Methods for colon cancer detection and treatment monitoring

Abstract

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

Description

[0001] Cross-reference to related applications

[0002] This application claims priority to and the benefit of 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] Sequence Listing

[0004] This application contains a sequence listing, which has been submitted in ASCII form via EFS-Web and is hereby incorporated by reference in its entirety. The ASCII copy was created on January 21, 2019, is named "LBIO-004_001WO.txt", and is 111 KB in size. Field of the Invention

[0005] The present invention relates to colon cancer detection. Background of the Invention

[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 it is in Europe. Worldwide, it is the fourth most common cause of cancer death. Although surgical resection followed by chemotherapy is the main treatment option, about half of them eventually die due to distant metastasis. Currently, the 5-year overall survival rate of primary CRC patients can reach up to 90%, but it will decrease to ~50% in patients with advanced non-metastatic tumors and further decrease to <10% in patients whose disease is resected at its earliest stage, due to the incomplete understanding of the molecular mechanisms supporting its pathogenesis.

[0008] Overall survival is associated with disease stage at diagnosis, suggesting that early detection of disseminated disease is of great importance. Therefore, the development of new diagnostic methods that better define disease stage and can better monitor disease progression is critical.

[0009] Surveillance remains a fundamental method for early detection of recurrence and planning of further treatment strategies. After potentially curative resection, monitoring can be performed by measuring blood biomarkers and / or imaging like CT to detect asymptomatic metastatic disease earlier. However, the pooled data from randomized trials published from 1995 to 2016 determined that there was no benefit from surgical treatment resulting from earlier detection of metastasis. This may reflect the poor sensitivity of current biomarkers.

[0010] The current biomarker is carcinoembryonic antigen (CEA), a glycoprotein involved in cell adhesion that is not normally expressed in adult tissues except in heavy smokers. Its specialized sialofucosylated glycoforms serve as functional colon cancer L-selectin and E-selectin ligands, which may play a role in the metastatic spread of colon cancer cells. CEA is mainly used to monitor the treatment of colorectal cancer, to identify recurrence after surgical resection, for staging or to locate cancer spread by measuring biological fluids. However, there are significant limitations. Although preoperative CEA levels have been shown to be associated with (disease-free) survival, this is mainly because it is a surrogate for the display of metastases. However, it has been shown that the predictive value of extrapolating preoperative CEA is of limited significance for predicting long-term outcomes in individual cases. This has been independently supported by prospective analyses that determined that levels of CEA and other biomarkers such as CA19-9 did not indicate metastasis even at a time point when clinical signs and imaging techniques had shown metastasis.

[0011] Although the molecular basis of colorectal cancer disease has been well characterized, e.g., microsatellite instability, K-RAS mutations, etc., the development of diagnostic and prognostic markers, e.g., in urine or feces or as circulating free DNA that captures this information, is still nascent but is beginning to be developed. Examples include measurement of methylation of Septin 9, a tumor suppressor gene involved in cytokinesis during cell division. This has been used to detect colon cancer; measurements range from 60-70%. Assessment of circulating free DNA (based on PCR for Line 1 and Alu) has a predictive value of 81% with an ROC of 0.86 as a diagnosis, while measurement of circulating tumor cells is also considered useful. TPS (tissue polypeptide specific antigen) can be used as a monitor for colon cancer, as can TAG-72 (tumor associated glycoprotein), but measurement of other single analytes such as CEA or CA19-9 is nonspecific. SUMMARY OF THE INVENTION

[0013] Disclosed herein, among other things, is a 14-gene expression tool for colon cancer detection.

[0014] In one aspect, the present disclosure provides a method for detecting colon cancer in a subject in need thereof, comprising: (a) determining the expression levels of at least 14 biomarkers from the test sample by contacting the test sample from the subject with a plurality of reagents 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) ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2 and UMPSThe expression level of each of the genes is normalized to the expression level of the housekeeping gene to obtain 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 to a predetermined cutoff value; and (e) identifying the presence of colon cancer in the subject when the score is equal to or greater than the predetermined cutoff value, or identifying the absence of colon cancer in the subject when the score is less than the predetermined cutoff value.

[0015] In one aspect, the present disclosure provides a method for detecting colon cancer in a subject in need thereof, the method comprising: (a) determining the expression levels of at least 14 biomarkers from the test sample by contacting the test sample from the subject with a plurality of reagents 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) ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2 and UMPS The expression level of each of the genes is normalized to the expression level of the housekeeping gene to obtain 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 to a first predetermined cutoff value; and (e) generating a report, wherein when the score is equal to or greater than the first predetermined cutoff value, the report identifies the presence of colon cancer in the subject, or when the score is less than the first predetermined cutoff value, the report identifies the absence of colon cancer in the subject, wherein the first predetermined cutoff value is 50% on a scale of 0-100%.

[0016] In one aspect, the present disclosure provides a method for determining whether colon cancer in a subject is stable or progressive, the method comprising: (a) determining the expression levels of at least 14 biomarkers from the test sample by contacting the test sample from the subject with a plurality of reagents 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) ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2 and UMPS The expression level of each of the genes is normalized to the expression level of the housekeeping gene to obtain 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 to a predetermined cutoff value; and (e) identifying the colon cancer in the subject as progressive when the score is equal to or greater than the predetermined cutoff value, or identifying the colon cancer in the subject as stable when the score is less than the predetermined cutoff value.

[0017] In one aspect, the present disclosure provides a method for determining whether colon cancer in a subject is stable or progressive, the method comprising: (a) determining the expression levels of at least 14 biomarkers from the test sample by contacting the test sample from the subject with a plurality of reagents 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) ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2 and UMPS The expression level of each of the genes is normalized to the expression level of the housekeeping gene to obtain 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 to a second predetermined cutoff value; and (e) generating a report, wherein when the score is equal to or greater than the second predetermined cutoff value, the report identifies the colon cancer as progressive, or when the score is less than the second predetermined cutoff value, the report identifies the colon cancer as stable, wherein the second predetermined cutoff value is 60% on a scale of 0-100%.

[0018] In one aspect, a method for determining the completeness of a surgical procedure in a subject having colon cancer, the method comprising: (a) determining the expression levels of at least 14 biomarkers from the test sample by contacting the test sample from the subject after the surgical procedure with a plurality of reagents 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) ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2 and UMPS The expression level of each of the genes is normalized to the expression level of the housekeeping gene to obtain 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 to a predetermined cutoff value; and (e) identifying that colon cancer in the subject has not been completely removed when the score is equal to or greater than the predetermined cutoff value, or identifying that colon cancer in the subject has been completely removed when the score is less than the predetermined cutoff value.

[0019] In one aspect, the present disclosure provides a method for determining the completeness of a surgical procedure in a subject having colon cancer, the method comprising: (a) determining the expression levels of at least 14 biomarkers from the test sample by contacting the test sample from the subject after the surgical procedure with a plurality of reagents 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) ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2 and UMPS The expression level of each of the genes is normalized to the expression level of the housekeeping gene to obtain 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 to a first predetermined cutoff value; and (e) generating a report, wherein when the score is equal to or greater than the first predetermined cutoff value, the report identifies that the colon cancer is not completely removed, or when the score is less than the first predetermined cutoff value, the report identifies that the colon cancer is completely removed, wherein the first predetermined cutoff value is 50% on a scale of 0-100%.

[0020] In one aspect, the present disclosure provides a method comprising: (a) determining the expression levels of at least 14 biomarkers from a test sample by contacting the test sample from a subject with a plurality of reagents 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) ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2 and UMPS The expression level of each of the genes is normalized to the expression level of the housekeeping gene to obtain 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 to a predetermined cutoff value; and (e) administering a first therapy to the subject when the score is equal to or greater than the predetermined cutoff value.

[0021] In one aspect, the present disclosure provides a method for evaluating a subject having colon cancer in response to a first therapy, the method comprising: (1) at a first time point: (a) determining the expression levels of at least 14 biomarkers from a first test sample by contacting the first test sample with a plurality of reagents 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) ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2 and UMPS The expression level of each of the genes is normalized to the expression level of the housekeeping gene to obtain ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2 and UMPS (a) determining the expression levels of at least 14 biomarkers from a second test sample of the subject by contacting the second test sample with a plurality of reagents 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) ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2 and UMPS The expression level of each of the genes is normalized to the expression level of the housekeeping gene to obtain 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 second score; (3) comparing the first score to the second score; and (4) identifying the subject as responsive to the first therapy when the second score is significantly reduced compared to the first score, or identifying the subject as not responsive to the first therapy when the second score is not significantly reduced compared to the first score.

[0022] In one aspect, the present disclosure provides a method for evaluating a subject having colon cancer in response to therapy, the method comprising: (1) at a first time point, performing the following steps, which include (a) determining the expression levels of at least 14 biomarkers from a first test sample by contacting the first test sample with a plurality of reagents 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)ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2 and UMPS The expression level of each of the genes is normalized to the expression level of the housekeeping gene to obtain 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 first score; and (2) at a second time point, performing the following steps, which include (d) determining the expression levels of at least 14 biomarkers from a second test sample by contacting the second test sample from the subject with a plurality of reagents 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; (e) ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2 and UMPS The expression level of each of the genes is normalized to the expression level of the housekeeping gene to obtain ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2 and UMPS (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 treatment is administered to the subject; (3) comparing the first score to the second score; and (4) generating a report, wherein when the second score is significantly reduced compared to the first score, the report identifies that the subject has responded to the treatment, or when the second score is not significantly reduced compared to the first score, the report identifies that the subject has not responded to the treatment.

[0023] In some aspects, the methods of the present disclosure may further include continuing to administer the first therapy to the subject when the second score is significantly reduced compared to the first score.

[0024] In some aspects, the methods of the present disclosure may further include ceasing administration of the first therapy to the subject when the second score is not significantly reduced compared to the first score.

[0025] In some aspects, the methods of the present disclosure may further comprise administering a second therapy to the subject when the second score is not significantly decreased compared to the first score.

[0026] In some aspects, the second score is significantly reduced compared to the first score when the second score is at least 25% less than the first score.

[0027] In some aspects, the predetermined cutoff value can be 50% on a scale of 0-100%. The predetermined cutoff value can be 60% on a scale of 0-100%.

[0028] In some aspects of any of the methods disclosed herein, the housekeeping gene can be selected from MRPL19, PSMC4, SF3A1, PUM1, ACTB, GAPD, GUSB, RPLP0, TFRC , MORF4L1, 18S, PPIA, PGK1, RPL13A, B2M, YWHAZ, SDHA and HPRT1 For example, a housekeeping gene could be MORF4L1 .

[0029] In some aspects, the methods of the present disclosure may have a sensitivity greater than 85%. In some aspects, the methods of the present disclosure may have a specificity greater than 85%.

[0030] In some aspects, a biomarker can include RNA, cDNA, protein, or any combination thereof.

[0031] In some aspects, where the biomarker is RNA, the RNA can be reverse transcribed to produce cDNA, and the expression level of the produced cDNA can be detected.

[0032] In some aspects, a biomarker or expression of a biomarker can be detected by forming a complex between the biomarker and a labeled probe or primer.

[0033] In some aspects, when the biomarker is a protein, the protein can be detected by forming a complex between the protein and a labeled antibody.

[0034] In some aspects, 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 can be a hybridization complex.

[0035] In some aspects, the predetermined cutoff value can be obtained from a number of reference samples obtained from subjects not suffering from or not diagnosed as suffering from a neoplastic disease.The neoplastic disease can be colon cancer.

[0036] In some aspects, the algorithm can be XGBoost (XGB), Random Forest (RF), glmnet, cforest, Classification and Regression Trees for Machine Learning (CART), treebag, K-neighbor (kNN), neural network (nnet), Support Vector Machine radial (SVM-radial), Support Vector Machine linear (SVM-linear), Naïve Bayes (NB), multi-layer perception (mlp), or any combination thereof.

[0037] In some aspects, the methods of the present disclosure may further comprise administering a first therapy to the subject when the score is equal to or greater than a predetermined cutoff value.

[0038] In some aspects, the first time point can be before the first therapy is administered to the subject.The first time point can be after the first therapy is administered to the subject.

[0039] In some aspects, therapy may include anti-cancer therapy, surgery, chemotherapy, targeted drug therapy, radiation therapy, immunotherapy, or any combination thereof.

[0040] In some aspects, the surgical procedure may include removal of a polyp during a colonoscopy, endoscopic mucosal resection, segmental colectomy, ostomy, removal of at least one cancerous lesion from the liver, or any combination thereof.

[0041] In some aspects, chemotherapy can include FOLFOX, FOLFIRI, a combination of 5-FU and leucovorin, capecitabine, irinotecan, CapeOx, or any combination thereof.

[0042] In some aspects, the targeted drug therapy can include bevacizumab, cetuximab, panitumumab, regorafenib, a combination of trifluridine and tipiracil, an EGFR TKI inhibitor, or any combination thereof.

[0043] In some aspects, the anti-cancer therapy may include an anti-colon cancer therapy.

[0044] In some aspects, immunotherapy can include pembrolizumab, nivolumab, or a combination of pembrolizumab and nivolumab.

[0045] In some aspects, the test sample can be blood, serum, plasma, neoplastic tissue, or any combination thereof. The reference sample can be blood, serum, plasma, non-neoplastic tissue, or any combination thereof.

[0046] Any of the above aspects may be combined with any other aspect.

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by a person of ordinary skill in the art to which the present disclosure belongs. In this specification, the singular also includes the plural, unless the context clearly dictates otherwise; as an example, the terms "a", "an", and "the" are understood to be singular or plural, and the term "or" is understood to be inclusive. As an example, "an element" refers to one or more elements. Throughout the specification, the word "comprising" or variations such as "comprises" or "comprising" will be understood to mean including the stated elements, integers or steps, or groups of elements, integers or steps, but not excluding any other elements, integers or steps, or groups of elements, integers or steps. About can be understood to be within 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, 0.5%, 0.1%, 0.05% or 0.01% of the stated value. Unless otherwise clear from the context, all numerical values ​​provided herein are modified by the term "about."

[0048] Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present disclosure, suitable methods and materials are described below. All publications, patent applications, patents and other references mentioned herein are incorporated by reference in their entirety. The references cited herein are not considered to be prior art of the invention claimed for protection. In the event of a conflict, this specification, including definitions, will prevail. In addition, materials, methods and embodiments are illustrative only and are not intended to be restrictive. Other features and advantages of the present disclosure will be apparent from the following detailed description and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1A-1B Is to show the colon mucosa ( Figure 1A ) and cell lines ( Figure 1B ) in the normalized gene expression profile of the 13 gene signatures in the matched normal mucosa ( n = 7) compared to colon cancer samples ( n = 7) p<0.0001). Levels were ~20-fold elevated in colon cancer tumor tissues compared to normal mucosa. All genes were expressed in three different colon cancer cell lines. Levels were ~1000x elevated compared to normal mucosa. Horizontal lines mark median normalized expression of the 13 genes.

[0051] Figure 2A-2B is the display test group ( Figure 2A ) and independent groups ( Figure 2B ). Each cohort included 136 cancers and 60 controls. The AUROC in the test group was 0.9, and the Youden J index was 0.71. In the independent group, the AUROC was 0.86, and the Youden index was 0.6. The Z-statistics were in the range of 11.2-15.6 and were highly significant ( p <0.0001). The sensitivity and specificity of the test reached 85-87.5% and 75-83%, respectively.

[0052] Figure 3A-3B is shown in the entire cohort (control: n = 120; Colon cancer cases: n = 272) were significantly (62.7 ± 14%) higher in cases compared with controls (34.6 ± 18%). p <0.0001) increased graph ( Figure 3A ). AUROC is 0.88, p <0.0001( Figure 3B ). The horizontal line marks the normalized median expression of the 13-gene signature (ColoTest).

[0053] Figure 4A-4B This is a decision curve analysis of ColoTest. Figure 4A ) and risk analysis ( Figure 4B ). This shows a standardized predictive benefit of >50% all the way up to a risk threshold of 80%. The Probit Risk Assessment plot determined that a ColoTest score >50% was 75% accurate for predicting colon cancer in a blood sample. This increased to >80% at ColoTest scores >60%.

[0054] Figure 5is a graph showing the effect of surgery on ColoTest. Levels were elevated before surgery (84 ± 6%). In those with no evidence of disease (NED), levels were reduced to 14 ± 9% by surgery (*p = 0.0001). In those with disease after surgery (D), levels remained similar to pre-surgery values ​​(74 ± 4%).

[0055] Figures 6A-6C is a graph showing ColoTest scores in stable and progressive disease. Test scores were not significantly different between those identified as having stable disease and those with progressive disease at the time of assessment ( Fig. 6A Of the 17 cases with stable disease, 12 showed disease progression at 3-month follow-up. Levels were low (16 ± 10%) among those with truly demonstrable stable disease ( Figure 6B Among those who did progress within 3 months, levels were not different from those with progressive disease (73±16% vs. 68±25%). The AUROC for distinguishing stable from ongoing / progressive disease was 0.97, p <0.0001( Figure 6C ).

[0056] Figure 7 Graph showing comparison of AUROC between ColoTest and CEA for distinguishing stable from progressive disease. ColoTest was significantly more sensitive than CEA (difference in AUC: 0.18, z-statistic: 2.1, p = 0.03).

[0057] Figure 8 is a graph showing the effect of treatment on ColoTest. Levels were elevated before treatment (82 ± 9%). In those responding to therapy with stable disease, levels decreased to 14 ± 7% (*p < 0.0001). In those showing disease progression due to treatment failure, levels increased (69 ± 21%). DETAILED DESCRIPTION OF THE INVENTION

[0059] The details of the present invention are set forth in the accompanying description below. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, illustrative methods and materials are now described. Other features, objects and advantages of the present invention will be apparent from the specification and claims. In the specification and the appended claims, the singular also includes the plural, unless the context clearly specifies otherwise. Unless otherwise defined, all technical and scientific terms used herein have the same meanings as those of ordinary skill in the art to which the present invention belongs. All patents and publications cited in this specification are incorporated herein by reference in their entirety.

[0060] Colon cancer is cancer of the large intestine (colon). Symptoms of colon 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 cramping, gas, or pain, (d) a feeling that the stool is not completely empty, (e) weakness or fatigue, and (f) unexplained weight loss.

[0061] Described herein are methods for quantifying (scoring) circulating colon cancer molecular markers with high sensitivity and specificity for purposes including, but not limited to, detecting colon cancer, determining whether colon cancer is stable or progressive, determining the completeness of surgical intervention, and evaluating response to colon cancer therapy. Specifically, the invention is based on the discovery that, compared to healthy subjects, circulating colon cancer markers expressed by housekeeping genes, such as MORF4L1 The expression level was normalized ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2 and UMPS The expression level of α is elevated in subjects with colon cancer.

[0062] Thus, the present disclosure provides a method for detecting colon cancer in a subject in need thereof, the method comprising: (a) determining the expression levels of at least 14 biomarkers from the test sample by contacting the test sample from the subject with a plurality of reagents 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) ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2 and UMPS The expression level of each of the genes is normalized to the expression level of the housekeeping gene to obtain 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 to a first predetermined cutoff value; and (e) identifying the presence of colon cancer in the subject when the score is equal to or greater than the predetermined cutoff value, or identifying the absence of colon cancer in the subject when the score is less than the predetermined cutoff value.

[0063] In some aspects of the foregoing methods, step (e) may include generating a report, wherein when the score is equal to or greater than the first predetermined cutoff value, the report identifies the presence of colon cancer in the subject, or when the score is less than the first predetermined cutoff value, the report identifies the absence of colon cancer in the subject.

[0064] In some aspects, the foregoing method may further comprise administering a first therapy to the subject. The foregoing method may further comprise administering a first therapy to the subject when the score is equal to or greater than a predetermined cutoff value.

[0065] The present disclosure also provides a method for determining whether colon cancer in a subject is stable or progressive, the method comprising: (a) determining the expression levels of at least 14 biomarkers from the test sample by contacting the test sample from the subject with a plurality of reagents 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) contacting the test sample from the subject with a plurality of reagents 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; The expression level of each of NRPA, SORD, STOML2 and UMPS is normalized relative to the expression level of the housekeeping gene to obtain a normalized expression level 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 to a second predetermined cutoff value; and (e) when the score is equal to or greater than the predetermined cutoff value, identifying that the colon cancer in the subject is progressive, or when the score is less than the predetermined cutoff value, identifying that the colon cancer in the subject is stable.

[0066] In some aspects of the foregoing method, step (e) may include generating a report, wherein when the score is equal to or greater than the second predetermined cutoff value, the report identifies the colon cancer as progressive, or when the score is less than the second predetermined cutoff value, the report identifies the colon cancer as stable.

[0067] In some aspects, the foregoing method may further comprise administering a first therapy to the subject. The foregoing method may further comprise administering a first therapy to the subject when the score is equal to or greater than a predetermined cutoff value.

[0068] In some aspects, the method further comprises treating the subject having progressive colon cancer with surgery, chemotherapy, targeted drug therapy, radiation therapy, immunotherapy, or a combination thereof.

[0069] The present disclosure also provides a method for determining the completeness of a surgical procedure in a subject suffering from colon cancer, the method comprising: (a) determining the expression levels of at least 14 biomarkers from the test sample by contacting the test sample from the subject after the surgical procedure with a plurality of reagents 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) ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2 and UMPS The expression level of each of the genes is normalized to the expression level of the housekeeping gene to obtain 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 to a first predetermined cutoff value; and (e) identifying that colon cancer in the subject has not been completely removed when the score is equal to or greater than the predetermined cutoff value, or identifying that colon cancer in the subject has been completely removed when the score is less than the predetermined cutoff value.

[0070] In some aspects of the foregoing method, step (e) may include generating a report, wherein when the score is equal to or greater than the first predetermined cutoff value, the report identifies that the colon cancer is not completely removed, or when the score is less than the first predetermined cutoff value, the report identifies that the colon cancer is completely removed.

[0071] In some aspects, the foregoing method may further comprise administering a first therapy to the subject. The foregoing method may further comprise administering a first therapy to the subject when the score is equal to or greater than a predetermined cutoff value.

[0072] The present disclosure also provides a method comprising: (a) determining the expression levels of at least 14 biomarkers from a test sample by contacting the test sample from a subject with a plurality of reagents 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) ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2 and UMPS The expression level of each of the genes is normalized to the expression level of the housekeeping gene to obtain 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 to a predetermined cutoff value; and (e) administering a first therapy to the subject when the score is equal to or greater than the predetermined cutoff value.

[0073] The response of a subject with colon cancer to a 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 can be before or after the therapy is administered to the subject; the second time point is after the first time point and after the therapy is administered to the subject. A first score is generated at the first time point, and a second score is generated at the second time point. When the second score is significantly lower than the first score, the subject is considered to be responsive to the therapy.

[0074] Thus, the present disclosure provides a method for evaluating a subject having colon cancer in response to a first therapy, the method comprising: (1) at a first time point: (a) determining the expression levels of at least 14 biomarkers from a first test sample by contacting the first test sample with a plurality of reagents 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) ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2 and UMPS The expression level of each of the genes is normalized to the expression level of the housekeeping gene to obtain ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2 and UMPS (a) determining the expression levels of at least 14 biomarkers from a second test sample of the subject by contacting the second test sample with a plurality of reagents 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) ADRM1、CDK4、 COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2 and UMPS The expression level of each of the genes is normalized to the expression level of the housekeeping gene to obtain 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 second score; (3) comparing the first score to the second score; and (4) identifying the subject as responsive to the first therapy when the second score is significantly reduced compared to the first score, or identifying the subject as not responsive to the first therapy when the second score is not significantly reduced compared to the first score.

[0075] In some aspects of the foregoing method, step (4) may include generating a report, wherein when the second score is significantly reduced compared to the first score, the report identifies the subject as responding to the first therapy, or when the second score is not significantly reduced compared to the first score, the report identifies the subject as not responding to the first therapy.

[0076] In some aspects of the foregoing methods, the second score is significantly reduced compared to the first score when the second score is at least about 10% less than the first score, or at least about 20% less than the first score, or at least about 25% less than the first score, at least about 40% less than the first score, at least about 50% less than the first score, or at least about 60% less than the first score, or at least about 70% less than the first score, or at least about 75% less than the first score, or at least about 80% less than the first score, or at least about 90% less than the first score, or at least about 95% less than the first score, or at least about 95% less than the first score. In some aspects, when the second score is not significantly reduced compared to the first score, the subject is considered to be unresponsive to therapy.

[0077] In some aspects of the foregoing methods, the first time point can be before the first therapy is administered to the subject.The first time point can be after the first therapy is administered to the subject.

[0078] In some aspects, the foregoing methods may further comprise continuing to administer the first therapy to the subject when the second score is significantly reduced compared to the first score.

[0079] In some aspects, the foregoing method may further comprise ceasing administration of the first therapy to the subject when the second score is not significantly reduced compared to the first score.

[0080] In some aspects, the foregoing methods may further comprise administering a second therapy to the subject when the second score is not significantly reduced compared to the first score.

[0081] In some aspects of the methods of the present disclosure, the predetermined cutoff value may be about 50% within a gradation range of 0-100%. The predetermined cutoff value may be about 60% within a gradation range of 0-100%. The 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% within a gradation range of 0-100%.

[0082] In some aspects of the methods of the present disclosure, the test sample can be any biological fluid obtained from a subject. The test sample can be blood, serum, plasma, neoplastic tissue, or any combination thereof. In some aspects, the test sample is blood. In some aspects, the test sample is serum. In some aspects, the test sample is plasma.

[0083] In some aspects of the methods of the present disclosure, housekeeping genes may include, but are not limited to, MRPL19, PSMC4, SF3A1, PUM1, ACTB, GAPD, GUSB, RPLP0, TFRC, MORF4L1, 18S, PPIA, PGK1, RPL13A, B2M, YWHAZ, SDHA and HPRT1 In some respects, housekeeping genes are MORF4L1 .

[0084] The methods 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 methods 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%.

[0085] The methods 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 methods 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%.

[0086] The methods 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 methods of the present disclosure may have an accuracy of 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%.

[0087] In some aspects of the methods of the present disclosure, the predetermined cutoff value can be obtained from a plurality of reference samples obtained from subjects not suffering from or not diagnosed as suffering from a neoplastic disease. In some aspects, the neoplastic disease can be colon cancer.

[0088] Many reference samples can include about 2-500, 2-200, 10-100 or 20-80 reference samples. Each reference sample generates a score using an algorithm, and the first predetermined cutoff value can be the arithmetic mean of these scores. Each reference sample can be blood, serum, plasma or non-neoplastic tissue. In some aspects, each reference sample is blood. In some aspects, each reference sample has the same type as the test sample.

[0089] Each biomarker disclosed herein can have one or more transcript variants. The methods disclosed herein can measure the expression level of any transcript variant of each biomarker.

[0090] Expression levels can be measured by a variety of methods, 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 a protein encoded by a selected gene.

[0091] In some aspects of the methods of the present disclosure, the biomarker can be RNA, cDNA, protein, or any combination thereof. When the biomarker is RNA, the RNA can be reverse transcribed to produce cDNA (e.g., by RT-PCR), and the expression level of the produced cDNA can be detected. The expression level of the biomarker can be detected by forming a complex between the biomarker and a labeled probe or primer. 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 can be a hybridization complex.

[0092] In some aspects of the methods of the present disclosure, gene expression can be detected by microarray analysis. Microarray technology can also be used to identify or confirm differential gene expression. Therefore, microarray technology can be used to measure expression profile biomarkers in fresh or fixed tissues. In this method, polynucleotide sequences of interest (including cDNAs and oligonucleotides) are covered or arranged on a microchip substrate. The arranged sequences are then hybridized with specific DNA probes from cells or tissues of interest. The source of mRNA is generally total RNA isolated from a biological sample, and corresponding normal tissues or cell lines can be used to determine differential expression.

[0093] In some aspects of microarray technology, the insert fragments of cDNA clones amplified by PCR are applied to a substrate in a dense array. In some aspects, at least 10,000 nucleotide sequences are applied to the substrate. The microarrayed genes fixed on the microchip in each of 10,000 elements are suitable for hybridization under stringent conditions. Fluorescently labeled cDNA probes can be produced by reverse transcription of RNA extracted from the tissue of interest by incorporating fluorescent nucleotides. The labeled cDNA probes applied to the chip are specifically hybridized with each DNA point on the array. After strict washing to remove non-specifically bound probes, the microarray chip is scanned by equipment such as confocal laser microscopy or by another detection method such as a CCD camera. The quantification of the hybridization of each array element allows the evaluation of the corresponding mRNA abundance. Using dual-color fluorescence, the cDNA probes labeled separately produced from two RNA sources are hybridized in pairs with the array. Therefore, the relative abundance of transcripts from two sources corresponding to each specified gene can be determined simultaneously. Microarray analysis can be performed by commercially available equipment according to the manufacturer's procedures.

[0094] In some aspects of the methods of the present disclosure, qRT-PCR can be used to detect biomarkers in biological samples. The first step in gene expression profiling by RT-PCR is to extract RNA from the biological sample, followed by reverse transcription of the RNA template into cDNA and amplification by PCR reaction. Depending on the goal of the expression profiling, the reverse transcription reaction step is usually initiated using specific primers, random hexamers, or oligo-dT primers. Two commonly used reverse transcriptases are avian myeloblastosis virus reverse transcriptase (AMV-RT) and Moloney murine leukemia virus reverse transcriptase (MLV-RT).

[0095] In some aspects of the methods of the present disclosure, when the biomarker is a protein, the protein can be detected by forming a complex between the protein and a labeled antibody. The label can be any label, such as a fluorescent label, a chemiluminescent label, a radioactive label, etc. Exemplary methods for protein detection include, but are not limited to, enzyme immunoassay (EIA), radioimmunoassay (RIA), protein blot analysis, and enzyme-linked immunosorbent assay (ELISA). For example, biomarkers can be detected in ELISA, where the biomarker antibody is bound to a solid phase and the enzyme-antibody conjugate is used to detect and / or quantify the biomarker present in the sample. On the other hand, a protein blot assay can be used, where the dissolved and separated biomarker is bound to nitrocellulose paper. The combination of a highly specific, stable liquid conjugate with a sensitive chromogenic substrate allows for rapid and accurate identification of samples.

[0096] In some aspects of the methods of the present disclosure, the methods described herein can have a specificity, sensitivity, and / or accuracy of at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

[0097] In some aspects of the methods of the disclosure, the labeled probe, labeled primer, labeled antibody, or labeled nucleic acid may comprise a fluorescent label.

[0098] Any algorithm that can generate scores for samples by evaluating where sample values ​​fall on a prediction model generated using different techniques such as decision trees can be used in the methods disclosed herein. The algorithm analyzes data (i.e., expression levels) and then assigns scores. In some aspects, the algorithm can be a machine learning algorithm. Exemplary algorithms that can be used in the methods disclosed herein can include, but are not limited to, XGBoost (XGB), random forests (RF), glmnet, cforest, classification and regression trees (CART) of machine learning, treebag, K-neighbors (kNN), neural networks (nnet), support vector machines radial (SVM-radial), support vector machines linear (SVM-linear), naive Bayes (NB), multi-layer perception (mlp) or any combination thereof.

[0099] In some aspects of the methods of the present disclosure, the algorithm may be XGB (also known as XGBoost). XGB is an implementation of gradient boosted decision trees designed for speed and performance.

[0100] In some aspects of the methods of the present disclosure, therapy may include anti-cancer therapy, surgery, chemotherapy, targeted drug therapy, radiation therapy, immunotherapy, or any combination thereof.

[0101] In some aspects of the methods of the present disclosure, the surgical procedure may include removal of a polyp during a colonoscopy, endoscopic mucosal resection, segmental colectomy, ostomy, removal of at least one cancerous lesion from the liver, or any combination thereof.

[0102] In some aspects of the methods of the present disclosure, the anti-cancer therapy may include an anti-colon cancer therapy.

[0103] In some aspects of the methods of the present disclosure, chemotherapy may include FOLFOX, FOLFIRI, a combination of 5-FU and leucovorin, capecitabine, irinotecan, CapeOx, or any combination thereof.

[0104] In some aspects of the methods 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.

[0105] In some aspects of the methods of the present disclosure, the immunotherapy may include pembrolizumab, nivolumab, or a combination of pembrolizumab and nivolumab.

[0106] For early-stage colon cancer, minimally invasive surgical methods may be used to remove the cancer. For example, if the cancer is completely contained within a polyp, the polyp may be removed during a colonoscopy. Endoscopic mucosal resection may be done to remove larger polyps. Polyps that cannot be removed during a colonoscopy may be removed using laparoscopic surgery.

[0107] If the cancer has grown into or through the colon, a partial colectomy may be done to remove the portion of the colon containing the cancer along with a margin of normal tissue on either side of the cancer. When it is not possible to reattach a healthy portion of the colon or rectum, an ostomy may be done to create an opening in the abdominal wall from a portion of the remaining intestine for drainage of stool into a bag that fits securely over the opening. Lymph node removal may also be done.

[0108] For advanced colon cancer, surgery to relieve colon blockage or other conditions may also be done. In certain cases where the cancer has only spread to the liver, surgery may be done to remove cancerous lesions from the liver.

[0109] For chemotherapy, the FOLFOX (5-FU, leucovorin, and oxaliplatin) or CapeOx (capecitabine and oxaliplatin) regimens are most often used, but some patients may get 5-FU and leucovorin or capecitabine alone based on their age and health needs. Irinotecan can also be used as a chemotherapeutic agent to treat colon cancer.

[0110] Targeted drug therapies target the specific malfunctions that allow cancer cells to grow. These therapies include, but are not limited to, bevacizumab, cetuximab, panitumumab, ramucirumab, regorafenib, ziv-aflibercept, the combination of trifluridine and tipiracil, and EGFR TKI inhibitors.

[0111] Immunotherapy for colon cancer includes, but is not limited to, pembrolizumab (Keytruda®) and nivolumab (Opdivo®).

[0112] Table 1 shows the sequence information of colon cancer biomarkers and housekeepers.

[0113] Table 1. Sequence information of colon cancer biomarkers / housekeeping genes

[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]

[0139]

[0140]

[0141]

[0142]

[0143]

[0144]

[0145]

[0146]

[0147]

[0148] definition

[0149] The articles “a” and “an” are used in this disclosure to refer to one or to more than one (ie, to at least one) of the grammatical object of the article. For example, “an element” means one element or more than one element.

[0150] The term "and / or" used in this disclosure means "and" or "or" unless stated otherwise.

[0151] As used herein, the terms "polynucleotide" and "nucleic acid molecule" are used interchangeably to refer to a polymeric form of nucleotides of at least 10 bases or base pairs in length, or a ribonucleotide or a deoxyribonucleotide or a modified form of any type of nucleotide, and are intended to include DNA in single-stranded and double-stranded forms. As used herein, nucleic acid molecules or nucleic acid sequences used as probes in microarray analysis preferably comprise nucleotide chains, more preferably DNA and / or RNA. In other aspects, nucleic acid molecules or nucleic acid sequences comprise other types of nucleic acid structures, such as, for example, DNA / RNA helices, peptide nucleic acids (PNA), locked nucleic acids (LNA) and / or ribozymes. Therefore, as used herein, the term "nucleic acid molecule" also includes chains comprising non-natural nucleotides, modified nucleotides and / or non-nucleotide building blocks that exhibit the same functions as natural nucleotides.

[0152] As used herein, the terms "hybridize," "hybridizing," "hybridizes," and the like used in the context of polynucleotides refer to conventional hybridization conditions, such as hybridization in 50% formamide / 6XSSC / 0.1% SDS / 100 μg / ml ssDNA, wherein the hybridization temperature is above 37 degrees Celsius, and the temperature for washing in 0.1 XSSC / 0.1% SDS is above 55 degrees Celsius, and preferably to stringent hybridization conditions.

[0153] As used herein, the term "normalization" or "normalizer" refers to expressing differential values ​​according to a standard value to adjust for effects due to technical variations in sample handling, sample preparation, and measurement methods rather than biological variations in the concentration of biomarkers in the sample. For example, when measuring the expression of a differentially expressed protein, the absolute value of the expression of the protein can be expressed according to the absolute value of the expression of a standard protein whose expression is substantially constant.

[0154] The terms "diagnosis" and "diagnostics" also include the terms "prognosis" and "prognostics," respectively, as well as the application of such procedures at two or more time points to monitor the diagnosis and / or prognosis over time, and statistical modeling thereon. In addition, the term diagnosis includes: a. prediction (determining whether a patient will likely develop aggressive disease (hyperproliferative / aggressive)), b. prognosis (predicting whether a patient will have a better or worse outcome at a preselected time in the future); c. therapy selection, d. therapeutic drug monitoring, and e. recurrence monitoring.

[0155] "Accuracy" refers to the closeness of agreement between a measured or calculated quantity (the value reported by a test) and its actual (or true) value. Clinical accuracy involves the ratio of true results (true positives (TP) or true negatives (TN)) to misclassified results (false positives (FP) or false negatives (FN)), and may be expressed as sensitivity, specificity, positive predictive value (PPV) or negative predictive value (NPV), or as likelihood, odds ratio, among other measures.

[0156] As used herein, the term "biological sample" refers to any sample of biological origin that may contain one or more biomarkers. Examples of biological samples include tissues, organs, or body fluids, such as whole blood, plasma, serum, tissue, lavage fluid, or any other sample used to detect disease.

[0157] As used herein, the term "subject" refers to a mammal, preferably a human. In some aspects, the subject may have at least one symptom of colon cancer. In some aspects, the subject may have a predisposition or family history for developing colon cancer. The subject may also have been previously diagnosed with colon cancer and tested for cancer recurrence.

[0158] As used herein, "treating" or "treatment" with respect to a condition can mean preventing the condition, slowing the onset or development of a condition, reducing the risk of developing a condition, preventing or delaying the development of symptoms associated with a condition, reducing or stopping symptoms associated with a condition, causing complete or partial regression of a condition, or some combination thereof.

[0159] The level of a biomarker may change due to treatment of a disease. Changes in the level of a biomarker can be measured by the present disclosure. Changes in the level of a biomarker can be used to monitor the progress of a disease or therapy.

[0160] "Altered," "varied," or "significantly different" refers to a detectable change or difference compared to a reasonably comparable state, profile, measurement, etc. Such changes may be all or nothing. They may be incremental, and need not be linear. They may be by orders of magnitude. The 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%. On the other hand, the change may be 1-fold, 1.5-fold, 2-fold, 3-fold, 4-fold, 5-fold or more, or any value between 1-fold and 5-fold. The change may be statistically significant, with a p-value of 0.1, 0.05, 0.001, or 0.0001.

[0161] The term "stable disease" refers to a diagnosis of the presence of colon cancer, however the colon cancer has been treated and remains in a stable condition, ie, not progressive, as determined by imaging data and / or best clinical judgment.

[0162] The term "progressive disease" refers to a diagnosis of the presence of colon cancer in a highly active state, i.e., colon cancer that has not been treated and is unstable or has been treated and is not responding to therapy, or has been treated and active disease continues, as determined by imaging data and / or best clinical judgment.

[0163] The term "neoplastic disease" refers to any abnormal growth of cells or tissues, benign (non-cancerous) or malignant (cancerous). For example, a neoplastic disease may be colon cancer.

[0164] The term "neoplastic tissue" refers to an abnormal growth of cells.

[0165] The term "non-neoplastic tissue" refers to a normally growing mass of cells.

[0166] The term "immunotherapy" may refer to activating immunotherapy or suppressing immunotherapy. As will be understood by those skilled in the art, activating immunotherapy refers to the use of therapeutic agents that induce, enhance or promote an immune response (including, for example, a T cell response), while suppressing immunotherapy refers to the use of therapeutic agents that interfere with, suppress or inhibit an immune response (including, for example, a T cell response). Activating immunotherapy may include the use of a restriction point inhibitor. Activating immunotherapy may include administering to a subject a therapeutic agent that activates a stimulatory restriction point molecule. Stimulatory restriction point molecules include, but are not limited to, CD27, CD28, CD40, CD122, CD137, OX40, GITR, and ICOS. Therapeutic agents that activate stimulatory restriction point molecules include, but are not limited to, MEDI0562, TGN1412, CDX-1127, lipocalin.

[0167] The term "antibody" herein is used in the broadest sense and includes various antibody structures, including but not limited to monoclonal antibodies, polyclonal antibodies, multispecific antibodies (e.g., bispecific antibodies) and antibody fragments, as long as they exhibit the desired antigen-binding activity. An antibody that binds to a target refers to an antibody that can bind to a target with sufficient affinity so that the antibody can be used as a diagnostic and / or therapeutic agent in targeting the target. In one embodiment, the degree of binding of an 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 a radioimmunoassay (RIA) or a biacore assay. In certain embodiments, the antibody that binds 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., from 108 M to 1013 M, e.g., from 109 M to 1013 M). In certain embodiments, an anti-target antibody binds to an epitope of the target that is conserved between different species.

[0168] "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 it binds. For example, antagonist antibodies can block signaling through immune cell receptors (e.g., T cell receptors) to restore functional responses (e.g., proliferation, cytokine production, target cell killing) by T cells from a dysfunctional state to antigen stimulation.

[0169] "Agonist antibodies" or "activating antibodies" are antibodies that simulate, promote, stimulate or enhance the normal biological activity of the antigen to which they bind. Agonist antibodies can also enhance or initiate signal transduction through the antigen to which they bind. In some embodiments, agonist antibodies cause or activate signal transduction in the absence of natural ligands. For example, agonist antibodies can increase memory T cell proliferation, increase cytokine production of memory T cells, inhibit regulatory T cell function and / or inhibit regulatory T cell suppression of effector T cell function, such as effector T cell proliferation and / or cytokine production.

[0170] "Antibody fragment" refers to a molecule other than an intact antibody that contains a portion of an intact antibody that binds to the 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; diabodies; linear antibodies; single-chain antibody molecules (e.g., scFv); and multispecific antibodies formed from antibody fragments.

[0171] Administering chemotherapy to a subject may include administering a therapeutically effective dose of at least one chemotherapeutic agent. Chemotherapeutic agents include, but are not limited to, 13-cis-retinoic acid, 2-CdA, 2-chlorodeoxyadenosine, 5-azacytidine, 5-fluorouracil, 5-FU, 6-mercaptopurine, 6-MP, 6-TG, 6-thioguanine, Abemaciclib, Abiraterone acetate, Abraxane, Isotretinoin, Actinomycin-D, Adcetris, Ado-Trastuzumab Emtansine, doxorubicin, fluorouracil (Adrucil), afatinib, Afinitor, anagrelide, hydrocortisone, aledurazine, alemtuzumab, alecensa, alectinib, Alimta, alitretinoin, alkaban-AQ, phenylalanine nitrogen mustard, all-trans retinoic acid, interferon alpha, hexamethylmelamine, brigatinib (Alunbrig), methotrexate (Ameth opterin), Amistin, Aminoglutethimide, Alarini, Linulactam, Anastrozole, Apalutamide, Cytarabine, Ara-C, Darbepoetin alfa (Aranesp), Pamidronate, Anasterazole, Aromasin, Nelarabine (Arranon), Arsenic trioxide, Arzerra, Asparaginase, Atezolizumab, All-trans retinoic acid (Atra), Avastin, Avelumab, Axicabtagene Ciloleucel, Axitinib, Azacytidine, Bavencio, Bcg, Beleodaq, Belinostat, Bindamustine, Bendeka, Besponsa, Bevacizumab, Bexarotene, Bexxar, Bicalutamide, Bicnu, Bleomycin, Blinatumomab, Blincyto, Bortezomib, Bosulif, Bosutinib, BrentuximabVedotin), Brigatinib, busulfan, busulfan, C225, Cabazitaxel, Cabozantinib, leucovorin calcium, humanized anti-lymphocyte monoclonal antibody, Irinotecan (Camptosar), Camptothecin-11, Capecitabine, Vandetanib (Caprelsa), Carac, Carboplatin, Carfilzomib, Carmustine, Carmustine tablets (Carmustine Wafer), Cassodix, CCI-779, Lomustine (Ccnu), Cisplatin (Cddp), Ceenu, Ceritinib, Daunorubicin, Cetuximab, Chlorambucil, Cisplatin, Calcium Folinate, Cladribine, Clofarabine, Clolar, Cobimetinib, Cometriq, Cortisone, Dactinomycin, Cotellic, Cpt-11, Crizotinib, Cyclophosphamide, Cyramza, Ethylbenzimidone, Cytarabine, Cytarabine Liposomal, Cytarabine-U, Andorin, Dabrafenib, Dacarbazine, Dacogen, Actinomycin D, Daratumumab, Darbepoetin α Alfa), Darzalex, dasatinib, daunomycin, daunomycin, daunomycin arabinoside (liposomal), daunomycin hydrochloride, daunomycin liposomal, DaunoXome, mefluene, deoxyazacytidine, degarelix, prednisolone, prednisone, denileukinDiftitox, Denosumab, Extended-release cytarabine, Dexamethasone, Dexamethasone acetate, Dexamethasone sodium phosphate, Flumethasone, Dexrazoxane, Dhad, Dic, Diodex, Docetaxel, Doxil, Afamicin, Afamicin liposomal, Droxia, DTIC, Dtic-Dome, Duralone, Durvalumab, Eculizumab, Efudex, Ellence, Elotuzumab, Eloxatin, Eltrombopag, Estramustine, Empliciti, Enasidenib, Enzalutamide lutamide), epirubicin, recombinant human erythropoietin alfa, Erbitux, Eribulin, Erivedge, Erleada, Erlotinib, Erwinia L-asparaginase, estramustine, Amistin, Etopophos, Etoposide, Etoposide phosphate, Flutamide, Everolimus, Raloxifene, Exemestane, Toremifene, Farydak, Faslodex, Letrozole, Filgrastim, Firmagon, Floxuridine, Fludanagan, Fludarabine, Fluoroplex, Fluorouracil, Fluorouracil (cream), Fluoxymesterone, Fluitan, FolinicAcid), Folotyn, Fudr, Fulvestrant, G-Csf, Gazyva, Gefitinib, Gemtuzumab, Gemtuzumab, Gemcitabine, Gilotrif, Gleevec, Gleostine, Carmustine Polyphenylpropen 20 Film, Gm-Csf, Sexrelin, Grani x, granulocyte colony stimulating factor, granulocyte macrophage colony stimulating factor, Halaven, flumethasone, Herceptin, flumethasone sodium phosphate, hexamethylmelamine, hexamethylpolynitrile, hexamethyltrinitrile (Hmm), topotecan hydrochloride, carbamylhydroxylamine, hydrocortisone acetate (HydrocortAcetate), hydrocortisone, hydrocortisone sodium phosphate, hydrocortisone sodium succinate, hydrocortisone phosphate (Hydrocortone Phosphate, hydroxyurea, Ibrance, Ibritumomab, Ibritumomab Tiuxetan, Ibrutinib, Iclusig, Idarubicin, Idiocytoxan, Idelalisib, Idhifa, Ifosamide, IFN-α, Ifosfamide, IL-11, IL-2, Imbruvica, Imatinib Mesylate, Imfinzi, ImidazoleCarboxamide), Imlygic, Inlyta, Inotuzumab Ozogamicin, Interferon-α, Interferon α-2b (PEG conjugate), Interleukin-2, Interleukin-11, Human lymphoblastoid interferon (Interferon α-2b), Ipilimumab, Iressa, Irinotecan, Irinotecan (liposomal), Isotrione, Istodax, Ixabepilone, Ixazomib, Ixempra, Jakafi, Je vtana, Kadcyla, Keytruda, L-asparaginase, Kisqali, Kymriah, Kyprolis, Lanacort, extended-release lanreotide, Lapatinib, Lartruvo, L-asparaginase, Lbrance, vinblastine (Lcr), Lenalidomide, Lenvatinib, Lenvima, Furui, leucovorin, leukocytol, leuprolide acetate, vincristine, clalipine, liposomal Ara-C, prednisone (Liquid Pred), lomustine, Lonsurf, L-PAM, L-sarcolysin, leuprolide, leuprolide depot (Lupron Depot), Lynparza, Marqibo, Procarbazine Hydrochloride, Fluprednisolone, Mechlorethamine, Mechlorethamine Hydrochloride, Medralone, Methylprednisolone, Mecozeb, Megestrol, Megestrol Acetate, Mekinist, Mercaptopurine, Mesna, Sodium Mercaptoethanesulfonate, Methotrexate, Methotrexate Sodium, Methylprednisolone, Prednisone, Midostaurin, Mitomycin, Mitomycin-C, Mitoxantrone, M-Prednisolone, MTC, MTX, Mechlorethamine ustargen), Enbixin, Mutamycin, Myleran, Mylocel, Mylotarg, Navelbine, Necitumumab, Nelarabine, Neosar, Neratinib, Nerlynx, Neulasta, Neumega, Nipoqin, Nexavar, Nilandron, Nilotinib, Nilutamide, Ninlaro, Nipent, Niraparib, Nitrogen mustardMustard), Nivolumab, Tamoxifen Citrate, Nplate, Obinutuzumab, Sandostatin, Sandostatin Acetate, Odomzo, Ofatumumab, Olaparib, Olaratumab, Omacetaxine, Oncospar, Onivyde, Ontak, Onxal, Opdivo ), Oprelvekin, Orapred, Orasone, Osimertinib, Otrexup, Oxaliplatin, Paclitaxel, Protein-bound Paclitaxel, Palbociclib, Pamidronate, Panitumumab, Panobinostat, Panretin, Carboplatin, Pazopanib, Prednisolone, PEGylated interferon Interferon, Pegapakase, Pegfilgrastim, Peg-Intron, PEG-L-asparaginase, Pembrolizumab, Pemetrexed, Pentostatin, Perjeta, Pertuzumab, Phenylalanine mustard, Cisplatin, Cisplatin-AQ, Pomalidomide, Pomalyst, Ponatinib, Portrazza, Pralatrexate, Prednisolone, Prednisone, Prelone, Procarbazine, Ipozemide, Recombinant interleukin-2, Prolia, Prolifeprospan 20 with Carmustine Implant, Promacta, Provenge, Levitra, Radium 223 Dichloride), Raloxifene, Ramucirumab, Rasuvo, Regorafenib, Revlimid, Rheumatrex, Ribociclib, monoclonal antibody IDEC-C2B8, RituxanHycela, Rituximab, Rituximab Hyaluronidase, Interferon alfa-2a (Interferon alfa-2a), Romidepsin, Romiplostim, Rubex, Daunorubicin hydrochloride, Rubraca, Rucaparib, Ruxolitinib, Rydapt, Octreotide, Octreotide LAR, Sargramostim, Siltuximab, Sipuleucel- T, Soliris, hydrocodone sodium, methylprednisolone, somatuline, Sonidegib, Sorafenib, Sprycel, Sti-571, Stivarga, streptozotocin, SU11248, Sunitinib, Sutent, Sylvant, Synribo, Tafinlar, Tagrisso, Talimogene Laherparepvec, Tamoxifen, Tarceva, Targretin, Tasigna, Tacozole, Taxotere, Tecentriq, Temozolomide, Temozolo, Temsirolimus, Teniposide, Thiotepa, Thalidomide, Thalidomide, TheraCys, Thioguanine, Thioguanine Tablets (Tabloid), Thiophosphoamide, Thioplex, Triamterene, Tice, Tisagenlecleucel, Toposar, Topotecan, Toremifene, Torisel, Tositumomab, Trabectedin, Trametinib ib), Trastuzumab, Treanda, Trelstar, Retinoic acid, Trexall, Trifluridine / Tipiricil, Triptorelin, Trisenox, Tspa, T-VEC, Tykerb, Valrubicin, Valstar, Vandetanib, VCR, Vectibix, Vinblastine, Velcade, Vemurafenib, Venclexta, Venetoclax, Verzenio, Vesanoid, Viadur, Vidaza, Vinblastine, Vinblastine sulfate, Vincasar PFSPfs), Vinblastine, Vinblastine Liposomal, Vinorelbine, Vinorelbine, Vismodegib, Vlb, VM-26, Vorinostat, Votrient, VP-16, Vyxeos, Xalkori Capsules, Xeloda, Xgeva, Xofigo, Xtandi, Yervoy, Yescarta, Yondelis, Zaltrap, Streptozocin, Zarxio, Zejula, Zelboraf, Zevalin, Dirazoxane, Ziv-aflibercept, Goserin, Zoledronic Acid, Zolinza, Zometa, Zydelig, Zykadia, Zytiga, or any combination thereof.

[0172] The terms "effective amount" and "therapeutically effective amount" of an agent or compound are used in the broadest sense to refer to a nontoxic but sufficient amount of the active agent or compound to provide the desired effect or benefit.

[0173] The term "benefit" is used in the broadest sense and refers to any desired effect, and specifically includes clinical benefits as defined herein. Clinical benefits can be measured by evaluating various endpoints, for example, inhibition of disease progression to some extent, including reduction and complete stagnation; reduction in the number of disease attacks and / or symptoms; reduction in lesion size; inhibition (i.e., reduction, reduction or complete cessation) of disease cell infiltration into adjacent peripheral organs and / or tissues; inhibition (i.e., reduction, reduction or complete cessation) of disease spread; reduction in autoimmune response, which may but not necessarily lead to regression or ablation of disease lesions; alleviation of one or more symptoms associated with the disorder to some extent; increased duration of disease-free manifestation after treatment, such as progression-free survival; improved overall survival; higher response rate; and / or reduced mortality at a given time point after treatment.

[0174] The terms "cancer" and "cancerous" refer to or describe the physiological condition in mammals that is generally characterized by unregulated cell growth. Included in the definition are both benign and malignant cancers. Examples of cancer include, but are not limited to, carcinomas, lymphomas, blastomas, sarcomas, and leukemias. More specific examples of such cancers include adrenocortical carcinoma, bladder urothelial carcinoma, breast invasive carcinoma, cervical squamous cell carcinoma, endocervical adenocarcinoma, bile duct carcinoma, colon adenocarcinoma, lymphoid neoplasm, diffuse large B-cell lymphoma, esophageal carcinoma, glioblastoma multiforme, head and neck squamous cell carcinoma, kidney chromophobe, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, acute myeloid leukemia, brain lower grade glioma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma, paraganglioma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, skin cutaneous melanoma, stomach adenocarcinoma, testicular germ cell tumor, thyroid carcinoma, thymoma, uterine carcinosarcoma, uveal melanoma. Other examples include breast cancer, lung cancer, lymphoma, melanoma, liver cancer, colorectal cancer, ovarian cancer, bladder cancer, kidney cancer or gastric cancer. Further examples of cancer include neuro-endocrine cancer, non-small cell lung cancer (NSCLC), small cell lung cancer, thyroid cancer, endometrial cancer, bile duct cancer, esophageal cancer, anal cancer, salivary gland cancer, vulvar cancer or cervical cancer.

[0175] 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," "cell proliferative disease," "proliferative disease," and "tumor" as referred to herein are not mutually exclusive.

[0176] Example

[0177] The present disclosure is further illustrated by the following examples, which are not to be construed as limiting the present disclosure to the specific procedures described herein in terms of scope or spirit. It is to be understood that these examples are provided to illustrate certain aspects, and are not intended to limit the scope of the present disclosure. It is to be further understood that various other aspects, embodiments, modifications, and equivalents thereof that may be presented to those skilled in the art may be used without departing from the spirit of the present disclosure and / or the scope of the appended claims.

[0178] Example 1.13- Derivation of marker gene experimental panel

[0179] Will come from n = 24. The original probe intensity of the colon cancer tumor tissue sample is n = 22 control colon mucosa to identify the genes that best discriminate disease using transcriptional profiles of E-MTAB-57. A gene co-expression network was generated to identify temporal patterns of gene regulation associated with colon cancer. A total of 513 nodes with 53,786 links were identified. Differential expression analysis identified 103 genes that were upregulated in tumor tissue compared to blood. To identify blood-specific colon cancer gene biomarkers, we evaluated the expression of 103 genes in the peripheral blood transcriptome (n = 7). 33 of the 103 genes (32%) were below detection levels in blood, identifying them as candidate genes. Evaluation of transcripts in a preliminary dataset of blood samples from colon cancer (n = 20) and matched normal blood (n = 20) identified thirteen genes and one housekeeping gene as markers for colon cancer (Table 2). These genes were shown to be highly expressed in colon cancer tumor tissue compared with normal mucosa, as well as in three different colon cancer cell lines: LOVO (a metastatic, hyperdiploid, MSI-unstable cell line), LS-180 (derived from Duke's B, colorectal adenocarcinoma), and Colo 320DM (derived from Duke's C, colorectal adenocarcinoma). These data confirm that the target transcripts are produced by neoplastically transformed colon mucosal cells ( Figure 1A-1B ).

[0180] Using the control ( n = 120) and colon cancer ( n = 272) samples, and an artificial intelligence model for colon cancer disease was established. The data set was randomly divided into training and test parts, which were used for model building and validation, respectively. 12 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") best predicted the training data. In the test set, XGB produced probability scores for the predicted samples. Each probability score reflects the "certainty" of the algorithm that the unknown sample belongs to the "control" or "colon cancer" class. For example, the unknown sample S1 can have the following probability vector [control = 20%, colon cancer = 80%]. This sample will be considered a colon cancer sample.

[0181] Example 2. Clinical efficacy

[0182] Figure 2A-2B Included in the test are patients with colon cancer in the training and test groups (n = 136) and controls ( n = 60) for discrimination (Receiver Operating Curve Analysis and Metrics). The scores showed an area under the curve (AUC) of 0.90 (training) and 0.86 (test group). The metrics were: Sensitivity: 85.3-87.5% and Specificity: 75-83.3%.

[0183] Overall, ColoTest scores were significantly elevated in cancer (63 ± 1%) and controls (34 ± 2%). Figure 3A-3B ). The overall accuracy (training and test cohorts) was 84%, and the AUC was 0.88. The z-statistic for discrimination against controls was 18.5.

[0184] Decision curve analysis for the clinical benefit of quantitative diagnostic tests ( Figure 4A-4B ). The ColoTest showed a standardized predictive benefit of >50% all the way up to a risk threshold of 80%. The probit risk assessment plot determined that a ColoTest score >50% was 75% accurate for predicting colon cancer in a blood sample. > 60%, which increased to >80%. Thus, the tool can accurately distinguish controls from colon cancer disease.

[0185] Specific evaluation of the colon cancer cohort before and after surgery determined that complete removal of the tumor and absence of disease evidence was associated with a significant decrease in ColoTest ( p <0.0001) ( Figure 5 Levels were not significantly different in those with evidence of residual disease.

[0186] ColoTest was confirmed in stable ( n = 17: 56 ± 7%) and progressive disease ( n = 32: 68 ± 4%) were not significantly different ( Figures 6A-6C However, 12 of the 17 patients progressed within 3 months of blood draw. Those that did progress showed elevated ColoTest scores at the time of the blood draw ( n = 12: 73 ± 4%), which was comparable to those with progressive disease at the time of blood draw ( n = 32: 68 ± 4%) no different ( Figures 6A-6C Patients with stable disease had significantly lower levels ( n = 5: 16 ± 4%,p <0.0001). A direct comparison between ColoTest and CEA in these samples determined that the gene expression assay was significantly more sensitive than CEA for predicting disease progression ( p <0.05) ( Figure 7 ). Therefore, the ColoTest tool can accurately predict progressive colon cancer disease.

[0187] ROC analysis determined that the ColoTest had an AUC of 0.97 for distinguishing stable from progressive disease. The z-statistic for distinguishing controls was 20.6. Further evaluation of the cohort determined that patients who showed disease progression despite therapy showed higher scores than those who responded to therapy ( Figure 8 ). Therapies included bevacizumab, chemotherapy, and EGFR TKI inhibitors. Therefore, the tool can accurately identify treatment failure in colon cancer disease.

[0188] Table 2.

[0189]

[0190]

[0191] References:

[0192]

[0193]

[0194] Equivalent solution

[0195] Although the present invention has been described in conjunction with the above specific aspects, many substitutions, modifications and other variations will be apparent to those skilled in the art. All such substitutions, modifications and variations are intended to fall within the spirit and scope of the present invention.

Claims

1. Use of a plurality of reagents specific for detecting the expression of 14 biomarkers in the preparation of a kit for detecting colon cancer in a subject in need thereof by a method comprising: determining the expression levels of the 14 biomarkers from the blood sample by contacting the blood sample from the subject with the reagent, wherein the 14 biomarkers are ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, UMPS, and a housekeeping gene, wherein the housekeeping gene is MORF4L1; normalizing the expression level of each of ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, and UMPS to the expression level of the housekeeping gene to obtain a normalized expression level of each of ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, and UMPS; inputting each normalized expression level into an algorithm to generate a score, wherein the algorithm is a prediction model generated using the XGB algorithm; comparing the score to a predetermined cutoff value, wherein the predetermined cutoff value is at least 50% on a scale of 0-100%; and When the score is equal to or greater than the predetermined cutoff value, colon cancer is identified as being present in the subject, or when the score is less than the predetermined cutoff value, colon cancer is identified as not being present in the subject.

2. Use of a plurality of reagents specific for detecting the expression of 14 biomarkers in the preparation of a kit for determining whether colon cancer in a subject is stable or progressive by a method comprising: determining the expression levels of the 14 biomarkers from the blood sample by contacting the blood sample from the subject with the reagent, wherein the 14 biomarkers are ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, UMPS, and a housekeeping gene, wherein the housekeeping gene is MORF4L1; normalizing the expression level of each of ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, and UMPS to the expression level of the housekeeping gene to obtain a normalized expression level of each of ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, and UMPS; inputting each normalized expression level into an algorithm to generate a score, wherein the algorithm is a prediction model generated using the XGB algorithm; comparing the score to a predetermined cutoff value, wherein the predetermined cutoff value is at least 50% on a scale of 0-100%; and When the score is equal to or greater than the predetermined cutoff value, the colon cancer in the subject is identified as progressive, or when the score is less than the predetermined cutoff value, the colon cancer in the subject is identified as stable.

3. Use of a plurality of reagents specific for detecting the expression of 14 biomarkers in the preparation of a kit for determining the completeness of post-surgical tumor removal in a subject with colon cancer by a method comprising: Determining the expression levels of the 14 biomarkers from the blood sample obtained from the subject after the surgical procedure by contacting the blood sample with the reagent, wherein the 14 biomarkers are ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, UMPS, and a housekeeping gene, wherein the housekeeping gene is MORF4L1; normalizing the expression level of each of ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, and UMPS to the expression level of the housekeeping gene to obtain a normalized expression level of each of ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, and UMPS; inputting each normalized expression level into an algorithm to generate a score, wherein the algorithm is a prediction model generated using the XGB algorithm; comparing the score to a predetermined cutoff value, wherein the predetermined cutoff value is at least 50% on a scale of 0-100%; and When the score is equal to or greater than the predetermined cutoff value, it is identified that the colon cancer tumor in the subject is not completely removed, or when the score is less than the predetermined cutoff value, it is identified that the colon cancer tumor in the subject is completely removed.

4. Use of a plurality of reagents specific for detecting the expression of 14 biomarkers in the preparation of a kit for evaluating a subject with colon cancer in response to a first therapy by a method comprising: (1) At the first point in time: (a) determining the expression levels of 14 biomarkers from a first blood sample from the subject by contacting the first blood sample with the reagent, wherein the 14 biomarkers are ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, UMPS, and a housekeeping gene, wherein the housekeeping gene is MORF4L1; (b) normalizing the expression level of each of ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2 and UMPS relative to the expression level of the housekeeping gene to obtain a normalized expression level 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 first score, wherein the algorithm is a prediction model generated using an XGB algorithm; (2) at a second time point, wherein the second time point is after the first time point and after administration of a therapy to the subject: (a) determining the expression levels of the at least 14 biomarkers from a second blood sample from the subject by contacting the second blood sample with the reagent, wherein the 14 biomarkers are ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2, UMPS, and a housekeeping gene, wherein the housekeeping gene is MORF4L1; (b) normalizing the expression level of each of ADRM1, CDK4, COMT, DHCR7, HMOX2, MCM2, PDXK, POP7, S100P, SNRPA, SORD, STOML2 and UMPS relative to the expression level of the housekeeping gene to obtain a normalized expression level 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 second score, wherein the algorithm is a prediction model generated using the XGB algorithm; (3) comparing the first score to the second score; and (4) when the second score is significantly lower than the first score, identifying the subject as responding to the first therapy, or when the second score is not significantly lower than the first score, identifying the subject as not responding to the first therapy.

5. The use according to any one of claims 1 to 3, wherein the predetermined cut-off value is at least 60% on a scale of 0-100%.

6. The use according to any one of claims 1 to 4, having a sensitivity greater than 85%.

7. The use according to any one of claims 1 to 4, having a specificity greater than 85%.

8. The use according to any one of claims 1 to 4, wherein at least one of the 14 biomarkers is RNA, cDNA or protein.

9. The use of claim 8, wherein when the biomarker is RNA, the RNA is reverse transcribed to produce cDNA, and the expression level of the produced cDNA is detected.

10. The use of claim 8, wherein the expression level of the biomarker is detected by forming a complex between the biomarker and a labeled probe or primer.

11. The use of any one of claims 1 to 3, wherein the predetermined cut-off value is obtained from a plurality of reference samples obtained from subjects not suffering from or not diagnosed as suffering from colon cancer.

12. The use of claim 4, wherein the first time point is prior to administering the first therapy to the subject.

13. The use of claim 4, wherein the first time point is after administration of the first therapy to the subject.

14. The use of claim 4, wherein the second fraction is significantly reduced compared to the first fraction when the second fraction is at least 25% less than the first fraction.

15. The use of claim 4, wherein the first therapy comprises an anti-cancer therapy.

16. The use of claim 4, wherein the first therapy comprises surgery, chemotherapy, targeted drug therapy, radiation therapy, immunotherapy, or any combination thereof.

17. The use of claim 4, wherein when the first therapy comprises surgery, the surgery comprises removal of polyps during colonoscopy, endoscopic mucosal resection, segmental colectomy, ostomy, removal of at least one cancerous lesion from the liver, or any combination thereof.

18. The use of claim 4, wherein the first therapy comprises chemotherapy comprising FOLFOX, FOLFIRI, a combination of 5-FU and leucovorin, capecitabine, irinotecan, CapeOx, or any combination thereof.

19. The use of claim 4, wherein the first therapy comprises 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.

20. The use of claim 4, wherein the first therapy comprises an anti-cancer therapy, wherein the anti-cancer therapy comprises an anti-colon cancer therapy.

21. The use of claim 4, wherein the first therapy comprises immunotherapy, wherein the immunotherapy comprises pembrolizumab, nivolumab, or a combination of pembrolizumab and nivolumab.

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