Systems and methods for cancer monitoring using minimal residual disease analysis - Patents.com

Abstract

Methods and systems for monitoring cancer using minimal residual disease analysis are provided herein. The methods may include detecting a set of biomarkers from a sample by assaying a plurality of nucleic acids. The methods may include sequencing the nucleic acids. The methods may include creating a probe panel. The methods may include processing the set of biomarkers to determine the presence of cancer or a parameter of cancer. The processing may be performed by an algorithm.

Description

[Technical field]

[0001] cross reference This application claims the benefit of U.S. Provisional Patent Application No. 63 / 306,466, filed February 3, 2022, U.S. Provisional Patent Application No. 63 / 306,468, filed February 3, 2022, and U.S. Provisional Patent Application No. 63 / 354,938, filed June 23, 2022, all of which are incorporated by reference in their entireties herein. [Background technology]

[0002] Cancer is a common leading cause of death worldwide. Detecting cancer in an individual can be crucial in providing treatment and improving patient outcomes. Cancer can be caused by genetic abnormalities that can lead to unregulated cell growth. Detecting this genetic abnormality can be important in detecting cancer. Sequencing of nucleic acids in samples from patients can be used to detect genetic abnormalities. Summary of the Invention

[0003] Systems and methods are provided herein for detecting the presence or absence of cancer in a subject. The systems and methods provided herein include identifying biomarkers of cancer in a subject by assaying polynucleotides. Detection of the type of cancer or specific biomarkers in a given cancer can allow for the provision of effective treatment to an individual and can result in improved outcomes. In multiple types of cancer, specific biomarkers indicative of a specific cancer type (or subtype) can be used to identify the prognosis of an individual suffering from cancer. In some cases, multiple analytes are tested to provide accurate detection and prognosis of cancer. By analyzing a larger number of analytes (and sets of biomarkers from these analytes), detection of cancer (or parameters of cancer) can be improved, effective treatment recommendations can be made, and even more accurate prognosis can be provided.

[0004] In one aspect, the disclosure provides a method for detecting the presence or absence of minimal residual disease (MRD) in a subject, the method comprising: (a) assaying deoxyribonucleic acid (DNA) molecules from a first biological sample obtained or removed from the subject at a first time point; (b) detecting a set of biomarkers from the DNA molecules based at least in part on the assaying step of (a); (c) creating a plurality of probe nucleic acids customized for the subject, wherein the probe nucleic acids comprise sequences of at least a subset of the set of biomarkers; and (d) assaying a first biological sample obtained or removed from the subject at a second time point using the plurality of probe nucleic acids. (e) detecting the presence or absence of a subset of the set of biomarkers by sequencing cell-free deoxyribonucleic acid (cfDNA) from a second biological sample obtained or removed from the subject, wherein the sequencing is performed at a depth of at least 80-fold; (e) determining the copy number of at least one region of the subject's genome by sequencing the nucleic acid obtained or removed from the subject by using whole genome sequencing; and (f) detecting the presence or absence of minimal residual disease in the subject by computing the copy number of the subset of the set of biomarkers and the at least one region of the genome.

[0005] In some embodiments, the first biological sample or the second biological sample is selected from the group consisting of a cell-free deoxyribonucleic acid (cfDNA) sample, a cell-free ribonucleic acid (cfRNA) sample, a plasma sample, a serum sample, a buffy coat sample, a peripheral blood mononuclear cell (PBMC) sample, a red blood cell sample, a urine sample, a saliva sample, a tissue biopsy, a pleural fluid sample, a peritoneal fluid sample, an amniotic fluid sample, a cerebrospinal fluid sample, a lymphatic fluid sample, a sweat sample, a tear sample, a semen sample, or a derivative of any of these, and any combination thereof. In some embodiments, the first biological sample or the second biological sample comprises a plasma sample. In some embodiments, the first biological sample or the second biological sample comprises a urine sample. In some embodiments, the first biological sample or the second biological sample is obtained or removed from the subject using an ethylenediaminetetraacetic acid (EDTA) collection tube, a cell-free RNA collection tube, or a cell-free deoxyribonucleic acid (DNA) collection tube, other blood collection tubes, and CTC collection tubes.

[0006] In some embodiments, (a) comprises exposing the first biological sample or the second biological sample to conditions sufficient to isolate, enrich, or extract DNA molecules. In some embodiments, the method further comprises fractionating a first biological sample from the subject to obtain DNA molecules, where the first biological sample is a whole blood sample. In some embodiments, the method further comprises fractionating a second biological sample from the subject to obtain cfDNA molecules, where the second biological sample is a whole blood sample.

[0007] In some embodiments, at least one of the DNA molecules is assayed using DNA sequencing to generate a nucleic acid sequencing read. In some embodiments, the DNA sequencing comprises whole exome sequencing. In some embodiments, the method further comprises filtering at least one subset of the nucleic acid sequencing reads based on a quality score. In some embodiments, the method further comprises performing error correction on the nucleic acid sequencing reads using a sample barcode or molecular barcode attached to at least one of the cfDNA molecules. In some embodiments, the method further comprises performing at least one of single-stranded and double-stranded consensus calling on the nucleic acid sequencing reads, thereby suppressing sequencing and PCR errors in the nucleic acid sequencing reads.

[0008] In some embodiments, the whole genome sequencing of (e) comprises low-pass whole genome sequencing. In some embodiments, the whole genome sequencing of (e) is performed at an average depth of 2x or less.

[0009] In some embodiments, the sequencing of (d) is performed at a depth of at least 100 times. In some embodiments, the sequencing of (d) is performed at a depth of at least 1,000 times. In some embodiments, the sequencing of (d) is performed at a depth of at least 10,000 times. In some embodiments, the sequencing of (d) is performed at a depth of at least 100,000 times. In some embodiments, the sequencing of (e) comprises sequencing a nucleic acid removed from a first biological sample. In some embodiments, the sequencing of (e) comprises sequencing a nucleic acid removed from a second biological sample. In some embodiments, the sequencing of (e) comprises sequencing a nucleic acid of a sample obtained at a first time point and sequencing a nucleic acid of a sample obtained at a second time point. In some embodiments, the method further comprises determining the copy number of at least one region of the subject's genome by comparing results of sequencing the nucleic acid of the sample obtained at the first time point with results of sequencing the nucleic acid of the sample obtained at the second time point. In some embodiments, the method further comprises generating a baseline copy number based on the sequencing of the nucleic acid of the sample obtained at least at the first time point.

[0010] In some embodiments, the assaying of (a), the sequencing of (d), or the sequencing of (e) comprises nucleic acid amplification. In some embodiments, the nucleic acid amplification comprises polymerase chain reaction (PCR) or isothermal amplification.

[0011] In some embodiments, the cancer is selected from the group consisting of genitourinary cancer, breast cancer, lung cancer, prostate cancer, colon cancer, melanoma, bladder cancer, non-Hodgkin's lymphoma, kidney cancer, endometrial cancer, leukemia, pancreatic cancer, thyroid cancer, and liver cancer, and any combination thereof. In some embodiments, the cancer comprises bladder cancer. In some embodiments, the bladder cancer is muscle invasive bladder cancer. In some embodiments, the subject is asymptomatic for the cancer.

[0012] In some embodiments, the method comprises detecting the presence or absence of minimal residual disease in a subject with an accuracy of at least about 60%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 98%, or at least about 99%. In some embodiments, the method comprises detecting the presence or absence of minimal residual disease in a subject with a sensitivity of at least about 60%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 98%, or at least about 99%. In some embodiments, the method comprises detecting the presence or absence of minimal residual disease in a subject with a specificity of at least about 60%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 98%, or at least about 99%. In some embodiments, the method comprises detecting the presence or absence of minimal residual disease in a subject with a positive predictive value of at least about 60%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 98%, or at least about 99%. In some embodiments, the method comprises detecting the presence or absence of minimal residual disease in a subject with a negative predictive value of at least about 60%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 98%, or at least about 99%.

[0013] In some embodiments, the biological sample is obtained or removed from the subject before the subject receives treatment for cancer. In some embodiments, the biological sample is obtained or removed from the subject during treatment for cancer. In some embodiments, the biological sample is obtained or removed from the subject after receiving treatment for cancer. In some embodiments, the treatment is selected from the group consisting of surgical resection, chemotherapy, radiation therapy, immunotherapy, cell therapy, adjuvant therapy, neoadjuvant therapy, androgen suppression therapy, and combinations thereof.

[0014] In some embodiments, the method further comprises identifying a clinical intervention for the subject based at least in part on the presence or absence of detection of cancer. In some embodiments, the clinical intervention is selected from a plurality of clinical interventions.

[0015] In some embodiments, the clinical intervention is selected from the group consisting of surgical resection, chemotherapy, radiation therapy, immunotherapy, adjuvant therapy, neoadjuvant therapy, androgen suppression therapy, and combinations thereof. In some embodiments, the method further comprises administering the clinical intervention to the subject.

[0016] In some embodiments, the set of biomarkers comprises one or more members selected from the group consisting of the genes listed in Table 1. In some embodiments, the set of biomarkers comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, or 180 members selected from the group consisting of the genes listed in Table 1. In some embodiments, the set of biomarkers comprises one or more members selected from the group consisting of the genes listed in Table 7. In some embodiments, the set of biomarkers is 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, In some embodiments, the set of biomarkers comprises one or more members selected from the group consisting of the genes listed in Table 8.In some embodiments, the set of biomarkers is 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, In some embodiments, the set of biomarkers comprises one or more members selected from the group consisting of the genes listed in Table 9. In some embodiments, the set of biomarkers is 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, In some embodiments, the subset of the set of biomarkers comprises one or more members selected from the group consisting of the genes listed in Table 9.In some embodiments, the subset of the set of biomarkers is 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 1 Includes 80, 185, 190, 195, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500, 520, 540, 560, 580, 600, 700, 800, 900, or 1000 members.

[0017] In some embodiments, the plurality of probes comprises a nucleic acid primer. In some embodiments, the plurality of probes comprises a nucleic acid capture probe. In some embodiments, the plurality of probes comprises sequence complementarity with at least a portion of the nucleic acid sequences of the set of biomarkers. In some embodiments, the plurality of probes comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, or 180 different probes.

[0018] In some embodiments, (d) further comprises sequencing using a plurality of immobilized probes, wherein the probes of the plurality of immobilized probes comprise probes that do not comprise sequences of a subset of the set of biomarkers. In some embodiments, the plurality of immobilized probes comprises one or more members selected from the group consisting of the genes listed in Table 10. In some embodiments, the method further comprises determining the likelihood of determining the presence or absence of cancer in the subject.

[0019] In some embodiments, the method further comprises monitoring the presence or absence of cancer in the subject, comprising assessing the presence or absence of cancer in the subject at each of a plurality of time points.

[0020] In some embodiments, the difference in the assessment of the presence or absence of cancer in the subject between multiple time points indicates one or more clinical indicators selected from the group consisting of: (i) a diagnosis of cancer, (ii) a prognosis of cancer, and (iii) the effectiveness or ineffectiveness of a course of treatment for treating the subject's cancer. In some embodiments, the prognosis comprises expected progression-free survival (PFS) or overall survival (OS). In some embodiments, the set of biomarkers from the cfDNA molecule comprises tumor-associated alterations selected from the group consisting of single nucleotide variants (SNVs), insertions or deletions (indels), and translocations.

[0021] In some embodiments, the method further comprises detecting copy number variation or copy number loss based on at least (e).

[0022] In some embodiments, the method further comprises determining a mutant allele frequency of the set of somatic mutations among the set of biomarkers. In some embodiments, the method further comprises determining a circulating tumor DNA (ctDNA) fraction of the cancer of the subject based at least in part on the set of mutant allele frequencies.

[0023] In some embodiments, the method further comprises determining a tumor mutation burden (TMB) of the subject's cancer. In some embodiments, the method further comprises determining an aberration score for the subject's cancer based at least in part on the set of variant allele frequencies.

[0024] Another aspect of the disclosure provides a non-transitory computer readable medium comprising machine executable code that, upon execution by one or more computer processors, implements any of the methods described above or elsewhere herein.

[0025] Another aspect of the present disclosure provides a system comprising one or more computer processors and a computer memory coupled thereto, the computer memory comprising machine executable code that, upon execution by the one or more computer processors, implements any of the methods described above or elsewhere herein.

[0026] Further aspects and advantages of the present disclosure will become readily apparent to those skilled in the art from the following detailed description, in which only illustrative embodiments of the present disclosure are shown and described. As will be understood, the present disclosure is capable of other and different embodiments, and its several details are capable of modification in various obvious respects, all without departing from the present disclosure. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.

[0027] Incorporation by Reference All publications, patents, and patent applications mentioned in this specification are incorporated herein by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. In the event that the publications and patents or patent applications incorporated by reference conflict with the disclosure contained herein, the specification is intended to supersede and / or take precedence over any such conflicting material. [Brief description of the drawings]

[0028] The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings (also referred to herein as "figure" and "FIG."). [Figure 1] FIG. 1 shows a schematic diagram for liquid biopsy assays across a cancer treatment timeline. [Diagram 2] Figure 2 shows an exemplary workflow for tissue-independent actionable MRD assay. [Diagram 3] FIG. 3 shows an exemplary workflow for an MRD assay. [Figure 4] FIG. 4 shows the specificity of mutation detection in an exemplary assay. [Diagram 5] FIG. 5 shows an exemplary workflow for generating a baseline profile. [Figure 6] FIG. 6 shows a chart of the relationship between tumor fraction and MRD sensitivity. [Figure 7] FIG. 7 shows a chart of tumor mutation allele frequency (MAF) in clinical samples. [Figure 8] FIG. 8 shows a chart of the analytical sensitivity of an exemplary Predicine BEACON assay using different numbers of target mutations. [Figure 9A]9A-9H show an exemplary study design, treatment evaluation, and biomarker discovery. FIG. 9A shows a scheme depicting the study design, sample collection, and biomarker analysis of the RJBLC-I2N003 study. Patients underwent TURBT for tumor resection, pathological diagnosis, disease staging, and risk stratification. All enrolled patients received preoperative toripalimab at 3 mg / kg every 2 weeks for up to 4 cycles. Imaging evaluations were performed at baseline and every 2 treatment cycles. Radical cystectomy was planned within 4±2 weeks after the last dose of toripalimab treatment, after which surgical tissues were subjected to pathological evaluation and biomarker analysis. Urine and plasma samples were collected during the course of neoadjuvant immunotherapy. PredicineBEACON™ MRD assay was performed to analyze utDNA and ctDNA. [Figure 9B] Figures 9A-9H show an exemplary study design, treatment evaluation, and biomarker discovery. Figure 9B shows a swimmer plot showing the treatment course and clinical response by RECIST 1.1. Reasons for early toripalimab termination are shown in circled text. Reasons for surgical delay are shown in boxed text. [Figure 9C] Figures 9A-H show an exemplary study design, treatment evaluation, and biomarker discovery. Figure 9C shows a waterfall plot of the best changes in target lesions in 20 patients. The best overall response of each patient by RECIST 1.1 is listed along the x-axis. The color of the bars indicates the pathological outcome of neoadjuvant toripalimab. [Figure 9D] Figures 9A-9H show exemplary study design, treatment evaluation, and biomarker discovery. Figure 9D shows a Sankey plot showing the pathological results of neoadjuvant toripalimab. Pre-treatment tumor stage was assessed by MRI imaging, and post-treatment tumor stage was assessed by pathological examination. [Figure 9E]Figures 9A-9H show exemplary study design, treatment evaluation, and biomarker discovery. Figure 9E shows the ROC curves of pretreatment TFsm, TFcn, and MRI measurements in predicting ypCR, along with their corresponding AUC values. [Figure 9F] Figures 9A-9H show exemplary study design, treatment evaluation, and biomarker discovery. Figure 9F shows ROC curves of post-treatment TFsm, TFcn, and MRI measurements in predicting ypCR, along with their corresponding AUC values. [Figure 9G] Figures 9A-9H show exemplary study design, treatment evaluation, and biomarker discovery. Figure 9G shows a heat map showing the relationship between pre- or post-treatment urinary MRD status and radiographic or pathological results. Patients with utDNA clearance (defined by TFsm+TFcn<10%) or FGFR3 mutations after neoadjuvant toripalimab are also shown. [Figure 9H] Figures 9A-H show exemplary study design, treatment evaluation, and biomarker discovery. Figure 9H shows a proposed workflow for actionable utDNA MRD testing and clinical decision-making in MIBC patients undergoing neoadjuvant therapy. Urine-based non-invasive MRD analysis allows for adaptive management of individual patients undergoing either bladder-preserving or radical cystectomy based on their real-time MRD status. TURBT = transurethral resection of bladder tumor, MRD = minimal residual disease, AE = adverse event, SAE = serious adverse event, RECIST = Response Evaluation Criteria in Solid Tumors, CR = complete response, PR = partial response, SD = stable disease, PD = progressive disease, ypCR = pathologic complete response, TFsm = somatic mutation-based tumor fraction estimate, TFcn = copy number-based tumor fraction estimate, MRI = magnetic resonance imaging, AUC = area under the receiver operating characteristic curve, utDNA = urinary tumor DNA, utDNA-pre = pre-treatment utDNA, utDNA-post = post-treatment utDNA. [Figure 10]10A-10B show representative images for evaluation of toripalimab. (A) Representative MRI image for radiographic evaluation of neoadjuvant toripalimab. (B) Representative hematoxylin and eosin staining for tissue pathology evaluation of neoadjuvant toripalimab. CR=complete response, PR=partial response, SD=stable disease, PD=progressive disease. The modifier "p" refers to pathological staging after cystectomy. [Figure 11] Figure 11A shows stacked bar graphs depicting the percentage of patients with negative or positive PD-L1, low or high TMB, and negative or positive TLS. Figure 11B shows Oncoprint charts for the tDNA mutation landscape in patients with ypCR or non-ypCR. Samples were analyzed by whole exome sequencing, and the mutation frequency of each gene is shown on the right. Figure 11C shows line plots depicting the change in tumor size as measured by MRI imaging before and after neoadjuvant toripalimab. PD-L1=programmed death ligand 1, TMB=tumor mutation burden, TLS=tertiary lymphoid structures, ypCR=pathological complete response, pre-tx=pre-treatment, post-tx=post-treatment, MRI=magnetic resonance imaging. [Figure 12]Figure 12A shows Oncoprint charts for tDNA and utDNA mutational landscapes. Samples were analyzed by whole exome sequencing, and the mutation frequency for each gene is shown on the right. Figure 12B shows the TMB correlation between tDNA and utDNA assessed by whole exome sequencing. Shading indicates 95% confidence intervals. Spearman correlation coefficients (r) and P values ​​are shown. Figure 12C shows variant and sample-level sensitivity across 93 titrated SeraCare reference samples. Each row shows a targeted mutation and each column corresponds to a sample. MRD-positive events were called if the MRD score in the sample was ≥2. Figure 12D shows variant and sample-level specificity across anuric cellular DNA samples from 16 healthy donors. Each row shows a targeted mutation and each column corresponds to a sample. MRD-positive events were called if the MRD score in the sample was ≥2. tDNA = tumor DNA, utDNA = urinary tumor DNA, TMB = tumor mutation burden, MRD = minimal residual disease, AA = amino acids, VAF = variant allele frequency [Figure 13] Figure 13A shows box plots comparing TFsm in utDNA and ctDNA samples taken at baseline. Figure 13B shows Venn plots showing the number of shared and unique variants in matched utDNA and ctDNA samples. Figure 13C shows box plots comparing TFcn in utDNA and ctDNA samples at baseline. Figure 13D shows utDNA and ctDNA copy number gains and losses identified by GISTIC2.0-based tumor fraction estimation. TFsm = somatic mutations, utDNA = urinary tumor DNA, ctDNA = circulating tumor DNA, TFcn = copy number-based tumor fraction estimate. [Figure 14]Figure 14A shows a line plot showing TFsm changes in utDNA samples with neoadjuvant toripalimab. Figure 14B shows a line plot showing TFcn changes in utDNA samples before and after neoadjuvant toripalimab. Figure 14C shows a stacked bar graph showing the percentage of patients with low or high pre-treatment TFsm, TFcn, and MRI measurements according to the optimal cut-off point defined by ROC analysis. Figure 14D shows a stacked bar graph showing the percentage of patients with low or high post-treatment TFsm, TFcn, and MRI measurements according to the optimal cut-off point defined by ROC analysis. TFsm = tumor fraction estimate based on somatic mutations, TFcn = tumor fraction estimate based on copy number, MRI = magnetic resonance imaging, utDNA = urinary tumor DNA, utDNA-pre = pre-treatment utDNA, utDNA-post = post-treatment utDNA, ypCR = pathological complete response, pre-tx = pre-treatment, posr-tx = post-treatment. [Figure 15] Figure 15A shows spider plots depicting the dynamic changes in TFsm, TFcn, and MRI measurements for each patient during neoadjuvant toripalimab. Figure 15B shows box plots depicting utDNA clearance (defined by TFsm+TFcn<10%) in utDNA-pre vs. utDNA-post samples. Figure 15C shows IGV plots depicting FGFR3 S249C mutations in patient RZ12 detected by MRD panel sequencing or whole exome sequencing. Figure 15D shows VAF changes of FGFR3 S249C mutations in patient RZ12 with progressive disease. TFsm = somatic mutation-based tumor fraction estimate, TFcn = copy number-based tumor fraction estimate, MRI = magnetic resonance imaging, C1 = cycle 1, C2 = cycle 2, C3 = cycle 3, C4 = cycle 4, RC = radical cystectomy, ypCR = pathologic complete response, MRD = minimal residual disease, WES = whole exome sequencing, tDNA = tumor DNA, utDNA = urinary tumor DNA, utDNA-pre = pre-treatment utDNA, utDNA-post = post-treatment utDNA, C2D1 = day 1 of cycle 2, VAF = variant allele frequency, PD = progressive disease. [Figure 16] FIG. 16 illustrates a computer control system that is programmed or otherwise configured to implement the methods provided herein. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0029] While various embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions may occur to those skilled in the art without departing from the invention. It is understood that various alternatives to the embodiments of the invention described herein may be employed.

[0030] Provided herein is a system and method for detecting the presence or absence of cancer in a subject. Provided herein is a system and method that includes assaying polynucleotides to identify biomarkers of cancer in a subject. Biomarkers can be processed to identify the presence or absence of cancer. The methods described herein can process multiple types of analytes or analytes from different sources or samples to determine the presence or absence of cancer. Multiple types of analytes can include DNA or RNA, such as cfDNA. Multiple analytes can be cfDNA, germline DNA, and cfRNA. By analyzing multiple different analytes or different sources or samples, the method can allow for improved detection or determination of prognosis compared to methods that are performed with fewer analytes or only one of many different analytes.

[0031] Circulating tumor DNA (ctDNA) can be used as a biomarker for non-invasive monitoring of treatment response and disease progression in many patients with cancer. Longitudinal and personalized ctDNA detection to monitor treatment efficacy and minimal residual disease (MRD) can be used to detect recurrence early and evaluate treatment response. However, most current methods lack adequate sensitivity and are actionable variants that require detecting residual disease during or after completion of treatment in non-metastatic cancer patients. To achieve high sensitivity for the detection of actionable MRD, a method could be to use high-coverage sequencing and ctDNA analysis that integrates results from multiple mutations in each patient.

[0032] Patient-specific, custom-made liquid biopsy NGS assays for detecting MRD and monitoring treatment response or recurrence can be performed by using cell-free DNA (cfDNA) from blood or urine. The assays can: 1) establish a baseline using either tissue, blood, or urine samples to detect genome-wide ctDNA variants (e.g., single nucleotide variants (SNVs) / indels, fusions, and copy number variants (CNVs)); 2) monitor ctDNA variants using a set of personalized mutation probes, fixed core probes with actionable genes, and assay genome-wide CNV measurements.

[0033] The assay can identify somatic mutations in a subject. Somatic mutations can be identified by using a sequencing assay. Somatic mutations can be identified by using whole exome sequencing. Whole exome sequencing can include sequencing across at least 20,000 genes. Furthermore, sequencing can be boosted to achieve higher depth in specific exons or genes of interest, such as cancer-related genes. For example, at least 100, 200, 300, 400, 500, 600, 700, 800, 900 or more genes can be sequenced at a higher depth (e.g., the average depth of whole exome sequencing) than the rest of the exome.

[0034] A matched control sample can be used to detect reliable somatic mutations or fusions for individualized probe design. The matched controls can be compared to identify mutations or other variants. For example, a peripheral blood mononuclear cell (PBMC) normal control sample can be used. The PBMC sample can help remove indeterminate potential clonal hematopoiesis (CHIP) mutations, germline variants and other background variants. Figure 5 shows an exemplary workflow for obtaining a matched PMBC sample. A subject's whole blood sample can be collected and then separated into a plasma fraction and a PBMC sample. These two samples can be analyzed and compared to identify somatic mutations or fusions.

[0035] Once somatic mutations are detected and identified in a subject, a set of probes personalized or customized for the mutations can be selected for the subject. For example, for a given patient, 16-50 personalized somatic mutations or fusions can be selected. In addition to this personalized panel, a fixed MRD core panel can be utilized for MRD detection and monitoring of treatment efficacy. In this way, genes specific to a given subject's cancer can be assayed simultaneously while analyzing genes that are associated with a particular type of cancer or are otherwise known to be associated with cancer. The fixed MRD core panel allows for the detection of novel and actionable mutations beyond the variants identified at baseline, which is important for treatment monitoring, studying drug resistance, and guiding personalized therapy. Based at least on this multi-layer panel design, the mutant allele fraction (MAF) limit of detection (LOD) for these assays can reach 0.005%.

[0036] Once the panel is constructed, the subject can be monitored by obtaining a sample from the subject and assaying the sample using the panel. The subject can be monitored using sequencing. The depth of this sequencing reaction can be increased to improve detection sensitivity. For example, ultra-deep sequencing (e.g., 100,000 times) can be performed, allowing high detection sensitivity.

[0037] In addition to the high sensitivity of detecting mutations, companion whole genome sequencing (WGS) can be performed to monitor CNV changes at both baseline and follow-up time points. Whole genome sequencing can be low-pass whole genome sequencing (LP-WGS, e.g., 1x). This can allow for more economical sequencing that can identify CNVs and SNVs (or other genetic variants and mutations) with high accuracy and sensitivity.

[0038] Tumor tissue is used for baseline profiling with most MRD assays. However, in many cases, tumor tissue is not available or the tissue quality does not meet the assay requirements. Thus, there is a significant clinical need to establish MRD mutation baselines without the need for tissue samples.

[0039] The subject may be suspected of having cancer. The cancer may be specific to an organ or other area of ​​the subject, or may originate. For example, the cancer may be breast cancer, lung cancer, prostate cancer, colorectal cancer, melanoma, bladder cancer, non-Hodgkin's lymphoma, kidney cancer, endometrial cancer, leukemia, pancreatic cancer, thyroid cancer, and liver cancer, and any combination thereof. The cancer may be hormone-sensitive prostate cancer (HSPC), castration-resistant prostate cancer (CRPC), metastatic prostate cancer, and any combination thereof. The cancer may be muscle-invasive bladder cancer (MIBC). The cancer may include a biomarker specific to a particular cancer. A particular biomarker may indicate the presence of a particular cancer. For example, the biomarker may indicate that castration-resistant prostate cancer is present. The biomarker may indicate that MIBC is present. Identification of the presence of a type of cancer may allow for the determination of treatment options or recommendations.

[0040] In some cases, the subject may be asymptomatic for cancer. For example, the cancer may not show any symptoms and the subject may not be aware of the presence of cancer. The methods described herein may allow cancer to be identified at an earlier stage than otherwise. Identifying the presence of cancer at an early stage may allow treatment options or recommendations to be determined at an early stage and may allow the subject to have an improved prognosis. The subject may have cancer and no longer show symptoms of cancer. Identifying the presence of cancer at an earlier stage or recurrence or recurrence of cancer may allow the subject to have an improved prognosis. Identifying the presence of cancer may allow the effectiveness of treatment to be determined.

[0041] The biological sample may include nucleic acid. The biological sample is a cell-free deoxyribonucleic acid (cfDNA) sample or a cell-free ribonucleic acid (cfRNA) sample. The biological sample may include genomic DNA or germline DNA (gDNA). The nucleic acid may be DNA (e.g., double-stranded DNA, single-stranded DNA, single-stranded DNA hairpin, cDNA, genomic DNA, germline DNA, circulating tumor DNA (ctDNA), cell-free DNA (cfDNA)), RNA (e.g., cfRNA, mRNA, cRNA, miRNA, siRNA, miRNA, snoRNA, piRNA, tiRNA, snRNA), or DNA / RNA hybrid. The biological sample may be derived from or contain a biological fluid. For example, the biological sample may be a plasma sample, a serum sample, a buffy coat sample, a peripheral blood mononuclear cell (PBMC) sample, a red blood cell sample, a urine sample, a saliva sample, or other bodily fluid sample. Biological sample can include or be pleural fluid sample, ascites sample, amniotic fluid sample, cerebrospinal fluid sample, lymphatic fluid sample, sweat sample, tear sample, semen sample, or any combination of biological fluid.In some cases, sample can include RNA and DNA.For example, sample can include cfDNA and cfRNA.

[0042] The biological sample may be collected, obtained, or removed from the subject using a collection tube. The collection tube may be an ethylenediaminetetraacetic acid (EDTA) collection tube, acellular RNA collection tube, or acellular deoxyribonucleic acid (DNA) collection tube and CTC collection tube, or other blood collection tube. The collection tube may include additional reagents to stabilize the nucleic acid molecules or blood cells. The collection tube may allow the nucleic acid or blood cells to stabilize so as to minimize degradation of the biological sample prior to assay. The additional reagents may include buffer salts or chelating agents.

[0043] Biological samples can be obtained or removed from a subject at various time points. Biological samples can be obtained or removed from a subject before the subject is treated for cancer. Biological samples can be obtained or removed from a subject while the subject is being treated for cancer. Biological samples can be obtained or removed from a subject after the subject is being treated for cancer. Biological samples can be taken over 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 or more time points. The time points can occur over a period of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, 55, or 60 hours or more. The time points can occur over a period of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, 55, or 60 days or more. The time points can occur over a period of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, 55, or 60 weeks or more. The time points can occur over a period of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, 55, or 60 months or more. The time points may occur over a period of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, 55, or 60 years or more.

[0044] In various aspects described herein, a clinical intervention or treatment can be identified based at least in part on the identification of the presence of cancer or the presence of a parameter of cancer. The clinical intervention can be a plurality of clinical interventions. The clinical intervention can be selected from a plurality of clinical interventions. The clinical intervention can be surgical resection, chemotherapy, radiation therapy, immunotherapy, adjuvant therapy, neoadjuvant therapy, androgen deprivation therapy, or a combination thereof. In some cases, the clinical intervention can be administered to the subject. After administration of the clinical intervention, a sample can be obtained or removed from the subject to monitor the cancer or the cancer parameter. Thus, the methods and systems disclosed herein can be repeatedly performed so that the monitoring of cancer can be performed. Furthermore, by repeatedly performing the method or system, the treatment or clinical intervention can be updated based on the results of the method. The monitoring of cancer can include evaluation and the difference of the evaluation with the evaluation generated previously. The difference in the evaluation of the cancer in the subject between the plurality of time points (or samples) can indicate one or more clinical signs, such as the diagnosis of cancer, the prognosis of cancer, or the effectiveness or ineffectiveness of a course of treatment for treating the cancer of the subject. Prognosis may include expected progression-free survival (PFS), overall survival (OS), or other metrics related to cancer severity or survival.

[0045] The biological sample may be subjected to additional reactions or conditions prior to the assay. For example, the biological sample may be subjected to conditions sufficient to isolate, enrich or extract nucleic acids, such as cfDNA or cfRNA molecules.

[0046] The methods disclosed herein may include performing one or more enrichment reactions on one or more nucleic acid molecules in a sample. The enrichment reaction may include contacting the sample with one or more beads or bead sets. The enrichment reaction may include one or more hybridization reactions. For example, the enrichment reaction may include contacting the sample with one or more probes (e.g., capture probes) or bait molecules that hybridize to the nucleic acid molecules of the biological sample. The enrichment reaction may include differential amplification of a set of nucleic acid molecules. The enrichment reaction may enrich for multiple loci or sequences corresponding to loci. For example, the enrichment reaction may enrich for sequences corresponding to genes from Table 1, Table 7, Table 8, Table 9, or Table 10. The enrichment reaction may include the use of primers or probes that may be complementary to the sequence (or upstream or downstream sequence) of the sequence to be enriched. For example, the capture probe may include sequence complementarity to a set of genomic loci, allowing for enrichment of genomic loci. The enrichment reaction may include multiple probes or primers. The plurality of probes may include 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, or 180 different probes. The probe may be a biotinylated probe. The probe may be attached to a bead or other solid support. The probe may be attached to a bead or other solid support via non-covalent (e.g., biotin-streptavidin interaction) or covalent interaction. The solid support may be a magnetic solid support.

[0047] The methods disclosed herein may include performing one or more isolation or purification reactions on one or more nucleic acid molecules in a sample. The isolation or purification reaction may include contacting the sample with one or more beads or bead sets. The isolation or purification reaction may include one or more hybridization reactions, enrichment reactions, amplification reactions, sequencing reactions, or combinations thereof. The isolation or purification reaction may include the use of one or more separators. The one or more separators may include a magnetic separator. The isolation or purification reaction may include separating bead-bound nucleic acid molecules from bead-free nucleic acid molecules. The isolation or purification reaction may include separating capture probe-hybridized nucleic acid molecules from nucleic acid molecules that do not contain a capture probe. The isolation reaction may include removing or separating a group of nucleic acid molecules from another group of nucleic acids.

[0048] The methods disclosed herein may include a conductive extraction reaction for one or more nucleic acids in a biological sample. The extraction reaction may lyse cells or disrupt nucleic acid interactions with cells so that the nucleic acids can be isolated, purified, concentrated, or subjected to other reactions.

[0049] The method disclosed herein may include an amplification or extension reaction. The amplification reaction may include a polymerase chain reaction. The amplification reaction may include a PCR-based amplification, a non-PCR-based amplification, or a combination thereof. The one or more PCR-based amplifications may include PCR, qPCR, nested PCR, linear amplification, or a combination thereof. The one or more non-PCR-based amplifications may include multiple displacement amplification (MDA), transcription-mediated amplification (TMA), nucleic acid sequence-based amplification (NASBA), strand displacement amplification (SDA), real-time SDA, rolling circle amplification, circle-to-circle amplification, or a combination thereof. The amplification reaction may include isothermal amplification.

[0050] The methods disclosed herein may include a barcoding reaction. The barcoding reaction may include the addition of a barcode or tag to a nucleic acid. The barcode may be a molecular barcode or a sample barcode. For example, the barcode nucleic acid may include a barcode sequence, which may be a degenerate n-mer. The sequence may be generated randomly or may be generated to synthesize a specific barcode sequence. The barcode nucleic acid may be added to a sample to label the nucleic acid molecules in the sample. The barcode may be specific to the sample. For example, multiple barcode nucleic acids may be added to a sample where the barcode sequence is the same. In barcoding nucleic acids, those originating from the same sample may have the same barcode sequence, allowing the nucleic acid to be identified as belonging to a specific or given sample. Molecular barcodes may also be used such that each molecule (or molecules) in the same volume has a different molecular barcode. This barcode may be subjected to amplification such that all amplicons originating from the molecule have the same barcode. In this way, molecules originating from the same molecule may be identified. Sequence reads may be processed based on the barcode sequence. For example, the processing may reduce errors or allow the molecules to be tracked. The barcode sequence may be added or otherwise added or incorporated into the sequence by various reactions, such as amplification, extension, or ligation reactions, or may be performed enzymatically using a nucleic acid polymerase or ligase. The ligation may be an overhang or blunt end ligation, and the barcode may include complementarity to the nucleic acid to be barcoded. This complementarity may be a sequence derived from a sample from a subject, or may be a constant sequence generated via a reaction performed on the nucleic acid in the sample.

[0051] In some cases, a biological sample may contain multiple components. For example, the biological sample may be a whole blood sample. The biological sample may be subjected to a reaction, such as separating or fractionating the biological sample. For example, a whole blood sample may be fractionated to obtain cell-free nucleic acid. A whole blood sample may be fractionated using centrifugation so that blood cells may be separated from plasma (which may contain cell-free nucleic acid). The sample may be subjected to multiple separations or fractionations.

[0052] A given biological sample can be subjected to multiple different reactions.For example, a given biological sample can be subjected to multiple different sequencing reactions.For example, a sample can be subjected to whole exome sequencing reaction and whole genome sequencing.Also, a sample can be divided into multiple samples, and some of the samples can be reacted and other parts can be reacted.

[0053] For example, a biological sample can be divided or otherwise split to form multiple samples. The resulting samples may contain the same composition. A biological sample can be fractionated, for example, into plasma and red blood cell fractions.

[0054] In various embodiments described throughout this disclosure, nucleic acid can be subjected to a sequencing reaction. Sequencing reaction can be used for DNA, RNA, or other nucleic acid molecules. Examples of sequencing reactions that can be used include capillary sequencing, next generation sequencing, Sanger sequencing, sequencing by synthesis, single molecule nanopore sequencing, sequencing by ligation, sequencing by hybridization, sequencing by nanopore current restriction, or combinations thereof. Sequencing by synthesis can include reversible transcription termination sequencing, forward single molecule sequencing, continuous nucleotide flow sequencing, or combinations thereof. Continuous nucleotide flow sequencing can include pyrosequencing, pH-mediated sequencing, semiconductor sequencing, or combinations thereof. The sequencing reaction may include whole genome sequencing, whole exome sequencing, low-pass whole genome sequencing, target sequencing, methylation recognition sequencing, enzymatic methylation sequencing, bisulfite methylation sequencing. The sequencing reaction may be transcriptome sequencing, mRNA-seq, totalRNA-seq, smallRNA-seq, exosome sequencing, or a combination thereof. A combination of sequencing reactions may be used in the methods described elsewhere herein. For example, a sample may be subjected to whole genome sequencing and whole transcriptome sequencing. Since a sample may contain multiple types of nucleic acids (e.g., RNA and DNA), a sequencing reaction specific to DNA or RNA may be used to obtain sequence readings related to the nucleic acid type.

[0055] The sequencing reaction can be performed at various sequencing depths. The sequencing depth of the sequencing reaction can be selected or adjusted. The sequencing reaction can be performed at least 1x, 2x, 3x, 4x, 5x, 6x, 7x, 8x, 9x, 10x, 11x, 12x, 13x, 14x, 15x, 16x, 17x, 18x, 19x, 20x, 25x, 30x, 35x, 40x, 45x, 50x, 60x, 70x, 80x, 90x, 100x, 200x, 300x, 400x, 500x, 600x, 700x, 800x. , 900x, 1000x, 2000x, 3000x, 4000x, 5000x, 6000x, 7000x, 8000x, 9000x, 10,000x, 20,000x, 30,000x, 40,000x, 50,000x, 60,000x, 70,000x, 80,000x, 90,000x, or 100,000x or greater depth. Sequencing reactions were performed at the following concentrations: 1x, 2x, 3x, 4x, 5x, 6x, 7x, 8x, 9x, 10x, 11x, 12x, 13x, 14x, 15x, 16x, 17x, 18x, 19x, 20x, 25x, 30x, 35x, 40x, 45x, 50x, 60x, 70x, 80x, 90x, 100x, 200x, 300x, 400x, 500x, 600x, 700x, 800x, 90x, 100x, 200x, 300x, 400x, 500x, 600x, 700x, 800x, 90x, 100x, 120x, 140x, 150x, 160x, 17x, 18x, 19x, 20x, 25x, 30x, 35x, 40x, 45x, 50x, 60x, 70x, 8 ... In some embodiments, sequencing may include sequencing the region at a depth of less than 0x, 1000x, 2000x, 3000x, 4000x, 5000x, 6000x, 7000x, 8000x, 9000x, 10,000x, 20,000x, 30,000x, 40,000x, 50,000x, 60,000x, 70,000x, 80,000x, 90,000, or 100,000x.

[0056] In various embodiments, low-pass whole genome sequencing is used to sequence nucleic acid. Low-pass whole genome sequencing can be performed at least 1x, 2x, 3x, 4x, 5x, 6x, 7x, 8x, 9x, or 10x or more average sequencing depth. Low-pass whole genome sequence can be performed at 1x, 2x, 3x, 4x, 5x, 6x, 7x, 8x, 9x, 10x or less average sequencing depth, or less than 10x. Low-pass whole genome sequencing can be performed at 1x to 2x average depth.

[0057] In various embodiments, the sequencing reaction may be performed using an individualized or customized set of probes. The sequencing reaction using an individualized or customized set of probes may be a deep or ultra-deep sequencing reaction. For example, sequencing reactions using individualized or customized probe sets can be performed at a sequencing depth of 50x, 60x, 70x, 80x, 90x, 100x, 200x, 300x, 400x, 500x, 600x, 700x, 800x, 900x, 1000x, 2000x, 3000x, 4000x, 5000x, 6000x, 7000x, 8000x, 9000x, 10,000x, 20,000x, 30,000x, 40,000x, 50,000x, 60,000x, 70,000x, 80,000x, 90,000x, or 100,000x or more.

[0058] In various embodiments, whole exome sequencing is used to sequence the nucleic acid of the subject. Whole exome sequencing can be performed at uneven depth. For example, some regions of the exome can be boosted or otherwise sequenced at a deeper depth than other regions, or at a deeper depth than the average depth of whole exome sequencing. By sequencing certain regions at a higher depth, more interesting genes or regions can be analyzed with higher sensitivity, accuracy, and / or precision. Genes or regions that are associated with or related to cancer can be sequenced at a deeper depth. For example, at least 100, 200, 300, 400, 500, 600, 700, 800, or 900 or more genes can be sequenced at a higher depth (e.g., the average depth of whole exome sequencing) than the rest of the exome.

[0059] Sequencing of nucleic acids may generate sequencing read data. Sequencing reads may be processed to generate data of improved quality. Sequencing reads may be generated with a quality score. A quality score may indicate the accuracy of the sequence read for a given base call, or the level or signal above a nose threshold. A quality score may be used to filter sequencing reads. For example, sequencing reads that do not meet a certain quality score threshold may be removed. Sequencing reads may be processed to generate consensus sequences or consensus base calls. A given nucleic acid (or nucleic acid fragment) may be sequenced, and errors in the sequence may be generated due to reactions prior to or during sequencing. For example, amplification or PCR may generate errors in the amplicon such that the sequence is not identical to the parent sequence. Error correction may be performed using sample barcodes or molecular barcodes. Error correction may include identifying sequence reads that do not corroborate with other sequences from the same sample or the same original parent molecule. The use of barcodes may allow for the identification of the same parent or sample. Additionally, sequence reads can be processed by making single-stranded or double-stranded consensus calls, thereby reducing or suppressing errors.

[0060] The methods disclosed herein may include determining allele frequencies or other cancer-related metrics. The methods may include the mutant allele frequencies of a set of somatic mutations between a set of biomarkers. The mutant allele frequencies may be used to determine the circulating tumor DNA (ctDNA) fraction of the cancer of the subject. The plasma tumor mutation burden (pTMB) of the cancer of the subject may be determined based at least in part on the set of mutant allele frequencies. The detection of microsatellite instability may also be used to determine the presence or absence of cancer or cancer metrics. The methylation status may be determined using the methods described herein and used to identify the presence of cancer or cancer parameters.

[0061] In various embodiments, the set of biomarkers is processed to generate data corresponding to the biomarkers. The set of biomarkers may include quantitative or qualitative measures from a set of genomic loci. The set of genomic loci may include a set of cancer-associated genomic loci. The set of biomarkers may correspond to a set of genes. The set of biomarkers may include one or more genes selected from Table 1. In some cases, the set of biomarkers may include 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, or 180 members selected from the group consisting of the genes listed in Table 1.

[0062] [Table 1]

[0063] The set of biomarkers may include one or more genes selected from Table 7. In some cases, the set of biomarkers may include 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, or 180 members selected from the group consisting of the genes listed in Table 7.

[0064] The set of biomarkers may include one or more genes selected from Table 8. In some cases, the set of biomarkers may include 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, or 180 members selected from the group consisting of the genes listed in Table 8. The set of biomarkers may include one or more genes selected from Table 9. In some cases, the set of biomarkers may include 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, or 180 members selected from the group consisting of the genes listed in Table 9. The set of biomarkers may include one or more genes selected from Table 10. In some cases, the set of biomarkers may include 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, or 180 members selected from the group consisting of the genes listed in Table 10.

[0065] The set of biomarkers can correspond to genetic abnormalities of loci. The genetic abnormalities can be tumor-associated changes. The genetic abnormalities can be copy number alterations (CNAs), copy number losses (CNLs), single nucleotide variants (SNVs), insertions or deletions (indels), and rearrangements. The set of biomarkers can be identified in various nucleic acid types. For example, tumor-associated changes can be identified in cfDNA. Tumor-associated changes can include allele expression or gene expression changes. The methods and systems disclosed herein can enable gene expression profiling and identification of changes to the expression levels of genes.

[0066] In various embodiments, the method can include identifying copy number or the presence of copy number polymorphism.The method can include using whole genome sequencing reaction to identify the copy number of a gene or region.The method can include identifying the copy number of a gene or region at a first time point or in a first sample or a part of a sample.The identified copy number can be used as a baseline.The method can include identifying the copy number of a gene or region at different time points, samples or parts of samples, and can be used to compare with the baseline.By comparing with the baseline, the method can allow the identification of the change in copy number over time, or the difference in copy number between two samples.

[0067] In various embodiments, a baseline measurement of a parameter is generated. The sample taken at a first time point can be used to compare with the sample taken at a second time point. Deviation from the baseline sample can indicate the presence or absence of genetic abnormality I in the subject. For example, the increase in copy number compared to the baseline can be used to evaluate the presence of the increase in copy number. In another example, the baseline can include a mutation, and the mutation can be identified in the sample taken at another time point. The presence of the mutation in the latter sample can indicate that cancer exists in the subject.

[0068] In various aspects, the method may include identifying the presence of cancer or cancer parameters. The method may process multiple data sets to identify the presence of cancer or cancer parameters. For example, the method may include using data from whole genome reaction and targeted sequencing reaction. The method may include determining the probability or likelihood of the presence of cancer or cancer parameters. For example, instead of a binary output indicating presence or absence, an output may be generated indicating the probability that a subject has cancer. This probability may be determined based on the algorithm described elsewhere herein. Similarly, the probability or likelihood of response to a particular treatment, or the probability of recurrence, may be output.

[0069] In various aspects, the set of biomarkers is processed using an algorithm. The algorithm may be a trained algorithm. The trained algorithm may use the set of biomarkers as input and generate an output regarding the presence or absence of cancer. The output may be specific to a type of cancer or a subtype of cancer. For example, the output may indicate the presence of muscle invasive bladder cancer.

[0070] The trained algorithm may be trained on a number of samples. For example, the trained algorithm may be trained on at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 1 It may be trained using 90, 195, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, or 10000 or more independent training samples. The trained algorithms are: 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 205, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540, 550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 650, 660, 670, 680, 690, 700, 710, 720, 730, 740, 750, 760, 770, 780, 790, 80 The algorithm may be trained using up to 5, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, or 10000 independent training samples. The training samples may be associated with the presence or absence of cancer. The training samples may be associated with the recurrence of cancer. The training samples may be associated with cancer that is resistant to a particular drug or treatment. An individual training sample may be positive for a particular cancer. An individual training sample may be negative for a particular cancer. By using the training samples, the trained algorithm may be capable of detecting cancer, determining the recurrence or probability of recurrence of cancer, or determining whether a cancer contains a set of biomarkers. The training samples may be associated with additional clinical health data of the subject.For example, the additional clinical health data may include the gender, weight, height, or levels of metabolites or antibodies in the subject. The additional clinical health data may include indicators of other diseases, disorders, or disease states.

[0071] The trained algorithm can be trained using multiple sets of training samples. The sets can include training samples as described elsewhere herein. For example, training can be performed using a first set of independent training samples related to the presence of cancer and a second set of independent training samples related to the absence of cancer. Similarly, the first set can be associated with recurrence, and the second set can be associated with the absence of recurrence.

[0072] The trained algorithm may also process additional clinical health data of the subject. For example, the additional clinical health data may include the gender, weight, height, or levels of metabolites or antibodies in the subject. The additional clinical health data may include indicators of other diseases, disorders, or disease states that the subject may suffer from. By using the additional clinical health data in conjunction with the biomarkers, the trained algorithm may output the presence or absence of cancer, the probability of recurrence, or resistance to drug treatment, which may differ from the output of an algorithm that does not process the additional clinical health.

[0073] The trained algorithm may be an unsupervised machine learning algorithm. For example, the unsupervised machine learning algorithm may utilize cluster analysis to identify attributes of interest. The trained algorithm may be a supervised machine learning algorithm. For example, the algorithm may be input with training data to generate an expected or desired output. The supervised learning algorithm may include a deep learning algorithm, a support vector machine (SVM), a neural network, or a random forest. Through the machine learning algorithm, the trained algorithm may be able to identify the relationship of a biomarker to a particular cancer prognosis or diagnosis. Without the trained algorithm, it may be difficult to identify the relationship of a biomarker to accurately identify the presence of cancer or other parameters associated with cancer.

[0074] In various embodiments, the system and method may include the accuracy, sensitivity, or specificity of detection of cancer or a parameter of cancer. For example, the method or system may include detecting the presence or absence of cancer (or the presence of a parameter of cancer, such as recurrence, recurrence, or drug resistance) in a subject with an accuracy of at least about 60%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 98%, or at least about 99%. The method or system may include detecting the presence or absence of cancer (or the presence of a parameter of cancer, such as recurrence, recurrence, or drug resistance) in a subject with a sensitivity of at least about 60%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 98%, or at least about 99%. The method or system may include detecting the presence or absence of cancer (or the presence of a parameter of cancer, such as recurrence, relapse, or drug resistance) in a subject with a specificity of at least about 60%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 98%, or at least about 99%. The method or system may include detecting the presence or absence of cancer (or the presence of a parameter of cancer, such as recurrence, relapse, or drug resistance) in a subject with a positive predictive value of at least about 60%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 98%, or at least about 99%. The method or system may include detecting the presence or absence of cancer (or the presence of a parameter of cancer, such as recurrence, recurrence, or drug resistance) in a subject with a negative predictive value of at least about 60%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 98%, or at least about 99%.

[0075] Computer Control System The present disclosure provides a computer system programmed to implement the methods of the present disclosure. Figure 16 shows a computer system 1601 programmed or otherwise configured to perform an analysis or step of a method, for example, determining the likelihood of the presence of cancer based on a set of biomarkers of an individual, or performing an algorithm. The computer system 1601 can coordinate various aspects of the methods and systems of the present disclosure, such as, for example, performing an algorithm, inputting training data, analyzing a set of biomarkers, or outputting a result to a user regarding the presence or absence of cancer. The computer system 1601 can be a user's electronic device, or a computer system located remotely to the electronic device. The electronic device can be a mobile electronic device.

[0076] The computer system 1601 includes a central processing unit (CPU, also "processor" and "computer processor" herein) 1605, which may be a single-core or multi-core processor, or multiple processors for parallel processing. The computer system 1601 also includes memory or memory locations 1610 (e.g., random access memory, read-only memory, flash memory), an electronic storage unit 1615 (e.g., hard disk), a communication interface 1620 (e.g., network adapter) for communicating with one or more other systems, and peripheral devices 1625 such as cache, other memory, data storage, and / or electronic display adapters. The memory 1610, the storage unit 1615, the interface 1620, and the peripheral devices 1625 communicate with the CPU 1605 via a communication bus (solid lines), such as a motherboard. The storage unit 1615 may be a data storage unit (or data repository) for storing data. The computer system 1601 can be operatively coupled to a computer network ("network") 1630 using the communication interface 1620. The network 1630 can be the Internet, an Internet and / or an extranet, or an intranet and / or an extranet in communication with the Internet. The network 1630 is, in some cases, a telecommunications and / or data network. The network 1630 can include one or more computer servers that can enable distributed computing, such as cloud computing. The network 1630 can, in some cases, implement a peer-to-peer network that can enable devices coupled to the computer system 1601 to behave as clients or servers with the aid of the computer system 1601.

[0077] The CPU 1605 may execute sequences of machine-readable instructions, which may be embodied in a program or software. The instructions may be stored in a memory location, such as the memory 1610. The instructions may be directed to the CPU 1605, which may then be programmed or configured to perform the methods of the present disclosure. Examples of operations performed by the CPU 1605 may include fetch, decode, execute, and writeback.

[0078] The CPU 1605 may be part of a circuit, such as an integrated circuit. One or more other components of the system 1601 may be included in the circuit. In some cases, the circuit is an application specific integrated circuit (ASIC).

[0079] The storage unit 1615 can store files such as drivers, libraries, and saved programs. The storage unit 1615 can store user data, such as user preferences and user programs. The computer system 1601 can optionally include one or more additional data storage units that are external to the computer system 1601, such as located on a remote server that communicates with the computer system 1601 via an intranet or the Internet.

[0080] Computer system 1601 can communicate with one or more remote computer systems via network 1630. For example, computer system 1601 can communicate with a remote computer system of a user (e.g., a medical professional or a patient). Examples of remote computer systems include a personal computer (e.g., a portable PC), a slate or tablet PC (e.g., an Apple™ iPad, a Samsung™ Galaxy Tab), a phone, a smartphone (e.g., an Apple™ iPhone, an Android-enabled device, a Blackberry™), or a personal digital assistant. A user can access computer system 1601 via network 1630.

[0081] The methods described herein can be implemented by machine (e.g., computer processor) executable code stored in electronic storage locations of the computer system 1601, such as memory 1610 or electronic storage unit 1615. The machine executable or machine readable code can be provided in the form of software. In use, the code can be executed by the processor 1605. In some cases, the code can be retrieved from the storage unit 1615 and stored in the memory 1610 for easy access by the processor 1605. In some situations, the electronic storage unit 1615 can be eliminated and machine executable instructions are stored in the memory 1610.

[0082] The code may be pre-compiled and configured for use with a machine having a processor adapted to execute the code, or may be compiled during run-time. The code may be supplied in a programming language that may be selected to allow the code to be executed in a pre-compiled or as-compiled manner.

[0083] Aspects of the systems and methods provided herein, such as computer system 1601, can be embodied in programming. Various aspects of the technology can be considered as an "article of manufacture" or "article of manufacture," typically in the form of machine (or processor) executable code, and / or associated data carried on or embodied in some type of machine-readable medium. The machine-executable code can be stored in an electronic storage unit, such as a memory (e.g., read-only memory, random access memory, flash memory) or hard disk. A "storage" type medium can include any or all of the tangible memory of a computer, a processor, or its associated modules, such as various semiconductor memories, tape drives, disk drives, etc., which can provide non-transitory storage at any time for software programming. All or portions of the software can be communicated from time to time over the Internet or various other telecommunications networks. Such communication can, for example, enable loading of the software from one computer or processor to another, for example, from a management server or host computer to the computer platform of an application server. Thus, other types of media that may carry software elements include optical, electrical, and electromagnetic waves, such as those used across physical interfaces between local devices, through wired and optical landline networks, and over various air links. Physical elements that carry such waves, such as wired or wireless links, optical links, etc., may also be considered software-bearing media. As used herein, unless limited to non-transitory, tangible "storage" media, terms such as computer or machine "readable medium" refer to any medium that participates in providing instructions to a processor for execution.

[0084] Thus, a machine-readable medium such as a computer executable code may take many forms, including but not limited to a tangible storage medium, a carrier wave medium, or a physical transmission medium. Non-volatile storage media include optical or magnetic disks, such as any of the storage devices in any computer, such as may be used to implement the databases, etc., shown in the figures. Volatile storage media include dynamic memory, such as the main memory of such a computer platform. Tangible transmission media include coaxial cables, copper wire and optical fiber, including the wires that make up a bus within a computer system. Carrier wave transmission media may take the form of electric or electromagnetic signals, or acoustic or light waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Thus, common forms of computer readable media include, for example, a floppy disk, a flexible disk, a hard disk, a magnetic tape, any other magnetic medium, a CD-ROM, a DVD or DVD-ROM, any other optical medium, punch cards paper tape, any other physical storage medium with a pattern of holes, a RAM, a ROM, a PROM and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave carrying data or instructions, a cable or link carrying such a carrier wave, or any other medium from which a computer can read programming code and / or data. Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.

[0085] The computer system 1601 may include or communicate with an electronic display 1635 that includes a user interface (UI) 1640 for inputting, for example, biomarker or sequencing data, or providing visual output related to detection, diagnosis, or prognosis. Examples of UIs include, but are not limited to, graphical user interfaces (GUIs) and web-based user interfaces.

[0086] The method and system of the present disclosure can be implemented by one or more algorithms. The algorithm can be implemented by software when executed by the central processing unit 1605. The algorithm can, for example, determine the presence or absence of cancer or cancer parameters based on a set of input sequencing data from a sample taken from a subject. EXAMPLES

[0087] Example 1: Analysis of cell-free DNA The PredicineBEACON assay uses blood or urine samples to + And LP-WGS can be used to provide highly sensitive baseline profiling.

[0088] Somatic mutations are identified by whole-exome sequencing across 20,000 genes with boosted sequencing of 600 cancer-related genes in untreated tissue, blood, or urine samples. Between 16-50 somatic mutations or fusions are selected for each patient based on sequencing. This personalized panel, along with a fixed MRD core panel, is utilized for MRD detection and treatment efficacy monitoring. The fixed MRD core panel allows for the detection of novel and actionable mutations beyond the variants identified at baseline, which is important for treatment monitoring, studying drug resistance, and guiding personalized therapy. Based on this multi-panel design, the mutant allele fraction (MAF) limit of detection (LOD) of PredicineBEACON can reach 0.005%. At each MRD monitoring time point, two tubes of blood (total 20 ml) or 40 ml of urine (30–60 ng cfDNA input) are recommended, and ultra-deep sequencing (e.g., 100,000x) can be used for high detection sensitivity. Companion low-pass whole-genome sequencing (LP-WGS) is performed to monitor CNV alterations for both baseline and follow-up time points. To establish a baseline for detection of MRD by cfDNA, the PredicineBEACON process is performed using PredicineWES + We started with the panel, which is an enhanced WES panel targeting 20,000 genes with boosted sequencing of 600 specific cancer-related genes. PredicineWES + The panel also covers important DNA fusions and includes genome-wide single nucleotide polymorphism (SNP) scaffold probes for loss of heterozygosity (LOH) and CNV detection.

[0089] PredicineWES +The cfDNA assay includes deep sequencing (20,000x, 0.25% LOD) for 600 cancer-associated genes and DNA fusions. The remaining whole-exome coverage averages 2,500x to enable genomic profiling at 1% LOD.

[0090] The clinical sensitivity of PredicineBEACON for MRD detection depends on the number of somatic mutations tracked. Figure 6 shows the relationship between tumor fraction and MRD sensitivity based on different numbers of patient mutations tracked by the assay. The model is based on the probability of detecting more than one single-stranded ctDNA molecule with an input of 30 ng of cfDNA. Based on this statistical model, more than four valid mutation targets are required to reliably detect ctDNA at a mutant allele frequency (MAF) of 0.05%, and 16 or more valid mutation targets are required to detect ctDNA at a MAF of 0.01%. Based on these data, conventional MRD assays targeting 16 or fewer mutations have a detection range of 0.1 to 0.01. With PredicineBEACON targeting 16 to 50 mutations, the MRD detection range can be improved to 0.0025%.

[0091] In real clinical samples, tumor MAF is often widely distributed. Figure 7 shows a typical MAF distribution from one clinical sample. This means that some high MAF mutations are easier to detect in MRD tracking, but many others with low MAF are more difficult to detect. Considering this factor, MRD assays need to target more mutations than the effective number of mutations shown in Figure 6.

[0092] Although the use of more targeted mutations may result in higher MRD detection sensitivity, assay specificity must also be considered. Assay specificity is an important consideration in baseline-independent assays because of the need to detect tens of thousands of unknown mutations covered by the panel. In baseline-information-based MRD assays such as PredicineBEACON, there is less concern about the risk of false positives because detection focuses on known mutations detected in the baseline sample. However, due to issues with multiple testing, false positive rates still need to be considered with increasing numbers of targeted mutations. To control the false positive rate of MRD detection, the PredicineBEACON assay needs to track up to 50 known baseline mutations and detect at least two known mutations to call an MRD event. Based on the titration experiment results with SeraCare reference material, the PredicineBEACON assay can achieve a high degree of sensitivity with an acceptable false positive rate (<1%) when tracking 50 baseline mutations.

[0093] Individualized probes allow for highly sensitive MRD detection. However, when novel mutations are induced during treatment, individualized probes may not be able to detect these. To meet such needs, PredicineBEACON can include a fixed core panel. The fixed 100kb core panel can cover more than 500 hotspot or actionable mutations in about 200 genes. Copy number alterations are another important cancer driver mechanism. Therefore, PredicineBEACON can include companion low-pass whole genome sequencing (LP-WGS) performed on both baseline and follow-up longitudinal samples. LP-WGS can further increase MRD detection sensitivity, especially for patients with genome-wide copy number alterations but few point mutations.

[0094] MRD detection often requires detecting trace amounts of ctDNA (<0.1%) by ultra-deep sequencing. Because the MAF ratio of ctDNA in MRD monitoring samples is often below the sequencing and PCR error rates, it is required to distinguish true mutations from sequencing and PCR errors. The use of Unique Molecular Identifiers (UMIs) can enable the identification of reads from the same single-stranded molecule, as well as the suppression of sequencing and late PCR errors during the consensus fragment construction process. However, single-stranded UMIs may not be able to distinguish early PCR errors from real mutations. A dual-UMI design can help recognize single-stranded molecules from the same double-stranded origin, thus suppressing early PCR errors. Importantly, a dual-UMI design may be infeasible with amplicon-based NGS assays utilized by some other MRD assays.

[0095] By using the dual UMI adapter design, both sequencing and PCR errors (including initial PCR errors) can be suppressed during the double-stranded consensus molecule construction step. The PredicineBEACON assay can make reliable MRD variant calls using one double-stranded ctDNA molecule. In the absence of dual UMI adapters, two or more supporting mutant fragments can be used for MRD variant calling.

[0096] Analytical validation of PredicineBEACON was performed based on two sets of titration samples: commercial reference material and real-world clinical patient blood samples. MAF from 0.025% to 0.0125%, up to 20 replicates at each titration level. Mutations in the SeraCare reference material have the same expected MAF, making them ideal for evaluating the relationship between the number of targeted mutations and MRD detection sensitivity. Among the mutations covered by the MRD core panel, 16 mutations were selected for MRD performance evaluation. Figure 8 shows the MRD detection sensitivity at different titration levels based on an input of 30 ng of DNA.

[0097] [Table 2]

[0098] To further evaluate the performance of PredicineBEACON, analytical validation was performed using three titrated sets of blood samples from patients with cancer in a real-world setting. Three metastatic castration-resistant prostate cancer (mCRPC) blood samples were analyzed using PredicineWES + As a baseline, PredicineWES + The cfDNA samples were sequenced by ELISA. Matched PBMC control samples were sequenced to remove CHIP, germline, and background variants. 50 somatic mutations were selected for the personalized probes for each patient sample. To mimic actual MRD monitoring patient samples with low MAF, cfDNA from patient samples was spiked into 30 ng of cfDNA from healthy donors. The titration ratio was calculated based on the estimated tumor fraction of the blood sample (maximum MAF after excluding variants with CNV alterations). Patient blood samples were titrated at MAFs of 0.05%, 0.025%, 0.01%, 0.005%–0.0025%, with four replicates at each titration level. A pooled panel of 50 personalized probes and the MRD core panel were used for MRD profiling. Table 3 shows a summary of the MRD detection results.

[0099] [Table 3]

[0100] Using the PredicineBEACON assay, MRD events can be detected in samples with tumor fractions as low as 0.005%. The actual MRD detection sensitivity depends on the number of traceable somatic mutations and the distribution of MAFs. Reproducibility was calculated as the percent coefficient of variation (%CV) of the median MAF of positive targets (Tables 2 and 3).

[0101] The PredicineBEACON assay allows for the establishment of MRD baselines utilizing blood or urine samples without the need for baseline tissue samples, which greatly expands the clinical application of MRD testing. A high level of MRD detection sensitivity is achieved by targeting up to 50 personalized mutation probes for each patient in addition to the analysis of a 100kb core panel and LP-WGS to assess CNV alterations. Furthermore, a high level of MRD detection specificity is achieved by utilizing double-stranded molecules identified by dual UMI. Based on the results of titration experiments with actual patient plasma samples, MRD detection with PredicineBEACON can reach MAF detection limits as low as 0.005%, improving the performance of PCR amplicon-based MRD assays.

[0102] The fixed MRD core panel and accompanying LP-WGS allow PredicineBEACON to have potential clinical applications including guiding treatment selection for MRD-positive patients, predicting the likelihood of recurrence at diagnosis, monitoring response to neoadjuvant treatment, detecting minimal residual disease, monitoring recurrence after adjuvant treatment, monitoring treatment resistance, and even the opportunity to treat disease when at MRD stage. Furthermore, sequential ctDNA detection by PredicineBEACON at different time points during treatment trials can be used as a measure of treatment response.

[0103] PredicineBEACON integrates tissue-independent, ultrasensitive MRD variant detection with actionable guidance on potential next treatment options, providing the first truly actionable MRD test for patients with cancer.

[0104] Example 2: Development and clinical application of the PredicineBEACON™ next-generation minimal residual disease (MRD) assay for genitourinary cancer Tumor-informed minimal residual disease (MRD) analysis has been previously evaluated in muscle-invasive bladder cancer (MIBC) patients undergoing neoadjuvant immunotherapy (NAT). However, many methods require the use of tumor tissue and do not provide actionable insights. In this study, we develop a tumor-independent MRD assay (PredicineBEACON™) with high sensitivity and ability to detect actionable mutations and genome-wide copy number variations in blood- or urine-based circulating tumor DNA.

[0105] The PredicineBEACON™ tumor-agnostic MRD assay includes three components: 1) baseline mutation identification and personalized variant panel design based on tissue or liquid biopsy, 2) ultra-deep next-generation sequencing of personalized cancer variants and actionable variants, and 3) genome-wide copy number variation assessment. In summary, PredicineWES with boosted depth in 600 cancer-associated genes + Whole-exon sequencing (WES) is performed using baseline tumor tissue or liquid biopsies (blood or urine) to identify somatic mutations. This allows ultra-deep sequencing (e.g., 100,000x) of up to 50 individualized somatic mutations along with a fixed core panel of 500 actionable / hotspot variants. Additionally, low-pass whole-genome sequencing (LP-WGS) is performed to monitor MRD by copy number burden (CNB) calculated from genome-wide copy number changes.

[0106] In this study of patients with MIBC, urine samples collected at baseline were analyzed to generate personalized variant profiles. Urine samples were then collected during the assay and tested with the PredicineBEACON™ MRD assay (Figure 2). Figure 2 shows the workflow of the PredicineBEACON tissue-agnostic actionable MRD assay.

[0107] Plasma cfDNA from cancer patients was diluted in a normal cfDNA background at five tumor fraction levels: 0.05%, 0.025%, 0.01%, 0.005%, and 0.0025%. Thirty-two somatic mutations were selected from baseline and used for MRD tracking. The assay reached 100% sensitivity at tumor fractions above 0.005%.

[0108] [Table 4]

[0109] FIG. 4 shows the specificity of mutation detection in the Predicine BEACON™ MRD assay.

[0110] To assess the specificity of mutation calling, plasma samples from healthy donors were tested with the Predicine BEACON™ MRD assay. By tracking up to 50 mutations, the assay reached a specificity of >99% when 2 or more mutations needed to be detected to call a mutation MRD positive event.

[0111] Urine-Based PredicineBEACON™ MRD Detection in MIBC Cancer Patients:

[0112] Four patients with MIBC undergoing NAT were tested with the Predicine BEACON™ assay. Urine samples were collected before and after neoadjuvant therapy. For MRD testing, urine samples collected before NAT were processed as baseline and were compared with the Predicine WES assay. +We tested 50 somatic variants in each patient with 100% genomic DNA in our study to generate personalized profiles of 50 somatic variants selected for mutation tracking along with a fixed core of 500 actionable / hotspot variants. LP-WGS sequencing was performed for genome-wide copy number analysis. Both tumor fraction inferred from mutation testing and copy number burden (CNB) derived from LP-WGS testing correlate with clinical response during NAT.

[0113] Example 3

[0114] Muscle-invasive bladder cancer (MIBC) is a challenging disease with a poor prognosis. Currently, the standard of care for resectable MIBC is neoadjuvant chemotherapy followed by radical cystectomy (RC), which suffers from widespread contraindications and a high rate of tumor recurrence. Immune checkpoint inhibitors (ICIs) are cytotoxic drugs 2 Perioperative immunotherapy has emerged as a promising treatment modality, especially in patients who are ineligible or refractory to cisplatin, as it has shown sustained efficacy and a favorable side effect profile compared with perioperative immunotherapy. Several ICIs, including pembrolizumab, atezolizumab, durvalumab, and nivolumab, have been evaluated as monotherapy or in combination in the neoadjuvant setting. However, established biomarkers currently fail to identify individuals who may benefit.

[0115] Toripalimab is a humanized IgG4 monoclonal antibody against programmed cell death 1 (PD-1) and is approved for second-line use in metastatic urothelial carcinoma. A study was conducted to further test the tolerability and efficacy of toripalimab given to MIBC patients before surgery. A total of 20 patients with newly diagnosed T2-4N0M0 MIBC were recruited (Table 5) and treated with toripalimab (3 mg / kg) every 2 weeks for up to 4 cycles before RC (Figure 9A). Overall, the safety signals were consistent with previous findings (Table 6). Nineteen (95%) and six (30%) patients experienced treatment- and immune-related adverse events (AEs), respectively. Grade 3 AEs occurred in three patients (15%) and caused the discontinuation of toripalimab in two cases. No grade 4 or 5 AEs were observed. All 20 patients were successfully subjected to RC with a median interval of 5.3 weeks (interquartile range, 4.1–6.5 weeks) from the last dose. Three patients (15%) underwent surgery that was postponed due to toxicity (Figure 9b). Upon use of toripalimab, most patients demonstrated extensive tumor regression on radiographic (Figure 9c, Figure 10A) and histopathological (Figure 9D, Figure 10B) examination, with a few exceptions. Eight patients (40%) achieved pathological complete response (ypCR, defined by pT0N0) and a further eight (40%) downstaged to nonmuscle-invasive disease (pT1 or less). One patient with locoregional progression while receiving toripalimab (RZ12) was able to undergo definitive resection. During a median postoperative follow-up time of 10.2 months (interquartile range, 7.6-13.1 months), only one patient (RZ17) recurred with a urethral mass, and all treated patients were alive. We conclude that the two co-primary endpoints of RJBLC-I2N003, including safety and efficacy, were both met, and the field 5-7 We replicated results from other ICI trials in

[0116] The clinical benefits of neoadjuvant immunotherapy must be weighed against the potential drawbacks of tumor progression during treatment and adverse events leading to surgical delay. Novel alternatives, including targeted agents and antibody-drug conjugates, are increasingly becoming viable options. 8 In addition, bladder preservation may be attempted in well-selected cases, associated with a better quality of life and with complete eradication of malignant cells. Taken together, these considerations highlight the urgency of developing reliable biomarkers for active disease monitoring and accurate patient stratification throughout the course of neoadjuvant therapy. To this end, prespecified exploratory biomarker discovery was pursued in prospectively collected biospecimens. Measurement of traditional biomarkers at baseline, such as PD-L1 expression, tumor mutation burden, and tertiary lymphoid structures, failed to show predictive value (Figure 11A). Similarly, PredicineWES, a WES assay with enhanced coverage of 600 cancer-related genes, was developed to assess the predictive value of 600 cancer-related genes. + Whole-exome sequencing (WES) of untreated neoplastic tissue using ELISA (Table 7) did not find a statistically significant correlation between specific somatic variants and patient outcome (Figure 11B). Furthermore, radiological reduction in tumor size as measured by repeat magnetic resonance imaging (MRI) showed only modest utility in identifying pathological responders (Figure 19c, Figure 11c), indicating a pressing need for more robust biomarkers.

[0117] Accumulating evidence suggests that liquid biopsy may be useful for predicting and monitoring ICI efficacy 9 Primary bladder cancer cells shed DNA directly into the urine, and we recently demonstrated that urinary tumor DNA (utDNA) is superior to blood-based circulating tumor DNA (ctDNA) in the treatment of MIBC. 10We have demonstrated that baseline utDNA can serve as a reliable and accurate surrogate of tumor-derived DNA (tDNA) in patients with advanced ovarian cancer. In this study, we evaluated the potential utility of tissue prognostic urinary minimal residual disease (MRD) analysis to identify true responders to neoadjuvant immunotherapy. Baseline utDNA was used in conjunction with PredicineWES for personalized MRD test design. + (Table 8). Notably, the mutational landscapes retrieved from utDNA and tDNA profiling were highly similar (Figure 12A), as were the estimated tumor mutational burdens (Figure 12B). Given the likely low abundance of utDNA following transurethral resection of bladder tumors (TURBT) and toriparimab treatment, we devised the PredicineBEACON™ MRD assay to profile up to 50 patient-specific somatic abnormalities (Table 9) using the Bespoke panel in combination with ultra-deep sequencing, targeted sequencing of a fixed set of 500 actionable / hotspot variants (Table 10), and low-pass whole genome sequencing (LP-WGS) to detect tumor-derived copy number alterations. Based on titration experiments with standard reference materials, PredicineBEACON™ reached 100% sensitivity with mutant allele frequencies >0.005% and a specificity of >99% for called MRD-positive events (data not shown). We applied PredicineBEACON™ to the RJBLC-I2N003 cohort to obtain tumor fraction (TF) estimates according to somatic mutations (TFsm) or copy number (TFcn) (Table 11, consistent with our previous observations), and utDNA performed better than ctDNA, representing urothelial neoplasms, as reflected by a clearly higher TFsm (Figure 13A). Indeed, utDNA contained all substitutions detected in ctDNA (Figure 13B). Moreover, more genome-wide copy number variations were identified in utDNA compared to ctDNA, as assessed by the TFcn score (Figure 13C) or the GISTIC algorithm (Figure 13D).

[0118] Focusing on quantitative metrics derived from urinary analytes, we found that TFsm was selectively decreased in triparimab responders (Figure 14A) and TFcn was similar, albeit modestly (Figure 14B), implicating utDNA reduction as a potential biological marker of tumor remission. At baseline, the area under the receiver operating characteristic (ROC) curve (AUC) values ​​for TFsm, TFcn, and MRI measures in predicting ypCR were 0.708, 0.760, and 0.531, respectively (Figure 9e). After completion of neoadjuvant therapy, the AUC values ​​were 0.969, 0.854, and 0.729, respectively (Figure 9f). Using optimal cut-off points defined by ROC analysis, TFsm and TFcn levels in post-treatment utDNA were significantly correlated with pathological outcomes (Fig. 14D), compared with TFsm and TFcn levels in pre-treatment utDNA (Fig. 14C), a correlation superior to that observed with MRI imaging. Finally, we determined the MRD status of utDNA samples according to the PredicineBEACON™ test. All patients were presumed to be MRD positive after TURBT, but three (RZ10, RZ15, and RZ20) became MRD negative before bladder removal and achieved ypCR without exception at the end of the study (Fig. 9g). These findings lay the foundation for histodiagnostic urinary MRD assessment to identify exceptional responders to neoadjuvant confirmatory point blockade who may be candidates for bladder preservation. Such a paradigm-shifting clinical approach would likely reduce the significant burden of unnecessary radical cystectomies.

[0119] We believed that continuous utDNA monitoring would be useful to measure the optimal duration of neoadjuvant immunotherapy. Indeed, the utDNA kinetic profile suggested that at least 3-4 cycles of pre-surgery toripalimab would likely be required in most cases to result in a dramatic reduction of both TFsm and TFcn (Figure 15A). Interestingly, utDNA clearance was observed in 8 of 8 (100%) patients who achieved ypCR. Similarly, 7 of 17 MRD-positive patients (41%) indeed showed utDNA clearance with minimal TFsm and TFcn (Figure 9G, Figure 15B), and may require continuous toripalimab to completely eradicate remaining cancer cells. Furthermore, to explore the possibility of treating MRD with alternative drugs, we explored the dynamic changes of actionable variants contained in PredicineBEACON™. For example, a recurrent FGFR3 S249C mutation was identified in a utDNA sample from patient RZ12 (Figure 15C), and an increase in mutant allele frequency was observed following neoadjuvant toripalimab, following progressive disease during treatment (Figure 15D). It is noteworthy that FGFR3 S249C was not detected in the analysis of bulky tissue, implying that subclonal disease was responsible for tumor growth. These results suggested that patient RZ12 is a candidate for subsequent FGFR-targeted therapy, such as erdafitinib. In total, four of the 17 MRD-positive subjects (RZ01, RZ06, RZ11, RZ12) could potentially benefit from genotype-matched FDA-approved regimens targeting FGFR3 variants (Figure 9G, Table 10). Taken together, 14 of the 20 patients in this study (70%, 14 / 20) could be promising candidates for a bladder-preserving strategy. Taken together, we suggest that longitudinal urinary MRD analysis should be performed to allow for adapted management of individual patients (Figure 9H).

[0120] Our data show, for the first time, that neoadjuvant toripalimab after RC is feasible and effective in patients with localized MIBC. Specifically, neoadjuvant toripalimab was associated with a low incidence of immune-related adverse events and no significant delay or complication in subsequent surgical procedures. Pathological complete response occurred in 40% of enrolled patients, in accordance with the most recent clinical studies of other ICIs in this setting. Thus, this study supports the exploration of the potential of neoadjuvant toripalimab in randomized controlled trials.

[0121] The current proof-of-concept biomarker analysis lays an unprecedented foundation for the incorporation of actionable utDNA MRD testing into future clinical trials and possibly into preoperative MIBC management in routine practice. Such a non-invasive approach is poised to not only facilitate the selection of individually tailored therapies but also enable real-time monitoring of various treatments, including emerging neoadjuvant immunotherapies, targeted agents and antibody-drug conjugates, among others. We envision that the tissue-independent urine-based MRD assay described herein holds great promise for informing clinical decision-making about organ preservation opportunities and may fundamentally transform health care guidelines for MIBC patients.

[0122] Patients and study design

[0123] A total of 20 patients with resectable MIBC were analyzed. Patient ages ranged from 18 to 75 years. All patients had an Eastern Cooperative Oncology Group performance status (ECOG PS) of 0 to 1 and underwent transurethral resection of bladder tumors (TURBT) for tumor resection, pathological diagnosis, and disease staging. Important exclusion criteria included documented severe autoimmune or chronic infection and use of systemic immunosuppressive drugs. Patients were treated with preoperative toripalimab at 3 mg / kg every 2 weeks for up to 4 cycles unless there was unacceptable toxicity or spontaneous retreatment. Radical cystectomy was planned within 4 ± 2 weeks after the last dose of toripalimab treatment. The primary efficacy endpoint was pathological complete response (ypCR) at surgical resection (defined by pT0N0). Safety was assessed at each patient's clinical visit and documented according to the National Cancer Institute Common Terminology Criteria for Adverse Events, v.4.03. Magnetic resonance imaging (MRI) was performed at baseline and every two cycles of toripalimab treatment. Radiographic images were evaluated by clinical investigators according to the Response Evaluation Criteria in Solid Tumors, v.1.1 (RECIST1.1). Median follow-up was 10.4 months (interquartile range, 8.0-13.2 months).

[0124] Sample collection and processing

[0125] FFPE (formalin-fixed and paraffin-embedded) tumor samples were obtained and histologically evaluated. FFPE with tumor content ≥20% were quantified for DNA extraction and whole exome sequencing (WES). PD-L1 immunohistochemistry (Dako, Canada) was performed, assessed by a certified pathologist, and quantified using a combined positive score (CPS), i.e., the number of stained positive cells divided by the total number of viable tumor cells and multiplied by 100. TLS (tertiary lymphoid structures) were assessed on FFPE tissue sections by hematoxylin and eosin (H&E) staining, and TLS positivity was defined as a TLS count ≥1. A volume of 40 ml of early morning first urine and a volume of 10 ml of peripheral blood were prospectively collected before and after neoadjuvant immunotherapy into a storage buffer-filled urine collection kit and BD Vacutainer EDTA tubes, respectively. Plasma and buffy coat were separated by centrifugation at 1600 xg for 10 min, followed by 3200 xg for 10 min at room temperature within 2 h after collection and immediately stored at -80°C.

[0126] DNA extraction was performed in a CAP-accredited laboratory (Huidu Shanghai). All collected biological specimens, including urine samples, plasma samples, tumor tissues, and peripheral blood mononuclear cells (PBMCs), were processed for DNA extraction and library preparation. Plasma and urine cell-free DNA (cfDNA) was extracted using the QIAamp Circulating Nucleic Acid Kit (Qiagen). The quantity and quality of purified cfDNA was confirmed using a Qubit fluorometer and a Bioanalyzer 2100. Genomic DNA (gDNA) was extracted from PBMCs and tumor tissues. Up to 250 ng of gDNA was enzymatically fragmented and purified.

[0127] MRD assay design

[0128] The PredicineBEACON™ personalized MRD assay involved whole-exome sequencing of baseline samples using either urine or tumor tissue from TURBT, followed by ultra-deep sequencing of subsequent longitudinal urine samples using a personalized MRD panel (personalized mutations, and a fixed panel of actionable / hotspot mutations). Matched PBMC samples were sequenced to obtain high-confidence somatic mutation calls. Up to 50 somatic mutations were selected to design a personalized panel for each patient.

[0129] Library preparation and sequencing

[0130] 5–30 ng of extracted cfDNA was subjected to library construction including end-repair dA tailing and adapter ligation. Ligated library fragments with appropriate adapters were amplified by PCR. Amplified DNA libraries were confirmed using Bioanalyzer 2100 and samples with sufficient yields were advanced to hybrid capture. Library capture was performed using biotin-labeled DNA probes. Briefly, libraries were hybridized overnight with PredicineBEACON™ panels and paramagnetic beads. Unbound fragments were washed away and enriched fragments were amplified by PCR amplification. Purified products were confirmed with Bioanalyzer 2100 and then loaded onto Illumina NovaSeq 6000 for sequencing with paired-end 2 × 150 bp reads.

[0131] Analysis of sequencing data from gDNA

[0132] Sequencing data from gDNA was analyzed using a developed analysis pipeline that started with raw sequencing data (BCL files) and ended with the output of final variant calling. Briefly, the pipeline first performs adapter trimming, barcode validation and correction. The cleaned paired FASTQ files were aligned to the human reference genome build hg19 using the BWA alignment tool. Candidate variants consisting of point mutations, small insertions and deletions, and structural mutations were identified across the target regions covered by the PredicineBEACON™ panel.

[0133] Analysis of sequencing data from cfDNA

[0134] NGS data were analyzed using the Predicine DeepSea NGS analysis pipeline, which started with raw sequencing data (BCL files) and ended with the output of final variant calls. Briefly, the pipeline first performed adapter trimming, barcode validation, and correction. The cleaned paired FASTQ files were aligned to the human reference genome build hg19 using the BWA alignment tool. A consensus bam file was then obtained by merging paired-end reads originating from the same molecule (based on mapping position and unique molecular identifiers) as single-stranded fragments. Single-stranded fragments originating from the same double-stranded DNA molecule were then further merged as double strands. Error suppression methods previously described 2 By using , both sequencing and PCR errors were largely corrected during this process.

[0135] Candidate variants were called by comparison with the local variant background (defined based on plasma and urine samples from healthy donors and historical data). Variants were called by comparing the logarithm of odds (LOD) threshold 3 , and were further filtered by base and mapping quality thresholds, repeat regions and other quality metrics.

[0136] Candidate somatic mutations were further filtered based on gene annotation to identify those occurring in protein-coding regions. Introns and silent changes were excluded, but mutations resulting in missense, nonsense, frameshift, or splice site changes were retained. Mutations annotated as benign or likely benign were also included in the ClinVar database. 4 or 1000 genomes with a population allele frequency >0.5% 5,6 ,ExAC 7 , gnomAD and KAVIAR 8 We excluded variants based on the common germline variants database, including 14466, 14455, 14456, 14457, 14459 ...

[0137] MRD Call

[0138] At least one fragment with confident variant support was required to detect known variants selected for MRD tracking at the following time points: MRD variants without double-stranded fragment support were classified as low confidence. For a sample to be considered MRD positive, one of the following criteria should have been met: (1) three or more low-confidence MRD variants were detected, or (2) two or more MRD variants were detected, one of which had double-stranded variant support.

[0139] Tumor fraction estimation

[0140] Tumor fractions were divided into 10 groups based on the number of somatic mutations (TFs) detected by MRD assay. sm ) or copy number (TF) detected from low-pass whole genome sequencing (LP-WGS) assay cn ) was estimated according to TF sm was estimated based on the allele fractions of the following autosomal somatic mutations:

[0141]

number

[0142] As previously described, ichorCNA was used to identify TF cn Briefly, low-pass whole genome sequencing (LP-WGS) with 5-fold global mean coverage was performed on patient samples. The ichorCNA algorithm was applied to GC- and mappability-normalized reads to estimate copy number variation using a hidden Markov model (HMM). TFs were then analyzed using the ichorCNA R software package. cn A prediction was made.

[0143] R version 4.0.0 (https: / / www.R-project.org) was used for statistical analysis and graph plotting with ggplot2 and ComplexHeatmap packages. ROC analysis was performed using the R package pROC. The optimal cutoff point was defined based on the maximum Youden index (sensitivity + specificity -1). Copy number G scores were calculated by the GISTIC2.0 pipeline via GenePattern (https: / / www.broadinstitute.org / cancer / software / genepattern). Numerical variables were compared using Wilcoxon rank sum test or Student's t test. Categorical variables were compared using Fisher's exact test. All tests were two-sided and considered statistically significant at P < 0.05.

[0144] [Table 5-1]

[0145]

Table 5-2

[0146]

Table 5-3

[0147]

Table 5-4

[0148]

Table 6-1

[0149]

Table 6-2

[0150]

Table 6-3

[0151]

Table 7-1

[0152]

Table 7-2

[0153]

Table 7-3

[0154]

Table 7-4

[0155]

Table 7-5

[0156]

Table 7-6

[0157]

Table 7-7

[0158]

Table 7-8

[0159]

Table 7-9

[0160]

Table 7-10

[0161]

Table 8-1

[0162]

Table 8-2

[0163]

Table 8-3

[0164]

Table 8-4

[0165]

Table 8-5

[0166]

Table 8-6

[0167]

Table 8-7

[0168]

Table 8-8

[0169]

Table 8-9

[0170]

Table 8-10

[0171]

Table 8-11

[0172]

Table 8-12

[0173]

Table 8-13

[0174]

Table 9-1

[0175]

Table 9-2

[0176]

Table 10

[0177]

Table 11-1

[0178]

Table 11-2

[0179] [Table 11-3]

[0180] [Table 11-4]

[0181] [Table 11-5]

[0182] [Table 11-6]

[0183] C1=Cycle 1, C2=Cycle 2, C3=Cycle 3, C4=Cycle 4, RC=Radical Cystectomy, MRD=Minimal Residual Disease, TFsm=Somatic Mutation-Based Tumor Fraction Estimate, TFcn=Copy Number-Based Tumor Fraction Estimate, CR=Complete Response, PR=Partial Response, SD=Stable Disease, PD=Progressive Disease, ypCR=Yield-Pathological Complete Response.

[0184] While preferred embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. The present invention is not intended to be limited by the specific examples provided herein. Although the present invention has been described with reference to the foregoing specification, the description and illustration of the embodiments herein are not meant to be construed in a limiting sense. Numerous variations, changes, and substitutions will occur to those skilled in the art without departing from the present invention. Furthermore, it should be understood that all aspects of the present invention are not limited to the specific depictions, configurations, or relative proportions described herein, which depend upon a variety of conditions and variables. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed in carrying out the present invention. It is therefore contemplated that the present invention shall also encompass any such alternatives, modifications, variations, or equivalents. It is intended that the following claims define the scope of the present invention, and that methods and structures within the scope of these claims and their equivalents are covered thereby.

Claims

1. 1. A method for detecting the presence or absence of minimal residual disease (MRD) in a subject, comprising: (a) assaying deoxyribonucleic acid (DNA) molecules from a first biological sample obtained or removed from said subject at a first time point; (b) detecting a set of biomarkers from said DNA molecules based at least in part on said assaying step of (a), wherein said set of biomarkers comprises differentially expressed markers or mutations; (c) creating a plurality of probe nucleic acids customized for the subject, the probe nucleic acids comprising sequences of at least a subset of the set of biomarkers; (d) detecting the presence or absence of said subset of said set of biomarkers by sequencing cell-free deoxyribonucleic acid (cfDNA) from a second biological sample obtained or removed from said subject at a second time point using said plurality of probe nucleic acids, wherein said sequencing is performed to a depth of at least 80 times; (e) determining the copy number of at least one region of the subject's genome by sequencing nucleic acid obtained or removed from the subject using whole genome sequencing; (f) detecting the presence or absence of minimal residual disease in the subject by computationally processing the subset of the set of biomarkers and the copy number of the at least one region of the genome; A method comprising:

2. 2. The method of claim 1, wherein the first biological sample or the second biological sample is selected from the group consisting of a cell-free deoxyribonucleic acid (cfDNA) sample, a cell-free ribonucleic acid (cfRNA) sample, a plasma sample, a serum sample, a buffy coat sample, a peripheral blood mononuclear cell (PBMC) sample, a red blood cell sample, a urine sample, a saliva sample, a tissue biopsy, a pleural effusion sample, a peritoneal fluid sample, an amniotic fluid sample, a cerebrospinal fluid sample, a lymph sample, a sweat sample, a tear sample, a semen sample, or a derivative of any of these, and any combination thereof.

3. 3. The method of claim 2, wherein the first biological sample or the second biological sample comprises the plasma sample.

4. 3. The method of claim 2, wherein the first biological sample or the second biological sample comprises the urine sample.

5. The method of claim 1, wherein (a) comprises exposing the first biological sample or the second biological sample to conditions sufficient to isolate, enrich, or extract the DNA molecules.

6. The method described in claim 1, further comprising the step of obtaining the DNA molecule by fractionating the first biological sample of the subject, wherein the first biological sample is a whole blood sample.

7. The method described in claim 1, further comprising a step of obtaining cfDNA molecules by fractionating the second biological sample of the subject, wherein the second biological sample is a whole blood sample.

8. The method of claim 1, wherein at least one of the DNA molecules is assayed using DNA sequencing to generate a nucleic acid sequencing read.

9. The method described in claim 8, wherein the DNA sequencing includes whole exome sequencing.

10. The method of claim 9, further comprising filtering at least one subset of the nucleic acid sequencing reads based on a quality score.

11. The method of claim 9, further comprising a step of performing error correction on the nucleic acid sequencing reads using a sample barcode or molecular barcode attached to at least one of the cfDNA molecules.

12. The method of claim 9, further comprising the step of performing at least one of single-stranded consensus calling and double-stranded consensus calling on the nucleic acid sequencing reads, thereby suppressing sequencing and PCR errors in the nucleic acid sequencing reads.

13. The method of claim 1, wherein the whole genome sequencing in (e) includes low-pass whole genome sequencing.

14. The method of claim 1, wherein the sequencing in (e) includes sequencing nucleic acids of a sample obtained at the first time point and sequencing nucleic acids of a sample obtained at the second time point.

15. The method of claim 14, further comprising a step of determining the copy number of at least one region of the subject's genome by comparing the results of sequencing the nucleic acid of the sample obtained at the first time point with the results of sequencing the nucleic acid of the sample obtained at the second time point.

16. The method described in claim 14, further comprising a step of generating a baseline copy number based on the sequencing of the nucleic acid of the sample obtained at least at the first time point.

17. The method of claim 1, wherein the cancer is selected from the group consisting of genitourinary cancer, breast cancer, lung cancer, prostate cancer, colon cancer, melanoma, bladder cancer, non-Hodgkin's lymphoma, kidney cancer, endometrial cancer, leukemia, pancreatic cancer, thyroid cancer, and liver cancer, and any combination thereof.

18. The method of claim 1, wherein the subject is asymptomatic for cancer.

19. The method described in claim 1, further comprising a step of determining mutant allele frequencies of a set of somatic mutations among the set of biomarkers.

20. The method of claim 19, further comprising determining an abnormality score for the cancer of the subject based at least in part on the set of variant allele frequencies.

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