Marker for early detection of proliferative disorders of colonic cells

Abstract

The systems, media, compositions, methods, and kits disclosed herein relate to a panel of protein biomarkers for the early detection of proliferative disorders of colorectal cells, including colorectal cancer. The presence or level of a protein in a biological sample for the protein panel described herein can be used for classifier generation and can be used as an input in machine learning that is useful for classifying subjects in a population for the detection of proliferative disorders of colorectal cells.

Description

Technical Field

[0001] Cross-reference This application claims the benefit of U.S. Provisional Patent Application No. 63 / 348,666, filed on June 3, 2022, which is hereby incorporated by reference in its entirety.

[0002] The present disclosure relates to biomarkers and methods for the early determination of proliferative disorders of colorectal cells, including advanced adenoma and colorectal cancer.

Background Art

[0003] Colorectal cancer (CRC) is a leading cause of cancer-related death in the Western world. CRC is one of the most characterized solid tumors, yet CRC continues to be one of the leading causes of death in developed countries due to late diagnosis. Among other reasons, the late diagnosis of patients is due to the fact that diagnostic tests, such as colonoscopy, are carried out too late. Deaths from CRC can be prevented by effective early screening.

Summary of the Invention

[0004] The present disclosure provides methods and systems for protein profiling of biological samples associated with the detection of CRC and disease progression. The studies described herein enable determining a specific protein signature of CRC, indicating the presence of specific biomarkers of proliferative disorders of colorectal cells that may detect proliferative disorders of colorectal cells, stratifying patient populations, and classifying populations with high specificity and high sensitivity using plasma from subjects with proliferative disorders of colorectal cells.

[0005] In one aspect, the present disclosure provides a predetermined protein panel that characterizes the proliferative disorders of colonic cells, the panel comprising at least 4 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta 42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, Calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, Ferritin, FGF23, free PSA, Gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, Pepsinogen 1, Pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total N-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40.

[0006] In some embodiments, the panel comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total N-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40.

[0007] In some embodiments, the panel comprises CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, IL-8, sTREM-1, or a combination thereof.

[0008] In some embodiments, the panel comprises CXCL13, CYFRA21-1, FGF23, GDF-15, HE4, IL-8, or a combination thereof.

[0009] In some embodiments, the panel comprises Abeta38, Abeta40, Abeta42, Abeta 42.2, Ang-2, CA125, calcitonin, CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, hGH, IGFBP-7, IL-8, IL-6, HS IL-6, MMP-3, OPN, BNP, proBNP, NT-proBNP, total N-proBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or a combination thereof.

[0010] In some embodiments, the panel comprises Ang-2, CA125, Calcitonin, CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, hGH, IGFBP-7, IL-8, IL-6, HS IL-6, MMP-3, OPN, BNP, proBNP, NT-proBNP, total NT-proBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or a combination thereof.

[0011] In some embodiments, the panel comprises free PSA.

[0012] In some embodiments, the panel is configured to discriminate between healthy subjects, subjects with benign colorectal polyps, subjects with advanced adenomas, or subjects with colorectal cancer.

[0013] In some embodiments, the panel is configured to suggest an advanced adenoma and comprises at least 4 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta 42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, Calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, Ferritin, FGF23, free PSA, Gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, Pepsinogen 1, Pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40.

[0014] In some embodiments, the panel is configured to suggest advanced adenomas and comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta 42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, Calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, Ferritin, FGF23, Free PSA, Gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, Pepsinogen 1, Pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40.

[0015] In some embodiments, the panel is configured to suggest colorectal cancer and comprises at least 4 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta 42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, Calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, Ferritin, FGF23, Free PSA, Gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, Pepsinogen 1, Pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40.

[0016] In some embodiments, the panel is configured to suggest colorectal cancer and comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40.

[0017] In some embodiments, the proliferative disorder of colonic cells is selected from adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0018] In another aspect, the present disclosure provides a classifier configured to discriminate between a population of healthy subjects and subjects having a proliferative disorder of colorectal cells, the classifier including a set of measurements representing proteins from a predetermined protein panel indicative of a proliferative disorder of colorectal cells, the set of measurements being obtained from protein expression data from samples of healthy subjects and samples of subjects having a proliferative disorder of colorectal cells, the measurements being used to generate a set of features corresponding to characteristics of the protein expression data, the set of features being computer-processed using a machine learning or statistical model, the machine learning or statistical model providing a feature vector useful as a classifier capable of discriminating between a population of healthy subjects and subjects having a proliferative disorder of colorectal cells.

[0019] In some embodiments, the predetermined protein panel includes at least 4 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta 42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40.

[0020] In some embodiments, a given protein panel comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta 42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, Calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, Ferritin, FGF23, Free PSA, Gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, Pepsinogen 1, Pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40.

[0021] In some embodiments, the panel comprises CXCL13, CYFRA21-1, FGF23, GDF-15, HE4, IL-8, sTREM-1, or a combination thereof.

[0022] In some embodiments, the panel comprises CXCL13, CYFRA21-1, FGF23, GDF-15, HE4, IL-8, or a combination thereof.

[0023] In some embodiments, the panel comprises Abeta38, Abeta40, Abeta42, Abeta 42.2, Ang-2, CA125, Calcitonin, CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, hGH, IGFBP-7, IL-8, IL-6, HS IL-6, MMP-3, OPN, BNP, proBNP, NT-proBNP, total NT-proBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or a combination thereof.

[0024] In some embodiments, the panel comprises Ang-2, CA125, Calcitonin, CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, hGH, IGFBP-7, IL-8, IL-6, HS IL-6, MMP-3, OPN, BNP, proBNP, NT-proBNP, total NT-proBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or a combination thereof.

[0025] In some embodiments, the panel comprises measuring total PSA.

[0026] In some embodiments, the classifier is configured to discriminate between a healthy subject, a subject having a benign colorectal polyp, a subject having an advanced adenoma, or a subject having colorectal cancer.

[0027] In some embodiments, the panel is configured to suggest an advanced adenoma and comprises at least 4 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, Calcitonin, CEA, CXCL13, CYFRA21-1, DKK-3, ESM-1, Ferritin, FGF23, free PSA, Gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, Pepsinogen 1, Pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40.

[0028] In some embodiments, the panel is configured to suggest advanced adenomas and comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40.

[0029] In some embodiments, the panel is configured to suggest colorectal cancer and comprises at least 4 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta 42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40.

[0030] In some embodiments, the panel is configured to suggest colorectal cancer and comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta 42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40.

[0031] In some embodiments, a given protein panel comprises at least 4 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA21-1, ferritin, FGF23, gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL.

[0032] In some embodiments, a predetermined protein panel comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA 21-1, ferritin, FGF23, gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL.

[0033] In some embodiments, a predetermined protein panel comprises at least 4 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CEA, CXCL13, CYFRA 21-1, ferritin, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, and sFlt-1.

[0034] In some embodiments, a predetermined protein panel comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CEA, CXCL13, CYFRA21-1, ferritin, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, and sFlt-1.

[0035] In some embodiments, a predetermined protein panel comprises at least 4 proteins selected from the group consisting of AFP, AFP-L3, hGH, IL8, NSE, CEA, anti-p53, sFlt-1, ferritin, CYFRA 21-1, CA125, IGF-1, CXCL-13, and YKL.

[0036] In some embodiments, a predetermined protein panel comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, hGH, IL8, NSE, CEA, anti-p53, sFlt-1, ferritin, CYFRA 21-1, CA125, IGF-1, CXCL-13, and YKL.

[0037] In some embodiments, a predetermined protein panel comprises at least 4 proteins selected from the group consisting of CA15-13, CA-19-9, CYFRA 21-1, IL-6, IL-6hs, OPN, and total PSA.

[0038] A predetermined protein panel comprises at least 5, at least 6, or at least 7 proteins selected from the group consisting of CA15-13, CA-19-9, CYFRA 21-1, IL-6, IL-6hs, OPN, and total PSA.

[0039] In some embodiments, a predetermined protein panel comprises at least 4 proteins selected from the group consisting of AFP, AFP-L3, Ang-2, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA 21-1, DKK-3, ferritin, FGF23, gastrin-17, GDF-15, hGH, IGF-1, IGFBP-3, IL-6, IL-8, MMP-3, NSE, pepsinogen 2, PRL2, S100, sFlt-1, sTREM-1, total PSA, VEGF-A, and YKL.

[0040] In some embodiments, a predetermined protein panel comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, Ang-2, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA 21-1, DKK-3, ferritin, FGF23, gastrin-17, GDF-15, hGH, IGF-1, IGFBP-3, IL-6, IL-8, MMP-3, NSE, pepsinogen 2, PRL2, S100, sFlt-1, sTREM-1, total PSA, VEGF-A, and YKL.

[0041] In some embodiments, a predetermined protein panel comprises at least 4 proteins selected from the group consisting of AFP, AFP-L3, CA125 II, CA15-3, CEA, CXCL13, CYFRA 21-1, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, PRL2, sFlt-1, and YKL.

[0042] In some embodiments, a predetermined protein panel comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, CA125 II, CA15-3, CEA, CXCL13, CYFRA21-1, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, PRL2, sFlt-1, and YKL.

[0043] In some embodiments, the proliferative disorder of colonic cells is selected from adenoma (adenomatous polyps), polyposis disorder, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0044] In another aspect, the present disclosure provides a system comprising a classifier of a machine learning model for detecting a proliferative disorder of colonic cells, the system comprising a computer-readable medium including a classifier operable to classify a subject based on a predetermined protein panel, and one or more processors for executing instructions stored in the computer-readable medium.

[0045] In some embodiments, the system provides a classifier configured to discriminate between a population of healthy subjects and subjects having a proliferative disorder of the colorectal cells, the classifier including a set of measurements representative of proteins from a predetermined protein panel indicative of the characteristics of the proliferative disorder of the colorectal cells, the set of measurements being obtained from protein expression data from samples of healthy subjects and samples of subjects having a proliferative disorder of the colorectal cells, the measurements being used to generate a set of features corresponding to the characteristics of the protein expression data, the set of features being computer-processed using a machine learning or statistical model, the machine learning or statistical model providing a feature vector useful as a classifier capable of discriminating between a population of healthy subjects and subjects having a proliferative disorder of the colorectal cells, the classifier being loaded into the memory of a computer system, the machine learning model being trained using training data obtained from training biological samples, a first subset of the training biological samples being identified as corresponding to subjects having a proliferative disorder of the colorectal cells, and a second subset of the training biological samples being identified as corresponding to subjects not having a proliferative disorder of the colorectal cells.

[0046] In some embodiments, the panel includes at least 4 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta 42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40.

[0047] In some embodiments, the panel comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, Calcitonin, CEA, CXCL13, CYFRA21-1, DKK-3, ESM-1, Ferritin, FGF23, Free PSA, Gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, Pepsinogen 1, Pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40.

[0048] In some embodiments, a given protein panel comprises at least 4 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA 21-1, Ferritin, FGF23, Gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL.

[0049] In some embodiments, a given protein panel comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA21-1, Ferritin, FGF23, Gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL.

[0050] In some embodiments, a predetermined protein panel comprises at least 4 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CEA, CXCL13, CYFRA 21-1, ferritin, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, and sFlt-1.

[0051] In some embodiments, a predetermined protein panel comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CEA, CXCL13, CYFRA21-1, ferritin, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, and sFlt-1.

[0052] In some embodiments, a predetermined protein panel comprises at least 4 proteins selected from the group consisting of AFP, AFP-L3, hGH, IL8, NSE, CEA, anti-p53, sFlt-1, ferritin, CYFRA 21-1, CA125, IGF-1, CXCL-13, and YKL.

[0053] In some embodiments, a predetermined protein panel comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, hGH, IL8, NSE, CEA, anti-p53, sFlt-1, ferritin, CYFRA21-1, CA125, IGF-1, CXCL-13, and YKL.

[0054] In some embodiments, a predetermined protein panel comprises at least 4 proteins selected from the group consisting of CA15-13, CA-19-9, CYFRA 21-1, IL-6, IL-6hs, OPN, and total PSA.

[0055] In some embodiments, a given protein panel comprises at least 5, at least 6, or at least 7 proteins selected from the group consisting of CA15-13, CA-19-9, CYFRA21-1, IL-6, IL-6hs, OPN, and total PSA.

[0056] In some embodiments, a given protein panel comprises at least 4 proteins selected from the group consisting of AFP, AFP-L3, Ang-2, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA21-1, DKK-3, ferritin, FGF23, gastrin-17, GDF-15, hGH, IGF-1, IGFBP-3, IL-6, IL-8, MMP-3, NSE, pepsinogen 2, PRL2, S100, sFlt-1, sTREM-1, total PSA, VEGF-A, and YKL.

[0057] In some embodiments, a given protein panel comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, Ang-2, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA21-1, DKK-3, ferritin, FGF23, gastrin-17, GDF-15, hGH, IGF-1, IGFBP-3, IL-6, IL-8, MMP-3, NSE, pepsinogen 2, PRL2, S100, sFlt-1, sTREM-1, total PSA, VEGF-A, and YKL.

[0058] In some embodiments, a given protein panel comprises at least 4 proteins selected from the group consisting of AFP, AFP-L3, CA125 II, CA15-3, CEA, CXCL13, CYFRA21-1, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, PRL2, sFlt-1, and YKL.

[0059] In some embodiments, a predetermined protein panel comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, CA125 II, CA15-3, CEA, CXCL13, CYFRA21-1, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, PRL2, sFlt-1, and YKL.

[0060] In some embodiments, a machine learning model is trained using training data obtained from training biological samples, wherein a first subset of the training biological samples is identified as corresponding to subjects having a proliferative disorder of colonic cells, and a second subset of the training biological samples is identified as corresponding to subjects not having a proliferative disorder of colonic cells.

[0061] In some embodiments, a classifier is provided in a system for detecting a proliferative disorder of colonic cells, the system comprising: a) a computer-readable medium including a classifier operable to classify a subject based on a protein signature panel; and b) one or more processors for executing instructions stored in the computer-readable medium.

[0062] In some embodiments, the system comprises a classification circuit configured as a machine learning classifier selected from the group consisting of a deep learning classifier, a neural network classifier, a linear discriminant analysis (LDA) classifier, a quadratic discriminant analysis (QDA) classifier, a support vector machine (SVM) classifier, a random forest (RF) classifier, a k-nearest neighbor search, a linear kernel support vector machine classifier, a first or second order polynomial kernel support vector machine classifier, a ridge regression classifier, an elastic net algorithm classifier, a sequential minimal optimization algorithm classifier, a naive bayes algorithm classifier, and a principal component analysis classifier.

[0063] In another aspect, the present disclosure provides a method for determining a protein profile of a biological sample from a subject, the method comprising a) obtaining a biological sample containing a protein derived from a subject; b) measuring the amount of protein from a predetermined panel of proteins comprising at least 4 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta 42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, YKL40 in the biological sample, thereby providing a protein profile of the subject;

[0064] In some embodiments, a predetermined panel of proteins comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta 42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, Calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, Ferritin, FGF23, Free PSA, Gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, Pepsinogen 1, Pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total-NTproBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40.

[0065] In some embodiments, the panel comprises CXCL13, CYFRA21-1, FGF23, GDF-15, HE4, IL-8, sTREM-1, or a combination thereof.

[0066] In some embodiments, the panel comprises CXCL13, CYFRA21-1, FGF23, GDF-15, HE4, IL-8, or a combination thereof.

[0067] In some embodiments, the panel comprises Abeta38, Abeta40, Abeta42, Abeta42.2, Ang-2, CA125, Calcitonin, CXCL13, CYFRA21-1, FGF23, GDF-15, HE4, hGH, IGFBP-7, IL-8, IL-6, HS IL-6, MMP-3, OPN, BNP, proBNP, NT-proBNP, total N-proBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or a combination thereof.

[0068] In some embodiments, the panel comprises Ang-2, CA125, calcitonin, CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, hGH, IGFBP-7, IL-8, IL-6, HS IL-6, MMP-3, OPN, BNP, proBNP, NT-proBNP, total NT-proBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or a combination thereof.

[0069] In some embodiments, the panel comprises total PSA.

[0070] In some embodiments, the protein profile is associated with a proliferative disorder of colonic cells and provides a classification of a subject as having a proliferative disorder of colonic cells.

[0071] In some embodiments, the biological sample from the subject is selected from the group consisting of body fluids, feces, colonic effluent, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsies, and combinations thereof.

[0072] In some embodiments, the proliferative disorder of colonic cells is selected from adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0073] In some embodiments, the method further comprises treating the subject with surgery and / or a therapeutic agent based on the protein profile of the subject.

[0074] In some embodiments, the method further includes treating the subject based on the detected proliferative disorder of the colorectal cells. In some embodiments, the treatment includes chemotherapy, radiation therapy, immunotherapy, or surgery.

[0075] In another aspect, the present disclosure provides a method for detecting a proliferative disorder of colorectal cells in a subject, the method comprising: a) obtaining a biological sample containing a protein from the subject; b) measuring the amount of proteins from a predetermined protein panel comprising at least 4 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta 42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40 in the biological sample, thereby providing a protein profile of the subject; c) using a machine learning model trained to discriminate between a healthy subject and a subject having a cell proliferative disorder to computer-process the protein profile to provide an output value associated with the presence or absence of a proliferative disorder of the colorectal cells, thereby suggesting the presence or absence of a proliferative disorder of the colorectal cells in the subject.

[0076] In some embodiments, a predetermined panel of proteins comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta 42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total-NTproBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40.

[0077] In some embodiments, the panel comprises CXCL13, CYFRA21-1, FGF23, GDF-15, HE4, IL-8, sTREM-1, or a combination thereof.

[0078] In some embodiments, the panel comprises CXCL13, CYFRA21-1, FGF23, GDF-15, HE4, IL-8, or a combination thereof.

[0079] In some embodiments, the panel comprises Abeta38, Abeta40, Abeta42, Abeta42.2, Ang-2, CA125, calcitonin, CXCL13, CYFRA21-1, FGF23, GDF-15, HE4, hGH, IGFBP-7, IL-8, IL-6, HS IL-6, MMP-3, OPN, BNP, proBNP, NT-proBNP, total N-proBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or a combination thereof.

[0080] In some embodiments, the panel comprises Ang-2, CA125, Calcitonin, CXCL13, CYFRA21-1, FGF23, GDF-15, HE4, hGH, IGFBP-7, IL-8, IL-6, HS IL-6, MMP-3, OPN, BNP, proBNP, NT-proBNP, total NT-proBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or a combination thereof.

[0081] In some embodiments, the panel comprises measuring total PSA.

[0082] In some embodiments, the protein profile is associated with a proliferative disorder of colon cells and provides a classification of a subject as having a proliferative disorder of colon cells.

[0083] In some embodiments, the biological sample from the subject is selected from the group consisting of body fluid, feces, colonic effluent, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

[0084] In some embodiments, the proliferative disorder of colon cells is selected from adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0085] In some embodiments, the method further comprises treating the subject with surgery and / or a therapeutic agent based on the protein profile of the subject.

[0086] In some embodiments, the method further comprises treating the subject based on the detected proliferative disorder of the colorectal cells. In some embodiments, the treatment includes chemotherapy, radiation therapy, immunotherapy, or surgery.

[0087] In another aspect, a method for monitoring minimal residual disease in a subject previously treated for a disease is provided, the method comprising: a) determining a protein profile of a biological sample from the subject using a panel of at least 4 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta 42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40, thereby generating a baseline protein state; b) determining a protein profile of a biological sample obtained from the subject at one or more time points after generation of the baseline protein state, thereby generating a current protein state; c) determining a difference between the baseline protein state and the current protein state, thereby detecting a change in minimal residual disease in the subject. comprising.

[0088] In some embodiments, minimal residual disease is selected from the group consisting of response to treatment, tumor burden, residual tumor after surgery, recurrence, secondary screening, primary screening, and cancer progression. In some embodiments, the method further comprises administering a treatment to the subject based on a change in the detected minimal residual disease in the subject. In some embodiments, the treatment comprises chemotherapy, radiation therapy, immunotherapy, or surgery. The treatment is selected (e.g., from among a plurality of possible treatment options) and administered to the subject based at least in part on the protein profile of the subject and / or a set of the subject's biological traits. The biological trait can be a measurement, a diagnosis, a prognosis, or a prediction (e.g., determined using a trained machine learning classifier).

[0089] In some embodiments, the biological trait comprises malignancy. In some embodiments, the biological trait comprises cancer type. In some embodiments, the biological trait comprises cancer stage. In some embodiments, the biological trait comprises cancer classification. In some embodiments, the cancer classification comprises cancer grade. In some embodiments, the cancer classification comprises tissue classification. In some embodiments, the biological trait comprises a metabolic profile. In some embodiments, the biological trait comprises a mutation. In some embodiments, the mutation is a disease-related mutation. In some embodiments, the biological trait comprises clinical outcome. In some embodiments, the biological trait comprises drug response.

[0090] In another aspect, a method for determining a subject's response to a treatment is provided, the method comprising a) Using a panel of at least 4 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40, determining the protein profile of a biological sample from a subject, thereby generating a baseline protein state; b) After generating the baseline protein state, determining the protein profile of a biological sample obtained from the subject at one or more time points, thereby generating a current protein state; c) Determining the difference between the baseline protein state and the current protein state, thereby determining the response of the subject to the treatment. A method is provided that includes the above steps.

[0091] In some embodiments, the method further includes administering treatment to the subject based on the determined response of the subject to the treatment. In some embodiments, the treatment includes chemotherapy, radiation therapy, immunotherapy, or surgery.

[0092] In another aspect, a method for determining the tumor burden of a subject is provided, the method comprising: a) Determining a protein profile of a biological sample derived from a subject using a panel of at least 4 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40, thereby generating a baseline protein state; b) After generating the baseline protein state, determining a protein profile of a biological sample obtained from the subject at one or more time points, thereby generating a current protein state; c) Determining the difference between the baseline protein state and the current protein state, thereby determining the tumor burden of the subject; and including.

[0093] In some embodiments, the method further includes treating the subject based on the subject's tumor burden. In some embodiments, the treatment includes chemotherapy, radiation therapy, immunotherapy, or surgery.

[0094] In another aspect, a method for detecting residual tumor after surgery of a subject is provided, the method comprising: a) Using a panel of at least 4 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40, determining the protein profile of a biological sample from a subject, thereby generating a baseline protein state; b) After generating the baseline protein state, determining the protein profile of a biological sample obtained from the subject at one or more time points, thereby generating a current protein state; c) Determining the difference between the baseline protein state and the current protein state, thereby detecting residual tumor after surgery on the subject. Including.

[0095] In some embodiments, the method further includes administering treatment to the subject based on the residual tumor detected after surgery on the subject. In some embodiments, the treatment includes chemotherapy, radiotherapy, immunotherapy, or surgery.

[0096] In another aspect, a method for detecting recurrence of a subject is provided, the method comprising: a) Determining the protein profile of a biological sample from a subject using a panel of at least 4 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40, thereby generating a baseline protein state; b) After generating the baseline protein state, determining the protein profile of a biological sample obtained from the subject at one or more time points, thereby generating a current protein state; c) Determining the difference between the baseline protein state and the current protein state, thereby determining recurrence of the subject; comprising.

[0097] In some embodiments, the method further comprises administering treatment to the subject based on the detected recurrence of the subject. In some embodiments, the treatment includes chemotherapy, radiation therapy, immunotherapy, or surgery.

[0098] In another aspect, a method for performing secondary screening is provided, at least partially based on the protein profile of a subject.

[0099] In another aspect, a method for performing primary screening is provided, at least partially based on the protein profile of a subject.

[0100] In another aspect, a method for monitoring the progression of a target cancer is provided, the method comprising: a) determining a protein profile of a biological sample from a subject using at least a panel of 4 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40, thereby generating a baseline protein state; b) after generating the baseline protein state, determining a protein profile of a biological sample obtained from the subject at one or more time points, thereby generating a current protein state; c) determining the difference between the baseline protein state and the current protein state, thereby determining the progression of the target cancer.

[0101] In some embodiments, the panel comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, Calcitonin, CEA, CXCL13, CYFRA21-1, DKK-3, ESM-1, Ferritin, FGF23, Free PSA, Gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, Pepsinogen 1, Pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40.

[0102] In another aspect, the present disclosure provides a predetermined protein panel that characterizes a proliferative disorder of colon cells, the panel comprising at least 4 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA21-1, Ferritin, FGF23, Gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL.

[0103] In some embodiments, the panel comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA21-1, Ferritin, FGF23, Gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL.

[0104] In some embodiments, the panel is configured to discriminate between healthy subjects, subjects having benign colorectal polyps, subjects having advanced adenomas, or subjects having colorectal cancer.

[0105] In some embodiments, the proliferative disorder of the colorectal cells is selected from the group consisting of adenomas (adenomatous polyps), polyposis disorders, Lynch syndrome, sessile serrated adenomas (SSA), advanced adenomas, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumors, gastrointestinal carcinoid tumors, gastrointestinal stromal tumors (GIST), lymphomas, and sarcomas.

[0106] In another aspect, the present disclosure provides a method for determining a protein profile of a biological sample from a subject, the method comprising: (a) obtaining a biological sample containing proteins from the subject; and (b) measuring the amount of proteins from a predetermined panel of proteins in the biological sample, the proteins including at least 4 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA21-1, ferritin, FGF23, gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL. and

[0107] In some embodiments, the panel comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA21-1, ferritin, FGF23, gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL.

[0108] In some embodiments, the protein profile is associated with a proliferative disorder of colon cells and provides a classification of a subject as having a proliferative disorder of colon cells.

[0109] In some embodiments, the biological sample from the subject is selected from the group consisting of body fluids, feces, colonic effluent, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsies, and combinations thereof.

[0110] In some embodiments, the proliferative disorder of colon cells is selected from the group consisting of adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0111] In some embodiments, the method further comprises c) treating the subject with surgery and / or a therapeutic agent based on the protein profile of the subject.

[0112] In another aspect, the present disclosure provides a method for detecting a proliferative disorder of colonic cells in a subject, the method comprising: (a) obtaining a biological sample containing a protein derived from the subject; (b) measuring the amount of a protein from a predetermined protein panel comprising at least 4 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA 21-1, ferritin, FGF23, gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL in the biological sample, thereby providing a protein profile of the subject; (c) computer-processing the protein profile using a machine learning model trained to discriminate between a healthy subject and a subject having a proliferative disorder of colonic cells to provide an output value associated with the presence or absence of a proliferative disorder of colonic cells, thereby suggesting the presence or absence of a proliferative disorder of colonic cells in the subject; and comprising.

[0113] In some embodiments, the predetermined panel of proteins comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA21-1, ferritin, FGF23, gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL.

[0114] In some embodiments, the protein profile is associated with a proliferative disorder of colonic cells and provides a classification of the subject as having a proliferative disorder of colonic cells.

[0115] In some embodiments, the biological sample from the subject is selected from the group consisting of body fluid, feces, colonic effluent, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

[0116] In some embodiments, the proliferative disorder of colonic cells is selected from the group consisting of adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0117] In some embodiments, the method further comprises d) treating the subject with surgery and / or therapeutic agents based on the protein profile of the subject.

[0118] In another aspect, the present disclosure provides a predetermined protein panel that characterizes a proliferative disorder of colonic cells, the panel comprising at least 4 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CEA, CXCL13, CYFRA21-1, ferritin, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, and sFlt-1.

[0119] In some embodiments, the panel comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CEA, CXCL13, CYFRA21-1, ferritin, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, and sFlt-1.

[0120] In some embodiments, the panel is configured to discriminate between healthy subjects, subjects having benign colorectal polyps, subjects having advanced adenomas, or subjects having colorectal cancer.

[0121] In some embodiments, the proliferative disorder of the colorectal cells is selected from the group consisting of adenomas (adenomatous polyps), polyposis disorders, Lynch syndrome, sessile serrated adenomas (SSA), advanced adenomas, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumors, gastrointestinal carcinoid tumors, gastrointestinal stromal tumors (GIST), lymphomas, and sarcomas.

[0122] In another aspect, the present disclosure provides a method for determining a protein profile of a biological sample derived from a subject, the method comprising: (a) obtaining a biological sample containing proteins derived from the subject; and (b) measuring the amount of proteins from a predetermined panel of proteins that includes at least 4 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CEA, CXCL13, CYFRA21-1, ferritin, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, and sFlt-1 in the biological sample. The method includes.

[0123] In some embodiments, the panel includes at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CEA, CXCL13, CYFRA21-1, ferritin, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, and sFlt-1.

[0124] In some embodiments, the protein profile is associated with a proliferative disorder of colonic cells and provides a classification of a subject as having a proliferative disorder of colonic cells.

[0125] In some embodiments, the biological sample from the subject is selected from the group consisting of a body fluid, feces, colonic effluent, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, a tissue biopsy, and combinations thereof.

[0126] In some embodiments, the proliferative disorder of colonic cells is selected from the group consisting of an adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0127] In some embodiments, the method further comprises c) treating the subject with surgery and / or a therapeutic agent based on the protein profile of the subject.

[0128] In another aspect, the present disclosure provides a method for detecting a proliferative disorder of colonic cells in a subject, the method comprising (a) obtaining a biological sample containing proteins from the subject; (b) A step of measuring the amount of proteins from a predetermined protein panel comprising at least 4 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CEA, CXCL13, CYFRA 21-1, ferritin, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, and sFlt-1 in a biological sample, thereby providing a protein profile of the subject. (c) A step of computer-processing the protein profile using a machine learning model trained to discriminate between a healthy subject and a subject having a proliferative disorder of colorectal cells to provide an output value associated with the presence or absence of a proliferative disorder of colorectal cells, thereby suggesting the presence or absence of a proliferative disorder of colorectal cells in the subject. (including)

[0129] In some embodiments, the predetermined panel of proteins comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CEA, CXCL13, CYFRA 21-1, ferritin, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, and sFlt-1.

[0130] In some embodiments, the protein profile is associated with a proliferative disorder of colorectal cells and provides a classification of the subject as having a proliferative disorder of colorectal cells.

[0131] In some embodiments, the biological sample derived from the subject is selected from the group consisting of body fluids, feces, colorectal effluent, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsies, and combinations thereof.

[0132] In some embodiments, the proliferative disorder of colonic cells is selected from the group consisting of adenomas (adenomatous polyps), polyposis disorders, Lynch syndrome, sessile serrated adenomas (SSA), advanced adenomas, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumors, gastrointestinal carcinoid tumors, gastrointestinal stromal tumors (GISTs), lymphomas, and sarcomas.

[0133] In some embodiments, the method further comprises d) treating the subject with surgery and / or a therapeutic agent based on the protein profile of the subject.

[0134] In another aspect, the disclosure provides a predetermined protein panel that characterizes a proliferative disorder of colonic cells, the panel comprising at least 4 proteins selected from the group consisting of AFP, AFP-L3, hGH, IL8, NSE, CEA, anti-p53, sFlt-1, ferritin, CYFRA21-1, CA125, IGF-1, CXCL-13, and YKL.

[0135] In some embodiments, the panel comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, hGH, IL8, NSE, CEA, anti-p53, sFlt-1, ferritin, CYFRA21-1, CA125, IGF-1, CXCL-13, and YKL.

[0136] In some embodiments, the panel is configured to discriminate between a healthy subject, a subject having a benign colonic polyp, a subject having an advanced adenoma, or a subject having colorectal cancer.

[0137] In some embodiments, the proliferative disorder of colonic cells is selected from the group consisting of adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0138] In another aspect, the present disclosure provides a method for determining a protein profile of a biological sample from a subject, the method comprising (a) obtaining a biological sample containing proteins from the subject; (b) measuring the amount of proteins from a predetermined panel of proteins that includes at least 4 proteins selected from the group consisting of AFP, AFP-L3, hGH, IL8, NSE, CEA, anti-p53, sFlt-1, ferritin, CYFRA21-1, CA125, IGF-1, CXCL-13, and YKL in the biological sample; and

[0139] In some embodiments, the panel includes at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, hGH, IL8, NSE, CEA, anti-p53, sFlt-1, ferritin, CYFRA21-1, CA125, IGF-1, CXCL-13, and YKL.

[0140] In some embodiments, the protein profile is associated with a proliferative disorder of colonic cells and provides a classification of the subject as having a proliferative disorder of colonic cells.

[0141] In some embodiments, the biological sample from the subject is selected from the group consisting of body fluids, feces, colonic effluent, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsies, and combinations thereof.

[0142] In some embodiments, the proliferative disorder of colonic cells is selected from the group consisting of adenomas (adenomatous polyps), polyposis disorders, Lynch syndrome, sessile serrated adenomas (SSA), advanced adenomas, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumors, gastrointestinal carcinoid tumors, gastrointestinal stromal tumors (GIST), lymphomas, and sarcomas.

[0143] In some embodiments, the method further comprises c) treating the subject with surgery and / or therapeutic agents based on the protein profile of the subject.

[0144] In another aspect, the present disclosure provides a method for detecting a proliferative disorder of colonic cells in a subject, the method comprising: (a) obtaining a biological sample containing a protein from the subject; (b) measuring the amount of a protein from a predetermined protein panel comprising at least 4 proteins selected from the group consisting of AFP, AFP-L3, hGH, IL8, NSE, CEA, anti-p53, sFlt-1, ferritin, CYFRA21-1, CA125, IGF-1, CXCL-13, and YKL in the biological sample, thereby providing a protein profile of the subject; (c) processing the protein profile by computer using a machine learning model trained to be able to discriminate between a healthy subject and a subject having a proliferative disorder of colorectal cells, thereby providing an output value associated with the presence or absence of a proliferative disorder of colorectal cells, thereby suggesting the presence or absence of a proliferative disorder of colorectal cells in the subject including.

[0145] In some embodiments, the predetermined panel of proteins comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, hGH, IL8, NSE, CEA, anti-p53, sFlt-1, ferritin, CYFRA21-1, CA125, IGF-1, CXCL-13, and YKL.

[0146] In some embodiments, the protein profile is associated with a proliferative disorder of colorectal cells and provides a classification of the subject as having a proliferative disorder of colorectal cells.

[0147] In some embodiments, the biological sample from the subject is selected from the group consisting of body fluid, feces, colorectal effluent, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

[0148] In some embodiments, the proliferative disorder of colorectal cells is selected from the group consisting of adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0149] In some embodiments, the method further comprises d) treating the subject with surgery and / or therapeutic agents based on the protein profile of the subject.

[0150] In another aspect, the present disclosure provides a predetermined protein panel that characterizes a proliferative disorder of colorectal cells, the panel comprising at least 4 proteins selected from the group consisting of CA15-13, CA-19-9, CYFRA21-1, IL-6, IL-6hs, OPN, and total PSA.

[0151] In some embodiments, the panel comprises at least 5, at least 6, or at least 7 proteins selected from the group consisting of CA15-13, CA-19-9, CYFRA21-1, IL-6, IL-6hs, OPN, and total PSA.

[0152] In some embodiments, the panel is configured to discriminate between healthy subjects, subjects with benign colorectal polyps, subjects with advanced adenomas, or subjects with colorectal cancer.

[0153] In some embodiments, the proliferative disorder of colorectal cells is selected from the group consisting of adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0154] In another aspect, the present disclosure provides a method for determining a protein profile of a biological sample from a subject, the method comprising (a) Obtaining a biological sample containing a protein derived from a subject; (b) Measuring the amount of protein from a predetermined panel of proteins including at least 4 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA21-1, ferritin, FGF23, gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL in the biological sample; comprising.

[0155] In some embodiments, the panel comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA21-1, ferritin, FGF23, gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL.

[0156] In some embodiments, the protein profile is associated with a proliferative disorder of colon cells and provides a classification of the subject as having a proliferative disorder of colon cells.

[0157] In some embodiments, the biological sample derived from the subject is selected from the group consisting of body fluid, feces, colon effluent, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

[0158] In some embodiments, the proliferative disorder of colonic cells is selected from the group consisting of adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0159] In some embodiments, the method further comprises the step of (c) treating the subject with surgery and / or a therapeutic agent based on the protein profile of the subject.

[0160] In another aspect, the present disclosure provides a method for detecting a proliferative disorder of colonic cells in a subject, the method comprising: (a) obtaining a biological sample containing proteins from the subject; (b) measuring the amount of proteins from a predetermined protein panel comprising at least 4 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA21-1, ferritin, FGF23, gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL in the biological sample, thereby providing a protein profile of the subject; (c) computer-processing the protein profile using a machine learning model trained to distinguish between a healthy subject and a subject having a proliferative disorder of colonic cells to provide an output value associated with the presence or absence of a proliferative disorder of colonic cells, thereby suggesting the presence or absence of a proliferative disorder of colonic cells in the subject.

[0161] In some embodiments, a predetermined panel of proteins comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA21-1, ferritin, FGF23, gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL.

[0162] In some embodiments, the protein profile is associated with a proliferative disorder of colonic cells and provides a classification of a subject as having a proliferative disorder of colonic cells.

[0163] In some embodiments, a biological sample from a subject is selected from the group consisting of body fluid, feces, colonic effluent, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

[0164] In some embodiments, the proliferative disorder of colonic cells is selected from the group consisting of adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0165] In some embodiments, the method further comprises d) treating the subject with surgery and / or a therapeutic agent based on the protein profile of the subject.

[0166] In another aspect, the present disclosure provides a predetermined protein panel that characterizes a proliferative disorder of colonic cells, the panel comprising at least 4 proteins selected from the group consisting of AFP, AFP-L3, Ang-2, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA21-1, DKK-3, ferritin, FGF23, gastrin-17, GDF-15, hGH, IGF-1, IGFBP-3, IL-6, IL-8, MMP-3, NSE, pepsinogen 2, PRL2, S100, sFlt-1, sTREM-1, total PSA, VEGF-A, and YKL.

[0167] In some embodiments, the panel comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, Ang-2, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA21-1, DKK-3, ferritin, FGF23, gastrin-17, GDF-15, hGH, IGF-1, IGFBP-3, IL-6, IL-8, MMP-3, NSE, pepsinogen 2, PRL2, S100, sFlt-1, sTREM-1, total PSA, VEGF-A, and YKL.

[0168] In some embodiments, the panel is configured to discriminate between healthy subjects, subjects having benign colonic polyps, subjects having advanced adenomas, or subjects having colorectal cancer.

[0169] In some embodiments, the proliferative disorder of the large intestine cells is selected from the group consisting of adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0170] In another aspect, the present disclosure provides a method for determining the protein profile of a biological sample from a subject, the method comprising: (a) obtaining a biological sample containing proteins from the subject; and (b) measuring the amount of proteins from a predetermined panel of proteins in the biological sample, the panel comprising at least 4 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA21-1, ferritin, FGF23, gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL. The method includes the steps above.

[0171] In some embodiments, the panel comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA21-1, ferritin, FGF23, gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL.

[0172] In some embodiments, the protein profile is associated with a proliferative disorder of colon cells and provides a classification of a subject as having a proliferative disorder of colon cells.

[0173] In some embodiments, a biological sample from a subject is selected from the group consisting of a body fluid, feces, colonic effluent, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, a tissue biopsy, and combinations thereof.

[0174] In some embodiments, the proliferative disorder of colon cells is selected from the group consisting of an adenoma (adenomatous polyp), a polyposis disorder, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0175] In some embodiments, the method further comprises c) treating the subject with surgery and / or a therapeutic agent based on the protein profile of the subject.

[0176] In another aspect, the present disclosure provides a method for detecting a proliferative disorder of colon cells in a subject, the method comprising (a) obtaining a biological sample containing proteins from the subject; and (b) A step of measuring the amount of a protein from a predetermined protein panel comprising at least 4 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA21-1, ferritin, FGF23, gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL, thereby providing a protein profile of the subject. (c) A step of computer-processing the protein profile using a machine learning model trained to distinguish between a healthy subject and a subject having a proliferative disorder of colorectal cells, thereby providing an output value associated with the presence or absence of a proliferative disorder of colorectal cells, and thereby indicating the presence or absence of a proliferative disorder of colorectal cells in the subject.

[0177] In some embodiments, the predetermined panel of proteins comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA21-1, ferritin, FGF23, gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL.

[0178] In some embodiments, the protein profile is associated with a proliferative disorder of colorectal cells and provides a classification of the subject as having a proliferative disorder of colorectal cells.

[0179] In some embodiments, the biological sample from the subject is selected from the group consisting of body fluids, feces, colorectal effluent, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsies, and combinations thereof.

[0180] In some embodiments, the proliferative disorder of colonic cells is selected from the group consisting of adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0181] In some embodiments, the method further comprises d) treating the subject with surgery and / or a therapeutic agent based on the protein profile of the subject.

[0182] In another aspect, the present disclosure provides a predetermined protein panel indicative of a proliferative disorder of colonic cells, the panel comprising at least 4 proteins selected from the group consisting of AFP, AFP-L3, CA125 II, CA15-3, CEA, CXCL13, CYFRA21-1, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, PRL2, sFlt-1, and YKL.

[0183] In some embodiments, the panel comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, CA125 II, CA15-3, CEA, CXCL13, CYFRA21-1, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, PRL2, sFlt-1, and YKL.

[0184] In some embodiments, the panel is configured to discriminate between a healthy subject, a subject having a benign colonic polyp, a subject having an advanced adenoma, or a subject having colorectal cancer.

[0185] In some embodiments, the proliferative disorder of the large intestine cells is selected from the group consisting of adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0186] In another aspect, the present disclosure provides a method for determining the protein profile of a biological sample derived from a subject, the method comprising: (a) obtaining a biological sample containing a protein derived from the subject; and (b) measuring the amount of protein from a predetermined panel of proteins comprising at least 4 proteins selected from the group consisting of AFP, AFP-L3, CA125 II, CA15-3, CEA, CXCL13, CYFRA21-1, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, PRL2, sFlt-1, and YKL in the biological sample. The method includes the steps above.

[0187] In some embodiments, the panel comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, CA125 II, CA15-3, CEA, CXCL13, CYFRA21-1, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, PRL2, sFlt-1, and YKL.

[0188] In some embodiments, the protein profile is associated with a proliferative disorder of colon cells and provides a classification of a subject as having a proliferative disorder of colon cells.

[0189] In some embodiments, a biological sample from a subject is selected from the group consisting of a body fluid, feces, colonic effluent, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, a tissue biopsy, and combinations thereof.

[0190] In some embodiments, the proliferative disorder of colon cells is selected from the group consisting of an adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0191] In some embodiments, the method further comprises (c) treating the subject with surgery and / or a therapeutic agent based on the protein profile of the subject.

[0192] In another aspect, the present disclosure provides a method for detecting a proliferative disorder of colon cells in a subject, the method comprising: (a) obtaining a biological sample containing proteins from the subject; (b) measuring the amount of proteins from a predetermined protein panel comprising at least 4 proteins selected from the group consisting of AFP, AFP-L3, CA125 II, CA15-3, CEA, CXCL13, CYFRA21-1, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, PRL2, sFlt-1, and YKL in a biological sample; thereby providing a protein profile of the subject; (c) computer-processing the protein profile using a machine learning model trained to distinguish between a healthy subject and a subject having a proliferative disorder of colorectal cells to provide an output value associated with the presence or absence of a proliferative disorder of colorectal cells, thereby suggesting the presence or absence of a proliferative disorder of colorectal cells in the subject; comprising.

[0193] In some embodiments, the predetermined panel of proteins comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, CA125 II, CA15-3, CEA, CXCL13, CYFRA21-1, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, PRL2, sFlt-1, and YKL.

[0194] In some embodiments, the protein profile is associated with a proliferative disorder of colorectal cells and provides a classification of the subject as having a proliferative disorder of colorectal cells.

[0195] In some embodiments, the biological sample derived from the subject is selected from the group consisting of body fluids, feces, colorectal effluent, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsies, and combinations thereof.

[0196] In some embodiments, the proliferative disorder of the large intestine cells is selected from the group consisting of adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0197] In some embodiments, the method further comprises the step of treating the subject with surgery and / or a therapeutic agent based on the protein profile of the subject.

[0198] In some embodiments, the method further comprises the step of treating the subject based on the progression of the monitored cancer of the subject. In some embodiments, the treatment includes chemotherapy, radiation therapy, immunotherapy, or surgery.

[0199] In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a sensitivity of at least about 25%. In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a sensitivity of at least about 30%. In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a sensitivity of at least about 35%. In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a sensitivity of at least about 40%. In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a sensitivity of at least about 50%. In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a sensitivity of at least about 60%. In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a sensitivity of at least about 70%. In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a sensitivity of at least about 80%. In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a sensitivity of at least about 90%. In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a sensitivity of at least about 95%.

[0200] In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a positive predictive value (PPV) of at least about 5%. In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a positive predictive value (PPV) of at least about 10%. In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a positive predictive value (PPV) of at least about 15%. In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a positive predictive value (PPV) of at least about 20%. In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a positive predictive value (PPV) of at least about 25%. In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a positive predictive value (PPV) of at least about 30%. In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a positive predictive value (PPV) of at least about 40%. In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a positive predictive value (PPV) of at least about 50%. In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a positive predictive value (PPV) of at least about 60%. In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a positive predictive value (PPV) of at least about 70%. In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a positive predictive value (PPV) of at least about 80%. In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a positive predictive value (PPV) of at least about 90%. In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a positive predictive value (PPV) of at least about 95%. In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a positive predictive value (PPV) of at least about 99%.

[0201] In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a negative predictive value (NPV) of at least about 40%. In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a negative predictive value (NPV) of at least about 50%. In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a negative predictive value (NPV) of at least about 60%. In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a negative predictive value (NPV) of at least about 70%. In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a negative predictive value (NPV) of at least about 80%. In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a negative predictive value (NPV) of at least about 90%. In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a negative predictive value (NPV) of at least about 95%. In some embodiments, the protein profile indicates the presence or susceptibility of colorectal cancer with a negative predictive value (NPV) of at least about 99%.

[0202] In some embodiments, the trained algorithm determines the presence or susceptibility of colorectal cancer in a subject with an area under the curve (AUC) of at least about 0.50. In some embodiments, the trained algorithm determines the presence or susceptibility of colorectal cancer in a subject with an area under the curve (AUC) of at least about 0.60. In some embodiments, the trained algorithm determines the presence or susceptibility of colorectal cancer in a subject with an area under the curve (AUC) of at least about 0.70. In some embodiments, the trained algorithm determines the presence or susceptibility of colorectal cancer in a subject with an area under the curve (AUC) of at least about 0.80. In some embodiments, the trained algorithm determines the presence or susceptibility of colorectal cancer in a subject with an area under the curve (AUC) of at least about 0.90. In some embodiments, the trained algorithm determines the presence or susceptibility of colorectal cancer in a subject with an area under the curve (AUC) of at least about 0.95. In some embodiments, the trained algorithm determines the presence or susceptibility of colorectal cancer in a subject with an area under the curve (AUC) of at least about 0.99.

[0203] In some embodiments, the method further includes presenting a report or a graphical user interface of the user's electronic device. In some embodiments, the user is a subject, an individual, or a patient.

[0204] In some embodiments, the method further includes determining the likelihood of the presence or susceptibility of colorectal cancer in a subject, an individual, or a patient.

[0205] In some embodiments, the trained algorithm (e.g., a machine learning model or classifier) includes a supervised or semi-supervised machine learning algorithm. In some embodiments, the supervised machine learning algorithm includes a deep learning algorithm, a support vector machine (SVM), a neural network, or a random forest.

[0206] In some embodiments, the method further comprises providing a therapeutic intervention to the subject, such as a therapeutic intervention (e.g., chemotherapy, radiation therapy, immunotherapy, or surgery) for treating a patient having colorectal cancer, based at least in part on the protein profile or analysis, or administering a treatment to the subject.

[0207] In some embodiments, the method further comprises monitoring the presence or susceptibility of colorectal cancer, the monitoring comprising evaluating the presence or susceptibility of the subject's colorectal cancer at multiple time points, the evaluating being based at least on the presence or susceptibility of colorectal cancer determined at each of the multiple time points.

[0208] In some embodiments, the difference in the evaluation of the presence or susceptibility of the subject's colorectal cancer between multiple time points indicates one or more clinical implications selected from the group consisting of (i) the diagnosis of the presence or susceptibility of the subject's colorectal cancer, (ii) the prognosis of the presence or susceptibility of the subject's colorectal cancer, and (iii) the effectiveness or ineffectiveness of a treatment course for treating the presence or susceptibility of the subject's colorectal cancer.

[0209] In some embodiments, the method further comprises stratifying the subject's colorectal cancer by using an algorithm trained to determine the subtype of the subject's colorectal cancer from among a plurality of distinct subtypes or stages of colorectal cancer.

[0210] Another aspect of the present disclosure provides a computer-readable medium comprising machine-executable code that, when executed by one or more computer processors, performs any of the methods described above or elsewhere in this specification.

[0211] Another aspect of the present disclosure provides a system comprising one or more computer processors and a computer memory coupled thereto. The computer memory comprises machine-executable code that, when executed by the one or more computer processors, implements any of the methods described above or elsewhere in this specification.

[0212] Another aspect of the present disclosure provides a system comprising: a) a computer-readable medium comprising a classifier for discriminating, using a machine learning model, between a population of subjects having a proliferative disorder of colonic cells and subjects not having a proliferative disorder of colonic cells based on a protein signature panel; and b) one or more processors for executing instructions stored on the computer-readable medium.

[0213] Further aspects and advantages of the present disclosure will be readily apparent to those skilled in the art from the following detailed description, wherein only exemplary embodiments of the present disclosure are shown and described. As will be understood, the present disclosure is capable of other embodiments and different embodiments, and various details thereof can be modified in various obvious respects without departing from the present disclosure. Accordingly, the drawings and description are to be regarded as illustrative in nature and not restrictive.

[0214] Incorporation by reference All publications, patents, and patent applications mentioned in this specification are hereby incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. To the extent that the incorporated publications and patents or patent applications conflict with the disclosure contained herein, this specification is intended to supersede and / or take precedence over any such conflicting material. Brief description of the drawings

[0215] Embodiments of the present disclosure are described by way of example only with reference to the accompanying drawings. The novel features of the present invention are specifically described together with the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description, which describes exemplary embodiments in which the principles of the present invention are used, and the following accompanying drawings (also referred to herein as "figures" ("Figure" and "FIG.")).

[0216]

Figure 1

DETAILED DESCRIPTION OF THE INVENTION

[0217] Although various embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, modifications, and substitutions may be contemplated by those skilled in the art without departing from the present invention. It should be understood that various alternatives to the embodiments of the present invention described herein may be utilized.

[0218] CRC is a leading cause of cancer-related deaths in the Western world. CRC is one of the most characterized solid tumors, yet CRC continues to be one of the leading causes of death in developed countries due to late diagnosis. Among other reasons, the late diagnosis of patients is due to the fact that diagnostic tests such as colonoscopy are carried out too late. Deaths due to CRC can be prevented by effective screening. Currently, there is no universal detailed screening test or panel that aids in clinical decision-making. Cancer screening and monitoring improve survival outcomes as early detection enables the elimination of cancer before its growth and spread. In CRC, for example, colonoscopy plays a role in improving early diagnosis. Unfortunately, the patient compliance rate is low, and screening is carried out below the recommended periodicity due to the invasiveness of the procedure. The implementation of non-invasive and simpler diagnostic methods that enable the early detection of colorectal neoplasms can be based on the identification of proteins detectable in serum or plasma. The non-invasive approach can be the basis for a more compliant and earlier screening test for colorectal neoplasms.

[0219] Tumor-related antigens can be present in patients with cancer. Since tumorigenesis is associated with changes in the structure or expression of self-proteins in tumor cells, these changes can serve as potential immunological markers of cancer.

[0220] The presence of cancer and tumors in humans is also associated with the presence of proteins in the serum from patients with cancer. Even before cancer can be detected by other techniques, proteins can be detected at the early stages of the disease, indicating the potential of these proteins as biomarkers of the disease. These proteins can be any of those affected by changes in expression levels, isolated mutations, irregular folding, overexpression, abnormal glycosylation, truncated, or experiencing abnormal modifications.

[0221] Accordingly, protein biomarkers can enable the diagnosis of colorectal neoplasms, the classification of colorectal neoplasms at different stages (such as adenomas or tumor progression), the prognosis of disease progression, the assessment of disease response to treatment, and the detection of recurrence or seeding of colorectal neoplasms by a simple, effective, and non-invasive method. The diagnostic potential of proteins associated with colorectal neoplasms can be useful in the early detection, diagnosis, and prognosis of colorectal neoplasms.

[0222] The present disclosure generally relates to cancer detection, disease treatment, and disease monitoring. In particular, the present disclosure relates to the detection of cancer-related proteins in proliferative disorders of colorectal cells such as early colorectal cancer, and disease monitoring. Specifically, provided are a signature panel of circulating proteins and their use for identifying human subjects having or at risk of developing a proliferative disorder of colorectal cells, such as colorectal cancer (CRC) and / or colorectal adenoma (CA), e.g., advanced colorectal adenoma (AA). Also disclosed herein are tumor-related proteins in a subject that indicate the presence of a proliferative disorder of colorectal cells or a high risk of developing a proliferative disorder of colorectal cells, e.g., when the subject has a colorectal lesion.

[0223] Some embodiments of the disclosure provide proteins that are differentially abundant when compared to corresponding samples from subjects without a proliferative disorder of colorectal cells or subjects having a low risk of developing a proliferative disorder of colorectal cells, in a sample from a subject having a proliferative disorder of colorectal cells or a subject having a high risk of developing a proliferative disorder of colorectal cells. In some aspects, each of a subject having a high risk of developing a proliferative disorder of colorectal cells and a subject having a low risk of developing a proliferative disorder of colorectal cells has a non-invasive precursor lesion (hereinafter, a colorectal lesion) occurring within the colorectal mucosa. Proteins that are present in different abundances in samples from healthy subjects and subjects having a proliferative disorder of colorectal cells can be used as biomarkers for the diagnosis, treatment, and / or prevention of a proliferative disorder of colorectal cells.

[0224] In some embodiments, the method further comprises comparing the protein profile of a biological sample from a subject to a database of reference protein profiles from healthy subjects, and determining that the subject has an elevated risk of having a proliferative disorder of colorectal cells based at least in part on measuring a change of at least 5%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, or at least 50% in the protein expression level of the protein profile relative to the reference protein profile.

[0225] A machine learning approach can be used to characterize protein data from a biological sample to identify a panel of informative proteins. The identified panel of informative proteins for a proliferative disorder of colorectal cells can be useful for training a classifier model useful for discriminating between samples from healthy subjects and samples from subjects having a proliferative disorder of colorectal cells.

[0226] For identifying informative proteins for the methods and classifiers described herein, plasma from subjects having a proliferative disorder of colorectal cells and plasma from subjects not having a proliferative disorder of colorectal cells (control plasma or reference plasma) have been investigated for the purpose of identifying a signature panel of proteins that are produced by patients having a proliferative disorder of colorectal cells in response to the proliferative disorder of colorectal cells or that are differentially expressed in patients having a proliferative disorder of colorectal cells. Accordingly, plasma from patients having a proliferative disorder of colorectal cells and control plasma have been assayed for protein expression to identify biomarkers.

[0227] The proteins identified herein can be used to identify subjects with a proliferative disorder of colorectal cells and distinguish them from subjects without a proliferative disorder of colorectal cells, or to identify subjects at high risk of developing a proliferative disorder of colorectal cells and distinguish them from subjects at low risk of developing a proliferative disorder of colorectal cells, or to identify subjects with a precursor lesion of a proliferative disorder of colorectal cells. Thus, these proteins can be used as an auxiliary tool to guide decisions regarding the monitoring, treatment, and management of proliferative disorders of colorectal cells.

[0228] Certain other embodiments of the present disclosure provide a classifier of a machine learning model trained against the proteins described herein that are expressed in plasma samples of healthy subjects and plasma samples from subjects with a proliferative disorder of colorectal cells. Training of the machine learning model provides a classifier having a predetermined set of protein biomarkers (a "protein panel" or "signature panel") useful for classifying healthy subjects or subjects with a proliferative disorder of colorectal cells. In one example, a method is provided for a minimally invasive blood-based protein assay that can be used to assess histological severity in subjects with colorectal lesions. In another embodiment, proteins indicative of a proliferative disorder of colorectal cells are detected in cell-free samples from a subject. Body fluid samples from a subject containing cell-free molecules such as whole blood, plasma, or serum containing proteins. Thus, provided herein are proteins that can be used to discriminate the presence or absence of a proliferative disorder of colorectal cells, to discriminate high-risk or low-risk colorectal lesions that form the basis for treatment such as surgical resection, immunotherapy, radiation, or chemotherapy, and to identify low-risk colorectal lesions that can be monitored. Monitoring and confirmation of the presence of a proliferative disorder or lesion of colorectal cells can be performed, for example, by colonoscopy, ultrasound, MM, or CT scan.

[0229] In some embodiments, a predetermined panel of plasma protein biomarkers for the early detection of colorectal growth disorders and related to the early detection of CRC is disclosed herein. The predetermined protein panel can be used in a classifier that indicates a cell growth disorder such as an advanced adenoma or colorectal cancer.

[0230] In other embodiments, disclosed herein are methods related to detection, diagnosis, and treatment. Plasma derived from a patient can be screened for a predetermined panel of proteins as an indicator of a proliferative disorder of the large intestine.

[0231] Described herein are methods for screening, discriminating, and / or treating a subject having or at risk of having a proliferative disorder of colorectal cells based on the expression profile or abundance of a protein that is upregulated or overexpressed in a subject suffering from a proliferative disorder of colorectal cells. Further described herein are methods for obtaining data useful for the diagnosis and / or treatment of a proliferative disorder of colorectal cells in a subject, such as a human subject.

[0232] The proliferative disorder of colorectal cells can be of any tumor stage (e.g., TX, TO, Tis, T1, T2, T3, T4); of any regional lymph node or distant metastasis stage (e.g., NX, NO, NI, MO, M1); of any stage (e.g., stage 0 (Tis, NO, MO), stage IA (T1, NO, MO), stage IIA (T3, NO, MO), stage IIB (T1-3, N1, MO), stage III (T4, Any N, MO), or stage IV (Any T, Any N, MI)); resectable; locally advanced (unresectable); or metastatic.

[0233] Current screening tools can face issues due to false positive and false negative results, as well as issues related to specificity and sensitivity. An ideal cancer screening tool could have a high Positive Predictive Value (PPV), which minimizes unnecessary investigations (low false positives) while detecting the majority of cancers (low false negatives). Another important trade-off is the "detection sensitivity", which, unlike test sensitivity, is the lower limit of tumor detection based on size. Allowing tumors to grow to a size where they release circulating tumor markers at detectable levels undermines the goal of early detection and prevention of cancer progression. Accordingly, the present disclosure addresses the need for a highly sensitive and effective blood-based screening for the early diagnosis of colorectal cancer.

[0234] The detection of circulating tumor DNA, also referred to as "liquid biopsy", enables the detection and profiling of tumors in a non-invasive manner. The identification of tumor-specific mutations in these liquid biopsies can be used to diagnose, for example, colorectal cancer, breast cancer, and prostate cancer. However, due to the high background of normal (i.e., non-tumor-derived) DNA present in circulation, these techniques can be limited in sensitivity. Accordingly, there remains a need for a more sensitive and specific screening tool for detecting early or low tumor burden colorectal cancer tumor markers for recurrence screening and primary screening of at-risk populations. Circulating proteins against tumor-associated antigens provide another source of informative biomarkers in liquid biopsy samples that can be used in the machine learning models described herein.

[0235] The present disclosure provides methods and systems directed to profiling circulating proteins associated with proliferative disorders of colonic cells and their progression, such as colorectal cancer. Proteins that indicate the presence of a proliferative disorder of colonic cells or a high risk of developing a proliferative disorder of colonic cells can be used, for example, to diagnose, treat, or prevent the progression of a proliferative disorder of colonic cells as early as possible, such as when a subject has only a colorectal lesion. Further provided herein are kits and methods for diagnosing a proliferative disorder of colonic cells in a subject or for assessing the risk of developing a proliferative disorder of colonic cells, particularly when the subject has a colonic lesion.

[0236] In one aspect, provided herein is a method of using a panel of proteins useful for discriminating a sample from a subject based on a disease state. In other aspects, provided herein are methods, assays, and kits directed to detecting, differentiating, and discriminating a proliferative disorder of colonic cells using a panel of proteins. Non-limiting examples of proliferative disorders of colonic cells include adenomas (adenomatous polyps), polyp disorders, Lynch syndrome, sessile serrated adenomas (SSA), advanced adenomas, colorectal dysplasia, colorectal adenomas, colorectal cancer, colon cancer, rectal cancer, colorectal adenocarcinoma, carcinoid tumors, gastrointestinal carcinoid tumors, gastrointestinal stromal tumors (GIST), lymphomas, and sarcomas.

[0237] In some embodiments, the method includes the use of one or more proteins as markers for the differentiation, detection, and discrimination of a proliferative disorder of colonic cells.

[0238] Definitions As used in this specification and the claims, the singular forms "a", "an", and "the" include the plural unless the context clearly dictates otherwise. For example, the term "a nucleic acid" includes a plurality of nucleic acids and mixtures thereof.

[0239] As used in this specification, the term "subject" generally refers to an entity or medium having testable or detectable genetic information. A subject can be a human, an individual, or a patient. A subject can be a vertebrate, such as a mammal, for example. Non-limiting examples of mammals include humans, monkeys, livestock, sport animals, rodents, and pets. A subject can be a human having or suspected of having cancer. A subject can exhibit symptoms indicative of the subject's health or physiological state, such as the subject's cancer or other disease, disorder, or illness. Alternatively, a subject can be asymptomatic with respect to such a health or physiological state or condition.

[0240] As used in this specification, the term "sample" generally refers to a biological sample obtained from or derived from one or more subjects. The biological sample can be a cell-free biological sample or a substantially cell-free biological sample, or can be processed or fractionated to produce a cell-free biological sample. For example, cell-free biological samples can include cell-free ribonucleic acid (cfRNA), cell-free deoxyribonucleic acid (cfDNA), cell-free fetal DNA (cffDNA), proteins, antibodies, plasma, serum, urine, saliva, amniotic fluid, and derivatives thereof. Cell-free biological samples can be obtained or derived from a subject using an ethylenediaminetetraacetic acid (EDTA) collection tube, a cell-free RNA collection tube (e.g., Streck® RNA Complete BCT®), or a cell-free DNA collection tube (e.g., Streck® Cell-Free DNA BCT®). Cell-free biological samples can be derived from a whole blood sample by fractionation (e.g., by fractionation centrifugation). A biological sample or a derivative thereof can contain cells. For example, a biological sample can be a blood sample or a derivative thereof (e.g., blood or a blood droplet collected by a blood collection tube).

[0241] As used herein, the term "cell-free sample" generally refers to a biological sample that is substantially lacking in intact cells. A cell-free sample may be derived from a biological sample that is itself substantially cell-free or from a sample from which cells have been removed. Non-limiting examples of cell-free samples include those derived from blood, serum, plasma, urine, semen, saliva, feces, effusions, lymph, and recovered lavage.

[0242] As used herein, the term "colon cell proliferative disorder" generally refers to a disorder or disease that includes neoplastic or dysplastic cell proliferation in the colon or rectum. Non-limiting examples of colon cell proliferative disorders include adenomas (adenomatous polyps), polyposis disorders, Lynch syndrome, sessile serrated adenomas (SSA), advanced adenomas, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumors, gastrointestinal carcinoid tumors, gastrointestinal stromal tumors (GIST), lymphomas, and sarcomas. As used herein, the abbreviation "CRC" is used to identify biological samples from subjects diagnosed with colorectal cancer. As used herein, the abbreviation "AA" is used to identify samples from subjects diagnosed with at least one advanced adenoma. As used herein, the abbreviation "NAA" is used to identify samples from subjects diagnosed with a benign colorectal tumor other than an advanced adenoma or colorectal cancer.

[0243] As used herein, the term "colorectal cancer" refers to a medical condition generally characterized by cancer of the cells of the intestinal tract below the small intestine (i.e., the large intestine / colon, such as the cecum, ascending colon, transverse colon, descending colon, sigmoid colon, and rectum).

[0244] As used herein, the term "colorectal adenoma" refers to an adenoma of the large intestine, called an adenomatous polyp, which is generally a benign and pre-cancerous stage of colorectal cancer. Colorectal adenomas may exhibit a high risk of progression to colorectal cancer.

[0245] As used herein, the term "advanced colorectal adenoma" generally refers to an adenoma having a size of at least 10 mm, or an adenoma having a high histological grade of dysplasia, or an adenoma having a villous component of more than 20%.

[0246] As used herein, the terms "at risk of developing a proliferative disorder of colonic cells" or "at high risk of developing a proliferative disorder of colonic cells" generally refer to a subject who has a higher risk of developing a proliferative disorder of colonic cells in the near future compared to a subject who does not have a proliferative disorder of colonic cells or who has a low risk of developing a proliferative disorder of colonic cells in the near future. As used herein, the term "near future" refers to a period of about 1 month to about 2 years, about 6 months to about 18 months, or about 1 year.

[0247] As used herein, the terms cancer "type" and "subtype" are generally used relatively herein such that one "type" of cancer, such as breast cancer, can be a "subtype" based on, for example, stage, morphology, histology, gene expression, receptor profile, mutation profile, aggressiveness, prognosis, and malignant characteristics. Similarly, "type" and "subtype" can be applied at a finer level to differentiate, for example, one histological "type" into "subtypes" defined according to, for example, mutation profile or gene expression. Cancer "stage" is also used to refer to the classification of cancer types based on histological and pathological features regarding disease progression.

[0248] As used herein, the term "neoplasm" generally refers to any new and abnormal growth of tissue. Thus, a neoplasm can be a pre-malignant neoplasm or a malignant neoplasm. The term "neoplasm-specific marker" refers to any biological material that can be used to indicate the presence of a neoplasm. Examples of biological materials include, but are not limited to, nucleic acids, polypeptides, carbohydrates, fatty acids, cellular components (e.g., cell membranes and mitochondria), and whole cells. The term "colorectal neoplasm-specific marker" refers to any biological material that can be used to indicate the presence of a colorectal neoplasm (e.g., a pre-malignant colorectal neoplasm or a malignant colorectal neoplasm).

[0249] As used herein, the term "healthy" generally refers to a subject without impaired proliferation of colonic cells. Health is a dynamic state, but as used herein, the term refers to the pathological state of a subject lacking the disease state being referred to in a particular description. In one example, when referring to a signature panel that can classify subjects with colorectal cancer, a healthy individual, a healthy sample, or a sample from a healthy individual refers to an individual lacking colorectal cancer (CRC), advanced adenoma (AA), or non-adenomatous adenoma (NAA). As used herein, the abbreviation "NAA" is used to identify samples from individuals evaluated as negative for colorectal tumors, and thus, in some embodiments, samples identified as NAA are included in the group of healthy samples. As used herein, even if other diseases or health conditions may be present in the subject, the term "healthy" represents the absence of the disease being discussed for the purpose of comparison or classification between subjects with and without the disease state being discussed.

[0250] As used herein, the term "minimal residual disease" or "MRD" generally refers to a small number of cancer cells in the body of a subject after cancer treatment. MRD testing can be performed to determine the effectiveness of cancer treatment and to guide further treatment plans.

[0251] As used herein, the term "screening" generally refers to a test or examination of a population of subjects at risk of having colorectal cancer or colorectal adenoma, for the purpose of identifying healthy subjects, subjects with undiagnosed colorectal cancer or colorectal adenoma, or subjects at high risk of having the indication.

[0252] As used herein, the terms "minimally invasive biological sample" or "non-invasive sample" generally refer to any sample taken from a patient's body without the need for instruments other than a fine needle used to obtain blood from the subject. In some embodiments, minimally invasive biological samples include blood, serum, or plasma samples.

[0253] As used herein, the terms "up-regulated" or "overexpressed" generally refer to an increase of at least 5%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 100%, at least 110%, at least 120%, at least 130%, at least 140%, at least 150%, or more than 150% in the expression level relative to a given "threshold" or "cutoff value".

[0254] As used herein, the terms "threshold" or "cutoff value", when referring to an expression level, generally refer to a reference expression level indicating that a subject is likely to have colorectal cancer or colorectal adenoma with a given sensitivity and specificity when the subject's expression level exceeds the threshold or cutoff value or reference level.

[0255] As used herein, the term "kit" is not limited to any particular device and includes any device suitable for implementing the systems and methods of the present disclosure, such as, but not limited to, microarrays, bioarrays, biochips, biochip arrays, or bead-based assays.

[0256] Analysis of Samples Cell-free biological samples can be obtained or derived from human subjects. Prior to processing, cell-free biological samples can be stored under various storage conditions such as different temperatures (e.g., at room temperature, refrigerated, or frozen conditions (e.g., at 25°C, 4°C, -18°C, -20°C, or -80°C)), or in different suspensions (e.g., EDTA collection tubes, cell-free RNA collection tubes, or cell-free DNA collection tubes).

[0257] Cell-free biological samples can be derived from subjects with cancer, subjects suspected of having cancer, or subjects without or not suspected of having cancer.

[0258] Cell-free biological samples can be obtained before and / or after treatment of a subject with cancer. Cell-free biological samples can be derived from a subject during treatment or a treatment regimen. Multiple cell-free biological samples can be derived from a subject for monitoring the effect of treatment over time. Cell-free biological samples can be collected from a subject having or suspected of having cancer for which a definitive positive or negative diagnosis cannot be obtained by clinical examination. Samples can be collected from a subject suspected of having cancer. Cell-free biological samples can be collected from a subject experiencing symptoms of unknown origin such as fatigue, nausea, weight loss, pain and discomfort, weakness, or bleeding. Cell-free biological samples can be collected from a subject having the described symptoms. Cell-free biological samples can be collected from a subject at risk of developing cancer due to factors such as family history, age, hypertension or prehypertension, diabetes or prediabetes, overweight or obesity, environmental exposure, lifestyle risk factors (e.g., smoking, alcohol consumption, or drug use), or the presence of other risk factors.

[0259] A cell-free biological sample can contain one or more analytes that can be assayed, such as cell-free ribonucleic acid (cfRNA) molecules suitable for an assay to generate transcriptome data, cell-free deoxyribonucleic acid (cfDNA) molecules suitable for an assay to generate genomic data, protein molecules suitable for an assay to generate proteomics data, or mixtures thereof, or combinations thereof.

[0260] After obtaining a cell-free biological sample from a subject, the cell-free biological sample can be processed to generate a dataset indicative of a proliferative disorder of the subject's colorectal cells. For example, the presence, absence, or quantitative assessment of protein molecules of the cell-free biological sample in a panel of proteins. Processing of the cell-free biological sample from the subject can include (i) subjecting the cell-free biological sample to conditions sufficient to isolate, enrich, or extract a plurality of proteins, and (ii) assaying the plurality of protein molecules to generate a dataset.

[0261] A biological sample can be used directly in an assay to detect one or more proteins in order to generate a protein profile of the sample. In some embodiments, the biological sample can have the proteins concentrated prior to the assay (e.g., using protein conjugate microbeads). In some embodiments, the biological sample is a plasma sample and the proteins are concentrated. The biological sample can be assayed using various laboratory methodologies to determine the presence and / or concentration or level of one or more proteins in the biological sample. In some embodiments, such approaches include, but are not limited to, mass spectrometry, protein microarrays, high-density protein microarrays, e.g., CDI proteome arrays, ELISA, Meso Scale Discovery (e.g., Pacific Biolabs), bead-based immunoassays (e.g., Luminex® magnetic bead-based capture assays), secondary fluorescent antibody assays, DNA-antibody conjugates or antibody-metal conjugates (e.g., mass cytometry, CyTOF®), HD-XTM and SR-XTM Ultra-Sensitive Biomarker Detection Systems (e.g., Quanterix®), aptamer-based oligohybridization MEMS (e.g., Somalogic), flow cytometry, FirePlex® particle technology (e.g., Abcam®), or combinations thereof to determine the protein profile of a biological sample from a subject. Panel of signature proteins

[0262] The present disclosure provides methods and systems for analyzing a biological sample to obtain measurable features associated with the development of a proliferative disorder of colorectal cells from a combination of protein molecules identified in the sample. The collection of identified protein molecules described herein is useful for creating a classifier for the detection of a proliferative disorder of colorectal cells or a stage thereof, and in its model. The identified protein molecules may be individually informative and useful, but the protein molecules may be used in the combinations described herein to form a signature panel of signatures that are characteristic of a proliferative disorder of colorectal cells or a stage thereof. Features from the signature panel can be processed using a trained algorithm (e.g., a machine learning model) to create a classifier configured to stratify a population of subjects having a proliferative disorder of colorectal cells. The method is characterized by using one or more proteins described in the signature panel. In some embodiments, a signature panel of at least three proteins is useful for the classifiers and methods described herein.

[0263] The protein signature panel described herein enables rapid and specific analysis of specific proteins associated with a proliferative disorder of colorectal cells. The signature panel described and employed in the methods herein can be used for improved diagnosis, prognosis, treatment selection, and monitoring (e.g., treatment monitoring) of a proliferative disorder of colorectal cells.

[0264] Signature panels and methods can provide significant improvements over current approaches for detecting early-stage proliferative disorders of colorectal cells from bodily fluid samples such as whole blood, plasma, or serum. Current methods used to detect and diagnose proliferative disorders of colorectal cells include colonoscopy, sigmoidoscopy, and fecal occult blood colorectal cancer. In comparison to these methods, the methods provided herein are much less invasive than colonoscopy and, when less sensitive, can be of equivalent invasiveness to sigmoidoscopy, fecal immunochemical test (FIT), and fecal occult blood test (FOBT). The methods provided herein can offer significant advantages in terms of sensitivity and specificity through an advantageous combination of using a panel of proteins and a very sensitive assay technique.

[0265] The present disclosure provides methods and systems for detecting proliferative disorders of colorectal cells and profiling proteins associated with disease progression. To identify informative proteins for the methods and classifiers described herein, plasma from patients with proliferative disorders of colorectal cells and plasma from subjects without proliferative disorders of colorectal cells (control plasma or reference plasma) were investigated to identify a signature panel of proteins and their respective reactive proteins produced in response to the proliferative disorders of colorectal cells by patients with the proliferative disorders of colorectal cells. For that purpose, plasma from patients with proliferative disorders of colorectal cells and control plasma can be tested using a high-density antibody microarray. Antibody microarrays have a number of advantages over other approaches used to identify proteins: i) the proteins printed in the array are known in advance, thereby obviating subsequent identification and eliminating the possibility of mimotope selection, and ii) since the proteins are printed at all similar concentrations, there is no tendency to select any one protein. This combination of factors results in high sensitivity for identifying biomarkers.

[0266] The proteins identified herein can be used to identify subjects having a proliferative disorder of colonic cells and distinguish them from subjects without a proliferative disorder of colonic cells, or to identify subjects at high risk of developing a proliferative disorder of colonic cells and distinguish them from subjects at low risk of developing a proliferative disorder of colonic cells, or to identify subjects having a precursor lesion of a proliferative disorder of colonic cells. Accordingly, these proteins can be used as an auxiliary tool to guide decisions regarding the monitoring, treatment, and management of proliferative disorders of colonic cells.

[0267] In some embodiments, a panel of plasma protein biomarkers is disclosed herein that is useful for the early detection of colorectal proliferative disorders and for the early detection of colorectal cancer.

[0268] In other embodiments, disclosed herein are methods related to detection, diagnosis, and treatment. Patient-derived plasma is screened for tumor-related or tumor-induced proteins as signs of a proliferative disorder of the colon.

[0269] In one aspect, the present disclosure provides a protein panel that characterizes the proliferative disorders of colonic cells, the panel comprising at least 4 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40.

[0270] In one embodiment, a protein panel that characterizes the proliferative disorder of colonic cells comprises at least 3 proteins, at least 4 proteins, at least 5 proteins, at least 6 proteins, at least 7 proteins, at least 8 proteins, at least 9 proteins, or at least 10 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, Calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, Ferritin, FGF23, Free PSA, Gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, Pepsinogen 1, Pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total N-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40.

[0271] In some embodiments, the panel comprises CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, IL-8, sTREM-1, or a combination thereof.

[0272] In some embodiments, the panel comprises Ang-2, CA125, Calcitonin, CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, hGH, IGFBP-7, IL-8, IL-6, HS IL-6, MMP-3, OPN, BNP, proBNP, NT-proBNP, total NT-proBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or a combination thereof.

[0273] In some embodiments, the panel comprises Abeta38, Abeta40, Abeta42, Abeta42.2, Ang-2, CA125, Calcitonin, CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, hGH, IGFBP-7, IL-8, IL-6, HS IL-6, MMP-3, OPN, BNP, proBNP, NT-proBNP, total N-proBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or combinations thereof.

[0274] In some embodiments, the panel comprises CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, IL-8, or combinations thereof.

[0275] In some embodiments, the panel comprises measuring total PSA.

[0276] In some embodiments, the protein signature panel is useful for discriminating between healthy subjects, subjects having benign colorectal polyps, subjects having advanced adenomas, or subjects with colorectal cancer.

[0277] In some embodiments, the panel is useful for suggesting an advanced adenoma and comprises at least 4 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40.

[0278] In some embodiments, the protein panel indicative of a proliferative disorder of colonic cells comprises at least 3 proteins, at least 4 proteins, at least 5 proteins, at least 6 proteins, at least 7 proteins, at least 8 proteins, at least 9 proteins, or at least 10 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total N-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40.

[0279] In some embodiments, the panel is useful for suggesting colorectal cancer and comprises at least 4 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total N-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40.

[0280] In some embodiments, the protein panel indicative of the proliferative disorder of colonic cells comprises at least 5 proteins, at least 6 proteins, at least 7 proteins, at least 8 proteins, at least 9 proteins, or at least 10 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40.

[0281] In some embodiments, a predetermined set of proteins includes proteins such as those described herein, at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, at least 45, at least 46, at least 47, at least 48, at least 49, at least 50, at least 51, at least 52, at least 53, at least 54, at least 55, at least 56, at least 57, at least 58, at least 59, at least 60, or more. In some embodiments, a predetermined set of proteins includes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, or more proteins.In some embodiments, the predetermined set of proteins is selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40.

[0282] In some embodiments, the predetermined set of proteins includes proteins selected from the group listed in Table 2.

[0283] In some embodiments, the proteins in a predetermined panel include proteins of functional classes such as interleukins, complement activation pathway mediators, complement proteins, chemokines, growth factors, cytokines, globulin proteins, mucins, and proteases.

[0284] Classifiers, machine learning models, and systems A machine learning approach is used to characterize protein data from a biological sample from a subject to identify an informative panel of proteins. The identified panel of informative proteins for proliferative disorders of the large intestine is useful for training a classifier model useful for discriminating between samples from healthy subjects and samples from subjects having proliferative disorders of the large intestine.

[0285] Furthermore, described herein is a machine learning model classifier trained on the proteins described herein that are expressed in plasma samples from healthy subjects and plasma samples from subjects having a proliferative disorder of the large intestine cells. Training of the machine learning model provides a classifier having a predetermined set of protein biomarkers (a "protein panel" or "signature panel") useful for classifying healthy subjects from subjects having a proliferative disorder of the large intestine cells. In one example, a method is provided for a minimally invasive, blood-based protein assay that can be used in subjects having colorectal lesions to assess histological severity. In another embodiment, proteins indicative of a proliferative disorder of the large intestine cells are detected in a cell-free sample from a subject, such as a body fluid sample from the subject, such as whole blood, plasma, or serum. Thus, the proteins disclosed herein can be used to discriminate the presence or absence of high-risk, low-risk, or low-risk colorectal lesions of the large intestine cells that are the basis for treatment such as surgical resection, immunotherapy, radiation, or chemotherapy, as well as to monitor low-risk colorectal lesions. Monitoring and confirmation of the presence of a proliferative disorder or lesion of the large intestine cells can be performed, for example, by colonoscopy, ultrasound, MM, or CT scan.

[0286] In some examples, the protein features are used as an input data set into a trained algorithm (e.g., a machine learning model or classifier) to find correlations between the protein profile and a subject group (e.g., a patient group). Examples of such patient groups include the presence or absence of a disease or disorder, an increased or non-increased risk of a disease or disorder, the stage of a disease or disorder, the subtype of a disease or disorder, responders to treatment versus non-responders to treatment, and progressors versus non-progressors. In some examples, a feature matrix is generated to compare samples from subjects having known states or characteristics. In some embodiments, the samples are from healthy subjects or subjects having no known indication, as well as samples from patients known to have cancer.

[0287] As used herein, in the context of machine learning and pattern recognition, the term "feature" generally refers to an individual measurable characteristic or a feature of an observed phenomenon. The concept of "feature" is related to, for example, but not limited to, the concept of explanatory variables used in statistical techniques such as linear regression and logistic regression. Features can be numerical or categorical (e.g., structural features such as strings and graphs are used in syntactic pattern recognition).

[0288] As used herein, the term "input feature" (or "feature") generally refers to a variable used by a trained algorithm (e.g., a machine learning model or classifier) to predict the output classification (label) of a sample, such as a state, protein identity, protein sequence content (e.g., mutations), a proposed data collection operation, or a proposed treatment. The values of the variables may be determined for one sample and used to determine the classification.

[0289] For multiple assays, the system identifies a set of features to input into a trained algorithm (e.g., a machine learning model or classifier). The system performs an assay on each biological sample and forms a feature vector from the measurements. The system inputs the feature vector into a machine learning model and obtains an output classification as to whether the biological sample has the specified characteristics.

[0290] In some embodiments, the machine learning model outputs a classifier that can distinguish between two or more groups or classes of subjects, or features in a population of subjects, or features of the population. In some embodiments, the classifier is a trained machine learning classifier.

[0291] In some embodiments, informative loci or features of biomarkers in cancer tissue are assayed to form a profile. A receiver operating characteristic (ROC) curve can be generated by plotting the results of a particular feature (e.g., any of the biomarkers described herein and / or any item of additional biomedical information) when discriminating between two populations (e.g., subjects responsive to a therapeutic agent and non-responsive subjects). In some embodiments, feature data across the entire population (e.g., cases and controls) is sorted in ascending order based on the value of a single feature.

[0292] In various examples, the detailed characteristics are selected from the group consisting of healthy versus cancer, increased versus non-increased risk of disease, disease subtype, disease stage, progressive versus non-progressive, and responder versus non-responder.

[0293] In some embodiments, the proliferative disorders of the large intestine cells are selected from the group consisting of adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0294] A. Data Analysis In some examples, the present disclosure provides a system, method, or kit having data analysis implemented in software applications, computing hardware, or both. In some examples, the analysis application or system comprises at least a data receiving module, a data preprocessing module, a data analysis module (which can operate on one or more types of protein data), a data interpretation module, or a data visualization module. In some embodiments, the data receiving module includes a computer system that connects laboratory hardware or equipment to a computer system that processes laboratory data. In some embodiments, the data preprocessing module includes a hardware system or computer software that performs operations on the data in preparation for analysis. Examples of operations that can be applied to the data within the preprocessing module include affine transformation, noise removal operations, data cleaning, reformatting, or subsampling. A data analysis module that can be specialized to analyze genomic data from one or more genomic materials can, for example, obtain an assembled genomic sequence and perform probabilistic and statistical analyses to identify abnormal patterns related to a disease, condition, state, risk, status, or phenotype. The data interpretation module can, for example, use analysis methods drawn from statistics, mathematics, or biology to assist in understanding the relationship between the identified abnormal patterns and a health state, functional state, prognosis, or risk. The data visualization module can create a visual representation of the data that can facilitate understanding or interpretation of the results using methods of mathematical modeling, computer graphics, or rendering.

[0295] In some examples, machine learning methods are applied to discriminate samples in a population of samples. In some embodiments, machine learning methods are applied to discriminate healthy samples from samples of a progressive disease (e.g., adenoma).

[0296] In some embodiments, one or more machine learning operations used to train the prediction engine include one or more of a generalized linear model, a generalized additive model, a nonparametric regression operation, a random forest classifier, a spatial regression operation, a Bayesian regression model, time series analysis, a Bayesian network, a Gaussian network, a decision tree learning operation, an artificial neural network, a recurrent neural network, a reinforcement learning operation, a linear or non-linear regression operation, a support vector machine, a clustering operation, and a genetic algorithm operation.

[0297] In some examples, the computer processing method is selected from the group consisting of logistic regression, multiple linear regression (MLR), dimensionality reduction, partial least squares (PLS) regression, principal component regression, autoencoders, variational autoencoders, singular value decomposition, Fourier-based, wavelets, discriminant analysis, support vector machines, decision trees, classification and regression trees (CART), tree-based methods, random forests, gradient boosted trees, logistic regression, matrix factorization, multidimensional scaling (MDS), dimensionality reduction methods, t-distributed stochastic neighbor embedding (t-SNE), multi-layer perceptrons (MLP), network clustering, neuro-fuzzy, and artificial neural networks.

[0298] In some examples, the methods disclosed herein can include computer analysis of nucleic acid sequencing data of a sample from a subject or subjects.

[0299] B. Generation of Classifier In one aspect, the systems and methods disclosed herein provide a classifier generated based on feature information derived from protein analysis from a biological sample containing proteins. The classifier forms part of a prediction engine for discriminating groups in a population based on features identified in a biological sample, such as proteins. The collective representation of protein information in a biological sample may be referred to as a protein profile.

[0300] In some embodiments, the classifier creates the prediction engine by normalizing protein information by formatting similar portions of the protein information into an integrated format and scale, storing the normalized protein information in a column database, and training the prediction engine by applying one or more machine learning operations to the stored normalized protein information, whereby the prediction engine is configured to map combinations of one or more features for a particular population to define at least two classes. In some embodiments, the method includes applying the prediction engine to a population of subjects, where the normalized protein features are obtained and associated with each subject, evaluating the output of the prediction engine to identify subjects associated with one group, and classifying the subjects into one group.

[0301] Specificity, as used herein, generally refers to "the probability of a negative test among people without the disease." Specificity can be calculated by dividing the number of disease-free subjects tested negative by the total number of disease-free subjects.

[0302] In some examples, the model, classifier, or prediction test has a specificity of at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, 85%, at least 90%, at least 95%, or at least 99%.

[0303] Sensitivity, as used herein, generally refers to "the probability of a positive test among people with the disease." Sensitivity can be calculated by dividing the number of affected subjects tested positive by the total number of affected subjects.

[0304] In some examples, the model, classifier, or prediction test has a sensitivity of at least 25%, at least 30%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, 85%, at least 90%, at least 95%, or at least 99%.

[0305] C. Digital Processing Device In some examples, a digital processing device or its use is described herein. In some examples, in some examples, a digital processing device can include one or more hardware central processing units (CPUs), graphics processing units (GPUs), or tensor processing units (TPUs) that execute the functions of the device. In some examples, a digital processing device can include an operating system configured to execute executable instructions.

[0306] In some examples, the digital processing device is optionally connected to a computer network. In some examples, the digital processing device is optionally connected to the Internet. In some examples, the digital processing device is optionally connected to a cloud computing infrastructure. In some examples, the digital processing device is optionally connected to an intranet. In some examples, the digital processing device is optionally connected to a data storage unit.

[0307] Non-limiting examples of suitable digital processing devices include server computers, desktop computers, laptop computers, notebook computers, subnotebook computers, netbook computers, netpad computers, set-top computers, handheld computers, Internet appliances, mobile smartphones, and tablet computers. Suitable tablet computers can include, for example, those having a booklet, slate, and convertible configuration.

[0308] In some examples, the digital processing device may include an operating system configured to execute executable instructions. For example, the operating system may include software including programs and data that control the hardware of the device and provide services for the execution of applications. For example, non-limiting examples of operating systems include Ubuntu, FreeBSD, OpenBSD, NetBSD®, Linux, Apple® Mac OS X Server®, Oracle® Solaris®, Windows Server®, and Novell® NetWare®. Non-limiting examples of suitable personal computer operating systems include operating systems such as UNIX® such as Microsoft® Windows®, Apple® Mac OS X®, UNIX®, and GNU / Linux®. In some examples, the operating system may be provided by cloud computing, and the cloud computing resources may be provided by one or more service providers.

[0309] In some examples, the device may include a storage and / or memory device. The storage and / or memory device can be one or more physical devices used to store data or programs temporarily or permanently. In some examples, the device can be volatile memory and may require power to maintain the stored information. In some examples, the device can be non-volatile memory and can retain the stored information when power is not supplied to the digital processing device. In some examples, the non-volatile memory can include flash memory. In some examples, the non-volatile memory can include dynamic random access memory (DRAM). In some examples, the non-volatile memory can include ferroelectric random access memory (FRAM). In some examples, the non-volatile memory can include phase change random access memory (PRAM).

[0310] In some examples, the device can be a storage device, including, for example, a CD-ROM, a DVD, a flash memory device, a magnetic disk drive, an optical disk drive, and a cloud computing-based storage device. In some examples, the storage and / or memory device can be a combination of devices as disclosed herein. In some examples, the digital processing device includes a display for sending visual information to the user. In some embodiments, the display can be a cathode ray tube (CRT). In some embodiments, the display can be a liquid crystal display (LCD). In some examples, the display can be a thin film transistor liquid crystal display (TFT-LCD). In some embodiments, the display can be an organic light emitting diode (OLED) display. In some examples, the OLED display can be a passive matrix OLED (PMOLED) display, or an active matrix OLED (AMOLED) display. In some examples, the display can be a plasma display. In some examples, the display can be a video projector. In some examples, the display can be a combination of devices as disclosed herein.

[0311] In some examples, the digital processing device can include an input device for receiving information from the user. In some examples, the input device can be a keyboard. In some examples, the input device can be a pointing device, including, for example, a mouse, a trackball, a trackpad, a joystick, a game controller, or a stylus. In some examples, the input device can be a touch screen or a multi-touch screen. In some examples, the input device can be a microphone for capturing voice or other audio input. In some examples, the input device can be a video camera for capturing motion or visual input. In some examples, the input device can be a combination of devices, such as those disclosed herein.

[0312] D. Non-transitory computer-readable storage medium In some examples, the subject matter disclosed herein can optionally include one or more non-transitory computer-readable storage media encoded with a program including instructions executable by an operating system of a network-connected digital processing device. In some examples, the computer-readable storage medium can be a tangible component of the digital processing device. In some examples, the computer-readable storage medium can optionally be removable from the digital processing device. Examples of computer-readable storage media include, for example, CD-ROMs, DVDs, flash memory devices, solid state memories, magnetic disk drives, magnetic tape drives, optical disk drives, cloud computing systems and services, and the like. In some examples, the program and instructions can be encoded permanently, substantially permanently, semi-permanently, or non-permanently on the medium.

[0313] E. Computer system The present disclosure provides a computer system programmed to implement the methods described herein. FIG. 1 shows a computer system (101) programmed or otherwise configured to store, process, identify, or interpret patient data, biological data, biological sequences, reference sequences, and protein profiles. The computer system (101) can process various aspects of the patient data, biological data, biological sequences, reference sequences, and protein profiles of the present disclosure. The computer system (101) can be an electronic device of a user located remotely from the computer system, or a computer system. The electronic device can be a mobile electronic device.

[0314] The computer system (101) includes a central processing unit (CPU, referred to herein as "processor" and "computer processor") (105) that can be a single-core or multi-core processor, or multiple processors for parallel processing. The computer system (101) also includes a memory or storage location (110) (e.g., random access memory, read-only memory, flash memory), an electronic storage unit (115) (e.g., hard disk), a communication interface (120) (e.g., network adapter) for communicating with one or more other systems, and peripheral devices (125) such as cache, other memory, data storage devices, and / or an electronic display adapter. The memory 110, storage unit (115), interface (120), and peripheral devices (125) communicate with the CPU (105) through a communication bus (solid line) such as a motherboard. The storage unit 115 may be a data storage unit (or data repository) for storing data. The computer system (101) is operably coupled to a computer network ("network") (130) with the assistance of the communication interface (120). The network (130) can be the Internet, the Internet and / or an extranet, or an intranet and / or an extranet communicating with the Internet. The network (130) is, in some examples, a telecommunications and / or data network. The network (130) can include one or more computer servers that can enable distributed computing such as cloud computing. The network (130) can, in some examples, implement a peer-to-peer network that can enable devices coupled to the computer system (101) to operate as clients or servers with the help of the computer system (101).

[0315] The CPU (105) can execute a sequence of machine-readable instructions that can be embodied in a program or software. The instructions can be stored in a memory location such as the memory (110). The instructions may be directed to the CPU (105), and the CPU (105) can then be programmed or otherwise configured to implement the methods of the present disclosure. Examples of operations performed by the CPU (105) can include fetch, decode, execute, and write-back.

[0316] The CPU (105) may be part of a circuit such as an integrated circuit. One or more other components of the system (101) can be included in the circuit. In some examples, the circuit is an application specific integrated circuit (ASIC).

[0317] The storage unit (115) can store files such as drivers, libraries, and saved programs. The storage unit (115) can store user data, such as user personal settings, and user programs. The computer system (101) can include one or more additional data storage units external to the computer system (101), such as being located on a remote server that communicates with the computer system (101) through an intranet or the Internet in some examples.

[0318] The computer system (101) can communicate with one or more remote computer systems via a network (130). For example, the computer system (101) can communicate with a user's remote computer system. Examples of remote computer systems include personal computers (e.g., portable PCs), slates or tablet PCs (e.g., Apple® iPad, Samsung® Galaxy Tab), telephones, smartphones (e.g., Apple® iPhone, Android-enabled devices, Blackberry®), or personal digital assistants. A user can access the computer system (101) via the network (130).

[0319] The methods described herein may be implemented, for example, by machine (e.g., computer processor) executable code stored on an electronic storage location of a computer system (101) such as, for example, on a memory (110) or an electronic storage unit (115). The machine executable code or machine readable code may be provided in the form of software. In use, the code may be executed by a processor (105). In some examples, the code may be retrieved from the storage unit (115) and stored in the memory (110) for easy access by the processor (105). In some examples, the electronic storage unit (115) may be excluded and the machine executable instructions may be stored in the memory (110).

[0320] The code may be pre-compiled and configured for use with a machine having a processor adapted to execute the code, or may be compiled at runtime. The code may be provided in a programming language selected to be executable in a pre-compiled manner or in a manner that is compiled during execution.

[0321] Aspects of the systems and methods provided herein, such as computer system (101), may be embodied in programming. Various aspects of this technology may typically be considered as a "product" or "manufactured article" in the form of machine (or, processor) executable code and / or associated data that is executed or embodied on a type of machine-readable medium. The machine executable code may be stored in an electronic storage device such as a memory (e.g., read-only memory, random access memory, flash memory) or a hard disk. A "storage" type medium can include any or all of various semiconductor memories, tape drives, disk drives, etc., which are tangible memories of a computer or processor, or related modules thereof, and which can provide a non-transitory recording medium at any time for the programming of software. All or part of the software may sometimes be communicated via the Internet or various other electrical communication networks. Such communication may enable the loading of software, for example, from one computer or processor to another, such as from an administrative server or host computer to the computer platform of an application server. Accordingly, another type of medium that can hold software elements includes optical, electrical, and electromagnetic waves such as those used through various wired and terrestrial optical communication line networks between local devices, and various air links. Physical elements that carry such waves, such as wired links or wireless links, optical links, etc., may also be considered as media that hold software. As used herein, unless restricted to non-transitory and tangible "storage" media, terms such as computer or machine "readable media" generally refer to any medium involved in providing instructions to a processor for execution.

[0322] Thus, machine-readable media such as computer-executable code may take many forms including, but not limited to, tangible storage media, carrier wave media, or physical transmission media. Non-volatile storage media includes, for example, optical or magnetic disks such as any of the storage devices in any computer that may be used to implement the databases described herein. Volatile storage media includes dynamic memory such as the main memory of the computer platforms described herein. Tangible transmission media includes copper wires and fiber optics including coaxial cables; wires including buses within computer systems. Carrier wave transmission media may take the form of electrical or electromagnetic signals, or acoustic or light waves such as those generated during radio frequency (RF) and infrared (IR) data communications. Thus, common forms of computer-readable media include, for example: floppy disks, flexible disks, hard disks, magnetic tape, other magnetic media, CD-ROM, DVD or DVD-ROM, other optical media, punch cards, paper tape, other physical storage media with patterns of holes, RAM, ROM, PROM and EPROM, FLASH-EPROM, other memory chips or cartridges, carrier waves transporting data or instructions, cables or links transporting such carrier waves, or other media from which a computer can read programming code and / or data. Many forms of computer-readable media may be involved in transporting one or more sequences of one or more instructions to a processor for execution.

[0323] A computer system (101) may include, or be communicable with, an electronic display (135) including a user interface (UI) (140) for providing, for example, analysis of nucleic acid sequences, concentrated nucleic acid sample data, protein profiles, expression profiles, and RNA expression profiles. Examples of UIs include, but are not limited to, graphical user interfaces (GUIs) and web-based user interfaces.

[0324] The methods and systems of the present disclosure can be implemented by one or more algorithms. When executed by a central processing unit 105, the algorithms can be implemented by software. The algorithms can, for example, store, process, identify, or interpret patient data, biological data, biological sequences, reference sequences, and protein profiles.

[0325] In some examples, the subject matter disclosed herein can include at least one computer program or its use. A computer program can be a sequence of instructions written to perform a specified task that is executable on a CPU, GPU, or TPU of a digital processing device. The computer-readable instructions can be implemented as program modules such as functions, objects, application programming interfaces (APIs), data structures, etc., that perform a particular task or implement a particular abstract data type. In the context of the disclosure provided herein, the computer program can be written in various versions of various languages.

[0326] The functions of the computer-readable instructions can be combined or distributed as desired in various environments. In some examples, the computer program can include one sequence of instructions. In some examples, the computer program can include multiple sequences of instructions. In some examples, the computer program can be provided from one location. In some examples, the computer program can be provided from multiple locations. In some examples, the computer program can include one or more software modules. In some examples, the computer program can include, in whole or in part, one or more web applications, one or more mobile applications, one or more stand-alone applications, one or more web browser plugins, extensions, add-ins, or add-ons, or combinations thereof.

[0327] In some examples, the computer processing can be a method of statistics, mathematics, biology, or a combination thereof. In some examples, the computer processing method can include, for example, dimensionality reduction methods including logistic regression, dimensionality reduction, principal component analysis, autoencoders, singular value decomposition, Fourier-based, singular value decomposition, wavelets, discriminant analysis, support vector machines, tree-based methods, random forests, gradient boosting trees, logistic regression, matrix factorization, network clustering, and neural networks.

[0328] In some examples, the computer processing method is a supervised machine learning method including, for example, regression, support vector machines, tree-based methods, and networks.

[0329] In some examples, the computer processing method is an unsupervised machine learning method including, for example, clustering, networks, principal component analysis, and matrix factorization.

[0330] F. Database In some examples, the subject matter disclosed herein can include one or more databases for storing patient data, biological data, biological sequences, reference sequences, or protein profiles, or the use thereof. The reference sequences can be obtained from a database. Considering the disclosure provided herein, many databases can be suitable for storing and retrieving sequence information. In some examples, suitable databases can include, for example, relational databases, non-relational databases, object-oriented databases, object databases, entity-relationship model databases, associative databases, and XML databases. In some examples, the database can be internet-based. In some examples, the database can be web-based. In some examples, the database can be cloud computing-based. In some examples, the database can be based on one or more local computer storage devices.

[0331] In one aspect, the present disclosure provides a non - transitory computer - readable medium comprising instructions that direct a processor to execute the methods disclosed herein.

[0332] In one aspect, the present disclosure provides a computing device comprising a computer - readable medium.

[0333] In another aspect, the present disclosure provides a system for classifying a biological sample, the system comprising: a) a receiver for receiving a plurality of training samples, each of the plurality of training samples having a plurality of molecular classes and each of the plurality of training samples including one or more known labels; b) a feature module for identifying a set of features corresponding to an assay, operable to be computer - processed using a machine - learning model for each of the plurality of training samples, the set of features corresponding to the properties of the molecules in the plurality of training samples, and for each of the plurality of training samples, the system being operable to subject the plurality of classes of molecules in the training sample to a plurality of different assays to obtain a set of measurement values, each set of measurement values being from one assay applied to the class of molecules in the training sample, and the plurality of sets of measurement values being obtained for the plurality of training samples; c) an analysis module for analyzing a set of measurement values to obtain a training vector for a training sample, the training vector including N sets of features of the features of the corresponding assay, each feature value corresponding to a feature and including one or more measurement values, the training vector being formed using at least one feature from at least two of the N sets of features corresponding to a first subset of a plurality of different assays; d) a labeling module for notifying the system about the training vector using the parameters of a machine - learning model to obtain an output label for the plurality of training samples; e) a comparison module for comparing the output label with the known label of the training sample; f) A training module that iteratively searches for optimal values of parameters as part of the training of a machine learning model based on a comparison between an output label and a known label of a training sample. g) An output module that provides a set of parameters of the machine learning model and features of the machine learning model and includes.

[0334] A method for classifying objects in a population The disclosed method relates to identifying parameters of protein expression associated with proliferative disorders of colorectal cells through analysis of expressed proteins in an object. This method is a method for use in improved diagnosis, treatment, and monitoring of proliferative disorders of colorectal cells, and more specifically, is a method by enabling improved identification of the above disorders and genetic factors for the above disorders and improved identification of stages or subclasses.

[0335] In some embodiments, the method includes analyzing differential expression of proteins in a biological sample from an object in a population.

[0336] The present disclosure provides a method for detecting a proliferative disorder of colorectal cells that can be applied to a cell-free sample, for example, to detect the presence and characteristics of proteins between an object having a proliferative disorder of colorectal cells and an object not having the disorder, or between different proliferative disorders of colorectal cells. This method utilizes the detection of proteins as a basic "positive" or "negative" signal of a proliferative disorder of colorectal cells compared to a healthy object not having a proliferative disorder of colorectal cells.

[0337] In some embodiments, the proliferative disorder of colonic cells is selected from the group consisting of adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0338] In one aspect, the present disclosure provides a method for determining the protein profile of a biological sample from a subject, the method comprising: a) obtaining a biological sample containing a protein from the subject; b) measuring the presence and amount of a predetermined panel of at least 4 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40 in the biological sample to provide the protein profile of the subject.

[0339] In some embodiments, the protein profile is associated with a proliferative disorder of colonic cells and provides a classification of a subject as having a proliferative disorder of colonic cells.

[0340] In some embodiments, the biological sample from the subject is selected from the group consisting of a body fluid, feces, colonic effluent, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, and combinations thereof.

[0341] In some embodiments, the proliferative disorder of colonic cells is selected from the group consisting of an adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0342] In some embodiments, the proliferative disorder of colonic cells is selected from the group consisting of stage 1 colorectal cancer, stage 2 colorectal cancer, stage 3 colorectal cancer, and stage 4 colorectal cancer.

[0343] In some embodiments, the advanced adenoma is a tubular adenoma, tubulovillous adenoma, villous adenoma, adenocarcinoma, or hyperplastic polyp.

[0344] In another aspect, the present disclosure provides a method for detecting a proliferative disorder of colonic cells in a subject, the method comprising: a) obtaining a biological sample containing a protein from the subject; b) measuring the presence and amount of a predetermined panel of at least 4 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta 42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, YKL40 in a biological sample to provide a protein profile of the subject; c) computer processing the protein profile using a machine learning model trained to distinguish between a healthy subject and a subject having a proliferative disorder of colonic cells to provide an output value associated with the presence or absence of a proliferative disorder of colonic cells, thereby suggesting the presence or absence of a proliferative disorder of colonic cells in the subject; comprising.

[0345] In some embodiments, the biological sample from the subject is selected from the group consisting of body fluid, feces, colonic effluent, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, and combinations thereof.

[0346] In another aspect, disclosed herein is a method for detecting proteins to generate a protein profile in a sample, the method comprising: a) obtaining a biological sample containing proteins from a subject; b) measuring the presence and amount of a predetermined panel of at least 4 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, YKL40 in a biological sample, the step of providing a protein profile of the biological sample and comprising.

[0347] In another aspect, disclosed herein is a method for obtaining data in a biological sample from a subject, the method comprising the step of detecting at least four proteins, wherein the at least four proteins are selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40, and, optionally, the step of determining the levels of the at least four proteins in the sample.

[0348] In some embodiments, the proliferative disorder of the large intestine cells is selected from the group consisting of adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0349] In some embodiments, the proliferative disorder of colonic cells is selected from the group consisting of stage 1 colorectal cancer, stage 2 colorectal cancer, stage 3 colorectal cancer, and stage 4 colorectal cancer.

[0350] In another aspect, the present disclosure provides a method for determining a protein profile of a biological sample from a subject, the method comprising: a) obtaining a biological sample containing proteins from the subject; b) measuring the presence and amount of a predetermined panel of at least 4 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, Calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40 in the biological sample to provide a protein profile of the subject, thereby determining the protein profile of the subject; and

[0351] In some embodiments, the predetermined panel of proteins comprises CXCL13, CYFRA21-1, FGF23, GDF-15, HE4, IL-8, or a combination thereof.

[0352] In some embodiments, a predetermined panel of proteins includes CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, IL-8, sTREM-1, or a combination thereof.

[0353] In some embodiments, the panel includes Ang-2, CA125, calcitonin, CXCL13, CYFRA21-1, FGF23, GDF-15, HE4, hGH, IGFBP-7, IL-8, IL-6, HS IL-6, MMP-3, OPN, BNP, proBNP, NT-proBNP, total NT-proBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or a combination thereof.

[0354] In some embodiments, the panel includes Abeta38, Abeta40, Abeta42, Abeta42.2, Ang-2, CA125, calcitonin, CXCL13, CYFRA21-1, FGF23, GDF-15, HE4, hGH, IGFBP-7, IL-8, IL-6, HS IL-6, MMP-3, OPN, BNP, proBNP, NT-proBNP, total N-proBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or a combination thereof.

[0355] In some embodiments, the panel includes measuring total PSA.

[0356] In another aspect, the present disclosure provides a method for detecting a proliferative disorder of colonic cells in a subject, the method comprising a) obtaining a biological sample containing a protein from the subject; b) measuring the presence and amount of a predetermined panel of at least 4 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, Calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40 in a biological sample to provide a protein profile of the subject; c) computer processing the protein profile of the subject using a machine learning model trained to discriminate between a subject without a proliferative disorder of colonic cells and a subject with a proliferative disorder of colonic cells; and d) outputting a value associated with a subject having a proliferative disorder of colonic cells by a machine learning model based on the protein profile, thereby detecting a proliferative disorder of colonic cells in the subject. Including.

[0357] In another aspect, the present disclosure provides a method for monitoring minimal residual disease in a subject previously treated for a disease, the method comprising determining a protein profile of a biological sample from the subject using a predetermined panel of proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, Calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, Ferritin, FGF23, Free PSA, Gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, Pepsinogen 1, Pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40, thereby generating a baseline protein state; and after generating the baseline protein state, determining a protein profile of a biological sample from the subject at one or more time points, thereby generating a current protein state, wherein a change between the baseline protein state and the current protein state indicates a change in minimal residual disease in the subject.

[0358] In some embodiments, the minimal residual disease is selected from the group consisting of response to treatment, tumor burden, residual tumor after surgery, recurrence, secondary screening, primary screening, and cancer progression.

[0359] The trained machine learning methods, models, and discriminative classifiers described herein may be applied to various medical applications, including cancer detection, diagnosis, and treatment responsiveness. Since the models can be trained using subject metadata and features from analytes, the applications can be individually tailored to stratify subjects within a population and accordingly guide treatment decisions.

[0360] Diagnosis The methods and systems provided herein may perform predictive analytics using an artificial intelligence-based approach to analyze data obtained from a subject (patient) to generate an output for the diagnosis of a subject having cancer (e.g., colorectal cancer). For example, the application can apply a prediction algorithm to the obtained data to generate a diagnosis of a subject having cancer. The prediction algorithm may include an artificial intelligence-based predictor, such as a machine learning-based predictor, configured to process the obtained data to generate a diagnosis of a subject having cancer.

[0361] The machine learning predictor can be trained using, as an input to the machine learning predictor, a dataset generated by performing a protein assay on a biological sample of a subject derived from one or more sets of cohorts of patients having cancer using the signature panel described herein, and, as an output, the known diagnosis (e.g., stage determination and / or tumor ratio) results of the subject.

[0362] A training dataset (e.g., a dataset generated by performing an assay using the signature panel described herein on a subject's biological sample) may be generated from, for example, one or more sets of subjects having common characteristics (features) and outcomes (labels). The training dataset may include a set of features and labels corresponding to features related to diagnosis. Features may include, for example, a range or category of protein assay measurements such as the presence or characteristics of one or more proteins in biological samples from healthy and diseased subjects. For example, a set of features collected from a given subject at a given time point may collectively serve as a diagnostic signature that may suggest the identified cancer of the subject at the given time point. Features may also include, for example, labels indicating the diagnostic results of the subject for one or more cancers.

[0363] Labels may include, for example, outcomes such as the results of a known diagnosis of the subject (e.g., a stage classification diagnosis and / or a tumor proportion diagnosis). Outcomes may include features related to cancer in the subject. The results may include features related to cancer in the subject. For example, the features may suggest a subject having one or more cancers.

[0364] A training set (e.g., a training data set) may be selected by random sampling of a set of data corresponding to one or more sets of subjects (e.g., retrospective and / or prospective cohorts of patients with or without one or more cancers). Alternatively, a training set (e.g., a training data set) may be selected by proportional sampling of a set of data corresponding to one or more sets of subjects (e.g., retrospective and / or prospective cohorts of patients with or without one or more cancers). The training set may be balanced across a set of data corresponding to one or more sets of subjects (e.g., patients from different clinical sites or clinical trials). The machine learning predictor may be trained until certain predetermined conditions regarding accuracy or performance are met, such as having a minimum desired value corresponding to a diagnostic accuracy metric. For example, the diagnostic accuracy metric may correspond to the diagnosis of one or more cancers in a subject, staging, or prediction of tumor fraction.

[0365] Examples of diagnostic accuracy metrics can include sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and the area under the curve (AUC) of a Receiver Operating Characteristic (ROC) curve corresponding to the diagnostic accuracy for detecting or predicting cancer (e.g., colorectal cancer).

[0366] In one aspect, the present disclosure provides a method of using a classifier capable of discriminating a population of subjects, the method comprising: a) obtaining a biological sample containing a protein from a subject; b) measuring the presence and amount of a predetermined panel of at least 4 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, Calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40 in a biological sample, thereby providing a protein profile of the subject; c) computer-processing the protein profile of the subject using a machine learning model trained to discriminate between two or more populations; d) outputting a value associated with a population by the machine learning model based on the protein profile, thereby discriminating the population; and including.

[0367] In some embodiments, the panel includes CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, IL-8, sTREM-1, or a combination thereof.

[0368] In some embodiments, the panel comprises Ang-2, CA125, Calcitonin, CXCL13, CYFRA21-1, FGF23, GDF-15, HE4, hGH, IGFBP-7, IL-8, IL-6, HS IL-6, MMP-3, OPN, BNP, proBNP, NT-proBNP, total NT-proBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or a combination thereof.

[0369] In some embodiments, the panel comprises Abeta38, Abeta40, Abeta42, Abeta42.2, Ang-2, CA125, Calcitonin, CXCL13, CYFRA21-1, FGF23, GDF-15, HE4, hGH, IGFBP-7, IL-8, IL-6, HS IL-6, MMP-3, OPN, BNP, proBNP, NT-proBNP, total N-proBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or a combination thereof.

[0370] In some embodiments, the panel comprises CXCL13, CYFRA21-1, FGF23, GDF-15, HE4, IL-8, or a combination thereof.

[0371] In some embodiments, the panel comprises measuring total PSA.

[0372] In another aspect, the present disclosure provides a method for identifying cancer in a subject, the method comprising: a) obtaining a biological sample containing a protein from the subject; b) measuring the presence and amount of a predetermined panel of at least 4 proteins selected from the group consisting of Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, Calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40 in a biological sample, thereby providing a protein profile of the subject; c) computer processing the protein profile using a machine learning model trained to distinguish between a healthy subject and a subject having a proliferative disorder of colorectal cells to provide an output value associated with the presence or absence of a proliferative disorder of colorectal cells, thereby suggesting the presence or absence of a proliferative disorder of colorectal cells in the subject and generating the likelihood that the subject has cancer; comprising.

[0373] In some embodiments, the panel comprises CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, IL-8, sTREM-1, or a combination thereof.

[0374] In some embodiments, the panel comprises Ang-2, CA125, Calcitonin, CXCL13, CYFRA21-1, FGF23, GDF-15, HE4, hGH, IGFBP-7, IL-8, IL-6, HS IL-6, MMP-3, OPN, BNP, proBNP, NT-proBNP, total NT-proBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or a combination thereof.

[0375] In some embodiments, the panel comprises Abeta38, Abeta40, Abeta42, Abeta42.2, Ang-2, CA125, Calcitonin, CXCL13, CYFRA21-1, FGF23, GDF-15, HE4, hGH, IGFBP-7, IL-8, IL-6, HS IL-6, MMP-3, OPN, BNP, proBNP, NT-proBNP, total N-proBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or a combination thereof.

[0376] In some embodiments, the panel comprises CXCL13, CYFRA21-1, FGF23, GDF-15, HE4, IL-8, or a combination thereof.

[0377] In some embodiments, the panel comprises measuring total PSA.

[0378] Various statistical and mathematical methods may be used to establish thresholds or cutoffs for expression levels. The threshold or cutoff for the expression level of a particular biomarker can be selected, for example, based on data from a Receiver Operating Characteristic (ROC) plot, as described in the examples and drawings disclosed herein. These threshold or cutoff expression levels may be varied, for example, along the ROC plot for a particular biomarker or combination thereof, to obtain different values for sensitivity or specificity, thereby affecting the overall assay performance. For example, if the goal is to have a robust diagnostic method from a clinical perspective, high sensitivity should be prioritized. However, if the goal is to have a cost-effective method, high specificity should be prioritized. The best cutoff refers to the value that yields the best sensitivity and specificity obtained from the ROC plot for a particular biomarker. The values of sensitivity and specificity are calculated over a range of thresholds (cutoffs). Thus, the threshold or cutoff value can be selected such that the sensitivity and / or specificity is at least about 50% in at least 60%, at least 65%, at least 70%, at least 75%, or at least 80% of the patient population being assayed, for example, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99%, or at least 100%.

[0379] Accordingly, some embodiments of the present disclosure determine the presence and / or level of at least the previously cited protein in a minimally invasive sample isolated from a subject to be diagnosed or screened, and compare the presence and / or level of the protein to a predetermined threshold or cut-off value, where the predetermined threshold or cut-off value corresponds to the expression level of the protein that correlates with the highest specificity at a desired sensitivity in a ROC curve calculated based on the expression levels of the protein determined in a patient population at risk of having colorectal cancer or colorectal adenoma, and at least one overexpression of the protein relative to the predetermined cut-off value indicates, at the desired sensitivity, that the subject has colorectal cancer or colorectal adenoma.

[0380] As another example, such predetermined conditions may include a specificity for predicting a proliferative disorder of colonic cells of a value of, for example, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%.

[0381] As another example, such predetermined conditions may include a positive predictive value (PPV) for predicting a proliferative disorder of colonic cells of a value of, for example, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%.

[0382] As another example, such predetermined conditions may include that the negative predictive value (NPV) for predicting a proliferative disorder of colon cells is, for example, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%.

[0383] As another example, such predetermined conditions may include that the area under the curve (AUC) of the Receiver Operating Characteristic (ROC) curve for predicting a proliferative disorder of colon cells is at least about 0.50, at least about 0.55, at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.85, at least about 0.90, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, or at least about 0.99.

[0384] Monitoring colorectal cancer After processing a dataset using a trained algorithm, colorectal cancer can be identified or monitored in a subject. The identification can be based at least in part on the quantitative measures of the proteins of the dataset in a panel of proteins related to colorectal cancer. For example, monitoring can include evaluating the subject's colorectal cancer at each of two or more time points.

[0385] In some embodiments, the methods disclosed herein can be applied to monitor and / or predict tumor burden.

[0386] In some embodiments, the methods disclosed herein can be applied to detect and / or predict residual tumors after surgery.

[0387] In some embodiments, the methods disclosed herein can be applied to detect and / or predict minimal residual disease after treatment.

[0388] In some embodiments, the methods disclosed herein can be applied to detect and / or predict recurrence.

[0389] In one aspect, the methods disclosed herein can be applied as a secondary screening.

[0390] In one aspect, the methods disclosed herein can be applied as a primary screening.

[0391] In one aspect, the methods disclosed herein can be applied to monitor the development of cancer.

[0392] In one aspect, the methods disclosed herein can be applied to monitor and / or predict the risk of cancer.

[0393] Colorectal cancer can be identified in a subject with at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more accuracy. The accuracy of identifying colorectal cancer by a trained algorithm can be calculated as the percentage of independent test samples (e.g., subjects known to have colorectal cancer or subjects with negative clinical test results for colorectal cancer) that are accurately identified or classified as having, or not having, colorectal cancer.

[0394] Colorectal cancer can be identified in a subject with a positive predictive value (PPV) of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more. The PPV for identifying colorectal cancer using a trained algorithm can be calculated as the percentage of cell-free biological samples identified or classified as having colorectal cancer that correspond to subjects truly having colorectal cancer.

[0395] Colorectal cancer can be identified in a subject with a negative predictive value (NPV) of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more. The NPV for identifying colorectal cancer using a trained algorithm can be calculated as the percentage of cell-free biological samples identified or classified as not having colorectal cancer that correspond to subjects truly not having colorectal cancer.

[0396] Colorectal cancer can be identified in a subject with a clinical sensitivity of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, at least about 99.1%, at least about 99.2%, at least about 99.3%, at least about 99.4%, at least about 99.5%, at least about 99.6%, at least about 99.7%, at least about 99.8%, at least about 99.9%, at least about 99.99%, at least about 99.999%, or more. The clinical sensitivity for identifying colorectal cancer using a trained algorithm can be calculated as the percentage of independent test samples associated with the presence of colorectal cancer (e.g., subjects known to have colorectal cancer) that are accurately identified or classified as having colorectal cancer.

[0397] Colorectal cancer can be identified in the subject with a clinical specificity of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, at least about 99.1%, at least about 99.2%, at least about 99.3%, at least about 99.4%, at least about 99.5%, at least about 99.6%, at least about 99.7%, at least about 99.8%, at least about 99.9%, at least about 99.99%, at least about 99.999%, or more. The clinical specificity for identifying colorectal cancer using a trained algorithm can be calculated as the proportion of independent test samples associated with the absence of colorectal cancer (e.g., subjects with negative clinical test results for colorectal cancer) that are correctly identified or classified as not having colorectal cancer.

[0398] In some embodiments, the trained algorithm can determine that the subject is at risk of colorectal cancer at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more.

[0399] The trained algorithm can determine that the subject is at risk of colorectal cancer with at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, at least about 99.1%, at least about 99.2%, at least about 99.3%, at least about 99.4%, at least about 99.5%, at least about 99.6%, at least about 99.7%, at least about 99.8%, at least about 99.9%, at least about 99.99%, at least about 99.999%, or more accuracy.

[0400] When it is identified that the subject has colorectal cancer, the subject may optionally be provided with a therapeutic intervention (e.g., prescribing an appropriate treatment course for treating the subject's colorectal cancer). The therapeutic intervention may include prescribing an effective dose of a drug, further testing or evaluating the colorectal cancer, further monitoring the colorectal cancer, or a combination thereof. If the subject is currently undergoing treatment for colorectal cancer by a certain treatment course, the therapeutic intervention may include a subsequent different treatment course (e.g., to increase treatment effectiveness due to the ineffectiveness of the current treatment course).

[0401] The therapeutic intervention may include recommending to the subject a secondary clinical examination to confirm the diagnosis of colorectal cancer. This secondary clinical examination may include an imaging test, a blood test, a computed tomography (CT) scan, a magnetic resonance imaging (MRI) scan, an ultrasound scan, a chest X-ray, a positron emission tomography (PET) scan, a PET-CT scan, a cell-free biological cytology, a FIT test, a FOBT test, or a combination thereof.

[0402] The subject's colorectal cancer may be monitored by monitoring a treatment course for treating the subject's colorectal cancer. Monitoring may include evaluating the subject's colorectal cancer at two or more time points. Evaluating may be based on quantitative measures of proteins in a panel of colorectal cancer-related proteins, including at least quantitative measures of a panel of colorectal cancer-related proteins obtained at each of two or more time points.

[0403] In some embodiments, differences in the quantitative measurement of proteins in a dataset of a panel of colorectal cancer-related proteins, including the quantitative measurement of the panel of colorectal cancer-related proteins determined between two or more time points, may indicate one or more clinical implications such as (i) diagnosis of colorectal cancer in a subject, (ii) prognosis of colorectal cancer in a subject, (iii) increased risk of colorectal cancer in a subject, (iv) decreased risk of colorectal cancer in a subject, (v) effectiveness of a treatment process for treating colorectal cancer in a subject, and (vi) ineffectiveness of a treatment process for treating colorectal cancer in a subject.

[0404] In some embodiments, differences in the quantitative measurement of proteins, including the quantitative measurement of a panel of colorectal cancer-related proteins determined between two or more time points, can be an indicator for the diagnosis of colorectal cancer in a subject. For example, if colorectal cancer was not detected in a subject at an earlier time point but was detected in the subject at a later time point, the difference can be an indicator for the diagnosis of colorectal cancer in the subject. Based on this indicator for the diagnosis of colorectal cancer in a subject, clinical measures or decisions can be made, such as prescribing a new therapeutic intervention for the subject. The clinical measures or decisions can include recommending a secondary clinical examination to confirm the diagnosis of colorectal cancer in the subject. This secondary clinical examination can include an imaging test, a blood test, a computed tomography (CT) scan, a magnetic resonance imaging (MRI) scan, an ultrasound scan, a chest x-ray, a positron emission tomography (PET) scan, a PET-CT scan, a cell-free biopsy, a fecal immunochemical test (FIT), a fecal occult blood test (FOBT), or a combination thereof.

[0405] In some embodiments, differences in the quantitative measurement of proteins in a dataset of a panel of colorectal cancer-related proteins, including the quantitative measurement of the panel of colorectal cancer-related proteins determined between two or more time points, can be an indicator for the prognosis of colorectal cancer in a subject.

[0406] In some embodiments, a difference in the quantitative measure of a protein in a dataset in a panel of colorectal cancer-related proteins, including the quantitative measure of a panel of colorectal cancer-related proteins determined between two or more time points, can be an indicator of a subject having an increased risk of colorectal cancer. For example, in a subject where colorectal cancer is detected at both an earlier time point and a later time point, and the difference is a positive difference (e.g., the quantitative measure of the protein in the dataset in the panel of colorectal cancer-related proteins increases from the earlier time point to the later time point), the difference can be an indicator of a subject having an increased risk of colorectal cancer. Based on this indicator of increased risk of colorectal cancer, clinical measures or decisions can be made, such as, for example, prescribing a new therapeutic intervention for the subject or switching a therapeutic intervention (e.g., ending the current treatment and prescribing a new treatment). Clinical measures or decisions can include recommending to the subject a secondary clinical examination to confirm an increased risk of colorectal cancer. This secondary clinical examination can include an imaging test, a blood test, a computed tomography (CT) scan, a magnetic resonance imaging (MRI) scan, an ultrasound scan, a chest x-ray, a positron emission tomography (PET) scan, a PET-CT scan, a cell-free bioscreening, a fecal immunochemical test (FIT), a fecal occult blood test (FOBT), or a combination thereof.

[0407] In some embodiments, differences in the quantitative measurement of proteins in a dataset in a panel of colorectal cancer-related proteins, including the quantitative measurement of a panel of colorectal cancer-related proteins determined between two or more time points, can be an indicator of a subject having a reduced risk of colorectal cancer. For example, in a subject where colorectal cancer is detected at both an earlier time point and a later time point, and the difference is a negative difference (e.g., the quantitative measurement of the proteins in the dataset in a panel of colorectal cancer-related proteins including the quantitative measurement of the panel of colorectal cancer-related proteins decreases from the earlier time point to the later time point), the difference can be an indicator of a subject having a reduced risk of colorectal cancer. Based on this indicator of a decrease in the risk of colorectal cancer in a subject, clinical measures or decisions can be made (e.g., continuing or ending current therapeutic interventions). Clinical measures or decisions can include recommending a secondary clinical trial to confirm a decrease in the risk of colorectal cancer in the subject. This secondary clinical trial can include imaging tests, blood tests, computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, ultrasound scans, chest x-rays, positron emission tomography (PET) scans, PET-CT scans, cell-free bioscreening, fecal immunochemical test (FIT), fecal occult blood test (FOBT), or combinations thereof.

[0408] In some embodiments, differences in the quantitative measurement of proteins in a dataset in a panel of colorectal cancer-related proteins, including the quantitative measurement of a panel of colorectal cancer-related proteins determined between two or more time points, can be an indicator of the effectiveness of a treatment process for treating a subject's colorectal cancer. For example, if colorectal cancer was detected in a subject at an earlier time point but not detected in the subject at a later time point, the difference can be an indicator of the effectiveness of a treatment process for treating the subject's colorectal cancer. Based on this indicator of the effectiveness of a treatment process for treating a subject's colorectal cancer, clinical measures or decisions, such as continuing or ending a current therapeutic intervention, can be made. The clinical measures or decisions can include recommending to the subject a secondary clinical examination to confirm the effectiveness of a treatment process for treating colorectal cancer. This secondary clinical examination can include an imaging test, a blood test, a computed tomography (CT) scan, a magnetic resonance imaging (MRI) scan, an ultrasound scan, a chest x-ray, a positron emission tomography (PET) scan, a PET-CT scan, a cell-free biopsy, a fecal immunochemical test (FIT), a fecal occult blood test (FOBT), or a combination thereof.

[0409] In some embodiments, a difference in the quantitative measurement of a protein in a dataset in a panel of colorectal cancer-related proteins, including the quantitative measurement of a panel of colorectal cancer-related proteins determined between two or more time points, can be an indicator of the ineffectiveness of a treatment process for treating a subject's colorectal cancer. For example, if colorectal cancer is detected in a subject at both an earlier time point and a later time point, and the difference is a positive or zero difference (e.g., the quantitative measurement of the protein in the dataset in the panel of colorectal cancer-related proteins, including the quantitative measurement of the panel of colorectal cancer-related proteins, increases or remains at a certain level from the earlier time point to the later time point), and an effective treatment is suggested at the earlier time point, the difference can be an indicator of the ineffectiveness of the treatment process for treating the subject's colorectal cancer. Based on this indicator of the ineffectiveness of the treatment process for treating the subject's colorectal cancer, clinical treatments or decisions can be made, such as, for example, terminating the current therapeutic intervention and / or changing (e.g., prescribing) a different new therapeutic intervention for the subject. The clinical treatment or decision can include recommending to the subject a secondary clinical examination to confirm the ineffectiveness of the treatment process for treating colorectal cancer. This secondary clinical examination can include imaging tests, blood tests, computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, ultrasound scans, chest X-rays, positron emission tomography (PET) scans, PET-CT scans, cell-free biopsy, fecal immunochemical test (FIT), fecal occult blood test (FOBT), or combinations thereof.

[0410] Kit The present disclosure provides a kit for identifying or monitoring a target cancer. The kit can include antibodies, probes, or primers for identifying a quantitative measure (e.g., indicating presence, absence, or relative amount) of a protein in each of a plurality of cancer-related proteins in a cell-free biological sample of a subject. A quantitative measure (e.g., indicating presence, absence, or relative amount) of a panel of proteins in a cell-free biological sample can suggest one or more cancers. The probe can be selective for a protein in a cell-free biological sample. The kit can include instructions for using the probe to process a cell-free biological sample to generate a dataset indicative of a quantitative measure (e.g., indicating presence, absence, or relative amount) of a protein in the cell-free biological sample of the subject.

[0411] The probe in the kit can be selective for a protein or a sequence encoding a protein in a plurality of cancer-related proteins in a cell-free biological sample. The probe in the kit can be configured to selectively enrich protein molecules corresponding to a plurality of cancer-related proteins. The probe in the kit can be an antibody that is recognized by a protein and is tagged to enable isolation after binding to a protein in a biological sample.

[0412] The instructions in the kit can include instructions for assaying a cell-free biological sample using a probe selective for a cancer-related protein in the cell-free biological sample. A quantitative measure (e.g., indicating presence, absence, or relative amount) of a protein or a sequence encoding a protein in each of a plurality of cancer-related proteins in a cell-free biological sample can suggest one or more cancers.

[0413] The instructions in the kit can optionally include instructions for measuring and interpreting an assay readout value, which can be quantified in one or more of a plurality of cancer-related proteins to generate a dataset indicative of a quantitative measure (e.g., indicating presence, absence, or relative amount) of a protein or a sequence encoding a protein in each of a plurality of cancer-related proteins in a cell-free biological sample.

Example

[0414] Example 1: Analysis of Proteins in Patient Plasma Samples In cancer, either a protein, cancer neoantigen, or canonical protein represents a potential source of biomarkers for early diagnosis of colorectal cancer. The characteristics of proteins can be determined from plasma by assessing overexpression, depletion, or mutation of the proteins in cancer patients. Several proteins have been identified as being associated with breast, prostate, colorectal, lung, and ovarian cancers.

[0415] To identify informative proteins for the methods and classifiers described herein, plasma from patients with a proliferative disorder of colonic cells and plasma from subjects without a proliferative disorder of colonic cells (control plasma or reference plasma) were investigated to identify a signature panel of proteins and their respective reactive proteins produced in response to the proliferative disorder of colonic cells by patients with a proliferative disorder of colonic cells. For this purpose, both mass spectrometry-based proteomics and immunoaffinity assays were used to profile plasma from patients with a proliferative disorder of intestinal cells and control plasma to identify and quantify circulating plasma proteins. Plasma may or may not be depleted of highly abundant proteins (e.g., albumin, immunoglobulins) prior to characterization.

[0416] The protein panel identified by this analysis enabled discrimination between plasma from subjects with a proliferative disorder of colonic cells and plasma from healthy subjects.

[0417] Method Sample Classification To detect proteins in plasma samples, immunoassays and mass spectrometry assays were performed using plasma samples collected from subjects, and then subjects were identified as having colorectal cancer (CRC), advanced adenoma (AA), non-adenomatous polyp (NAA), or none of these (NEG).

[0418] Plasma samples were obtained using a standardized blood collection and processing protocol and then stored at -80 °C until use. Written consent was obtained from all subjects under the approval of the institutional review board.

[0419] Table 1 provides a description of the study cohort, which shows the number of healthy and cancer samples used in the CRC experiment in the classification model (by gender and age).

[0420]

Table 1

[0421] The aim of this study was to identify serum protein biomarkers that can discriminate between colorectal cancer, advanced adenomas, benign diseases, and healthy controls, improve the sensitivity of current biomarkers, and guide clinical decisions.

[0422] Plasma was separated from subject samples representing the NEG, CRC, AA, and NAA subject populations and screened on a protein array. A total of 1,472 features were identified among the NEG, CRC, AA, and NAA subject populations, and differential expression in plasma from subjects with proliferative disorders of colorectal cells and in plasma from healthy subjects was investigated.

[0423] Protein quantification data was normalized and reported on either a relative or absolute scale.

[0424] Filtering feature values:

[0425] The ability of each protein to discriminate between patients with a particular disease and those without the disease was evaluated by calculating metrics for three groups for discrimination. The discrimination groups were as follows: disease negative vs. colorectal cancer (NEG vs. CRC), disease negative vs. advanced adenoma (NEG vs. AA), and disease negative vs. advanced and non-advanced adenoma (NEG vs. AA+NAA). The metrics calculated for this comparison included the Hedges’ G effect size metric, the Wasserstein distance metric, the feature weights in single-assay linear logistic regression with elastic net regularization, the feature importance in single-assay non-linear random forest, and the feature weights in multi-assay logistic regression models.

[0426] For each protein, the percentile of the protein in the permetric distribution across all proteins was calculated. Proteins were ranked by their percentile in the permetric distribution. Proteins were retained for further consideration if they met at least one of the following criteria: A) From the analysis of the protein alone, it had a maximum metric percentile of 95 or above and a median metric percentile of 90 or above; B) It was ranked in the top 25 for the maximum metric percentile or the median metric percentile; C) It was ranked in the top 25 by the feature weights in the multi-assay logistic regression model.

[0427] Some additional features were also considered. Features that met the above criteria, including the additional features, were further investigated for reagent availability and those features were selected.

[0428] Results NEG, NAA, AA, CRC Table 2 provides a list of the identified proteins of the panel of protein biomarkers for CRC discrimination.

[0429]

Table 2-1

Table 2-2

Table 2-3

[0430] The fold-averaged metric of the ROC cross-validation test. The average performance metric is determined over 20 folds of the exploratory data using all data for feature selection, while the model weights are defined within the cross-validation.

[0431] For the "Target 0.9 Specificity" metric, predictions are made based on the predicted probability (score) and the label. A threshold is selected that maximizes sensitivity while achieving a specificity slightly above 0.9. For the "empirical" metric, predictions are made by using the default prediction threshold from the classifier, without targeting any specific specificity and not based on the test samples. Confidence intervals are reported within square brackets and are the average of the confidence intervals for each fold.

[0432] The 53 classification specifications are mainly CRC vs. NEG in the exploratory data where all data is used for feature selection, while the model weights are defined within the cross-validation (there is no true holdout in this performance).

[0433] Preferred embodiments of the present invention are shown and described herein, but it will be apparent to those skilled in the art that such embodiments are provided by way of example only. It is not intended that the present invention be limited by the specific examples provided within the specification. The present invention has been described with respect to the foregoing specification, but the description and illustration of the embodiments in this specification are not meant to be construed in a limiting sense. Numerous variations, modifications, and substitutions are presently contemplated by those skilled in the art without departing from the present invention. Further, it will be understood that all aspects of the present invention are not limited to the specific depictions, configurations, or relative ratios described herein, which depend on various conditions and variables. It should be understood that various alternatives of the embodiments of the present invention described herein are available for use in practicing the disclosed invention. Accordingly, it is contemplated that the present invention also encompasses such alternatives, modifications, variations, or equivalents. The following claims define the scope of the present invention, and it is intended that methods and structures within the scope of these claims and their equivalents be thereby encompassed.

Claims

1. A predetermined protein panel exhibiting characteristics of proliferative disorders of colon cells, comprising: Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS A protein panel comprising at least four proteins selected from the group consisting of IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40.

2. A classifier configured to distinguish between a group of healthy subjects and subjects having proliferative disorders of colon cells, The classifier includes a set of measurements representing proteins from a predetermined protein panel that exhibits characteristics of proliferative disorders in the colon cells, The aforementioned set of measurements was obtained from protein expression data from samples of healthy subjects and samples of subjects with proliferative disorders of colon cells. The measured values ​​are used to generate a set of features corresponding to the characteristics of the protein expression data, and the set of features is computer-processed using machine learning or statistical models. The machine learning or statistical model provides a feature vector useful as a classifier capable of distinguishing between a population of healthy subjects and subjects having proliferative disorders of the colon cells. Classifier.

3. A method for determining the protein profile of a biological sample derived from a target, a) In the biological sample containing the protein derived from the target, Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS A step of measuring the amount of protein from a predetermined panel of proteins comprising at least four proteins selected from the group consisting of IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40, thereby providing the protein profile of the target. Methods that include...

4. The predetermined panel of proteins is Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, Calcitonin, CEA, CXCL13, CYFRA21-1, DKK-3, ESM-1, Ferritin, FGF23, Free PSA, Gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS The method according to claim 3, comprising at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40.

5. The method according to claim 3, wherein the predetermined panel of proteins includes CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, IL-8, or a combination thereof.

6. The method according to claim 3, wherein the predetermined panel of proteins includes Abeta 38, Abeta 40, Abeta 42, Abeta 42.2, Ang-2, CA125, calcitonin, CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, hGH, IGFBP-7, IL-8, IL-6, HS IL-6, MMP-3, OPN, BNP, proBNP, NT-proBNP, total-NTproBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or a combination thereof.

7. The method according to claim 3, wherein the predetermined panel of proteins includes Ang-2, CA125, calcitonin, CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, hGH, IGFBP-7, IL-8, IL-6, HS IL-6, MMP-3, OPN, BNP, proBNP, NT-proBNP, total NT-proBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or a combination thereof.

8. The method according to claim 3, wherein the protein profile is associated with proliferative disorders of colon cells, and the classification of the subject is provided as having proliferative disorders of colon cells.

9. The method according to claim 3, wherein the biological sample derived from the subject is selected from the group consisting of body fluids, feces, colonic effluent, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

10. The method according to claim 3, wherein the proliferative disorder of the colonic cells is selected from the group consisting of adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile hyperplastic adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

11. A method for detecting proliferative disorders of colon cells in a subject, a) In a biological sample containing the protein derived from the subject, Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA 21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS A step of measuring the amount of protein from a predetermined protein panel comprising at least four proteins selected from the group consisting of IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total-NTproBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40, thereby providing the target protein profile. b) A step of computerizing the protein profile using a machine learning model trained to distinguish between healthy subjects and subjects having the proliferative disorder of colon cells, to provide output values ​​associated with the presence or absence of the proliferative disorder of colon cells, thereby suggesting the presence or absence of the proliferative disorder of colon cells in the subject. Methods that include...

12. A predetermined panel of proteins includes Abeta38, Abeta40, Abeta42, Abeta42.2, AFP, AFP-L3, Ang-2, anti-p53, CA125, CA15-3, CA19-9, CA72-4, CA242, CA50, calcitonin, CEA, CXCL13, CYFRA21-1, DKK-3, ESM-1, ferritin, FGF23, free PSA, gastrin-17, GDF-15, HE4, hGH, IGF-1, IGFBP-3, IGFBP-7, IL-22, IL-8, IL-6, HS The method according to claim 11, comprising at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of IL-6, MMP-3, NSE, OPN, pepsinogen 1, pepsinogen 2, PIVKA, BNP, proBNP, NT-proBNP, total-NTproBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40.

13. The method according to claim 11, wherein the predetermined panel of proteins includes CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, IL-8, or a combination thereof.

14. The method according to claim 11, wherein the predetermined panel of proteins includes Abeta 38, Abeta 40, Abeta 42, Abeta 42.2, Ang-2, CA125, calcitonin, CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, hGH, IGFBP-7, IL-8, IL-6, HS IL-6, MMP-3, OPN, BNP, proBNP, NT-proBNP, total NT-proBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or a combination thereof.

15. The method according to claim 11, wherein the predetermined panel of proteins includes Ang-2, CA125, calcitonin, CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, hGH, IGFBP-7, IL-8, IL-6, HS IL-6, MMP-3, OPN, BNP, proBNP, NT-proBNP, total NT-proBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or a combination thereof.

16. The method according to claim 11, wherein the protein profile is associated with proliferative disorders of colon cells, and the subject is classified as having proliferative disorders of colon cells.

17. The method according to claim 11, wherein the biological sample derived from the subject is selected from the group consisting of body fluids, feces, colonic effluent, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

18. The method according to claim 11, wherein the proliferative disorder of the colonic cells is selected from the group consisting of adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile hyperplastic adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

19. A predetermined protein panel exhibiting characteristics of proliferative disorders of colon cells, comprising at least four proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA 21-1, ferritin, FGF23, gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL.

20. A method for determining the protein profile of a biological sample derived from a target, a) A step of measuring the amount of protein from a predetermined panel of proteins in the biological sample containing the protein derived from the target, the panel of proteins including at least four proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA 21-1, ferritin, FGF23, gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL. Methods that include...

21. The method according to claim 20, wherein the panel comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA 21-1, ferritin, FGF23, gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL.

22. The method according to claim 20, wherein the protein profile is associated with proliferative disorders of colorectal cells, and the subject is classified as having such proliferative disorders.

23. The method according to claim 20, wherein the biological sample derived from the subject is selected from the group consisting of body fluids, feces, colonic effluent, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

24. The method according to claim 20, wherein the proliferative disorder of colorectal cells is selected from the group consisting of adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile hyperplastic adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colorectal cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

25. A method for detecting proliferative disorders of colon cells in a subject, a) A step of measuring the amount of protein from a predetermined protein panel containing at least four proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA 21-1, ferritin, FGF23, gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL in a biological sample containing the protein derived from the target, thereby providing a protein profile of the target; b) A step of computerizing the protein profile using a machine learning model trained to distinguish between healthy subjects and subjects having the proliferative disorder of colon cells, to provide output values ​​associated with the presence or absence of the proliferative disorder of colon cells, thereby suggesting the presence or absence of the proliferative disorder of colon cells in the subject. Methods that include...

26. The method according to claim 25, wherein a predetermined panel of proteins comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA21-1, ferritin, FGF23, gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL.

27. ​​The method according to claim 25, wherein the protein profile is associated with proliferative disorders of colorectal cells, and the subject is classified as having such proliferative disorders.

28. The method according to claim 25, wherein the biological sample derived from the subject is selected from the group consisting of body fluids, feces, colonic effluent, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

29. The method according to claim 25, wherein the proliferative disorder of the colonic cells is selected from the group consisting of adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile hyperplastic adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

30. A predetermined protein panel exhibiting characteristics of proliferative disorders of colon cells, comprising at least four proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CEA, CXCL13, CYFRA 21-1, ferritin, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, and sFlt-1.

31. A method for determining the protein profile of a biological sample derived from a target, a) A step of measuring the amount of protein from a predetermined panel of proteins in the biological sample containing the protein derived from the target, the panel of proteins including at least four proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CEA, CXCL13, CYFRA 21-1, ferritin, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, and sFlt-1. Methods that include...

32. The method according to claim 31, wherein the panel comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CEA, CXCL13, CYFRA 21-1, ferritin, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, and sFlt-1.

33. The method according to claim 31, wherein the protein profile is associated with proliferative disorders of colon cells, and the subject is classified as having proliferative disorders of colon cells.

34. The method according to claim 31, wherein the biological sample derived from the subject is selected from the group consisting of body fluids, feces, colonic effluent, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

35. The method according to claim 31, wherein the proliferative disorder of colorectal cells is selected from the group consisting of adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile hyperplastic adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colorectal cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

36. A method for detecting proliferative disorders of colon cells in a subject, a) A step of measuring the amount of protein from a predetermined protein panel containing at least four proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CEA, CXCL13, CYFRA 21-1, ferritin, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, and sFlt-1 in a biological sample containing the protein derived from the target, thereby providing a protein profile of the target; b) A step of computerizing the protein profile using a machine learning model trained to distinguish between healthy subjects and subjects having the proliferative disorder of colon cells, to provide output values ​​associated with the presence or absence of the proliferative disorder of colon cells, thereby suggesting the presence or absence of the proliferative disorder of colon cells in the subject. Methods that include...

37. The method according to claim 36, wherein a predetermined panel of proteins comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CEA, CXCL13, CYFRA 21-1, ferritin, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, and sFlt-1.

38. The method according to claim 36, wherein the protein profile is associated with proliferative disorders of colon cells, and the subject is classified as having proliferative disorders of colon cells.

39. The method according to claim 36, wherein the biological sample derived from the subject is selected from the group consisting of body fluids, feces, colonic effluent, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

40. The method according to claim 36, wherein the proliferative disorder of the colonic cells is selected from the group consisting of adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile hyperplastic adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

41. A predetermined protein panel exhibiting characteristics of proliferative disorders of colon cells, comprising at least four proteins selected from the group consisting of AFP, AFP-L3, hGH, IL8, NSE, CEA, anti-p53, sFlt-1, ferritin, CYFRA 21-1, CA125, IGF-1, CXCL-13, and YKL.

42. A method for determining the protein profile of a biological sample derived from a target, a) A step of measuring the amount of protein from a predetermined panel of proteins in the biological sample containing the protein derived from the target, the panel of proteins including at least four proteins selected from the group consisting of AFP, AFP-L3, hGH, IL8, NSE, CEA, anti-p53, sFlt-1, ferritin, CYFRA 21-1, CA125, IGF-1, CXCL-13, and YKL. Methods that include...

43. The method according to claim 42, wherein the panel comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, hGH, IL8, NSE, CEA, anti-p53, sFlt-1, ferritin, CYFRA 21-1, CA125, IGF-1, CXCL-13, and YKL.

44. The method according to claim 42, wherein the protein profile is associated with proliferative disorders of colon cells, and the classification of the subject is provided as having proliferative disorders of colon cells.

45. The method according to claim 42, wherein the biological sample derived from the subject is selected from the group consisting of body fluids, feces, colonic effluent, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

46. The method according to claim 42, wherein the proliferative disorder of colorectal cells is selected from the group consisting of adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile hyperplastic adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colorectal cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

47. A method for detecting proliferative disorders of colon cells in a subject, a) A step of measuring the amount of protein from a predetermined protein panel containing at least four proteins selected from the group consisting of AFP, AFP-L3, hGH, IL8, NSE, CEA, anti-p53, sFlt-1, ferritin, CYFRA 21-1, CA125, IGF-1, CXCL-13, and YKL in a biological sample containing the protein derived from the target, thereby providing a protein profile of the target; b) A step of computerizing the protein profile using a machine learning model trained to distinguish between healthy subjects and subjects having the proliferative disorder of colon cells, to provide output values ​​associated with the presence or absence of the proliferative disorder of colon cells, thereby suggesting the presence or absence of the proliferative disorder of colon cells in the subject. Methods that include...

48. The method according to claim 47, wherein a predetermined panel of proteins comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, hGH, IL8, NSE, CEA, anti-p53, sFlt-1, ferritin, CYFRA 21-1, CA125, IGF-1, CXCL-13, and YKL.

49. The method according to claim 47, wherein the protein profile is associated with proliferative disorders of colon cells, and the subject is classified as having proliferative disorders of colon cells.

50. The method according to claim 47, wherein the biological sample derived from the subject is selected from the group consisting of body fluids, feces, colonic effluent, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

51. The method according to claim 47, wherein the proliferative disorder of the colonic cells is selected from the group consisting of adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile hyperplastic adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

52. A predetermined protein panel exhibiting characteristics of proliferative disorders of colon cells, comprising at least four proteins selected from the group consisting of CA15-13, CA-19-9, CYFRA 21-1, IL-6, IL-6hs, OPN, and total PSA.

53. A method for detecting proliferative disorders of colon cells in a subject, a) A step of measuring the amount of protein from a predetermined protein panel containing at least four proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA 21-1, ferritin, FGF23, gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL in a biological sample containing the protein derived from the target, thereby providing a protein profile of the target; b) A step of computerizing the protein profile using a machine learning model trained to distinguish between healthy subjects and subjects having the proliferative disorder of colon cells, to provide output values ​​associated with the presence or absence of the proliferative disorder of colon cells, thereby suggesting the presence or absence of the proliferative disorder of colon cells in the subject. Methods that include...

54. The method according to claim 53, wherein a predetermined panel of proteins comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA 21-1, ferritin, FGF23, gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL.

55. The method according to claim 53, wherein the protein profile is associated with proliferative disorders of colon cells, and the subject is classified as having proliferative disorders of colon cells.

56. The method according to claim 53, wherein the biological sample derived from the subject is selected from the group consisting of body fluids, feces, colonic effluent, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

57. The method according to claim 53, wherein the proliferative disorder of the colonic cells is selected from the group consisting of adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile hyperplastic adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

58. A predetermined protein panel exhibiting characteristics of proliferative disorders of colorectal cells, comprising at least four proteins selected from the group consisting of AFP, AFP-L3, Ang-2, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA 21-1, DKK-3, ferritin, FGF23, gastrin-17, GDF-15, hGH, IGF-1, IGFBP-3, IL-6, IL-8, MMP-3, NSE, pepsinogen 2, PRL2, S100, sFlt-1, sTREM-1, total PSA, VEGF-A, and YKL.

59. A method for determining the protein profile of a biological sample derived from a target, a) A step of measuring the amount of protein from a predetermined panel of proteins in the biological sample containing the protein derived from the target, the panel of proteins including at least four proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA 21-1, ferritin, FGF23, gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL. Methods that include...

60. The method according to claim 59, wherein the panel comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA 21-1, ferritin, FGF23, gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL.

61. The method according to claim 59, wherein the protein profile is associated with proliferative disorders of colon cells, and the subject is classified as having proliferative disorders of colon cells.

62. The method according to claim 59, wherein the biological sample derived from the subject is selected from the group consisting of body fluids, feces, colonic effluent, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

63. The method according to claim 59, wherein the proliferative disorder of the colonic cells is selected from the group consisting of adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile hyperplastic adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

64. A method for detecting proliferative disorders of colon cells in a subject, a) A step of measuring the amount of protein from a predetermined protein panel containing at least four proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA 21-1, ferritin, FGF23, gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL in a biological sample containing the protein derived from the target, thereby providing a protein profile of the target; b) A step of computerizing the protein profile using a machine learning model trained to distinguish between healthy subjects and subjects having the proliferative disorder of colon cells, to provide output values ​​associated with the presence or absence of the proliferative disorder of colon cells, thereby suggesting the presence or absence of the proliferative disorder of colon cells in the subject. Methods that include...

65. The method according to claim 64, wherein a predetermined panel of proteins comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, anti-p53, CA125 II, CA15-3, CA72-4, CEA, CXCL13, CYFRA 21-1, ferritin, FGF23, gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL.

66. The method according to claim 64, wherein the protein profile is associated with proliferative disorders of colon cells, and the subject is classified as having proliferative disorders of colon cells.

67. The method according to claim 64, wherein the biological sample derived from the subject is selected from the group consisting of body fluids, feces, colonic effluent, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

68. The method according to claim 64, wherein the proliferative disorder of the colonic cells is selected from the group consisting of adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile hyperplastic adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

69. A predetermined protein panel exhibiting characteristics of proliferative disorders of colorectal cells, comprising at least four proteins selected from the group consisting of AFP, AFP-L3, CA125 II, CA15-3, CEA, CXCL13, CYFRA 21-1, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, PRL2, sFlt-1, and YKL.

70. The protein panel according to claim 69, comprising at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, CA125 II, CA15-3, CEA, CXCL13, CYFRA 21-1, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, PRL2, sFlt-1, and YKL.

71. The protein panel according to claim 69, configured to distinguish between a healthy subject, a subject with a benign colon polyp, a subject with an advanced adenoma, or a subject with colorectal cancer.

72. The protein panel according to claim 69, wherein the proliferative disorder of the colorectal cells is selected from the group consisting of adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile hyperplastic adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colorectal cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

73. A method for determining the protein profile of a biological sample derived from a target, a) A step of measuring the amount of protein from a predetermined panel of proteins in the biological sample containing the protein derived from the target, the panel of proteins including at least four proteins selected from the group consisting of AFP, AFP-L3, CA125 II, CA15-3, CEA, CXCL13, CYFRA 21-1, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, PRL2, sFlt-1, and YKL. Methods that include...

74. The method according to claim 73, wherein the panel comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, CA125 II, CA15-3, CEA, CXCL13, CYFRA 21-1, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, PRL2, sFlt-1, and YKL.

75. The method according to claim 73, wherein the protein profile is associated with proliferative disorders of colon cells, and the subject is classified as having proliferative disorders of colon cells.

76. The method according to claim 73, wherein the biological sample derived from the subject is selected from the group consisting of body fluids, feces, colonic effluent, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

77. The method according to claim 73, wherein the proliferative disorder of the colonic cells is selected from the group consisting of adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile hyperplastic adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

78. A method for detecting proliferative disorders of colon cells in a subject, a) A step of measuring the amount of protein from a predetermined protein panel containing at least four proteins selected from the group consisting of AFP, AFP-L3, CA125 II, CA15-3, CEA, CXCL13, CYFRA 21-1, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, PRL2, sFlt-1, and YKL in a biological sample containing the protein derived from the target, thereby providing a protein profile of the target; b) A step of computerizing the protein profile using a machine learning model trained to distinguish between healthy subjects and subjects having the proliferative disorder of colon cells, to provide output values ​​associated with the presence or absence of the proliferative disorder of colon cells, thereby suggesting the presence or absence of the proliferative disorder of colon cells in the subject. Methods that include...

79. The method according to claim 78, wherein a predetermined panel of proteins comprises at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 proteins selected from the group consisting of AFP, AFP-L3, CA125 II, CA15-3, CEA, CXCL13, CYFRA 21-1, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, PRL2, sFlt-1, and YKL.

80. The method according to claim 78, wherein the protein profile is associated with proliferative disorders of colon cells, and the subject is classified as having proliferative disorders of colon cells.

81. The method according to claim 78, wherein the biological sample derived from the subject is selected from the group consisting of body fluids, feces, colonic effluent, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

82. The method according to claim 78, wherein the proliferative disorder of the colonic cells is selected from the group consisting of adenoma (adenomatous polyp), polyposis disorder, Lynch syndrome, sessile hyperplastic adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

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