Method for detecting colon cancer and monitoring treatment

Abstract

The present invention relates to a method for detecting colon cancer, a method for determining whether colon cancer is stable or progressive, a method for determining the risk of disease recurrence, and a method for determining the response of a subject with colon cancer to a therapy.

Description

Technology Field

[0001] 관련 출원에 대한 상호 참조

[0002] This application claims priority to U.S. Provisional Application No. 62 / 620,015 filed on January 22, 2018, the contents of which are incorporated herein by reference in their entirety.

[0003] 서열 목록

[0004] The present application includes a list of sequences submitted in ASCII format via EFS-Web, the entirety of which is incorporated herein by reference. The name of the said ASCII copy created on January 21, 2019, is "LBIO-004_001WO.txt" and its size is 111 KB.

[0005] 기술분야

[0006] The present invention relates to the detection of colon cancer. Background Technology

[0007] Colorectal cancer (CRC) is one of the most commonly diagnosed cancers worldwide. In the United States, CRC is the second leading cause of death after lung cancer, as is the case in Europe. Globally, it is the fourth most common cause of cancer death. Although chemotherapy following surgical resection is the primary treatment option, approximately half of patients eventually die due to distant metastasis. Currently, the 5-year overall survival rate for patients with primary CRC can be up to 90%, but it is expected to decrease to ~50% in patients with advanced non-metastatic tumors, and further decrease to <10% in patients resected at an early stage of the disease due to the incomplete understanding of the molecular mechanisms underlying its onset.

[0008] Overall survival is associated with the disease stage at diagnosis, suggesting that early detection of disseminated disease is significantly important. Consequently, the development of new diagnostic methods capable of better defining disease stages and monitoring disease progression is crucial.

[0009] Surveillance remains a cornerstone approach for detecting recurrence at an early stage and planning additional treatment strategies. Following potentially curative resection, monitoring via the measurement of blood biomarkers and / or imaging, such as CT, may be performed to detect asymptomatic metastatic disease early. However, pooled data from randomized trials published from 1995 to 2016 confirm that there is no benefit from surgical treatment resulting from the early detection of metastasis. This likely reflects the poor sensitivity of current biomarkers.

[0010] The current biomarker is Carcinoembryonic Antigen (CEA), a glycoprotein involved in cell adhesion that is not typically expressed in adult tissues, except in heavy smokers. Its specific sialofucosylated glycoform acts as a functional colon carcinoma L-selectin and E-selectin ligand, which may play a role in the metastatic dissemination of colon carcinoma cells. CEA is primarily used to monitor the treatment of colorectal carcinoma, confirm recurrence after surgical resection for stage determination, or localize cancer spread through the measurement of biological fluids. However, there are significant limitations. While preoperative CEA levels have shown an association with (disease-free) survival, this was primarily because they served as a surrogate for metastatic indication. Nevertheless, estimating preoperative CEA values ​​has been shown to be of limited importance for predicting long-term outcomes in individual cases. This was independently supported by a prospective analysis confirming that levels of CEA and other biomarkers, such as CA19-9, did not indicate metastasis even at a point when clinical signs and imaging techniques had already demonstrated metastasis.

[0011] Although the molecular basis of colorectal cancer, such as microsatellite instability and K-RAS mutations, has been widely elucidated, the development of diagnostic and prognostic markers that capture this information—e.g., in urine or stool, or as circulating cell-free DNA—has begun but is still in its early stages. Examples include the measurement of methylation of septin 9, a tumor suppressor involved in cytokinesis during cell division. This has been used to detect colorectal cancer; metrics range from 60 to 70%. Evaluation of circulating cell-free DNA (Line 1 and Alu-based PCR) yields an 81% predictive value with an ROC of 0.86 as a diagnostic tool, while the measurement of circulating tumor cells is also considered useful. Tissue polypeptide-specific antigen (TPS) can be used as a monitor for colon cancer, similar to TAG-72 (tumor-associated glycoprotein), but the measurement of other single analytes, such as CEA or CA19-9, is non-specific.

[0012] Among them, 14 gene expression tools for detecting colon cancer are disclosed herein.

[0013] In one embodiment, the present disclosure is a method for detecting colon cancer in a subject requiring detection of colon cancer, comprising: (a) determining the expression level of at least 14 biomarkers in a test sample from a subject by contacting the test sample from the subject with a plurality of agents specific for detecting the expression of at least 14 biomarkers, wherein the 14 biomarkers are ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA, DEAF , STOML2 , UMPS , and Step of including a housekeeping gene; (b) ADRM1, CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA , DEAF , STOML2, and UMPS By normalizing each expression level with respect to the expression level of the housekeeping gene, ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA , DEAF, STOML2 , and UMPSA method for detecting colon cancer in a subject requiring detection of colon cancer is provided, comprising the steps of: (c) obtaining each normalized expression level; (d) inputting each normalized expression level into an algorithm to generate a score; (e) comparing the score with a predetermined cutoff value; and (e) confirming the presence of colon cancer in the subject if the score is greater than or equal to the predetermined cutoff value, or confirming the absence of colon cancer in the subject if the score is less than the predetermined cutoff value.

[0014] In one embodiment, the present disclosure is a method for detecting colon cancer in a subject requiring detection of colon cancer, comprising: (a) determining the expression level of at least 14 biomarkers in a test sample from a subject by contacting the test sample from the subject with a plurality of agents specific for detecting the expression of at least 14 biomarkers, wherein the 14 biomarkers are ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA, DEAF , STOML2 , UMPS A step comprising , and housekeeping genes; (b) ADRM1, CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA , DEAF , STOML2, and UMPS By normalizing each expression level with respect to the expression level of the housekeeping gene, ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA , DEAF, STOML2 , and UMPS A method for detecting colon cancer in a subject requiring detection of colon cancer is provided, comprising the steps of: (c) obtaining each normalized expression level; (d) inputting each normalized expression level into an algorithm to generate a score; (e) comparing the score with a first predetermined cutoff value; and (e) generating a report, wherein the report confirms the presence of colon cancer in the subject if the score is greater than or equal to the first predetermined cutoff value, or confirms the absence of colon cancer in the subject if the score is less than the first predetermined cutoff value, and the first predetermined cutoff value is 50% on a scale of 0-100%.

[0015] In one embodiment, the present disclosure is a method for determining whether colon cancer in a subject is stable or advanced, comprising the step of (a) contacting a test sample from a subject with a plurality of agents specific for detecting the expression of at least 14 biomarkers to determine the expression levels of at least 14 biomarkers in a test sample from a subject, wherein the 14 biomarkers are ADRM1, CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA , DEAF, STOML2 , UMPS , and a step including housekeeping genes; (b) ADRM1 , CDK4, COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA , DEAF , STOML2 , and UMPS By normalizing each expression level with respect to the expression level of the housekeeping gene, ADRM1, CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA , DEAF , STOML2, and UMPSA method for determining whether colon cancer in a subject is stable or advanced is provided, comprising the steps of: (c) obtaining each normalized expression level; (d) inputting each normalized expression level into an algorithm to generate a score; (e) comparing the score with a predetermined cutoff value; and (e) confirming that the colon cancer in the subject is advanced if the score is greater than or equal to the predetermined cutoff value, or confirming that the colon cancer in the subject is stable if the score is less than the predetermined cutoff value.

[0016] In one embodiment, the present disclosure is a method for determining whether colon cancer in a subject is stable or advanced, comprising the step of (a) determining the expression level of at least 14 biomarkers in a test sample from a subject by contacting the test sample from the subject with a plurality of agents specific to detecting the expression of at least 14 biomarkers, wherein the 14 biomarkers are ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA, DEAF , STOML2 , UMPS , and Step of including a housekeeping gene; (b) ADRM1, CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA , DEAF , STOML2, and UMPS By normalizing each expression level with respect to the expression level of the housekeeping gene, ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA , DEAF, STOML2 , and UMPS A method for determining whether colon cancer in a subject is stable or advanced, comprising the steps of: (c) obtaining each normalized expression level; (d) inputting each normalized expression level into an algorithm to generate a score; (e) comparing the score with a second predetermined cutoff value; and (e) generating a report, wherein the report confirms that the colon cancer is advanced if the score is greater than or equal to the second predetermined cutoff value, or confirms that the colon cancer is stable if the score is less than the second predetermined cutoff value, and the second predetermined cutoff value is 60% on a scale of 0-100%.

[0017] In one embodiment, the present disclosure is a method for determining the completion of surgery in a subject having colon cancer, comprising the step of (a) determining the expression level of at least 14 biomarkers in a test sample from a subject after surgery by contacting a test sample from the subject with a plurality of agents specific to detecting the expression of at least 14 biomarkers, wherein the 14 biomarkers are ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P, SNRPA , DEAF , STOML2 , UMPS Step (b) including , and housekeeping genes ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA , DEAF , STOML2, and UMPS By normalizing each expression level with respect to the expression level of the housekeeping gene, ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA , DEAF, STOML2 , and UMPS A method for determining the completion of surgery in a subject having colon cancer is provided, comprising the steps of: (c) obtaining each normalized expression level; (d) inputting each normalized expression level into an algorithm to generate a score; (e) comparing the score with a predetermined cutoff value; and (e) confirming that the colon cancer in the subject has not been completely removed if the score is greater than or equal to the predetermined cutoff value, or confirming that the colon cancer in the subject has been completely removed if the score is less than the predetermined cutoff value.

[0018] In one embodiment, the present disclosure is a method for determining the completion of surgery in a subject having colon cancer, comprising the step of (a) determining the expression level of at least 14 biomarkers in a test sample from a subject after surgery by contacting a test sample from the subject with a plurality of agents specific to detecting the expression of at least 14 biomarkers, wherein the 14 biomarkers are ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P, SNRPA , DEAF , STOML2 , UMPS A step comprising , and housekeeping genes; (b) ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA , DEAF , STOML2, and UMPS By normalizing each expression level with respect to the expression level of the housekeeping gene, ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA , DEAF, STOML2 , and UMPS A method for determining the completion of surgery in a subject having colon cancer comprises the steps of: (c) obtaining each normalized expression level; (d) inputting each normalized expression level into an algorithm to generate a score; (e) comparing the score with a first predetermined cutoff value; and (e) generating a report, wherein the report confirms that the colon cancer has not been completely removed if the score is greater than or equal to the first predetermined cutoff value, or confirms that the colon cancer has been completely removed if the score is less than or equal to the first predetermined cutoff value, and the first predetermined cutoff value is 50% on a scale of 0-100%.

[0019] In one embodiment, the present disclosure comprises the step of (a) determining the expression levels of at least 14 biomarkers in a test sample from a subject by contacting a test sample from a subject with a plurality of agents specific for detecting the expression of at least 14 biomarkers, wherein the 14 biomarkers are ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA, DEAF , STOML2 , UMPS , and Step of including a housekeeping gene; (b) ADRM1, CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA , DEAF , STOML2, and UMPS By normalizing each expression level with respect to the expression level of the housekeeping gene, ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA , DEAF, STOML2 , and UMPS A method is provided comprising the steps of: obtaining each normalized expression level; (c) inputting each normalized expression level into an algorithm to generate a score; (d) comparing the score with a predetermined cutoff value; and (e) performing a first therapy on a subject if the score is greater than or equal to the predetermined cutoff value.

[0020] In one embodiment, the present disclosure is a method for evaluating the response of a subject having colon cancer to a first therapy, comprising: (1) at a first time point: (a) determining the expression level of at least 14 biomarkers in a first test sample from a subject by contacting a first test sample from the subject with a plurality of agents specific to detecting the expression of at least 14 biomarkers, wherein the 14 biomarkers are ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK, POP7 , S100P , SNRPA , DEAF , STOML2 , UMPS , Step (b) including housekeeping genes ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA, DEAF , STOML2 , and UMPS By normalizing each expression level with respect to the expression level of the housekeeping gene, ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA, DEAF , STOML2 , and UMPS (a) a step of obtaining each normalized expression level; (c) a step of inputting each normalized expression level into an algorithm to generate a first score; (2) at a second time point after the first time point and after the performance of therapy on the subject: (a) a step of determining the expression levels of at least 14 biomarkers from a second test sample from the subject by contacting the second test sample from the subject with a plurality of agents specific to detecting the expression of at least 14 biomarkers, wherein the 14 biomarkers are ADRM1 , CDK4 , COMT , DHCR7 , HMOX2, MCM2 , PDXK , POP7, S100P , SNRPA , DEAF , STOML2 , UMPS , and Step of including a housekeeping gene; (b) ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7, S100P , SNRPA , DEAF , STOML2 , and UMPS By normalizing each expression level with respect to the expression level of the housekeeping gene, ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK, POP7 , S100P , SNRPA , DEAF , STOML2 , and UMPSA method for evaluating the response of a subject with colon cancer to a first therapy is provided, comprising the steps of: obtaining each normalized expression level; (c) inputting each normalized expression level into an algorithm to generate a second score; (3) comparing a first score with a second score; and (4) confirming that the subject is responsive to the first therapy if the second score is significantly reduced compared to the first score, or confirming that the subject is not responsive to the first therapy if the second score is not significantly reduced compared to the first score.

[0021] In one embodiment, the present disclosure is a method for evaluating the response of a subject with colon cancer to a therapy, comprising: (1) at a first time point, (a) determining the expression level of at least 14 biomarkers in a first test sample from a subject by contacting a first test sample from the subject with a plurality of agents specific to detecting the expression of at least 14 biomarkers, wherein the 14 biomarkers are ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK, POP7 , S100P , SNRPA , DEAF , STOML2 , UMPS A step comprising , and housekeeping genes; (b) ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2, PDXK , POP7 , S100P , SNRPA, DEAF , STOML2 , and UMPS By normalizing each expression level with respect to the expression level of the housekeeping gene, ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA, DEAF , STOML2 , and UMPS (2) a step of obtaining each normalized expression level; (c) a step of inputting each normalized expression level into an algorithm to generate a first score; and (2) at a second time point, (d) a step of determining the expression levels of at least 14 biomarkers from a second test sample from a subject by contacting the second test sample from the subject with a plurality of agents specific to detecting the expression of at least 14 biomarkers, wherein the 14 biomarkers are ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK, POP7 , S100P , SNRPA , DEAF , STOML2 , UMPS A step comprising , and housekeeping genes; (e) ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA, DEAF , STOML2 , and UMPS By normalizing each expression level with respect to the expression level of the housekeeping gene, ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA, DEAF , STOML2 , and UMPSThe present invention provides a method for evaluating the response of a subject with colon cancer to a therapy, comprising the steps of: obtaining each normalized expression level; (f) inputting each normalized expression level into an algorithm to generate a second score, wherein the second time point is after the first time point and after the subject has received the therapy; (3) comparing the first score with the second score; and (4) generating a report, wherein the report confirms that the subject is responsive to the therapy if the second score is significantly reduced compared to the first score, or that the subject is not responsive to the therapy if the second score is not significantly reduced compared to the first score.

[0022] In some embodiments, the method of the present disclosure may further include the step of continuing to perform the first therapy on a subject when the second score is significantly reduced compared to the first score.

[0023] In some embodiments, the method of the present disclosure may further include the step of stopping the first therapy on a subject when the second score is not significantly reduced compared to the first score.

[0024] In some embodiments, the method of the present disclosure may further include the step of performing a second therapy on a subject when the second score is not significantly reduced compared to the first score.

[0025] In some aspects, when the second score is at least 25% lower than the first score, the second score is significantly reduced compared to the first score.

[0026] In some embodiments, the predetermined cutoff value may be 50% on a scale of 0-100%. The predetermined cutoff value may be 60% on a scale of 0-100%.

[0027] In some embodiments of any method disclosed herein, the housekeeping gene is MRPL19 , PSMC4, SF3A1 , PUM1 , ACTB , GAPD , GUSB , RPLP0 , TFRC , MORF4L1 , 18S, PPIA , PGK1 , RPL13A , B2M , YWHAZ , SDHA , and HPRT1 It can be selected from a group consisting of. For example, housekeeping genes are MORF4L1 It could be.

[0028] In some embodiments, the method of the present disclosure may have a sensitivity of more than 85%. In some embodiments, the method of the present disclosure may have a specificity of more than 85%.

[0029] In some embodiments, the biomarker may include RNA, cDNA, protein, or any combination thereof.

[0030] In some embodiments, when the biomarker is RNA, the RNA can be reverse transcribed to produce cDNA, and the expression level of the produced cDNA can be detected.

[0031] In some embodiments, a biomarker or the expression of a biomarker can be detected by forming a complex between the biomarker and a labeled probe or primer.

[0032] In some embodiments, when the biomarker is a protein, the protein can be detected by forming a complex between the protein and the labeled antibody.

[0033] In some embodiments, when the biomarker is RNA or cDNA, the RNA or cDNA can be detected by forming a complex between the RNA or cDNA and a labeled nucleic acid probe or primer. The complex between the RNA or cDNA and the labeled nucleic acid probe or primer may be a hybridization complex.

[0034] In some embodiments, a predetermined cutoff value may be derived from a plurality of reference samples obtained from subjects who do not have a neoplastic disease or have not been diagnosed with a neoplastic disease. The neoplastic disease may be colon cancer.

[0035] In some embodiments, the algorithm may be XGBoost (XGB), Random Forest (RF), glmnet, cforest, Classification and Regression Trees for Machine Learning (CART), treebag, K-Nearest Neighbors (kNN), neural network (nnet), Support Vector Machine Radial (SVM-radial), Support Vector Machine Linear (SVM-linear), Naive Bayes (NB), multilayer perceptron (mlp), or any combination thereof.

[0036] In some embodiments, the method of the present disclosure may further include the step of performing a first therapy on a subject when the score is greater than or equal to a predetermined cutoff.

[0037] In some embodiments, the first time point may be before performing the first therapy on the subject. The first time point may be after performing the first therapy on the subject.

[0038] In some embodiments, the therapy may include anticancer therapy, surgery, chemotherapy, targeted drug therapy, radiation therapy, immunotherapy, or any combination thereof.

[0039] In some embodiments, the surgery may include removing a polyp during a colonoscopy, endoscopic mucosal resection, partial colectomy, ostomy, removing at least one cancerous lesion from the liver, or any combination thereof.

[0040] In some embodiments, chemotherapy may include a combination of FOLFOX, FOLFIRI, 5-FU and leucovorin, capecitabine, irinotecan, CapeOx, or any combination thereof.

[0041] In some embodiments, the targeted drug therapy may include bevacizumab, cetuximab, panitumumab, regorafenib, a combination of trifluridine and tipiracil, EGFR TKI inhibitors, or any combination thereof.

[0042] In some embodiments, anticancer therapy may include anti-colon cancer therapy.

[0043] In some embodiments, immunotherapy may include pembrolizumab, nivolumab, or a combination of pembrolizumab and nivolumab.

[0044] In some embodiments, the test sample may be blood, serum, plasma, neoplastic tissue, or any combination thereof. The reference sample may be blood, serum, plasma, non-neoplastic tissue, or any combination thereof.

[0045] Any of the above modes may be combined with any other mode.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as generally understood by those skilled in the art to which this disclosure pertains. In this specification, the singular form also includes the plural unless the context clearly indicates otherwise; for example, the terms "a," "an," and "the" are understood as singular or plural, and the term "or" is understood as inclusive. For example, "element" means one or more elements. Throughout the specification, variations of the word "containing" or "to contain" will be understood to imply the inclusion of the mentioned element, integer, or step, or group of elements, integers, or steps, but not the exclusion of any other element, integer, or step, or group of elements, integers, or steps. "Approximately" may be understood as within 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, 0.5%, 0.1%, 0.05%, or 0.01% of the mentioned value. Unless otherwise evident from the context, all numerical values ​​provided herein are modified by the term "approximately".

[0047] Methods and materials similar or equivalent to those described herein may be used to practice or test the present disclosure, but suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated herein by reference in their entirety. References cited herein are not recognized as prior art for the claimed invention. In the event of a conflict, this specification, including definitions, shall prevail. Furthermore, materials, methods, and examples are merely illustrative and are not intended to be limiting. Other features and benefits of the present disclosure will become apparent from the following detailed description and claims. Brief explanation of the drawing

[0048] Figures 1a-1b are graphs showing the normalized gene expression of 13 gene signatures in the colon mucosa (Figure 1a) and cell line (Figure 1b). Gene expression is matched with normal mucosa ( n colon cancer sample (compared to =7) n Significantly at =7( p <0.0001) increased. Levels were ~20-fold higher in colon cancer tumor tissue than in normal mucosa. All genes were expressed in three different colon cancer cell lines. Levels were ~1000x higher compared to normal mucosa. Horizontal lines confirm the median-normalized expression of 13 genes. Figures 2a and 2b are graphs showing the analysis of receiver manipulation curves for the test set (Figure 2a) and the independent set (Figure 2b). Each cohort included 136 cancers and 60 controls. In the test set, the AUROC was 0.9 and the Youden J index was 0.71. In the independent set, the AUROC was 0.86 and the Youden index was 0.6. The Z-statistic ranged from 11.2 to 15.6 and was highly significant ( p <0.0001). The sensitivity and specificity of the test ranged from 85-87.5% and 75-83%, respectively. Figures 3a-3b show the entire cohort (control group: n =120; Colon cancer cases: n The level of gene expression in =272) was significantly higher in the cases (62.7±14%) compared to the control group (34.6±18%) ( p This is a graph showing that it was confirmed to have increased (<0.0001) (Fig. 3a). The AUROC is 0.88, p It was <0.0001 (Fig. 3b). The horizontal line confirms the central expression of the 13 normalized gene signatures (ColoTest). Figures 4a and 4b are graphs showing the decision curve analysis (Figure 4a) and risk analysis (Figure 4n) for ColoTest. This showed a standardized predictive benefit of >50% up to 80% of the risk threshold. The probit risk assessment plot confirmed that a ColoTest score of >50% was 75% accurate in predicting colon cancer in blood samples. This increased from a ColoTest score of >60% to >80%. Figure 5 is a graph showing the effect of surgery on ColoTest. The pre-operative level increased (84±6%). In subjects with no evidence of disease (NED), the level decreased to 14±9% (*p=0.0001) by surgery. In subjects with residual disease (D) after surgery, the level remained similar to the pre-operative value (74±4%). Figures 6a–6c are graphs showing ColoTest scores for stable and progressive disease. The test scores did not differ significantly between subjects confirmed to be stable at the time of evaluation and subjects with progressive disease (Figure 6a). Of the 17 subjects with stable disease, 12 showed disease progression during the 3-month follow-up. The level in subjects with truly evident stable disease was low (16±10%) (Figure 6b). In subjects who progressed within 3 months, the level did not differ from that of subjects with progressive disease (73±16% vs. 68±25%). The AUROC for distinguishing between stable disease and progressing / progressive disease was 0.97, p It was <0.0001 (Fig. 6c). Figure 7 is a graph showing the comparison of AUROCs between ColoTest and CEA for distinguishing between stable and progressive disease. ColoTest was significantly more sensitive than CEA (AUC difference: 0.18, z-statistic: 2.1, p =0.03). Figure 8 is a graph showing the effect of treatment on ColoTest. The pre-treatment level increased (82±9%). In subjects who responded to therapy with disease stabilization, the level decreased to 14±7% (* p< 0.0001). In subjects showing disease progression due to treatment failure, the level increased (69±21%). Specific details for implementing the invention

[0049] Details of the invention are set forth in the following appended description. Although methods and materials similar or equivalent to those described herein may be used in the practice or testing of the invention, exemplary methods and materials are described. Other features, objects, and advantages of the invention will be apparent from the description and claims. In the specification and appended claims, the singular form also includes the plural unless the context otherwise clearly indicates. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as generally understood by those skilled in the art to which the invention pertains. All patents and publications cited herein are incorporated herein by reference in their entirety.

[0050] Colorectal cancer is cancer of the large intestine (colon). Symptoms of colorectal cancer include, but are not limited to: (a) changes in bowel habits, (b) rectal bleeding or blood in the stool, (c) persistent abdominal discomfort such as cramps, gas, or pain, (d) a feeling that the bowel is not completely empty, (e) weakness or fatigue, and (f) unexplained weight loss.

[0051] A method for quantifying (scoring) circulating colon cancer molecular signatures with high sensitivity and specificity for purposes including, but not limited to, detecting colon cancer, determining whether colon cancer is stable or advanced, determining the completion of surgery, and evaluating the response to colon cancer therapy is described herein. Specifically, the present invention MORF4L1Normalized by the expression level of housekeeping genes such as, ADRM1 , CDK4 , COMT, DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA , DEAF , STOML2 , and UMPS It is based on the finding that the expression level of is elevated in subjects with colon cancer compared to healthy subjects.

[0052] Accordingly, the present disclosure comprises the step of (a) determining the expression levels of at least 14 biomarkers from a test sample from a subject by contacting the test sample with a plurality of agents specific for detecting the expression of at least 14 biomarkers, wherein the 14 biomarkers are ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA , DEAF , STOML2, UMPS , and Step of including a housekeeping gene; (b) ADRM1 , CDK4 , COMT, DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA , DEAF , STOML2 , and UMPS By normalizing each expression level with respect to the expression level of the housekeeping gene, ADRM1 , CDK4, COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA , DEAF , STOML2 , and UMPS A method for detecting colon cancer in a subject requiring this is provided, comprising the steps of: (c) obtaining each normalized expression level; (d) inputting each normalized expression level into an algorithm to generate a score; (d) comparing the score with a first predetermined cutoff value; and (e) confirming the presence of colon cancer in the subject if the score is greater than or equal to the predetermined cutoff value, or confirming the absence of colon cancer in the subject if the score is less than the predetermined cutoff value.

[0053] In some embodiments of the method described above, step (e) is a step of generating a report, the report may include confirming the presence of colon cancer in the subject if the score is greater than or equal to a first predetermined cutoff value, or confirming the absence of colon cancer in the subject if the score is less than a first predetermined cutoff value.

[0054] In some embodiments, the above-described method may further include the step of performing a first therapy on a subject. The above-described method may further include the step of performing a first therapy on a subject when the score is greater than or equal to a predetermined cutoff value.

[0055] The present disclosure also comprises the step of determining the expression levels of at least 14 biomarkers from a test sample from a subject by contacting the test sample with a plurality of agents specific for detecting the expression of at least 14 biomarkers, wherein the 14 biomarkers include ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, UMPS, and housekeeping genes; (b) normalizing the expression levels of each of ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, and UMPS with respect to the expression levels of the housekeeping gene to obtain the normalized expression levels of each of ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, and UMPS; (c) inputting each normalized expression level into an algorithm to generate a score; (d) comparing the score with a second predetermined cutoff value; and (e) confirming that the colon cancer in the subject is advanced if the score is greater than or equal to the predetermined cutoff value, or confirming that the colon cancer in the subject is stable if the score is less than the predetermined cutoff value. A method for determining whether colon cancer in a subject is stable or advanced is provided.

[0056] In some embodiments of the method described above, step (e) is a step of generating a report, the report may include confirming that the colon cancer is advanced if the score is greater than or equal to a second predetermined cutoff value, or confirming that the colon cancer is stable if the score is less than the second predetermined cutoff value.

[0057] In some embodiments, the above-described method may further include the step of performing a first therapy on a subject. The above-described method may further include the step of performing a first therapy on a subject when the score is greater than or equal to a predetermined cutoff value.

[0058] In some embodiments, the method further comprises the step of treating a subject with advanced colon cancer with surgery, chemotherapy, targeted drug therapy, radiation therapy, immunotherapy, or a combination thereof.

[0059] The present disclosure also comprises the step of (a) determining the expression levels of at least 14 biomarkers from a test sample from a postoperative subject by contacting the test sample with a plurality of agents specific for detecting the expression of at least 14 biomarkers, wherein the 14 biomarkers are ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, UMPS Step (b) including , and housekeeping genes ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, and UMPS By normalizing each expression level with respect to the expression level of the housekeeping gene, ADRM1, CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA , DEAF , STOML2, and UMPSA method for determining the completion of surgery in a subject having colon cancer is provided, comprising the steps of: (c) obtaining each normalized expression level; (d) inputting each normalized expression level into an algorithm to generate a score; (e) comparing the score with a first predetermined cutoff value; and (e) confirming that the colon cancer in the subject has not been completely removed if the score is greater than or equal to the predetermined cutoff value, or confirming that the colon cancer in the subject has been completely removed if the score is less than the predetermined cutoff value.

[0060] In some embodiments of the method described above, step (e) may include a step of generating a report, which is to confirm that the colon cancer has not been completely removed if the score is greater than or equal to a first predetermined cutoff value, or to confirm that the colon cancer has been completely removed if the score is less than or equal to a first predetermined cutoff value.

[0061] In some embodiments, the above-described method may further include the step of performing a first therapy on a subject. The above-described method may further include the step of performing a first therapy on a subject when the score is greater than or equal to a predetermined cutoff value.

[0062] The present disclosure also provides a step of (a) determining the expression levels of at least 14 biomarkers from a test sample from a subject by contacting the test sample with a plurality of agents specific for detecting the expression of at least 14 biomarkers, wherein the 14 biomarkers are ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P, SNRPA , DEAF , STOML2, UMPS , and Step of including a housekeeping gene; (b) ADRM1 , CDK4 , COMT, DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA , DEAF , STOML2 , and UMPS By normalizing each expression level with respect to the expression level of the housekeeping gene, ADRM1 , CDK4, COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA , DEAF , STOML2 , and UMPS A method is provided comprising the steps of: obtaining each normalized expression level; (c) inputting each normalized expression level into an algorithm to generate a score; (d) comparing the score with a predetermined cutoff value; and (e) performing a first therapy on a subject if the score is greater than or equal to the predetermined cutoff value.

[0063] The response of a subject with colon cancer to the therapy can also be evaluated by comparing scores determined by the same algorithm at different time points of the therapy. For example, the first time point may be before or after the administration of the therapy to the subject; and the second time point is after the first time point and after the administration of the therapy to the subject. The first score is generated at the first time point, and the second score is generated at the second time point. If the second score is significantly reduced compared to the first score, the subject is considered responsive to the therapy.

[0064] Accordingly, the present disclosure comprises (1) at a first time step: (a) determining the expression level of at least 14 biomarkers from a first test sample from a subject by contacting the first test sample with a plurality of agents specific for detecting the expression of at least 14 biomarkers, wherein the 14 biomarkers are ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7, S100P , SNRPA , DEAF , STOML2 , UMPS , Step (b) including housekeeping genes ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P, SNRPA , DEAF, STOML2 , and UMPS By normalizing each expression level with respect to the expression level of the housekeeping gene, ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA , SORD, STOML2 , and UMPS (a) a step of obtaining each normalized expression level; (c) a step of inputting each normalized expression level into an algorithm to generate a first score; (2) at a second time point after the first time point and after the performance of therapy on the subject: (a) a step of determining the expression levels of at least 14 biomarkers from a second test sample from the subject by contacting the second test sample with a plurality of agents specific to detecting the expression of at least 14 biomarkers, wherein the 14 biomarkers are ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK, POP7 , S100P , SNRPA , SORD , STOML2 , UMPS , and Step of including a housekeeping gene; (b) ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA, SORD , STOML2 , and UMPS By normalizing each expression level with respect to the expression level of the housekeeping gene, ADRM1 , CDK4 , COMT , DHCR7 , HMOX2 , MCM2 , PDXK , POP7 , S100P , SNRPA, SORD , STOML2 , and UMPSA method for evaluating the response of a subject with colon cancer to a first therapy is provided, comprising the steps of: obtaining each normalized expression level; (c) inputting each normalized expression level into an algorithm to generate a second score; (3) comparing a first score with a second score; and (4) confirming that the subject is responsive to the first therapy if the second score is significantly reduced compared to the first score, or confirming that the subject is not responsive to the first therapy if the second score is not significantly reduced compared to the first score.

[0065] In some embodiments of the method described above, step (4) is a step of generating a report, the report may include confirming that the subject is responsive to the first therapy if the second score is significantly reduced compared to the first score, or confirming that the subject is not responsive to the first therapy if the second score is not significantly reduced compared to the first score.

[0066] In some embodiments of the method described above, if the second score is at least about 10% lower than the first score, or at least about 20% lower than the first score, or at least about 25% lower than the first score, or at least about 40% lower than the first score, or at least about 50% lower than the first score, or at least about 60% lower than the first score, or at least about 70% lower than the first score, or at least about 75% lower than the first score, or at least about 80% lower than the first score, or at least about 90% lower than the first score, or at least about 95% lower than the first score, the second score is significantly reduced compared to the first score. In some embodiments, if the second score is not significantly reduced compared to the first score, the subject is considered not responsive to the therapy.

[0067] In some embodiments of the above-described method, the first time point may be before performing the first therapy on the subject. The first time point may be after performing the first therapy on the subject.

[0068] In some embodiments, the above-described method may additionally include the step of continuing to perform the first therapy on the subject when the second score is significantly reduced compared to the first score.

[0069] In some embodiments, the above-described method may additionally include the step of stopping the first therapy on the subject if the second score is not significantly reduced compared to the first score.

[0070] In some embodiments, the above-described method may additionally include the step of performing a second therapy on a subject when the second score is not significantly reduced compared to the first score.

[0071] In some embodiments of the method of the present disclosure, a predetermined cutoff value may be about 50% on a scale of 0-100%. A predetermined cutoff value may be about 60% on a scale of 0-100%. A predetermined cutoff value may be about 10%, or about 20%, or about 30%, or about 40%, or about 70%, or about 80%, or about 90% on a scale of 0-100%.

[0072] In some embodiments of the method of the present disclosure, the test sample may be any biological fluid obtained from a subject. The test sample may be blood, serum, plasma, neoplastic tissue, or any combination thereof. In some embodiments, the test sample is blood. In some embodiments, the test sample is serum. In some embodiments, the test sample is plasma.

[0073] In some embodiments of the method of the present disclosure, the housekeeping gene is, without limitation, MRPL19, PSMC4 , SF3A1 , PUM1 , ACTB , GAPD , GUSB , RPLP0 , TFRC , MORF4L1 , 18S, PPIA, PGK1 , RPL13A , B2M , YWHAZ , SDHA , and HPRT1 It may include. In some embodiments, housekeeping genes MORF4L1 am.

[0074] The method of the present disclosure may have a sensitivity of at least about 50%, or at least about 60%, or at least about 70%, or at least about 75%, or at least about 80%, or at least about 85%, or at least about 90%, or at least about 95%, or at least about 99%. The method of the present disclosure may have a sensitivity greater than about 50%, or greater than about 60%, or greater than about 70%, or greater than about 75%, or greater than about 80%, or greater than about 85%, or greater than about 90%, or greater than about 95%, or greater than about 99%.

[0075] The method of the present disclosure may have a specificity of at least about 50%, or at least about 60%, or at least about 70%, or at least about 75%, or at least about 80%, or at least about 85%, or at least about 90%, or at least about 95%, or at least about 99%. The method of the present disclosure may have a specificity greater than about 50%, or greater than about 60%, or greater than about 70%, or greater than about 75%, or greater than about 80%, or greater than about 85%, or greater than about 90%, or greater than about 95%, or greater than about 99%.

[0076] The method of the present disclosure may have an accuracy of at least about 50%, or at least about 60%, or at least about 70%, or at least about 75%, or at least about 80%, or at least about 85%, or at least about 90%, or at least about 95%, or at least about 99%. The method of the present disclosure may have an accuracy greater than about 50%, or greater than about 60%, or greater than about 70%, or greater than about 75%, or greater than about 80%, or greater than about 85%, or greater than about 90%, or greater than about 95%, or greater than about 99%.

[0077] In some embodiments of the method of the present disclosure, a predetermined cutoff value may be derived from a plurality of reference samples obtained from a subject who does not have a neoplastic disease or has not been diagnosed as having one. In some embodiments, the neoplastic disease may be colon cancer.

[0078] Multiple reference samples may include approximately 2-500, 2-200, 10-100, or 20-80 reference samples. Each reference sample generates a score using an algorithm, and a first predetermined cutoff value may be the arithmetic mean of these scores. Each reference sample may be blood, serum, plasma, or non-neoplastic tissue. In some embodiments, each reference sample is blood. In some embodiments, each reference sample is of the same type as the test sample.

[0079] Each biomarker disclosed herein may have one or more transcriptional variants. The method disclosed herein may measure the expression level of any one of the transcriptional variants for each biomarker.

[0080] Expression levels can be measured in many ways, including, but not limited to: measuring mRNA encoded by a selected gene; measuring the amount of protein encoded by a selected gene; and measuring the activity of the protein encoded by a selected gene.

[0081] In some embodiments of the method of the present disclosure, the biomarker may be RNA, cDNA, protein, or any combination thereof. If the biomarker is RNA, the RNA may be reverse transcribed to produce cDNA (e.g., by RT-PCR), and the expression level of the produced cDNA may be detected. The expression level of the biomarker may be detected by forming a complex between the biomarker and a labeled probe or primer. If the biomarker is RNA or cDNA, the RNA or cDNA may be detected by forming a complex between the RNA or cDNA and a labeled nucleic acid probe or primer. The complex between the RNA or cDNA and the labeled nucleic acid probe or primer may be a hybridization complex.

[0082] In some embodiments of the method of the present disclosure, gene expression may be detected by microarray analysis. Differential gene expression may also be identified or determined using microarray technology. Thus, expression profile biomarkers may be measured in fresh or fixed tissues using microarray technology. In this method, polynucleotide sequences of interest (including cDNA and oligonucleotides) may be plated or arrayed on a microchip substrate. The arrayed sequences are then hybridized with specific DNA probes from cells or tissues of interest. The source of mRNA is typically total RNA isolated from a biological sample, and the corresponding normal tissue or cell line may be used to determine differential expression.

[0083] In some embodiments of microarray technology, PCR-amplified inserts of cDNA clones are applied to a substrate within a dense array. In some embodiments, at least 10,000 nucleotide sequences are applied to the substrate. Microarrayed genes, immobilized on a microchip with 10,000 elements each, are suitable for hybridization under strict conditions. Fluorescently labeled cDNA probes can be generated through the incorporation of fluorescent nucleotides via reverse transcription of RNA extracted from tissues of interest. The labeled cDNA probes applied to the chip specifically hybridize to each spot of DNA on the array. After rigorous washing to remove non-specifically bound probes, the microarray chip is scanned by a device such as a confocal laser microscope or by another detection method such as a CCD camera. Quantification of the hybridization of each arrayed element allows for the evaluation of the corresponding mRNA abundance. Using dual-color fluorescence, individually labeled cDNA probes generated from two sources of RNA are hybridized in pairs to the array. Therefore, the relative abundance of transcripts from two sources corresponding to each specific gene is determined simultaneously. Microarray analysis can be performed by commercially available equipment according to the manufacturer's protocol.

[0084] In some embodiments of the method of the present disclosure, biomarkers may be detected in biological samples using qRT-PCR. The first step of gene expression profiling by RT-PCR is to extract RNA from a biological sample, then reverse transcribe the RNA template into cDNA and amplify it by a PCR reaction. The reverse transcription step is typically primed using specific primers, random hexamers, or oligo-dT primers, depending on the goal of the expression profiling. Two commonly used reverse transcriptases are avilo myeloblastosis virus reverse transcriptase (AMV-RT) and Moloney murine leukemia virus reverse transcriptase (MLV-RT).

[0085] In some embodiments of the method of the present disclosure, when the biomarker is a protein, the protein may be detected by forming a complex between the protein and the labeled antibody. The label may be any label, e.g., fluorescent label, chemiluminescent label, radioactive label, etc. Exemplary methods for protein detection include, but are not limited to, enzyme immunoassay (EIA), radioimmunoassay (RIA), Western blot analysis, and enzyme-linked immunosorbent assay (ELISA). For example, the biomarker may be detected in an ELISA in which an enzyme-antibody conjugate is used to detect and / or quantify the biomarker present in the sample, where the biomarker antibody is bound to a solid phase. Alternatively, Western blot analysis may be used in which the solubilized and isolated biomarker is bound to nitrocellulose paper. The combination of a highly specific and stable liquid conjugate and a sensitive chromogenic substrate allows for rapid and accurate identification of the sample.

[0086] In some embodiments of the method of the present disclosure, the method described herein may have a specificity, sensitivity, and / or accuracy of at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

[0087] In some embodiments of the method of the present disclosure, the labeled probe, labeled primer, labeled antibody, or labeled nucleic acid may include a fluorescent label.

[0088] Any algorithm capable of generating a score for a sample by evaluating where the sample value belongs in a predictive model generated using different techniques, e.g., a decision tree, may be used in the method disclosed herein. The algorithm analyzes the data (i.e., expression levels) and then assigns a score. In some embodiments, the algorithm may be a machine learning algorithm. Exemplary algorithms that can be used in the methods disclosed herein may include, but are not limited to, XGBoost (XGB), Random Forest (RF), glmnet, cforest, Classification and Regression Trees for Machine Learning (CART), treebag, K-Nearest Neighbors (kNN), neural network (nnet), Support Vector Machine Radial (SVM-radial), Support Vector Machine Linear (SVM-linear), Naive Bayes (NB), multilayer perceptron (mlp), or any combination thereof.

[0089] In some embodiments of the method of the present disclosure, the algorithm may be XGB (also called XGBoost). XGB is an implementation of a gradient boosted decision tree designed for speed and performance.

[0090] In some embodiments of the method of the present disclosure, the therapy may include anticancer therapy, surgery, chemotherapy, targeted drug therapy, radiation therapy, immunotherapy, or any combination thereof.

[0091] In some embodiments of the method of the present disclosure, the surgery may include removing a polyp during a colonoscopy, endoscopic mucosal resection, partial colectomy, ostomy, removing at least one cancerous lesion from the liver, or any combination thereof.

[0092] In some embodiments of the method of the present disclosure, the anticancer therapy may include an anti-colon cancer therapy.

[0093] In some embodiments of the method of the present disclosure, the chemotherapy may include a combination of FOLFOX, FOLFIRI, 5-FU and leucovorin, capecitabine, irinotecan, CapeOx, or any combination thereof.

[0094] In some embodiments of the method of the present disclosure, the targeted drug therapy may include bevacizumab, cetuximab, panitumumab, regorafenib, a combination of trifluridine and tipiracil, an EGFR TKI inhibitor, or any combination thereof.

[0095] In some embodiments of the method of the present disclosure, the immunotherapy may include pembrolizumab, nivolumab, or a combination of pembrolizumab and nivolumab.

[0096] For early-stage colon cancer, the cancer can be removed using a minimally invasive surgical approach. For example, if the cancer is completely contained within a polyp, the polyp can be removed during a colonoscopy. Endoscopic mucosal resection may be performed to remove larger polyps. Polyps that cannot be removed during a colonoscopy can be removed using laparoscopic surgery.

[0097] If the cancer has grown into or through the colon, a partial colectomy may be performed to remove the portion of the colon containing the cancer, along with the margins of normal tissue on either side. If the healthy portions of the colon or rectum cannot be reconnected, a scrotum may be performed to create an opening in the abdominal wall from the remaining portion of the intestine to remove stool through a sac that fits tightly over the opening. Lymph node removal may also be performed.

[0098] In cases of advanced colon cancer, surgery to relieve colon obstruction or other pathological conditions may also be performed. In specific cases where the cancer has spread only to the liver, surgery to remove the cancerous lesion from the liver may be performed.

[0099] For chemotherapy, FOLFOX (5-FU, leucovorin, and oxaliplatin) or CapeOx (capecitabine and oxaliplatin) regimens are most frequently used, but some patients may receive 5-FU with leucovorin and capecitabine alone based on their age and health needs. Irinotecan can also be used as a chemotherapy agent for the treatment of colon cancer.

[0100] Targeted drug therapies target specific dysfunctions that cause cancer cells to grow. These therapies include, but are not limited to, bevacizumab, cetuximab, panitumumab, ramucirumab, regorafenib, ziv-aflibercept, combinations of trifluridine and tipiracil, and EGFR TKI inhibitors.

[0101] Immunotherapy for colorectal cancer includes, to a non-limited extent, pembrolizumab (Keytruda®) and nivolumab (Opdivo®).

[0102] Sequence information of colon cancer biomarkers and housekeepers is shown in Table 1.

[0103] Table 1. Colon Cancer Biomarker / Housekeeper Sequence Information

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113]

[0114]

[0115]

[0116]

[0117]

[0118]

[0119]

[0120]

[0121]

[0122]

[0123]

[0124]

[0125]

[0126]

[0127]

[0128]

[0129]

[0130]

[0131]

[0132]

[0133]

[0134]

[0135]

[0136]

[0137]

[0138] definition

[0139] The articles "a" and "an" are used in this disclosure to refer to two or more (i.e., at least one) grammatical objects of the article. For example, "element" means one element or two or more elements.

[0140] The term "and / or" is used in this disclosure to mean "and" or "or" unless otherwise indicated.

[0141] As used herein, the terms “polynucleotide” and “nucleic acid molecule” are used interchangeably to mean a polymeric form of at least 10 bases or base pairs in length that is a modified form of ribonucleotide, deoxynucleotide, or any type of nucleotide, and includes single and double-stranded forms of DNA. As used herein, a nucleic acid molecule or nucleic acid sequence serving as a probe in microarray analysis preferably comprises a chain of nucleotides, more preferably DNA and / or RNA. In other embodiments, a nucleic acid molecule or nucleic acid sequence comprises other types of nucleic acid structures, such as DNA / RNA helices, peptide nucleic acids (PNA), lock nucleic acids (LNA), and / or ribozymes. Accordingly, as used herein, the term “nucleic acid molecule” also comprises a chain comprising non-natural nucleotides, modified nucleotides, and / or non-nucleotide building blocks that exhibit the same function as natural nucleotides.

[0142] As used herein, terms such as “hybridize,” “hybridizing,” etc., used in the context of polynucleotides mean hybridization conditions such as hybridization in 50% formamide / 6XSSC / 0.1% SDS / 100 μg / ml ssDNA, hybridization conditions in which the hybridization temperature is greater than 37°C and the washing temperature in 0.1 XSSC / 0.1% SDS is greater than 55°C, and preferably strict hybridization conditions.

[0143] As used herein, the terms “normalization” or “normalizer” refer to the expression of differential values ​​in terms of standard values ​​to adjust for effects arising from technical variations due to sample handling, sample preparation, and measurement methods, rather than biological variations in the concentration of biomarkers within a sample. For example, when measuring the expression of differentially expressed proteins, the absolute value of the protein expression may be expressed in terms of the absolute value of the expression of a standard protein of substantially constant expression.

[0144] The term "diagnosis" (diagnostics) also includes the term "prognosis" (prognostics), as well as the application of such procedures across two or more time points to monitor diagnosis and / or prognosis over time, and statistical modeling based thereon. Additionally, the term diagnosis includes a. prognosis (determining whether a patient is likely to develop an aggressive disease (hyperproliferative / infiltrative), b. prognosis (predicting whether a patient is likely to have a better or worse outcome at a predetermined time in the future), c. selection of therapy, d. monitoring of therapeutic drugs, and e. monitoring of recurrence.

[0145] "Accuracy" refers to the degree to which a measured or calculated quantity (test-reported value) corresponds to its actual (or true) value. Clinical accuracy refers to the ratio of true results (True Positive (TP) or True Negative (TN)) to misclassified results (False Positive (FP) or False Negative (FN)), and among other measurements, it may be expressed as sensitivity, specificity, positive predictive value (PPV) or negative predictive value (NPV), or as probability or odds ratio.

[0146] As used herein, the term “biological sample” refers to any sample of biological origin that potentially contains one or more biomarkers. Examples of biological samples include tissues, organs, or body fluids, such as whole blood, plasma, serum, tissue, washing fluid, or any other specimen used for the detection of a disease.

[0147] As used herein, the term "subject" refers to a mammal, preferably a human. In some embodiments, the subject may have at least one symptom of colon cancer. In some embodiments, the subject may have a predisposition or family history of colon cancer. The subject may also have been previously diagnosed with colon cancer and is tested for cancer recurrence.

[0148] As used herein with respect to a condition, "treating" or "treatment" may refer to preventing a condition, slowing the onset or development of a condition, reducing the risk of the condition developing, preventing or delaying the development of symptoms associated with the condition, reducing or ending symptoms associated with the condition, causing complete or partial regression of the condition, or any combination thereof.

[0149] Biomarker levels may change due to treatment of the disease. Changes in biomarker levels can be measured by the present disclosure. Changes in biomarker levels can be used to monitor the progression of the disease or therapy.

[0150] "Changed," "altered," or "significantly different" refers to a change or difference detectable from reasonably equivalent states, profiles, measurements, etc. Such changes may be all or none at all. They may be numerical comparisons. A change may be an increase or decrease of 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, 100%, or more, or any value between 0% and 100%. Alternatively, a change may be 1x, 1.5x, 2x, 3x, 4x, 5x, or more, or any value between 1x and 5x. A change may be statistically significant with a p-value of 0.1, 0.05, 0.001, or 0.0001.

[0151] The term "stable disease" refers to a diagnosis of colon cancer that has been treated and remains in a stable state, that is, non-advanced colon cancer as determined by imaging data and / or the best clinical judgment.

[0152] The term "advanced disease" refers to the diagnosis of a highly active state of colon cancer, that is, the presence of colon cancer that is untreated and unstable, treated and unresponsive to therapy, or treated and remains active, as determined by imaging data and / or the best clinical judgment.

[0153] The term "neoplastic disease" refers to any abnormal growth of cells or tissues that are benign (non-cancerous) or malignant (cancerous). For example, a neoplastic disease may be colon cancer.

[0154] The term "neoplastic tissue" refers to a mass of abnormally growing cells.

[0155] The term "non-neoplastic tissue" refers to a mass of normally growing cells.

[0156] The term "immunotherapy" may refer to activating or inhibiting immunotherapy. As understood by those skilled in the art, activating immunotherapy refers to the use of a therapeutic agent that induces, enhances, or promotes an immune response, such as a T cell response, whereas inhibiting immunotherapy refers to the use of a therapeutic agent that interferes with, suppresses, or inhibits an immune response, such as a T cell response. Activating immunotherapy may include the use of a checkpoint inhibitor. Activating immunotherapy may include the step of administering a therapeutic agent that activates stimulating checkpoint molecules to a subject. Stimulating checkpoint molecules include, but are not limited to, CD27, CD28, CD40, CD122, CD137, OX40, GITR, and ICOS. Therapeutics that activate stimulating checkpoint molecules include, but are not limited to, MEDI0562, TGN1412, CDX-1127, and lipocalin.

[0157] In this document, the term “antibody” is used in a broad sense and includes, but is not limited to, monoclonal antibodies, polyclonal antibodies, multispecific antibodies (e.g., bispecific antibodies), and antibody fragments that exhibit desired antigen-binding activity. An antibody that binds to a target refers to an antibody capable of binding to a target with sufficient affinity to be useful as a diagnostic and / or therapeutic agent in targeting the target. In one embodiment, the degree of binding of the anti-target antibody to an unrelated non-target protein is less than about 10% of the binding of the antibody to the target, as measured, for example, by radioimmunoassay (RIA) or biacore analysis. In a specific embodiment, the antibody binding to the target has a dissociation constant (Kd) of < 1 μM, < 100 nM, < 10 nM, < 1 nM, < 0.1 nM, < 0.01 nM, or < 0.001 nM (e.g., 108 M or less, e.g., 108 M to 1013 M, e.g., 109 M to 1013 M). In a specific embodiment, the anti-target antibody binds to an epitope of the target conserved between different species.

[0158] "Blocking antibodies" or "antagonist antibodies" are antibodies that partially or completely block, inhibit, interfere with, or neutralize the normal biological activity of the antigen to which they bind. For example, antagonist antibodies can block signaling through immune cell receptors (e.g., T cell receptors) to restore functional responses by T cells (e.g., proliferation, cytokine production, target cell death) from a dysfunctional state to antigen stimulation.

[0159] An "agonist antibody" or "activating antibody" is an antibody that mimics, promotes, stimulates, or enhances the normal biological activity of the antigen to which it binds. An agonist antibody may also enhance or initiate signaling by the antigen to which it binds. In some embodiments, an agonist antibody induces or activates signaling without the presence of a natural ligand. For example, an agonist antibody increases memory T cell proliferation, increases cytokine production by memory T cells, inhibits regulatory T cell function and / or inhibits regulatory T cell inhibition of effector T cell function, such as effector T cell proliferation and / or cytokine production.

[0160] “Antibody fragment” refers to a molecule other than an intact antibody that contains a portion of an intact antibody that binds to an antigen to which the intact antibody binds. Examples of antibody fragments include, but are not limited to, Fv, Fab, Fab', Fab'-SH, F(ab')2; diabadies; linear antibodies; single-stranded antibody molecules (e.g., scFv); and multispecific antibodies formed from antibody fragments.

[0161] Performing chemotherapy on a subject may include administering a therapeutically effective dose of at least one chemotherapy agent. Chemotherapy agents, to the non-restrictive extent, 13-cis-retinoic acid, 2-CdA, 2-chlorodeoxyadenosine, 5-azacitidine, 5-fluorouracil, 5-FU, 6-mercaptopurine, 6-MP, 6-TG, 6-thioguanine, abemaciclib, abiraterone acetate, Abraxane, Accutane, Actinomycin-D, Adcetris, Ado-Trastuzumab Emtansine, Adriamycin, Adrucil, Afatinib, Afinitor, Agrylin, Ala-Cort, Aldesleukin, Alemtuzumab, Alecensa, Alectinib, Alimta, Alitretinoin, Alkaban-AQ, Alkeran, All-transretinoic acid, Alpha Interferon, Altretamine, Alunbrig, Amethopterin, Amifostine, Aminoglutethimide, Anagrelide, Anandron, Anastrozole, Apalutamide, Arabinosylcytosine, Ara-C, Aranesp, Aredia, Arimidex, Aromasin, Arranon,Arsenic Trioxide, Arzerra, Asparaginase, Atezolizumab, Atra, Avastin, Avelumab, Axicabtagene Ciloleucel, Axitinib, Azacitidine, Bavencio, Bcg, Beleodaq, Belinostat, Bendamustine, Bendeka, Besponsa, bevacizumab, Bexarotene, Bexxar, Bicalutamide, Bicnu, Blenoxane, Bleomycin, Blinatumomab, Blincyto, Bortezomib, Bosulif, Bosutinib, Brentuximab Vedotin, Brigatinib, Busulfan, Busulex, C225, Cabazitaxel, Cabozantinib, Calcium leucovorin, Camppath, Camptosar, Camptothecin-11, Capecitabine, Caprelsa, Carac, Carboplatin, Carfilzomib, Carmustine, Carmustine Wafer, Casodex, CCI-779, Ccnu, Cddp, Ceenu, Ceritinib, Cerubidine, Cetuximab, Chlorambucil,Cisplatin, Citroborum Factor, Cladribine, Clofarabine, Chlorar, Cobimetinib, Cometriq, Cortisone, Cosmegen, Cotellic, Cpt-11, Crizotinib, Cyclophosphamide, Cyramza, Cytadren, Cytarabine, Cytarabine Liposomal, Cytosar-U, Cytoxan, Dabrafenib, Dacarbazine, Dacogen, Dactinomycin, Daratumumab, Darbepoetin Alfa, Darzalex, Dasatinib, Daunomycin, Daunorubicin, Daunorubicin Cytarabine (Liposome), Daunorubicin-hydrochloride, Daunorubicin Liposomal, DaunoXome, Decadron, Decitabine, Degarelix, Delta-Cortef, Deltasone, Denileukin Diftitox, Denosumab, DepoCyt, Dexamethasone, Dexamethasone acetate, Dexamethasone sodium phosphate, Dexasone, Dexrazoxane, Dhad, Dic, Diodex, Docetaxel, Doxil,Doxorubicin, Doxorubicin Liposome, Droxia, DTIC, Dtic-Dome, Duralone, Durvalumab, Eculizumab, Efudex, Ellence, Elotuzumab, Eloxatin, Elspar, Eltrombopag, Emcyt, Empliciti, Enasidenib, Enzalutamide, Epirubicin, Epoetin Alfa, Erbitux, Eribulin, Eriveedge, Erleada, Erlotinib, Erwinia L-asparaginase, Estramustine, Ethyol, Etopophos, Etoposide, Etoposide Phosphate, Eulexin, Everolimus, Evista, Exemestane, Fareston, Fairydak, Faslodex, Femara, Filgrastim, Firmagon, Floxuridine, Fludara, Fludarabine, Fluoroplex, Fluorouracil, Fluorouracil (cream), Fluoxymesterone, Flutamide, Folinic Acid, Folotyn, Fudr, Fulvestrant, G-Csf, Gazyva, Gefitinib, Gemcitabine,Gemtuzumab ozogamicin, Gemzar, Gilotrif, Gleevec, Gleostine, Gliadel Wafer, Gm-Csf, Goserelin, Granix, Granulocyte-colony-stimulating factor, Granulocyte-macrophage colony-stimulating factor, Halaven, Halotestin, Herceptin, Hexadrol, Hexalen, Hexamethylmelamine, Hmm, Hycamtin, Hydrodrea, Hydrocort Acetate, Hydrocortisone, Hydrocortisone sodium phosphate, hydrocortisone sodium succinate, hydrocortisone phosphate, hydroxyurea, Ibrance, Ibritumomab, Ibritumomab Tiuxetan, Ibrutinib, Iclusig, Idamycin, Idarubicin, Idelalisib, Idhifa, Ifex, IFN-alpha, Ifosfamide, IL-11, IL-2, Imbruvica, Imatinib Mesylate, Imfinzi, Imidazole Carboxamide, Imlygic, Inlyta, Inotuzumab Ozogamicin, Interferon-alpha, Interferon alpha-2b (PEG conjugate), Interleukin-2, Interleukin-11, Intron A (Interferon alpha-2b), Ipilimumab, Iressa, Irinotecan,Irinotecan (liposomal), Isotretinoin, Istodax, Ixabepilone, Ixazomib, Ixempra, Jakafi, Jevtana, Kadcyla, Keytruda, Kidrolase, Kisqali, Kymriah, Kyprolis, Lanacort, Lanreotide, Lapatinib, Lartruvo, L-Asparaginase, Lbrance, Lcr, Lenalidomide, Lenvatinib, Lenvima, Letrozole, leucovorin, Leukeran, Leukine, Leuprolide, Leurocristine, Leustatin, Liposomal Ara-C, Liquid Pred, Lomustine, Lonsurf, L-PAM, L-Sarcolysin, Lupron, Lupron Depot, Lynparza, Marqibo, Matulane, Maxidex, Mechlorethamine, Mechlorethamine Hydrochloride, Medralone, Medrol, Megace, Megestrol, Megestrol Acetate, Mekinist, Mercaptopurine, Mesna, Mesnex, Methotrexate, Methotrexate Sodium, Methylprednisolone, Meticorten, Midostaurin,Mitomycin, Mitomycin-C, Mitoxantrone, M-Prednisol, MTC, MTX, Mustargen, Mustine, Mutamycin, Myleran, Mylocel, Mylotarg, Navelbine, Necitumumab, Nelarabine, Neosar, Neratinib, Nerlynx, Neulasta, Neumega, Neupogen, Nexavar, Nilandron, Nilotinib, Nilutamide, Ninlaro, Nipent, Niraparib, Nitrogen Mustard, nivolumab, Nolvadex, Novantrone, Nplate, Obinutuzumab, Octreotide, Octreotide Acetate, Odomzo, Ofatumumab, Olaparib, Olaratumab, Omacetaxine, Oncospar, Oncovin, Onivyde, Ontak, Onxal, Opdivo, Ofrelvekin, Orapred, Orasone, Osimertinib, Otrexup, Oxaliplatin, Paclitaxel, Paclitaxel protein-binding, Palbociclib, Pamidronate, Panitumumab, Panobinostat, Panretin,Paraplatin, Pazopanib, Pediapred, Peg Interferon, Pegaspargase, Pegfilgrastim, Peg-Intron, PEG-L-asparaginase, pembrolizumab, Pemetrexed, Pentostatin, Perjeta, Pertuzumab, Phenylalanine Mustard, Platinol, Platinol-AQ, Pomalidomide, Pomalyst, Ponatinib, Portrazza, Pralatrexate, Prednisolone, Prednisone, Prelon, Procarbazine, Procrit, Proleukin, Prolia, Prolifeprospan 20 with Carmustine Implant, Promacta, Provenge, Purinethol, Radium 223 Dichloride, Raloxifene, Ramucirumab, Rasuvo, Regorafenib, Revlimid, Rheumatorex, Ribociclib, Rituxan, Rituxan Rituxan Hycela, Rituximab, Rituximab Hyalurodinase, Roferon-A (Interferon Alfa-2a), Romidepsin, Romiplostim,Rubex, Rubidomycin Hydrochloride, Rubraca, Rucaparib, Ruxolitinib, Rydapt, Sandostatin, Sandostatin LAR, Sargramostim, Siltuximab, Sipuleucel-T, Soliris, Solu-Cortef, Solu-Medrol, Somatuline, Sonidegib, Sorafenib, Sprycel, Sti-571, Stivarga, Streptozocin, SU11248, Sunitinib, Sutent, Sylvant, Synribo, Tafinlar, Tagrisso, Talimogene Laherparepvec, Tamoxifen, Tarceva, Targretin, Tasigna, Taxol, Taxotere, Tecentriq, Temodar, Temozolomide, Temsirolimus, Teniposide, Tespa, Thalidomide, Thalomid, TheraCys, Thioguanine, Thioguanine Tabloid, Thiophosphoamide, Thioplex, Thiotepa, Tice, Tisagenlecleucel, Toposar, Topotecan, Toremifene, Torisel,Tositumomab, Trabectedin, Trametinib, Trastuzumab, Treanda, Trelstar, Tretinoin, Trexall, Trifluridine / Tipiricil, Triptorelin pamoate, Trisenox, Tspa, T-VEC, Tykerb, Valrubicin, Valstar, Vandetanib, VCR, Vectibix, Velban, Velcade, Vemurafenib, Venclexta, Venetoclax, VePesid, Verzenio, Vesanoid, Viadur, Vidaza, Vinblastine, Vinblastine Sulfate, Vincasar Pfs, Vincristine, Vincristine Liposomal, Vinorelbine, Vinorelbine Tartrate, Vismodegib, Vlb, VM-26, Vorinostat, Votrient, VP-16, Vumon, Vyxeos, Xalkori Capsule, Xeloda, Xgeva, Xofigo, Xtandi, Yervoy, Yescarta, Yondelis, Zaltrap, Zanosar, Zarxio, Zejula, Zelboraf, Zevalin, Zinecard, Ziv-aflibercept,Includes Zoladex, Zoledronic Acid, Zolinza, Zometa, Zydelig, Zykadia, Zytiga, or any combination thereof.

[0162] The terms "effective dose" and "therapeutic effective dose" of a preparation or compound are used broadly to refer to a non-toxic but sufficient amount of an active preparation or compound to provide a desired effect or benefit.

[0163] The term "benefit" is used in a broad sense and refers to any desirable effect, specifically including clinical benefits as defined herein. Clinical benefits may be measured by evaluating various endpoints of disease progression to some degree, such as inhibition, including: delay and complete cessation; reduction in the number of disease episodes and / or symptoms; reduction in lesion size; inhibition of disease cell infiltration into adjacent peripheral organs and / or tissues (i.e., reduction, delay, or complete cessation); inhibition of disease spread (i.e., reduction, delay, or complete cessation); reduction in autoimmune responses that may, but do not necessarily, cause regression or resection of disease lesions; some degree of alleviation of one or more symptoms associated with the disorder; increased length of disease-free presentation after treatment, e.g., progression-free survival; increased overall survival; higher response rates; and / or reduced mortality at a given time after treatment.

[0164] The terms "cancer" and "cancerous" refer to or describe physiological conditions in mammals typically characterized by uncontrolled cell growth. This definition includes benign and malignant cancers. Examples of cancer include, but are not limited to, carcinomas, lymphomas, blastomas, sarcomas, and leukemias. More specific examples of these cancers include adrenocortical carcinoma, bladder urinary tract carcinoma, invasive breast carcinoma, cervical squamous cell carcinoma, intracervical adenocarcinoma, cholangiocarcinoma, colorectal adenocarcinoma, diffuse large B-cell lymphoma (lymphoid neoplasm), esophageal carcinoma, glioblastoma pleomorphic, head and neck squamous cell carcinoma, nephrotic chromophobe, renal clear cell carcinoma, renal papillary cell carcinoma, acute myeloid leukemia, brain low-grade glioma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma, paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, cutaneous melanoma, gastric adenocarcinoma, testicular germ cell tumor, thyroid carcinoma, thymoma, and uterus. Includes carcinosarcoma and uveal melanoma. Other examples include breast cancer, lung cancer, lymphoma, melanoma, liver cancer, colorectal cancer, ovarian cancer, bladder cancer, kidney cancer, or stomach cancer. Additional examples of cancer include neuroendocrine cancer, non-small cell lung cancer (NSCLC), small cell lung cancer, thyroid cancer, endometrial cancer, biliary tract cancer, esophageal cancer, anal cancer, salivary cancer, vulvar cancer, or cervical cancer.

[0165] The term "tumor" refers to all neoplastic cell growth and proliferation, whether malignant or benign, and all precancerous and cancerous cells and tissues. The terms "cancer," "cancerous," "proliferative disorder," "proliferative disorder," and "tumor" are not mutually exclusive as used herein.

[0166] Examples

[0167] The present disclosure is further illustrated by the following examples, which should not be construed as limiting the present disclosure in scope or spirit to the specific procedures described herein. It should be understood that the examples are provided to illustrate specific aspects and are not intended to limit the scope of the present disclosure. It should also be understood that one may rely on various other aspects, embodiments, modifications, and equivalents thereof that may be presented to those skilled in the art without departing from the spirit of the present disclosure and / or the scope of the appended claims.

[0168] Example 1. Derivation of a 13-Marker Gene Panel

[0169] n = 24 raw probe intensity from colon cancer tumor tissue sample n = 22 Using the transcriptional profile of E-MTAB-57 compared to control colon mucosa, the genes that best distinguished the disease were identified. A gene co-expression network was constructed to identify transient patterns of gene regulation associated with colon cancer. A total of 513 nodes with 53,786 links were identified. Differential expression analysis confirmed that 103 genes were upregulated in tumor tissue compared to blood. To identify blood-specific colon cancer gene biomarkers, the expression of 103 genes was evaluated in the peripheral blood transcriptome (n = 7). Of the 103 genes, 33 (32%) were lower than the detection levels in blood, leading to their identification as candidate genes. Evaluation of the transcriptome in a preliminary dataset of blood samples from colon cancer (n = 20) and matched normal blood (n = 20) identified 13 genes and 1 housekeeping gene as markers for colon cancer ( Table 2These genes were demonstrated to be highly expressed in colon cancer tumor tissues compared to normal mucosa and in three different colon cancer cell lines: LOVO (metastatic, hyperdiploid, MSI-unstable cell line), LS-180 (Duke's B, derived from colorectal adenocarcinoma), and Colo 320DM (Duke's C, derived from colorectal adenocarcinoma). These data demonstrate that target transcripts are produced by neoplastically transformed colon mucosal cells ( Fig. 1a-1b ).

[0170] Artificial intelligence model of colon cancer (control group) n =120) and colon cancer( n It was generated using the normalized gene expression of these 13 markers in whole blood from samples (=272). The dataset was randomly split into training and test partitions for model generation and validation, respectively. Twelve algorithms were evaluated (XGB, RF, glmnet, cforest, CART, Treebag, knn, nnet, SVM-Radial, SVM-Linear, NB, and mlp). The best-performing algorithm (XGB – "Gradient Boosting") predicted the training data best. In the test set, XGB generated probability scores for predicting samples. Each probability score reflects the algorithm's "certainty" that an unknown sample belongs to the "control" or "colon cancer" class. For example, an unknown sample S1 could have the following probability vector [Control = 20%, Colon Cancer = 80%]. This sample would be considered a colon cancer sample.

[0171] Example 2. Clinical Utility

[0172] Patients with colon cancer in training and test sets ( n =136) and control group( n Data on the usefulness of the test for distinguishing =60 (receiver operation curve analysis and scale) Figs. 2a-2bIt is included in. The scores showed an area under the curve (AUC) of 0.90 (training) and 0.86 (test set). The scales are sensitivity: 85.3–87.5% and specificity: 75–83.3%.

[0173] Overall, ColoTest scores were significantly elevated in cancer (63±1%) and the control group (34±2%). Figs. 3a-3b The overall accuracy (training and test cohorts) was 84%, and the AUC was 0.88. The z-statistic for distinguishing the control group was 18.5.

[0174] The clinical benefit of the diagnostic test was quantified using decision curve analysis. Figs. 4a-4b ColoTest showed a standardized predictive gain of >50% up to 80% of the risk threshold. The probit risk assessment plot confirmed that a ColoTest score of >50% was 75% accurate in predicting colon cancer in blood samples. This increased to >80% at a ColoTest score of ≥60%. Therefore, the tool can accurately distinguish between control groups and colon cancer.

[0175] Specific evaluation of the colon cancer cohort before and after surgery indicates complete removal of the tumor and absence of evidence of disease, with a significant decrease in ColoTest ( p It was confirmed that it is related to (<0.0001) Fig. 5 The levels did not differ significantly in the cohort with evidence of residual disease.

[0176] Investigation of individual colon cancer cohorts based on disease status (clinical evaluation at the time of blood collection) using ColoTest for stable disease ( n =17: 56±7%) and progressive disease( n It was confirmed that there was no significant difference between =32: 68±4%) ( Figs. 6a-6cHowever, 12 of the 17 patients proceeded with blood collection at 3 months. The patients who proceeded showed elevated ColoTest scores at the time of blood collection ( n =12: 73±4%), which was not different from patients with progressive disease at the time of blood collection( n =32: 68±4%( Figs. 6a-6c The level in patients with stable disease was significantly lower ( n =5: 16±4%, p A direct comparison between ColoTest and CEA in these samples showed that gene expression analysis was significantly more sensitive than CEA in predicting disease progression ( p confirmed (<0.05) Fig. 7 Therefore, the ColoTest tool can accurately predict advanced colon cancer.

[0177] ROC analysis confirmed that the ColoTest has an AUC of 0.97 in distinguishing between stable and progressive disease. The z-statistic for distinguishing the control group was 20.6. Further evaluation of this cohort confirmed that patients who showed disease progression despite therapy had higher scores than patients who responded to therapy ( Fig. 8 The regimen included bevacizumab, chemotherapy, and EGFR TKI inhibitors. Therefore, the above tool can accurately identify treatment failure in colorectal cancer.

[0178] Table 2.

[0179]

[0180] References:

[0181] 1. Siegel RL, Miller KD, Jemal A. Cancer Statistics, 2017. CA Cancer J Clin. 2017; 67: 7-30. doi: 10.3322 / caac.21387. Epub 2017 Jan 5.

[0182] 2. Ferlay J, Steliarova-Foucher E, Lortet-Tieulent J, Rosso S, Coebergh JW, Comber H, Forman D, Bray F. Cancer incidence and mortality patterns in Europe: estimates for 40 countries in 2012. Eur J Cancer. 2013; 49: 1374-403.

[0183] 3. Fritzmann J, Morkel M, Besser D, Budczies J, Kosel F, Brembeck FH, Stein U, Fichtner I, Schlag PM, Birchmeier W. A colorectal cancer expression profile that includes transforming growth factor beta inhibitor BAMBI predicts metastatic potential. Gastroenterology. 2009; 137: 165-75.

[0184] 4. Chen VW, Hsieh MC, Charlton ME, Ruiz BA, Karlitz J, Altekruse SF, Ries LA, Jessup JM. Analysis of stage and clinical / prognostic factors for colon and rectal cancer from SEER registries: AJCC and collaborative stage data collection system. Cancer. 2014; 120: 3793-806.

[0185] 5. Heald RJ, Lockhart-Mummery HE. The lesion of the second cancer of the large bowel. Br J Surg. 1972; 59: 16-9.

[0186] 6. Mokhles S, Macbeth F, Farewell V, Fiorentino F, Williams NR, Younes RN, Takkenberg JJ, Treasure T. Meta-analysis of colorectal cancer follow-up after potentially curative resection. Br J Surg. 2016; 103: 1259-68.

[0187] 7. Thomas SN, Zhu F, Schnaar RL, Alves CS, Konstantopoulos K. Carcinoembryonic antigen and CD44 variant isoforms cooperate to mediate colon carcinoma cell adhesion to E- and L-selectin in shear flow. J Biol Chem. 2008; 283: 15647-55.

[0188] 8. Amri R, Bordeianou LG, Sylla P, Berger DL. Preoperative carcinoembryonic antigen as an outcome predictor in colon cancer. J Surg Oncol. 2013; 108: 14-8.

[0189] 9. Jansen N, Coy JF. Diagnostic use of epitope detection in monocytes blood test for early detection of colon cancer metastasis. Future Oncol. 2013; 9: 605-9.

[0190] 10. Locker GY, Hamilton S, Harris J, Jessup JM, Kemeny N, Macdonald JS, Somerfield MR, Hayes DF, Bast RC, Jr. ASCO 2006 update of recommendations for the use of tumor markers in gastrointestinal cancer. J Clin Oncol. 2006; 24: 5313-27.

[0191] 11. Warren JD, Xiong W, Bunker AM, Vaughn CP, Furtado LV, Roberts WL, Fang JC, Samowitz WS, Heichman KA. Septin 9 methylated DNA is a sensitive and specific blood test for colorectal cancer. BMC Med. 2011; 9:133.: 10.1186 / 741-7015-9-133.

[0192] 12. Mead R, Duku M, Bhandari P, Cree IA. Circulating tumour markers can define patients with normal colons, benign polyps, and cancers. Br J Cancer. 2011; 105: 239-45.

[0193] 13. Molnar B, Floro L, Sipos F, Toth B, Sreter L, Tulassay Z. Elevation in peripheral blood circulating tumor cell number correlates with macroscopic progression in UICC stage IV colorectal cancer patients. Dis Markers. 2008; 24: 141-50. doi:

[0194] 14. Mishaeli M, Klein B, Sadikov E, Bayer I, Koren R, Gal R, Rakowsky E, Levin I, Kfir B, Schachter J, Klein T. Initial TPS serum level as an indicator of relapse and survival in colorectal cancer. Anticancer Res. 1998; 18: 2101-5.

[0195] 15. Piepoli A, Cotugno R, Merla G, Gentile A, Augello B, Quitadamo M, Merla A, Panza A, Carella M, Maglietta R, D'Addabbo A, Ancona N, Fusilli S, et al. Promoter methylation correlates with reduced NDRG2 expression in advanced colon tumor. BMC Med Genomics. 2009; 2:11.:10.1186 / 755-8794-2-11.

[0196] Equal range

[0197] Although the present invention has been described in relation to the specific embodiments presented above, many alternatives, variations, and other modifications will be apparent to those skilled in the art. All such alternatives, variations, and other modifications are intended to fall within the spirit and scope of the present invention.

Claims

청구항 1 A method for providing information for detecting colon cancer in a subject requiring detection of colon cancer, comprising the step of determining the expression levels of at least 14 biomarkers from a test sample from a subject by contacting the test sample from the subject with a plurality of agents specific for detecting the expression of at least 14 biomarkers, wherein the 14 biomarkers are ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, UMPS, and Step of including a housekeeping gene; ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, and UMPS By normalizing each expression level with respect to the expression level of the housekeeping gene, ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, and UMPS A method for providing information for detecting colon cancer in a subject, comprising: a step of obtaining each normalized expression level; a step of inputting each normalized expression level into an algorithm to generate a score; a step of comparing the score with a predetermined cutoff value; and a step of confirming the presence of colon cancer in a subject if the score is greater than or equal to the predetermined cutoff value, or confirming the absence of colon cancer in a subject if the score is less than the predetermined cutoff value, wherein the confirming step is not performed by a clinician. 청구항 2 A method for providing information for determining whether colon cancer in a subject is stable or advanced, comprising the step of contacting a test sample from a subject with a plurality of agents specific for detecting the expression of at least 14 biomarkers to determine the expression levels of at least 14 biomarkers from a test sample from a subject, wherein the 14 biomarkers are ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, UMPS, and a step including housekeeping genes; ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, and UMPS By normalizing each expression level with respect to the expression level of the housekeeping gene, ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, and UMPS A method for providing information for determining whether the colon cancer in a subject is stable or advanced, comprising: a step of obtaining each normalized expression level; a step of inputting each normalized expression level into an algorithm to generate a score; a step of comparing the score with a predetermined cutoff value; and a step of confirming that the colon cancer in the subject is advanced if the score is greater than or equal to the predetermined cutoff value, or confirming that the colon cancer in the subject is stable if the score is less than or equal to the predetermined cutoff value, wherein the confirming step is not performed by a clinician. 청구항 3 A method for providing information for determining the completion of surgery in a subject having colon cancer, comprising the step of determining the expression levels of at least 14 biomarkers from a test sample from a subject after surgery by contacting the test sample from the subject after surgery with a plurality of agents specific to detecting the expression of at least 14 biomarkers, wherein the 14 biomarkers are ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, UMPS A step comprising , and housekeeping genes; ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, and UMPS By normalizing each expression level with respect to the expression level of the housekeeping gene, ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, and UMPS A method for providing information for determining the completion of surgery in a subject with colon cancer, comprising the steps of: obtaining each normalized expression level; inputting each normalized expression level into an algorithm to generate a score; comparing the score with a predetermined cutoff value; and confirming that the colon cancer in the subject has not been completely removed if the score is greater than or equal to the predetermined cutoff value, or confirming that the colon cancer in the subject has been completely removed if the score is less than or equal to the predetermined cutoff value, wherein the confirming step is not performed by a clinician. 청구항 4 A method for providing information for evaluating the response of a subject having colon cancer to a first therapy, comprising: (1) at a first time point, (a) determining the expression level of at least 14 biomarkers from a first test sample from a subject by contacting the first test sample from the subject with a plurality of agents specific to detecting the expression of at least 14 biomarkers, wherein the 14 biomarkers are ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, UMPS, Step (b) including housekeeping genes ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, and UMPS By normalizing each expression level with respect to the expression level of the housekeeping gene, ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, and UMPS (a) a step of obtaining each normalized expression level; (c) a step of inputting each normalized expression level into an algorithm to generate a first score; (2) at a second time point, which is after the first time point and after the performance of therapy on the subject, (a) a step of determining the expression levels of at least 14 biomarkers from a second test sample from the subject by contacting the second test sample from the subject with a plurality of agents specific to detecting the expression of at least 14 biomarkers, wherein the 14 biomarkers are ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, UMPS, and Step of including a housekeeping gene; (b) ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, and UMPS By normalizing each expression level with respect to the expression level of the housekeeping gene, ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, and UMPS A method for providing information for evaluating the response of a subject with colon cancer to a first therapy, comprising the steps of: obtaining each normalized expression level; (c) inputting each normalized expression level into an algorithm to generate a second score; (3) comparing a first score with a second score; and (4) confirming that the subject is responsive to the first therapy if the second score is significantly reduced compared to the first score, or confirming that the subject is not responsive to the first therapy if the second score is not significantly reduced compared to the first score, wherein the confirming step is not performed by a clinician. Claim 5 A method according to any one of claims 1 to 3, wherein the predetermined cutoff value is at least 50% on a scale of 0-100%. Claim 6 A method according to any one of claims 1 to 3, wherein the predetermined cutoff value is at least 60% on a scale of 0-100%. Claim 7 In any one of paragraphs 1 to 4, the housekeeping gene MRPL19, PSMC4, SF3A1, PUM1, ACTB, GAPD, GUSB, RPLP0, TFRC , MORF4L1, 18S, PPIA, PGK1, RPL13A, B2M, YWHAZ, SDHA, and HPRT1 A method selected from a group consisting of . Claim 8 In paragraph 7, the housekeeping gene is MORF4L1 The method. Claim 9 A method having a sensitivity of more than 85% in any one of paragraphs 1 to 4. Claim 10 A method having a specificity of more than 85% in any one of paragraphs 1 to 4. Claim 11 A method according to any one of claims 1 to 4, wherein at least one of the 14 biomarkers is RNA, cDNA, or protein. Claim 12 A method according to claim 11, wherein when the biomarker is RNA, the RNA is reverse transcribed to produce cDNA, and the expression level of the produced cDNA is detected. Claim 13 A method according to claim 11, wherein the expression level of a biomarker is detected by forming a complex between the biomarker and a labeled probe or primer. Claim 14 A method according to any one of claims 1 to 3, wherein a predetermined cutoff value is derived from a plurality of reference samples obtained from a subject that does not have a neoplastic disease or has not been diagnosed with a neoplastic disease. Claim 15 In paragraph 14, the neoplastic disease is colon cancer. Claim 16 A method according to any one of claims 1 to 4, wherein the algorithm is XGBoost (XGB), Random Forest (RF), glmnet, cforest, Classification and Regression Trees for Machine Learning (CART), treebag, K-Nearest Neighbors (kNN), neural network (nnet), Support Vector Machine Radial (SVM-radial), Support Vector Machine Linear (SVM-linear), Naive Bayes (NB), or multilayer perceptron (mlp). Claim 17 In Clause 16, the algorithm is a method that is XGBoost. Claim 18 In paragraph 4, the first point in time is a method before performing the first therapy on the subject. Claim 19 In paragraph 4, the method in which the first point in time is after the first therapy has been performed on the subject. Claim 20 A method in which, in any one of paragraphs 4, 18 and 19, if the second score is at least 25% lower than the first score, the second score is significantly reduced compared to the first score. Claim 21 A method according to any one of paragraphs 4, 18 and 19, wherein the first therapy comprises anticancer therapy, surgery, chemotherapy, targeted drug therapy, radiation therapy, immunotherapy, or any combination thereof. Claim 22 In paragraph 21, where the first therapy includes surgery, the surgery comprises removing a polyp during a colonoscopy, endoscopic mucosal resection, partial colectomy, ostomy, removing at least one cancerous lesion from the liver, or any combination thereof. Claim 23 A method according to claim 21, wherein the first regimen comprises chemotherapy, the chemotherapy comprises FOLFOX, FOLFIRI, a combination of 5-FU and leucovorin, capecitabine, irinotecan, CapeOx, or any combination thereof. Claim 24 A method according to claim 21, wherein the first therapy comprises a targeted drug therapy, wherein the targeted drug therapy comprises bevacizumab, cetuximab, panitumumab, regorafenib, a combination of trifluridine and tipiracil, an EGFR TKI inhibitor, or any combination thereof. Claim 25 A method according to claim 21, wherein the first therapy includes an anticancer therapy, the anticancer therapy includes an anti-colon cancer therapy. Claim 26 A method according to claim 21, wherein the first therapy comprises immunotherapy, the immunotherapy comprises pembrolizumab, nivolumab, or a combination of pembrolizumab and nivolumab. Claim 27 delete Claim 28 delete Claim 29 delete Claim 30 delete Claim 31 delete

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