Item 54) The method according to item 53, further comprising detecting the tumor condition of the subject with an area under the curve (AUC) of at least about 0.90. (Item 55) If no tumor progression is detected, further comprising determining that the tumor in the subject is not progressing, The method described in any one of items 1 through 54. (Item 56) The method according to any one of items 1 to 55, further comprising administering a therapeutically effective dose of treatment to treat the cancer of the subject based on the determined tumor state of the subject. (Item 57) The method according to item 56, wherein the treatment includes surgery, chemotherapy, radiotherapy, targeted therapy, immunotherapy, cell therapy, antihormone agents, antimetabolites, kinase inhibitors, methyltransferase inhibitors, peptides, gene therapy, vaccines, platinum-based chemotherapeutic agents, antibodies, or checkpoint inhibitors. (Item 58) The method according to any one of items 1 to 57, wherein the detected tumor condition indicates tumor progression, non-progression, regression, or recurrence. (Item 59) The method according to any one of items 1 to 58, wherein the first and second WGS data are obtained by pyrosequencing, synthesis sequencing, single-molecule sequencing, nanopore sequencing, semiconductor sequencing, ligation sequencing, hybridization sequencing, ultra-parallel sequencing, chain-end sequencing, single-molecule real-time sequencing, Polony sequencing, combinatorial probe-anchor synthesis, or hybrid trapping sequencing. (Item 60) The method according to any one of items 1 to 59, wherein the first and second WGS data are acquired by a sequencing device or a computer processor. (Item 61) A computer system for evaluating the tumor status of a subject with cancer, A database configured to store (i) first whole-genome sequencing (WGS) data of a first plurality of cell-free DNA (cfDNA) molecules, wherein the first plurality of cfDNA molecules are obtained from or derived from a first bodily fluid sample of the subject at a first time point, the first time point being before the therapeutic agent configured to treat the cancer is administered to the subject; and (ii) second whole-genome sequencing (WGS) data of a second plurality of cell-free DNA (cfDNA) molecules, wherein the second plurality of cfDNA molecules are obtained from or derived from a second bodily fluid sample of the subject at a second time point, the second time point being after the therapeutic agent is administered to the subject. One or more computer processors operablely coupled to the database, wherein the one or more computer processors individually or collectively, Based on the first WGS data, (i) a first set of copy number anomalies (CNAs) in the first set of cfDNA molecules, and (ii) a first set of fragment lengths in the first set of cfDNA molecules are determined. Based on the second WGS data, (iii) a second set of copy number anomalies (CNAs) in the second set of cfDNA molecules, and (iv) a second set of fragment lengths in the second set of cfDNA molecules are determined. The first set of CNAs is compared with the second set of CNAs to determine the change in the CNA profile. Based on the first and second sets of fragment lengths, the fragment length profile change is determined. At least partially, the first tumor percentage of the subject at the first time point or the second tumor percentage of the subject at the second time point is determined based on the CNA profile change and the fragment length profile change, and At least partially, based on the first tumor percentage or the second tumor percentage, A computer system comprising one or more computer processors programmed to detect a target tumor condition. (Item 62) A non-temporary computer-readable medium, which, when executed by one or more computer processors, includes machine-executable instructions for performing a method for evaluating the tumor state of a subject having cancer, wherein the method Obtaining first whole-genome sequencing (WGS) data of a first group of cell-free DNA (cfDNA) molecules, wherein the first group of cfDNA molecules are obtained from or derived from a first bodily fluid sample of the subject at a first time point, and the first time point is before the therapeutic agent configured to treat the cancer is administered to the subject. Based on the first WGS data, (i) a first set of copy number anomalies (CNAs) in the first set of cfDNA molecules, and (ii) a first set of fragment lengths in the first set of cfDNA molecules are determined. Obtaining second whole-genome sequencing (WGS) data of a second group of cell-free DNA (cfDNA) molecules, wherein the second group of cfDNA molecules are obtained from or derived from a second bodily fluid sample of the subject at a second time point, the second time point being after the subject has been administered the therapeutic agent. Based on the second WGS data, (iii) a second set of copy number anomalies (CNAs) in the second set of cfDNA molecules, and (iv) a second set of fragment lengths in the second set of cfDNA molecules, The first set of CNAs is compared with the second set of CNAs to determine the change in the CNA profile, Based on the first plurality of fragment lengths and the second plurality of fragment lengths, the change in the fragment length profile is determined, At least in part, the first tumor percentage of the subject at the first time point or the second tumor percentage of the subject at the second time point is determined based on the CNA profile change and the fragment length profile change, A non-temporary computer-readable medium comprising detecting the tumor status of the subject based at least in part on the first tumor percentage or the second tumor percentage. (Item 63) A method for evaluating the tumor status of a subject with cancer, Obtaining first methylation sequencing (MS) data of a first group of cell-free DNA (cfDNA) molecules across a region of the genome, wherein the first group of cfDNA molecules are obtained from or derived from a first bodily fluid sample of the subject at a first time point, and the first time point is before the subject is administered a therapeutic agent configured to treat the cancer. Based on the first MS data, the mean methylation rate for each of one or more CpG islands within the region of the genome is determined, thereby obtaining a first mean methylation rate profile. Acquiring second MS data of a second plurality of cell-free DNA (cfDNA) molecules across the entire region of the genome, wherein the second plurality of cfDNA molecules are acquired from or derived from a second bodily fluid sample of the subject at a second time point, the second time point being after the subject has been administered the therapeutic agent. Based on the second MS data, the mean methylation rate for each of one or more CpG islands within the region of the genome is determined, thereby obtaining a second mean methylation rate profile. The methylation ratio profile is determined by comparing the first average methylation ratio profile across one or more CpG islands with the second average methylation ratio profile across one or more CpG islands. At least partially, determining the first tumor percentage of the subject at the first time point or the second tumor percentage of the subject at the second time point based on the respective methylation percentage profiles, A method comprising detecting the tumor status of the subject based at least in part on the first tumor percentage or the second tumor percentage. (Item 64) The method according to item 63, wherein the first or second body fluid sample is selected from the group consisting of blood, serum, plasma, vitreous humor, sputum, urine, tears, sweat, saliva, semen, mucosal discharge, mucus, cerebrospinal fluid, cerebrospinal fluid (CSF), pleural fluid, ascites, amniotic fluid, and lymph. (Item 65) The method according to item 63, wherein obtaining the first MS data comprises performing methylation sequencing of the first plurality of cfDNA molecules to generate a first plurality of sequencing reads, or obtaining the second MGS data comprises performing methylation sequencing of the second plurality of cfDNA molecules to generate a second plurality of sequencing reads. (Item 66) The method according to item 65, wherein the methylation sequencing includes whole-genome bisulfite sequencing. (Item 67) The method according to item 65, wherein the methylation sequencing includes whole-genome enzymatic methyl-seq. (Item 68) The method according to item 65, wherein the methylation sequencing includes oxidative bisulfite sequencing, TET-assisted pyridineborane sequencing (TAPS), TET-assisted bisulfite sequencing (TABS), oxidative bisulfite sequencing (oxBS-Seq), APOBEC-bound epigenetic sequencing (ACE-seq), methylated DNA immunoprecipitation (MeDIP) sequencing, hydroxymethylated DNA immunoprecipitation (hMeDIP) sequencing, methylation array analysis, reduced-expression bisulfite sequencing (RRBS-Seq), or cytosine 5-hydroxymethylation sequencing. (Item 69) The method according to item 65, wherein the methylation sequence determination is performed at a depth of approximately 25 or fewer steps. (Item 70) The method according to item 65, wherein the methylation sequence determination is performed at a depth of approximately 10 steps or less. (Item 71) The method according to item 65, wherein the methylation sequence determination is performed at a depth of approximately 8 steps or less. (Item 72) The method according to item 65, wherein the methylation sequence determination is performed at a depth of approximately 6 times or less. (Item 73) The method according to item 65, further comprising aligning the first or second sequence readings with a reference genome to generate a plurality of aligned sequence readings. (Item 74) The method according to item 65, further comprising enriching the first or second plurality of cfDNA molecules with respect to the region of the genome. (Item 75) The method according to item 74, wherein the enrichment comprises amplifying the first or second plurality of cfDNA molecules. (Item 76) The method according to item 75, wherein the amplification includes selective amplification. (Item 77) The method of item 75, wherein the amplification includes universal amplification. (Item 78) The method according to item 74, wherein the enrichment comprises selectively isolating at least a portion of the first or second plurality of cfDNA molecules. (Item 79) The method according to item 78, wherein selective isolation of the first or second plurality of cfDNA molecules comprises using a plurality of probes, each of which probes is sequence-complementary to at least a portion of the region of the genome. (Item 80) The method according to item 78, wherein at least a portion of the above includes a tumor marker gene locus. (Item 81) The method according to item 80, wherein at least a portion of the above includes multiple tumor marker gene loci. (Item 82) The aforementioned multiple tumor marker gene loci are identified in The Cancer Genome Atlas (TCGA) or Catalogue of Somatic Mutations The method described in item 81, comprising one or more gene loci selected from in cancer (COSMIC). (Item 83) The method according to item 63, wherein the region of the genome comprises one or more of the following: CpG islands, CpG Shores, patient-specific partial methylation domains, general partial methylation domains, promoters, gene entities, equally spaced bins throughout the genome, and transposable elements. (Item 84) The method according to item 63, wherein the region of the genome includes a plurality of non-overlapping regions of the genome. (Item 85) The method according to item 84, wherein the plurality of non-overlapping regions of the genome have predetermined sizes. (Item 86) The method described in item 85, wherein the predetermined size is approximately 50 kilobases (kb), approximately 100 kb, approximately 200 kb, approximately 500 kb, approximately 1 megabase (Mb), approximately 2 Mb, approximately 5 Mb, or approximately 10 Mb. (Item 87) The method according to item 84, wherein the plurality of non-overlapping regions of the genome include at least about 1,000 distinct regions. (Item 88) The method according to item 87, wherein the plurality of non-overlapping regions of the genome include at least about 2,000 distinct regions. (Item 89) The method according to item 63, wherein determining the first or second tumor percentage comprises comparing the methylation percentage profile with one or more reference methylation percentage profiles, the one or more reference methylation percentage profiles being obtained from or derived from additional cfDNA molecules of additional subjects. (Item 90) The method according to item 89, wherein the aforementioned additional subjects include one or more subjects having cancer. (Item 91) The method according to item 89, wherein the aforementioned additional subjects include one or more subjects who do not have cancer. (Item 92) The method according to item 89, wherein the aforementioned additional subjects include one or more subjects with tumor progression. (Item 93) The method according to item 89, wherein the aforementioned additional subjects include one or more subjects who do not have tumor progression. (Item 94) The method according to item 89, wherein the one or more reference methylation ratio profiles are obtained using additional body fluid samples of the subject obtained at one or more subsequent time points after the first time point. (Item 95) The method according to item 63, further comprising detecting that the tumor condition includes tumor progression of the subject if the first tumor percentage or the second tumor percentage is greater than 1, greater than 1.1, greater than 1.2, greater than 1.3, greater than 1.4, greater than 1.5, greater than 1.6, greater than 1.7, greater than 1.8, greater than 1.9, greater than 2, greater than 3, greater than 4, or greater than 5. (Item 96) The method according to item 63, further comprising detecting the major molecular response (MMR) of the subject when the first tumor percentage or the second tumor percentage is less than 0.01, less than 0.05, less than 0.1, less than 0.2, less than 0.3, less than 0.4, or less than 0.5. (Item 97) The method according to any one of items 63 to 96, further comprising detecting the tumor condition of the subject with a sensitivity of at least about 50%. (Item 98) The method according to item 97, further comprising detecting the tumor condition of the subject with a sensitivity of at least about 70%. (Item 99) The method according to item 98, further comprising detecting the tumor condition of the subject with a sensitivity of at least about 90%. (Item 100) The method according to any one of items 63 to 99, further comprising detecting the tumor condition of the subject with a specificity of at least about 50%. (Item 101) The method according to item 100, further comprising detecting the tumor condition of the subject with a specificity of at least about 70%. (Item 102) The method according to item 101, further comprising detecting the tumor condition of the subject with a specificity of at least about 90%. (Item 103) The method according to item 102, further comprising detecting the tumor condition of the subject with a specificity of at least about 98%. (Item 104) The method according to any one of items 63 to 103, further comprising detecting the tumor condition of the subject with a positive predictive value (PPV) of at least about 50%. (Item 105) The method according to item 104, further comprising detecting the tumor condition of the subject with a positive predictive value (PPV) of at least about 70%. (Item 106) The method according to item 105, further comprising detecting the tumor condition of the subject with a positive predictive value (PPV) of at least about 90%. (Item 107) The method according to any one of items 63 to 106, further comprising detecting the tumor condition of the subject with a negative predictive value (NPV) of at least approximately 50%. (Item 108) The method according to item 107, further comprising detecting the tumor condition of the subject with a negative predictive value (NPV) of at least about 70%. (Item 109) The method according to item 108, further comprising detecting the tumor condition of the subject with a negative predictive value (NPV) of at least approximately 90%. (Item 110) The method according to any one of items 63 to 109, further comprising detecting the progression of the state of the object with an area under the curve (AUC) of at least about 0.60. (Item 111) The method according to item 110, further comprising detecting the tumor condition of the subject with an area under the curve (AUC) of at least about 0.75. (Item 112) The method according to item 111, further comprising detecting the tumor condition of the subject with an area under the curve (AUC) of at least about 0.90. (Item 113) The method according to any one of items 63 to 112, further comprising determining that the subject is not tumor-progressing if no tumor progression is detected. (Item 114) The method according to any one of items 63 to 113, further comprising administering a therapeutically effective dose of a second therapeutic agent to treat the cancer of the subject based on the determined tumor state of the subject. (Item 115) The method according to item 114, wherein the second therapeutic agent includes surgery, chemotherapy, radiotherapy, targeted therapy, immunotherapy, cell therapy, antihormone agents, antimetabolitic chemotherapeutic agents, kinase inhibitors, methyltransferase inhibitors, peptides, gene therapy, vaccines, platinum-based chemotherapeutic agents, antibodies, or checkpoint inhibitors. (Item 116) The method according to any one of items 63 to 115, wherein the first and second plurality of cfDNA molecules are derived from the target immune cells. (Item 117) The method according to any one of items 63 to 116, wherein the detected tumor condition indicates tumor progression, non-progression, regression, or recurrence. (Item 118) The method according to any one of items 63 to 117, wherein the first and second MS data are acquired by a sequencing device or a computer processor. (Item 119) The method according to any one of items 1-60 and 63-118, wherein the subject has brain cancer, bladder cancer, breast cancer, cervical cancer, colorectal cancer, endometrial cancer, esophageal cancer, stomach cancer, kidney cancer, hepatobiliary tract cancer, leukemia, liver cancer, lung cancer, lymphoma, ovarian cancer, pancreatic cancer, prostate cancer, skin cancer, stomach cancer, thyroid cancer, or urinary tract cancer. (Item 120) A computer system for evaluating the tumor status of a subject with cancer, (i) First methylation sequencing (MS) data of a first group of cell-free DNA (cfDNA) molecules across a region of the genome, wherein the first group of cfDNA molecules are obtained from or derived from a first bodily fluid sample of the subject at a first time point, and the first time point is before the subject is administered a therapeutic agent configured to treat the cancer. (i) first methylation sequencing (MS) data, and (ii) second MS data of a second plurality of cell-free DNA (cfDNA) molecules across the entire region of the genome, wherein the second plurality of cfDNA molecules are obtained from or derived from a second bodily fluid sample of the subject at a second time point, the second time point being after the subject has been administered the therapeutic agent; One or more computer processors operablely coupled to the database, wherein the one or more computer processors individually or collectively, Based on the first MS data, the mean methylation rate for each of one or more CpG islands within the region of the genome is determined, thereby obtaining a first mean methylation rate profile. Based on the second MS data, the mean methylation rate for each of one or more CpG islands within the region of the genome is determined, thereby obtaining a second mean methylation rate profile. The methylation ratio profile is determined by comparing the first average methylation ratio profile across one or more CpG islands with the second average methylation ratio profile across one or more CpG islands. At least partially, based on the respective methylation ratio profiles, the first tumor percentage of the subject at the first time point or the second tumor percentage of the subject at the second time point is determined, and A computer system comprising one or more computer processors programmed, at least in part, to detect the tumor condition of the subject based on the first tumor percentage or the second tumor percentage. (Item 121) A non-temporary computer-readable medium, which, when executed by one or more computer processors, includes machine-executable instructions for performing a method for evaluating the tumor state of a subject having cancer, wherein the method Obtaining first methylation sequencing (MS) data of a first group of cell-free DNA (cfDNA) molecules across a region of the genome, wherein the first group of cfDNA molecules are obtained from or derived from a first bodily fluid sample of the subject at a first time point, and the first time point is before the subject is administered a therapeutic agent configured to treat the cancer. Based on the first MS data, the mean methylation rate for each of one or more CpG islands within the region of the genome is determined, thereby obtaining a first mean methylation rate profile. Acquiring second MS data of a second plurality of cell-free DNA (cfDNA) molecules across the entire region of the genome, wherein the second plurality of cfDNA molecules are acquired from or derived from a second bodily fluid sample of the subject at a second time point, and the second time point is after the subject has been administered the therapeutic agent. Based on the second MS data, the mean methylation rate for each of one or more CpG islands within the region of the genome is determined, thereby obtaining a second mean methylation rate profile. The methylation ratio profile is determined by comparing the first average methylation ratio profile across one or more CpG islands with the second average methylation ratio profile across one or more CpG islands. At least partially, determining the first tumor percentage of the subject at the first time point or the second tumor percentage of the subject at the second time point based on the respective methylation percentage profiles, A non-temporary computer-readable medium comprising detecting the tumor status of the subject based at least in part on the first tumor percentage or the second tumor percentage. (Item 122) The detected tumor progression is based, at least in part, on one or more statistical modeling analyses of the respective methylation rate profiles of the computer system described in item 120 or the non-temporary computer-readable media described in item 112. (Item 123) The system or medium described in item 122, wherein one or more of the statistical modeling analyses include linear regression, simple regression, binary regression, Bayesian linear regression, Bayesian modeling, multinomial regression, Gaussian process regression, Gaussian modeling, logistic regression, or nonlinear regression. (Item 124) The system or medium according to item 122 or 123, wherein one or more statistical modeling analyses compare the detected tumor progression with MS data derived from a sample having a known tumor proportion, MS data derived from a pure tumor sample, or MS data derived from a healthy sample. (Item 125) A method for evaluating the tumor status of a subject with cancer, Obtaining first methylation sequencing (MS) data of a first group of cell-free DNA (cfDNA) molecules across a region of the genome, wherein the first group of cfDNA molecules are obtained from or derived from a first bodily fluid sample of the subject at a first time point, and the first time point is before the subject is administered a therapeutic agent configured to treat the cancer. Based on the first MS data, the methylation profile for each of one or more gene loci in the genome is determined, thereby obtaining the first methylation profile. Acquiring second MS data of a second plurality of cell-free DNA (cfDNA) molecules across the entire region of the genome, wherein the second plurality of cfDNA molecules are acquired from or derived from a second bodily fluid sample of the subject at a second time point, the second time point being after the subject has been administered the therapeutic agent. Based on the second MS data, the methylation profile for each of one or more gene loci in the genome is determined, thereby obtaining a second methylation profile. Comparing the first methylation profile across one or more gene loci with the second methylation profile across one or more gene loci, At least partially, determining the first tumor percentage of the subject at the first time point or the second tumor percentage of the subject at the second time point based on the respective methylation profiles, A method comprising detecting the tumor status of the subject based at least in part on the first tumor percentage or the second tumor percentage. (Item 126) The method according to item 125, wherein the first and second methylation profiles include a 5-hydroxymethylcytosine state, a 5-methylcytosine state, a concentrated methylation evaluation, a central methylation level, a modal methylation level, a maximum methylation level, or a minimum methylation level. (Item 127) The method according to item 125 or 126, wherein the first or second body fluid sample is selected from the group consisting of blood, serum, plasma, vitreous humor, sputum, urine, tears, sweat, saliva, semen, mucosal discharge, mucus, cerebrospinal fluid, cerebrospinal fluid (CSF), pleural fluid, ascites, amniotic fluid, and lymph. (Item 128) Item 125, where obtaining the first MS data includes performing methylation sequencing of the first plurality of cfDNA molecules to generate the first plurality of sequencing reads, or where obtaining the second MGS data includes performing methylation sequencing of the second plurality of cfDNA molecules to generate the second plurality of sequencing reads. Methods used. (Item 129) The method according to item 128, wherein the methylation sequencing includes whole-genome bisulfite sequencing. (Item 130) The method described in item 128, wherein the methylation sequencing includes whole-genome enzymatic methyl-seq. (Item 131) The method according to item 128, wherein the methylation sequencing includes oxidative bisulfite sequencing, TET-assisted pyridineborane sequencing (TAPS), TET-assisted bisulfite sequencing (TABS), oxidative bisulfite sequencing (oxBS-Seq), APOBEC-bound epigenetic sequencing (ACE-seq), methylated DNA immunoprecipitation (MeDIP) sequencing, hydroxymethylated DNA immunoprecipitation (hMeDIP) sequencing, methylation array analysis, reduced-expression bisulfite sequencing (RRBS-Seq), or cytosine 5-hydroxymethylation sequencing. (Item 132) The method according to item 128, further comprising aligning the first or second sequence readings with a reference genome to thereby generate a plurality of aligned sequence readings. (Item 133) The method according to item 128, further comprising enriching the first or second plurality of cfDNA molecules with respect to the region of the genome. (Item 134) The method according to item 128, wherein the region of the genome comprises one or more of the following: CpG islands, CpG Shores, patient-specific partial methylation domains, general partial methylation domains, promoters, gene entities, equally spaced bins throughout the genome, and transposable elements. (Item 135) The method according to item 128, wherein the region of the genome includes a plurality of non-overlapping regions of the genome. (Item 136) The method according to item 128, wherein determining the first or second tumor percentage comprises comparing the methylation percentage profile with one or more reference methylation percentage profiles, the one or more reference methylation percentage profiles being obtained from or derived from additional cfDNA molecules of additional subjects. (Item 137) The method according to item 128, further comprising detecting that the tumor condition includes tumor progression of the subject if the first tumor percentage or the second tumor percentage is greater than 1, greater than 1.1, greater than 1.2, greater than 1.3, greater than 1.4, greater than 1.5, greater than 1.6, greater than 1.7, greater than 1.8, greater than 1.9, greater than 2, greater than 3, greater than 4, or greater than 5. (Item 138) The method according to item 128, further comprising detecting the major molecular response (MMR) of the subject when the first tumor percentage or the second tumor percentage is less than 0.01, less than 0.05, less than 0.1, less than 0.2, less than 0.3, less than 0.4, or less than 0.5. (Item 139) The method according to any one of items 128 to 138, further comprising determining that the subject is not tumor-progressing if no tumor progression is detected. (Item 140) Based on the determined tumor state of the subject, in order to treat the cancer of the subject The method according to any one of items 128 to 139, further comprising administering a second therapeutic dose of the therapeutic agent. (Item 141) The method according to any one of items 128 to 140, wherein the first and second plurality of cfDNA molecules are derived from the target immune cells. (Item 142) The method according to any one of items 128 to 141, wherein the detected tumor condition indicates tumor progression, non-progression, regression, or recurrence. (Item 143) The method according to any one of items 128 to 142, wherein the first and second MS data are acquired by a sequencing device or a computer processor. (Item 144) The method according to any one of items 128 to 143, wherein the subject has brain cancer, bladder cancer, breast cancer, cervical cancer, colorectal cancer, endometrial cancer, esophageal cancer, stomach cancer, kidney cancer, hepatobiliary tract cancer, leukemia, liver cancer, lung cancer, lymphoma, ovarian cancer, pancreatic cancer, prostate cancer, skin cancer, stomach cancer, thyroid cancer, or urinary tract cancer. (Item 145) A computer system for evaluating the tumor status of a subject with cancer, (i) First methylation sequencing (MS) data of a first plurality of cell-free DNA (cfDNA) molecules across a region of the genome, wherein the first plurality of cfDNA molecules are obtained from or derived from a first bodily fluid sample of the subject at a first time point, the first time point being before the subject is administered a therapeutic agent configured to treat the cancer; and (ii) Second MS data of a second plurality of cell-free DNA (cfDNA) molecules across the region of the genome, wherein the second plurality of cfDNA molecules are obtained from or derived from a second bodily fluid sample of the subject at a second time point, the second time point being after the subject is administered the therapeutic agent; One or more computer processors operablely coupled to the database, wherein the one or more computer processors individually or collectively, Based on the first MS data, the methylation profile for each of one or more CpG islands within the region of the genome is determined, thereby obtaining the first methylation profile. Based on the second MS data, the methylation profile for each of one or more CpG islands within the region of the genome is determined, thereby obtaining a second methylation profile. The first methylation profile across one or more CpG islands is compared with the second methylation profile across one or more CpG islands. At least partially, based on the respective methylation profiles, the first tumor percentage of the subject at the first time point or the second tumor percentage of the subject at the second time point is determined, and A computer system comprising one or more computer processors programmed, at least in part, to detect the tumor condition of the subject based on the first tumor percentage or the second tumor percentage. (Item 146) A non-temporary computer-readable medium, which, when executed by one or more computer processors, includes machine-executable instructions for performing a method for evaluating the tumor state of a subject having cancer, wherein the method The method involves obtaining first methylation sequencing (MS) data of a first group of cell-free DNA (cfDNA) molecules across a region of the genome, wherein the first group of cfDNA molecules The child is obtained from or derived from a first bodily fluid sample of the subject at a first time point, and the first time point is before the subject is administered a therapeutic agent configured to treat the cancer. Based on the first MS data, the methylation profile for each of one or more CpG islands within the region of the genome is determined, thereby obtaining the first methylation profile. Acquiring second MS data of a second plurality of cell-free DNA (cfDNA) molecules across the entire region of the genome, wherein the second plurality of cfDNA molecules are acquired from or derived from a second bodily fluid sample of the subject at a second time point, and the second time point is after the subject has been administered the therapeutic agent. Based on the second MS data, the methylation profile for each of one or more CpG islands within the region of the genome is determined, thereby obtaining a second methylation profile. Comparing the first average methylation ratio profile across one or more CpG islands with the second average methylation ratio profile across one or more CpG islands, At least partially, determining the first tumor percentage of the subject at the first time point or the second tumor percentage of the subject at the second time point based on the respective methylation profiles, A non-temporary computer-readable medium comprising detecting the tumor status of the subject based at least in part on the first tumor percentage or the second tumor percentage. (Item 147) A method for evaluating the tumor status of a subject with cancer, Obtaining first whole-genome sequencing (WGS) data of a first group of cell-free DNA (cfDNA) molecules, wherein the first group of cfDNA molecules are obtained from or derived from a first bodily fluid sample of the subject at a first time point, and the first time point is before the subject is administered a therapeutic agent configured to treat cancer. Based on the first WGS data, (i) a first set of copy number anomalies (CNAs) in the first set of cfDNA molecules, and (ii) a first set of fragment lengths in the first set of cfDNA molecules are determined. Obtaining first methylation sequencing (MS) data of a first group of cell-free DNA (cfDNA) molecules across a region of the genome, wherein the first group of cfDNA molecules are obtained from or derived from the subject bodily fluid sample at a first time point. Based on the first MS data, the mean methylation rate for each of one or more CpG islands within the region of the genome is determined, thereby obtaining a first mean methylation rate profile. Obtaining second whole-genome sequencing (WGS) data for a second set of cell-free DNA (cfDNA) molecules, wherein the second set of cfDNA molecules are obtained from or derived from a second bodily fluid sample of the subject at a second time point, and the second time point is after the subject has been administered the therapeutic agent. Based on the second WGS data, (iii) a second set of copy number anomalies (CNAs) in the second set of cfDNA molecules, and (iv) a second set of fragment lengths in the second set of cfDNA molecules, Obtaining second MS data of a second plurality of cell-free DNA (cfDNA) molecules across the entire region of the genome, wherein the second plurality of cfDNA molecules are obtained from or derived from the subject body fluid sample at a second time point. Based on the second MS data, the mean methylation rate for each of one or more CpG islands within the region of the genome is determined, thereby determining the second mean methylation rate Obtaining a matching profile, The first set of CNAs is compared with the second set of CNAs to determine the change in the CNA profile, Based on the first plurality of fragment lengths and the second plurality of fragment lengths, the change in the fragment length profile is determined, The methylation ratio profile is determined by comparing the first average methylation ratio profile across one or more CpG islands with the second average methylation ratio profile across one or more CpG islands. Determining, at least in part, the first tumor percentage of the subject at the first time point or the second tumor percentage of the subject at the second time point based on the CNA profile change, the fragment length profile change, and the respective methylation percentage profiles, A method comprising detecting a tumor condition of interest based at least partially on a first tumor percentage or a second tumor percentage. (Item 148) A method for evaluating the tumor status of a subject with cancer, Obtaining first whole-genome sequencing (WGS) data of a first group of cell-free DNA (cfDNA) molecules, wherein the first group of cfDNA molecules are obtained from or derived from a first bodily fluid sample of the subject at a first time point, and the first time point is before the therapeutic agent configured to treat the cancer is administered to the subject. Based on the first WGS data, (i) a first set of copy number anomalies (CNAs) in the first set of cfDNA molecules, and (ii) a first set of fragment lengths in the first set of cfDNA molecules are determined. Obtaining first methylation sequencing (MS) data of a first group of cell-free DNA (cfDNA) molecules across a region of the genome, wherein the first group of cfDNA molecules are obtained from or derived from the subject bodily fluid sample at a first time point. Based on the first MS data, the methylation profile for each of one or more gene loci in the genome is determined, thereby obtaining the first methylation profile. Obtaining second whole-genome sequencing (WGS) data of a second group of cell-free DNA (cfDNA) molecules, wherein the second group of cfDNA molecules are obtained from or derived from a second bodily fluid sample of the subject at a second time point, the second time point being after the subject has been administered the therapeutic agent. Based on the second WGS data, (iii) a second set of copy number anomalies (CNAs) in the second set of cfDNA molecules, and (iv) a second set of fragment lengths in the second set of cfDNA molecules, Obtaining second MS data of a second plurality of cell-free DNA (cfDNA) molecules across the entire region of the genome, wherein the second plurality of cfDNA molecules are obtained from or derived from the subject body fluid sample at a second time point. Based on the second MS data, the methylation profile for each of one or more gene loci in the genome is determined, thereby obtaining a second methylation profile. The first set of CNAs is compared with the second set of CNAs to determine the change in the CNA profile, Based on the first plurality of fragment lengths and the second plurality of fragment lengths, the change in the fragment length profile is determined, The first methylation profile spanning the entirety of one or more gene loci, and the one or more By comparing this with the aforementioned second methylation profile across the entire gene locus, Determining, at least in part, the first tumor percentage of the subject at the first time point or the second tumor percentage of the subject at the second time point based on the CNA profile change, the fragment length profile change, and the respective methylation percentage profiles, A method comprising detecting the tumor status of the subject based at least in part on the first tumor percentage or the second tumor percentage. (Item 149) The method according to item 148, wherein the first and second methylation profiles include a 5-hydroxymethylcytosine state, a 5-methylcytosine state, a concentrated methylation assessment, a central methylation level, a modal methylation level, a maximum methylation level, or a minimum methylation level. (Item 150) The method according to any one of items 147 to 149, wherein the first WGS data and the first MS data are obtained from the same sample. (Item 151) The method according to any one of items 147 to 149, wherein the first WGS data and the first MS data are obtained from different samples. (Item 152) The method according to any one of items 147 to 151, wherein the second WGS data and the second MS data are obtained from the same sample. (Item 153) The method according to any one of items 147 to 151, wherein the second WGS data and the second MS data are obtained from different samples. (Item 154) The method according to any one of items 147 to 153, wherein the first or second body fluid sample is selected from the group consisting of blood, serum, plasma, vitreous humor, sputum, urine, tears, sweat, saliva, semen, mucosal discharge, mucus, cerebrospinal fluid, cerebrospinal fluid (CSF), pleural fluid, ascites, amniotic fluid, and lymph. (Item 155) The method according to any one of items 147 to 154, wherein obtaining the first WGS data comprises sequencing the first plurality of cfDNA molecules to generate a first plurality of sequencing reads, or obtaining the second WGS data comprises sequencing the second plurality of cfDNA molecules to generate a second plurality of sequencing reads. (Item 156) The method according to any one of items 147 to 155, further comprising enriching the first or second plurality of cfDNA molecules with respect to a plurality of genomic regions. (Item 157) The method according to item 155 or item 156, wherein determining the first plurality of CNAs comprises determining a quantitative measure of CNAs in each of the plurality of genomic regions of the first plurality of sequencing reads, and determining the second plurality of CNAs comprises determining a quantitative measure of CNAs in each of the plurality of genomic regions of the second plurality of sequencing reads. (Item 158) The method according to any one of items 147 to 157, wherein determining the CNA profile change comprises comparing the plurality of CNAs of item 157 and the second plurality of CNAs with a plurality of reference CNA values, the plurality of reference CNA values ​​being obtained from or derived from additional cfDNA molecules of an additional subject. (Item 159) The first and second WGS data mentioned above were subjected to pyro-sequencing, synthesis-based sequencing, and single-component sequencing. The method described in any one of items 147-158, obtained by sub-sequence sequencing, nanopore sequencing, semiconductor sequencing, sequencing by ligation, sequencing by hybridization, ultra-parallel sequencing, end-chain sequencing, single-molecule real-time sequencing, Polony sequencing, combinatorial probe anchor synthesis, or hybrid capture system sequencing. (Item 160) The method according to any one of items 147 to 159, wherein obtaining the first MS data comprises performing methylation sequencing of the first plurality of cfDNA molecules to generate a first plurality of sequencing reads, or obtaining the second MGS data comprises performing methylation sequencing of the second plurality of cfDNA molecules to generate a second plurality of sequencing reads. (Item 161) The method according to any one of items 147 to 160, further comprising enriching the first or second plurality of cfDNA molecules with respect to the region of the genome. (Item 162) The method according to any one of items 147 to 161, wherein the region of the genome comprises one or more of the following: CpG islands, CpG Shores, patient-specific partial methylation domains, general partial methylation domains, promoters, gene entities, equally spaced bins throughout the genome, and transposable elements. (Item 163) The method according to any one of items 147 to 162, wherein determining the first or second tumor percentage comprises comparing the methylation percentage profile with one or more reference methylation percentage profiles, the one or more reference methylation percentage profiles being obtained from or derived from additional cfDNA molecules of additional subjects. (Item 164) The method according to any one of items 147 to 163, further comprising administering a therapeutically effective dose of treatment to treat the cancer of the subject based on the determined tumor state of the subject. (Item 165) The method according to item 164, wherein the treatment includes surgery, chemotherapy, radiotherapy, targeted therapy, immunotherapy, cell therapy, antihormone agents, antimetabolitic chemotherapeutic agents, kinase inhibitors, methyltransferase inhibitors, peptides, gene therapy, vaccines, platinum-based chemotherapeutic agents, antibodies, or checkpoint inhibitors. (Item 166) The method according to any one of items 147 to 165, wherein the first and second plurality of cfDNA molecules are derived from the target immune cells. (Item 167) The method according to any one of items 147 to 166, wherein the detected tumor condition indicates tumor progression, non-progression, regression, or recurrence. (Item 168) The method according to any one of items 147 to 167, wherein the first and second MS data are acquired by a sequencing device or a computer processor. (Item 169) The method according to any one of items 147 to 168, wherein the subject has brain cancer, bladder cancer, breast cancer, cervical cancer, colorectal cancer, endometrial cancer, esophageal cancer, stomach cancer, kidney cancer, hepatobiliary tract cancer, leukemia, liver cancer, lung cancer, lymphoma, ovarian cancer, pancreatic cancer, prostate cancer, skin cancer, stomach cancer, thyroid cancer, or urinary tract cancer.

[0072] Novel features of the present invention are described in detail in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by referring to the following detailed description illustrating exemplary embodiments in which the principles of the present invention are utilized, and to the appended drawings (furthermore, "Figure" and "FIG" in this specification). [Brief explanation of the drawing]

[0073] [Figure 1] This document describes exemplary methods for evaluating tumor progression in a subject using a change in deviation (CID) score, according to several embodiments. [Figure 2] This describes a computer system that is programmed or otherwise configured to carry out the methods provided herein. [Figure 3A]An overview of clinical settings in several embodiments is presented. Figure 3A shows a comparison of radiographic response assessment for evaluating molecular responses with the potential use of cfDNA. Figure 3B shows the timing of imaging and blood sampling for patients in study. [Figure 3B] An overview of clinical settings in several embodiments is presented. Figure 3A shows a comparison of radiographic response assessment for evaluating molecular responses with the potential use of cfDNA. Figure 3B shows the timing of imaging and blood sampling for patients in study. [Figure 4A] The sequential evaluation of ctDNA to determine molecular progression is shown in several embodiments. Figure 4A shows a genome-wide plot of CNA detected in patient LS030178. T0 baseline blood samples were collected 13 days before the initiation of treatment, and T1 samples were collected 21 days after the initiation of treatment. Figure 4B shows that normalized fragment lengths show an inverse pattern compared to CNA. Figure 4C shows that, overall, there was a strong negative correlation between normalized fragment length and estimated copy number at each genomic location (Spearman's rho = -0.57, P < 1E-10). Figure 4D shows that patient LS030178 had an increase in TFR at follow-up time points T1 and T2, detectable prior to imaging indicating progressive disease. Figure 4E shows that patient LS030093, who responded to therapy, showed a significant decrease in TFR at T1 and T2, consistent with imaging after a partial response. [Figure 4B] Same as above. [Figure 4C] Same as above. [Figure 4D] Same as above. [Figure 4E] Same as above. [Figure 5A]Figure 5A shows ctDNA evaluation after the first or second cycle of therapy predicted progression in several embodiments. Figure 5A compares imaging results at the first FUI (SLD as assessed by RECIST 1.1) with ctDNA evaluation of molecular progression, indicated by a definite increase in TFR in either post-treatment sample (sensitivity = 54%, specificity = 100%, PPV = 100%, NPV = 85%). Cases with footnotes showed clear clinical progression. Figure 5B shows TFR of progression and non-progression patients at T1 (left) and T2 (right) compared with radiography or clinical evaluation of PD or non-PD, showing predictive performance at each time point. Figure 5C shows that for patients with molecular progression, detection of molecular progression precedes detection of progression by standard therapy imaging with a median of 40 days (range of -21 to 103 days). [Figure 5B] Same as above. [Figure 5C] Same as above. [Figure 6A]Several embodiments demonstrate that early molecular response assessment during the course of therapy was associated with favorable PFS. Figure 6A shows that the complete cohort (n=92) had a median PFS of 211 days. Figure 6B shows that patients with molecular progression detected from cfDNA at T1 or T2 (n=14, median PFS=62 days) had a significantly worse PFS compared to patients without molecular progression (n=78, median PFS=263 days; HR=12.6 [95% CI: 5.8~27.3]; log-rank P<1E-10). Figures 6C-6D show subset analyses based on treatment mode for patients receiving immunotherapy with or without chemotherapy (n=34; log-rank P=2E-12) (Figure 6C) and patients receiving chemotherapy with or without targeted therapy (n=42; log-rank P=7E-6) (Figure 6D). Figures 6E–6F show subset analyses based on cancer type for lung cancer patients (n=40; ​​log-rank P=8E-8) (Figure 6E) and breast cancer patients (n=25; log-rank P=3E-4) (Figure 6F). Figure 6G shows that patients with MMR had a significantly longer PFS after considering predictions based on molecular progression (Cox P=0.011). Figures 6H–6I show subset analyses of patients with either stable disease or partial response as determined by radiography in the first FUI (n=65), stratified by response status (log-rank P=0.4) (Figure 6H) or MMR (log-rank P=0.02) (Figure 6I). [Figure 6B] Same as above. [Figure 6C] Same as above. [Figure 6D] Same as above. [Figure 6E] Same as above. [Figure 6F] Same as above. [Figure 6G] Same as above. [Figure 6H] Same as above. [Figure 6I] Same as above. [Figure 7A]Several embodiments demonstrate that methylation can provide orthogonal signals to CNA for response monitoring. These figures show the distribution of genome-wide 1 megabasebin mean methylation levels for patients LS030083 (Figure 7A) and LS030078 (Figure 7B) at baseline (black line) and either T1 or T2 (orange line). [Figure 7B] Same as above. [Figure 8] Longitudinal WGS data for healthy individuals in several embodiments are shown. This figure includes a genome-wide plot, as shown in Figure 4A, showing that no CNA was detected for participant LB-S00129 at the time of initial blood collection (top) and 34 days later (bottom). [Figure 9] This section compares tumor percentage ratios across the entire sequencing protocol using several embodiments. The figure shows results for 20 post-treatment samples from 13 participants treated with both WGS and WGBS. Two samples from patients with PD at the first FUI lacked a matched classification of molecular progression, and TFR measurements were close to the call boundary. [Figure 10] The timing and sensitivity of sample collection in several embodiments are shown. This figure shows the molecular progression and blood sample collection timing for 42 samples from 26 participants with PD in the first FUI (Kolmogorov-Smirnov test for 2 samples, P=0.15). [Figure 11] The molecular response assessment and PFS for other cancers in several embodiments are shown. This figure shows non-lung, non-breast cancers (n=27; log-rank P=5E-6) plotted as shown in Figures 6E-6F. [Figure 12A] The following figures show MMR and PFS for patients with non-PD in the first FUI according to several embodiments. These figures show results from all patients with partial response (n=30) (Figure 12A) or stable disease (n=35) (Figure 12B) on radiography. [Figure 12B]The following figures show MMR and PFS for patients with non-PD in the first FUI according to several embodiments. These figures show results from all patients with partial response (n=30) (Figure 12A) or stable disease (n=35) (Figure 12B) on radiography. [Figure 13A] Examples of Kaplan-Meier progression-free survival (PFS) and overall survival (OS) plots for each of these three patient categories (MP, MMR, and neither MP nor MMR) in a patient cohort are shown in several embodiments. These figures demonstrate that the survival curves are highly separated from each other. Furthermore, molecular progression prediction predicts radiographic progression with high specificity. [Figure 13B] Examples of Kaplan-Meier progression-free survival (PFS) and overall survival (OS) plots for each of these three patient categories (MP, MMR, and neither MP nor MMR) in a patient cohort are shown in several embodiments. These figures demonstrate that the survival curves are highly separated from each other. Furthermore, molecular progression prediction predicts radiographic progression with high specificity. [Figure 14A] Examples of strong mean reductions in methylation observed in three MAGE genes (MAGEA1, MAGEA3, and MAGEA4) in several embodiments are shown. [Figure 14B] Examples of strong mean reductions in methylation observed in three MAGE genes (MAGEA1, MAGEA3, and MAGEA4) in several embodiments are shown. [Figure 14C] Examples of strong mean reductions in methylation observed in three MAGE genes (MAGEA1, MAGEA3, and MAGEA4) in several embodiments are shown. [Figure 15A] Quantifying changes in the intensity of specific copy number anomalies (CNAs) across multiple patient samples throughout the entire treatment course demonstrates fewer tendencies of specific error modes that arise from separately quantifying tumor proportions in different samples based on CNAs. [Figure 15B]Quantifying changes in the intensity of specific copy number anomalies (CNAs) across multiple patient samples throughout the entire treatment course demonstrates fewer tendencies of specific error modes that arise from separately quantifying tumor proportions in different samples based on CNAs. [Modes for carrying out the invention]

[0074] As used herein, the terms “nucleic acid” or “polynucleotide” generally refer to a molecule comprising one or more nucleic acid subunits, or nucleotides. A nucleic acid may comprise one or more nucleotides selected from adenosine (A), cytosine (C), guanine (G), thymine (T), and uracil (U), or their variants. A nucleotide generally comprises a nucleoside and at least one, two, three, four, five, six, seven, eight, nine, ten, or more phosphate (PO3) groups. A nucleotide may comprise a nucleic acid base, a five-carbon sugar (either ribose or deoxyribose), and one or more phosphate groups, individually or in combination.

[0075] Ribonucleotides are nucleotides in which the sugar is ribose. Deoxyribonucleotides are nucleotides in which the sugar is deoxyribose. Nucleotides can be nucleoside monophosphates or nucleoside polyphosphates. Nucleotides can be deoxyribonucleoside polyphosphates, such as deoxyribonucleoside triphophosphates (dNTPs), and can be selected from deoxyadenosine triphophosphates (dATP), deoxycytidine triphophosphates (dCTP), deoxyguanosine triphophosphates (dGTP), uridine triphophosphates (dUTP), and deoxythymidine triphophosphates (dTTP) dNTPs, which include detectable tags such as luminescent tags or markers (e.g., fluorophores). Nucleotides can contain any subunits that can be incorporated into a growing nucleic acid chain. Such subunits may be specific to A, C, G, T, or U, or one or more complementary A, C, G, T, or U, or any other subunit complementary to a purine (i.e., A or G, or a variant thereof) or a pyrimidine (i.e., C, T, or U, or a variant thereof). In some examples, nucleic acids are deoxyribonucleic acid (DNA), ribonucleic acid (RNA), or derivatives or variants thereof. Nucleic acids may be single-stranded or double-stranded. Nucleic acid molecules may be linear, curvilinear, cyclic, or any combination thereof.

[0076] As used herein, the terms “nucleic acid molecule,” “nucleic acid sequence,” “nucleic acid fragment,” “oligonucleotide,” and “polynucleotide” generally refer to polynucleotides, which may have a variety of lengths, including either deoxyribonucleotides or ribonucleotides (RNA), or analogs thereof. A nucleic acid molecule may have a length of at least about 5 nucleotides, 10 nucleotides, 20 nucleotides, 30 nucleotides, 40 nucleotides, 50 nucleotides, 60 nucleotides, 70 nucleotides, 80 nucleotides, 90, 100 nucleotides, 110 nucleotides, 120 nucleotides, 130 nucleotides, 140 nucleotides, 150 nucleotides, 160 nucleotides, 170 nucleotides, 180 nucleotides, 190 nucleotides, 200 nucleotides, 300 nucleotides, 400 nucleotides, 500 nucleotides, 1 kilobase (kb), 2 kb, 3 kb, 4 kb, 5 kb, 10 kb, or 50 kb, or it may have any number of nucleotides between any two of the aforementioned values. Oligonucleotides typically consist of a specific sequence of four nucleotide bases: adenine (A); cytosine (C); guanine (G); and thymine (T) (or uracil (U) if the polynucleotide is RNA). Therefore, the terms “nucleic acid molecule,” “nucleic acid sequence,” “nucleic acid fragment,” “oligonucleotide,” and “polynucleotide” are intended, at least in part, to be alphabetical representations of polynucleotide molecules. Alternatively, these terms may apply to the polynucleotide molecule itself. This alphabetical representation can be used for inputting into databases in centrally controlled computers, as well as for bioinformatics applications such as functional genomics and homology searches. Oligonucleotides may contain one or more non-standard nucleotides, nucleotide analogs, and / or modified nucleotides.

[0077] As used herein, the term “sample” generally refers to a biological sample. Examples of biological samples include nucleic acid molecules, amino acids, polypeptides, proteins, carbohydrates, lipids, or viruses. For example, a biological sample is a nucleic acid sample containing one or more nucleic acid molecules. Nucleic acid molecules may be cell-free or cell-free nucleic acid molecules, such as cell-free DNA (cfDNA) or cell-free RNA (cfRNA). Nucleic acid molecules may originate from a variety of sources, including humans, mammals, non-human mammals, apes, monkeys, chimpanzees, reptiles, amphibians, or birds. Furthermore, samples may be extracted from the bodily fluids of various animals containing cell-free sequences, including but not limited to bodily fluid samples such as blood, serum, plasma, vitreous humor, sputum, urine, tears, sweat, saliva, semen, mucosal discharge, mucus, cerebrospinal fluid, cerebrospinal fluid (CSF), pleural fluid, ascites, amniotic fluid, and lymph. Cell-free polynucleotides (e.g., cfDNA) may be of fetal origin (through fluids collected from a pregnant subject) or may originate from the subject's own tissues.

[0078] As used herein, the term “subject” generally refers to an individual having a biological sample undergoing processing or analysis. A subject may be an animal or a plant. A subject may be a mammal, such as a human, dog, cat, horse, pig, or rodent. A subject may be a patient who has or is suspected of having a disease, such as one or more cancers (e.g., brain cancer, breast cancer, cervical cancer, colorectal cancer, endometrial cancer, esophageal cancer, stomach cancer, hepatobiliary cancer, leukemia, liver cancer, lung cancer, lymphoma, ovarian cancer, pancreatic cancer, skin cancer, urinary tract cancer), one or more infections, one or more genetic disorders, or one or more tumors, or any combination thereof. For a subject having or being suspected of having one or more tumors, the tumors may be of one or more types.

[0079] As used herein, the term “whole blood” generally refers to a blood sample that has not been separated into smaller components (e.g., by centrifugation). Whole blood in a blood sample may contain cfDNA and / or germline DNA. Whole blood DNA (which may contain cfDNA and / or germline DNA) can be extracted from a blood sample. Whole blood DNA sequencing reads (which may contain cfDNA sequencing reads and / or germline DNA sequencing reads) can be extracted from whole blood DNA.

[0080] Tumor progression assessment using cell-free DNA sequence data from subjects When the majority of samples taken from a subject (e.g., >80%) are obtained from or derived from tumor cells, assessing tumor progression is relatively straightforward. However, with cell-free DNA (cfDNA) preparations from subject plasma derived from blood samples, detecting tumor DNA from cfDNA and assessing tumor progression therefrom can be an insensitive and noisy process. Due to the vast amount of signal from non-tumor DNA (e.g., from germline DNA from non-tumor-derived germ cells), detecting tumor DNA and assessing tumor progression from such insensitive and / or noisy signals can be challenging. This disclosure provides a method and system for assessing tumor progression from cell-free DNA (cfDNA) sequence data (e.g., cfDNA sequencing readout) of cfDNA molecules obtained from or derived from a sample of a subject (e.g., a patient with cancer). cfDNA sequence data can be received from the analysis of a sample from a subject, and one or more bioinformatics processes can be used to assess tumor progression or non-tumor progression in the subject. In some embodiments, immune cell DNA can be detected from cfDNA and optionally used to assess tumor progression.

[0081] In one embodiment, the present disclosure is a method for evaluating tumor progression in a subject having cancer, comprising: obtaining first whole-genome sequencing (WGS) data of a first plurality of cell-free DNA (cfDNA) molecules, wherein the first plurality of cfDNA molecules are obtained from or derived from a first bodily fluid sample of the subject at a first time point, the first time point being before a therapeutic agent configured to treat cancer is administered to the subject; processing the first WGS data to determine (i) a first plurality of copy number anomalies (CNAs) in the first plurality of cfDNA molecules, and (ii) a first plurality of fragment lengths of the first plurality of cfDNA molecules; and obtaining second whole-genome sequencing (WGS) data of a second plurality of cell-free DNA (cfDNA) molecules, wherein the second plurality of cfDNA molecules are obtained from a second bodily fluid sample of the subject at a second time point. A method is provided which includes obtaining, or derived therefrom, a second time point in time being after the subject has been administered a therapeutic agent, and processing the second WGS data to determine (iii) a second set of copy number anomalies (CNAs) in a second set of cfDNA molecules, and (iv) a second set of fragment lengths in a second set of cfDNA molecules; processing the first set of CNAs with the second set of CNAs to determine changes in the CNA profile; processing the first set of fragment lengths with the second set of fragment lengths to determine changes in the fragment length profile; determining, at least in part, a first tumor percentage of the subject at the first time point or a second tumor percentage of the subject at the second time point based on the changes in the CNA profile and the changes in the fragment length profile; and at least in part, detecting tumor progression in the subject based on the first or second tumor percentage.

[0082] Figure 1 shows exemplary methods for evaluating tumor progression in a subject according to several embodiments. In operation 102, first whole-genome sequencing (WGS) data for a first group of cell-free DNA (cfDNA) molecules are obtained. The first group of cfDNA molecules may be obtained from or derived from a first fluid sample of the subject at a first time point. The first time point may be before the subject is administered a therapeutic agent configured to treat cancer. In operation 104, the first WGS data are processed to determine (i) a first group of copy number anomalies (CNAs) in the first group of cfDNA molecules, and (ii) a first group of fragment lengths in the first group of cfDNA molecules. In operation 106, second whole-genome sequencing (WGS) data for a second group of cell-free DNA (cfDNA) molecules are obtained. The second group of cfDNA molecules may be obtained from or derived from a second fluid sample of the subject at a second time point. The second time point may be after the subject is administered a therapeutic agent configured to treat cancer. In operation 108, the second WGS data is processed to determine (i) a second set of copy number anomalies (CNAs) in the second set of cfDNA molecules, and (ii) a second set of fragment lengths in the second set of cfDNA molecules. In operation 110, the first set of CNAs is processed (e.g., compared) with the second set of CNAs to determine changes in the CNA profile. In operation 112, the first set of fragment lengths is processed (e.g., compared) with the second set of fragment lengths to determine changes in the fragment length profile. In operation 114, the first tumor percentage of the subject at the first time point and / or the second tumor percentage of the subject at the second time point are determined, at least in part, based on changes in the CNA profile and changes in the fragment length profile. In operation 116, tumor progression in the subject is detected, at least in part, based on the first tumor percentage and / or the second tumor percentage.

[0083] In some embodiments, the method includes identifying one or more libraries (for example, by using CNA patterns or based on the fact that the libraries are from a control sample, the tumor proportion can be determined). For each library, the methylation status (e.g., mean methylation proportion) can be calculated from one or more regions of the genome (e.g., one, part, or all CpG islands, promoters, etc.) using methylation sequencing as described herein. Statistical modeling (e.g., linear regression or another technique of this disclosure) can be used to regularize known tumor proportions against methylation patterns, for example, cross-validation leaving one participant. While we do not wish to be bound by theory, these methods are considered to enable the prediction of tumor proportions in a sample based on methylation patterns, even when CNAs are undetectable. In some embodiments, the method further includes comparing the above analyses over two or more time points.

[0084] For example, sequence readouts can be generated from cfDNA using any suitable sequencing method known to those skilled in the art. The sequencing method may be a first-generation sequencing method such as Maxam-Gilbert or Sanger sequencing, or a high-throughput sequencing method (e.g., next-generation sequencing or NGS). High-throughput sequencing methods can sequence at least 10,000, 100,000, 1 million, 10 million, 100 million, 1 billion, or more polynucleotide molecules simultaneously (or substantially simultaneously). Sequencing methods include, but are not limited to, pyro-sequencing, synthetic sequencing, single-molecule sequencing, nanopore sequencing, semiconductor sequencing, ligation sequencing, hybridization sequencing, digital gene expression (Helicos®), and ultra-parallel sequencing, such as sequencing using the Helicos®, cloned single-molecule array (Solexa® / Illumina®), PacBio®, SOLiD®, Ion Torrent®, or Nanopore® platform. In some embodiments, sequencing is performed by nanopore sequencing, end-chain (Sanger) sequencing, synthetic sequencing (e.g., Illumina or Solexa sequencing), single-molecule real-time sequencing, ultra-parallel signature sequencing, Polony sequencing, 454 pyro-sequencing, combinatorial probe anchor synthesis, ligation sequencing (SOLiD sequencing), or GenapSys sequencing. In some embodiments, sequencing includes hybrid capture sequencing (hybrid capture NGS), for example, using an adapter ligation library. See, for example, Frampton, GM et al. (2013) Nat. Biotech. 31:1023-1031.

[0085] In some embodiments, the sequencing method includes bisulfite sequencing. Bisulfite sequencing typically involves treating the DNA with a bisulfite before sequencing, which converts unmethylated cytosines to uracil without converting 5-methylcytosine, thereby enabling the detection of the DNA methylation status (although additional methods are required to distinguish between 5-methylcytosine and 5-hydroxymethylcytosine, as shown below). Various standard sequencing methods can be used after bisulfite treatment, including methods that are either specific or nonspecific to the detection of methylation. Sequencing methods include, but are not limited to, pyrosequencing, direct sequencing (e.g., using PCR), high-resolution thaw analysis, methylation-sensitive single-strand higher-order structure analysis, methylation-sensitive single-nucleotide primer extension, base-specific cleavage / MALDI-TOF, microarray sequencing, and methylation-specific PCR.

[0086] In some embodiments, the sequencing method includes oxidative bisulfite sequencing. Using oxidative bisulfite sequencing, 5-methylcytosine and 5-hydroxymethylcytosine can be distinguished by chemical oxidation of 5-hydroxymethylcytosine to 5-formylcytosine, which can then be converted to uracil via bisulfite treatment.

[0087] In some embodiments, the sequencing method includes TET-assisted pyridineborane sequencing (TAPS) or TET-assisted bisulfite sequencing (TABS or TAB-Seq). TAB-Seq allows for the separation of 5-hydroxymethylcytosine using the 11-11 translocation (TET) dioxygenase enzyme. In an exemplary method, β-glucosyltransferase (βGT) is used to convert 5-hydroxymethylcytosine to β-glucosyl-5-hydroxymethylcytosine (blocking further modification by TET and oxidation by bisulfite), and the TET enzyme is used to oxidize 5-hydroxymethylcytosine to 5-carboxylcytosine, which is sensitive to uracil conversion via bisulfite. See, for example, Yu, M. et al. (2012) Cell 149:1368-1380. In the case of TAPS, the TET enzyme is used to oxidize 5-methylcytosine and 5-hydroxymethylcytosine to 5-carboxylcytosine, and then 5-carboxylcytosine is converted to dihydrouracil (DHU) by pyridineborane reduction, which can be read as thymine via PCR. See, for example, Liu, Y. et al. (2019) Nat. Biotechnol. 37:424-429.

[0088] In some embodiments, the sequencing method includes oxidative bisulfite sequencing (oxBS-Seq). In this method, potassium perruthenate can be used to convert 5-hydroxymethylcytosine to 5-formylcytosine without affecting 5-methylcytosine. Then, bisulfite treatment can convert 5-formylcytosine to uracil.

[0089] In some embodiments, the sequencing method includes APOBEC-bound epigenetic sequencing (ACE-seq). This method uses apolipoprotein B mRNA editing enzyme subunit 3A (APOBEC3A) to deaminate cytosine and 5-methylcytosine and sequence them as thymine, while using β-glucosyltransferase (βGT) to convert 5-hydroxymethylcytosine to β-glucosyl-5-hydroxymethylcytosine (which blocks deamination by APOBEC).

[0090] In some embodiments, the sequencing method includes methylated DNA immunoprecipitation sequencing, such as methylated DNA immunoprecipitation (MeDIP) or hydroxymethylated DNA immunoprecipitation (hMeDIP) sequencing. In these techniques, methylated DNA is isolated from total DNA by immunoprecipitation, followed by purification and sequencing, using antibodies specific to 5-methylcytosine or 5-hydroxymethylcytosine.

[0091] In some embodiments, the sequencing method includes a methylation array. This method uses microarray technology to investigate the methylation status of multiple genomic loci. For example, DNA can be treated with bisulfite, and oligonucleotide probes can be designed to detect unmethylated (by detecting uracil) or methylated (by detecting cytosine) versions of the same locus. By detecting which probes hybridize with the sequence, it is identified whether the sequence is methylated or not.

[0092] In some embodiments, the sequencing method includes reduced-expression bisulfite sequencing (RRBS-Seq). In this method, DNA is digested with a methylation-insensitive restriction enzyme (e.g., MspI), and a sequence adapter is added to the fragment after repair of adherent ends and A-tailing. The DNA can then be treated with a disulfide, amplified by PCR, and sequenced. See, for example, Meissner, A. et al. (2005) Nucleic Acids Res. 33:5868-5877.

[0093] In some embodiments, the sequencing method includes cytosine 5-hydroxymethylation sequencing, e.g., hMe-Seal. In this method, β-glucosyltransferase (βGT) is used to transfer the glucose moiety containing the azide group to 5-hydroxymethylcytosine, which is then chemically modified with biotin to enable detection, affinity enrichment, and sequencing of DNA fragments. See, for example, Song, CX et al. (2011) Nat. Biotechnol. 29:68-72.

[0094] In some embodiments, the sequencing method includes whole-genome sequencing (WGS). Sequencing may be performed to a depth sufficient to assess tumor progression or non-progression in a subject with desired performance (e.g., precision, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), or area under the curve (AUC) of receiver operating characteristics (ROC). In some embodiments, sequencing is performed in a “low-pass” manner to a depth of, for example, about 12 or fewer passes, about 11 or fewer passes, about 10 or fewer passes, about 9 or fewer passes, about 8 or fewer passes, about 7 or fewer passes, about 6 or fewer passes, about 5 or fewer passes, about 4 or fewer passes, about 3.5 or fewer passes, about 3 or fewer passes, about 3 or fewer passes, about 2.5 or fewer passes, about 2 or fewer passes, about 1.5 or fewer passes, or about 1 or fewer passes.

[0095] In some embodiments, evaluating tumor progression or non-progression in a subject may involve aligning cfDNA sequencing reads to a reference genome. The reference genome may include at least a portion of a genome (e.g., the human genome). The reference genome may include an entire genome (e.g., the entire human genome). The reference genome may include an entire genome to which specific base conversions have been applied (e.g., an entire human genome to which unmethylated cytosines have been converted to thymine) so that it can be used for methylation data alignment. The reference genome may include a database containing multiple genomic regions corresponding to coding and / or non-coding genomic regions of the genome. The database may include multiple genomic regions corresponding to cancer-related (or tumor-related) coding and / or non-coding genomic regions of the genome, such as cancer-derived mutations (e.g., single nucleotide variants (SNVs), copy number variations (CNAs), insertions or deletions (indels), as well as other rearrangements, fusion genes, and genomic regions (such as mononucleotides and / or dinucleotides)). Sorting can be performed using the Burrows-Wheeler algorithm (BWA), the Sambamba algorithm, the Samtools algorithm, or other suitable sorting algorithms.

[0096] In some embodiments, evaluating tumor progression or non-progression in a subject may involve generating quantitative measures of cfDNA sequencing reads for each of several genomic regions. These quantitative measures of cfDNA sequencing reads may be generated, for example, by counting DNA sequencing reads aligned with a given genomic region. A CfDNA sequencing read having part or all of a sequencing read aligned with a given genomic region may be counted as a quantitative measure for that genomic region.

[0097] In some embodiments, genomic regions may include tumor markers. Patterns of specific and non-specific genomic regions may indicate tumor progression or non-tumor progression. Changes in these patterns of genomic regions over time may indicate changes in tumor progression or non-tumor progression.

[0098] In some embodiments, cfDNA can be assayed by performing binding assays on multiple cfDNA molecules in each of multiple genomic regions. In some embodiments, performing binding assays involves assaying multiple cfDNA molecules using a probe that is selective for at least a portion of the multiple genomic regions in the multiple cfDNA molecules. In some embodiments, the probe is a nucleic acid molecule having sequence complementarity with the nucleic acid sequences of the multiple genomic regions. In some embodiments, the nucleic acid molecule is a primer or enriched sequence. In some embodiments, the assay involves the use of array hybridization, or polymerase chain reaction (PCR), or nucleic acid sequencing.

[0099] In some embodiments, the method further includes enriching multiple cfDNA molecules for at least a portion of multiple genomic regions. In some embodiments, enrichment includes amplifying the multiple cfDNA molecules. For example, multiple cfDNA molecules may be amplified by selective amplification (e.g., by using a set of primers or probes containing nucleic acid molecules having sequence complementarity with the nucleic acid sequences of multiple genomic regions). Alternatively, or in combination, multiple cfDNA molecules may be amplified by universal amplification (e.g., by using universal primers). In some embodiments, enrichment includes selectively isolating at least a portion of the multiple cfDNA molecules (e.g., a portion of the multiple cfDNA molecules enriched for shorter cfDNA molecules).

[0100] In some embodiments, the methods of the present disclosure include obtaining one or more quantitative measurements, such as fragment length, nucleotide number, etc. In some embodiments, the quantitative measurements are statistical measurements. Preferred statistical measurements are known in the art. For example, in some embodiments, a statistical measurement of deviation includes a z-score against a set of reference samples or a set of reference values ​​(e.g., a set of baseline values).

[0101] In some embodiments, a method for evaluating tumor progression or non-progression in a subject includes processing multiple counts to obtain a quantitative measurement (e.g., a statistical measurement) of the fragment lengths of multiple cfDNA molecules. In some embodiments, the quantitative measurement of the fragment lengths of multiple cfDNA molecules includes the number of nucleotides in each of the multiple cfDNA molecules. Reference samples may be obtained from one or more subjects with tumor progression and / or from subjects without tumor progression (e.g., subjects with non-progression or unaffected patients). Reference samples may be obtained from one or more subjects with a cancer type or from subjects without a cancer type (e.g., brain cancer, bladder cancer, breast cancer, cervical cancer, colorectal cancer, endometrial cancer, esophageal cancer, gastric cancer, kidney cancer, hepatobiliary cancer, leukemia, liver cancer, lung cancer, lymphoma, ovarian cancer, pancreatic cancer, prostate cancer, skin cancer, stomach cancer, thyroid cancer, or urinary tract cancer). Reference samples may be obtained from one or more subjects with or without advanced-stage cancer (e.g., early-stage cancer or no cancer).

[0102] In some embodiments, cfDNA sequencing reads may be normalized or corrected. For example, cfDNA sequencing reads may be deduplication, normalized, and / or corrected to account for known trends in sequencing and library preparation, and / or known trends in sequencing and library preparation. In some embodiments, a subset of quantitative measurements (e.g., statistical measurements) may be excluded, for example, on the basis that changes in such quantitative measurements (e.g., over different time points) are significantly different from those observed in unaffected subjects (e.g., background profiles of cfDNA molecules). Quantitative measurements may be excluded, for example, if the absolute value of the z-score of the quantitative measurement is less than (or less than) a predetermined number. The predetermined number may be about 0.1, about 0.2, about 0.5, about 1, about 1.5, about 2, about 2.5, about 3, about 3.5, about 4, about 4.5, about 5, or greater than about 5.

[0103] In some embodiments, the multiple genomic regions include mononucleotides and / or dinucleotides. The multiple genomic regions include at least about 10 distinct genomic regions, at least about 50 distinct genomic regions, at least about 100 distinct genomic regions, at least about 500 distinct genomic regions, at least about 1000 distinct genomic regions, at least about 5000 distinct genomic regions, at least about 10,000 distinct genomic regions, at least about 50,000 distinct genomic regions, at least about 100,000 distinct genomic regions, at least about 500,000 distinct genomic regions, and at least about 1,000,000 distinct genomic regions. The genome region may include at least approximately 2 million distinct genome regions, at least approximately 3 million distinct genome regions, at least approximately 4 million distinct genome regions, at least approximately 5 million distinct genome regions, at least approximately 10 million distinct genome regions, at least approximately 15 million distinct genome regions, at least approximately 20 million distinct genome regions, at least approximately 25 million distinct genome regions, at least approximately 30 million distinct genome regions, or more than 30 million distinct genome regions.

[0104] In some embodiments, the genomic region includes one or more MAGE (melanoma-associated antigen) genes, e.g., the human MAGE gene. In some embodiments, the genomic region includes one or more promoters corresponding to one or more MAGE (melanoma-associated antigen) genes, e.g., the human MAGE gene. MAGE genes (e.g., the human MAGE gene) are known in the art; see, for example, Chomez, P. et al. (2001) Cancer Res. 61:5544-5551 and Weon, J. Land Potts, PR (2015) Curr. Opin. Cell Biol. 37:1-8. The following are examples of MAGE genes (e.g., human MAGE genes), but are not limited to MAGE-A genes (e.g., MAGE-A1, MAGE-A2, MAGE-A2B, MAGE-A3, MAGE-A4, MAGE-A5, MAGE-A6, MAGE-A8, MAGE-A9, MAGE-A10, MAGE-A11, and MAGE-A12), MAGE-B genes (e.g., MAGE-B1, MAGE-B2, MAGE-B3, MAGE-B4, MAGE-B5, MAGE-A12), MAGE-B genes (e.g., MAGE-B1, MAGE-B2, MAGE-B3, MAGE-B4, MAGE-B5, MAGE-A12). This includes MAGE-B6, MAGE-B6B, MAGE-B10, MAGE-B16, MAGE-B17, and MAGE-B18), MAGE-C genes (e.g., MAGE-C1, MAGE-C2, and MAGE-C3), and type II MAGE genes (e.g., MAGE-D1, MAGE-D2, MAGE-D3, MAGE-D4, MAGE-E1, MAGE-E2, MAGE-F1, MAGE-G1, MAGE-H1, MAGE-L2, NDN, and NDNL2).

[0105] In some embodiments, the target tumor progression is detected with a sensitivity of at least approximately 10%, at least approximately 20%, at least approximately 30%, at least approximately 40%, at least approximately 50%, at least approximately 60%, at least approximately 70%, at least approximately 80%, at least approximately 90%, at least approximately 95%, at least approximately 96%, at least approximately 97%, at least approximately 98%, or at least approximately 99%.

[0106] In some embodiments, the target tumor progression is detected with a specificity of at least approximately 10%, at least approximately 20%, at least approximately 30%, at least approximately 40%, at least approximately 50%, at least approximately 60%, at least approximately 70%, at least approximately 80%, at least approximately 90%, at least approximately 95%, at least approximately 96%, at least approximately 97%, at least approximately 98%, or at least approximately 99%.

[0107] In some embodiments, the target tumor progression is detected with a positive predictive value (PPV) of at least approximately 10%, at least approximately 20%, at least approximately 30%, at least approximately 40%, at least approximately 50%, at least approximately 60%, at least approximately 70%, at least approximately 80%, at least approximately 90%, at least approximately 95%, at least approximately 96%, at least approximately 97%, at least approximately 98%, or at least approximately 99%.

[0108] In some embodiments, the target tumor progression is detected with a negative predictive value (NPV) of at least approximately 10%, at least approximately 20%, at least approximately 30%, at least approximately 40%, at least approximately 50%, at least approximately 60%, at least approximately 70%, at least approximately 80%, at least approximately 90%, at least approximately 95%, at least approximately 96%, at least approximately 97%, at least approximately 98%, or at least approximately 99%.

[0109] In some embodiments, the tumor progression in question is detected by an area under the receiver operating characteristic (ROC) curve (AUC) of at least about 0.5, at least about 0.6, at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.9, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, or at least about 0.99.

[0110] In some embodiments, a method for evaluating tumor progression in a subject further includes determining that the subject is tumor-free if no tumor progression is detected.

[0111] In some embodiments, the target tumor non-progression is detected with a sensitivity of at least approximately 10%, at least approximately 20%, at least approximately 30%, at least approximately 40%, at least approximately 50%, at least approximately 60%, at least approximately 70%, at least approximately 80%, at least approximately 90%, at least approximately 95%, at least approximately 96%, at least approximately 97%, at least approximately 98%, or at least approximately 99%.

[0112] In some embodiments, the target tumor non-progression is detected with a specificity of at least approximately 10%, at least approximately 20%, at least approximately 30%, at least approximately 40%, at least approximately 50%, at least approximately 60%, at least approximately 70%, at least approximately 80%, at least approximately 90%, at least approximately 95%, at least approximately 96%, at least approximately 97%, at least approximately 98%, or at least approximately 99%.

[0113] In some embodiments, the target tumor non-progression is detected with a positive predictive value (PPV) of at least approximately 10%, at least approximately 20%, at least approximately 30%, at least approximately 40%, at least approximately 50%, at least approximately 60%, at least approximately 70%, at least approximately 80%, at least approximately 90%, at least approximately 95%, at least approximately 96%, at least approximately 97%, at least approximately 98%, or at least approximately 99%.

[0114] In some embodiments, the target tumor non-progression is detected with a negative predictive value (NPV) of at least approximately 10%, at least approximately 20%, at least approximately 30%, at least approximately 40%, at least approximately 50%, at least approximately 60%, at least approximately 70%, at least approximately 80%, at least approximately 90%, at least approximately 95%, at least approximately 96%, at least approximately 97%, at least approximately 98%, or at least approximately 99%.

[0115] In some embodiments, non-progression of the target tumor is detected by an area under the receiver operating characteristic (ROC) curve (AUC) of at least about 0.5, at least about 0.6, at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.9, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, or at least about 0.99.

[0116] In some embodiments, the subject is diagnosed with cancer. For example, cancer may be one or more types, including but not limited to brain cancer, bladder cancer, breast cancer, cervical cancer, colorectal cancer, endometrial cancer, esophageal cancer, stomach cancer, kidney cancer, hepatobiliary tract cancer, leukemia, liver cancer, lung cancer, lymphoma, ovarian cancer, pancreatic cancer, prostate cancer, skin cancer, stomach cancer, thyroid cancer, or urinary tract cancer.

[0117] In some embodiments, the method further comprises administering a therapeutically effective dose of treatment to treat the target tumor based on the determined tumor progression of the target. In some embodiments, the treatment includes treatment with surgery, chemotherapy, therapeutic agents, radiotherapy, targeted therapy, immunotherapy, cell therapy, antihormone agents, antimetabolitic chemotherapeutic agents, kinase inhibitors, methyltransferase inhibitors, peptides, gene therapy, vaccines, platinum-based chemotherapeutic agents, antibodies, or checkpoint inhibitors.

[0118] The tumor progression or non-progression of a subject can be evaluated to determine the diagnosis of cancer, the prognosis of cancer, the likelihood of cancer recurrence, or signs of tumor progression or regression in the subject. In addition, one or more clinical outcomes may be assigned based on the evaluation or monitoring of tumor progression or non-progression (e.g., the difference in tumor progression or non-progression status between two or more time points). Such clinical outcomes may include diagnosing a subject with cancer, including one or more types of tumors; diagnosing a subject with cancer, including one or more types and stages of tumors; making a prognosis for a subject with cancer (e.g., indicating the clinical course of treatment (e.g., surgery, chemotherapy, therapeutic agents, radiotherapy, targeted therapy, immunotherapy, cell therapy, antihormone agents, antimetabolites, kinase inhibitors, methyltransferase inhibitors, peptides, gene therapy, vaccines, platinum-based chemotherapy agents, antibodies, or checkpoint inhibitors, or other treatments)); indicating an alternative clinical course (e.g., no treatment, continuous monitoring such as prescribed time interval basis, discontinuation of current treatment, switching to another treatment); or indicating the expected survival time of the subject.

[0119] In some embodiments, a method for evaluating tumor progression in a subject further includes processing a first plurality of CNAs with a second plurality of CNAs to determine a change in the CNA profile. In some embodiments, a method for evaluating tumor progression in a subject further includes processing a first plurality of fragment lengths with a second plurality of fragment lengths to determine a change in the fragment length profile. In some embodiments, a method for evaluating tumor progression in a subject further includes, at least in part, determining a first tumor percentage of the subject at a first time point or a second tumor percentage of the subject at a second time point based on the CNA profile change and the fragment length profile change. In some embodiments, a method for evaluating tumor progression in a subject further includes, at least in part, detecting tumor progression in the subject based on the first or second tumor percentage. For example, tumor progression may be determined based on whether the first or second tumor percentage meets a predetermined criterion (e.g., at least a predetermined threshold, above a predetermined threshold, at most a predetermined threshold, or below a predetermined threshold). Predetermined thresholds can be obtained from one or more reference subjects (e.g., patients known to have a specific tumor type, patients known to have a specific tumor type at a specific stage, or healthy subjects showing no cancer) or by performing tumor progression or non-progression assessments on one or more reference samples derived from or from the reference subjects, and identifying suitable predetermined thresholds based on the tumor progression or non-progression of the reference samples obtained from or derived from the reference subjects.

[0120] The predetermined thresholds may be adjusted based on the desired sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), or accuracy in assessing the tumor progression or non-progression status of the subject. For example, if high sensitivity is desired in assessing the tumor progression or non-progression status of the subject, the predetermined thresholds may be adjusted lower. Alternatively, if high specificity is desired in assessing the tumor progression or non-progression status of the subject, the predetermined thresholds may be adjusted higher. The predetermined thresholds may be adjusted to achieve a desired balance between false positives (FP) and false negatives (FN) in assessments obtained from or derived from one or more reference subjects of cancer, including one or more types of tumors.

[0121] In some embodiments, the method for evaluating tumor progression or non-progression further includes repeating the evaluation at a second subsequent time point. The second time point may be selected for a suitable comparison of the evaluation of tumor progression or non-progression with that of the first time point. Examples of the second time point may correspond to the period after surgical resection, during the administration of therapy, or the period after the administration of therapy to treat cancer in a subject in order to monitor the effectiveness of the therapy, or the subsequent period after treatment in which cancer is not detected in the subject in order to monitor residual disease or recurrence of cancer in the subject.

[0122] In some embodiments, the methods of the present disclosure include determining a difference in state (e.g., tumor progression or non-progression) at two or more distinct time points. The difference in state between time points (e.g., a comparison of the state at an initial time point with the state at a subsequent or current time point) can be used to indicate, for example, tumor progression, regression, recurrence, or stability. In some embodiments, the difference in state over time can be plotted to represent, for example, tumor progression, regression, recurrence, or stability. For example, in some embodiments, the method for evaluating tumor progression or non-progression further includes determining a difference between a first tumor progression / non-progression state and a second tumor progression / non-progression state. In some embodiments, the difference indicates progression or regression of the tumor in question. Alternatively, or in combination, the method may further include, by a computer processor, generating plots of the first tumor progression / non-progression state and the second tumor progression / non-progression state as functions of the first and second time points. In some embodiments, the plots indicate progression or regression of the tumor in question. For example, a computer processor can generate a plot of two or more tumor progression / non-progression states on the y-axis against time corresponding to the data collection time for two or more tumor progression or non-progression states on the x-axis.

[0123] The difference in tumor status over time between the first and second tumor progression / non-progression states, as determined or plotted above, may indicate tumor progression, regression, recurrence, or stability in the subject. For example, if the subsequent tumor progression / non-progression state (e.g., the second state) is greater than the initial tumor progression / non-progression state (e.g., the first state), this difference may indicate, for example, tumor progression, inefficiency of treatment for the tumor in the subject, resistance of the tumor to ongoing treatment, metastasis of the tumor to other sites in the subject, or residual disease or recurrence of cancer in the subject. If the subsequent tumor progression / non-progression state (e.g., the second state) is smaller than the initial tumor progression / non-progression state (e.g., the first state), this difference may indicate, for example, tumor regression, effectiveness of surgical resection of the tumor in the subject, effectiveness of treatment for the tumor in the subject, or absence of residual disease or recurrence of cancer in the subject.

[0124] After evaluating and / or monitoring tumor progression or non-progression, one or more clinical outcomes may be assigned based on the evaluation or monitoring of tumor progression or non-progression (e.g., the difference in tumor progression or non-progression between two or more time points). Such clinical outcomes may include diagnosing a subject with cancer, including one or more types of tumors; diagnosing a subject with cancer, including one or more types and stages of tumors; prognosticating a subject with cancer (e.g., indicating the clinical course of treatment (e.g., surgery, chemotherapy, therapeutic agents, radiotherapy, targeted therapy, immunotherapy, cell therapy, antihormone agents, antimetabolites, kinase inhibitors, methyltransferase inhibitors, peptides, gene therapy, vaccines, platinum-based chemotherapy agents, antibodies, or checkpoint inhibitors, or other treatments)); identifying the origin of tumor cDNA within the subject; indicating an alternative clinical course (e.g., no treatment, continuous monitoring such as prescribed time interval basis, discontinuation of current treatment, switching to another treatment); or indicating the expected survival time of the subject.

[0125] treatment Certain aspects of this disclosure relate, for example, to treatment with one or more therapeutic agents. For example, in some embodiments, treatment may include surgery, chemotherapy, therapeutic agents, radiotherapy, targeted therapy, immunotherapy, cell therapy, antihormone agents, antimetabolitic chemotherapeutic agents, kinase inhibitors, methyltransferase inhibitors, peptides, gene therapy, vaccines, platinum-based chemotherapeutic agents, antibodies, or checkpoint inhibitors. Exemplary and non-limiting descriptions of treatments are provided herein.

[0126] For example, treatment may involve the use of cytotoxic agents or cell proliferation inhibitors. Exemplary cytotoxic agents include antimicrotubule agents, topoisomerase inhibitors, taxanes, antimetabolites, mitotic inhibitors, alkylating agents, inserts, agents that can interfere with signaling pathways, and agents that promote apoptosis, as well as irradiation. In yet another embodiment, the method may be used in combination with immunomodulators, such as IL-1, 2, 4, 6, or 12, or interferon alpha or gamma, or immune cell growth factors such as GM-CSF.

[0127] In some embodiments, the treatment may be immunotherapy or immunomodulatory therapy, such as compound-based, antibody-based, or cell-based immunotherapy. Examples of immunotherapies include, but are not limited to, checkpoint inhibitors, cancer vaccines, cell-based therapies, T-cell receptor (TCR) system therapies, adjuvant immunotherapy, cytokine immunotherapy, or oncolytic virus therapy. In some embodiments, cancer immunotherapy may include small molecules, nucleic acids, polypeptides, carbohydrates, toxins, cell systems, or conjugate therapies. Examples of cancer immunotherapies are described in more detail below, but are not intended to be limiting.

[0128] In some embodiments, cancer immunotherapy comprises one or more of the following: checkpoint inhibitors, cancer vaccines, cell line therapies, T cell receptor (TCR) system therapies, adjuvant immunotherapy, cytokine immunotherapy, and oncolytic virus therapy. In some embodiments, cancer immunotherapy comprises small molecules, nucleic acids, polypeptides, carbohydrates, toxins, cell lines, or conjugate therapeutic agents. Examples of cancer immunotherapy are described in more detail below, but are not intended to be limiting. In some embodiments, cancer immunotherapy activates one or more situations of the immune system to attack cells expressing the novel antigens of the Disclosure (e.g., tumor cells). The cancer immunotherapy of the Disclosure is intended for use as monotherapy or in combination approaches comprising any combination or number of two or more, subject to medical judgment. Any cancer immunotherapy (optionally, as monotherapy or in combination with another cancer immunotherapy or other therapeutic agent described herein) may be used in any of the methods described herein.

[0129] In some embodiments, cancer immunotherapy includes cancer vaccines. A range of cancer vaccines utilizing various approaches to enhance tumor-directed immune responses have been tested (see, for example, Emens LA, Expert Opin Emerg Drugs 13(2):295-308 (2008), and US20190367613). The approaches are designed to enhance the response of B cells, T cells, or professional antigen-presenting cells to tumors. Exemplary types of cancer vaccines include, but are not limited to, DNA vaccines, RNA vaccines, transdextrin vaccines, peptide vaccines, dendritic cell vaccines, oncolytic viruses, whole tumor cell vaccines, and tumor antigen vaccines. In some embodiments, cancer vaccines may be prophylactic or therapeutic. In some embodiments, cancer vaccines are formulated as peptide vaccines, nucleoside vaccines, antibody vaccines, or cell-based vaccines. For example, vaccine compositions include naked cDNA in cationic lipid preparations, lipopeptides (e.g., Vitiello, A. et al, J. Clin. Invest. 95:341, 1995), naked cDNA or peptides, encapsulated (e.g., poly(DL-lactide-co-glycolide) ("PLG") microspheres (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), and peptide compositions contained in immunostimulatory complexes (ISCOMS) (e.g., Takahashi et al, Nature 344:873-875, 1990, Hu et al. This may include a combination of antigenic peptide systems (MAPs) (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 vaccine, or a nucleic acid vaccine in which the nucleic acid encodes a polypeptide. In some embodiments, the cancer vaccine is formulated as an antibody vaccine. In some embodiments, the cancer vaccine is formulated as a cell vaccine. In some embodiments, the cancer vaccine is a peptide cancer vaccine, and in some embodiments, it is a personalized peptide vaccine. In some embodiments, the cancer vaccine is a polyvalent long peptide, a multipeptide, a peptide mixture, a hybrid peptide, or a peptide pulsed dendritic cell vaccine (see, e.g., Yamada et al, Cancer Sci, 104:14-21), 2013). In some embodiments, such a cancer vaccine enhances the antitumor response.

[0130] In some embodiments, the cancer vaccine is selected from ciproisel-T (Provenge®, Dendreon / Valeant Pharmaceuticals), which is approved for the treatment of asymptomatic or minimally symptomatic metastatic castration-resistant (hormone-resistant) prostate cancer, and tarimozine laharpalepvek (Imlygic®, BioVex / Amgen, formerly known as T-VEC), a recombinant oncolytic virus therapy approved for the treatment of unresectable cutaneous, subcutaneous, and nodular lesions of melanoma. In some embodiments, cancer vaccines are oncolytic virus therapies, such as Pexastimogene devacirepvec (PexaVec / JX-594, SillaJen / formerly Jennerex Biotherapeutics), thymidine kinase-(TK-) deficient vaccinia virus engineered to express GM-CSF for hepatocellular carcinoma (NCT02562755) and melanoma (NCT00429312); Pelareorep (Reolysin®, Oncolytics Biotech); colorectal cancer (NCT01622543); prostate cancer (NCT01619813); head and neck squamous cell carcinoma (NCT01166542); pancreatic adenocarcinoma (NCT00998322); and non-small cell lung cancer (NSCLC) (NCT01622543); In many cancers, including 00861627), a variant of reovirus (reovirus) that does not replicate in RAS-unactivated cells; Enadenotucirev (NG-348, PsiOxus, formerly known as ColoAdl), ovarian cancer (NCT02028117); adenovirus engineered to express full-length CD80 and T-cell receptor CD3 protein-specific antibody fragments in metastatic or advanced epithelial tumors such as colorectal cancer, bladder cancer, head and neck squamous cell carcinoma, and salivary gland cancer (NCT02636036); ONCOS-102 (Targovax / formerly Oncos), melanoma (NCT03003676); and adenovirus engineered to express GM-CSF in peritoneal diseases, colorectal cancer, or ovarian cancer (NCT02963831);Select from GL-ONC1 (GLV-1h68 / GLV-1h153, Genelux GmbH), vaccinia viruses engineered to express beta-galactosidase (beta-gal) / beta-glucolonidase or beta-gal / human sodium iodide cotransporter (hNIS), as studied in peritoneal carcinomatosis (NCT01443260), fallopian tube cancer, and ovarian cancer (NCT 02759588); or adenoviruses engineered to express GM-CSF in CG0070 (Cold Genesys) and bladder cancer (NCT02365818); anti-gp100; STINGVAX; GVAX; DCVaxL; and DNX-2401. In some embodiments, the cancer vaccines include: JX-929 (SillaJen / formerly Jennerex Biotherapeutics), a TK and vaccinia growth factor-deficient vaccinia virus engineered to express cytosine deaminase capable of converting the prodrug 5-fluorocytosine to the cytotoxic agent 5-fluorouracil; TGO1 and TG02 (Targovax / formerly Oncos), peptide-based immunotherapies targeting difficult-to-treat RAS mutations; TILT-123 (TILT Biotherapeutics), an engineered adenovirus named Ad5 / 3-E2F-delta24-hTNFα-IRES-hIL20; and VSV-GP (ViraTherapeutics), an antigen-specific CD8. +A selection is made from vesicular stomatitis virus (VSV) designed to express the glycoprotein (GP) of lymphocytic choriomeningitis virus (LCMV), which can be further manipulated to express antigens designed to produce a T cell response. In some embodiments, cancer vaccines include vector-based tumor antigen vaccines. Vector-based tumor antigen vaccines can be used as a method to provide a stable supply of antigens to stimulate an anti-tumor immune response. In some embodiments, a vector encoding a tumor antigen is injected into the patient (perhaps together with other attractants such as inflammatory or GM-CSF), taken up by cells in vivo, and produces a specific antigen, thereby triggering a desired immune response. In some embodiments, the vector can be used to deliver more than one tumor antigen at a time to increase the immune response. In addition, recombinant viruses, bacteria, or yeast vectors should induce their own immune responses, and it may also enhance the overall immune response.

[0131] In some embodiments, cancer vaccines include DNA-based vaccines. In some embodiments, DNA-based vaccines can be used to stimulate an antitumor response. The ability of directly injected DNA encoding antigenic proteins to induce a protective immune response has been demonstrated in many experimental systems. Vaccination that induces a protective immune response by directly injecting DNA encoding antigenic proteins often elicits both cellular and humoral responses. Furthermore, reproducible immune responses to DNA encoding various antigens have been reported in mice to persist essentially throughout the animal's lifetime (see, for example, Yankauckas et al. (1993) DNA Cell Biol., 12:771-776). In some embodiments, plasmid (or other vector) DNA containing a sequence encoding a protein operably linked to regulatory elements necessary for gene expression is administered to an individual (e.g., a human patient, a non-human mammal, etc.). In some embodiments, the individual's cells take up the administered DNA and the coding sequence is expressed. In some embodiments, the produced antigen becomes the target to which the immune response is directed.

[0132] In some embodiments, cancer vaccines include RNA-based vaccines. In some embodiments, RNA-based vaccines can be used to stimulate an antitumor response. In some embodiments, RNA-based vaccines include self-replicating RNA molecules. In some embodiments, self-replicating RNA molecules may be alphavirus-derived RNA replicons. Self-replicating RNA (or "SAM") molecules are well known in the art and can be produced, for example, by using replicating elements derived from alphaviruses and substituting a structural viral protein with a nucleotide sequence encoding the protein of interest. Self-replicating RNA molecules are typically +-chain molecules that can be directly translated after delivery to cells, and this translation provides RNA-dependent RNA polymerase to produce both antisense and sense transcripts from the delivered RNA. Thus, the delivered RNA leads to the generation of multiple daughter RNAs. These daughter RNAs, and collinear subgenomic transcripts, can be translated themselves to provide in-situ expression of the encoded polypeptide (i.e., including the HPV antigen) or to provide further transcripts in the same sense as the delivered RNA, which are translated to provide in-situ expression of the antigen.

[0133] In some embodiments, cancer immunotherapy includes cell lineage therapy. In some embodiments, cancer immunotherapy includes T cell lineage therapy. In some embodiments, cancer immunotherapy includes adoptive therapy, e.g., adoptive T cell lineage 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 infection in which immune cells are administered to a host with the aim of the cells directly or indirectly mediating specific immunity against tumor cells (i.e., initiating an immune response). In some embodiments, the immune response results in inhibition of tumor and / or metastatic cell growth and / or proliferation, and in relevant embodiments, results in tumor cell death and / or resorption. Immune cells may originate from a different organism / host (exogenous immune cells) or may be cells obtained from a target organism (autoimmune 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 gamma delta T cells), NK cells, invariant NK cells, or NKT cells) may be genetically engineered to express antigen receptors such as genetically engineered TCRs and / or chimeric antigen receptors (CARs). For example, host cells (e.g., autologous or allogeneic T cells) are modified to express T cell receptors (TCRs) that have antigen specificity for cancer antigens. In some embodiments, NK cells are engineered to express TCRs. NK cells may be further engineered to express CARs. Multiple CARs and / or TCRs for different antigens, etc., can be added to a single cell type such as T cells or NK cells. In some embodiments, cells include one or more nucleic acids / expression constructs / vectors introduced via genetic engineering techniques encoding one or more antigen receptors, and genetically engineered products of such nucleic acids.In some embodiments, nucleic acids are heterogeneous, i.e., typically not present in cells or samples obtained from cells, e.g., those obtained from another organism or cell, and not typically found in the manipulated cells and / or the organism from which such cells originate. In some embodiments, nucleic acids are not naturally occurring, e.g., not found in nature (e.g., chimeras). In some embodiments, a population of immune cells may be obtained from a subject in need of treatment or suffering from a disease associated with reduced immune cell activity. Thus, the cells are autologous to the subject in need of therapy. In some embodiments, a population of immune cells may be obtained from a donor, such as a histocompatibility-matched donor. In some embodiments, a population of immune cells may be collected 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, immune cells may be isolated from a pool of subject and / or donor, such as pooled umbilical cord blood. In some embodiments, when a population of immune cells is obtained from a donor different from the target, the donor may be allogeneic, provided that the obtained cells are target-compatible in the sense that they can be introduced into the target. In some embodiments, allogeneic donor cells may or may not be human leukocyte antigen (HLA) compatible. In some embodiments, allogeneic cells may be treated to reduce immunogenicity in order to make them compatible with the target.

[0134] In some embodiments, cell lineage therapy includes T cell lineage therapy. Several basic approaches for inducing, activating, and expanding functional antitumor effector cells have been described over the past 20 years. These include autologous cells, e.g., tumor-infiltrating lymphocytes (TILs); T cells, lymphocytes, artificial antigen-presenting cells (APCs) or beads coated with T cell ligands and activating antibodies, or cells isolated by capturing target cell membranes, using autologous DCs; allogeneic cells that spontaneously 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-responsive TCRs or chimeric TCR molecules that exhibit antibody-like tumor recognition ability known as "T-bodies." In some embodiments, 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 those isolated directly from the subject and / or isolated and frozen from the subject. In some embodiments, the cells include one or more subsets of T cells or other cell types, e.g., the whole T cell population, CD4 + cells, CD8 + Cells, their subpopulations, are defined, for example, by function, activation state, maturity, potential for differentiation, expansion, recirculation, localization, and / or persistence, antigen specificity, type of antigen receptor, presence in a specific organ or compartment, marker or cytokine secretion profile, and / or degree of differentiation. In some embodiments, cells may be allogeneic and / or autologous. In some embodiments, such as with ready-made technologies, cells are pluripotent and / or multipotent stem cells, such as induced pluripotent stem cells (iPSCs). In some embodiments, the method includes isolating cells from a subject, preparing, processing, culturing, and / or manipulating them, and reintroducing them into the same patient before or after cryopreservation, as described herein. In some embodiments, T cell subtypes and subpopulations (e.g., CD4 + and / or CD8 +T cells include naive T (TN) cells, effector T cells (TEFF), memory T cells and their subtypes, such as stem cell memory T (TSCM), central memory T (TCM), effector memory T (TEM), or terminally differentiated effector memory T cells, tumor infiltrating lymphocytes (TIL), immature T cells, mature T cells, helper T cells, cytotoxic T cells, mucosal associated invariant T (MAIT) cells, natural and adaptive regulatory T (Treg) cells, helper T cells, such as TH1 cells, TH2 cells, TH3 cells, TH17 cells, TH9 cells, TH22 cells, follicular helper T cells, alpha / beta T cells, and delta / gamma T cells. In some embodiments, one or more T cell populations are enriched or depleted for cells that are positive or negative for a particular marker, such as a surface marker. In some embodiments, such a marker is not present or is expressed at a relatively low level in a particular population of T cells (e.g., non-memory cells), but is present or expressed at a relatively high level in some other particular population of T cells (e.g., memory cells). In some embodiments, T cells are separated from a PBMC sample by negative selection of markers expressed by non-T cells such as B cells, monocytes, or other white blood cells such as CD14. In some embodiments, CD4 + or CD8 + selection steps are used to separate CD4 + helper and CD8 + cytotoxic T cells. Such CD4 + and CD8 + populations can be further classified into subpopulations by positive or negative selection of markers expressed or expressed to a relatively high degree in one or more naive, memory, and / or effector T cell subpopulations. In some embodiments, CD8 +T cells are further enriched or depleted, for example, by positive or negative selection based on surface antigens associated with each subpopulation, for naive, central memory, effector memory, and / or central memory stem cells. In some embodiments, the T cells are autologous T cells. In this method, a tumor sample is obtained from the patient, and a single-cell suspension is obtained. The single-cell suspension can be obtained in any preferred manner, for example, mechanically (e.g., using gentleMACS® Dissociator, Miltenyi Biotec, Auburn, Calif. to degrade the tumor) or enzymatically (e.g., collagenase or DNase). The single-cell suspension of the tumor enzyme digest is cultured in interleukin-2 (IL-2). The cells are cultured until confluent (e.g., about 2 × 10). 6 Individual lymphocytes are cultured for, for example, about 5 to 21 days, or for example, about 10 to 14 days.

[0135] In some embodiments, cultured T cells can be pooled and rapidly expanded. Rapid expansion provides an increase in the number of antigen-specific T cells, for example, at least about 50-fold (e.g., 50-fold, 60-fold, 70-fold, 80-fold, 90-fold, or 100-fold or more) over a period of about 10 to about 14 days. In some embodiments, expansion can be achieved by any of several methods known in the art. For example, T cells can be rapidly expanded using nonspecific T cell receptor stimulation in the presence of supporting lymphocytes and either interleukin-2 (IL-2) or interleukin-15 (IL-15), with IL-2 being particularly intended. Nonspecific T cell receptor stimulation includes about 30 ng / ml of OKT3, a mouse monoclonal anti-CD3 antibody (available from Ortho-McNeil®, Raritan, NJ). In some embodiments, T cells can be rapidly expanded by stimulating peripheral blood mononuclear cells (PBMCs) in vitro with one or more cancer antigens (including their antigenic portion such as an epitope or cell), which can optionally be expressed from a vector such as a human leukocyte antigen A2 (HLA-A2) binding peptide in the presence of 300 IU / ml of IL-2 or IL-15, with IL-2 being intended. In vivo-induced T cells are rapidly expanded by restorative stimulation with the same cancer antigens pulsed onto antigen-presenting cells expressing HLA-A2. In some embodiments, T cells can be restoratively stimulated, for example, with irradiated autologous lymphocytes or irradiated HLA-A2+ allogeneic lymphocytes and IL-2. In some embodiments, autologous T cells can be modified to express T cell growth factors that promote autologous T cell growth and activation. In some embodiments, suitable T cell growth factors include, for example, interleukin (IL)-2, IL-7, IL-15, and IL-12. Suitable methods of modification are known in the art. For example, Sambrook et al, Molecular Cloning: A Laboratory Manual, 3 rdSee ed., Cold Spring Harbor Press, Cold Spring Harbor, NY 2001, and Ausubel et al, CURRENT PROTOCOLS IN MOLECULAR BIOLOGY, Greene Publishing Associates and John Wiley & Sons, NY, 1994. In some embodiments, modified autologous T cells express high levels of T cell growth factors. T cell growth factor coding sequences such as IL-12 are readily available in the Art and are promoters whose activatable binding promotes high levels of expression to T cell growth factor coding sequences. In some embodiments, autologous T cells may be engineered to express a defined T cell receptor (TCR) directed to a target TAA of either a wild-type TCR or a mutated / engineered TCR, toward a higher affinity for the antigen peptide / MHC molecular complex. In some embodiments, autologous T cells may be engineered to express a CAR, for example, as described below.

[0136] In some embodiments, T-cell therapy includes chimeric antigen receptor (CAR)-T-cell therapy. This approach involves manipulating a CAR that 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 the manipulated T cells (CAR-T), which are administered to a patient and result in a T-cell-specific immune response against cancer cells expressing the antigen. In some embodiments, the CAR specifically binds to a novel antigen of the present disclosure.

[0137] In some embodiments, T-cell therapy comprises T cells expressing recombinant T-cell receptors (TCRs). This approach involves identifying TCRs that specifically bind to an antigen of interest, and then using these to replace endogenous or native TCRs on the surface of engineered T cells administered to a patient, thereby resulting in a T-cell-specific immune response against cancer cells expressing the antigen. In some embodiments, the recombinant TCRs specifically bind to the novel antigens of the present disclosure.

[0138] In some embodiments, the T-cell therapy comprises tumor-infiltrating lymphocytes (TILs). For example, TILs may be isolated from the tumor or cancer of the present disclosure and then isolated and expanded in vitro. Some or all of these TILs may specifically recognize the novel antigens of the present disclosure. In some embodiments, after isolation, the TILs are exposed in vitro to one or more novel antigens of the present disclosure. The TILs are then administered to a patient (optionally in combination with one or more cytokines or other immunostimulants).

[0139] In some embodiments, cell lineage therapy includes natural killer (NK) cell lineage therapy. Natural killer (NK) cells are a subpopulation of lymphocytes that exhibit spontaneous cytotoxicity against various tumor cells, virus-infected cells, and some normal cells in the bone marrow and thymus. NK cells are a key effector of the initial innate immune response to transformed and virus-infected cells. NK cells make up about 10% of lymphocytes in human peripheral blood. When lymphocytes are cultured in the presence of interleukin-2 (IL-2), a strong cytotoxic reactivity develops. NK cells are effector cells known as large granular lymphocytes, characterized by their large size and the presence of azurophilic granules in their cytoplasm. NK cells differentiate and mature in the bone marrow, lymph nodes, spleen, tonsils, and thymus. NK cells can be detected by certain surface markers such as human CD16, CD56, and CD8. NK cells do not express T cell antigen receptors, the pan-T marker CD3, or surface immunoglobulin B cell receptors. In some embodiments, NK cells are derived from human peripheral blood mononuclear cells (PBMCs), unstimulated leukocyte depletion 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. In some embodiments, umbilical cord CBs are used to induce NK cells. In some embodiments, NK cells are isolated and grown by the aforementioned methods for ex vivo proliferation of NK cells (Spanholtz et al, 2011; Shah et al, 2013). In some embodiments, CB mononuclear cells are separated by Ficol density gradient centrifugation and cultured in a bioreactor containing IL-2 and artificial antigen-presenting cells (aAPCs). After 7 days, the cell culture is depleted of any cells expressing CD3 and recultured for a further 7 days. The cells are again depleted of CD3 and CD56 + / CD3 cells or NK cells are characterized to determine the percentage. In some embodiments, the umbilical cord CB is CD34 + CD56 was obtained by isolating cells and culturing them in a medium containing SCF, IL-7, IL-15, and IL-2. + It is used to induce NK cells by differentiating them into CD3 cells.

[0140] In some embodiments, cell therapy includes dendritic cell therapy, e.g., dendritic cell vaccines. In some embodiments, the DC vaccine comprises antigen-presenting cells capable of inducing specific T-cell immunity and is collected from a patient or donor. In some embodiments, the DC vaccine can then be in vitro exposed to peptide antigens that produce T cells in the patient's body. In some embodiments, the antigen-loaded dendritic cells are then injected into the patient. In some embodiments, immunization may be repeated multiple times as needed. Methods for collecting, expanding, and administering dendritic cells are known in the Art; see, for example, WO2019 / 178081. Dendritic cell vaccines (e.g., also known as Ciproisel-T, APC8015, and PROVENGE®) are vaccines involving the administration of dendritic cells that function as APCs presenting one or more cancer-specific antigens, e.g., novel antigens of the Disclosure to the patient's immune system. In some embodiments, the vaccine comprises dendritic cells exposed to one or more novel antigens of the Disclosure. In some embodiments, the vaccine comprises dendritic cells that present one or more novel antigens of this disclosure, for example, via MHC class I. In some embodiments, the dendritic cells are autologous or allogeneic to the recipient.

[0141] In some embodiments, cancer immunotherapy includes TCR-based therapy. In some embodiments, cancer immunotherapy includes administration of one or more TCRs or TCR-based biological agents that specifically bind to the novel antigens of the Disclosure. For example, TCR-based therapy may include a TCR or its extracellular component that specifically binds to the novel antigens of the Disclosure (e.g., so as to be presented on the cell surface via MHC class I), as well as a portion that binds to immune cells (e.g., T cells), such as an antibody or antibody fragment that specifically binds to T cell surface proteins or receptors (e.g., an anti-CD3 antibody or antibody fragment).

[0142] In some embodiments, cancer immunotherapy includes adjuvant immunotherapy. Adjuvant immunotherapy includes the use of one or more agents that activate components of the innate immune system, such as HILTONOL® (imiquimod), which targets the TLR7 pathway.

[0143] In some embodiments, cancer 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, aldethleukin (PROLEUKIN®; interleukin-2), interferon alpha-2a (ROFERON®-A), interferon alpha-2b (INTRON®-A), and pegylated interferon alpha-2b (PEGINTRON®).

[0144] In some embodiments, cancer immunotherapy includes oncolytic virus therapy. Oncolytic virus therapy uses a genetically modified virus to replicate and kill cancer cells, resulting in the release of an antigen (e.g., a novel antigen in the Disclosure) that stimulates an immune response. In some embodiments, the replication-capable oncolytic virus expressing a tumor antigen includes any naturally occurring (e.g., from a “field source”) or a modified replication-capable oncolytic virus. In some embodiments, the oncolytic virus may be modified to increase the virus’s selectivity for cancer cells in addition to expressing a tumor antigen. In some embodiments, the reproducible oncolytic viruses include Myoviridae, Cyphoviridae, Podoviridae, Tesiviridae, Corticoviridae, Plasmaviridae, Lipospiroidsviridae, Fuseroviridae, Poxyiridoviridae, Iridoviridae, Phycodnaviridae, Baculoviridae, Herpesviridae, Adnoviridae, Papovaviridae, Polidnaviridae, Inoviridae, Microviridae, Geminiviridae, Circoviridae, Parvoviridae, Hepadnaviridae, and Retroviridae. This includes, but is not limited to, oncolytic viruses that are members of the families Sulci, Cictoviridae, Reoviridae, Birnaviridae, Paramyxoviridae, Rhabdoviridae, Filoviridae, Orthomyxoviridae, Bunyaviridae, Arenaviridae, Leviviridae, Picornaviridae, Ceciviridae, Comoviridae, Potiviridae, Caliciviridae, Astroviridae, Nodaviridae, Tetraviridae, Tonbasviridae, Coronavirusidae, Graviviridae, Togaviridae, and Varnaviridae. In some embodiments, reproducible oncolytic viruses include adenoviruses, retroviruses, reoviruses, rhabdoviruses, Newcastle disease virus (NDV), polyomaviruses, vacciniaviruses (VacV), herpes simplex virus, picornaviruses, coxsackieviruses, and parvoviruses.In some embodiments, a replicating oncolytic vaccinia virus expressing a tumor antigen may be engineered to lack one or more functional genes in order to increase the virus's cancer selectivity. In some embodiments, the oncolytic vaccinia virus is engineered to lack thymidine kinase (TK) activity. In some embodiments, the oncolytic vaccinia virus may be engineered to lack vaccinia virus growth factor (VGF). In some embodiments, the oncolytic vaccinia virus may be engineered to lack both VGF and TK activity. In some embodiments, the oncolytic vaccinia virus may 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 replicating oncolytic vaccinia virus is a Western Reserve, Copenhagen, Lister, or Wyeth strain lacking a functional TK gene. In some embodiments, the oncolytic vaccinia virus is a Western Reserve, Copenhagen, Lister, or Wyeth strain lacking the functional B18R and / or B8R genes. In some embodiments, a replicating oncolytic vaccinia virus expressing a combination of tumor antigens may be administered topically or systemically to a subject, for example, via intratumoral, intraperitoneal, intravenous, intraarterial, intramuscular, intradermal, intracranial, subcutaneous, or intranasal administration.

[0145] In some embodiments, cancer immunotherapy includes checkpoint inhibitors. As is known in the art, checkpoint inhibitors target at least one immune checkpoint protein and alter the control of the immune response, for example, by downmodulating or inhibiting 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, SIRP alpha (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 the regulation of immune checkpoints include PD-1 (CD279), PD-L1 (B7-H1, CD274), PD-L2 (B7-CD, CD273), CTLA-4 (CD152), HVEM, BTLA (CD272), killer cell immunoglobulin-like receptor (KIR), LAG-3 (CD223), TIM-3 (HAVCR2), CEACAM, CEACAM-1, CEACAM-3, CEACAM-5, GAL9, VISTA (PD-1H), TIGIT, LAIR1, CD160, 2B4, TGFR beta, A2AR, GITR (CD357), CD80 (B7-1), CD86 (B7-2), CD276 (B This list includes, but is not limited to, 7-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 arginases. In some embodiments, checkpoint inhibitors reduce the activity of checkpoint proteins that negatively regulate immune cell function, for example, to enhance T cell activation and / or anti-cancer immune responses, while in other embodiments, checkpoint inhibitors increase the activity of checkpoint proteins that positively regulate immune cell function, for example, to enhance T cell activation and / or anti-cancer immune responses. In some embodiments, the checkpoint inhibitor is an antibody. In some embodiments, the checkpoint inhibitor is an antibody.Examples of checkpoint inhibitors include, but are not limited to, PD-L1 axis-conjugated antagonists (e.g., anti-PD-L1 antibodies, e.g., atezolizumab (MPDL3280A)), antagonists directed towards co-inhibitor 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, the immune checkpoint inhibitor comprises drugs such as small molecules, ligands, or recombinant receptors, or in particular antibodies such as human antibodies (see, for example, International Patent Publication No. 2015016718, 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 their analogues can be used, and in particular, chimeric, humanized, or human-type antibodies can be used.

[0146] In some embodiments, the checkpoint inhibitor is a PD-L1 axis-binding antagonist, such as 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 shown in UniProtKB / Swiss-Prot acceptance number Q15116. PD-L1 (programmed cell 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 shown in UniProtKB / Swiss-Prot acceptance number Q9NZQ7.1. PD-L2 (programmed cell death ligand 2) is also referred to in the art as "programmed cell death ligand 1-2," "PDCD1 LG2," "CD273," "B7-DC," "Btdc," and "PDL2." An exemplary human PD-L2 is shown in UniProtKB / Swiss-Prot acceptance number Q9BQ51. In some cases, PD-1, PD-L1, and PD-L2 are human PD-1, PD-L1, and PD-L2.

[0147] In some cases, a PD-1 binding antagonist 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 other cases, a PD-L1 binding antagonist 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 yet another case, a 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 ligand-binding partner is PD-1. The antagonist may be an antibody, its antigen-binding fragment, an immune adhesin, a fusion protein, or an oligopeptide. In some embodiments, the PD-1 binding antagonist may be a small molecule, a nucleic acid, a polypeptide (e.g., an antibody), a carbohydrate, a lipid, a metal, or a toxin.

[0148] In some cases, the PD-1 conjugated 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 cases, the anti-PD-1 antibody is selected from the group consisting of MDX-1 106 (nivolumab), MK-3475 (pembrolizumab), MEDI-0680 (AMP-514), PDR001, REGN2810, MGA-012, JNJ-63723283, BI 754091, and BGB-108. MDX-1 106, also known as MDX-1 106-04, ONO-4538, BMS-936558, or nivolumab, is an anti-PD-1 antibody described in WO2006 / 121168. MK-3475, also known as pembrolizumab or lambrolizumab, is an anti-PD-1 antibody described in WO2009 / 114335. In some cases, the PD-1 binding antagonist is an immunoadhesin containing an extracellular or PD-1 binding portion of PD-L1 or PD-L2 fused to a constant region (e.g., the Fc region of an immunoglobulin sequence). In some cases, the PD-1 binding antagonist is AMP-224. AMP-224, also known as B7-DCIg, is a PD-L2-Fc fusion soluble receptor described in WO2010 / 027827 and WO2011 / 066342.

[0149] In some embodiments, the anti-PD-1 antibody is nivolumab (CAS registry number: 946414-94-4). Nivolumab (Bristol-Myers Squibb / Ono) is also known as MDX-1106-04, MDX-1106, ONO-4538, BMS-936558, and OPDIVO®, and is an anti-PD-1 antibody described in WO2006 / 121168. In some embodiments, the anti-PD-1 antibody comprises heavy chain and light chain sequences.

[0150] (a) The heavy chain sequence has at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, or 100% sequence identity with the following heavy chain sequences: QVQLVESGGGVVQPGRSLRLDCKASGITFSNSGMHWVRQAPGKGLEWVAVIWYDGSKRYYADSVKGRFTISRDNSKNTLFLQMNSLRAEDTAVYYCATNDDYWGQGTLVTV SSASTKGPSVFPLAPCSRSTSESTAALGCLVKDYFPEPVTVSWNSGALTSGVHTFPAVLQSSGLYSLSSVVTVPSSSLGTKTYTCNVDHKPSNTKVDKRVESKYGPPCPPCP APEFLGGPSVFLFPPKPKDTLMISRTPEVTCVVVDVSQEDPEVQFNWYVDGVEVHNAKTKPREEQFNSTYRVVSVLTVLHQDWLNGKEYKCKVSNKGLPSSIEKTISKAKGQPREPQVYTLPPSQEEMTKNQVSLTCLVKGFYPSDIAVEWESNGQPENNYKTTPPVLDSDGSFFLYSRLTVDKSRWQEGNVFSCSVMHEALHNHYTQKSLSLSLG (Sequence ID 1),

[0151] (b) The light chain sequence has at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, or 100% sequence identity with the following light chain sequences:

[0152] EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQSSNWPRTFGQGTKVEIKRTVAAPSVFIFPPSDEQLKSGTASVVCLLNNFYPREAKVQWKVDNALQSGNSQESVTEQDSKDSTYSLSSTLTLSKADYEKHKVYACEVTHQGLSSPVTKSFNRGEC (Sequence ID 2).

[0153] In some embodiments, the anti-PD-1 antibody contains six HVR sequences from SEQ ID NO: 1 and SEQ ID NO: 2 (e.g., three heavy chain HVRs from SEQ ID NO: 1 and three light chain HVRs from SEQ ID NO: 2). In some embodiments, the anti-PD-1 antibody contains a heavy chain variable domain from SEQ ID NO: 1 and a light chain variable domain from SEQ ID NO: 2.

[0154] In some embodiments, the anti-PD-1 antibody is pembrolizumab (CAS registry number: 1374853-91-4). Pembrolizumab (Merck) is also known as MK-3475, Merck 3475, lambrolizumab, KEYTRUDA®, and SCH-900475, and is an anti-PD-1 antibody described in WO2009 / 114335. In some embodiments, the anti-PD-1 antibody comprises heavy chain and light chain sequences. (a) The heavy chain sequence has at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, or 100% sequence identity with the following heavy chain sequences: QVQLVQSGVEVKKPGASVKVSCKASGYTFTNYYMYWVRQAPGQGLEWMGG INPSNGGTNFNEKFKNRVTLTTDSSTTTAYMELKSLQFDDTAVYYCARRDYRFDMGFDYWGQGTTVTVSSASTKGPSVFPLAPCSRSTSESTAALGCLVKDYFPEPVTVSWNSGALTSGVHTFPAVLQSSGLYSLSSVVTVPSSSLGTKTYTCNVDHKPSNTKVDKRVESKYGPPCPPCPAPEFLGGPSVFLFPPKPKDTLMISRTPEVTCVVVDVSQEDPEVQFNWYVDGVEVHNAKTKPREEQFNSTYRVVSVLTVLHQDWLNGKEYKCKVSNKGLPSSIEKTISKAKGQPREPQVYTLPPSQEEMTKNQVSLTCLVKGFYPSDIAVEWESNGQPENNYKTTPPVLDSDGSFFLYSRLTVDKSRWQEGNVFSCSVMHEALHNHYTQKSLSLSLG (Sequence ID 3) (b) The light chain sequence has at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, or 100% sequence identity with the following light chain sequences: EIVLTQSPATLSLSPGERATLSCRASKGVSTSGYSYLHWYQQKPGQAPRLLIYLASYLESGVPARFSGSGSGTDFTLTISSLEPEDFAVYYCQHSRDLPLTFGGGTKVEIKRTVAAPSVFIFPPSDEQLKSGTASVVCLLNNFYPREAKVQWKVDNALQSGNSQESVTEQDSKDSTYSLSSTLTLSKADYEKHKVYACEVTHQGLSSPVTKSFNRGEC (Sequence ID 4).

[0155] In some embodiments, the anti-PD-1 antibody contains six HVR sequences from SEQ ID NO: 3 and SEQ ID NO: 4 (e.g., three heavy chain HVRs from SEQ ID NO: 3 and three light chain HVRs from SEQ ID NO: 4). In some embodiments, the anti-PD-1 antibody contains a heavy chain variable domain from SEQ ID NO: 3 and a light chain variable domain from SEQ ID NO: 4.

[0156] Other examples of anti-PD-1 antibodies include MEDI-0680 (AMP-514; AstraZeneca), PDR001 (CAS Registry No. 1859072-53-9; Novartis), REGN2810 (LIBTAYO® or semiprimab-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), ENUM 244C8 (Enumeral Biomedical Holdings), and ENUM This includes, but is not limited to, 388D4 (Enumeral Biomedical Holdings).In some embodiments, the PD-1 axially coupled antagonist is tisrelizumab (BGB-A317), BGB-108, STI-A1110, AM0001, BI 754091, cintilimab (IBI308), cetrerimab (JNJ-63723283), tripalimab (JS-001), camrelizumab (SHR-1210, INCSHR-1210, HR-301210), MEDI-0680 (AMP-514), MGA-012 (INCMGA 0012), nivolumab (BMS-936558, MDX1106, ONO-4538), spartalizumab (PDR00l), pembrolizumab (MK-3475, SCH 900475), PF-06801591, semiprimab (REGN-2810, REGEN2810), dostallimab (TSR-042, ANB011), FITC-YT-16 (PD-1 binding peptide), APL-501 or CBT-501 or genolimusumab (GB-226), AB-122, AK105, AMG 404, BCD-100, F520, HLX10, HX008, JTX-4014, LZM009, Sym021, PSB205, AMP-224 (PD-1 targeting fusion protein), 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 EISCOI 11-003, IKT-202, MCLA-134, MT-17000, PEGMP-7, PRS-332, RXI-762, STI-1110, VXM-10, XmAb-2 3104, AK-112, HLX-20, SSI-361, AT-16201, SNA-01, AB122, PD1-PIK, PF-06936308, RG-7769, CAB PD-1 Includes 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, CCX-4503, or derivatives thereof.In some embodiments, the PD-1 binding antagonist is a peptide or a small molecule compound. In some embodiments, the PD-1 binding antagonist is AUNP-12 (Pierre Fabre / Aurigene). In some embodiments, the PD-1 axial binding antagonist includes small molecule PD-1 axial binding antagonists as described in Guzik et al., Molecules (2019) May 30;24(11). Other PD-1 inhibitors for use in the methods provided herein are known in the art as described in U.S. Patents 8,735,553, 8,354,509, and 8,008,449.

[0157] 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 any of the examples herein, an isolated anti-PD-L1 antibody can conjugate to human PD-L1, e.g., human PD-L1 as shown in UniProtKB / Swiss-Prot acceptance number Q9NZQ7.1, or a variant thereof. In some embodiments, the PD-L1-binding antagonist is a small molecule, nucleic acid, polypeptide (e.g., antibody), carbohydrate, lipid, metal, or toxin.

[0158] 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 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 the group consisting of Fab, Fab'-SH, Fv, scFv, and (Fab')2 fragments. 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, anti-PD-L1 antibodies are selected from a group consisting of YW243.55.S70, MPDL3280A (atezolizumab), MDX-1 105, MEDI4736 (durvalumab), and MSB0010718C (avelumab). Antibody YW243.55.S70 is an anti-PD-L1 antibody described in WO2010 / 077634. ​​MDX-1 105, also known as BMS-936559, is an anti-PD-L1 antibody described in WO2007 / 005874. MEDI4736 (durvalumab) is an anti-PD-L1 monoclonal antibody described in WO2011 / 066389 and US2013 / 034559. Examples of anti-PD-L1 antibodies useful in the methods of this disclosure, and methods for producing the same, are described in PCT Patent Applications WO2010 / 077634, WO2007 / 005874, WO2011 / 066389, U.S. Patent No. 8,217,149, and U.S. Patent No. 2013 / 034559.In some embodiments, the PD-L1 axis-coupled antagonist is atezolizumab, avelumab, durvalumab (imfinzi), BGB-A333, SHR-1316 (HTI-1088), CK-301, BMS-936559, emvafolimab (KN035, ASC22), CS1001, MDX-1105 (BMS-936559), LY33 00054, STI-A1014, FAZ053, CX-072, INCB086550, GNS-1480, CA-170, CK-301, M-7824, HTI-1088( HTI-131, SHR-1316), MSB-2311, AK-106, AVA-004, BBI-801, CA-327, CBA-0710, CBT-502, FPT-15 5, IKT-201, IKT-703, 10-103, JS-003, KD-033, KY-1003, MCLA-145, MT-5050, SNA-02, BCD-135, A PL-502 (CBT-402 or TQB2450), IMC-001, KD-045, INBRX-105, KN-046, IMC-2102, IMC-2101, KD-0 Includes 05, 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, FS-118, or derivatives thereof.

[0159] In some embodiments, the anti-PDL1 antibody comprises a heavy chain variable region and a light chain variable region. (a) The heavy chain variable region comprises the HVR-H1, HVR-H2, and HVR-H3 sequences GFTFSDSWIH (SEQ ID NO: 5), AWISPYGGSTYYADSVKG (SEQ ID NO: 6), and RHWPGGFDY (SEQ ID NO: 7), respectively. (b) The light chain variable regions each contain the HVR-L1, HVR-L2, and HVR-L3 sequences of RASQDVSTAVA (SEQ ID NO: 8), SASFLYS (SEQ ID NO: 9), and QQYLYHPAT (SEQ ID NO: 10), respectively.

[0160] In some embodiments, the anti-PDL1 antibody is MPDL3280A, also known as atezolizumab and TECENTRIQ® (CAS Registry No.: 1422185-06-5). In some embodiments, the anti-PDL1 antibody comprises heavy chain and light chain sequences. (a) The heavy chain sequence has at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, or 100% sequence identity with the following heavy chain sequences: EVQLVESGGGLVQPGGSLRLSCAASGFTFSDSWIHWVRQAPGKGLEWVAWISPYGGSTYYADSVKGRFTISADTSKNTAYLQMNSLRAEDTAVYYCARRHWPGGFDYWGQGTLVTVSS (Sequence ID 11), (b) The light chain sequence has at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, or 100% sequence identity with the following light chain sequences: DIQMTQSPSSLSASVGDRVTITCRASQDVSTAVAWYQQKPGKAPKLLIY SASF LYSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYLYHPATFGQGTKVEIKR (Sequence ID 12).

[0161] In some embodiments, the anti-PDL1 antibody comprises a heavy chain and a light chain sequence. (a) The heavy chain sequence has at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, or 100% sequence identity with the following heavy chain sequences: EVQLVESGGGLVQPGGSLRLSCAASGFTFSDSWIHWVRQAPGKGLEWVAWISPYGGSTYYADSVKGRFTISADTSKNTAYLQMNSLRAEDTAVYYCARRHWPGGFDYWGQGTLVTVSSASTKGPSVFPLAPSSKSTSGGTAALGCLVKDYFPEPVTVSWNS GALTSGVHTFPAVLQSSGLYSLSSVVTVPSSSLGTQTYICNVNHKPSNTKVDKKVEPKSCDKTHTCPPCPAPELLGGPSVFLFPPKPKDTLMISRTPEVTCVVVDVSHEDPEVKFNWYVDGVEVHNAKTKPREEQYASTYRVVSVLTVLHQDWLNGKEYKCKVSNKALPAPIEKTISKAKGQPREPQVYTLPPSREEMTKNQVSLTCLVKGFYPSDIAVEWESNGQPENNYKTTPPVLDSDGSFFLYSKLTVDKSRWQQGNVFSCSVMHEALHNHYTQKSLSLSPG (Sequence ID 13), (b) The light chain sequence has at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, or 100% sequence identity with the following light chain sequence: DIQMTQSPSSLSASVGDRVTITCRASQDVSTAVAWYQQKPGKAPKLLIYSASFLYSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYLYHPATFGQGTKVEIKRTVAAPSVFIFPPSDEQLKSGTASVVCLLNNFYPREAKVQWKVDNALQSGNSQESVTEQDSKDSTYSLSSTLTLSKADYEKHKVYACEVTHQGLSSPVTKSFNRGEC (SEQ ID NO: 14).

[0162] In some cases, isolated anti-PD-L1 antibodies containing heavy and light chain sequences are provided, and the light chain sequence has at least 85%, at least 86%, at least 87%, at least 88%, at least 89%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% sequence identity with the amino acid sequence of SEQ ID NO: 14. In some cases, isolated anti-PD-L1 antibodies containing heavy and light chain sequences are provided, and the heavy chain sequence has at least 85%, at least 86%, at least 87%, at least 88%, at least 89%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% sequence identity with the amino acid sequence of SEQ ID NO: 13. In some cases, isolated anti-PD-L1 antibodies containing heavy and light chain sequences are provided, wherein the light chain sequence has at least 85%, at least 86%, at least 87%, at least 88%, at least 89%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% sequence identity with the amino acid sequence of SEQ ID NO: 14, and the heavy chain sequence has at least 85%, at least 86%, at least 87%, at least 88%, at least 89%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% sequence identity with the amino acid sequence of SEQ ID NO: 13.

[0163] In a further specific embodiment, the antibody further comprises a human or mouse constant region. In yet another embodiment, the human constant region is selected from the group consisting of IgG1, IgG2, IgG2, IgG3, and IgG4. In yet another embodiment, the human constant region is IgG1. In yet another embodiment, the mouse constant region is selected from the group consisting of IgG1, IgG2A, IgG2B, and IgG3. In yet another embodiment, the mouse constant region is IgG2A. In yet another embodiment, the antibody has reduced or minimal effector function. In yet another embodiment, minimal effector function is due to an "effector-reducing Fc mutation" or glycosylation. In yet another instance, the effector-reducing Fc mutation is an N297A or D265A / N297A substitution in the constant region.

[0164] In some cases, isolated anti-PD-L1 antibodies are non-glycosylated. Antibody glycosylation is typically either N-linked or O-linked. N-linked glycosylation refers to the binding of the carbohydrate moiety to the side chain of an asparagine residue. The tripeptide sequences asparagine-X-serine and asparagine-X-threonine, where X is any amino acid other than proline, are recognition sequences for the enzymatic binding of the carbohydrate moiety to the asparagine side chain. Therefore, the presence of either of these tripeptide sequences in a polypeptide creates a potential glycosylation site. O-linked glycosylation refers to the binding of one of the sugars N-acetylgalactosamine, galactose, or xylose to a hydroxyamino acid, most typically serine or threonine, although 5-hydroxyproline or 5-hydroxylysine may also be used. Removal of glycosylation sites from antibodies is conveniently achieved by modifying the amino acid sequence so that one of the aforementioned tripeptide sequences (in the case of N-linked glycosylation sites) is removed. The modification can be made by substituting an asparagine, serine, or threonine residue within the glycosylation site with another amino acid residue (e.g., glycine, alanine, or a conservative substitution).

[0165] In some embodiments, the anti-PDL1 antibody is avelumab (CAS registry number: 1537032-82-8). Avelumab, also known as MSB0010718C, is a human monoclonal IgG1 anti-PDL1 antibody (Merck KGaA, Pfizer). In some embodiments, the anti-PDL1 antibody comprises heavy chain and light chain sequences.

[0166] (a) The heavy chain sequence has at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, or 100% sequence identity with the following heavy chain sequences: EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYIMMWVRQAPGKGLEWVSSIYPSGGITFYADTVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARIKLGTVTTVDYWGQGTLVTVSSASTKGPSVFPLAPSSKSTSGGTAALGCLVKDYFPEPVTVSWN SGALTSGVHTFPAVLQSSGLYSLSSVVTVPSSSLGTQTYICNVNHKPSNTKVDKKVEPKSCDKTHTCPPCPAPELLGGPSVFLFPPKPKDTLMISRTPEVTCVVVDVSHEDPEVKFNWYVDGVEVHNAKTKPREEQYNSTYRVVSVLTVLHQDWLNGKEYKCKVSNKALPAPIEKTISKAKGQPREPQVYTLPPSRDELTKNQVSLTCLVKGFYPSDIAVEWESNGQPENNYKTTPPVLDSDGSFFLYSKLTVDKSRWQQGNVFSCSVMHEALHNHYTQKSLSLSPG (Sequence ID 15),

[0167] (b) The light chain sequence has at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, or 100% sequence identity with the following light chain sequence: QSALTQPASVSGSPGQSITISCTGTSSDVGGYNYVSWYQQHPGKAPKLMIYDVSNRPSGVSNRFSGSKSGNTASLTISGLQAEDEADYYCSSYTSSSTRVFGTGTKVTVLGQPKANPTVTLFPPSSEELQANKATLVCLISDFYPGAVTVAWKADGSPVKAGVETTKPSKQSNNKYAASSYLSLTPEQWKSHRSYSCQVTHEGSTVEKTVAPTECS (SEQ ID NO: 16).

[0168] In some embodiments, the anti-PDL1 antibody contains six HVR sequences from SEQ ID NO: 15 and SEQ ID NO: 16 (e.g., three heavy chain HVRs from SEQ ID NO: 15 and three light chain HVRs from SEQ ID NO: 16). In some embodiments, the anti-PDL1 antibody contains a heavy chain variable domain from SEQ ID NO: 15 and a light chain variable domain from SEQ ID NO: 16.

[0169] In some embodiments, the anti-PDL1 antibody is durvalumab (CAS registry number: 1428935-60-7). Durvalumab, also known as MEDI4736, is an Fc-optimized human monoclonal IgG1 kappa anti-PDL1 antibody (MedImmune, AstraZeneca) described in WO2011 / 066389 and US2013 / 034559. In some embodiments, the anti-PDL1 antibody comprises heavy chain and light chain sequences. (a) The heavy chain sequence has at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, or 100% sequence identity with the following heavy chain sequences: EVQLVESGGGLVQPGGSLRLSCAASGFTFSRYWMSWVRQAPGKGLEWVANIKQDGSEKYYVDSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCAREGGWFGELAFDYWGQGTLVTVSSASTKGPSVFPLAPSSKSTSGGTAALGCLVKDYFPEPVTVSWN SGALTSGVHTFPAVLQSSGLYSLSSVVTVPSSSLGTQTYICNVNHKPSNTKVDKRVEPKSCDKTHTCPPCPAPEFEGGPSVFLFPPKPKDTLMISRTPEVTCVVVDVSHEDPEVKFNWYVDGVEVHNAKTKPREEQYNSTYRVVSVLTVLHQDWLNGKEYKCKVSNKALPASIEKTISKAKGQPREPQVYTLPPSREEMTKNQVSLTCLVKGFYPSDIAVEWESNGQPENNYKTTPPVLDSDGSFFLYSKLTVDKSRWQQGNVFSCSVMHEALHNHYTQKSLSLSPG (Sequence ID 17), (b) The light chain sequence has at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, or 100% sequence identity with the following light chain sequence: EIVLTQSPGTLSLSPGERATLSCRASQRVSSSYLAWYQQKPGQAPRLLIYDASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYGSLPWTFGQGTKVEIKRTVAAPSVFIFPPSDEQLKSGTASVVCLLNNFYPREAKVQWKVDNALQSGNSQESVTEQDSKDSTYSLSSTLTLSKADYEKHKVYACEVTHQGLSSPVTKSFNRGEC (SEQ ID NO: 18).

[0170] In some embodiments, the anti-PDL1 antibody contains six HVR sequences from SEQ ID NO: 17 and SEQ ID NO: 18 (e.g., three heavy chain HVRs from SEQ ID NO: 17 and three light chain HVRs from SEQ ID NO: 18). In some embodiments, the anti-PDL1 antibody contains a heavy chain variable domain from SEQ ID NO: 17 and a light chain variable domain from SEQ ID NO: 18.

[0171] Other examples of anti-PD-L1 antibodies include, but are not limited to, MDX-1105 (BMS-936559; Bristol Myers Squibb), LY3300054 (Eli Lilly), STI-A1014 (Sorrento), KN035 (Suzhou Alphamab), FAZ053 (Novartis), or CX-072 (CytomX Therapeutics).

[0172] In some embodiments, the PD-L1 axis-binding antagonist includes the low-molecular-weight PD-L1 axis-binding antagonist GS-4224. In some embodiments, the PD-L1 axis-binding antagonist includes the low-molecular-weight PD-L1 axis-binding antagonist described in PCT / US2019 / 017721.

[0173] In some embodiments, the checkpoint inhibitor is CT-011, also known as hBAT, hBAT-1, or pizilizumab, which is an antibody described in WO2009 / 101611.

[0174] In some embodiments, the checkpoint inhibitor is an antagonist 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 that act to negatively regulate T cell activation, particularly the CD28-dependent T cell response. CTLA4 competes with CD28 for binding to common ligands such as CD80(B7-1) and CD86(B7-2), and binds to these ligands with higher affinity than CD28. Blocking CTLA4 activity (e.g., by using an anti-CTLA4 antibody) is thought to enhance CD28-mediated co-stimulation (leading to increased T cell activation / priming), affect T cell development, and / or deplete Tregs (such as intratumor Tregs). In some embodiments, the CTLA4 antagonist is a small molecule, nucleic acid, polypeptide (e.g., antibody), carbohydrate, lipid, metal, or toxin.

[0175] In some embodiments, the anti-CTLA4 antibody is ipilimumab (YERVOY®; CAS Registry No.: 477202-00-9). Ipilimumab is also known as BMS-734016, MDX-010, and MDX-101, and is a fully human monoclonal IgG1 kappa anti-CTLA4 antibody (Bristol-Myers Squibb) described in WO2001 / 14424. In some embodiments, the anti-CTLA4 antibody comprises heavy chain and light chain sequences. (a) The heavy chain sequence has at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, or 100% sequence identity with the following heavy chain sequences: QVQLVESGGGVVQPGRSLRLSCAASGFTFSSYTMHWVRQAPGKGLEWVTFISYDGNNKYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAIYYCARTGWLGPFDYWGQGTLVTVSS (Sequence ID 19), (b) The light chain sequence has at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, or 100% sequence identity with the following light chain sequences: EIVLTQSPGTLSLSPGERATLSCRASQSVGSSYLAWYQQKPGQAPRLLIYGAFSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYGSSPWTFGQGTKVEIK (Sequence ID 20).

[0176] In some embodiments, the anti-CTLA4 antibody contains six HVR sequences from SEQ ID NO: 19 and SEQ ID NO: 20 (e.g., three heavy chain HVRs from SEQ ID NO: 19 and three light chain HVRs from SEQ ID NO: 20). In some embodiments, the anti-CTLA4 antibody contains a heavy chain variable domain from SEQ ID NO: 19 and a light chain variable domain from SEQ ID NO: 20.

[0177] Other examples of anti-CTLA4 antibodies include, but are not limited to, APL-509, AGEN1884, and CS1002. In some embodiments, CTLA-4 inhibitors include ipilimumab (IBI310, BMS-734016, MDX010, MDX-CTLA4, MEDI4736), tremelimumab (CP-675, CP-675,206), APL-509, AGEN1884, and CS1002, AGEN1181, abatacept (Orencia, BMS-188667, RG2077), BCD-145, ONC-392, ADU-1604, REGN4659, ADG116, KN044, KN046, or derivatives thereof.

[0178] In some embodiments, the immune checkpoint inhibitor comprises a LAG-3 inhibitor (e.g., an antibody, an antibody complex, or an antigen-binding fragment thereof). In some embodiments, the LAG-3 inhibitor comprises a small molecule, nucleic acid, polypeptide (e.g., an antibody), carbohydrate, lipid, metal, or toxin. In some embodiments, the LAG-3 inhibitor comprises a small molecule. In some embodiments, the LAG-3 inhibitor comprises a LAG-3 conjugate. In some embodiments, the LAG-3 inhibitor comprises an antibody, an antibody complex, or an antigen-binding fragment thereof. In some embodiments, LAG-3 inhibitors include eftilagimod alfa (IMP321, IMP-321, EDDP-202, EOC-202), relatrimab (BMS-986016), GSK2831781 (IMP-731), LAG525 (IMP701), TSR-033, EVIP321 (soluble LAG-3 protein), BI 754111, 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, derivatives thereof, or antibodies that compete with any of the aforementioned.

[0179] In some embodiments, immune checkpoint inhibitors are monovalent and / or single-specific. In some embodiments, immune checkpoint inhibitors are multivalent and / or multiple-specific.

[0180] In some embodiments, immunotherapy includes immunomodulatory molecules or cytokines. An immunomodulatory profile is necessary to induce an efficient immune response and balance immunity in the subject. In some embodiments, the immunomodulatory molecules are included in any of the treatments detailed herein. 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, Lantes, macrophage colony-stimulating factor (M-CSF), granulocyte colony-stimulating factor (G-CSF), and granulocyte-macrophage colony-stimulating factor (GM-CSF), as well as their functional fragments. In some embodiments, any immunomodulatory chemokine that binds to a chemokine receptor, i.e., CXC, CC, C, or CX3C chemokine receptor, may be used in the context of the present invention. 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, and BCA-1(Blc), as well as their functional fragments.

[0181] The compositions used in the methods described herein (e.g., cancer immunotherapy) can be administered by any preferred method, including, for example, intravenous, intramuscular, subcutaneous, intradermal, percutaneous, intraarterial, intraperitoneal, intralesional, intracranial, intraarticular, intraprostatic, intrapleural, intratracheal, intrathecal, intranasal, vaginal, intrarectal, topical, intratumoral, peritoneal, subconjunctival, intravesicular, mucosal, intrapericardial, intraumbilical, intraocular, intraorbital, oral, topical, percutaneous, intravitreous (e.g., by intravitreal injection), ophthalmic, inhalation, injection, transplantation, infusion, continuous infusion, topical perfusion directly immersing target cells, catheter, lavage, cream, or lipid composition. The compositions used in the methods described herein can also be administered systemically or topically. The method of administration may vary depending on various factors (e.g., the compound or composition being administered, as well as the severity of the condition, disease, or disorder being treated). In some cases, checkpoint inhibitors are administered intravenously, intramuscularly, subcutaneously, topically, orally, percutaneously, intraperitoneally, or orbitally, implanted, by inhalation, intrathecally, intraventricularly, or intranasally. Dosage can be carried out by any preferred route, such as intravenous or subcutaneous infusion, depending in part whether the administration is short-term or chronic. Various dosing schedules are included herein, but are not limited to single or multiple doses, bolus doses, and pulse infusions at various time points.

[0182] The cancer immunotherapies described herein (e.g., antibodies, conjugated polypeptides, and / or small molecules) (and any additional therapeutic agents) may be formulated, administered, and given in a manner consistent with good medical practice. Factors to be considered in this context include the specific disorder being treated, the specific mammal being treated, the individual patient's clinical condition, the cause of the disorder, the site of drug delivery, the method of administration, the schedule of administration, and other factors known to the physician. The therapeutic agents may, but are not required, be formulated and / or given concurrently with one or more agents currently used to prevent or treat the disorder in question. The effective dose of such other agents depends on the amount of checkpoint inhibitors present in the formulation, the type of disorder or treatment, and other factors considered above. These are generally used in the same dosages and routes of administration as described herein, or in approximately 1–99% of the dosages described herein, or in any dosage and route that is determined to be empirically / clinically appropriate.

[0183] The progression of this therapy can be easily monitored using conventional techniques and assays. For example, as a general suggestion, the therapeutically effective dose of an immune checkpoint inhibitor, such as a PD-L1 axis-conjugated antagonist antibody, anti-CTLA-4 antibody, anti-TIM-3 antibody, or anti-LAG-3 antibody administered to a human, would be in the range of approximately 0.01 to approximately 50 mg / kg of the patient's body weight, whether administered in one or more doses. In some cases, the antibody used is administered at doses of approximately 0.01 mg / kg to 45 mg / kg, 0.01 mg / kg to 40 mg / kg, 0.01 mg / kg to 35 mg / kg, 0.01 mg / kg to 30 mg / kg, 0.01 mg / kg to 25 mg / kg, 0.01 mg / kg to 20 mg / kg, 0.01 mg / kg to 15 mg / kg, 0.01 mg / kg to 10 mg / kg, 0.01 mg / kg to 5 mg / kg, or 0.01 mg / kg to 1 mg / kg, for example, daily, weekly, every two weeks, every three weeks, or monthly. In some cases, the antibody is administered at 15 mg / kg. However, other drug regimens may be useful. In one case, the anti-PD-L1 antibody described herein is administered to humans in doses of approximately 100 mg, 200 mg, 300 mg, 400 mg, 500 mg, 600 mg, 700 mg, 800 mg, 900 mg, 1000 mg, 1100 mg, 1200 mg, 1300 mg, 1400 mg, 1500 mg, 1600 mg, 1700 mg, or 1800 mg on day 1 of a 21-day cycle (every three weeks, q3w). In some cases, the anti-PD-L1 antibody MPDL3280A is administered intravenously at a dose of 1200 mg every three weeks (q3w). The dose may be administered as a single dose or as multiple doses (e.g., two or three doses) by infusion or other means. The dose of antibody administered in combination therapy may be reduced compared to monotherapy. The progress of this therapy can be easily monitored using conventional techniques.

[0184] In some embodiments, the method further involves administering an effective amount of an additional therapeutic agent to the patient. In some embodiments, the additional anticancer therapy includes one or more of the following: surgery, radiotherapy, chemotherapy, anti-angiogenic therapy, anti-DNA repair therapy, and anti-inflammatory therapy. In some cases, the additional therapeutic agent is selected from the group consisting of antitumor agents, chemotherapeutic agents, growth inhibitors, anti-angiogenic agents, radiotherapy, cytotoxic agents, and combinations thereof. In some cases, cancer immunotherapy may be administered in combination with chemotherapy or a chemotherapeutic agent. In some embodiments, the chemotherapy or chemotherapeutic agent is a platinum-based agent (including, but not limited to, cisplatin, carboplatin, oxaliplatin, and stalaplatin). In some cases, cancer immunotherapy may be administered in combination with a radiotherapy agent. In some cases, cancer immunotherapy may be administered in combination with targeted therapy or a targeted therapy agent. In some cases, cancer immunotherapy may be administered in combination with another immunotherapy or immunotherapy agent, such as a monoclonal antibody. In some cases, the additional therapeutic agent is an agonist directed towards a co-stimulatory molecule. In some cases, the additional therapeutic agent is an antagonist that targets the co-inhibitor molecule. In other cases, cancer immunotherapy is administered as monotherapy.

[0185] Examples of chemotherapeutic agents include alkylating agents, e.g., thiotepa and cyclophosphamide; alkyl sulfonates, e.g., busulfan, improsulfan, and pigosulfan; aziridines, e.g., benzodopa, carbocon, methuredopa, and uredopa; ethyleneimines and methylamelamines, including altretamine, triethylenemelamine, triethylenephosphoramide, triethylenethiophosphoramide, and trimethyloromelamamine; acetogenins (especially bratacin and bratacinone); camptothecin (including its synthetic analog topotecan); briostatin; calistatin; CC-1065 (including its synthetic analogs adzeresin, karzeresin, and bizeresin); cryptophycin (especially cryptophycin 1 and cryptophycin 8); drastatin; duocalmycin (synthetic) Analogues, including KW-2189 and CB1-TM1); eryuterobin; pancratistatin; sarcodictiin; spongitatin; nitrogen mustards, e.g., chlorambucil, chromafadin, colophosphamide, estramustine, ifosfamide, mechloretamine, mechloretamine hydrochloride, melphalan, nobenbitin, fenestrine, prednimustine, trophosphamide, and uracil mustard; nitrosoureas, e.g., carmustine, chlorozotosin, fotemustine, lomustine, nimustine, and ranimustine; antibiotics, e.g., engine antibiotics (e.g., calicheamicin, especially calicheamicin gamma and calicheamicin omegal); dynemicins, including dynemicin A; bisphosphonates, e.g., clodronate; esperamicin;Furthermore, neocardinostatin chromophores and related pigment proteins, enediin antibiotic chromophores, acrasinomycin, actinomycin, autoramycin, azaserin, bleomycin, kactinomycin, carabicin, carminomycin, cardinophilin, chromomycin, dactinomycin, daunorubicin, detorubicin, 6-diazo-5-oxo-L-norleucine, doxorubicin (including morpholino-doxorubicin, cyanomorpholino-doxorubicin, 2-pyrrolino-doxorubicin and deoxydoxorubicin), epirubicin Mitomycin such as esorubicin, idarubicin, marcelomycin, mitomycin C, mycophenolate, nogaramycin, olibomycin, peplomycin, potophyllomycin, puromycin, keramycin, rhodorubicin, streptonigrin, streptozocin, tubercidine, ubenimex, dinostatin, and zolbicin; antimetabolites, e.g., methotrexate and 5-fluorouracil (5-FU); folate analogs, e.g., denopterin, pteropterin, and trimethrexate; purine analogs, e.g., flud Rabin, 6-mercaptopurine, thiamiprine, and thioguanine; pyrimidine analogs, e.g., ancitabine, azacitidine, 6-azauridine, carmofur, cytarabine, dideoxyuridine, doxifluridine, enocitabine, and phloxuridine; androgens, e.g., carsterone, drostanolone propionate, epithiostanol, mepithiostan, and testotrachtone; anti-adrenal agents, e.g., mitotane and trilostane; folic acid supplements, e.g., folic acid; acegraton; aldofsphamide glycosides; aminolevulinic acid; Eniluracil; Amsacrin; Bestrabusil; Bisanthren; Edatrexate; Defofamine; Demecolsin; Diadiquan; Eflornithine; Erliptinium acetate; Epotilon; Etoglucid; Gallium nitrate; Hydroxyurea; Lentinan; Ronidynin; Mytansinoids, e.g., Mytansin and Ansamitosin; Mitoguazone; Mitoxantrone; Mopidammol; Nitraerine; Pentostatin; Fenamet; Pirarubicin; Rosoxantrone; Podophyllic acid; 2-Ethylhydrazide; Procarbazine; PSK polysaccharide complex;Lazoxane; Rhizoxin; Schizophyllan; Spirogermanium; Tenuazonic acid; Triadiquan; 2,2',2"-Trichlorotriethylamine; Trichothecene (especially T-2 toxin, Beraclin A, Loridine A, Angidin); Urethane; Vindesine; Dacarbazine; Mannomustine; Mitobronitol; Mitractol; Pipobroman; Gacitosine; Arabinoside ("Ara-C"); Cyclophosphamide; Taxoids, e.g., Paclita This includes xel and docetaxel gemcitabine; 6-thioguanine; mercaptopurine; platinum-coordinated 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-11); topoisomerase inhibitor RFS2000; difluoromethylhyromycin (DMFO); retinoids, e.g., retinoic acid; capecitabine; carboplatin, procarbazine, precomycin, gemcitabine, navelbine, famesyl protein tanspherase inhibitor, trans platinum; and any pharmaceutically acceptable salts, acids, or derivatives of any of the above.

[0186] Some (non-exclusive) examples of chemotherapy drugs that can be combined with this disclosure include carboplatin (Paraplatin), cisplatin (Platinol, Platinol-AQ), cyclophosphamide (Citoxane, Neosal), docetaxel (Taxotere), doxorubicin (Adriamycin), erlotinib (Tarceva), etoposide (Bepecid), fluorouracil (5-FU), gemcitabine ( These include gemcitabine (Gemzar), imatinib mesylate (Gleevec), irinotecan (Camptosar), methotrexate (Forex, Mexart, Ametopterin), paclitaxel (Taxol, Abraxane), sorafenib (Nexavar), sunitinib (Sutent), topotecan (Hycamtin), vincristine (Oncovin, Vincasar PFS), and vinblastine (Vervan). Another large group of potential targets for complementary cancer therapies includes kinase inhibitors, as cancer cell growth and survival are closely related to the deregulation of kinase activity. A wide range of inhibitors are used to restore normal kinase activity and thereby reduce tumor growth. The group of target kinases includes 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, ALK; cytoplasmic tyrosine kinases, e.g., c-SRC, c-YES, Abl, JAK-2; serine / threonine kinases, e.g., ATM, Aurora A&B, CDK, mTOR, PKCi, PLK, b-Raf, S6K, STK1 1 / LKB1; and lipid kinases, e.g., PI3K, SKI. Examples of small molecule kinase inhibitors include PHA-739358, nilotinib, dasatinib, and PD166326, NSC 74341 1, lapatinib (GW-572016), canertinib (CI-1033), semaxinib (SU5416), batalanib (PTK787 / ZK222584), sutent (SU1 1248), sorafenib (BAY 43-9006), and leflunomide (SU101).For further details, see, for example, Zhang et al. 2009: Targeting cancer with small molecule kinase inhibitors. Nature Reviews Cancer 9, 28-39. Small molecule targeted therapies generally involve inhibitors of mutations, overexpression, or other important enzyme domains of proteins within cancer cells. Notable and non-limiting examples include the tyrosine kinase inhibitors imatinib (Gleevec) and gefitinib (Iressa).

[0187] In some embodiments, additional anticancer therapies include anti-angiogenic therapies. Angiogenesis inhibitors prevent the extensive growth of blood vessels (angiogenesis) necessary for tumor survival. Angiogenesis, which is promoted to allow tumor cells to receive increased nutrients and oxygen, can be blocked, for example, by targeting various molecules. Non-limiting examples of angiogenesis-mediated molecules or angiogenesis inhibitors that can be combined with the present invention include: soluble VEGF (VEGF isoforms VEGF121 and VEGF165, receptors VEGFR1, VEGFR2 and co-receptors neuropilin-1 and neuropilin-2)1 and NRP-1, angiopoietin 2, TSP-1 and TSP-2, angiostatins and related molecules, endostatins, vasostatins, calreticulin, platelet factor 4, TIMP and CDAI, Meth-1 and Meth-2, IFNα, -β and -γ, CXCL10, IL-4, -12 and -18, prothrombin (cringle domain-2), antithrombin I II fragments, prolactin, VEGI, SPARC, osteopontin, Maspin, canstatin, proliferin-related proteins, Restin, and drugs such as bevacizumab, itraconazole, carboxamide triazole, TNP-470, CM101, IFN-α, platelet factor IV, suramin, SU5416, thrombospondin, VEGFR antagonists, anti-angiogenic steroids + heparin, cartilage-derived angiogenesis inhibitors, matrix metalloproteinase inhibitors, 2-methoxyestradiol, tecogalan, tetrathiomolybdate, thalidomide, thrombospondin, prolactin νβ3 inhibitors, linamide, and tascinimod. In some embodiments, known therapeutic candidates include naturally occurring angiogenesis inhibitors, including but not limited to angiostatins, endostatins, and platelet factor IV. In another embodiment, potential therapeutic candidates include, but are not limited to, endothelial cell proliferation-specific inhibitors such as TNP-470, thalidomide, and interleukin-12.Further anti-angiogenic agents include, but are not limited to, antibodies against fibroblast growth factor, or antibodies against vascular endothelial growth factor, or antibodies against platelet-derived growth factor, or inhibitors of other types of receptors for EGF, VEGF, or PDGF, as well as agents that neutralize angiogenic molecules. In some embodiments, anti-angiogenic agents include, but are not limited to, suramin and its analogues, as well as tecogalan. In other embodiments, anti-angiogenic agents include, but are not limited to, agents that neutralize receptors for angiogenic factors, or agents that interfere with the vascular basement membrane and extracellular matrix, as well as metalloproteinase inhibitors and angiogenesis-inhibiting steroids. Another group of anti-angiogenic compounds includes, but are not limited to, anti-adhesion molecules such as antibodies against integrin alpha v beta 3. Other anti-angiogenic compounds or compositions include, but are not limited to, kinase inhibitors, thalidomide, itraconazole, carboxamide triazole, CM101, IFN-α, IL-12, SU5416, thrombospondin, cartilage-derived angiogenesis inhibitors, 2-methoxyestradiol, tetrathiomolybdate, thrombospondin, prolactin, and linamide. In one particular embodiment, the anti-angiogenic compound is an antibody against VEGF, such as Avastin® / bevacizumab (Genentech).

[0188] In some embodiments, additional anticancer therapy includes anti-DNA repair therapy. In some embodiments, DNA damage repair and response inhibitors are selected from PARP inhibitors, RAD51 inhibitors, or inhibitors of DNA damage response kinases selected from CHCK1, ATM, or ATR. In some embodiments, additional anticancer therapy includes radiosensitizers. Exemplary radiosensitizers include hypoxic radiosensitizers such as misonidazole, metronidazole, and trans sodium crocetin, a compound that helps increase oxygen diffusion into hypoxic tumor tissue. Radiosensitizers can also be DNA damage response inhibitors that interfere with recombination repair, including base excision repair (BER), nucleotide excision repair (NER), mismatch repair (MMR), homologous recombination (HR), and non-homologous end joining (NHEJ), as well as direct repair mechanisms. SSB repair mechanisms include the BER, NER, or MMR pathways, while DSB repair mechanisms include the HR and NHEJ pathways. Radiation causes DNA damage, which is fatal if not repaired. Single-strand breaks are repaired through a combination of BER, NER, and MMR mechanisms, using an 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) polymerase (PARP). Therefore, radiosensitizers can include DNA damage response inhibitors, such as poly(ADP-ribose) polymerase (PARP) inhibitors. In some embodiments, additional anticancer therapies include DNA repair and response pathway inhibitors, PARP inhibitors (e.g., talazoparib, rucaparib, olaparib), RAD51 inhibitors (RI-1), or inhibitors of DNA damage response kinases, e.g., CHCK1 (AZD7762), ATM (KU-55933, KU-60019, NU7026, VE-821), and ATR (NU7026).

[0189] In some embodiments, additional anticancer therapy includes an anti-inflammatory agent. In some embodiments, the anti-inflammatory agent is a drug that blocks, inhibits, or reduces signaling from inflammation or inflammatory signaling pathways, and in some embodiments, the anti-inflammatory agent is 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 inducer (IGIF), transformant The agent inhibits or reduces the activity of one or more of the following receptors: transforming growth factor-β (TGF-β), transforming growth factor-α (TGF-α), tumor necrosis factor (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, TL5, TLR6, TLR7, TLR8, TLR9, and / or any of their homologous receptors. In some embodiments, the anti-inflammatory agent is an IL-1 or IL-1 receptor antagonist such as anakinra (KINERET®), lilonacept, 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 anti-IL-6 receptor antibody, such as tocilizumab (ACTEMRA®), olokizumab, crazakizumab, sarilumab, sirucumab, 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.

[0190] In some embodiments, the anti-inflammatory agent is a corticosteroid. Exemplary corticosteroids include cortisone (hydrocortisone, hydrocortisone sodium phosphate, hydrocortisone sodium succinate, ALA-CORT®, HYDROCORT ACETATE (registered trademark), hydrocortisone phosphate LANACORT (registered trademark), SOLU-CORTEF (registered trademark), Decadron (dexamethasone, dexamethasone acetate, dexamethasone sodium phosphate, DEXASONE (registered trademark), DIODEX (registered trademark), HEXADROL (registered trademark), MAXIDEX (registered trademark)), methylprednisolone (6-methylprednisolone, methylprednisolone acetate, methylprednisolone sodium succinate, DURALONE (registered trademark), MEDRALONE (registered trademark), MEDROL (registered trademark), M-PREDNISOL (registered trademark), SOLU-MEDROL (registered trademark)), prednisolone (DELTA-CORTEF (registered trademark), ORAPRED (registered trademark), PEDIAPRED (registered trademark), PREZONE (registered trademark)), and prednisone (DELTASONE (registered trademark), LIQUID This includes, but is not limited to, PRED®, METICORTEN®, ORASONE®, and bisphosphonates (e.g., pamidronate (AREDIA®) and zoledronic acid (ZOMETAC®)).

[0191] The aforementioned combination therapies include both combined administration (where two or more therapeutic agents are contained in the same or separate formulations) and separate administrations, in which case the administration of cancer immunotherapy may occur before, simultaneously with, and / or after the administration of additional therapeutic agents or multiple drugs. In one case, the administration of cancer immunotherapy and the administration of additional therapeutic agents may occur within approximately one month of each other, or within approximately one, two, or three weeks, or within approximately one, two, three, four, five, or six days.

[0192] While we do not wish to be bound by theory, it is thought that enhancing T cell stimulation by promoting co-stimulatory molecules or inhibiting co-inhibitory molecules may promote tumor cell death, thereby treating or delaying cancer progression. In some cases, immune checkpoint inhibitors, such as PD-L1 axis-binding antagonists and / or CTLA4 antagonists, may be administered in combination with agonists directed towards co-stimulatory molecules. In some cases, the co-stimulatory molecules include CD40, CD226, CD28, OX40, GITR, CD137, CD27, HVEM, or CD127. In some cases, the agonists directed towards co-stimulatory molecules are agonist antibodies that bind to CD40, CD226, CD28, OX40, GITR, CD137, CD27, HVEM, or CD127. In some cases, immune checkpoint inhibitors, such as PD-L1 axis-conjugated antagonists and / or CTLA4 antagonists, may be administered in combination with antagonists targeting co-inhibitor molecules. In some cases, co-inhibitor molecules may include CTLA-4 (also known as CD1 52), TIM-3, BTLA, VISTA, LAG-3, B7-H3, B7-H4, IDO, TIGIT, MICA / B, or arginase. In some cases, the co-inhibitor-targeting antagonist is an antagonist antibody that binds to CTLA-4, TIM-3, BTLA, VISTA, LAG-3, B7-H3, B7-H4, IDO, TIGIT, MICA / B, or arginase.

[0193] In some cases, PD-L1 axis-conjugated antagonists can be administered in combination with CTLA-4 (also known as CD152) antagonists, such as blocking antibodies. In some cases, PD-L1 axis-conjugated antagonists can be administered in combination with ipilimumab (also known as MDX-010, MDX-101, or YERVOY®). In some cases, PD-L1 axis-conjugated antagonists can be administered in combination with tremelimumab (also known as tisilimucob or CP-675,206). In some cases, PD-L1 axis-conjugated antagonists can be administered in combination with B7-H3 (also known as CD276) antagonists, such as blocking antibodies. In some cases, PD-L1 axis-conjugated antagonists can be administered in combination with MGA271. In some cases, PD-L1 axis-binding antagonists may be administered in combination with TGF-β-targeting antagonists, such as meterimumab (also known as CAT-192), fresolimmab (also known as GC1008), or LY2157299.

[0194] In some cases, immune checkpoint inhibitors, such as PD-L1 axis-binding antagonists and / or CTLA4 antagonists, may be administered in combination with therapies including adoptive transfer of T cells expressing chimeric antigen receptors (CARs) (e.g., cytotoxic T cells or CTLs). In some cases, immune checkpoint inhibitors, such as PD-L1 axis-binding antagonists and / or CTLA4 antagonists, may be administered in combination with therapies including adoptive transfer of T cells containing dominant-negative TGF beta receptors, such as dominant-negative TGF beta type II receptors. In some cases, immune checkpoint inhibitors, such as PD-L1 axis-binding antagonists and / or CTLA4 antagonists, may be administered in combination with therapies including the HERCREEM protocol (e.g., ClinicalTrials.gov Identifier NCT00889954).

[0195] In some cases, immune checkpoint inhibitors, such as PD-L1 axis-conjugated antagonists and / or CTLA4 antagonists, may be administered in combination with CD137-targeted agonists (also known as TNFRSF9, 4-1 BB, or ILA), such as activating antibodies. In some cases, immune checkpoint inhibitors, such as PD-L1 axis-conjugated antagonists and / or CTLA4 antagonists, may be administered in combination with urelumab (also known as BMS-663513). In some cases, immune checkpoint inhibitors, such as PD-L1 axis-conjugated antagonists and / or CTLA4 antagonists, may be administered in combination with CD40-targeted agonists, such as activating antibodies. In some cases, immune checkpoint inhibitors, such as PD-L1 axis-conjugated antagonists and / or CTLA4 antagonists, may be administered in combination with CP-870893. In some cases, immune checkpoint inhibitors, such as PD-L1 axis-conjugated antagonists and / or CTLA4 antagonists, may be administered in combination with OX40-targeted agonists (also known as CD134), such as activating antibodies. In some cases, immune checkpoint inhibitors, such as PD-L1 axis-conjugated antagonists and / or CTLA4 antagonists, may be administered in combination with anti-OX40 antibodies (e.g., AgonOX). In some cases, immune checkpoint inhibitors, such as PD-L1 axis-conjugated antagonists and / or CTLA4 antagonists, may be administered in combination with CD27-targeted agonists, such as activating antibodies. In some cases, immune checkpoint inhibitors, such as PD-L1 axis-conjugated antagonists and / or CTLA4 antagonists, may be administered in combination with CDX-1127. In some cases, immune checkpoint inhibitors, such as PD-L1 axis-binding antagonists and / or CTLA4 antagonists, may be administered in combination with indoleamine-2,3-dioxygenase (IDO)-targeted antagonists.In some cases, the IDO antagonist is 1-methyl-D-tryptophan (also known as 1-D-MT).

[0196] In some cases, immune checkpoint inhibitors, such as PD-L1 axis-coupled antagonists and / or CTLA4 antagonists, may be administered in combination with antibody-drug conjugates. In some cases, the antibody-drug conjugates include meltansine or monomethyl auristatin E (MMAE). In some cases, immune checkpoint inhibitors, such as PD-L1 axis-coupled antagonists and / or CTLA4 antagonists, may be administered in combination with anti-NaPi2b antibody-MMAE conjugates (also known as DNIB0600A or RG7599). In some cases, immune checkpoint inhibitors, such as PD-L1 axis-coupled antagonists and / or CTLA4 antagonists, may be administered in combination with trastuzumab emtansine (T-DM1, ado-trastuzumab emtansine, or KADCYLA®, also known as Genentech). In some cases, immune checkpoint inhibitors, such as PD-L1 axis-coupled antagonists and / or CTLA4 antagonists, may be administered in combina...

Claims

1. A computer-based method for evaluating the tumor status of a subject with cancer, The computer obtains first sequencing data of a first plurality of cell-free DNA (cfDNA) molecules, wherein the first plurality of cfDNA molecules are obtained from or derived from a first bodily fluid sample of the subject at a first time point, and the first time point is before the administration of a therapeutic agent configured to treat the cancer. The computer determines, based on the first sequencing data, (i) a first plurality of copy number anomalies (CNAs) in the first plurality of cfDNA molecules and (ii) a first plurality of fragment lengths in the first plurality of cfDNA molecules. The computer acquires first methylation sequencing (MS) data of a first plurality of cell-free DNA (cfDNA) molecules across a region of the genome, wherein the first plurality of cfDNA molecules are acquired from or derived from a first bodily fluid sample of the subject at a first time point. The computer determines a methylation profile for each of one or more loci of the genome based on the first MS data, thereby obtaining a first methylation profile, wherein the methylation profile includes the average methylation rate for each of one or more CpG islands. The computer obtains second sequencing data of a second plurality of cell-free DNA (cfDNA) molecules, wherein the second plurality of cfDNA molecules are obtained from or derived from a second bodily fluid sample of the subject at a second time point, and the second time point is after the therapeutic agent has been administered to the subject. The computer determines, based on the second sequencing data, (iii) a second plurality of copy number anomalies (CNAs) in the second plurality of cfDNA molecules and (iv) a second plurality of fragment lengths in the second plurality of cfDNA molecules, The computer acquires second MS data of a second plurality of cell-free DNA (cfDNA) molecules across the entire region of the genome, wherein the second plurality of cfDNA molecules are acquired from or derived from a second bodily fluid sample of the subject at a second time point. The computer determines the methylation profile for each of one or more gene loci of the genome based on the second MS data, thereby obtaining the second methylation profile. The computer compares the first plurality of CNAs with the second plurality of CNAs to determine the change in the CNA profile, The computer determines the fragment length profile change based on the first plurality of fragment lengths and the second plurality of fragment lengths, The computer compares the first methylation profile across the entire one or more gene loci with the second methylation profile across the entire one or more gene loci, The computer determines, at least partially, the first tumor percentage of the subject at a first time point or the second tumor percentage of the subject at a second time point, based on the CNA profile change, the fragment length profile change, and the respective methylation profiles, wherein the first tumor percentage is the amount of circulating tumor DNA (ctDNA) molecules in the first fluid sample relative to the first plurality of cfDNA molecules, and the second tumor percentage is the amount of circulating tumor DNA (ctDNA) molecules in the second fluid sample relative to the second plurality of cfDNA molecules. The computer detects the tumor state of the subject based at least partially on the first tumor percentage or the second tumor percentage, Methods that include...

2. The method according to claim 1, wherein the first and second methylation profiles include a 5-hydroxymethylcytosine state, a 5-methylcytosine state, a concentrated methylation evaluation, a central methylation level, a modal methylation level, a maximum methylation level, or a minimum methylation level.

3. The method according to claim 1 or 2, wherein the first or second bodily fluid sample is selected from the group consisting of blood, serum, plasma, vitreous humor, sputum, urine, tears, sweat, saliva, semen, mucosal discharge, mucus, cerebrospinal fluid, cerebrospinal fluid (CSF), pleural fluid, ascites, amniotic fluid, and lymph.

4. The method according to claim 1, wherein obtaining the first MS data includes performing methylation sequencing of the first plurality of cfDNA molecules to generate a first plurality of sequence determination reads, or obtaining the second MS data includes performing methylation sequencing of the second plurality of cfDNA molecules to generate a second plurality of sequence determination reads.

5. The method according to claim 4, wherein the methylation sequence determination comprises whole-genome bisulfite sequencing or whole-genome enzymatic methyl-seq.

6. The method according to claim 4, wherein the methylation sequencing includes oxidative bisulfite sequencing, TET-assisted pyridineborane sequencing (TAPS), TET-assisted bisulfite sequencing (TABS), oxidative bisulfite sequencing (oxBS-Seq), ABOBEC-bound epigenetic sequencing (ACE-Seq), methylated DNA immunoprecipitation (MeDIP) sequencing, hydroxymethylated DNA immunoprecipitation (hMeDIP) sequencing, methylation array analysis, reduced-expression bisulfite sequencing (RRBS-Seq), or cytosine 5-hydroxymethylation sequencing.

7. The method according to claim 4, further comprising the computer aligning the first or second plurality of sequencing reads with a reference genome to generate a plurality of aligned sequencing reads.

8. The method according to claim 4, further comprising the computer enriching the first or second plurality of cfDNA molecules with respect to the region of the genome.

9. The method according to claim 4, wherein the region of the genome comprises one or more of the following: CpG islands, CpG Shore, patient-specific partial methylation domains, general partial methylation domains, promoters, gene bodies, equally spaced bins throughout the genome, and transposable elements.

10. The method according to claim 4, wherein the region of the genome includes a plurality of non-overlapping regions of the genome.

11. The method according to claim 4, wherein determining the first or second tumor percentage comprises comparing the methylation percentage profile with one or more reference methylation percentage profiles, the one or more reference methylation percentage profiles are obtained from or derived from additional cfDNA molecules of additional subjects.

12. The method according to claim 4, further comprising the computer detecting that the tumor condition includes tumor progression of the subject if the first tumor percentage or the second tumor percentage is greater than 1, greater than 1.1, greater than 1.2, greater than 1.3, greater than 1.4, greater than 1.5, greater than 1.6, greater than 1.7, greater than 1.8, greater than 1.9, greater than 2, greater than 3, greater than 4, or greater than 5.

13. The method according to claim 4, further comprising the computer detecting the major molecular response (MMR) of the subject if the first tumor percentage or the second tumor percentage is less than 0.01, less than 0.05, less than 0.1, less than 0.2, less than 0.3, less than 0.4, or less than 0.

5.

14. The method according to any one of claims 4 to 13, further comprising the computer determining that the target tumor is not progressing if no tumor progression is detected.

15. The method according to any one of claims 4 to 14, characterized in that, based on the determined tumor state of the subject, the subject is shown to be administered a treatment for treating the cancer.

16. The method according to any one of claims 4 to 15, wherein the first and second plurality of cfDNA molecules are derived from the target immune cells.

17. The method according to any one of claims 4 to 16, wherein the detected tumor condition indicates tumor progression, non-progression, regression, or recurrence.

18. The method according to any one of claims 4 to 17, wherein the first and second MS data are acquired by a sequencing device or a computer processor.

19. The method according to any one of claims 4 to 18, wherein the subject is brain cancer, bladder cancer, breast cancer, cervical cancer, colorectal cancer, endometrial cancer, esophageal cancer, stomach cancer, kidney cancer, hepatobiliary tract cancer, leukemia, liver cancer, lung cancer, lymphoma, ovarian cancer, pancreatic cancer, prostate cancer, skin cancer, stomach cancer, thyroid cancer, or urinary tract cancer.

20. A computer system for evaluating the tumor status of a subject with cancer, (i) First sequencing data of a first plurality of cell-free DNA (cfDNA) molecules, wherein the first plurality of cfDNA molecules are obtained from or derived from a first bodily fluid sample of the subject at a first time point, the first time point being before the subject is administered a therapeutic agent configured to treat the cancer, (ii) Second sequencing data of a second plurality of cell-free DNA (cfDNA) molecules, wherein the second plurality of cfDNA molecules are obtained from or derived from a second bodily fluid sample of the subject at a second time point, the second time point being after the therapeutic agent has been administered to the subject. (iii) First methylation sequencing (MS) data of multiple cell-free DNA (cfDNA) molecules across a region of the genome, and (iv) Second MS data of a second plurality of cell-free DNA (cfDNA) molecules across the entire region of the genome. A database configured to store and One or more computer processors operablely coupled to the database, wherein the one or more computer processors individually or collectively, Based on the first sequencing data, (i) a first set of copy number anomalies (CNAs) in the first set of cfDNA molecules and (ii) a first set of fragment lengths in the first set of cfDNA molecules are determined. Based on the first MS data, a methylation profile is determined for each of one or more loci of the genome, thereby obtaining a first methylation profile, wherein the methylation profile includes the average methylation rate for each of one or more CpG islands. Based on the second sequencing data, (iii) a second set of copy number anomalies (CNAs) in the second set of cfDNA molecules and (iv) a second set of fragment lengths in the second set of cfDNA molecules are determined. Based on the second MS data, the methylation profile for each of one or more gene loci of the genome is determined, thereby obtaining a second methylation profile. The first set of CNAs is compared with the second set of CNAs to determine the change in the CNA profile. Based on the first plurality of fragment lengths and the second plurality of fragment lengths, the fragment length profile change is determined. The first methylation profile across the entire one or more gene loci is compared with the second methylation profile across the entire one or more gene loci, At least partially, the first tumor percentage of the subject at a first time point or the second tumor percentage of the subject at a second time point is determined based on the CNA profile change, the fragment length profile change, and the respective methylation profiles, wherein the first tumor percentage is the amount of circulating tumor DNA (ctDNA) molecules in the first fluid sample relative to the first plurality of cfDNA molecules, and the second tumor percentage is the amount of circulating tumor DNA (ctDNA) molecules in the second fluid sample relative to the second plurality of cfDNA molecules, and The tumor status of the subject is detected, at least partially, based on the first tumor percentage or the second tumor percentage. One or more computer processors programmed in such a way, A computer system, including a computer system.

21. A non-temporary computer-readable medium, which, when executed by one or more computer processors, includes machine-executable instructions for performing a method for evaluating the tumor state of a subject having cancer, wherein the method Obtaining first sequencing data of a first plurality of cell-free DNA (cfDNA) molecules, wherein the first plurality of cfDNA molecules are obtained from or derived from a first bodily fluid sample of the subject at a first time point, and the first time point is before the administration of a therapeutic agent configured to treat the cancer. Based on the first sequencing data, (i) a first plurality of copy number anomalies (CNAs) in the first plurality of cfDNA molecules and (ii) a first plurality of fragment lengths of the first plurality of cfDNA molecules are determined. Obtaining first methylation sequencing (MS) data of a first group of cell-free DNA (cfDNA) molecules across a region of the genome, wherein the first group of cfDNA molecules are obtained from or derived from a first bodily fluid sample of the subject at a first time point. Based on the first MS data, the methylation profile for each of one or more loci of the genome is determined, thereby obtaining a first methylation profile, wherein the methylation profile includes the average methylation rate for each of one or more CpG islands. Obtaining second sequencing data of a second plurality of cell-free DNA (cfDNA) molecules, wherein the second plurality of cfDNA molecules are obtained from or derived from a second bodily fluid sample of the subject at a second time point, and the second time point is after the therapeutic agent has been administered to the subject. Based on the second sequencing data, (iii) a second set of copy number anomalies (CNAs) in the second set of cfDNA molecules and (iv) a second set of fragment lengths in the second set of cfDNA molecules, Acquiring second MS data of a second plurality of cell-free DNA (cfDNA) molecules across the entire region of the genome, wherein the second plurality of cfDNA molecules are acquired from or derived from a second bodily fluid sample of the subject at a second time point. Based on the second MS data, the methylation profile for each of one or more gene loci in the genome is determined, thereby obtaining a second methylation profile. The first set of CNAs is compared with the second set of CNAs to determine the change in the CNA profile, Based on the first plurality of fragment lengths and the second plurality of fragment lengths, the change in the fragment length profile is determined, Comparing the first average methylation rate profile across the entire one or more gene loci with the second average methylation rate profile across the entire one or more gene loci, Determining, at least partially, the first tumor percentage of the subject at a first time point or the second tumor percentage of the subject at a second time point based on the CNA profile change, the fragment length profile change, and the respective methylation profiles, wherein the first tumor percentage is the amount of circulating tumor DNA (ctDNA) molecules in the first fluid sample relative to the first plurality of cfDNA molecules, and the second tumor percentage is the amount of circulating tumor DNA (ctDNA) molecules in the second fluid sample relative to the second plurality of cfDNA molecules. The tumor status of the subject is detected, at least in part, based on the first tumor percentage or the second tumor percentage. Non-temporary computer-readable media, including [specific examples of such media].

Images