Systems and methods for minimal residual disease analysis

Abstract

Provided herein are methods and systems for cancer detection and monitoring. The methods may include minimal residual disease analysis. The methods may include using nucleic acids from a urine sample or a urine cell pellet sample. The methods may include assaying nucleic acids in urine to detect a set of biomarkers from the sample. The methods may include monitoring a subject after transurethral bladder tumor resection.

Description

[Technical Field]

[0001] cross reference This application claims the benefit of U.S. Application No. 63 / 445,151, filed February 13, 2023, which is incorporated herein by reference in its entirety. [Background technology]

[0002] Cancer is one of the leading causes of death worldwide. Detecting cancer in an individual can be important for providing treatment and improving patient outcomes. Cancer can be caused by genetic abnormalities that can lead to unregulated growth of cells. Detecting genetic abnormalities can be important in cancer detection. Sequencing of nucleic acids in patient-derived samples can be used to detect genetic abnormalities. Summary of the Invention

[0003] Provided herein are systems and methods for detecting the presence or absence of cancer in a subject. The systems and methods provided herein involve assaying polynucleotides to identify cancer biomarkers in a subject. Detection of specific biomarkers for a cancer type or a given cancer can enable effective treatment to be provided to an individual and can result in improved outcomes. For multiple types of cancer, specific biomarkers indicative of a particular cancer type (or subtype) can be used to identify the prognosis of an individual suffering from cancer. Multiple analytes can be examined to provide accurate cancer detection and prognosis. By analyzing an increased number of analytes (and sets of biomarkers from the analytes), detection of cancer (or cancer parameters) can be improved, allowing for effective treatment recommendations and more accurate prognosis.

[0004] In one aspect, the disclosure provides a method for identifying 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 derived 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 assay of (a), wherein the set of biomarkers comprises differentially expressed markers or variants; (c) generating 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) sequencing cellular DNA from a second biological sample obtained or derived from the subject at a second time point using the plurality of probe nucleic acids to detect the presence or absence of the subset of the set of biomarkers, wherein the sequencing is performed at a depth of at least 80x, and wherein the second biological sample is a blood sample, a urine sample, or a urine cell pellet sample. and (e) computationally processing a subset of the set of biomarkers to detect the presence or absence of minimal residual disease (MRD) in the subject. In some embodiments, the first 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 urine cell pellet 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 any derivative thereof, and any combination thereof. In some embodiments, the first biological sample comprises a plasma sample. In some embodiments, the first biological sample comprises a urine sample. In some embodiments, the first biological sample comprises a tumor tissue sample.In some embodiments, the first or second biological sample is obtained or derived from the subject using an ethylenediaminetetraacetic acid (EDTA) collection tube, acellular RNA collection tube, acellular deoxyribonucleic acid (DNA) collection tube, other blood collection tube, or CTC collection tube. In some embodiments, (a) comprises subjecting the first or second biological sample to conditions sufficient to isolate, enrich, or extract DNA molecules. In some embodiments, the method further comprises fractionating the subject's first biological sample to obtain DNA molecules, wherein the first biological sample is a whole blood sample. In some embodiments, at least one of the DNA molecules is assayed using DNA sequencing to generate nucleic acid sequencing reads. In some embodiments, the DNA sequencing comprises whole-exome sequencing. In some embodiments, the method further comprises filtering at least a 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 DNA molecules. In some embodiments, the method further includes 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. In some embodiments, the sequencing in (d) is performed at a depth of at least 100x. In some embodiments, the sequencing in (d) is performed at a depth of at least 1,000x. In some embodiments, the sequencing in (d) is performed at a depth of at least 10,000x. In some embodiments, the sequencing in (d) is performed at a depth of at least 100,000x. In some embodiments, the sequencing in (e) comprises sequencing nucleic acids from a first biological sample. In some embodiments, the sequencing in (e) comprises sequencing nucleic acids from a second biological sample.In some embodiments, the sequencing in (e) comprises sequencing the nucleic acid of a sample collected at a first time point and sequencing the nucleic acid of a sample collected at a second time point. In some embodiments, the assaying in (a), the sequencing in (d), or the sequencing in (e) comprises nucleic acid amplification. In some embodiments, the nucleic acid amplification comprises polymerase chain reaction (PCR) or isothermal amplification. In some embodiments, the cancer is selected from the group consisting of genitourinary cancer, prostate cancer, bladder 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.

[0005] In some embodiments, the methods comprise 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 methods comprise 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 methods comprise 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%. In some embodiments, the first biological sample is obtained or derived from the subject before the subject receives treatment for cancer. In some embodiments, the biological sample is obtained or derived from the subject during treatment for cancer. In some embodiments, the biological sample is obtained or derived 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 deprivation therapy, and combinations thereof.In some embodiments, the first biological sample is obtained or derived from the subject via transurethral resection of bladder tumor. In some embodiments, the first biological sample is obtained or derived from the subject after performing transurethral resection of bladder tumor. In some embodiments, the method includes identifying a clinical intervention for the subject based at least in part on the presence or absence of detected cancer. In some embodiments, the clinical intervention is selected from a plurality of clinical interventions. In some embodiments, the clinical intervention is selected from the group consisting of surgical resection, chemotherapy, radiation therapy, immunotherapy, adjuvant therapy, neoadjuvant therapy, androgen deprivation therapy, and combinations thereof. In some embodiments, the surgical resection is transurethral resection of bladder tumor (TURBT) or repeat transurethral resection of bladder tumor. In some embodiments, the method further includes administering the clinical intervention to the subject. In some embodiments, the set of biomarkers includes 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, or 85 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 2. In some embodiments, the set of biomarkers comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, or 40 members selected from the group consisting of the genes listed in Table 3. In some embodiments, the set of biomarkers comprises one or more members selected from the group consisting of the genes listed in Table 3. In some embodiments, the plurality of probes comprises nucleic acid primers. In some embodiments, the plurality of probes comprises nucleic acid capture probes. In some embodiments, the plurality of probes has sequence complementarity to 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. In some embodiments, (d) further comprises sequencing using the immobilized plurality of probes, wherein the probes of the immobilized plurality of probes comprise probes that do not comprise sequences of a subset of the set of biomarkers. In some embodiments, the immobilized plurality of probes comprises one or more members selected from the group consisting of the genes listed in Table 2. In some embodiments, the method further comprises determining the likelihood of determining the presence or absence of cancer in the subject. In some embodiments, the method further comprises monitoring the presence or absence of cancer in the subject, wherein the monitoring comprises assessing the presence or absence of cancer in the subject at each of a plurality of time points. In some embodiments, a difference in the assessment of the presence or absence of cancer in the subject between the plurality of time points indicates one or more clinical signs 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).

[0006] In some embodiments, the set of biomarkers derived from DNA molecules comprises tumor-associated alterations selected from the group consisting of single nucleotide variants (SNVs), insertions or deletions (indels), and rearrangements. In some embodiments, the method further comprises determining mutant allele frequencies 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 subject's cancer based at least in part on the set of mutant allele frequencies. In some embodiments, the method further comprises determining a tumor mutational burden (TMB) of the subject's cancer. In some embodiments, the method further comprises determining an abnormality score for the subject's cancer based at least in part on the set of mutant allele frequencies.

[0007] In another aspect, the disclosure provides a method for providing treatment to a subject, the method comprising: (a) assaying deoxyribonucleic acid (DNA) molecules from a tumor sample obtained or derived from the subject at a first time point, wherein the subject has undergone a bladder tumor procedure. (b) detecting a set of biomarkers from DNA molecules based at least in part on the assay of (a), where the set of biomarkers comprises differentially expressed markers or variants; (c) generating a plurality of probe nucleic acids customized for the subject, where the probe nucleic acids comprise sequences of at least a subset of the set of biomarkers; (d) sequencing DNA from a urine sample obtained or derived from the subject at a second time point using the plurality of probe nucleic acids to detect the presence or absence of the subset of the set of biomarkers, where the sequencing is performed to a depth of at least 80x and the second biological sample is a urine sample; (e) computer processing the subset of the set of biomarkers to detect the presence or absence of minimal residual disease (MRD) in the subject; and (f) performing a repeat transurethral resection of bladder tumor (rTURBT) procedure based at least on the presence or absence of minimal residual disease (MRD) in the subject.

[0008] Another aspect of the present disclosure provides a non-transitory computer readable medium comprising machine executable code that, when executed by one or more computer processors, implements any of the methods described above or elsewhere herein.

[0009] 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, when executed by the one or more computer processors, performs any of the methods described above or elsewhere herein.

[0010]

[0013] Further aspects and advantages of the present disclosure will become readily apparent to those skilled in the art from the following detailed description, wherein only illustrative embodiments of the present disclosure are shown and described. As will be realized, the present disclosure is capable of other and different embodiments, and its various details are capable of modifications 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.

[0011] Incorporation by Reference All publications, patents, and patent applications mentioned herein are herein incorporated 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. To the extent that the publications and patents or patent applications incorporated by reference conflict with the present disclosure contained herein, the present specification is intended to supersede and / or take precedence over any such conflicting material. [Brief explanation of the drawings]

[0012] 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] 1 illustrates an exemplary workflow. [Figure 2] Figure 1 shows an oncoplot of urinary tumor DNA genomic alterations in patients with NMIBC before repeat TURBT. [Figure 3A] WES+ variant frequencies between the index sample and the rTURBT sample are shown. [Figure 3B]Variant overlap panels between index TURBT (blue), rTURBT (green), and urine samples (yellow) are shown. [Figure 3C] utDNA positivity in patients with disease present at the time of rTURBT. [Figure 3D] Tumor fraction of utDNA before rTURBT is shown. [Figure 4] 1 illustrates a computer system that is programmed or otherwise configured to implement the methods provided herein. Detailed Description of the Invention

[0013] While various embodiments of the present invention have been shown and described herein, it will be obvious 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.

[0014] Provided herein are systems and methods for detecting the presence or absence of cancer in a subject. The systems and methods provided herein include assaying polynucleotides to identify cancer biomarkers in a subject. The biomarkers can be processed to identify the presence or absence of cancer. The methods described herein can process analytes to determine the presence or absence of cancer. The analytes can include cfDNA or other analytes that can be provided via non-invasive methods. By analyzing analytes obtained by non-invasive methods, the methods can enable improved or similar detection or determination of prognosis compared to methods using tumor or tissue biopsies.

[0015] Human urine may contain fragmented DNA known as urinary cell-free DNA (ucfDNA), derived from dead cells in the urogenital tract or circulating DNA that has passed through glomerular filtration. Given direct access to the urinary tract, urine is a viable source for detecting cfDNA biomarkers, and ucfDNA may improve the current diagnostic sensitivity of liquid biopsies in genitourinary cancers. Many genetic variants can be identified in ucfDNA from cancer patients, particularly those with bladder cancer. Urine may also contain whole cells, cell fragments, and other biomolecules, which can be precipitated by centrifugation to form a pellet. The urinary cell pellet (UCP), or urine pellet, may contain DNA that can be analyzed for biomarkers. The use of urine for liquid biopsy offers a completely noninvasive approach for the detection of genomic biomarkers to guide cancer treatment.

[0016] Next-generation sequencing (NGS) has revolutionized cancer genomic research over the past decade. NGS technologies are commercially available, cleared or approved by the FDA for use in processing DNA from patient tissue or blood samples to guide cancer patient treatment plans. Next-generation sequencing (NGS) assays on cfDNA can enable accurate detection of genomic alterations, including single nucleotide variants (SNVs), insertions and deletions (indels), copy number variations (CNVs), and DNA rearrangements. Urinary cfDNA is derived directly from exfoliated dead cells in urine and, due to tumor heterogeneity, can be considered more representative of the tumor than tissue biopsies, which only detect mutations found in specific areas of the tumor. Additionally, urine may contain fewer contaminating proteins than blood, and cfDNA levels in urine may be higher than in the bloodstream. Urinary cfDNA can be subjected to sequencing to enable the detection of cancer and cancer-related genetic alterations in a subject's urine. The methods and assays described in this disclosure represent an application of NGS technology to provide a non-invasive, cost-effective, and potentially more sensitive sampling method for patients with cancer, such as genitourinary cancers, including bladder cancer.

[0017] In addition to staging and grading a patient's tumor, tissue biopsy is often the gold standard for guiding cancer patient treatment. However, depending on the tumor location or the patient's disease, tumor biopsy can be painful, and patients may be at risk for complications, which can make the medical procedure expensive. In some cases, tissue biopsy may not be feasible. Less invasive sampling methods represent an unmet clinical need for the treatment of bladder cancer patients. Urine cfDNA assays and liquid biopsy options (e.g., from urine) may help fill this void. Additionally, in contrast to tissue biopsies, there is an opportunity for patients to be tested multiple times with urine biopsies. Thus, liquid biopsies from urine represent a noninvasive and cost-effective method for obtaining patient samples to determine molecular eligibility for specific treatment strategies.

[0018] Urine biopsy samples can also improve patient care. For NGS testing of bladder cancer patients, if tissue biopsy is not feasible, bladder cancer patients can be monitored using urine biopsies. The assay can detect other genetic mutations in urine samples from bladder cancer patients, including, but not limited to, alterations in CDKN2A, HRAS / KRAS, KDM6A, PIK3CA, TERT, TP53, and TSC1, which, if identified, can help inform patient treatment.

[0019] Bladder cancer is the 10th most common malignancy worldwide, with an estimated 550,000 new cases and 200,000 deaths reported in 2018. The majority of bladder cancer cases are non-muscle invasive bladder cancer (NMIBC), which requires frequent monitoring, local resection (transurethral resection of bladder tumor [TURBT]), and an intensive regimen of intravesical therapy to reduce the risk of both recurrent and progressive disease. Despite these efforts, within 5 years, 45%–60% of patients experience recurrent disease, and nearly 20% of patients with high-risk disease progress to muscle-invasive tumors requiring radical cystectomy (RC). The natural history of high-risk NMIBC is unpredictable, with rates of recurrence ranging from 15% to 78%, and rates of progression to muscle invasiveness and metastasis ranging from <1% to 45%. Long-term outcomes suggest that approximately 20% to 25% of patients with high-risk NMIBC will ultimately die from bladder cancer.

[0020] TURBT can be effective as a treatment for cancer. Even if tumors are completely removed by TURBT, cancer recurrence may still occur. Therefore, patients who have undergone TURBT need to be monitored to determine whether further therapeutic intervention is required. Specifically, repeat TURBT (rTURBT) can be performed on the subject. The method described herein can enable patient monitoring after initial TURBT. Based on monitoring and MRD, another therapeutic intervention, such as rTURBT, can be recommended or performed on the subject.

[0021] Analytes that can be used for tumor diagnosis from urine biopsies include cfDNA, non-coding RNA, exfoliated tumor cells, and proteins. During tumor-destructive therapy or during apoptosis and necrotic processes, both healthy and diseased cells can release cfDNA fragments, typically 100–200 base pairs in length. In disease-free patients, phagocytes engulf cellular debris and necrotic cells, resulting in very low levels of cfDNA. In diseased patients, phagocytosis is impaired, DNA digestion is minimal, and DNA fragments have random dimensions that can exceed 10,000 base pairs. Therefore, cfDNA levels in diseased patients are often elevated.

[0022] Urine cfDNA (ucfDNA) can be extracted from the urine of a subject, and the ucfDNA can be subjected to various reactions to allow sequencing of the ucfDNA.The library construction of ucfDNA can include amplification, ligation of adapters or additional sequences, and / or labeling with barcodes to generate sequencing libraries.Furthermore, cfDNA or ucfDNA library can be subjected to enrichment using capture probes or amplification primers to enrich specific sequences of interest from cfDNA.Then, the library can be subjected to sequencing reactions to generate sequencing data.

[0023] Urine samples can be isolated and collected from individuals.After collection, urine DNA or cfDNA can be extracted.Then, library construction can be carried out on the extracted urine DNA or cfDNA, and then specific target can be enriched.After enrichment is carried out, urine DNA or cfDNA can be sequenced, and then sequencing data can be processed.

[0024] Genetic modifications such as single nucleotide variations (SNVs), indels, DNA rearrangements, and copy number variations (CNVs) can be identified through bioinformatics analysis of sequencing data. Generally, a bioinformatics pipeline can utilize raw sequencing data (e.g., BCL files) and output mutational calls. The pipeline can perform various tasks to analyze sequencing data, such as adapter trimming, barcode checking, or error correction. Cleaned paired files (e.g., FASTQ files) can be aligned to the human reference genome using an alignment tool such as the BWA alignment tool. A consensus sequence can then be derived by merging single-stranded fragments and paired-end reads derived from the same molecule. Single-stranded fragments derived from the same double-stranded DNA molecule can be further merged as a duplex. These processes can allow for the correction of sequencing and PCR errors.

[0025] The subject may be suspected of having cancer. The cancer may be specific to an organ or other region of the subject, or may originate from any source. 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, or any combination thereof. The cancer may be hormone-sensitive prostate cancer (HSPC), castrate-resistant prostate cancer (CRPC), metastatic prostate cancer, or a combination thereof. The cancer may be cancer of tissues or cells of the genitourinary tract. For example, the cancer may be bladder cancer, kidney cancer, or prostate cancer. The cancer may include a biomarker specific to a particular cancer. A particular biomarker may indicate the presence of a particular cancer. For example, a biomarker may indicate the presence of castration-resistant prostate cancer. Identifying the presence of a type of cancer may allow for the determination of treatment options or recommendations.

[0026] In some cases, the subject may be asymptomatic for cancer.For example, cancer may not show any symptoms, and the subject may not be aware of the existence of cancer.The method described herein can allow cancer to be identified at an earlier stage than in other cases.Identifying the existence of cancer at an early stage can allow treatment options or recommendations to be determined at an early stage, and can allow the subject to have an improved prognosis.

[0027] The biological sample may contain nucleic acids. The biological sample may be a cell-free deoxyribonucleic acid (cfDNA) sample or a cell-free ribonucleic acid (cfRNA) sample. The biological sample may contain 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 a 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, serum sample, buffy coat sample, peripheral blood mononuclear cell (PBMC) sample, red blood cell sample, urine sample, saliva sample, or other bodily fluid sample. The biological sample may include or be a pleural fluid sample, an ascites sample, an amniotic fluid sample, a cerebrospinal fluid sample, a lymphatic fluid sample, a sweat sample, a tear sample, a semen sample, or any combination of biological fluids. The biological sample may include a urine sample.

[0028] The biological sample can be collected, obtained, or derived from a subject using a collection tube. The collection tube can be an ethylenediaminetetraacetic acid (EDTA) collection tube, a cell-free RNA collection tube, a cell-free deoxyribonucleic acid (DNA) collection tube, a CTC collection tube, or other blood collection tube. The collection tube can contain additional reagents to stabilize nucleic acid molecules or blood cells. The collection tube can allow the nucleic acid or blood cells to stabilize to minimize degradation of the biological sample before assay. The additional reagents can include buffer salts or chelating agents.

[0029] The biological sample may be obtained or derived from a subject at various times. The biological sample may be obtained or derived from a subject before the subject receives treatment for cancer. The biological sample may be obtained or derived from a subject while the subject is receiving treatment for cancer. The biological sample may be obtained or derived from a subject after receiving treatment for cancer. The biological sample may be obtained or derived from the subject via a transurethral bladder tumor resection. The biological sample may be obtained or derived from a subject after performing a transurethral bladder tumor resection.

[0030] Biological samples may 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 time points. 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, 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, 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, 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, 60 months 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, 60 years or more.

[0031] In various aspects described herein, a clinical intervention or treatment may be identified based at least in part on the identification of the presence of cancer or the presence of a cancer parameter. The clinical intervention may be a plurality of clinical interventions. The clinical intervention may be selected from a plurality of clinical interventions. The clinical intervention may be surgical resection, chemotherapy, radiation therapy, immunotherapy, adjuvant therapy, neoadjuvant therapy, androgen deprivation therapy, or a combination thereof. The clinical intervention may be transurethral resection of bladder tumor (TURBT). The clinical intervention may be repeat transurethral resection of bladder tumor (rTURBT). In some cases, the clinical intervention may be administered to the subject. After administration of the clinical intervention, a sample may be obtained or derived from the subject to monitor the cancer or cancer parameter. Thus, the methods and systems disclosed herein may be repeatedly performed so that cancer monitoring can be performed. Furthermore, by repeatedly performing the method or system, the treatment or clinical intervention may be updated based on the results of the method. Cancer monitoring may include an assessment and a difference between the assessment and a previously generated assessment. Differences in the assessment of cancer in a subject between multiple time points (or samples) can indicate one or more clinical indications, such as a diagnosis of cancer, a prognosis of cancer, or the effectiveness or ineffectiveness of a course of treatment for treating the subject's cancer. Prognosis can include predicted progression-free survival (PFS), overall survival (OS), or other metrics related to cancer severity or survival rate.

[0032] 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 molecules.

[0033] 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 capture probes or bait molecules that hybridize to nucleic acid molecules in 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. 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. For example, the capture probe may include sequence complementarity to a gene selected from Table 1, Table 2, or Table 3. Multiple probes are available at 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 5, 160, 165, 170, 175, 180, 185, 190, 195, 200, 205, 210, 215, 220, 225, 230, 235, 240, 245, 250, 255, 260, 265, 270, 275, 280, 285, 290, 295, 300, 305, 310 , 315, 320, 325, 330, 335, 340, 345, 350, 355, 360, 365, 370, 375, 380, 385, 390, 395, 400, 405, 410, 415, 420, 425, 430, 435, 440, 445, 450, 455, 460, 465, 470, 475, 480, 485, 490, 495, 500, 505, 510, 515, 520, 525, 530, 535, 540, 545, 550, 555, 560, 565, 570, 575, 580, 585, 590, 595, or 600 different probes.

[0034] 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 magnetic separators. 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 capture probe-free nucleic acid molecules. The isolation reaction may include removing or separating a group of nucleic acid molecules from another group of nucleic acids.

[0035] The methods disclosed herein can include a conduction extraction reaction for one or more nucleic acids in a biological sample. The extraction reaction can lyse cells or disrupt nucleic acid interactions with cells so that the nucleic acids can be isolated, purified, concentrated, or subjected to other reactions.

[0036] The methods 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.

[0037] The methods disclosed herein may include a barcoding reaction. The barcoding reaction may include adding 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 randomly generated or may be generated to synthesize a specific barcode sequence. The barcode nucleic acid may be added to a sample to label 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 with the same barcode sequence. When barcoding nucleic acids, those derived 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 so that each molecule (or multiple molecules) in the same volume has a different molecular barcode. This barcode may be subjected to amplification so that all amplicons derived from a molecule have the same barcode. In this way, molecules derived from the same molecule may be identified. Sequence reads may be processed based on the barcode sequence. For example, processing can reduce errors or allow molecules to be tracked. Barcode sequences can be added or otherwise added or incorporated into sequences by various reactions, such as amplification, extension, or ligation reactions, and can be enzymatically performed using nucleic acid polymerase or ligase. Ligation can be overhang or blunt end ligation, and the barcode can include complementarity to the barcoded nucleic acid. This complementarity can be a sequence derived from a sample from a subject, or a constant sequence generated through a reaction performed on the nucleic acid in the sample.

[0038] 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, and cell-free nucleic acids may be obtained. A whole blood sample may be fractionated using centrifugation, so that blood cells may be separated from plasma (which may contain cell-free nucleic acids). The sample may be subjected to multiple separations or fractionations.

[0039] In various embodiments described throughout this disclosure, nucleic acids can be subjected to a sequencing reaction. Sequencing reactions 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, nanopore current-limiting sequencing, or a combination thereof. Sequencing-by-synthesis can include reversible terminator sequencing, processive single-molecule sequencing, sequential nucleotide flow sequencing, or a combination thereof. Sequential nucleotide flow sequencing can include pyrosequencing, pH-mediated sequencing, semiconductor sequencing, or a combination thereof. The sequencing reactions can include whole genome sequencing, whole exome sequencing, low-pass whole genome sequencing, targeted sequencing, methylation-aware sequencing, enzymatic methylation sequencing, bisulfite methylation sequencing. The sequencing reactions can be transcriptome sequencing, mRNA-seq, total RNA-seq, small RNA-seq, exosome sequencing, or a combination thereof. Combinations of sequencing reactions can be used in the methods described elsewhere herein.For example, a sample can be subjected to whole genome sequencing and whole transcriptome sequencing. Because a sample can contain multiple types of nucleic acids (e.g., RNA and DNA), DNA- or RNA-specific sequencing reactions can be used to obtain sequence reads associated with the nucleic acid type.

[0040] Sequencing reactions can be performed at various sequencing depths. The sequencing depth of the sequencing reactions can be selected or adjusted. Sequencing reactions 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, It may include sequencing in a region of 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, 90,000, 100,000x, or more depth. Sequencing reactions are: 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, 100,000x, or less depth.

[0041] In various embodiments, low-pass whole genome sequencing is used to sequence nucleic acids. Low-pass whole genome sequencing can be performed at an average sequencing depth of at least 1x, 2x, 3x, 4x, 5x, 6x, 7x, 8x, 9x, 10x, or more. Low-pass whole genome sequencing can be performed at an average sequencing depth of 1x, 2x, 3x, 4x, 5x, 6x, 7x, 8x, 9x, 10x, or less. Low-pass whole genome sequencing can be performed at an average sequencing depth of 1x to 2x.

[0042] In various embodiments, the sequencing reaction can be performed using an individualized or customized set of probes, which can be a deep sequencing reaction or an 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, 100,000x, or more.

[0043] In various embodiments, whole exome sequencing is used to sequence the nucleic acid of interest. Whole exome sequencing can be performed at non-uniform depth. For example, certain regions of the exome can be boosted or otherwise sequenced at a greater depth than other regions, or at a greater depth than the average depth of whole exome sequencing. By sequencing certain regions at a greater depth, more interesting genes or regions can be analyzed with higher sensitivity, precision, and / or accuracy. Genes or regions associated with or related to cancer can be sequenced at a greater depth. For example, at least 100, 200, 300, 400, 500, 600, 700, 800, 900, or more genes can be sequenced at a greater depth than the rest of the exome (for example, the average depth of whole exome sequencing).

[0044] Sequencing of nucleic acids can generate sequencing read data. Sequencing reads can be processed to generate data of improved quality. Sequencing reads can be generated using quality scores. The quality score can indicate the accuracy of the sequence read for a given base call, or the level or signal above a threshold. The quality score can be used to filter sequencing reads. For example, sequencing reads that do not meet a certain quality score threshold can be removed. Sequencing reads can be processed to generate consensus sequences or consensus base calls. A given nucleic acid (or nucleic acid fragment) can be sequenced, and errors in the sequence can be generated due to reactions before or during sequencing. For example, amplification or PCR can generate errors in the amplicon so that the sequence is not identical to the parent sequence. Error correction can be performed using sample barcodes or molecular barcodes. Error correction can 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 can enable identification of the same parent or sample. Additionally, sequence reads can be processed by performing single-stranded or double-stranded consensus calling, thereby reducing or suppressing errors.

[0045] The methods disclosed herein may include determining allele frequencies or other cancer-related metrics. The methods may include determining mutant allele frequencies of a set of somatic mutations among a set of biomarkers. The mutant allele frequencies may be used to determine the circulating tumor DNA (ctDNA) fraction of a subject's cancer. The plasma tumor mutational burden (pTMB) of a subject's cancer may be determined based at least in part on the set of mutant allele frequencies. Detection of microsatellite instability may also be used to determine the presence or absence of cancer or a cancer metric. Methylation status may be determined using the methods described herein and used to identify the presence of cancer or a cancer parameter.

[0046] In various embodiments, a set of biomarkers is processed to generate data corresponding to the biomarkers. The set of biomarkers may include quantitative measures from a set of cancer-associated genomic loci. The cancer-associated genomic loci may correspond to a set of genes. The cancer-associated genomic loci may include one or more genes selected from Table 1. In some cases, the set of cancer-associated genomic loci includes 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 genes listed in Table 1.

[0047] [Table 1] The set of biomarkers may include one or more genes listed in Table 2. 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, or 85 members selected from the group consisting of the genes listed in Table 2.

[0048] The set of biomarkers may include one or more genes selected from Table 3. In some cases, the set of biomarkers may include 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, or 40 members selected from the group consisting of the genes listed in Table 3.

[0049] The set of biomarkers can correspond to genetic abnormalities of gene loci. The genetic abnormalities can be tumor-associated changes. The genetic abnormalities can include copy number alterations (CNAs), copy number losses (CNLs), single nucleotide variants (SNVs), insertions or deletions (indels), and / or rearrangements. The set of biomarkers can be identified in various nucleic acid types. For example, tumor-associated changes can be identified in cfDNA. The tumor-associated changes can include changes in allele expression or gene expression. The methods and systems disclosed herein can enable gene expression profiling and the identification of changes in gene expression levels.

[0050] In various embodiments, the method may include identifying the presence of cancer or a cancer parameter. The method may include determining the probability or likelihood of the presence of cancer or a cancer parameter. For example, instead of a binary output indicating presence or absence, an output may be generated indicating the probability that the subject has cancer. This probability may be determined based on an algorithm described elsewhere herein. Similarly, the probability or likelihood of response to a particular treatment, or the probability of relapse, may be output.

[0051] In various aspects, the set of biomarkers is processed using an algorithm. The algorithm can be a trained algorithm. The trained algorithm can use the set of biomarkers as input and generate an output regarding the presence or absence of cancer. The output can be specific to a type of cancer or a subtype of cancer. For example, the output can indicate the presence of bladder cancer.

[0052] The trained algorithm may be trained on a plurality 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, 190, 200, 210, 215, 220, 225, 230, 235, 240, 245, 250, 255, 260, 265, 270, 275, 280, 285, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 410, 420, 430, 440, 450, 460, 470, 480, 490, 510, 520, 530, 540, 550, 560, 570, 580, 590, 610, 620, 630, 640, 650, 660, 670, 680, 690, 700, 710, 720, 7 It may be trained using 5, 190, 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, 10000, or more independent training samples. The trained algorithms were: 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, 215, 220, 225, 230, 235, 240, 245, 250, 255, 260, 265, 270, 275, 280, 285, 290, 300, 305, 310, 315, 320, 325, 330, 335, 340, 345, 350, 355, 360, 365, 370, 375, 380, 385, 390, 410, 420, 430, 440, 450, 460, 470, 480, 490, 510, 520, 530, 540, 550, 560, 570, 580, 590, 610, 620, 630, 640, 650, 66 The method may be trained using 95, 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, 10000, or fewer 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. Individual training samples may be positive for a particular cancer. Individual training samples may be negative for a particular cancer. By using the training sample, the trained algorithm may be able to detect cancer, determine the probability of cancer recurrence or recurrence, or determine whether the cancer contains a set of biomarkers that may be resistant to treatment. The training sample 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.

[0053] The trained algorithm can be trained using multiple sets of training samples. Sets can include the training samples described elsewhere herein. For example, training can be carried out using a first set of independent training samples that are associated with the presence of cancer, and a second set of independent training samples that are associated with 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.

[0054] 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 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 data.

[0055] 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 trained algorithm may be trained using training data to generate expected or desired outputs. Supervised learning algorithms may include deep learning algorithms, support vector machines (SVMs), neural networks, or random forests. Through the machine learning algorithm, the trained algorithm may be able to identify relationships between biomarkers and the prognosis or diagnosis of a particular cancer. Without the trained algorithm, it may be otherwise difficult to identify biomarker relationships to accurately identify the presence of cancer or other parameters associated with cancer.

[0056] In various embodiments, the systems and methods may include accuracy, sensitivity, or specificity of cancer detection or a cancer parameter. For example, the method or system may include detecting the presence or absence of cancer (or the presence of a cancer parameter, 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 cancer parameter, 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, recurrence, 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, recurrence, 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%.

[0057] Computer Control System The present disclosure provides a computer system programmed to implement the disclosed methods. Figure 4 shows a computer system (401) programmed or otherwise configured to perform the analysis or operations of the method, such as, for example, determining the likelihood of the presence of cancer based on a set of biomarkers for an individual or executing an algorithm. The computer system (401) can coordinate various aspects of the disclosed methods and systems, such as, for example, executing 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 (401) can be a user's electronic device or a computer system located remotely relative to the electronic device. The electronic device can be a mobile electronic device.

[0058] The computer system (401) includes a central processing unit (CPU, further referred to herein as "processor" and "computer processor") (405), which may be a single-core or multi-core processor, or multiple processors for parallel processing. The computer system (401) also includes memory or memory locations (410) (e.g., random access memory, read-only memory, flash memory), an electronic storage unit (415) (e.g., hard disk), a communication interface (420) (e.g., network adapter) for communicating with one or more other systems, and peripheral devices (425), such as cache, other memory, data storage, and / or electronic display adapters. The memory (410), storage unit (415), interface (420), and peripheral devices (425) communicate with the CPU (405) through a communication bus (solid lines), such as a motherboard. The storage unit (415) may be a data storage unit (or data repository) for storing data. The computer system 401 may be operably coupled to a computer network ("network") 430 using a communication interface 420. The network 430 may be the Internet, an internet and / or extranet, or an intranet and / or extranet in communication with the Internet. The network 430, in some cases, is a telecommunications and / or data network. The network 430 may include one or more computer servers that may enable distributed computing, such as cloud computing.The network (430) may, in some cases, implement a peer-to-peer network using the computer system (401), which may allow devices coupled to the computer system (401) to function as clients or servers.

[0059] The CPU (405) can 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 (410). The instructions are directed to the CPU (405), which can then program or otherwise configure the CPU (405) to implement the methods of the present disclosure. Examples of operations performed by the CPU (405) may include fetch, decode, execute, and writeback.

[0060] The CPU 405 may be part of a circuit, such as an integrated circuit. One or more other components of the system 401 may be included in the circuit. In some cases, the circuit is an application-specific integrated circuit (ASIC).

[0061] The storage unit (415) can store files such as drivers, libraries, and saved programs. The storage unit (415) can store user data, such as user preferences and user programs. The computer system (401) may, in some cases, include one or more additional data storage units external to the computer system (401), such as located on a remote server in communication with the computer system (401) through an intranet or the Internet.

[0062] The computer system (401) can communicate with one or more remote computer systems via the network (430). For example, the computer system (401) can communicate with a remote computer system of a user (e.g., a medical professional or a patient). Examples of remote computer systems include personal computers (e.g., portable PCs), slate or tablet PCs (e.g., Apple® iPad, Samsung® Galaxy Tab), telephones, smartphones (e.g., Apple® iPhone, Android-enabled devices, Blackberry®), or personal digital assistants. A user can access the computer system (401) via the network (430).

[0063] The methods described herein can be implemented by machine (e.g., computer processor) executable code stored on an electronic storage location of the computer system (401), such as, for example, memory (410) or electronic storage unit (415). 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 (405). In some cases, the code can be retrieved from the storage unit (415) and stored in the memory (410) for easy access by the processor (405). In some situations, the electronic storage unit (415) can be omitted, and the machine-executable instructions are stored in the memory (410).

[0064] 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 compiled manner.

[0065] Aspects of the systems and methods provided herein, such as the computer system (401), can be embodied in programming. Various aspects of the present technology may be considered “products” or “articles 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 may be stored in an electronic storage unit, such as memory (e.g., read-only memory, random-access memory, flash memory) or a hard disk. “Storage” type media may include any or all of the tangible memory of a computer, processor, or its associated modules, such as various semiconductor memories, tape drives, disk drives, etc., which may provide non-transitory storage for software programming at any time. All or portions of the software may, from time to time, be communicated via the Internet or various other telecommunications networks. Such communication may, for example, enable loading of the software from one computer or processor to another, e.g., from an administrative server or host computer to the computer platform of an application server. Thus, another type of medium that may carry software elements includes light waves, radio waves, and electromagnetic waves, such as those used across physical interfaces between local devices, through wired and optical landline networks, and via various air links. The physical elements that carry such waves, such as wired or wireless links, optical links, etc., may 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.

[0066] 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 those shown in the figures, that may be used to implement the databases, etc. 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 comprise 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, magnetic tape, any other magnetic medium, a CD-ROM, a DVD or DVD-ROM, any other optical medium, punched cards, paper tape, any other physical storage medium with a pattern of holes, RAM, ROM, PROM and EPROM, FLASH-EPROM, any other memory chip or cartridge, a carrier wave transmitting 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.

[0067] The computer system (401) may include or communicate with an electronic display (435) that includes a user interface (UI) (440) for inputting, for example, biomarker or sequencing data, or for 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.

[0068] The methods and systems of the present disclosure may be implemented by one or more algorithms. The algorithms may be implemented by software when executed by a central processing unit (405). For example, the algorithms may determine the presence or absence of cancer or cancer parameters based on a set of input sequencing data from a sample derived from a subject. [Example]

[0069] Example 1. Detection of minimal residual disease before repeat transurethral resection of bladder tumor (TURBT) Cell-free urinary tumor DNA (utDNA), generated from tumor genome sequencing, is an emerging biomarker that shows enormous potential for detecting minimal residual disease (MRD) in muscle-invasive and metastatic bladder cancer. We conducted a study to evaluate the ability of utDNA to measure MRD at the time of standard-of-care repeat transurethral resection of the bladder tumor (rTURBT) in non-muscle-invasive bladder cancer (NMIBC).

[0070] Figure 1 shows a schematic diagram of the study. An initial sample is collected from the subject as an index sample. This initial sample can be before any transurethral resection of the bladder tumor (TURBT) or after the initial TURBT has been performed. Generally, rTURBT can be performed on subjects with high-risk tumors, HGTa (high-grade Ta tumors), and T1 tumors, or CIS (carcinoma in situ) malignant tumors. Patients with high-risk NMIBC were enrolled before rTURBT. Index tumor and rTURBT mutation profiles were performed via whole-exome sequencing (e.g., PredicineWES) across 20,000 genes. A urine sample (or urine cell pellet sample) was collected immediately before the repeat TURBT. A custom panel of up to 50 baseline mutations per patient was used, along with a fixed core panel (e.g., using PredicineBEACON, Table 2) covering hotspot regions and actionable variants. utDNA detection was performed by ultra-deep sequencing of urinary cfDNA. Urinary tumor fraction was used to determine utDNA positivity. The primary endpoint was detection of utDNA to predict MRD at the time of repeat TURBT.

[0071] [Table 2]

[0072] Figure 2 shows an oncoplot of urinary tumor DNA genomic alterations in a patient with NMIBC before recurrent TURBT. Genes with non-synonymous (NS) mutations observed in the index and rTURBT specimens were identified in at least two samples. Figure 3A shows the WES+ variant frequency between the index and rTURBT samples. Figure 3B shows the variant overlap panel between the index TURBT (blue), rTURBT (green), and urine samples (yellow). Figure 3C shows utDNA positivity in a patient with disease present in rTURBT. Figure 3D shows the tumor fraction of utDNA before rTURBT.

[0073] Eleven patients underwent rTURBT for high-risk NMIBC. Residual tumor was detected by rTURBT in 8 / 11 (73%) patients. We identified 50 genes with non-significant mutations found in at least two samples, including TP53, PIK3CA, RB1, MYC, CDKN1A, and ARID1A (Figure 2, Table 3). A median of 146 (range 39-418) and 91 non-significant mutations (range 2-312) were identified in the index and re-TUR specimens, respectively. Concordance rates averaged 83% between primary and rTURBT, were highest in patients with T2 disease and lowest in patients with CIS. The tumor fraction of utDNA was higher in patients with residual disease (mean 2.5% vs. 0.2%, p = 0.10) (Figure 3D). The area under the receiver-operator curve (AUC) of this test for predicting MRD using tumor fraction was 0.85. Using an optimal threshold of tumor fraction ≥ 3.3%, the test had a sensitivity of 75% with a specificity of 100% for defining utDNA positivity.

[0074] [Table 3]

[0075] conclusion Urinary tumor DNA is a promising surrogate for minimal residual disease and can predict TURBT pathology for NMIBC. Genomic alterations between index and rTURBT tumors are highly concordant in papillary tumors, even in the upstage setting, which may aid in the selection of targeted intravesical or systemic therapy. Larger cohorts and long-term follow-up are needed to determine whether utDNA can risk-stratify patients.

[0076] Example 2: Monitoring subjects for minimal residual disease The subject has cancer and is subjected to a treatment for treating the cancer. A cancer sample (for example, from tumor cells, or from biopsy, or from tumor tissue resection) is obtained from the subject. A mutation profile of the cancer is obtained through whole exome assay. The specific mutation of the cancer is identified through whole exome assay. Based on the cancer mutation in the profile, a custom panel of cancer-specific probes is created.

[0077] After treatment, the subject is monitored for minimal residual disease.Biological samples (such as whole blood, plasma, urine or urine cell pellet) are obtained from the subject.The DNA from the sample is subjected to sequencing by using custom panel (and optionally, the panel contains the hot spot mutation previously identified in cancer).Based on DNA sequencing, the subject is identified as having residual tumor.The subject is recommended to undergo additional therapeutic intervention.

[0078] Example 3: Monitoring of subjects after transurethral resection of bladder tumor A subject has bladder cancer and undergoes transurethral resection of the bladder tumor (TURBT) to remove the cancer. A mutational profile of the cancer is obtained via a whole-exome assay. Specific mutations in the cancer are identified. A custom panel of probes is created based on the cancer mutations in the profile.

[0079] The subject is observed to be cancer-free and monitored for minimal residual disease. A urine sample or a urine cell pellet sample is obtained from the subject. The urinary cell-free DNA is subjected to sequencing by using a custom panel and a panel containing previously identified hotspot mutations in cancer. Based on the urinary cell-free DNA sequencing, the subject is identified as having residual tumor. The subject is recommended to undergo additional therapeutic intervention, including the use of repeat transurethral resection of bladder tumor (rTURBT).

[0080] While preferred embodiments of the present invention have been shown and described herein, it will be obvious 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. While described with reference to the foregoing specification, the descriptions and illustrations of the embodiments herein are not intended to be construed in a limiting sense. Numerous variations, changes, and substitutions will occur to those skilled in the art without departing from the invention. Furthermore, it is to be understood that all aspects of the invention are not limited to the specific depictions, configurations, or relative proportions set forth herein, which vary depending upon a variety of conditions and variables. It is to be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. It is therefore contemplated that the present invention will further encompass any such alternatives, modifications, variations, or equivalents. It is intended that the following claims define the scope of the invention, and that methods and structures within the scope of these claims and their equivalents be covered thereby.

Claims

1. 1. A method for identifying 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 derived from said subject at a first time point; (b) detecting a set of biomarkers from the DNA molecule based at least in part on the assay of (a), wherein the set of biomarkers comprises differentially expressed markers or variants; (c) generating 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) using the plurality of probe nucleic acids to sequence cell-free deoxynucleic acid (cfDNA) from a second biological sample obtained or derived from the subject at a second time point to detect the presence or absence of the subset of the set of biomarkers, wherein sequencing is performed to a depth of at least 80x, and the second biological sample is a blood sample, a urine sample, or a urine cell pellet sample; (e) computing the subset of the set of biomarkers to detect the presence or absence of minimal residual disease (MRD) in the subject; A method comprising:

2. 2. The method of claim 1, wherein the first 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 urine cell pellet 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 any derivative thereof, and any combination thereof.

3. The method of any one of claims 1 to 2, wherein the first biological sample comprises a plasma sample.

4. The method of any one of claims 1 to 2, wherein the first biological sample comprises a urine sample.

5. The method of any one of claims 1 to 2, wherein the first biological sample comprises a tumor tissue sample.

6. The method of any one of claims 1 to 5, wherein the second sample comprises a urine sample.

7. The method of any one of claims 1 to 5, wherein the second sample comprises a urine cell pellet sample.

8. The method of any one of claims 1 to 5, wherein the second sample comprises a blood sample.

9. 9. The method of any one of claims 1 to 8, wherein the first or second biological sample is obtained or derived 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 tube, and CTC collection tube.

10. 10. The method of any one of claims 1 to 9, wherein the DNA molecule comprises a cell-free DNA (cfDNA) molecule.

11. 11. The method of any one of claims 1-10, wherein (a) comprises subjecting the first or second biological sample to conditions sufficient to isolate, enrich, or extract the DNA molecules.

12. 12. The method of any one of claims 1 to 11, wherein the method further comprises fractionating the first biological sample of the subject to obtain the DNA molecule, and wherein the first biological sample is a whole blood sample.

13. 13. The method of any one of claims 1 to 12, wherein at least one of the DNA molecules is assayed using DNA sequencing to generate nucleic acid sequencing reads.

14. 14. The method of claim 13, wherein the DNA sequencing comprises whole exome sequencing.

15. The method of any one of claims 13 to 14, further comprising filtering at least a subset of the nucleic acid sequencing reads based on a quality score.

16. 16. The method of any one of claims 13-15, further comprising performing error correction on the nucleic acid sequencing reads using a sample barcode or molecular barcode attached to at least one of the DNA molecules.

17. 17. The method of any one of claims 13 to 16, further comprising performing at least one of single-strand consensus calling and double-strand consensus calling on the nucleic acid sequencing reads, thereby suppressing sequencing and PCR errors in the nucleic acid sequencing reads.

18. 18. The method of any one of claims 1 to 17, wherein the sequencing in (d) is performed to a depth of at least 100x.

19. 19. The method of any one of claims 1 to 18, wherein the sequencing in (d) is performed at a depth of at least 1,000x.

20. 20. The method of any one of claims 1 to 19, wherein the sequencing in (d) is performed at a depth of at least 10,000x.

21. 21. The method of any one of claims 1 to 20, wherein the sequencing in (d) is performed to a depth of at least 100,000x.

22. 22. The method of any one of claims 1 to 21, wherein the sequencing in (e) comprises sequencing nucleic acid from the first biological sample.

23. 23. The method of any one of claims 1 to 22, wherein the sequencing in (e) comprises sequencing nucleic acid from the second biological sample.

24. 24. The method of any one of claims 1 to 23, wherein the sequencing in (e) comprises sequencing the nucleic acid of a sample taken at the first time point and sequencing the nucleic acid of a sample taken at the second time point.

25. 25. The method of any one of claims 1 to 24, wherein the assaying of (a), the sequencing of (d), or the sequencing of (e) comprises nucleic acid amplification.

26. 26. The method of claim 25, wherein the nucleic acid amplification comprises polymerase chain reaction (PCR) or isothermal amplification.

27. 27. The method of any one of claims 1 to 26, wherein the cancer is selected from the group consisting of genitourinary cancer, prostate cancer, bladder cancer, and any combination thereof.

28. 28. The method of claim 27, wherein the cancer comprises bladder cancer.

29. 29. The method of claim 28, wherein the bladder cancer is muscle-invasive bladder cancer.

30. The method of any one of claims 1 to 29, wherein the subject is asymptomatic for cancer.

31. 31. The method of any one of claims 1 to 30, wherein the method comprises detecting the presence or absence of minimal residual disease in the 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%.

32. 32. The method of any one of claims 1-31, wherein the method comprises detecting the presence or absence of minimal residual disease in the 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%.

33. 33. The method of any one of claims 1-32, wherein the method comprises detecting the presence or absence of minimal residual disease in the 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%.

34. 34. The method of any one of claims 1-33, wherein the method comprises detecting the presence or absence of minimal residual disease in the 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%.

35. 35. The method of any one of claims 1-34, wherein the method comprises detecting the presence or absence of minimal residual disease in the 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%.

36. 36. The method of any one of claims 1 to 35, wherein the first biological sample is obtained or derived from the subject before the subject receives treatment for cancer.

37. 37. The method of any one of claims 1 to 36, wherein the biological sample is obtained or derived from the subject during treatment for cancer.

38. 37. The method of any one of claims 1 to 36, wherein the biological sample is obtained or derived from the subject after undergoing treatment for cancer.

39. 39. The method of any one of claims 36 to 38, wherein the treatment is selected from the group consisting of surgical resection, chemotherapy, radiation therapy, immunotherapy, cell therapy, adjuvant therapy, neoadjuvant therapy, androgen deprivation therapy, and combinations thereof.

40. 40. The method of any one of claims 1 to 39, wherein the first biological sample is obtained or derived from the subject via transurethral resection of the bladder tumor.

41. 40. The method of any one of claims 1 to 39, wherein the first biological sample is obtained or derived from the subject after performing a transurethral resection of the bladder tumor.

42. 42. The method of any one of claims 1-41, further comprising identifying a clinical intervention for the subject based at least in part on the presence or absence of cancer detected.

43. 43. The method of claim 42, wherein the clinical intervention is selected from a plurality of clinical interventions.

44. 44. The method of any one of claims 42-43, wherein the clinical intervention is selected from the group consisting of surgical resection, chemotherapy, radiation therapy, immunotherapy, adjuvant therapy, neoadjuvant therapy, androgen deprivation therapy, and combinations thereof.

45. 45. The method of claim 44, wherein the surgical resection is a transurethral resection of the bladder tumor (TURBT) or a repeat transurethral resection of the bladder tumor.

46. 46. ​​The method of any one of claims 42 to 45, further comprising administering said clinical intervention to said subject.

47. 47. The method of any one of claims 1 to 46, wherein the set of biomarkers comprises one or more members selected from the group consisting of the genes listed in Table 1.

48. 48. The method of claim 47, wherein 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, or 85 members selected from the group consisting of the genes listed in Table 1.

49. 47. The method of any one of claims 1 to 46, wherein the set of biomarkers comprises one or more members selected from the group consisting of the genes listed in Table 2.

50. 50. The method of claim 49, wherein the set of biomarkers comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, or 40 members selected from the group consisting of the genes listed in Table 3.

51. 47. The method of any one of claims 1 to 46, wherein the set of biomarkers comprises one or more members selected from the group consisting of the genes listed in Table 3.

52. 52. The method of any one of claims 1 to 51, wherein the plurality of probes comprises nucleic acid primers.

53. The method of any one of claims 1 to 52, wherein the plurality of probes comprises nucleic acid capture probes.

54. 54. The method of any one of claims 1 to 53, wherein the plurality of probes have sequence complementarity to at least a portion of the nucleic acid sequences of the set of biomarkers.

55. 55. The method of any one of claims 1-54, wherein 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.

56. 56. The method of any one of claims 1 to 55, wherein (d) further comprises sequencing using a plurality of immobilized probes, wherein the probes of the plurality of immobilized probes include probes that do not contain sequences of the subset of the set of biomarkers.

57. 57. The method of Claim 56, wherein said immobilized plurality of probes comprises one or more members selected from the group consisting of the genes listed in Table 2.

58. 58. The method of any one of claims 1 to 57, further comprising determining the likelihood of determining the presence or absence of cancer in the subject.

59. 59. The method of any one of claims 1-58, wherein the method further comprises monitoring the presence or absence of cancer in the subject, wherein monitoring comprises assessing the presence or absence of cancer in the subject at each of a plurality of time points.

60. 60. The method of claim 59, wherein a difference in the assessment of the presence or absence of cancer in the subject between the multiple time points indicates one or more clinical indications selected from the group consisting of: (i) a diagnosis of the cancer, (ii) a prognosis of the cancer, and (iii) the effectiveness or ineffectiveness of a course of treatment for treating the cancer in the subject.

61. 61. The method of claim 60, wherein the prognosis comprises expected progression-free survival (PFS) or overall survival (OS).

62. 62. The method of any one of claims 1 to 61, wherein the set of biomarkers derived from the DNA molecule comprises tumor-associated alterations selected from the group consisting of single nucleotide variants (SNVs), insertions or deletions (indels), and rearrangements.

63. 63. The method of any one of claims 1 to 62, further comprising determining mutant allele frequencies of a set of somatic mutations among said set of biomarkers.

64. 64. The method of claim 63, further comprising determining a circulating tumor DNA (ctDNA) fraction of the subject's cancer based at least in part on the set of variant allele frequencies.

65. 65. The method of any one of claims 63-64, further comprising determining the tumor mutational burden (TMB) of the subject's cancer.

66. 66. The method of any one of claims 63-65, further comprising determining an aberration score for said subject's cancer based at least in part on said set of variant allele frequencies.

67. 1. A method for providing treatment to a subject, the method comprising: (a) assaying deoxyribonucleic acid (DNA) molecules from a tumor sample obtained or derived from the subject at a first time point, wherein the subject has undergone a transurethral resection of bladder tumor procedure; (b) detecting a set of biomarkers from the DNA molecule based at least in part on the assay of (a), wherein the set of biomarkers comprises differentially expressed markers or variants; (c) generating 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) using the plurality of probe nucleic acids to sequence deoxynucleic acid (DNA) from a urine sample obtained or derived from the subject at a second time point to detect the presence or absence of the subset of the set of biomarkers, wherein sequencing is performed to a depth of at least 80x and the second biological sample is a urine sample; (e) computing the subset of the set of biomarkers to detect the presence or absence of minimal residual disease (MRD) in the subject; (f) performing a repeat transurethral resection of bladder tumor (rTURBT) procedure based at least on the presence or absence of minimal residual disease (MRD) in the subject; A method comprising:

Images