Systems and methods for minimal residual disease analysis

Abstract

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

Description

Cross-references

[0001] 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 a leading cause of death worldwide. Cancer detection in individuals is crucial for providing treatment and improving patient outcomes. Cancer can be caused by genetic abnormalities, which can lead to uncontrolled cell growth. Detection of genetic abnormalities is important for cancer detection. Sequencing nucleic acids in samples from patients can be used to detect genetic abnormalities. Summary of the Invention

[0003] This article provides systems and methods for detecting the presence or absence of cancer in subjects. The systems and methods provided include the determination of polynucleotides as biomarkers to identify cancer in subjects. Detection of a type of cancer or a specific biomarker for a given cancer can allow for the provision of effective treatments to individuals and can produce improved outcomes. For multiple types of cancer, specific biomarkers indicating a particular cancer type (or subtype) can be used to identify the prognosis of an individual with that cancer. To provide accurate detection and prognosis of cancer, multiple analytes can be examined. By analyzing an increased number of analytes (and the biomarker sets derived from the analytes), the detection of cancer (or cancer parameters) can be improved, allowing for the recommendation of effective treatments and potentially more accurate prognosis.

[0004] In one aspect, this disclosure provides a method for identifying the presence or absence of minimal residual disease (MRD) in an object, comprising: (a) determining deoxyribonucleic acid (DNA) molecules from a first biological sample obtained or derived from the object at a first time point; (b) detecting a set of biomarkers from the DNA molecules, at least in part based on the determination of (a), wherein the set of biomarkers includes differentially expressed markers or variants; (c) generating a plurality of probe nucleic acids tailored for the object, wherein the probe nucleic acids include sequences of at least a subset of the set of biomarkers; (d) sequencing cellular DNA from a second biological sample obtained or derived from the object 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, wherein the second biological sample is a blood sample, a urine sample, or a urine cell sediment sample; and (e) computer processing of the subset of the set of biomarkers to detect the presence or absence of the minimal residual disease (MRD) in the object. In some embodiments, the first biological sample is selected from: cell-free deoxyribonucleic acid (cfDNA) samples, cell-free ribonucleic acid (cfRNA) samples, plasma samples, serum samples, erythrocyte sedimentation rate (ESR) amber layer samples, peripheral blood mononuclear cell (PBMC) samples, erythrocyte samples, urine samples, urine cytoretin samples, saliva samples, tissue biopsies, pleural fluid samples, peritoneal fluid samples, amniotic fluid samples, cerebrospinal fluid samples, lymphatic fluid samples, sweat samples, tear samples, semen samples, or any derivatives thereof, or any combination thereof. In some embodiments, the first biological sample includes the plasma sample. In some embodiments, the first biological sample includes the urine sample. In some embodiments, the first biological sample includes the tumor tissue sample. In some embodiments, the first biological sample or the second biological sample is obtained from or derived from the subject using EDTA collection tubes, cell-free RNA collection tubes, cell-free deoxyribonucleic acid (DNA) collection tubes, other blood collection tubes, and CTC collection tubes. In some embodiments, (a) includes subjecting the first biological sample or the second biological sample to conditions sufficient to isolate, enrich, or extract the DNA molecules. In some embodiments, the method further includes grading the first biological sample of the object to obtain the DNA molecules, wherein the first biological sample is a whole blood sample. In some embodiments, at least one of the DNA molecules is determined using DNA sequencing to generate nucleic acid sequencing reads. In some embodiments, the DNA sequencing includes whole exome sequencing. In some embodiments, the method further includes filtering at least one subset of the nucleic acid sequencing reads based on a quality score.In some embodiments, the method further includes error correction of 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 at least one of single-stranded common sequence identification and double-stranded common sequence identification of the nucleic acid sequencing reads to suppress 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) includes sequencing nucleic acids derived from the first biological sample. In some embodiments, the sequencing in (e) includes sequencing nucleic acids derived from the second biological sample. In some embodiments, the sequencing in (e) includes sequencing the nucleic acids of a sample collected at the first time point and sequencing the nucleic acids of a sample collected at a second time point. In some embodiments, the assay in (a), the sequencing in (d), or the sequencing in (e) includes nucleic acid amplification. In some embodiments, the nucleic acid amplification includes polymerase chain reaction (PCR) or isothermal amplification. In some embodiments, the cancer is selected from: urogenital cancers, prostate cancer, bladder cancer, and any combination thereof. In some embodiments, the cancer includes the 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 method includes detecting the presence or absence of the microresidual pathogen in the object 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 includes detecting the presence or absence of the microresidual pathogen in the object 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 includes detecting the presence or absence of the microresidual pathogen in the object 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 includes detecting the presence or absence of the 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%. In some embodiments, the method includes detecting the presence or absence of the 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%. In some embodiments, the first biological sample is obtained from or derived from the subject prior to the subject receiving therapy for the cancer. In some embodiments, the biological sample is obtained from or derived from the subject during therapy for the cancer. In some embodiments, the biological sample is obtained from or derived from the subject after receiving therapy for the cancer. In some embodiments, the therapy is selected from: surgical resection, chemotherapy, radiotherapy, immunotherapy, cell therapy, adjuvant therapy, neoadjuvant therapy, androgen deprivation therapy, and combinations thereof. In some embodiments, the first biological sample is obtained from or derived from the subject via transurethral resection of bladder tumor. In some embodiments, the first biological sample is obtained from or derived from the subject after 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 detected presence or absence of the cancer. In some embodiments, the clinical intervention is selected from a variety of clinical interventions. In some embodiments, the clinical intervention is selected from: surgical resection, chemotherapy, radiotherapy, 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 biomarker set includes one or more members selected from the genes listed in Table 1. In some embodiments, the biomarker set includes 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 genes listed in Table 1. In some embodiments, the biomarker set includes one or more members selected from the genes listed in Table 2. In some embodiments, the biomarker set includes 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, or 40 members selected from the genes listed in Table 3. In some embodiments, the biomarker set includes one or more members selected from the genes listed in Table 3. In some embodiments, the plurality of probes includes nucleic acid primers. In some embodiments, the plurality of probes includes nucleic acid capture probes. In some embodiments, the plurality of probes is sequence complementary to at least a portion of the nucleic acid sequence of the biomarker group. In some embodiments, the plurality of probes includes 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, step (d) further includes sequencing using a fixed plurality of probes, wherein the probes in the fixed plurality of probes include probes that do not include sequences of the subset of the biomarker group. In some embodiments, the fixed plurality of probes includes one or more members selected from the genes listed in Table 2. In some embodiments, the method further includes determining the probability of the presence or absence of the cancer in the subject. In some embodiments, the method further includes monitoring the presence or absence of the cancer in the subject, wherein the monitoring includes assessing the presence or absence of the cancer in the subject at each of a plurality of time points. In some embodiments, the difference in the assessment of the presence or absence of the cancer in the subject between the plurality of time points is selected from one or more of the following clinical indications: (i) diagnosis of the cancer, (ii) prognosis of the cancer, and (iii) effectiveness or ineffectiveness of a treatment process for the cancer in the subject. In some embodiments, the prognosis includes expected progression-free survival (PFS) or overall survival (OS).

[0006] In some embodiments, the biomarker set from the DNA molecule includes tumor-associated alterations selected from single nucleotide variants (SNVs), insertions or deletions (gains and losses), and rearrangements. In some embodiments, the method further includes determining the frequency of mutated alleles in the somatic mutation set within the biomarker set. In some embodiments, the method further includes determining the circulating tumor DNA (ctDNA) fraction of the subject's cancer based at least in part on the mutated allele frequency set. In some embodiments, the method further includes determining the tumor mutational burden (TMB) of the subject's cancer. In some embodiments, the method further includes determining an abnormality score of the subject's cancer based at least in part on the mutated allele frequency set.

[0007] On the other hand, this disclosure provides a method for providing treatment to a subject, comprising: (a) determining deoxyribonucleic acid (DNA) molecules from a tumor sample obtained or derived from the subject at a first time point, wherein the subject has undergone urethral bladder tumor resection surgery; (b) detecting a set of biomarkers from the DNA molecules, at least in part based on the determination in (a), wherein the set of biomarkers includes differentially expressed biomarkers or variants; (c) generating a plurality of probe nucleic acids tailored for the subject, wherein the probe nucleic acids include 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, wherein the sequencing is performed at a depth of at least 80x, wherein the second biological sample is a urine sample; (e) computer processing of the subset of the set of biomarkers to detect the presence or absence of minimal residual disease (MRD) in the subject; (f) A second transurethral resection of bladder tumor (rTURBT) is performed, at least in part based on the presence or absence of the minimal residual disease (MRD) in the subject.

[0008] Another aspect of this 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 this disclosure provides a system comprising one or more computer processors and computer memory coupled thereto. The computer memory includes machine-executable code that, when executed by the one or more computer processors, implements any of the methods described above or elsewhere herein.

[0010] Other aspects and advantages of this disclosure will be readily apparent to those skilled in the art from the following detailed description, which shows and describes only illustrative embodiments thereof. As will be appreciated, this disclosure is capable of having other and different embodiments, and several details thereof can be modified in various obvious respects, all without departing from this disclosure. Therefore, the drawings and descriptions should be considered illustrative in nature and not restrictive. Incorporation

[0011] All publications, patents, and patent applications mentioned in this specification are incorporated herein by reference to the extent that each individual publication, patent, or patent application is specifically and individually indicated to be incorporated by reference. If any publication, patent, or patent application incorporated by reference contradicts the contents of this disclosure contained herein, this specification is intended to supersede and / or give precedence to any such contradictory material. Attached Figure Description

[0012] The novel features of the invention are set forth in detail in the appended claims. The features and advantages of the invention will be better understood by referring to the following detailed description of illustrative embodiments in which the principles of the invention are utilized, and the accompanying drawings (also referred to herein as “Figures”), in which: Figure 1 An example workflow is shown.

[0013] Figure 2 A waterfall plot showing genomic alterations in urinary tumor DNA in patients with NMIBC prior to TURBT.

[0014] Figure 3A The frequency of WES+ variants between the indexed sample and the rTURBT sample is shown. Figure 3B The diagram shows the variant overlap plane between the index TURBT sample (blue), the rTURBT sample (green), and the urine sample (yellow). Figure 3C The study showed utDNA positivity in patients with the disease during rTURBT. Figure 3D The tumor score of utDNA prior to rTURBT is shown.

[0015] Figure 4 A computer system is shown that is programmed or otherwise configured to implement the methods provided herein. Detailed Implementation

[0016] Although various embodiments of the invention have been shown and described herein, it will be apparent to those skilled in the art that these embodiments are provided by way of example only. Many variations, modifications, and substitutions will occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed.

[0017] This article provides systems and methods for detecting the presence or absence of cancer in a subject. The systems and methods provided include the determination of polynucleotides as biomarkers to identify cancer in a subject. 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. Analytes may include cfDNA or other analytes that can be provided by non-invasive methods. By analyzing analytes obtained by non-invasive methods, these methods can allow for improved or similar detection or prognostic determination compared to methods using tumor or tissue biopsies.

[0018] Human urine can contain fragmented DNA known as urinary cell-free DNA (ucfDNA), which originates from dead cells in the urogenital tract or circulating DNA filtered by the glomeruli. Due to the direct accessibility of the urinary tract, urine is a viable source for detecting cfDNA biomarkers, and ucfDNA can improve the diagnostic sensitivity of current liquid biopsies for urogenital cancers. Many gene variants can be identified in ucfDNA from cancer patients, particularly those with bladder cancer. Urine may also contain whole cells, cell debris, and other biomolecules that can be precipitated by centrifugation to form a precipitate. Urinary cell precipitate (UCP), or urine sediment, can contain DNA that can be analyzed for biomarkers. Liquid biopsies using urine provide a completely non-invasive method for detecting genomic biomarkers to guide cancer treatment.

[0019] Over the past decade, next-generation sequencing (NGS) technology has revolutionized cancer genomics research. NGS technology is commercially available to guide treatment planning for cancer patients and has been FDA-approved or recognized for processing DNA from patient tissue or blood samples. Next-generation sequencing (NGS) assays of cfDNA allow for the precise detection of genomic alterations, including single nucleotide variants (SNVs), insertions and deletions (gains and losses), copy number variations (CNVs), and DNA rearrangements. Urinary cfDNA is derived directly from dead cells shed in urine and, due to tumor heterogeneity, can be considered more representative of tumors than tissue biopsies, which only reflect mutations found in specific regions of the tumor. Furthermore, urine may contain fewer contaminating proteins than blood, and cfDNA levels in urine can be higher than in the bloodstream. Sequencing of urinary cfDNA allows for the detection of cancer and cancer-related genetic alterations in the urine of subjects. The methods and assays described in this disclosure represent applications of NGS technology and provide a non-invasive, cost-effective, and potentially more sensitive sampling method for patients with cancers such as genitourinary cancers, including bladder cancer.

[0020] Besides being used for tumor staging and grading, tissue biopsy is often considered the gold standard for guiding cancer treatment. However, depending on the tumor location or patient condition, tumor biopsies can cause pain and pose a risk of complications, making medical treatment expensive. In some cases, tissue biopsy may not be feasible. For the treatment of bladder cancer patients, a less invasive sampling method remains an unmet clinical need. Urine cfDNA testing and liquid biopsy (e.g., from urine) options can help fill this gap. Furthermore, compared to tissue biopsy, patients have the opportunity to undergo multiple tests with urine liquid biopsy. Therefore, urine liquid biopsy represents a non-invasive and cost-effective method for obtaining patient samples to determine molecular fit for a specific treatment strategy.

[0021] Urine liquid biopsy samples can also improve patient care. When tissue biopsy is not available for NGS testing in bladder cancer patients, urine liquid biopsy can be used to monitor bladder cancer patients. This assay can also detect other gene mutations in urine samples from bladder cancer patients, including but not limited to... CDKN2A , HRAS / KRAS , KDM6A , PIK3CA , TERT , TP53 and TSC1 Changes in the body, if identified, can help guide patient treatment.

[0022] Bladder cancer is the tenth most common malignancy worldwide, with an estimated 550,000 new cases and 200,000 deaths reported in 2018. Most bladder cancer cases are non-muscle-invasive bladder cancer (NMIBC), requiring frequent monitoring, local resection (transurethral bladder tumor resection [TURBT]), and intensive intravesical therapy to reduce the risk of recurrence and progressive disease. Despite these efforts, 45% to 60% of patients experience disease recurrence within 5 years, and nearly 20% of high-risk patients progress to muscle-invasive tumors, requiring radical cystectomy (RC). The natural course of high-risk NMIBC is unpredictable; recurrence rates range from 15% to 78%, and the rate of progression to muscle-invasive and metastatic tumors ranges from <1% to 45%. Long-term outcomes indicate that approximately 20% to 25% of patients with high-risk NMIBC eventually die from bladder cancer.

[0023] TURBT can be an effective treatment for cancer. Even if the tumor is completely removed by TURBT, cancer recurrence can still occur. Therefore, it is necessary to monitor patients who have undergone TURBT to determine if they require additional treatment intervention. Specifically, a second TURBT (rTURBT) can be performed. The method described in this article allows for monitoring of patients after the initial TURBT. Based on monitoring and MRD (Medical Record), another treatment intervention such as rTURBT can be recommended or administered to the subject.

[0024] Analytes used in urine liquid biopsies for tumor diagnosis include cfDNA, non-coding RNA, shed tumor cells, and proteins. During tumor-destructive therapy or during apoptosis and necrosis, both healthy and diseased cells release cfDNA fragments, typically 100-200 base pairs in length. In healthy patients, phagocytes engulf cell debris and necrotic cells, resulting in very low cfDNA levels. In diseased patients, phagocytosis is impaired, DNA digestion is minimal, and DNA fragments are of random size, potentially exceeding 10,000 base pairs. Therefore, cfDNA levels are typically elevated in diseased patients.

[0025] Urinary cfDNA (ucfDNA) can be extracted from a subject's urine, and various reactions can be performed on the ucfDNA to allow for sequencing. Library construction of the ucfDNA may include amplification, ligation of adaptors or other sequences, and / or barcoding to generate a sequencing library. Furthermore, the ucfDNA or ucfDNA library can be enriched using capture probes or amplification primers to enrich specific target sequences from the cfDNA. The library can then be subjected to sequencing reactions to generate sequencing data.

[0026] Urine samples can be isolated and collected from individuals. After collection, urine DNA or cfDNA can be extracted. Then, libraries can be constructed from the extracted urine DNA or cfDNA, and specific targets can be enriched. Once enriched, the urine DNA or cfDNA can be sequenced, and the sequencing data can then be processed.

[0027] Genetic alterations, such as single nucleotide variants (SNVs), gain / loss sites, DNA rearrangements, and copy number variations (CNVs), can be identified through bioinformatics analysis of sequencing data. Typically, bioinformatics workflows utilize raw sequencing data (e.g., BCL files) and output mutational calls. This workflow can perform various tasks to analyze sequencing data, such as adaptor trimming, barcode checking, or error correction. Alignment tools (e.g., BWA alignment tools) can be used to align cleaned paired files (e.g., FASTQ files) with a human reference genome. Common sequences can then be derived by merging paired-end reads from molecules identical to single-stranded fragments. Single-stranded fragments from the same double-stranded DNA molecules can be further merged into double strands. These processes allow for the correction of sequencing and PCR errors.

[0028] The subject may be suspected of having cancer. The cancer can be specific to or originate from the subject's organ or other region. For example, cancer can 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. Cancer can be hormone-sensitive prostate cancer (HSPC), castration-resistant prostate cancer (CRPC), metastatic prostate cancer, and combinations thereof. Cancer can be a cancer of the tissues or cells of the genitourinary tract. For example, cancer can be bladder cancer, kidney cancer, or prostate cancer. Cancer may include biomarkers specific to a particular cancer. Specific biomarkers can indicate the presence of a particular cancer. For example, a biomarker can indicate the presence of castration-resistant prostate cancer. Identifying the presence of a cancer type can allow for the determination of treatment options or recommendations.

[0029] In some cases, subjects may not experience any symptoms of cancer. For example, cancer may not present any symptoms, and subjects may be unaware of its presence. The method described in this article allows for the identification of cancer at an earlier stage than other methods. Identifying the presence of cancer at an earlier stage allows for the determination of treatment options or recommendations at an earlier stage, and may allow subjects to have improved prognoses.

[0030] Biological samples may include nucleic acids. Biological samples may be cell-free deoxyribonucleic acid (cfDNA) or cell-free ribonucleic acid (cfRNA) samples. Biological samples may include genomic DNA or germline DNA (gDNA). Nucleic acids may be DNA (e.g., double-stranded DNA, single-stranded DNA, single-stranded DNA hairpins, 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 hybrids. Biological samples may be derived from or contain biological fluids. For example, biological samples may be plasma samples, serum samples, erythrocyte sedimentation rate (ESR) amber layer samples, peripheral blood mononuclear cell (PBMC) samples, erythrocyte blood cell samples, urine samples, saliva samples, or other bodily fluid samples. Biological samples may include or be any combination of pleural fluid samples, peritoneal fluid samples, amniotic fluid samples, cerebrospinal fluid samples, lymph fluid samples, sweat samples, tear samples, semen samples, or biological fluids. Biological samples can include urine samples.

[0031] Collection tubes can be used to collect, acquire, or derive biological samples from an object. Collection tubes can be EDTA collection tubes, cell-free RNA collection tubes, cell-free deoxyribonucleic acid (DNA) collection tubes, and CTC collection tubes, or other blood collection tubes. Collection tubes may include additional reagents for stabilizing nucleic acid molecules or blood cells. The collection tubes can stabilize nucleic acids or blood cells to minimize degradation of the biological sample prior to assay. Additional reagents may include buffer salts or chelating agents.

[0032] Biological samples can be obtained or derived from the subject at different times. Biological samples can be obtained or derived from the subject before receiving cancer-specific therapy. Biological samples can be obtained or derived from the subject during cancer-specific therapy. Biological samples can be obtained or derived from the subject after receiving cancer-specific therapy. Biological samples can be obtained or derived from the subject through transurethral resection of bladder tumors. Biological samples can be obtained or derived from the subject after performing transurethral resection of bladder tumors.

[0033] Biological samples can be obtained or derived from the subject at different times. Biological samples can be obtained or derived from the subject before receiving cancer-specific therapy. Biological samples can be obtained or derived from the subject during cancer-specific therapy. Biological samples can be obtained or derived from the subject after receiving cancer-specific therapy. Biological samples can be collected at 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. This time point can occur within 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 longer. This time point can also occur within 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 longer. This time point can occur within 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 longer. This time point can occur in 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 longer.

[0034] In all aspects described herein, clinical interventions or therapies can be identified, at least in part, based on the identification of the presence of cancer or cancer parameters. Clinical interventions can be multiple. A choice can be made from a variety of clinical interventions. Clinical interventions can be surgical resection, chemotherapy, radiotherapy, immunotherapy, adjuvant therapy, neoadjuvant therapy, androgen deprivation therapy, or combinations thereof. A clinical intervention can be transurethral resection of bladder tumor (TURBT). A clinical intervention can be re-transurethral resection of bladder tumor (rTURBT). In some cases, a clinical intervention can be administered to the subject. After the administration of a clinical intervention, samples can be obtained or derived from the subject to monitor for cancer or cancer parameters. Therefore, the methods and systems disclosed herein can be implemented iteratively to enable cancer monitoring. Furthermore, by iteratively implementing the methods or systems, the therapy or clinical intervention can be updated based on the results of the method. Cancer monitoring can include assessment and the difference between assessments and previously generated assessments. Differences in cancer assessments of a subject across multiple time points (or samples) can indicate one or more clinical indicators, such as the diagnosis of cancer, the prognosis of cancer, or the effectiveness or ineffectiveness of a treatment process used to treat the subject's cancer. Prognosis may include expected progression-free survival (PFS), overall survival (OS), or other measures related to cancer severity or survival ability.

[0035] Biological samples may undergo additional reactions or conditions before assaying. For example, biological samples may be subjected to conditions sufficient to isolate, enrich, or extract nucleic acids (such as cfDNA molecules).

[0036] The methods disclosed herein may include one or more enrichment reactions on one or more nucleic acid molecules in a sample. An enrichment reaction may include contacting the sample with one or more beads or groups of beads. An enrichment reaction may include one or more hybridization reactions. For example, an enrichment reaction may include contacting the sample with one or more capture probes or decoy molecules that hybridize with nucleic acid molecules in a biological sample. An enrichment reaction may include differential amplification of a set of nucleic acid molecules. An enrichment reaction may enrich multiple genetic loci or sequences corresponding to genetic loci. An enrichment reaction may include using primers or probes that are complementary to a sequence (or upstream or downstream sequence) of the sequence to be enriched. For example, a capture probe may include a sequence complementary to a set of genomic loci and allow for the enrichment of genomic loci. An enrichment reaction may include multiple probes or primers. For example, a capture probe may include a sequence complementary to a gene selected in Table 1, Table 2, or Table 3. Multiple probes can 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, 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, 30 5, 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.

[0037] The methods disclosed herein may include one or more isolation or purification reactions on one or more nucleic acid molecules in a sample. Isolation or purification reactions may include contacting the sample with one or more beads or groups of beads. Isolation or purification reactions may include one or more hybridization reactions, enrichment reactions, amplification reactions, sequencing reactions, or combinations thereof. Isolation or purification reactions may include using one or more separators. One or more separators may include magnetic separators. Isolation or purification reactions may include separating bead-bound nucleic acid molecules from bead-free nucleic acid molecules. Isolation or purification reactions may include separating nucleic acid molecules hybridized with capture probes from nucleic acid molecules without capture probes. Isolation reactions may include removing or isolating a group of nucleic acid molecules from another group of nucleic acids.

[0038] The methods disclosed herein may include extraction reactions of one or more nucleic acids in a biological sample. Extraction reactions may lyse cells or disrupt the interaction between nucleic acids and cells, thereby enabling the isolation, purification, enrichment, or further processing of nucleic acids.

[0039] The methods disclosed herein may include amplification or extension reactions. Amplification reactions may include polymerase chain reactions. Amplification reactions may include PCR-based amplification, non-PCR-based amplification, or combinations thereof. One or more PCR-based amplification methods may include PCR, qPCR, nested PCR, linear amplification, or combinations thereof. One or more non-PCR-based amplification methods may include multiple substitution amplification (MDA), transcription-mediated amplification (TMA), nucleic acid sequence-based amplification (NASBA), strand substitution amplification (SDA), real-time SDA, rolling circle amplification, loop-to-loop amplification, or combinations thereof. Amplification reactions may include isothermal amplification.

[0040] The methods disclosed herein may include barcoding reactions. Barcoding reactions may include adding barcodes or tags to nucleic acids. The barcodes may be molecular barcodes or sample barcodes. For example, barcoded nucleic acids may include barcode sequences, which may be degenerate n-mers. The sequences may be randomly generated or generated to synthesize specific barcode sequences. Barcoded nucleic acids may be added to a sample to label nucleic acid molecules in the sample. Barcodes may be sample-specific. For example, multiple barcoded nucleic acids may be added to samples with the same barcode sequence. After barcoding nucleic acids, nucleic acids from the same sample may have the same barcode sequence, allowing nucleic acids to be identified as belonging to a specific or given sample. Molecular barcoding may also be used, such that each molecule (or multiple molecules) of equal volume has a different molecular barcode. This barcode may undergo amplification, such that all amplicon derived from the molecule have the same barcode. In this way, molecules originating from the same molecule can be identified. Sequence reads may be processed based on the barcode sequence. For example, processing may reduce errors or allow for molecule tracking. Barcode sequences can be attached to or otherwise added to a sequence through various reactions (such as amplification, extension, or ligation reactions), and can be enzymatically performed using nucleic acid polymerases or ligases. Ligation can be overhang or blunt-end ligation, and the barcode can include complementarity with the nucleic acid to be barcoded. This complementarity can be a sequence derived from the sample of the object, or it can be a constant sequence generated by reacting nucleic acids in the sample.

[0041] In some cases, biological samples may include multiple components. For example, a biological sample may be a whole blood sample. Biological samples may undergo reactions to separate or fractionate the biological sample. For example, a whole blood sample can be fractionated to obtain cell-free nucleic acids. A whole blood sample can be fractionated using centrifugation to separate blood cells from plasma (which may contain cell-free nucleic acids). Samples may undergo multiple rounds of separation or fractionation.

[0042] In all respects described in this disclosure, nucleic acids can undergo sequencing reactions. 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, ligation-by-sequencing, hybridization sequencing, nanopore current-limited sequencing, or combinations thereof. Sequencing-by-synthesis can include reversible terminator sequencing, progressive single-molecule sequencing, sequential nucleotide stream sequencing, or combinations thereof. Sequential nucleotide stream sequencing can include pyrosequencing, pH-mediated sequencing, semiconductor sequencing, or combinations thereof. Sequencing reactions can include whole-genome sequencing, whole-exome sequencing, low-throughput whole-genome sequencing, targeted sequencing, methylation-sensitive sequencing, enzyme-catalyzed methylation sequencing, and bisulfite methylation sequencing. Sequencing reactions can be transcriptome sequencing, mRNA-seq, totalRNA-seq, smallRNA-seq, exosome sequencing, or combinations thereof. Combinations of sequencing reactions can be used in methods described elsewhere herein. For example, a sample can undergo whole-genome sequencing and whole-transcriptome sequencing. Since samples can include multiple types of nucleic acids (such as RNA and DNA), sequencing reactions specific to DNA or RNA can be used to obtain sequence reads associated with the nucleic acid type.

[0043] Sequencing reactions can be performed at different sequencing depths. The sequencing depth of a sequencing reaction can be selected or adjusted. Sequencing reactions can include depths of 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, and 700x. Sequencing of regions at depths of 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 higher. Sequencing reactions can include values ​​no higher than 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, and 700x. Sequencing of regions at depths of 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 lower.

[0044] In various implementations, low-throughput whole-genome sequencing is used to sequence nucleic acids. Low-throughput 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 higher. Low-throughput whole-genome sequencing can be performed at an average sequencing depth of no more than 1x, 2x, 3x, 4x, 5x, 6x, 7x, 8x, 9x, 10x or lower. Low-throughput whole-genome sequencing can be performed at an average depth between 1x and 2x.

[0045] In various implementations, a set of personalized or customized probes can be used for sequencing reactions. Sequencing reactions using a set of personalized or customized probes can be deep sequencing reactions or ultra-deep sequencing reactions. For example, sequencing reactions using a set of personalized or custom probes can be performed at depths 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 higher.

[0046] In various implementations, whole-exome sequencing is used to sequence the nucleic acids of a target. Whole-exome sequencing can be performed at non-uniform depths. For example, certain regions of the exome may be enhanced or otherwise sequenced at greater depths than other regions, or at depths greater than the average depth of whole-exome sequencing. By sequencing certain regions at greater depths, genes or regions of greater interest can be analyzed with higher sensitivity, accuracy, and / or precision. Cancer-related or associated genes or regions can be sequenced at greater depths. For example, at least 100, 200, 300, 400, 500, 600, 700, 800, 900, or more genes can be sequenced at depths greater than the rest of the exome (e.g., the average depth of whole-exome sequencing).

[0047] Nucleic acid sequencing generates sequencing read data. Sequencing reads can be processed to produce quality-improved data. Sequencing reads with quality scores can be generated. Quality scores can indicate the accuracy of a sequence read or a level or signal above a noise threshold for a given base identification. Quality scores can be used to filter sequencing reads. For example, sequencing reads that do not meet a specific quality score threshold can be removed. Sequencing reads can be processed to generate shared sequences or shared sequence base identification. A given nucleic acid (or nucleic acid fragment) can be sequenced, and errors can occur in the sequence due to reactions before or during sequencing. For example, amplification or PCR can produce errors in the amplicon, causing the sequence to be different from the parental sequence. Error correction can be performed using sample barcoding or molecular barcoding. Error correction can include identifying sequence reads that cannot be corroborated by other sequences from the same sample or the same original parent molecule. The use of barcoding can allow for the identification of identical parents or samples. Furthermore, sequence reads can be processed by performing single-stranded shared sequence identification or double-stranded shared sequence identification, thereby reducing or suppressing errors.

[0048] The methods disclosed herein may include determining allele frequencies or other cancer-related measures. These methods may include the frequency of mutant alleles in a somatic mutation set of biomarkers. Mutant allele frequencies can be used to determine the circulating tumor DNA (ctDNA) fraction of the target cancer. The plasma tumor mutational burden (pTMB) of the target cancer can be determined at least in part based on the mutant allele frequency set. Microsatellite instability detection can also be used to determine the presence or absence of cancer or cancer measures. Methylation status can be determined using the methods described herein and can be used to identify the presence of cancer or cancer parameters.

[0049] In each aspect, the biomarker set is processed and data corresponding to the biomarkers are generated. The biomarker set may include quantitative measurements from a set of cancer-associated genomic loci. Cancer-associated genomic loci may correspond to a genome. Cancer-associated genomic loci may include one or more genes selected from Table 1. In some cases, the cancer-associated genomic loci set 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 genes listed in Table 1.

[0050] Table 1: Gene List

[0051] A biomarker set may include one or more genes selected from Table 2. In some cases, a biomarker set 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 genes listed in Table 2.

[0052] A biomarker set may include one or more genes selected from Table 3. In some cases, a biomarker set may include 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, or 40 members selected from the genes listed in Table 3.

[0053] A biomarker set can correspond to genetic aberrations at a genetic locus. Genetic aberrations can be tumor-related alterations. These aberrations can include copy number alterations (CNA), copy number loss (CNL), single nucleotide variants (SNV), insertions or deletions (gains and losses), and / or rearrangements. Biomarker sets can be identified in various nucleic acid types. For example, tumor-related alterations can be identified in cfDNA. Tumor-related alterations can include changes in allele expression or gene expression. The methods and systems disclosed herein allow for gene expression profiling and the identification of changes in gene expression levels.

[0054] In various aspects, the method may include identifying the presence of cancer or cancer parameters. 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 indicating the probability that an object has cancer may be generated. This probability may be determined based on algorithms described elsewhere in this document. Similarly, a probability or likelihood of response to a particular treatment or a recurrence probability may be output.

[0055] In various aspects, algorithms are used to process sets of biomarkers. These algorithms can be trained. A trained algorithm can take a set of biomarkers as input and generate output regarding the presence or absence of cancer. The output can be specific to cancer type or cancer subtype. For example, the output could indicate the presence of bladder cancer.

[0056] The trained algorithm can be trained on multiple samples. For example, 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 samples can be used. The trained algorithm is trained using 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. No more than 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 can be used. The trained algorithm is trained using 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 fewer independent training samples. Training samples may be correlated with the presence or absence of cancer. Training samples may be correlated with cancer recurrence. Training samples may be correlated with cancer resistant to a specific drug or treatment. A single training sample may be positive for a specific cancer. A single training sample may be negative for a specific cancer. By using training samples, the trained algorithm can detect cancer, determine the probability of cancer recurrence or relapse, or determine whether cancer includes a group of biomarkers that may be resistant to treatment. Training samples can be correlated with additional clinical health data of the subjects. For example, additional clinical health data may include the subject's sex, weight, height, or levels of metabolites or antibodies. Additional clinical health data may also include indicators of other diseases, conditions, or disease statuses.

[0057] The trained algorithm can be trained using multiple sets of training samples. These sets can include the training samples described elsewhere in this document. For example, it can be trained using a first set of independent training samples associated with the presence of cancer and a second set of independent training samples 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.

[0058] The trained algorithm can also process additional clinical health data about the subject. For example, additional clinical health data may include the subject's sex, weight, height, or levels of metabolites or antibodies. Additional clinical health data may include indicators of other diseases, conditions, or disease statuses that the subject may have. By using additional clinical health data, combined with biomarkers, the trained algorithm can output the presence or absence of cancer, the probability of recurrence, or drug resistance, which may differ from the output of an algorithm that does not process additional clinical health data.

[0059] The trained algorithm can be an unsupervised machine learning algorithm. For example, an unsupervised machine learning algorithm can use cluster analysis to identify attributes of interest. The trained algorithm can also be a supervised machine learning algorithm. For example, a trained algorithm can be trained with training data to generate expected or desired outputs. Supervised learning algorithms can include deep learning algorithms, support vector machines (SVMs), neural networks, or random forests. Through machine learning algorithms, a trained algorithm can identify the relationship between biomarkers and the prognosis or diagnosis of a specific cancer. Without a trained algorithm, it may be difficult to identify relationships between biomarkers to accurately identify the presence of cancer or other cancer-related parameters.

[0060] In various aspects, systems and methods may include accuracy, sensitivity, or specificity in detecting cancer or cancer parameters. For example, a method or system may include detecting the presence or absence of cancer (or the presence of cancer parameters such as recurrence, relapse, 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%. A method or system may include detecting the presence or absence of cancer (or the presence of cancer parameters such as recurrence, relapse, 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%. Methods or systems may include detecting the presence or absence of cancer (or the presence of cancer parameters 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%. Methods or systems may include detecting the presence or absence of cancer (or the presence of cancer parameters 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%. Methods or systems may include detecting the presence or absence of cancer (or the presence of cancer parameters such as recurrence, relapse, 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%.

[0061] Computer System This disclosure provides computer systems that are programmed to implement the methods of this disclosure. Figure 4 A computer system 401 is illustrated, which is programmed or otherwise configured to perform the analysis or operations of the method, such as determining the probability of cancer presence based on an individual's biomarker set or running an algorithm. The computer system 401 can regulate various aspects of the methods and systems of this disclosure, such as, for example, executing algorithms, inputting training data, analyzing biomarker sets, or outputting results 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 remotely positioned relative to an electronic device. The electronic device can be a mobile electronic device.

[0062] Computer system 401 includes a central processing unit (CPU, also referred to herein as a “processor” and “computer processor”) 405, which may be a single-core or multi-core processor, or multiple processors for parallel processing. Computer system 401 also includes memory or storage location 410 (e.g., random access memory, read-only memory, flash memory), electronic storage unit 415 (e.g., hard disk), communication interface 420 for communicating with one or more other systems (e.g., network adapter), and peripheral devices 425, such as cache, other memory, data storage, and / or electronic display adapter. Memory 410, storage unit 415, interface 420, and peripheral devices 425 communicate with CPU 405 via a communication bus (solid line), such as a motherboard. Storage unit 415 may be a data storage unit (or data repository) for storing data. Computer system 401 may be operatively coupled to computer network (“network”) 430 via communication interface 420. Network 430 may be the Internet, the Internet of Things, and / or an extranet, or an intranet and / or extranet communicating with the Internet. In some cases, network 430 is a telecommunications and / or data network. Network 430 may include one or more computer servers that can enable distributed computing, such as cloud computing. In some cases, with the assistance of computer system 401, network 430 can implement a peer-to-peer network, which allows devices coupled to computer system 401 to act as clients or servers.

[0063] CPU 405 can execute a series of machine-readable instructions, which may be embodied in a program or software. These instructions may be stored in a memory location such as memory 410. The instructions may be directed to CPU 405, which may then be programmed or otherwise configured to implement the methods of this disclosure. Examples of operations performed by CPU 405 may include fetching, decoding, executing, and writing back.

[0064] CPU 405 may be part of a circuit such as an integrated circuit. One or more other components of system 401 may be included in the circuit. In some cases, the circuit is an application-specific integrated circuit (ASIC).

[0065] Storage unit 415 may store files, such as drivers, libraries, and saved programs. Storage unit 415 may store user data, such as user preferences and user programs. In some cases, computer system 401 may include one or more additional data storage units located outside computer system 401, such as those located on a remote server communicating with computer system 401 via an intranet or the Internet.

[0066] Computer system 401 can communicate with one or more remote computer systems via network 430. For example, computer system 401 can communicate with a remote computer system belonging to a user (e.g., a medical professional or patient). Examples of remote computer systems include personal computers (e.g., portable PCs), tablets or tablet computers (e.g., Apple® iPad, Samsung® Galaxy Tab), telephones, smartphones (e.g., Apple® iPhone, Android-enabled devices, Blackberry®), or personal digital assistants. Users can access computer system 401 via network 430.

[0067] The method described herein can be implemented via machine-executable code (e.g., a computer processor) stored in an electronic storage location (such as, for example, memory 410 or electronic storage unit 415) of computer system 401. The machine-executable or machine-readable code can be provided in the form of software. During use, processor 405 can execute the code. In some cases, the code can be retrieved from storage unit 415 and stored in memory 410 so that processor 405 is ready to access it. In some cases, electronic storage unit 415 can be removed, and machine-executable instructions can be stored in memory 410.

[0068] The code can be pre-compiled and configured for use with a machine that has a processor suitable for executing the code, or it can be compiled at runtime. The code can be provided in a programming language, which can be selected to enable the code to be executed in a pre-compiled or compiled manner.

[0069] Aspects of the systems and methods presented herein, such as computer system 401, can be embodied in programming. These aspects of the technology can be considered "products" or "manufactured goods," typically existing in the form of machine (or processor) executable code and / or associated data carried or embodied in a type of machine-readable medium. Machine-executable code can be stored in electronic storage units such as memory (e.g., read-only memory, random access memory, flash memory) or hard disks. "Storage" type media can include any or all tangible memory of computers, processors, etc., or related modules thereof, such as various semiconductor memories, tape drives, disk drives, etc., which can provide non-transitory storage for software programming at any time. All or part of the software can sometimes be communicated via the Internet or various other telecommunications networks. For example, such communication can load software from one computer or processor into another, such as from a management server or host computer into a computer platform for an application server. Therefore, another type of medium that can carry software elements includes light waves, radio waves, and electromagnetic waves, such as those used through physical interfaces between local devices, through wired and fiber optic fixed telephone networks, and through various air links. Physical elements carrying such waves (such as wired or wireless links, fiber optic links, etc.) can also be considered as media carrying software. As used herein, unless limited to non-transitory, tangible "storage" media, the term "readable medium" for a computer or machine refers to any medium that participates in providing instructions to a processor for execution.

[0070] Therefore, machine-readable media (such as computer-executable code) can take many forms, including but not limited to tangible storage media, carrier media, or physical transmission media. Non-volatile storage media include, for example, optical discs or disks, such as any storage device or the like in any computer, such as those used to implement the database shown in the figure. Volatile storage media include dynamic memory, such as the main memory of such a computer platform. Tangible transmission media include coaxial cables; copper wires and optical fibers, including wires that form the bus within a computer system. Carrier transmission media can take the form of electrical or electromagnetic signals or sound or light waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Therefore, common forms of computer-readable media include, for example: floppy disks, flexible disks, hard disks, magnetic tape, any other magnetic media, CD-ROMs, DVDs or DVD-ROMs, any other optical media, punched cardstock, any other physical storage media with a perforated pattern, RAM, ROM, PROM and EPROM, FLASH-EPROM, any other memory chips or cassette tapes, carriers for transmitting data or instructions, cables or links for transmitting such carriers, or any other media from which a computer can read programming code and / or data. Many of these forms of computer-readable media may involve passing one or more sequences of one or more instructions to a processor for execution.

[0071] Computer system 401 may include or communicate with an electronic display 435, the electronic display 435 including a user interface (UI) 440 for providing input, such as biomarkers or sequencing data, or 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.

[0072] The methods and systems disclosed herein can be implemented using one or more algorithms. The algorithms can be implemented using software executed by the central processing unit 405. For example, the algorithm can determine the presence or absence of cancer or cancer parameters based on a set of input sequencing data from a sample derived from the object.

[0073] Example Example 1. Minimal residual disease detection prior to transurethral resection of bladder tumor (TURBT) Cell-free urinary tumor DNA (utDNA) derived from tumor genome sequencing is an emerging biomarker that shows great potential for detecting minimal residual disease (MRD) in both muscle-invasive and metastatic bladder cancer. This study aimed to evaluate the ability of utDNA to measure MRD in non-muscle-invasive bladder cancer (NMIBC) following standard-of-care re-transurethral resection of bladder tumor (rTURBT).

[0074] Figure 1 A schematic diagram of the study is shown. Initial samples are obtained from subjects as index samples. These initial samples can be taken before or after any transurethral resection of bladder tumor (TURBT). Typically, rTURBT is performed on subjects showing high-risk tumors such as HGTa (high-grade Ta tumors) and T1 tumors or CIS (carcinoma in situ) grade tumors. Patients with high-risk NMIBC are enrolled prior to rTURBT. Tumor and rTURBT mutation profiles are indexed at 20,000 genes via whole-exome sequencing (e.g., PredicineWES). Urine samples (or urine cytospheric sediment samples) are collected immediately prior to re-TURBT. utDNA detection is performed via ultra-deep sequencing of urine cfDNA using a custom panel with up to 50 baseline mutations per patient and a fixed core panel covering hotspot regions and operable variants (e.g., using PredicineBEACON, Table 2). Urine tumor scores are used to identify utDNA positivity. The primary endpoint is the detection of utDNA to predict MRD at re-TURBT.

[0075] Table 2. List of genes detected by fixed operational / hotspot sequencing kits.

[0076] ACSF3, AHR, ALDH3A2, ANAPC1, ANK2, ANKHD1, AR, ARID1A, ARID1B, ATM, BLM, BRAF, BRCA1, BRCA2, C17orf97, C3orf70, CASP8, CDKN1A, CDKN2A, CER1, CREB BP, CTNNB1, CYC1, DIDO1, DNMT3A, EGFR, ELF3, EP300, EPHB1, EPYC, ERBB2, ERBB3, ERCC2, ESPL1, FBN3, FBXW7, FGFR1, FGFR2, FGFR3, FGFR4, FMN2, FOXA1, H ELZ, HIRIP3, HIST1H1C, HRAS, HRH4, IKZF1, KANSL1, KDM6A, KMT2A, KMT2D, KRAS, MROH2B, MYC, MYH9, NF1, NFE2L2, NTRK1, NTRK3, PIAS1, PIK3CA, PKHD1, P TEN, PTPRT, RARS2, RB1, RHOA, RHOB, RXRA, SACS, SF3B1, SF3B3, SOX2, SPOP, STAG2, SYNE1, TEKT1, TERT, TMEM132D, TNKS, TP53, TSC1, VHL, WDR66, ZFP36L1 Figure 2 A waterfall plot of genomic alterations in urinary tumor DNA in patients with NMIBC prior to re-TURBT is shown. Genes with non-synonymous (NS) mutations observed in index samples and rTURBT samples identified in at least two samples are reported. Figure 3A The frequency of WES+ variants between the indexed sample and the rTURBT sample is shown. Figure 3B The diagram shows the variant overlap plane between the index TURBT (blue) sample, the rTURBT (green) sample, and the urine sample (yellow). Figure 3C The study showed utDNA positivity in patients who developed the disease during rTURBT. Figure 3D The tumor score of utDNA prior to rTURBT is shown.

[0077] Eleven patients underwent rTURBT for high-risk NMIBC. Residual tumor was detected at rTURBT in 8 / 11 (73%) of the patients. Fifty genes with NS mutations were identified in at least two samples, including TP53 , PIK3CA , RB1 , MYC , CDKN1A and ARID1A ( Figure 2 (Table 3). A median of 146 (range 39–418) NS mutations and 91 (range 2–312) mutations were identified in the index sample and the re-TUR sample, respectively. The mean concordance rate between the initial TURBT and rTURBT was 83%, highest for patients upgraded to T2 disease and lowest for patients with CIS. In patients with residual disease, the tumor fraction of utDNA was higher (mean 2.5% vs. 0.2%, p = 0.10). Figure 3D Using tumor score to predict MRD, the area under the receiver operating characteristic curve (AUC) of this test is 0.85. Using an optimal threshold of >=3.3% tumor score to define utDNA positivity, the test has 75% sensitivity with 100% specificity.

[0078] Table 3. List of genes with mutations detected by personalized MRD kit sequencing.

[0079] TP53, KMT2D, EP300, RB1, AFF3, CDKN1A, EPHB1, FAT1, FLNA, FRY, GAK, MSH2, NEGR1, PCDHGA8, PIK3CA, SMARCA4, TSC1, AARS2, ABCA8, ABL1, ABL2, AC AP1, ACPP, ACSS3, ACVR1, ACAMDEC1, AHNAK, BRCA2, CACNB1, DNAJB1, ERBB3, FBXW7, FUBP3, KDM6A, MAP3K6, MEN1, NTRK1, PPP1R14C, PRKD1, TP53BP1 in conclusion Urinary tumor DNA (utDNA) shows promise as an alternative to minimal residual disease (NMIBC) and can predict TURBT pathology in NMIBC. Genomic alterations between index tumors and rTURBT tumors are highly concordant in papillomas, even in cases of staging progression, which could aid in targeted intravesical or systemic treatment options. Larger cohorts and long-term follow-up are needed to determine whether utDNA can be used for patient risk stratification.

[0080] Example 2: Minimal residual disease in monitored subjects The subject has cancer and is undergoing cancer treatment. A cancer sample is obtained from the subject (e.g., tumor cells, or from a biopsy, or from the excision of tumor tissue). The mutation profile of the cancer is obtained through whole-exome sequencing. Specific mutations in the cancer are identified through whole-exome sequencing. Based on the cancer mutations in the profile, a custom-designed probe kit specific to the cancer is prepared.

[0081] Following treatment, monitor for minimal residual disease in the subject. Obtain biological samples (e.g., whole blood, plasma, urine, or urinary cytate sediment) from the subject. Sequencing of the DNA from the samples is performed using a custom kit (and optionally a kit containing previously identified hotspot mutations in the cancer). Based on the DNA sequencing, the subject is identified as having residual tumor. Additional therapeutic interventions are recommended for the subject.

[0082] Example 3: Monitoring subjects after transurethral resection of bladder tumor The subject had bladder cancer and underwent transurethral resection of bladder tumor (TURBT) to remove the cancer. The mutation profile of the cancer was obtained through whole-exome sequencing. Specific mutations in the cancer were identified. Based on the cancer mutations in the profile, a custom probe kit was prepared.

[0083] The subject was found to be cancer-free, and minimal residual disease was monitored. Urine samples or urine cytate sediment samples were obtained from the subject. Cell-free DNA from the urine was sequenced using a custom kit and a kit containing previously identified hotspot mutations in the cancer. Based on the urine cell-free DNA sequencing, the subject was identified as having residual tumor. Additional therapeutic interventions were recommended for the subject, including re-transurethral bladder tumor resection (rTURBT).

[0084] While preferred embodiments of the invention have been shown and described herein, it will be apparent to those skilled in the art that these embodiments are provided by way of example only. The invention is not intended to be limited to the specific examples provided in the specification. Although the invention has been described with reference to the foregoing specification, the description and illustrations of the embodiments herein are not intended to be interpreted in a limiting sense. Many variations, modifications, and substitutions will now be conceived by those skilled in the art without departing from the invention. Furthermore, it should be understood that all aspects of the invention are not limited to the specific descriptions, configurations, or relative proportions set forth herein, which depend on various conditions and variables. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of the invention. Therefore, the invention should also cover any such alternatives, modifications, variations, or equivalents. The following claims are intended to define the scope of the invention and thereby cover the methods and structures within the scope of these claims and their equivalents.

Claims

1. A method for identifying the presence or absence of minimal residual disease (MRD) in a subject, comprising: (a) Determination of deoxyribonucleic acid (DNA) molecules from a first biological sample obtained from or derived from the object at a first time point; (b) Based at least in part on the determination of (a), detect a set of biomarkers from the DNA molecule, wherein the set of biomarkers includes differentially expressed markers or variants; (c) Generate a plurality of probe nucleic acids tailored for the object, wherein the probe nucleic acids comprise sequences of at least a subset of the biomarker set; (d) Using the plurality of probe nucleic acids, sequencing cell-free deoxyribonucleic acid (cfDNA) from a second biological sample obtained from or derived from the object at a second time point to detect the presence or absence of the subset of the biomarker set, wherein the sequencing is performed at a depth of at least 80x, wherein the second biological sample includes a blood sample, a urine sample, or a urine cell sediment sample. (e) The computer processes the subset of the biomarker group to detect the presence or absence of the minimal residual disease (MRD) in the object.

2. The method according to claim 1, wherein the first biological sample is selected from: cell-free deoxyribonucleic acid (cfDNA) samples, cell-free ribonucleic acid (cfRNA) samples, plasma samples, serum samples, erythrocyte sedimentation rate (ESR) amber layer samples, peripheral blood mononuclear cell (PBMC) samples, erythrocyte samples, urine samples, urine cell sediment samples, saliva samples, tissue biopsy, pleural fluid samples, peritoneal fluid samples, amniotic fluid samples, cerebrospinal fluid samples, lymph fluid samples, sweat samples, tear samples, semen samples, or any derivatives thereof and any combination thereof.

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

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

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

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

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

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

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

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

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

12. The method according to any one of claims 1-11, further comprising grading the first biological sample of the object to obtain the DNA molecules, wherein the first biological sample is a whole blood sample.

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

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

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

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

17. The method according to any one of claims 13-16, further comprising performing at least one of single-stranded common sequence identification and double-stranded common sequence identification on the nucleic acid sequencing read, thereby suppressing sequencing and PCR errors in the nucleic acid sequencing read.

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

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

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

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

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

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

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

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

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

27. The method according to any one of claims 1-26, wherein the cancer is selected from: urogenital cancers, prostate cancer, bladder cancer, and any combination thereof.

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

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

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

31. The method according to any one of claims 1-30, wherein the method comprises detecting the presence or absence of the minute residual disease in the object 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. The method according to any one of claims 1-31, wherein the method comprises detecting the presence or absence of the minute residual disease in the object 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. The method according to any one of claims 1-32, wherein the method comprises detecting the presence or absence of the microresidual disease in the object 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. The method according to any one of claims 1-33, wherein the method comprises detecting the presence or absence of the microresidual disease in the object 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. The method according to any one of claims 1-34, wherein the method comprises detecting the presence or absence of the microresidual disease in the object 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. The method according to any one of claims 1-35, wherein the first biological sample is obtained from or derived from the subject prior to the subject receiving a therapy for the cancer.

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

38. The method according to any one of claims 1-36, wherein the biological sample is obtained from or derived from the subject after receiving a therapy for the cancer.

39. The method according to any one of claims 36-38, wherein the therapy is selected from: surgical resection, chemotherapy, radiotherapy, immunotherapy, cell therapy, adjuvant therapy, neoadjuvant therapy, androgen deprivation therapy, and combinations thereof.

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

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

42. The method according to 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 the detected cancer.

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

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

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

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

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

48. The method of claim 47, wherein the biomarker group 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 genes listed in Table 1.

49. The method according to any one of claims 1-46, wherein the biomarker set comprises one or more members selected from the genes listed in Table 2.

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

51. The method according to any one of claims 1-46, wherein the biomarker set comprises one or more members selected from the genes listed in Table 3.

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

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

54. The method according to any one of claims 1-53, wherein the plurality of probes are sequence complementary to at least a portion of the nucleic acid sequence of the biomarker group.

55. The method according to 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. The method according to any one of claims 1-55, wherein (d) further comprises sequencing using a fixed plurality of probes, wherein the probes among the fixed plurality of probes include probes that do not include sequences of the subset of the biomarker group.

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

58. The method according to any one of claims 1-57, further comprising determining the probability of the presence or absence of the cancer in the object.

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

60. The method of claim 59, wherein the difference indication of the assessment of the presence or absence of the cancer in the subject between the plurality of time points is selected from one or more of the following clinical indications: (i) the diagnosis of the cancer, (ii) the prognosis of the cancer, and (iii) the effectiveness or ineffectiveness of the treatment process for the cancer in the subject.

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

62. The method according to any one of claims 1-61, wherein the group of biomarkers from the DNA molecule includes tumor-associated alterations selected from single nucleotide variants (SNVs), insertions or deletions (gains and losses), and rearrangements.

63. The method according to any one of claims 1-62, further comprising determining the frequency of mutant alleles in the somatic mutant group within the biomarker set.

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

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

66. The method according to any one of claims 63-65, further comprising determining an abnormal score of the cancer of the subject based at least in part on the mutation allele frequency set.

67. A method for providing treatment to a target, comprising: (a) Determining deoxyribonucleic acid (DNA) molecules from a tumor sample obtained from or derived from the subject at a first time point, wherein the subject has undergone urethral bladder tumor resection surgery; (b) Based at least in part on the determination of (a), detect a set of biomarkers from the DNA molecule, wherein the set of biomarkers includes differentially expressed markers or variants; (c) Generate a plurality of probe nucleic acids tailored for the object, wherein the probe nucleic acids comprise sequences of at least a subset of the biomarker set; (d) Using the plurality of probe nucleic acids, sequencing deoxyribonucleic acid (DNA) from a urine sample obtained from or derived from the object at a second time point to detect the presence or absence of the subset of the biomarker group, wherein the sequencing is performed at a depth of at least 80x, wherein the second biological sample is a urine sample; (e) The computer processes the subset of the biomarker group to detect the presence or absence of the minimal residual disease (MRD) in the object; (f) Perform a second transurethral resection of bladder tumor based at least in part on the presence or absence of the minimal residual disease (MRD) in the subject.

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