Markers for early detection of colonic cell proliferative disorders

Abstract

The systems, media, compositions, methods, and kits disclosed herein relate to a panel of protein biomarkers for the early detection of colonic cell proliferative disorders, including colorectal cancer. The presence and levels of proteins in biological samples for the protein groups described herein can be used for classifier generation and as inputs in machine learning models that can be used to classify subjects in a population to detect colonic cell proliferative disorders.

Description

[0001] Cross-references

[0002] This application claims the benefit of U.S. Provisional Application No. 63 / 348,666, filed on June 3, 2022, which is incorporated herein by reference in its entirety. Technical Field

[0003] The present disclosure relates to biomarkers and methods for early identification of cell proliferative disorders of the colon including advanced adenomas and colorectal cancer. Background Art

[0004] Colorectal cancer (CRC) is the leading cause of cancer-related death in the Western world. Although CRC is one of the best characterized solid tumors, it remains one of the leading causes of death in developed countries due to late diagnosis. Among other reasons, late diagnosis in patients is due to diagnostic tests such as colonoscopy being performed too late. Deaths due to CRC can be prevented by effective early screening. Summary of the invention

[0005] The present disclosure provides methods and systems involving protein profiling of biological samples associated with CRC detection and disease progression. The studies described herein are able to determine the presence of specific protein signatures of CRC, indicating the presence of specific biomarkers of colon cell proliferative disorders, with the potential to detect colon cell proliferative disorders, stratify patient populations, and classify populations using plasma from subjects with colon cell proliferative disorders, with high specificity and high sensitivity.

[0006] In one aspect, the present disclosure provides a panel of predetermined proteins characteristic of a cell proliferative disorder of the colon, 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 NT-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 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 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.

[0008] In some embodiments, the panel includes CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, IL-8, sTREM-1, or a combination thereof.

[0009] In some embodiments, the panel includes CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, IL-8, or a combination thereof.

[0010] In some embodiments, the panel includes 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 NT-proBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or a combination thereof.

[0011] In some embodiments, the panel 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.

[0012] In some embodiments, the panel includes free PSA.

[0013] In some embodiments, the panel is configured to distinguish between healthy subjects, subjects with benign colon polyps, subjects with advanced adenomas, or subjects with colorectal cancer.

[0014] In some embodiments, the panel is configured to indicate advanced adenoma and 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.

[0015] In some embodiments, the panel is configured to be indicative of advanced adenoma and 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 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.

[0016] In some embodiments, the panel is configured to be indicative of colorectal cancer and 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.

[0017] In some embodiments, the panel is configured to be indicative of colorectal cancer and 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 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.

[0018] In some embodiments, the cell proliferative disorder of the colon is selected from adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma and sarcoma.

[0019] In another aspect, the present disclosure provides a classifier configured to distinguish between a population of healthy subjects and subjects suffering from a cell proliferative disorder of the colon, the classifier comprising:

[0020] a measurement set representing proteins from a predetermined panel of proteins characteristic of a cell proliferative disorder of the colon,

[0021] wherein the set of measurements is obtained from protein expression data from samples from healthy subjects and samples from subjects having a cell proliferative disorder of the colon,

[0022] wherein the measurements are used to generate a feature set corresponding to properties of the protein expression data, wherein the feature set is processed computationally using a machine learning or statistical model, and

[0023] Wherein the machine learning or statistical model provides a feature vector that can be used as a classifier capable of distinguishing between a population of healthy subjects and subjects suffering from a cell proliferative disorder of the colon.

[0024] In some embodiments, the predetermined panel of proteins 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.

[0025] In some embodiments, the predetermined panel of proteins 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 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.

[0026] In some embodiments, the panel includes CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, IL-8, sTREM-1, or a combination thereof.

[0027] In some embodiments, the panel includes CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, IL-8, or a combination thereof.

[0028] In some embodiments, the panel includes 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 NT-proBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or a combination thereof.

[0029] In some embodiments, the panel 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.

[0030] In some embodiments, the panel includes measuring total PSA.

[0031] In some embodiments, the classifier is configured to distinguish between a healthy subject, a subject with benign colon polyps, a subject with advanced adenoma, or a subject with colorectal cancer.

[0032] In some embodiments, the panel is configured to indicate advanced adenoma and 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.

[0033] In some embodiments, the panel is configured to be indicative of advanced adenoma and 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 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.

[0034] In some embodiments, the panel is configured to be indicative of colorectal cancer and 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.

[0035] In some embodiments, the panel is configured to be indicative of colorectal cancer and 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 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.

[0036] In some embodiments, the predetermined panel of proteins includes 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.

[0037] In some embodiments, the predetermined panel of proteins 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, 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.

[0038] In some embodiments, the predetermined panel of proteins includes 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.

[0039] In some embodiments, the predetermined panel of proteins 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.

[0040] In some embodiments, the predetermined panel of proteins includes 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.

[0041] In some embodiments, the predetermined panel of proteins 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, CYFRA 21-1, CA125, IGF-1, CXCL-13, and YKL.

[0042] In some embodiments, the predetermined panel of proteins includes 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.

[0043] In some embodiments, the predetermined panel of proteins includes 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.

[0044] In some embodiments, the predetermined panel of proteins includes 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.

[0045] In some embodiments, the predetermined panel of proteins 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, 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.

[0046] In some embodiments, the predetermined panel of proteins includes 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.

[0047] In some embodiments, the predetermined panel of proteins 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, CA125 II, CA15-3, CEA, CXCL13, CYFRA 21-1, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, PRL2, sFlt-1, and YKL.

[0048] In some embodiments, the cell proliferative disorder of the colon is selected from adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma and sarcoma.

[0049] In another aspect, the present disclosure provides a system comprising a machine learning model classifier for detecting a cell proliferative disorder of the colon, the system comprising:

[0050] A computer-readable medium comprising a classifier operable to classify an object based on a predetermined panel of proteins; and

[0051] One or more processors for executing instructions stored on a computer-readable medium.

[0052] In some embodiments, a system includes a classifier configured to distinguish between a population of healthy subjects and subjects having a cell proliferative disorder of the colon, the classifier comprising:

[0053] a measurement set representing proteins from a predetermined panel of proteins characteristic of a cell proliferative disorder of the colon,

[0054] wherein the set of measurements is obtained from protein expression data from samples from healthy subjects and samples from subjects having a cell proliferative disorder of the colon,

[0055] wherein the measurements are used to generate a feature set corresponding to properties of the protein expression data, wherein the feature set is computer processed using a machine learning or statistical model,

[0056] wherein the machine learning or statistical model provides a feature vector that can be used as a classifier capable of distinguishing between a population of healthy subjects and subjects suffering from a cell proliferative disorder of the colon,

[0057] wherein the classifier is loaded into a memory of a computer system, wherein the machine learning model is trained using training data obtained from training biological samples, wherein a first subset of the training biological samples are identified as corresponding to subjects having a cell proliferative disorder of the colon, and wherein a second subset of the training biological samples are identified as corresponding to subjects not having a cell proliferative disorder of the colon.

[0058] 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.

[0059] 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 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.

[0060] In some embodiments, the predetermined panel of proteins includes 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.

[0061] In some embodiments, the predetermined panel of proteins 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, 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.

[0062] In some embodiments, the predetermined panel of proteins includes 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.

[0063] In some embodiments, the predetermined panel of proteins 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.

[0064] In some embodiments, the predetermined panel of proteins includes 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.

[0065] In some embodiments, the predetermined panel of proteins 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, CYFRA 21-1, CA125, IGF-1, CXCL-13, and YKL.

[0066] In some embodiments, the predetermined panel of proteins includes 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.

[0067] In some embodiments, the predetermined panel of proteins includes 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.

[0068] In some embodiments, the predetermined panel of proteins includes 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.

[0069] In some embodiments, the predetermined panel of proteins 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, 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.

[0070] In some embodiments, the predetermined panel of proteins includes 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.

[0071] In some embodiments, the predetermined panel of proteins 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, CA125 II, CA15-3, CEA, CXCL13, CYFRA 21-1, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, PRL2, sFlt-1, and YKL.

[0072] In some embodiments, the machine learning model is trained using training data obtained from training biological samples, a first subset of training biological samples are identified as corresponding to subjects having a cell proliferative disorder of the colon, and a second subset of training biological samples are identified as corresponding to subjects not having a cell proliferative disorder of the colon.

[0073] In some embodiments, a classifier is provided in a system for detecting a colon cell proliferative disorder, the system comprising: a) a computer readable medium comprising a classifier operable to classify an object based on a panel of protein features; and b) one or more processors for executing instructions stored on the computer readable medium.

[0074] In some embodiments, the system includes a classification circuit configured as a machine learning classifier selected from 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 classifier, a linear kernel support vector machine classifier, a first-order 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.

[0075] In another aspect, the present disclosure provides a method for determining a protein profile of a biological sample from a subject, the method comprising:

[0076] a) obtaining a biological sample containing a protein from a subject; and

[0077] b) measuring the amount of proteins from a predetermined panel of proteins in a biological sample, thereby providing a protein profile of the subject, the 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.

[0078] In some embodiments, the predetermined panel of proteins 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 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.

[0079] In some embodiments, the panel includes CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, IL-8, sTREM-1, or a combination thereof.

[0080] In some embodiments, the panel includes CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, IL-8, or a combination thereof.

[0081] In some embodiments, the panel includes 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 NT-proBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or a combination thereof.

[0082] In some embodiments, the panel 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.

[0083] In some embodiments, the panel includes total PSA.

[0084] In some embodiments, the protein profile is associated with a cell proliferative disorder of the colon and provides a classification of the subject as having a cell proliferative disorder of the colon.

[0085] In some embodiments, the biological sample from the subject is selected from the group consisting of body fluids, feces, colonic discharge, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

[0086] In some embodiments, the cell proliferative disorder of the colon is selected from adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma and sarcoma.

[0087] In some embodiments, the method further comprises treating the subject with surgery and / or a therapeutic agent based on the subject's protein profile.

[0088] In some embodiments, the method further comprises administering a treatment to the subject based on the detected colon cell proliferative disorder. In some embodiments, the treatment comprises chemotherapy, radiation therapy, immunotherapy, or surgery.

[0089] In another aspect, the present disclosure provides a method for detecting a colon cell proliferative disorder in a subject, the method comprising:

[0090] a) obtaining a biological sample containing a protein from a subject;

[0091] b) measuring the amount of proteins from a predetermined panel of proteins in a biological sample, thereby providing a protein profile of the subject, the 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, and YKL40; and

[0092] c) computer processing the protein profile using a machine learning model trained to be able to distinguish between healthy subjects and subjects suffering from a colon cell proliferative disorder to provide an output value associated with the presence or absence of the colon cell proliferative disorder, thereby indicating the presence or absence of the colon cell proliferative disorder in the subject.

[0093] In some embodiments, the predetermined panel of proteins 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 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.

[0094] In some embodiments, the panel includes CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, IL-8, sTREM-1, or a combination thereof.

[0095] In some embodiments, the panel includes CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, IL-8, or a combination thereof.

[0096] In some embodiments, the panel includes 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 NT-proBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or a combination thereof.

[0097] In some embodiments, the panel 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.

[0098] In some embodiments, the panel includes measuring total PSA.

[0099] In some embodiments, the protein profile is associated with a cell proliferative disorder of the colon and provides a classification of the subject as having a cell proliferative disorder of the colon.

[0100] In some embodiments, the biological sample from the subject is selected from the group consisting of body fluids, feces, colonic discharge, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

[0101] In some embodiments, the cell proliferative disorder of the colon is selected from adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma and sarcoma.

[0102] In some embodiments, the method further comprises treating the subject with surgery and / or a therapeutic agent based on the subject's protein profile.

[0103] In some embodiments, the method further comprises administering a treatment to the subject based on the detected colon cell proliferative disorder. In some embodiments, the treatment comprises chemotherapy, radiation therapy, immunotherapy, or surgery.

[0104] In another aspect, a method for monitoring minimal residual disease in a subject previously treated for a disease is provided, the method comprising:

[0105] a) determining a protein profile of a biological sample from the subject using a panel having 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;

[0106] b) determining a protein profile of a biological sample obtained from the subject at one or more time points after generating the baseline protein state, thereby generating a current protein state; and

[0107] c) determining the difference between the baseline protein status and the current protein status, thereby detecting a change in minimal residual disease in the subject.

[0108] In some embodiments, minimal residual disease is selected from treatment response, tumor burden, postoperative residual tumor, recurrence, secondary screening, primary screening, and cancer development. In some embodiments, the method further comprises administering treatment to the subject based on changes in minimal residual disease detected in the subject. In some embodiments, treatment comprises chemotherapy, radiotherapy, immunotherapy, or surgery. Treatment can be selected (e.g., from 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 the biological trait set of the subject. The biological trait can be a measurement, diagnosis, prognosis, or prediction (e.g., determined using a trained machine learning classifier).

[0109] In some embodiments, the biological traits include malignancies. In some embodiments, the biological traits include cancer types. In some embodiments, the biological traits include cancer stages. In some embodiments, the biological traits include cancer classifications. In some embodiments, cancer classifications include cancer grading. In some embodiments, cancer classifications include histological classifications. In some embodiments, the biological traits include metabolic profiles. In some embodiments, the biological traits include mutations. In some embodiments, mutations are mutations associated with diseases. In some embodiments, the biological traits include clinical outcomes. In some embodiments, the biological traits include drug responses.

[0110] In another aspect, a method for determining a subject's response to a treatment is provided, the method comprising:

[0111] a) determining a protein profile of a biological sample from the subject using a panel having 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;

[0112] b) determining a protein profile of a biological sample obtained from the subject at one or more time points after generating the baseline protein state, thereby generating a current protein state; and

[0113] c) determining the difference between the baseline protein status and the current protein status, thereby determining the subject's response to treatment.

[0114] In some embodiments, the method further comprises administering a treatment to the subject based on the determined response of the subject to the treatment. In some embodiments, the treatment comprises chemotherapy, radiotherapy, immunotherapy, or surgery.

[0115] In another aspect, a method for monitoring tumor burden in a subject is provided, the method comprising:

[0116] a) determining a protein profile of a biological sample from the subject using a panel having 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;

[0117] b) determining a protein profile of a biological sample obtained from the subject at one or more time points after generating the baseline protein state, thereby generating a current protein state; and

[0118] c) determining the difference between the baseline protein status and the current protein status, thereby monitoring the tumor burden in the subject.

[0119] In some embodiments, the method further comprises administering a treatment to the subject based on the subject's tumor burden. In some embodiments, the treatment comprises chemotherapy, radiotherapy, immunotherapy, or surgery.

[0120] In another aspect, a method for detecting residual tumor in a subject after surgery is provided, the method comprising:

[0121] a) determining a protein profile of a biological sample from the subject using a panel having 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;

[0122] b) determining a protein profile of a biological sample obtained from the subject at one or more time points after generating the baseline protein state, thereby generating a current protein state; and

[0123] c) determining the difference between the baseline protein status and the current protein status to detect residual tumor in the subject after surgery.

[0124] In some embodiments, the method further comprises administering treatment to the subject based on the detected residual tumor after surgery in the subject. In some embodiments, the treatment comprises chemotherapy, radiotherapy, immunotherapy or surgery.

[0125] In another aspect, a method for detecting recurrence in a subject is provided, the method comprising:

[0126] a) determining a protein profile of a biological sample from the subject using a panel having 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;

[0127] b) determining a protein profile of a biological sample obtained from the subject at one or more time points after generating the baseline protein state, thereby generating a current protein state; and

[0128] c) determining a difference between the baseline protein status and the current protein status, thereby detecting a relapse in the subject.

[0129] 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 comprises chemotherapy, radiotherapy, immunotherapy or surgery.

[0130] In another aspect, a method of performing secondary screening based at least in part on a protein profile of a subject is provided.

[0131] In another aspect, a method of performing a screening based at least in part on a protein profile of a subject is provided.

[0132] In another aspect, a method for monitoring the progression of cancer in a subject is provided, the method comprising:

[0133] a) determining a protein profile of a biological sample from the subject using a panel having 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;

[0134] b) determining a protein profile of a biological sample obtained from the subject at one or more time points after generating the baseline protein state, thereby generating a current protein state; and

[0135] c) determining the difference between the baseline protein status and the current protein status, thereby monitoring the progression of cancer in the subject.

[0136] 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 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.

[0137] In another aspect, the present disclosure provides a predetermined panel of proteins characteristic of a cell proliferative disorder of the colon, 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, CYFRA 21-1, ferritin, FGF23, gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1, and YKL.

[0138] 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, 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, proBNPII, sFlt-1, and YKL.

[0139] In some embodiments, the panel is configured to distinguish between healthy subjects, subjects with benign colon polyps, subjects with advanced adenomas, or subjects with colorectal cancer.

[0140] In some embodiments, the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0141] In another aspect, the present disclosure provides a method for determining a protein profile of a biological sample from a subject, the method comprising:

[0142] (a) obtaining a biological sample containing a protein from a subject; and

[0143] (b) measuring the amount of a protein from a predetermined panel of proteins in a biological sample, the predetermined panel of proteins 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.

[0144] 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, 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, proBNPII, sFlt-1, and YKL.

[0145] In some embodiments, the protein profile is associated with a cell proliferative disorder of the colon and provides a classification of the subject as having a cell proliferative disorder of the colon.

[0146] In some embodiments, the biological sample from the subject is selected from the group consisting of body fluids, feces, colonic discharge, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

[0147] In some embodiments, the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0148] In some embodiments, the method further comprises: c) treating the subject with surgery and / or a therapeutic agent based on the subject's protein profile.

[0149] In another aspect, the present disclosure provides a method for detecting a colon cell proliferative disorder in a subject, the method comprising:

[0150] (a) obtaining a biological sample containing a protein from a subject;

[0151] (b) measuring the amount of proteins from a predetermined panel of proteins in a biological sample, thereby providing a protein profile of the subject, the predetermined panel of proteins 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; and

[0152] (c) computer processing the protein profile using a machine learning model trained to be able to distinguish between healthy subjects and subjects suffering from a colon cell proliferative disorder to provide an output value associated with the presence or absence of the colon cell proliferative disorder, thereby indicating the presence or absence of the colon cell proliferative disorder in the subject.

[0153] In some embodiments, the predetermined panel of proteins 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, 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.

[0154] In some embodiments, the protein profile is associated with a cell proliferative disorder of the colon and provides a classification of the subject as having a cell proliferative disorder of the colon.

[0155] In some embodiments, the biological sample from the subject is selected from the group consisting of body fluids, feces, colonic discharge, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

[0156] In some embodiments, the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0157] In some embodiments, the method further comprises: d) treating the subject with surgery and / or a therapeutic agent based on the subject's protein profile.

[0158] In another aspect, the present disclosure provides a predetermined panel of proteins characteristic of a cell proliferative disorder of the colon, 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.

[0159] 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, CYFRA 21-1, ferritin, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, and sFlt-1.

[0160] In some embodiments, the panel is configured to distinguish between healthy subjects, subjects with benign colon polyps, subjects with advanced adenomas, or subjects with colorectal cancer.

[0161] In some embodiments, the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0162] In another aspect, the present disclosure provides a method for determining a protein profile of a biological sample from a subject, the method comprising:

[0163] (a) obtaining a biological sample containing a protein from a subject; and

[0164] (b) measuring the amount of a protein from a predetermined panel of proteins in a biological sample, the predetermined panel of proteins 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.

[0165] 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, CYFRA 21-1, ferritin, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, and sFlt-1.

[0166] In some embodiments, the protein profile is associated with a cell proliferative disorder of the colon and provides a classification of the subject as having a cell proliferative disorder of the colon.

[0167] In some embodiments, the biological sample from the subject is selected from the group consisting of body fluids, feces, colonic discharge, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

[0168] In some embodiments, the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0169] In some embodiments, the method further comprises: c) treating the subject with surgery and / or a therapeutic agent based on the subject's protein profile.

[0170] In another aspect, the present disclosure provides a method for detecting a colon cell proliferative disorder in a subject, the method comprising:

[0171] (a) obtaining a biological sample containing a protein from a subject;

[0172] (b) measuring the amount of proteins from a predetermined panel of proteins in the biological sample, thereby providing a protein profile of the subject, the predetermined panel of proteins 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; and

[0173] (c) computer processing the protein profile using a machine learning model trained to be able to distinguish between healthy subjects and subjects suffering from a colon cell proliferative disorder to provide an output value associated with the presence or absence of the colon cell proliferative disorder, thereby indicating the presence or absence of the colon cell proliferative disorder in the subject.

[0174] In some embodiments, the predetermined panel of proteins 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.

[0175] In some embodiments, the protein profile is associated with a cell proliferative disorder of the colon and provides a classification of the subject as having a cell proliferative disorder of the colon.

[0176] In some embodiments, the biological sample from the subject is selected from the group consisting of body fluids, feces, colonic discharge, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

[0177] In some embodiments, the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0178] In some embodiments, the method further comprises: d) treating the subject with surgery and / or a therapeutic agent based on the subject's protein profile.

[0179] In another aspect, the present disclosure provides a predetermined panel of proteins characteristic of a cell proliferative disorder of the colon, 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, CYFRA 21-1, CA125, IGF-1, CXCL-13 and YKL.

[0180] 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.

[0181] In some embodiments, the panel is configured to distinguish between healthy subjects, subjects with benign colon polyps, subjects with advanced adenomas, or subjects with colorectal cancer.

[0182] In some embodiments, the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0183] In another aspect, the present disclosure provides a method for determining a protein profile of a biological sample from a subject, the method comprising:

[0184] (a) obtaining a biological sample containing a protein from a subject; and

[0185] (b) measuring the amount of a protein from a predetermined panel of proteins in a biological sample, the predetermined panel of proteins comprising 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.

[0186] 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.

[0187] In some embodiments, the protein profile is associated with a cell proliferative disorder of the colon and provides a classification of the subject as having a cell proliferative disorder of the colon.

[0188] In some embodiments, the biological sample from the subject is selected from the group consisting of body fluids, feces, colonic discharge, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

[0189] In some embodiments, the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0190] In some embodiments, the method further comprises: c) treating the subject with surgery and / or a therapeutic agent based on the subject's protein profile.

[0191] In another aspect, the present disclosure provides a method for detecting a colon cell proliferative disorder in a subject, the method comprising:

[0192] (a) obtaining a biological sample containing a protein from a subject;

[0193] (b) measuring the amount of proteins from a predetermined panel of proteins in the biological sample, thereby providing a protein profile of the subject, the predetermined panel of proteins comprising 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; and

[0194] (c) computer processing the protein profile using a machine learning model trained to be able to distinguish between healthy subjects and subjects suffering from a colon cell proliferative disorder to provide an output value associated with the presence or absence of the colon cell proliferative disorder, thereby indicating the presence or absence of the colon cell proliferative disorder in the subject.

[0195] In some embodiments, the predetermined panel of proteins 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, CYFRA 21-1, CA125, IGF-1, CXCL-13, and YKL.

[0196] In some embodiments, the protein profile is associated with a cell proliferative disorder of the colon and provides a classification of the subject as having a cell proliferative disorder of the colon.

[0197] In some embodiments, the biological sample from the subject is selected from the group consisting of body fluids, feces, colonic discharge, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

[0198] In some embodiments, the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0199] In some embodiments, the method further comprises: d) treating the subject with surgery and / or a therapeutic agent based on the subject's protein profile.

[0200] In another aspect, the present disclosure provides a predetermined panel of proteins characteristic of a cell proliferative disorder of the colon, the panel comprising 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.

[0201] In some embodiments, the panel includes 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.

[0202] In some embodiments, the panel is configured to distinguish between healthy subjects, subjects with benign colon polyps, subjects with advanced adenomas, or subjects with colorectal cancer.

[0203] In some embodiments, the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0204] In another aspect, the present disclosure provides a method for determining a protein profile of a biological sample from a subject, the method comprising:

[0205] (a) obtaining a biological sample containing a protein from a subject; and

[0206] (b) measuring the amount of a protein from a predetermined panel of proteins in a biological sample, the predetermined panel of proteins 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.

[0207] 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, 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, proBNPII, sFlt-1, and YKL.

[0208] In some embodiments, the protein profile is associated with a cell proliferative disorder of the colon and provides a classification of the subject as having a cell proliferative disorder of the colon.

[0209] In some embodiments, the biological sample from the subject is selected from the group consisting of body fluids, feces, colonic discharge, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

[0210] In some embodiments, the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0211] In some embodiments, the method further comprises: c) treating the subject with surgery and / or a therapeutic agent based on the subject's protein profile.

[0212] In another aspect, the present disclosure provides a method for detecting a colon cell proliferative disorder in a subject, the method comprising:

[0213] (a) obtaining a biological sample containing a protein from a subject;

[0214] (b) measuring the amount of proteins from a predetermined panel of proteins in a biological sample, thereby providing a protein profile of the subject, the predetermined panel of proteins 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; and

[0215] (c) computer processing the protein profile using a machine learning model trained to be able to distinguish between healthy subjects and subjects suffering from a colon cell proliferative disorder to provide an output value associated with the presence or absence of the colon cell proliferative disorder, thereby indicating the presence or absence of the colon cell proliferative disorder in the subject.

[0216] In some embodiments, the predetermined panel of proteins 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, 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.

[0217] In some embodiments, the protein profile is associated with a cell proliferative disorder of the colon and provides a classification of the subject as having a cell proliferative disorder of the colon.

[0218] In some embodiments, the biological sample from the subject is selected from the group consisting of body fluids, feces, colonic discharge, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

[0219] In some embodiments, the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0220] In some embodiments, the method further comprises: d) treating the subject with surgery and / or a therapeutic agent based on the subject's protein profile.

[0221] In another aspect, the present disclosure provides a predetermined panel of proteins characteristic of a cell proliferative disorder of the colon, 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, 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.

[0222] 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, 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.

[0223] In some embodiments, the panel is configured to distinguish between healthy subjects, subjects with benign colon polyps, subjects with advanced adenomas, or subjects with colorectal cancer.

[0224] In some embodiments, the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0225] In another aspect, the present disclosure provides a method for determining a protein profile of a biological sample from a subject, the method comprising:

[0226] (a) obtaining a biological sample containing a protein from a subject; and

[0227] (b) measuring the amount of a protein from a predetermined panel of proteins in a biological sample, the predetermined panel of proteins 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.

[0228] 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, 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, proBNPII, sFlt-1, and YKL.

[0229] In some embodiments, the protein profile is associated with a cell proliferative disorder of the colon and provides a classification of the subject as having a cell proliferative disorder of the colon.

[0230] In some embodiments, the biological sample from the subject is selected from the group consisting of body fluids, feces, colonic discharge, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

[0231] In some embodiments, the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0232] In some embodiments, the method further comprises: c) treating the subject with surgery and / or a therapeutic agent based on the subject's protein profile.

[0233] In another aspect, the present disclosure provides a method for detecting a colon cell proliferative disorder in a subject, the method comprising:

[0234] (a) obtaining a biological sample containing a protein from a subject;

[0235] (b) measuring the amount of proteins from a predetermined panel of proteins in a biological sample, thereby providing a protein profile of the subject, the predetermined panel of proteins 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; and

[0236] (c) computer processing the protein profile using a machine learning model trained to be able to distinguish between healthy subjects and subjects suffering from a colon cell proliferative disorder to provide an output value associated with the presence or absence of the colon cell proliferative disorder, thereby indicating the presence or absence of the colon cell proliferative disorder in the subject.

[0237] In some embodiments, the predetermined panel of proteins 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, 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.

[0238] In some embodiments, the protein profile is associated with a cell proliferative disorder of the colon and provides a classification of the subject as having a cell proliferative disorder of the colon.

[0239] In some embodiments, the biological sample from the subject is selected from the group consisting of body fluids, feces, colonic discharge, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

[0240] In some embodiments, the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0241] In some embodiments, the method further comprises: d) treating the subject with surgery and / or a therapeutic agent based on the subject's protein profile.

[0242] In another aspect, the present disclosure provides a predetermined panel of proteins characteristic of a cell proliferative disorder of the colon, the panel comprising 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.

[0243] 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, CA125 II, CA15-3, CEA, CXCL13, CYFRA 21-1, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, PRL2, sFlt-1, and YKL.

[0244] In some embodiments, the panel is configured to distinguish between healthy subjects, subjects with benign colon polyps, subjects with advanced adenomas, or subjects with colorectal cancer.

[0245] In some embodiments, the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0246] In another aspect, the present disclosure provides a method for determining a protein profile of a biological sample from a subject, the method comprising:

[0247] (a) obtaining a biological sample containing a protein from a subject; and

[0248] (b) measuring the amount of a protein from a predetermined panel of proteins in a biological sample, the predetermined panel of proteins comprising 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.

[0249] 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, CA125 II, CA15-3, CEA, CXCL13, CYFRA 21-1, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, PRL2, sFlt-1, and YKL.

[0250] In some embodiments, the protein profile is associated with a cell proliferative disorder of the colon and provides a classification of the subject as having a cell proliferative disorder of the colon.

[0251] In some embodiments, the biological sample from the subject is selected from the group consisting of body fluids, feces, colonic discharge, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

[0252] In some embodiments, the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0253] In some embodiments, the method further comprises: c) treating the subject with surgery and / or a therapeutic agent based on the subject's protein profile.

[0254] In another aspect, the present disclosure provides a method for detecting a colon cell proliferative disorder in a subject, the method comprising:

[0255] (a) obtaining a biological sample containing a protein from a subject;

[0256] (b) measuring the amount of proteins from a predetermined panel of proteins in a biological sample, thereby providing a protein profile of the subject, the predetermined panel of proteins comprising at least 4 proteins selected from the group consisting of AFP, AFP-L3, CA125II, CA15-3, CEA, CXCL13, CYFRA 21-1, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, PRL2, sFlt-1, and YKL; and

[0257] (c) computer processing the protein profile using a machine learning model trained to be able to distinguish between healthy subjects and subjects suffering from a colon cell proliferative disorder to provide an output value associated with the presence or absence of the colon cell proliferative disorder, thereby indicating the presence or absence of the colon cell proliferative disorder in the subject.

[0258] In some embodiments, the predetermined panel of proteins 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, CA125 II, CA15-3, CEA, CXCL13, CYFRA 21-1, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, PRL2, sFlt-1, and YKL.

[0259] In some embodiments, the protein profile is associated with a cell proliferative disorder of the colon and provides a classification of the subject as having a cell proliferative disorder of the colon.

[0260] In some embodiments, the biological sample from the subject is selected from the group consisting of body fluids, feces, colonic discharge, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

[0261] In some embodiments, the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0262] In some embodiments, the method further comprises: d) treating the subject with surgery and / or a therapeutic agent based on the subject's protein profile.

[0263] In some embodiments, the method further comprises administering treatment to the subject based on the monitored cancer progression in the subject. In some embodiments, the treatment comprises chemotherapy, radiotherapy, immunotherapy, or surgery.

[0264] In some embodiments, the protein spectrum indicates the presence or susceptibility of colorectal cancer with a sensitivity of at least about 25%. In some embodiments, the protein spectrum indicates the presence or susceptibility of colorectal cancer with a sensitivity of at least about 30%. In some embodiments, the protein spectrum indicates the presence or susceptibility of colorectal cancer with a sensitivity of at least about 35%. In some embodiments, the protein spectrum indicates the presence or susceptibility of colorectal cancer with a sensitivity of at least about 40%. In some embodiments, the protein spectrum indicates the presence or susceptibility of colorectal cancer with a sensitivity of at least about 50%. In some embodiments, the protein spectrum indicates the presence or susceptibility of colorectal cancer with a sensitivity of at least about 60%. In some embodiments, the protein spectrum indicates the presence or susceptibility of colorectal cancer with a sensitivity of at least about 70%. In some embodiments, the protein spectrum indicates the presence or susceptibility of colorectal cancer with a sensitivity of at least about 80%. In some embodiments, the protein spectrum 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 of or susceptibility to colorectal cancer with a sensitivity of at least about 95%.

[0265] In some embodiments, the protein spectrum indicates the presence or susceptibility of colorectal cancer with a positive predictive value (PPV) of at least about 5%. In some embodiments, the protein spectrum indicates the presence or susceptibility of colorectal cancer with a positive predictive value (PPV) of at least about 10%. In some embodiments, the protein spectrum indicates the presence or susceptibility of colorectal cancer with a positive predictive value (PPV) of at least about 15%. In some embodiments, the protein spectrum indicates the presence or susceptibility of colorectal cancer with a positive predictive value (PPV) of at least about 20%. In some embodiments, the protein spectrum indicates the presence or susceptibility of colorectal cancer with a positive predictive value (PPV) of at least about 25%. In some embodiments, the protein spectrum indicates the presence or susceptibility of colorectal cancer with a positive predictive value (PPV) of at least about 30%. In some embodiments, the protein spectrum indicates the presence or susceptibility of colorectal cancer with a positive predictive value (PPV) of at least about 40%. In some embodiments, the protein spectrum indicates the presence or susceptibility of colorectal cancer with a positive predictive value (PPV) of at least about 50%. In some embodiments, the protein spectrum indicates the presence or susceptibility of colorectal cancer with a positive predictive value (PPV) of at least about 60%. In some embodiments, the protein spectrum indicates the presence or susceptibility of colorectal cancer with a positive predictive value (PPV) of at least about 70%. In some embodiments, the protein spectrum indicates the presence or susceptibility of colorectal cancer with a positive predictive value (PPV) of at least about 80%. In some embodiments, the protein spectrum indicates the presence or susceptibility of colorectal cancer with a positive predictive value (PPV) of at least about 90%. In some embodiments, the protein spectrum indicates the presence or susceptibility of colorectal cancer with a positive predictive value (PPV) of at least about 95%. In some embodiments, the protein spectrum indicates the presence or susceptibility of colorectal cancer with a positive predictive value (PPV) of at least about 99%.

[0266] In some embodiments, the protein spectrum indicates the presence or susceptibility of colorectal cancer with a negative predictive value (NPV) of at least about 40%. In some embodiments, the protein spectrum indicates the presence or susceptibility of colorectal cancer with a negative predictive value (NPV) of at least about 50%. In some embodiments, the protein spectrum indicates the presence or susceptibility of colorectal cancer with a negative predictive value (NPV) of at least about 60%. In some embodiments, the protein spectrum indicates the presence or susceptibility of colorectal cancer with a negative predictive value (NPV) of at least about 70%. In some embodiments, the protein spectrum indicates the presence or susceptibility of colorectal cancer with a negative predictive value (NPV) of at least about 80%. In some embodiments, the protein spectrum indicates the presence or susceptibility of colorectal cancer with a negative predictive value (NPV) of at least about 90%. In some embodiments, the protein spectrum 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 to colorectal cancer with a negative predictive value (NPV) of at least about 99%.

[0267] 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 a subject to colorectal cancer with an area under the curve (AUC) of at least about 0.99.

[0268] In some embodiments, the method further comprises presenting a report or a graphical user interface of an electronic device of a user.In some embodiments, the user is a subject, an individual, or a patient.

[0269] In some embodiments, the method further comprises determining a likelihood of determining a likelihood of the presence or susceptibility to colorectal cancer in the subject, individual, or patient.

[0270] In some embodiments, the trained algorithm (e.g., 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.

[0271] In some embodiments, the method further comprises providing a therapeutic intervention to a subject or administering a treatment to a subject based at least in part on the protein profile or analysis, such as a therapeutic intervention (e.g., chemotherapy, radiotherapy, immunotherapy, or surgery) to treat a patient with colorectal cancer.

[0272] In some embodiments, the method further includes monitoring the presence or susceptibility of colorectal cancer, wherein the monitoring includes assessing the patient's presence or susceptibility of colorectal cancer at multiple time points, wherein the assessment is based at least on the presence or susceptibility of colorectal cancer determined at each of the multiple time points.

[0273] In some embodiments, a difference in the assessment of the presence or susceptibility of colorectal cancer in a subject between multiple time points indicates one or more clinical indications selected from: (i) diagnosis of the presence or susceptibility of colorectal cancer in a subject, (ii) prognosis of the presence or susceptibility of colorectal cancer in a subject, and (iii) effectiveness or ineffectiveness of a course of treatment for treating the presence or susceptibility of colorectal cancer in a subject.

[0274] In some embodiments, the method further comprises stratifying the subject's colorectal cancer by using the trained algorithm to determine a subtype of the subject's colorectal cancer from a plurality of different subtypes or stages of colorectal cancer.

[0275] Another aspect of the present disclosure provides a non-transitory computer-readable medium comprising machine-executable code, which, when executed by one or more computer processors, implements any method described above or elsewhere herein.

[0276] 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, which, when executed by the one or more computer processors, implements any method described above or elsewhere herein.

[0277] Another aspect of the present disclosure provides a system comprising: a) a computer-readable medium comprising a classifier for distinguishing a population of subjects having a proliferative disorder of the colon from subjects not having the proliferative disorder of the colon using a machine learning model based on a panel of protein features; and b) one or more processors for executing instructions stored on the computer-readable medium.

[0278] According to the following specific embodiments, other aspects and advantages of the present disclosure will be easily clear to those skilled in the art, in which only illustrative embodiments of the present disclosure are shown and described. As will be understood, the present disclosure can have other and different embodiments, and its several details can be modified in various obvious aspects, all of which do not depart from the present disclosure. Accordingly, the drawings and description should be regarded as illustrative, rather than restrictive.

[0279] Incorporation by reference

[0280] All publications, patents, and patent applications mentioned in this specification are incorporated herein 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 publications and patents or patent applications incorporated by reference conflict with the disclosure contained in this specification, this specification is intended to supersede and / or take precedence over any such conflicting material. BRIEF DESCRIPTION OF THE DRAWINGS

[0281] Embodiments of the present disclosure will now be described, by way of example only, with reference to the accompanying drawings. The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the invention will be obtained by reference to the following detailed description and accompanying drawings (also referred to herein as "figures") which set forth illustrative embodiments utilizing the principles of the invention, in which:

[0282] Figure 1 Schematic diagrams of computer systems programmed or otherwise configured with machine learning models and classifiers for implementing the methods provided herein are provided. DETAILED DESCRIPTION

[0283] Although various embodiments of the present invention have been shown and described herein, it will be apparent to those of ordinary skill in the art that such embodiments are provided by way of example only. Various modifications, variations and alternatives may be envisioned by those of ordinary skill 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 employed.

[0284] CRC is the leading cause of cancer-related death in the Western world. Although CRC is one of the best characterized solid tumors, it remains one of the leading causes of death in developed countries due to late diagnosis. Among other reasons, late diagnosis in patients is due to diagnostic tests such as colonoscopy being performed too late. Death due to CRC could be prevented by effective screening. Currently, there are no universal specific screening tests or panels that help direct clinical decisions. Cancer screening and monitoring improve survival outcomes because early detection is able to eliminate the cancer before it grows and spreads. In CRC, for example, colonoscopy plays a role in improving early diagnosis. Unfortunately, patient compliance rates are low, and screening is performed less regularly than recommended due to the invasive nature of the procedure. The realization of non-invasive and simpler diagnostic methods that can detect colorectal tumors early may be based on the identification of proteins detectable in serum or plasma. Non-invasive methods may be the basis for more compliant and early screening tests for colorectal tumors.

[0285] Tumor-associated antigens may be present in cancer patients. Since tumorigenesis is associated with changes in the structure or expression of self-proteins in tumor cells, these changes may serve as potential immune markers for cancer.

[0286] The presence of cancer and tumors in humans has also been associated with the presence of proteins in the sera of cancer patients. The proteins can be detected at early stages of the disease, even before cancer is detected by other techniques, indicating the potential of these proteins as disease biomarkers. These proteins can be affected by changes in expression levels, isolated mutations, can have abnormal folding, be overexpressed, be abnormally glycosylated, can be truncated or undergo abnormal degradation.

[0287] Therefore, protein biomarkers can enable the diagnosis of colon tumors, the classification of colon tumors of different stages (such as adenoma or tumor progression), the prognosis of disease progression, the assessment of disease response to treatment, and the detection of recurrence or dissemination of colon tumors by means of simple, effective and non-invasive methods. The diagnostic potential of colon tumor-associated proteins may contribute to the early detection, diagnosis and prognosis of colon tumors.

[0288] The present disclosure generally relates to cancer detection, disease treatment and disease monitoring. More specifically, the present disclosure relates to cancer-associated protein monitoring and disease monitoring in colon cell proliferative disorders, such as early colorectal cancer. Specifically, a circulating protein signature panel and its use are provided for identifying a human subject with a risk of developing a colon cell proliferative disorder or a colon cell proliferative disorder such as colorectal cancer (CRC) and / or colorectal adenoma (CA) (e.g., advanced colorectal adenoma (AA)). Further disclosed herein are tumor-associated proteins in an object, which indicate the presence of a colon cell proliferative disorder, or a high risk of developing a colon cell proliferative disorder, for example, when the object suffers from colorectal lesions.

[0289] Some embodiments of the present disclosure provide proteins with differential abundance in samples of objects with colon cell proliferative diseases or with a high risk of developing colon cell proliferative diseases compared to corresponding samples of objects without colon cell proliferative diseases or with a low risk of developing colon cell proliferative diseases. In some embodiments, each of the objects with a high risk of developing colon cell proliferative disorders and the objects with a low risk of developing colon cell proliferative disorders has non-invasive pre-lesions (hereinafter referred to as colorectal lesions) in the colorectal mucosa. Proteins present in samples of healthy subjects and objects with colon cell proliferative disorders with different abundances can be used as biomarkers for diagnosing, treating and / or preventing colon cell proliferative disorders.

[0290] In some embodiments, the method further comprises comparing the protein profile of the biological sample of the subject to a database of reference protein profiles from healthy subjects; and determining that the subject has an increased risk of having a cell proliferative disorder of the colon based at least in part on a change in protein expression levels of the measured protein profile relative to the reference protein profile 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%.

[0291] Machine learning methods may be used to characterize protein data derived from biological samples to identify panels of informative proteins. The panels of informative proteins identified for colon cell proliferative disorders may be used to train a classifier model that can be used to distinguish samples from healthy subjects and subjects with colon cell proliferative disorders.

[0292] In order to identify proteins that are informative for the methods and classifiers described herein, plasma from subjects with a cell proliferative disorder of the colon and plasma from subjects without a cell proliferative disorder of the colon (control plasma or reference plasma) have been examined for the purpose of identifying a signature panel of proteins that are produced by or differentially expressed in patients with a cell proliferative disorder of the colon in response to the cell proliferative disorder of the colon. To this end, protein expression was determined in plasma from patients with a cell proliferative disorder of the colon and in control plasma to identify biomarkers.

[0293] The proteins identified herein can be used to identify subjects with a cell proliferative disorder of the colon to distinguish them from subjects without a cell proliferative disorder of the colon, to identify subjects at high risk of developing a cell proliferative disorder of the colon to distinguish them from subjects at low risk of developing a cell proliferative disorder of the colon, or to identify subjects with a precursor lesion of a cell proliferative disorder of the colon. Thus, these proteins can be used as auxiliary tools to guide decisions related to the monitoring, treatment, and management of cell proliferative disorders of the colon.

[0294] Some other embodiments of the present disclosure provide a machine learning model classifier trained on the protein described herein, which is expressed in plasma samples of healthy subjects and plasma samples from subjects suffering from colon cell proliferative disorders. The training machine learning model provides a classifier with a predetermined protein biomarker set ("protein panel" or "feature panel"), which can be used to classify healthy subjects or subjects suffering from colon cell proliferative disorders. In one example, a method for minimally invasive protein determination based on blood is provided, which can be used in subjects with colorectal lesions to assess histological severity. In another embodiment, a protein indicating colon cell proliferative disorders is detected in a cell-free sample from the object, a body fluid sample from the object such as whole blood, plasma, or serum containing cell-free molecules such as proteins. Therefore, a protein is provided herein, which can be used to identify the presence or absence of colon cell proliferative disorders, high-risk or low-risk colorectal lesions requiring treatment (such as surgical resection, immunotherapy, radiotherapy or chemotherapy) and identification of low-risk colorectal lesions that can be monitored. The presence of colon cell proliferative disorders or lesions can be monitored and confirmed, for example, by colonoscopy, ultrasound, MM or CT scanning.

[0295] In some embodiments, disclosed herein is a predetermined panel of plasma protein biomarkers for early detection of colorectal proliferative disorders and associated with early detection of CRC. The predetermined panel of proteins can be used in a classifier that indicates a cell proliferative disorder, such as advanced adenoma or colorectal cancer.

[0296] In other embodiments, disclosed herein are methods of detection, diagnosis, and treatment related. Plasma from a patient can be screened for a predetermined panel of proteins as an indicator of a colorectal proliferative disorder.

[0297] Described herein are methods for screening, identifying and / or treating subjects with or at risk for a proliferative disorder of the colon cell based on the expression profile or abundance of proteins that are upregulated or overexpressed in subjects with the proliferative disorder of the colon cell. Also described herein are methods for obtaining data useful for diagnosing and / or treating a proliferative disorder of the colon cell in a subject (e.g., a human subject).

[0298] The cell proliferative disorder of the colon can be any tumor stage (e.g., TX, T0, Tis, T1, T2, T3, T4); any regional lymph node or distant metastasis stage (e.g., NX, N0, N1, M0, M1); any stage (e.g., Stage 0 (Tis, N0, M0), Stage IA (T1, N0, M0), Stage IIA (T3, N0, M0), Stage IIB (T1-3, N1, M0), Stage III (T4, any N, M0), or Stage IV (any T, any N, M1)); resectable; locally advanced (unresectable); or metastatic.

[0299] Current screening tools can face challenges due to false positive and false negative results as well as specificity and sensitivity. An ideal cancer screening tool would have a high Positive Predictive Value (PPV), which would minimize unnecessary examinations (low false positives) but detect the vast majority of cancers (low false negatives). Another key compromise is "detection sensitivity," which is different from test sensitivity, which is the lower limit for detecting tumors based on size. Allowing tumors to grow to a sufficient size to release detectable levels of circulating tumor markers defeats the purpose of early detection and prevention of cancer progression. Therefore, the present disclosure addresses the need for highly sensitive and effective blood-based early diagnostic screening for colorectal cancer.

[0300] The detection of circulating tumor DNA, also known as "liquid biopsy", can detect and investigate tumors in a non-invasive manner. The identification of tumor-specific mutations in these liquid biopsies may be used to diagnose, for example, colon cancer, breast cancer, and prostate cancer. However, due to the presence of a high background of normal (i.e., non-tumor-derived) DNA in the circulation, the sensitivity of these techniques may be limited. Therefore, there is still a need for more sensitive and specific screening tools to detect colorectal cancer tumor markers in early or low tumor burdens, so as to perform recurrence screening and primary screening in high-risk groups. Circulating proteins for tumor-associated antigens provide another source of informative biomarkers in liquid biopsy samples, which can be used in the machine learning models described herein.

[0301] The present disclosure provides methods and systems for profiling circulating proteins associated with colon cell proliferative disorders and their progression (e.g., colorectal cancer). Those proteins that indicate the presence of a colon cell proliferative disorder or a high risk of developing a colon cell proliferative disorder can be used to diagnose, treat, or prevent the progression of a colon cell proliferative disorder as early as possible, for example, when the subject has only colorectal lesions. Also provided herein are kits and methods for diagnosing a colon cell proliferative disorder or assessing the risk of a subject developing a colon cell proliferative disorder, particularly when the subject has colorectal lesions.

[0302] In one aspect, methods using protein panels are provided herein that can be used to distinguish samples from a subject based on disease state. In other aspects, methods, assays and kits are provided herein that involve using protein panels to detect, identify and distinguish colon cell proliferative disorders. Non-limiting examples of colon cell proliferative disorders include adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma and sarcoma.

[0303] In some embodiments, the methods comprise using one or more proteins selected as markers for identifying, detecting, and distinguishing cell proliferative disorders of the colon.

[0304] definition

[0305] As used in the specification and claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. For example, the term "nucleic acid" includes a plurality of nucleic acids, including mixtures thereof.

[0306] As used herein, the term "subject" generally refers to an entity or medium with testable or detectable genetic information. The subject can be an individual, individual or patient. The subject can be a vertebrate, such as a mammal. Non-limiting examples of mammals include humans, apes, farm animals, competitive animals, rodents and pets. The subject can be a person suffering from cancer or suspected of suffering from cancer. The subject can show symptoms indicating the health, physiological state or condition of the subject, such as cancer or other diseases, disorders or conditions of the subject. As an alternative, the subject can be asymptomatic in terms of such health or physiological state or condition.

[0307] As used herein, the term "sample" generally refers to a biological sample obtained or derived from one or more subjects. A 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, a cell-free biological sample 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. Ethylenediaminetetraacetic acid (EDTA) collection tubes, cell-free RNA collection tubes (e.g., RNAComplete ) or cell-free DNA collection tubes (e.g., Cell-Free DNA ) obtain or derive an acellular biological sample from a subject. An acellular biological sample can be derived from a whole blood sample by fractionation (e.g., by differential centrifugation). The biological sample or its derivative can contain cells. For example, the biological sample can be a blood sample or its derivative (e.g., blood collected by a collection tube or blood drop).

[0308] As used herein, the term "cell-free sample" generally refers to a biological sample that is substantially lacking intact cells. A cell-free sample can be derived from a biological sample that is itself substantially lacking cells, or can be derived from a sample from which cells have been removed. Non-limiting examples of cell-free samples include those samples derived from blood, serum, plasma, urine, semen, sputum, feces, ductal exudate, lymph, and recovered lavage fluid.

[0309] As used herein, the term "colon cell proliferative disorder" generally refers to a disorder or disease comprising a disorder or abnormal proliferation of colon or rectal cells. Non-limiting examples of colon cell proliferative disorders include adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma and sarcoma. As used herein, the abbreviation "CRC" is used to identify a biological sample from a subject diagnosed with colorectal cancer. As used herein, the abbreviation "AA" is used to identify a sample from a subject diagnosed with at least one advanced adenoma. As used herein, the abbreviation "NAA" is used to identify a sample from a subject diagnosed with a benign colorectal tumor and not suffering from advanced adenoma or colorectal cancer.

[0310] As used herein, the term "colorectal cancer" is a medical condition generally characterized by cancerous changes in the intestinal cells below the small intestine, ie, the large intestine (colon), such as the cecum, ascending colon, transverse colon, descending colon, sigmoid colon, and rectum.

[0311] As used herein, the term "colorectal adenoma" generally refers to colon adenomas, also known as adenomatous polyps, which are benign and precancerous stages of colorectal cancer. Colorectal adenomas can indicate a high risk of progression to colorectal cancer.

[0312] As used herein, the term "advanced colorectal adenoma" generally refers to an adenoma that is at least 10 mm in size or has high-grade dysplasia histologically or a villous component greater than 20%.

[0313] As used herein, the terms "at risk of developing a cell proliferative disorder of the colon" or "at high risk of developing a cell proliferative disorder of the colon" generally refer to a subject having an increased risk of developing a cell proliferative disorder of the colon in the near future, compared to a subject not suffering from a cell proliferative disorder of the colon or a subject at low risk of developing a cell proliferative disorder of the colon in the near future. As used herein, the term "in the near future" refers to a duration of about 1 month to about 2 years, about 6 months to about 18 months, or about 1 year.

[0314] As used herein, the terms cancer "type" and "subtype" are generally used relative to one another herein, such that a "type" of cancer, such as breast cancer, may be a "subtype" based on, for example, stage, morphology, histology, gene expression, receptor profile, mutation profile, aggressiveness, prognosis, and malignancy characteristics. Likewise, "type" and "subtype" may be applied at a more granular level, e.g., identifying a histological "type" as a "subtype," defined, for example, based on mutation profile or gene expression. Cancer "stage" is also used to refer to a classification of cancer types based on histological and pathological characteristics associated with disease progression.

[0315] As used herein, the term "vegetation" generally refers to any new and abnormal tissue growth. Therefore, the neoplasm can be a precancerous neoplasm or a malignant neoplasm. The term "neoplastic 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, cell 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 precancerous colorectal neoplasm or a malignant colorectal neoplasm).

[0316] As used herein, the term "healthy" generally refers to that the object does not suffer from colorectal cell proliferation disorders. Although health is a dynamic state, as used herein, the term refers to the pathological state of the object lacking a disease state mentioned in a specific statement. In an example, when referring to a feature panel capable of classifying a colorectal cancer object, 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 benign adenoma (NAA). As used herein, the abbreviation "NAA" is used to identify samples from individuals assessed as colorectal tumor negative, and therefore, in some embodiments, samples identified as NAA are included in the healthy sample group. Although other diseases or health states may exist in this object, as used herein, the term "healthy" indicates a lack of a disease state, for the purpose of comparing or classifying between an object having and lacking the disease state discussed.

[0317] As used herein, the term "minimal residual disease" or "MRD" generally refers to a small amount of cancer cells in a subject's body after cancer treatment. MRD testing can be performed to determine the effectiveness of cancer treatment and to guide further treatment plans.

[0318] As used herein, the term "screening" generally refers to the examination or testing of a population of subjects at risk for colorectal cancer or colorectal adenoma for the purpose of distinguishing healthy subjects from subjects with undiagnosed colorectal cancer or colorectal adenoma or subjects at high risk for developing the indications.

[0319] As used herein, the term "minimally invasive biological sample" or "non-invasive sample" generally refers to any sample collected from a patient without any instrumentation other than a fine needle used to draw blood from the subject. In some embodiments, the minimally invasive biological sample comprises a blood, serum, or plasma sample.

[0320] As used herein, the terms "up-regulated" or "overexpressed" generally refer to an increase in expression levels by 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% relative to a given "threshold" or "cutoff value."

[0321] As used herein, the term "threshold" or "cutoff value", when referring to an expression level, generally refers to a reference expression level that indicates that the subject may have colorectal cancer or colorectal adenoma if the subject's expression level is above the threshold or cutoff or reference level, at a given sensitivity and specificity.

[0322] 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, a microarray, a bioarray, a biochip, a biochip array, or a bead-based array.

[0323] Measurement samples

[0324] The cell-free biological sample can be obtained or derived from a human subject. The cell-free biological sample can be stored under a variety of storage conditions before processing, such as different temperatures (e.g., room temperature, refrigerated or frozen conditions, such as 25°C, 4°C, -18°C, -20°C, or -80°C) or different suspensions (e.g., EDTA collection tubes, cell-free RNA collection tubes, or cell-free DNA collection tubes).

[0325] The cell-free biological sample may be from a subject having cancer, a subject suspected of having cancer, or a subject not having or not suspected of having cancer.

[0326] Cell-free biological samples can be obtained before and / or after the treatment of an object with cancer. Cell-free biological samples can be obtained from an object during treatment or a treatment regimen. Multiple cell-free biological samples can be obtained from an object to monitor the therapeutic effect over time. Cell-free biological samples can be obtained from an object known or suspected of having cancer, and the object suspected of having cancer cannot obtain a clear positive or negative diagnosis via clinical testing. Samples can be obtained from an object suspected of having cancer. Cell-free biological samples can be obtained from an object with the following unexplained symptoms, such as fatigue, nausea, weight loss, body pain, weakness or bleeding. Cell-free biological samples can be obtained from an object with explained symptoms. Cell-free biological samples can be obtained from an object with a 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 or drinking) or the presence of other risk factors.

[0327] A cell-free biological sample may contain one or more analytes capable of being assayed, such as cell-free ribonucleic acid (cfRNA) molecules suitable for assaying to generate transcriptomic data, cell-free deoxyribonucleic acid (cfDNA) molecules suitable for assaying to generate genomic data, protein molecules suitable for assaying to generate proteomic data, or mixtures or combinations thereof.

[0328] After obtaining the acellular biological sample from the subject, the acellular biological sample can be processed to generate a data set indicative of a colon cell proliferative disorder in the subject. For example, the presence, absence, or quantitative assessment of protein molecules in the acellular biological sample at a protein panel. Processing the acellular biological sample from the subject may include: (i) subjecting the acellular biological sample to conditions sufficient to separate, enrich, or extract a plurality of proteins; and (ii) assaying the plurality of protein molecules to generate a data set.

[0329] The biological sample may be used directly in an assay to detect one or more proteins to generate a protein profile of the sample. In some embodiments, the biological sample may be enriched for protein prior to the assay (e.g., using protein-conjugated microbeads). In some embodiments, the biological sample is a plasma sample and is protein enriched. The biological sample may be assayed using various laboratory methods to determine the presence and / or concentration or level of one or more proteins in the biological sample. In some embodiments, such methods may include, but are not limited to, mass spectrometry, protein microarrays, high density protein microarrays (e.g., CDI proteome arrays), ELISA, mesoscale discovery (e.g., Pacific Biolabs), bead-based immunoassays (e.g., based on bead capture assays), secondary fluoro-antibody assays, DNA antibody conjugates, or antibody metal conjugates (e.g., mass cytometry, ), HD-X TM and SR-X TM Ultrasensitive biomarker detection systems (e.g. ), aptamer-based oligohybrid MEMS (e.g., Somalogic), flow cytometry, Particle technology (e.g. ) or a combination thereof to determine a protein profile from a biological sample from a subject.

[0330] Featured Protein Panel

[0331] The present disclosure provides methods and systems for analyzing biological samples to obtain measurable features from combinations of protein molecules associated with the development of colon cell proliferative disorders identified in the sample. The set of identified protein molecules described herein has information value in creating a classifier and model for detecting colon cell proliferative disorders or its stage. Although the identified protein molecules can be individually informative and available, the protein molecules can be used in the combination described herein to form a feature panel, wherein the feature is unique to colon cell proliferative disorders or its stage. Features from the feature panel can be processed using a trained algorithm (e.g., a machine learning model) to create a classifier, which is configured to stratify a population of subjects suffering from colon cell proliferative disorders. The method is characterized by using one or more proteins described in the feature panel. In some embodiments, feature panels of at least 3 proteins can be used for classifiers and methods described herein.

[0332] The protein signature panels described herein enable rapid and specific analysis of specific proteins associated with cell proliferative disorders of the colon.The signature panels described and used in the methods herein can be used to improve diagnosis, prognosis, treatment selection and monitoring (eg, therapeutic monitoring) of cell proliferative disorders of the colon.

[0333] Compared with current methods, feature panels and methods provide significant improvements to detect early colon cell proliferative disorders from body fluid samples such as whole blood, plasma or serum. Current methods for detecting and diagnosing colon cell proliferative disorders include colonoscopy, sigmoidoscopy and fecal occult blood colon cancer. Compared with these methods, the methods provided herein can be much less invasive than colonoscopy, and at least as sensitive as or more sensitive than sigmoidoscopy, fecal immunochemical test (FIT) and fecal occult blood test (FOBT). The methods provided herein may provide significant advantages in terms of sensitivity and specificity, due to the use of a favorable combination of protein panels and highly sensitive assay techniques.

[0334] The present disclosure provides methods and systems for protein profiling associated with colon cell proliferative disorder detection and disease progression. In order to identify proteins that are informative for the methods and classifiers described herein, plasma from patients with colon cell proliferative disorders and plasma from subjects without colon cell proliferative disorders (control plasma or reference plasma) have been examined to identify a characteristic panel of proteins produced by patients with colon cell proliferative disorders in response to the colon cell proliferative disorders and corresponding reactive proteins. To this end, it is possible to test plasma and control plasma from patients with colon cell proliferative disorders using high-density protein microarrays. Antibody microarrays have a series of advantages over other methods for identifying proteins: i) the proteins printed in the array are known in advance, thereby avoiding subsequent identification and eliminating the possible selection of mimic epitopes, and ii) there is no tendency to select any protein because the proteins are all printed at similar concentrations. This combination of factors allows for high sensitivity in identifying biomarkers.

[0335] The proteins identified herein can be used to identify subjects with a cell proliferative disorder of the colon, to distinguish them from subjects who do not have a cell proliferative disorder of the colon, or to identify subjects at higher risk of developing a cell proliferative disorder of the colon, to distinguish them from subjects at lower risk of developing a cell proliferative disorder of the colon, or to identify subjects with precursor lesions of a cell proliferative disorder of the colon. Thus, these proteins can be used as auxiliary tools to guide decisions related to the monitoring, treatment, and management of cell proliferative disorders of the colon.

[0336] Disclosed herein, in some embodiments, is a plasma protein biomarker panel that can be used for early detection of colorectal proliferative disorders and correlates with early detection of colorectal cancer.

[0337] In other embodiments, disclosed herein are methods of detection, diagnosis, and treatment related to screening plasma from a patient for tumor-associated or tumor-derived proteins as an indicator of a colorectal proliferative disorder.

[0338] In one aspect, the present disclosure provides a panel of proteins characteristic of a cell proliferative disorder of the colon, 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, 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.

[0339] In one embodiment, the panel of proteins characteristic of a cell proliferative disorder of the colon includes 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 NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40.

[0340] In some embodiments, the panel includes CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, IL-8, sTREM-1, or a combination thereof.

[0341] In some embodiments, the panel 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.

[0342] In some embodiments, the panel includes 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 NT-proBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or a combination thereof.

[0343] In some embodiments, the panel includes CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, IL-8, or a combination thereof.

[0344] In some embodiments, the panel includes measuring total PSA.

[0345] In some embodiments, a protein signature panel can be used to distinguish between healthy subjects, subjects with benign colon polyps, subjects with advanced adenomas, or subjects with colorectal cancer.

[0346] In some embodiments, the panel is useful for indicating advanced adenoma and 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.

[0347] In some embodiments, the panel of proteins characteristic of a cell proliferative disorder of the colon includes 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 NT-proBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40.

[0348] In some embodiments, the panel is indicative of colorectal cancer and includes 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.

[0349] In some embodiments, the panel of proteins characteristic of a cell proliferative disorder of the colon includes 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, 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.

[0350] In some embodiments, the predetermined set of proteins contains 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 proteins, such as those described herein. In some embodiments, the predetermined set of proteins contains 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, 61, 62, 63, 6 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 such as those described herein.In some embodiments, the predetermined set of proteins is 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.

[0351] In some embodiments, the predetermined set of proteins contains proteins selected from the group consisting of proteins listed in Table 2.

[0352] In some embodiments, the proteins in the predetermined panel include proteins in functional categories such as interleukins, complement pathway mediators, complement proteins, chemokines, growth factors, cytokines, globulins, mucins, and proteases.

[0353] Classifiers, machine learning models, and systems

[0354] Machine learning methods are used to characterize protein data derived from biological samples from subjects to identify informative panels of proteins. The identified informative panels of proteins for colon cell proliferative disorders can be used to train a classifier model that can be used to distinguish samples from healthy subjects and subjects with colon cell proliferative disorders.

[0355] Also described herein is a machine learning model classifier trained on the protein described herein, which is expressed in plasma samples of healthy subjects and plasma samples from subjects suffering from colon cell proliferative disorders. The training machine learning model provides a classifier with a predetermined protein biomarker set ("protein panel" or "feature panel"), which can be used to classify healthy subjects or subjects suffering from colon cell proliferative disorders. In one example, a method for minimally invasive protein determination based on blood is provided, which can be used to assess the histological severity of subjects with colorectal lesions. In another embodiment, a protein indicating colon cell proliferative disorders is detected in a cell-free sample from the object, such as a body fluid sample such as whole blood, plasma or serum from the object. Therefore, the protein disclosed herein can be used to identify the presence or absence of colon cell proliferative disorders, high-risk colorectal lesions or low-risk colorectal lesions and low-risk colorectal lesions that require treatment (such as surgical resection, immunotherapy, radiotherapy or chemotherapy) Monitoring. The presence of colon cell proliferative disorders or lesions can be monitored and confirmed, for example, by colonoscopy, ultrasound, MM or CT scanning.

[0356] In some instances, protein signatures are used as input data sets to trained algorithms (e.g., machine learning models or classifiers) to find correlations between protein profiles and groups of subjects (e.g., patient groups). Examples of such patient groups include the presence or absence of a disease or condition, an increased or non-elevated risk of a disease or condition, the stage of a disease or condition, a subtype of a disease or condition, responders and non-responders to treatment, and progressors and non-progressors. In some instances, a signature matrix is ​​generated to compare samples from objects with known conditions or features. In some embodiments, the samples are from healthy subjects or objects without any known indications and the samples are from patients known to have cancer.

[0357] As used herein, with respect to machine learning and pattern recognition, the term "feature" generally refers to a single measurable property or characteristic of an observed phenomenon. The concept of a "feature" is related to the concept of an explanatory variable used in statistical techniques such as, but not limited to, linear regression and logistic regression. Features may be numeric or categorical (e.g., structural features such as strings and graphs are used in syntactic pattern recognition).

[0358] As used herein, the term "input feature" (or "feature") generally refers to a variable, such as a condition, protein identity, protein sequence content (e.g., mutation), a suggested data collection action, or a suggested treatment, that is used by a trained algorithm (e.g., a machine learning model or classifier) ​​to predict an output classification (label) for a sample. The value of the variable can be determined for a sample and used to determine the classification.

[0359] For multiple measurements, the system identifies feature sets to input into a trained algorithm (e.g., a machine learning model or classifier). The system performs a measurement on each biological sample and forms a feature vector from the measurements. The system inputs the feature vector into the machine learning model and obtains an output classification of whether the biological sample has a specified characteristic.

[0360] In some embodiments, the machine learning model outputs a classifier that is able to distinguish between features in two or more groups or categories of objects or populations of objects or features of populations. In some embodiments, the classifier is a trained machine learning classifier.

[0361] In some embodiments, the information loci or features of biomarkers in cancer tissue are determined to form a spectrum. A receiver operating characteristic (ROC) curve can be generated by plotting the performance of a specific feature (e.g., any biomarker described herein and / or any additional biomedical information item) in distinguishing two populations (e.g., objects that respond and do not respond to a therapeutic agent). In some embodiments, feature data across the entire population (e.g., cases and controls) is sorted in ascending order based on a single feature value.

[0362] In various examples, the specified characteristic is selected from healthy versus cancer, elevated versus non-elevated risk of disease, disease subtype, disease stage, progressor versus non-progressor, and responder versus non-responder.

[0363] In some embodiments, the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0364] A. Data Analysis

[0365] In some instances, the present disclosure provides a system, method or kit, wherein data analysis can be implemented in software applications, computing hardware or both. In some instances, the analysis application or system includes 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 instruments 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 in the preprocessing module include affine transformations, denoising operations, data cleaning, reformatting or subsampling. The data analysis module can be specifically used to analyze genomic data from one or more genomic materials, for example, an assembled genomic sequence can be obtained and probability and statistical analysis can be performed to identify abnormal patterns associated with diseases, pathologies, states, risks, conditions or phenotypes. The data interpretation module can use analytical methods, for example, analytical methods extracted from statistics, mathematics or biology, to support understanding of the relationship between identified abnormal patterns and health conditions, functional states, prognosis or risks. The Data Visualization module can use methods from mathematical modeling, computer graphics, or rendering to create visual representations of data to facilitate understanding or interpretation of the results.

[0366] In some examples, machine learning methods are applied to distinguish samples in a sample population. In some embodiments, machine learning methods are applied to distinguish healthy from advanced disease (eg, adenoma) samples.

[0367] In some embodiments, the one or more machine learning operations used to train the prediction engine include one or more of the following: generalized linear models, generalized additive models, nonparametric regression operations, random forest classifiers, spatial regression operations, Bayesian regression models, time series analysis, Bayesian networks, Gaussian networks, decision tree learning operations, artificial neural networks, recurrent neural networks, reinforcement learning operations, linear or nonlinear regression operations, support vector machines, clustering operations, and genetic algorithm operations.

[0368] In some instances, the computer processing method is selected from logistic regression, multivariate linear regression (MLR), dimensionality reduction, partial least squares (PLS) regression, principal component regression, autoencoder, variational autoencoder, singular value decomposition, Fourier basis, wavelet, discriminant analysis, support vector machine, decision tree, classification and regression tree (CART), tree-based methods, random forest, gradient boosted tree, logistic regression, matrix factorization, multidimensional scaling (MDS), dimensionality reduction methods, t-distributed stochastic neighbor embedding (t-SNE), multilayer perceptron (MLP), network clustering, neuro-fuzzy and artificial neural network.

[0369] In some examples, the methods disclosed herein can include computational analysis of nucleic acid sequencing data from samples from a subject or multiple subjects.

[0370] B. Classifier Generation

[0371] In one aspect, the systems and methods disclosed herein provide a classifier generated based on feature information derived from protein analysis of a biological sample containing proteins. The classifier forms part of a prediction engine for distinguishing groups in a population based on features identified in biological samples such as proteins. The collective representation of protein information in a biological sample can be referred to as a protein profile.

[0372] In some embodiments, a classifier is created by the following steps: normalizing protein information by formatting similar portions of protein information into a uniform format and uniform scale; storing the normalized protein information in a columnar database; training a prediction engine by applying one or more machine learning operations to the stored normalized protein information, thereby configuring the prediction engine to map a combination of one or more features for a specific population to define at least two classification groups. In some embodiments, the method includes applying the prediction engine to a population of objects from which the normalized protein features were obtained and associating with each object in the population; evaluating the output of the prediction engine to identify objects associated with a group; and classifying the objects into the group.

[0373] As used herein, specificity generally refers to “the probability that a test result is negative among individuals without the disease.” Specificity can be calculated by dividing the number of disease-free subjects with a negative test result by the total number of disease-free subjects.

[0374] In some instances, the model, classifier, or predictive 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%, at least 85%, at least 90%, at least 95%, or at least 99%.

[0375] As used herein, sensitivity generally refers to “the probability that a test result is positive among individuals with the disease.” Sensitivity can be calculated by dividing the number of diseased subjects with positive test results by the total number of diseased subjects.

[0376] In some instances, the model, classifier, or predictive test has a sensitivity of 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%, or at least 99%.

[0377] C. Digital processing device

[0378] In some embodiments, a digital processing device or its use is described herein. In some instances, the digital processing device may include one or more hardware central processing units (CPUs), graphics processing units (GPUs), or tensor processing units (TPUs) that perform device functions. In some instances, the digital processing device may include an operating system configured to execute executable instructions.

[0379] In some instances, the digital processing device may optionally be connected to a computer network. In some instances, the digital processing device may optionally be connected to the Internet. In some instances, the digital processing device may optionally be connected to a cloud computing facility. In some instances, the digital processing device may optionally be connected to an intranet. In some instances, the digital processing device may optionally be connected to a data storage device.

[0380] Non-limiting examples of suitable digital processing devices include server computers, desktop computers, laptop computers, notebook computers, sub-notebook computers, netbook computers, netpad computers, set-top computers, handheld computers, Internet appliances, mobile smart phones and tablet computers. Suitable tablet computers may include, for example, those with booklet, notepad and convertible configurations.

[0381] In some examples, the digital processing device may include an operating system configured to execute executable instructions. For example, an operating system may include software, including programs and data, for managing the hardware of the device and providing services for the execution of applications. Non-limiting examples of operating systems include Ubuntu, FreeBSD, OpenBSD, Linux, Mac OS X Windows and Non-limiting examples of suitable personal computer operating systems include MacOS and UNIX-like operating systems such as In some instances, the operating system may be provided by cloud computing, and the cloud computing resources may be provided by one or more service providers.

[0382] In some instances, the device may include a storage and / or memory device. The storage and / or memory device may be one or more physical devices for temporarily or permanently storing data or programs. In some instances, the device may be a volatile memory and requires power to maintain the stored information. In some instances, the device is a non-volatile memory and retains the stored information when the digital processing device is not powered. In some instances, the non-volatile memory may include flash memory. In some instances, the non-volatile memory may include a dynamic random access memory (DRAM). In some instances, the non-volatile memory may include a ferroelectric random access memory (FRAM). In some instances, the non-volatile memory may include a phase change random access memory (PRAM).

[0383] In some instances, the device may be a storage device, including, for example, a CD-ROM, a DVD, a flash memory device, a disk drive, a tape drive, an optical drive, and cloud computing-based storage. In some instances, the storage and / or memory device may be a combination of devices such as those disclosed herein. In some instances, the digital processing device may include a display that sends visual information to a user. In some instances, the display may be a cathode ray tube (CRT). In some instances, the display may be a liquid crystal display (LCD). In some instances, the display may be a thin film transistor liquid crystal display (TFT-LCD). In some instances, the display may be an organic light emitting diode (OLED) display. In some instances, the OLED display may be a passive matrix OLED (PMOLED) or an active matrix OLED (AMOLED) display. In some instances, the display may be a plasma display. In some instances, the display may be a video projector. In some instances, the display may be a combination of devices such as those disclosed herein.

[0384] In some instances, the digital processing device may include an input device for receiving information from a user. In some instances, the input device may be a keyboard. In some instances, the input device may be a pointing device, including, for example, a mouse, a trackball, a trackpad, a joystick, a game controller, or a stylus. In some instances, the input device may be a touch screen or a multi-touch screen. In some instances, the input device may be a microphone for capturing voice or other sound input. In some instances, the input device may be a camera for capturing motion or visual input. In some instances, the input device may be a combination of devices such as those disclosed herein.

[0385] D. Non-transitory computer-readable storage medium

[0386] In some instances, the subject matter disclosed herein may include one or more non-transitory computer-readable storage media, the storage media being encoded with a program containing instructions that can be executed by an operating system of an optional network digital processing device. In some instances, the computer-readable storage medium may be a tangible component of the digital processing device. In some instances, the computer-readable storage medium may optionally be removable from the digital processing device. In some instances, the computer-readable storage medium may include, for example, a CD-ROM, a DVD, a flash memory device, a solid-state memory, a disk drive, a tape drive, an optical drive, a cloud computing system and service, etc. In some instances, the program and instructions may be permanently, substantially permanently, semi-permanently, or non-temporarily encoded on the medium.

[0387] E.Computer System

[0388] The present disclosure provides computer systems programmed to implement the methods described herein. Figure 1 A computer system 101 is shown that is programmed or otherwise configured to store, process, identify, or interpret patient data, biological data, biological sequences, reference sequences, and protein spectra. The computer system 101 can process various aspects of the patient data, biological data, biological sequences, reference sequences, and protein spectra of the present disclosure. The computer system 101 can be a user's electronic device or a computer system located remotely from the electronic device. The electronic device can be a mobile electronic device.

[0389] Computer system 101 includes a central processing unit (CPU, also referred to herein as a "processor" and "computer processor") 105, which may be a single-core or multi-core processor, or multiple processors for parallel processing. Computer system 101 also includes memory or memory location 110 (e.g., random access memory, read-only memory, flash memory), an electronic storage unit 115 (e.g., a hard disk), a communication interface 120 (e.g., a network adapter) for communicating with one or more other systems, and peripheral devices 125, such as cache, other memory, data storage, and / or an electronic display adapter. Memory 110, storage unit 115, interface 120, and peripheral devices 125 communicate with CPU 105 via a communication bus (solid line) (such as a motherboard). Storage unit 115 may be a data storage unit (or data repository) for storing data. By means of communication interface 120, computer system 101 may be operably coupled to a computer network ("network") 130. Network 130 may be the Internet, an internetwork, and / or an extranet, or an intranet and / or an extranet in communication with the Internet. In some examples, network 130 is a telecommunications and / or data network. Network 130 may include one or more computer servers that may implement distributed computing, such as cloud computing. In some examples, network 130 may implement a peer-to-peer network with the aid of computer system 101, which may enable devices coupled to computer system 101 to behave as clients or servers.

[0390] The CPU 105 may execute a series of machine-readable instructions, which may be embodied in a program or software. The instructions may be stored in a memory location, such as the memory 110. The instructions may be directed to the CPU 105, which may then program or otherwise configure the CPU 105 to implement the methods of the present disclosure. Examples of operations performed by the CPU 105 may include fetching, decoding, executing, and writing back.

[0391] CPU 105 may be part of a circuit, such as an integrated circuit. One or more other components of system 101 may be included in the circuit. In some examples, the circuit is an application specific integrated circuit (ASIC).

[0392] Storage unit 115 may store files, such as drivers, libraries, and saved programs. Storage unit 115 may store user data, such as user preferences and user programs. In some instances, computer system 101 may include one or more additional data storage units external to computer system 101, such as located on a remote server that communicates with computer system 101 via an intranet or the Internet.

[0393] Computer system 101 may communicate with one or more remote computer systems via network 130. For example, computer system 101 may communicate with a user's remote computer system. Examples of remote computer systems include personal computers (e.g., portable PCs), slate / tablet PCs (e.g., iPad, Galaxy Tab), phones, smartphones (e.g. iPhone, Android-supported devices, ) or a personal digital assistant. A user can access the computer system 101 via the network 130.

[0394] The methods as described herein may be implemented by machine (e.g., computer processor) executable code stored in an electronic storage location of computer system 101, such as, for example, stored in memory 110 or electronic storage unit 115. The machine executable or machine readable code may be provided in the form of software. During use, the code may be executed by processor 105. In some instances, the code may be retrieved from storage unit 115 and stored on memory 110 for access by processor 105. In some instances, electronic storage unit 115 may be excluded and machine executable instructions may be stored on memory 110.

[0395] The code may be precompiled and configured for use with a machine having a processor suitable for executing the code, or may be interpreted or compiled at run time. The code may be provided in a programming language, which may be selected to enable the code to be executed in a precompiled, interpreted, or as-compiled manner.

[0396] Aspects of the systems and methods provided herein, such as computer system 101, can be embodied in programming. Various aspects of the technology can be considered as "products" or "articles", generally in the form of machine (or processor) executable code and / or associated data, which are carried or contained in a type of machine-readable medium. Machine executable code can be stored in an electronic storage unit such as a memory (e.g., read-only memory, random access memory, flash memory) or a hard disk. "Storage" type media can include any or all tangible memories of computers, processors, etc. or their associated modules, such as various semiconductor memories, tape drives, disk drives, etc., which can provide non-temporary storage for software programming at any time. All or part of the software can sometimes communicate through the Internet or various other telecommunications networks. For example, such communications can enable software to be loaded from one computer or processor to another computer or processor, for example, from a management server or host computer to a computer platform of an application server. Therefore, another type of medium that can carry software elements includes light, electricity, and electromagnetic waves, such as those used on physical interfaces between local devices through wired and optical landline networks and various air links. Physical elements that carry such waves, such as wired or wireless links, optical links, etc., can also be considered to be the medium that carries the software. As used herein, unless limited to non-transitory, tangible "storage" media, terms such as computer or machine "readable media" refer to any medium that participates in providing instructions to a processor for execution.

[0397] Thus, machine-readable media (such as computer executable code) can take many forms, including but not limited to tangible storage media, carrier media, or physical transmission media. Non-volatile storage media include, for example, optical or magnetic disks, such as any storage device in any one or more computers, such as can be used to implement the database described herein. Volatile storage media include dynamic memory, such as the main memory of the computer platform described herein. Tangible transmission media include coaxial cables; copper wire and optical fiber, including the wires that make up the bus within a computer system. Carrier transmission media can 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: a floppy disk, a diskette, a hard disk, magnetic tape, any other magnetic medium, a CD-ROM, a DVD or DVD-ROM, any other optical medium, a punched card stock tape, any other physical storage medium with a pattern of holes, a RAM, a ROM, a PROM and an EPROM, a FLASH-EPROM, any other memory chip or cassette, a carrier wave that transports data or instructions, a cable or link that transports such a carrier wave, or any other medium from which a computer can read programming code and / or data. Many of these forms of computer readable media may be involved in conveying one or more sequences of one or more instructions to a processor for execution.

[0398] The computer system 101 may include or communicate with an electronic display 135, which includes a user interface (UI) 140 for providing, for example, analysis of nucleic acid sequences, enriched 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.

[0399] The methods and systems of the present disclosure may be implemented by one or more algorithms. The algorithms may be implemented by software when executed by the central processing unit 105. For example, the algorithms may store, process, identify or interpret patient data, biological data, biological sequences, reference sequences and protein spectra.

[0400] In some instances, the subject matter disclosed herein may include at least one computer program or its use. A computer program may be a sequence of instructions that is executed in a CPU, GPU, or TPU of a digital processing device and is written to perform a specified task. Computer-readable instructions may be implemented as program modules that perform specific tasks or implement specific abstract data types, such as functions, objects, application programming interfaces (APIs), data structures, etc. In view of the disclosure provided herein, computer programs can be written in various versions of various languages.

[0401] In various environments, the functionality of the computer-readable instructions may be combined or distributed as needed. In some instances, the computer program may include an instruction sequence. In some instances, the computer program may include multiple instruction sequences. In some instances, the computer program may be provided by one location. In some instances, the computer program may be provided by multiple locations. In some instances, the computer program may include one or more software modules. In some instances, the computer program may include, in part or in whole, one or more web applications, one or more mobile applications, one or more stand-alone applications, one or more web browser plug-ins, extensions, add-ons or add-ons, or a combination thereof.

[0402] In some instances, the computer processing can be a method of statistics, mathematics, biology or a combination thereof. In some instances, the computer processing method includes a dimensionality reduction method, for example, including logistic regression, dimensionality reduction, principal component analysis, autoencoders, singular value decomposition, Fourier basis, singular value decomposition, wavelets, discriminant analysis, support vector machines, tree-based methods, random forests, gradient boosting trees, logistic regression, matrix decomposition, network clustering and neural networks.

[0403] In some instances, the computer processing method is a supervised machine learning method, including, for example, regression, support vector machines, tree-based methods, and networks.

[0404] In some instances, the computer processing method is an unsupervised machine learning method, including, for example, clustering, networks, principal component analysis, and matrix factorization.

[0405] F. Database

[0406] In some instances, the subject matter disclosed herein may include one or more databases, or use a database to store patient data, biological data, biological sequences, reference sequences, or protein spectra. Reference sequences may be derived from a database. In view of the disclosure provided herein, many databases may be suitable for storing and retrieving sequence information. In some instances, suitable databases may include, for example, relational databases, non-relational databases, object-oriented databases, object databases, entity-relationship model databases, associated databases, and XML databases. In some instances, the database may be based on the Internet. In some instances, the database may be based on a network. In some instances, the database may be based on cloud computing. In some instances, the database may be based on one or more local computer storage devices.

[0407] In one aspect, the present disclosure provides a non-transitory computer-readable medium comprising instructions for directing a processor to perform a method disclosed herein.

[0408] In one aspect, the present disclosure provides a computing device including a computer-readable medium.

[0409] In another aspect, the present disclosure provides a system for classifying a biological sample, the system comprising:

[0410] a) a receiver receiving a plurality of training samples, each of the plurality of training samples having a plurality of classes of molecules, wherein each of the plurality of training samples comprises one or more known labels;

[0411] b) a feature module for identifying a set of operable features corresponding to the assays for use in a computer processed machine learning model for each of a plurality of training samples, wherein the set of features corresponds to characteristics of molecules in the plurality of training samples, wherein for each of the plurality of training samples, the system is operable to subject a plurality of classes of molecules in the training sample to a plurality of different assays to obtain a set of measurements, wherein each set of measurements is derived from a single assay performed on a class of molecules in the training sample, wherein a plurality of sets of measurements are obtained for the plurality of training samples;

[0412] c) an analysis module for analyzing the set of measured values ​​to obtain a training vector for the training sample, wherein the training vector comprises feature values ​​of N feature sets corresponding to the assays, each feature value corresponding to a feature and comprising one or more measured values, wherein the training vector is formed using at least one feature from at least two of the N feature sets corresponding to a first subset of the plurality of different assays,

[0413] d) a labeling module, configured to inform the system about the training vectors using parameters of the machine learning model so as to obtain output labels for a plurality of training samples;

[0414] e) a comparator module for comparing the output labels with known labels of training samples;

[0415] f) a training module for iteratively searching for optimal values ​​of parameters as part of training a machine learning model based on comparing the output labels with known labels of training samples; and

[0416] g) An output module, used to provide parameters of the machine learning model and a feature set of the machine learning model.

[0417] Methods for classifying objects in a group

[0418] The disclosed methods involve determining parameters of protein expression associated with colon cell proliferative disorders via analysis of proteins expressed in a subject. The methods are useful for improving the diagnosis, treatment and monitoring of colon cell proliferative disorders, more specifically by improving the identification and differentiation between the stage or subtype of the disorder and the genetic predisposition to the disorder.

[0419] In some embodiments, the methods include analyzing differential expression of a protein in biological samples from subjects in a population.

[0420] The present disclosure provides a method for detecting a cell proliferative disorder of the colon that can be applied to a cell-free sample, for example, to detect the presence and characteristics of a protein between subjects with and without a cell proliferative disorder of the colon or between different cell proliferative disorders of the colon. The method utilizes the detection of a protein as a substantial "positive" or "negative" signal for a cell proliferative disorder of the colon compared to a healthy subject without a cell proliferative disorder of the colon.

[0421] In some embodiments, the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0422] In one aspect, the present disclosure provides a method for determining a protein profile of a biological sample from a subject, the method comprising:

[0423] a) obtaining a biological sample containing a protein from a subject; and

[0424] b) measuring the presence and amount of a predetermined panel in a biological sample to provide a protein profile of a subject, the predetermined panel having 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.

[0425] In some embodiments, the protein profile is associated with a cell proliferative disorder of the colon and provides a classification of the subject as having a cell proliferative disorder of the colon.

[0426] In some embodiments, the biological sample from the subject is selected from the group consisting of body fluids, feces, colonic discharge, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, and combinations thereof.

[0427] In some embodiments, the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0428] In some embodiments, the cell proliferative disorder of the colon is selected from stage 1 colorectal cancer, stage 2 colorectal cancer, stage 3 colorectal cancer, and stage 4 colorectal cancer.

[0429] In some embodiments, the advanced adenoma is a tubular adenoma, tubulovillous adenoma, villous adenoma, adenocarcinoma, or a hyperplastic polyp.

[0430] In another aspect, the present disclosure provides a method for detecting a colon cell proliferative disorder in a subject, the method comprising:

[0431] a) obtaining a biological sample containing a protein from a subject;

[0432] b) measuring the presence and amount of a predetermined panel in a biological sample to provide a protein profile of the subject, the predetermined panel having 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; and

[0433] c) computer processing the protein profile using a machine learning model trained to be able to distinguish between healthy subjects and subjects suffering from a colon cell proliferative disorder to provide an output value associated with the presence or absence of the colon cell proliferative disorder, thereby indicating the presence or absence of the colon cell proliferative disorder in the subject.

[0434] In some embodiments, the biological sample from the subject is selected from the group consisting of body fluids, feces, colon discharge, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, and combinations thereof.

[0435] In another aspect, disclosed herein is a method for detecting proteins to generate a protein profile in a sample, comprising:

[0436] a) obtaining a biological sample containing a protein from a subject; and

[0437] b) measuring the presence and amount of a predetermined panel in a biological sample to provide a protein profile of the biological sample, the predetermined panel having 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.

[0438] In another aspect, disclosed herein is a method of obtaining data from a biological sample from a subject, comprising detecting at least 4 proteins, wherein the at least 4 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, if necessary, determining the levels of at least 4 of the proteins in the sample.

[0439] In some embodiments, the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

[0440] In some embodiments, the cell proliferative disorder of the colon is selected from stage 1 colorectal cancer, stage 2 colorectal cancer, stage 3 colorectal cancer, and stage 4 colorectal cancer.

[0441] In another aspect, the present disclosure provides a method for determining a protein profile of a biological sample from a subject, the method comprising:

[0442] a) obtaining a biological sample containing a protein from a subject; and

[0443] b) measuring the presence and amount of a predetermined panel in a biological sample, the predetermined panel having 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,

[0444] To provide a protein profile of the subject, thereby determining the protein profile of the subject.

[0445] In some embodiments, the predetermined panel of proteins includes CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, IL-8, or a combination thereof.

[0446] In some embodiments, the predetermined panel of proteins includes CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, IL-8, sTREM-1, or a combination thereof.

[0447] In some embodiments, the panel 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.

[0448] In some embodiments, the panel includes 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 NT-proBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or a combination thereof.

[0449] In some embodiments, the panel includes measuring total PSA.

[0450] In another aspect, the present disclosure provides a method for detecting a colon cell proliferative disorder in a subject, the method comprising:

[0451] a) obtaining a biological sample containing a protein from a subject;

[0452] b) measuring the presence and amount of a predetermined panel in the biological sample to provide a protein profile of the subject, the predetermined panel having at least 4 proteins selected from the group consisting of:

[0453] 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;

[0454] c) computer processing of the subject's protein profile using a machine learning model trained to distinguish between subjects not suffering from a cell proliferative disorder of the colon and subjects suffering from a cell proliferative disorder of the colon; and

[0455] d) detecting a colon cell proliferative disorder in the subject by outputting a value associated with the subject having the colon cell proliferative disorder based on the protein profile of the machine learning model.

[0456] 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 protein panel 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 after generating a baseline protein state, determining a protein profile of a biological sample from the subject at one or more time points to generate a current protein state, wherein a change between the baseline protein state and the current protein state is indicative of a change in minimal residual disease in the subject.

[0457] In some embodiments, minimal residual disease is selected from the group consisting of treatment response, tumor burden, residual tumor after surgery, recurrence, secondary screening, primary screening, and cancer progression.

[0458] The trained machine learning methods, models, and discriminative classifiers described herein can be applied to a variety of medical applications, including cancer detection, diagnosis, and treatment responsiveness. Since the models can be trained with subject metadata and analyte-derived features, applications can be customized to stratify subjects in a population and guide treatment decisions accordingly.

[0459] diagnosis

[0460] The methods and systems provided herein can perform predictive analysis using artificial intelligence-based methods to analyze data acquired from an object (patient) to generate a diagnostic output for an object suffering from cancer (e.g., colorectal cancer). For example, an application can apply a predictive algorithm to the acquired data to generate a diagnosis for a subject suffering from cancer. The predictive algorithm can include an artificial intelligence-based predictor, such as a machine learning-based predictor, which is configured to process the acquired data to generate a diagnosis for a subject suffering from cancer.

[0461] A machine learning predictor can be trained using a dataset, for example, a dataset generated by performing protein assays on biological samples of subjects from one or more cohorts of cancer patients using the feature panels described herein as input, and the known diagnostic (e.g., stage and / or tumor score) results of the subjects as output of the machine learning predictor.

[0462] A training data set (e.g., a data set generated by assaying a subject's biological sample using a feature panel herein) can be generated from, for example, one or more sets of subjects having common features (features) and results (markers). A training data set can include a set of features and markers corresponding to features relevant to diagnosis. Features can include features, such as certain ranges or categories of protein assay measurements, such as the presence or characteristics of one or more proteins in biological samples from healthy subjects and subjects with a disease. For example, a set of features collected from a given subject at a given time point can collectively serve as a diagnostic feature, which can indicate that the subject suffers from an identified cancer at a given time point. Features can also include markers indicating a diagnostic result (such as one or more cancers) for the subject.

[0463] The marker may include a result, such as a known diagnosis (e.g., stage and / or tumor score) result for a subject. The result may include a characteristic associated with a subject's cancer. For example, a characteristic may indicate that a subject suffers from one or more cancers.

[0464] The training set (e.g., training data set) can be selected by random sampling of a data set corresponding to one or more subject sets (e.g., retrospective and / or prospective patient cohorts with or without one or more cancers). Alternatively, the training set (e.g., training data set) can be selected by proportional sampling of a data set corresponding to one or more subject sets (e.g., retrospective and / or prospective patient cohorts with or without one or more cancers). The training set can be balanced between data sets corresponding to one or more subject sets (e.g., patients from different clinical sites or trials). The machine learning predictor can be trained until certain predetermined accuracy or performance conditions are met, such as having a minimum expected value corresponding to a diagnostic accuracy metric. For example, a diagnostic accuracy metric can correspond to a prediction of a diagnosis, staging, or tumor score for one or more cancers of a subject.

[0465] Examples of diagnostic accuracy metrics may include sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the curve (AUC) of a receiver operating characteristic (ROC) curve, which correspond to the diagnostic accuracy of detecting or predicting cancer (e.g., colorectal cancer).

[0466] In one aspect, the present disclosure provides a method of using a classifier capable of distinguishing between groups of objects, the method comprising:

[0467] a) obtaining a biological sample containing a protein from a subject;

[0468] b) measuring the presence and amount of a predetermined panel in a biological sample, thereby providing a protein profile of the subject, the predetermined panel having 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;

[0469] c) computer processing of the protein profile of the subject using a machine learning model trained to distinguish between two or more populations; and

[0470] d) Outputting values ​​associated with the groups through a machine learning model based on protein spectra, thereby distinguishing the groups of objects.

[0471] In some embodiments, the panel includes CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, IL-8, sTREM-1, or a combination thereof.

[0472] In some embodiments, the panel 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.

[0473] In some embodiments, the panel includes 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 NT-proBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or a combination thereof.

[0474] In some embodiments, the panel includes CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, IL-8, or a combination thereof.

[0475] In some embodiments, the panel includes measuring total PSA.

[0476] In another aspect, the present disclosure provides a method for identifying cancer in a subject, the method comprising:

[0477] a) obtaining a biological sample containing a protein from a subject;

[0478] b) measuring the presence and amount of a predetermined panel in a biological sample, thereby providing a protein profile of the subject, the predetermined panel having 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;

[0479] c) computer processing the protein profile using a machine learning model trained to be able to distinguish between healthy subjects and subjects suffering from a colon cell proliferative disorder to provide an output value associated with the presence or absence of the colon cell proliferative disorder, thereby indicating the presence or absence of the colon cell proliferative disorder in the subject,

[0480] to generate a probability of said subject having said cancer.

[0481] In some embodiments, the panel includes CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, IL-8, sTREM-1, or a combination thereof.

[0482] In some embodiments, the panel 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.

[0483] In some embodiments, the panel includes 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 NT-proBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or a combination thereof.

[0484] In some embodiments, the panel includes CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, IL-8, or a combination thereof.

[0485] In some embodiments, the panel includes measuring total PSA.

[0486] A variety of statistical and mathematical methods can be used to establish thresholds or cutoff levels of expression. The threshold or cutoff expression level of a specific biomarker can be selected, for example, based on data from a receiver operating characteristic (ROC) plot (as described in the embodiments and figures disclosed herein). These thresholds or cutoff expression levels can be varied, for example, by moving along the ROC plot of a specific biomarker or a combination thereof to obtain different sensitivity or specificity values, thereby affecting the overall assay performance. For example, if the goal is to have a reliable diagnostic method from a clinical perspective, high sensitivity should be given priority. However, if the goal is to have a cost-effective method, high specificity should be given priority. The optimal cutoff refers to the value of a specific biomarker that produces the best sensitivity and specificity obtained from the ROC plot. Sensitivity and specificity values ​​are calculated within the threshold (cutoff) range. 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, and can be, 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%.

[0487] Therefore, some embodiments of the present disclosure are carried out by the following steps: determining the presence and / or level of at least previously recorded proteins in a minimally invasive sample isolated from a subject to be diagnosed or screened, and comparing the presence and / or level of the proteins with a predetermined threshold or cutoff value, wherein the predetermined threshold or cutoff value corresponds to an expression level of the protein that is associated with the highest specificity at a desired sensitivity in an ROC curve, the ROC curve being calculated based on protein expression levels determined in a patient population at risk of developing colorectal cancer or colorectal adenoma, wherein overexpression of at least one of the proteins relative to the predetermined cutoff value indicates that the subject has colorectal cancer or colorectal adenoma with the desired sensitivity.

[0488] As another example, such a predetermined condition can be a specificity for predicting a colon cell proliferative disorder having 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%.

[0489] As another example, such a predetermined condition can be that the positive predictive value (PPV) for predicting a colon cell proliferative disorder has 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%.

[0490] As another example, such a predetermined condition can be that the negative predictive value (NPV) for predicting a colon cell proliferative disorder has 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%.

[0491] As another example, such a predetermined condition can be that the area under the curve (AUC) of a receiver operating characteristic (ROC) curve predicting a colon cell proliferative disorder has a value of 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.

[0492] Surveillance for colorectal cancer

[0493] After processing the data set using the trained algorithm, colorectal cancer can be identified or monitored in the subject. Identification can be based at least in part on quantitative measurements of proteins in the data set on a panel of colorectal cancer-associated proteins. For example, monitoring may include colorectal cancer in each evaluated subject at two or more different time points.

[0494] In some embodiments, the methods disclosed herein can be applied to monitor and / or predict tumor burden.

[0495] In some embodiments, the methods disclosed herein can be applied to detect and / or predict residual tumor after surgery.

[0496] In some embodiments, the methods disclosed herein can be applied to detect and / or predict minimal residual disease after treatment.

[0497] In some embodiments, the methods disclosed herein can be applied to detect and / or predict recurrence.

[0498] In one aspect, the methods disclosed herein can be used as a secondary screen.

[0499] In one aspect, the methods disclosed herein can be used as a single screen.

[0500] In one aspect, the methods disclosed herein can be applied to monitoring cancer progression.

[0501] In one aspect, the methods disclosed herein can be applied to monitoring and / or predicting cancer risk.

[0502] Colorectal cancer can be identified in a subject with an accuracy of 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 greater. 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 correctly identified or classified as having or not having colorectal cancer.

[0503] 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 the trained algorithm can be calculated as the percentage of cell-free biological samples identified or classified as having colorectal cancer that correspond to subjects who actually have colorectal cancer.

[0504] 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 the trained algorithm can be calculated as the percentage of cell-free biological samples identified or classified as not having colorectal cancer corresponding to subjects who are truly free of colorectal cancer.

[0505] 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 100%, at least about 101%, at least about 102%, at least about 103%, at least about 104%, at least about 105%, at least about 106%, at least about 107%, at least about 108%, at least about 109%, at least about 110%, at least about 111%, at least about 112%, at least about 113%, at least about 114%, at least about 115%, 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 of identifying colorectal cancer using the 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 correctly identified or classified as having colorectal cancer.

[0506] Colorectal cancer can be identified in a 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 100%, at least about 101%, at least about 102%, at least about 103%, at least about 104%, at least about 105%, at least about 106%, at least about 107%, at least about 108%, at least about 109%, at least about 110%, at least about 111%, at least about 112%, at least about 113%, at least about 114%, at least about 115%, 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 of identifying colorectal cancer using a trained algorithm can be calculated as the percentage 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 suffering from colorectal cancer.

[0507] In some embodiments, the trained algorithm can determine that a subject's risk of developing colorectal cancer is 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.

[0508] The trained algorithm can determine that a subject is at risk for developing colorectal cancer with an accuracy of 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 100%, at least about 101%, at least about 102%, at least about 103%, at least about 104%, at least about 105%, at least about 106%, at least about 107%, at least about 108%, at least about 109%, at least about 110%, at least about 111%, at least about 112%, at least about 113%, at least about 114%, at least about 115%, at least about 116%, at least about 117%, at least about 118%, at least about 119%, 2%, 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.

[0509] After identifying that the subject suffers from colorectal cancer, therapeutic intervention can be optionally provided for the subject (e.g., prescribing an appropriate course of treatment for colorectal cancer for the subject). Therapeutic intervention can include prescribing an effective dose of medication, further testing or evaluation of colorectal cancer, further monitoring of colorectal cancer, or a combination thereof. If the subject is currently receiving treatment for colorectal cancer with a course of treatment, therapeutic intervention can include a subsequent different course of treatment (e.g., to increase therapeutic efficacy because the current course of treatment is ineffective).

[0510] Therapeutic intervention may include recommending that the subject undergo secondary clinical testing to confirm the diagnosis of colorectal cancer. Such secondary clinical testing may 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 biocytology, FIT tests, FOBT tests, or a combination thereof.

[0511] By monitoring the course of treatment of colorectal cancer in a subject, colorectal cancer in the subject can be monitored. Monitoring can include evaluating the colorectal cancer in the subject at two or more time points. The evaluation can be based at least on quantitative measures of proteins in a data set on a colorectal cancer associated protein panel, including quantitative measures of the colorectal cancer associated protein panel determined at each of the two or more time points.

[0512] In some embodiments, differences in quantitative measures of proteins in a data set on a colorectal cancer-associated protein panel (including quantitative measures of a colorectal cancer-associated protein panel determined between two or more time points) can indicate one or more clinical indications, 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 course of treatment for colorectal cancer in a subject; and (vi) ineffectiveness of a course of treatment for colorectal cancer in a subject.

[0513] In some embodiments, the difference in the quantitative measurement of proteins (including the quantitative measurement of the group of colorectal cancer-associated proteins determined between two or more time points) can indicate the diagnosis of colorectal cancer of the object. For example, if the object does not detect colorectal cancer at an earlier time point, but detects it at a later time point, the difference indicates the diagnosis of colorectal cancer of the object. Clinical action or decision can be made based on this indication of the diagnosis of colorectal cancer of the object, for example, prescribing a new therapeutic intervention for the object. Clinical action or decision can include advising the object to undergo secondary clinical testing to confirm the diagnosis of colorectal cancer. This secondary clinical test 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 biological cytology, FIT tests, FOBT tests or combinations thereof.

[0514] In some embodiments, differences in quantitative measures of proteins of a dataset on a colorectal cancer-associated protein panel, including quantitative measures of a colorectal cancer-associated protein panel determined between two or more time points, may indicate a prognosis for colorectal cancer in a subject.

[0515] In some embodiments, the difference in the quantitative measurement of the protein of the data set on the colorectal cancer associated protein panel (including the quantitative measurement of the colorectal cancer associated protein panel determined between two or more time points) can indicate that the subject has an increased risk of colorectal cancer. For example, if the subject is detected with colorectal cancer at both an earlier time point and a later time point, and if the difference is a positive difference (for example, the quantitative measurement of the protein of the data set on the colorectal cancer associated protein panel increases from an earlier time point to a later time point), the difference can indicate that the subject has an increased risk of colorectal cancer. Clinical action or decision can be made based on this indication of increased risk of colorectal cancer, for example, prescribing a new therapeutic intervention or switching therapeutic intervention (for example, ending the current treatment and prescribing a new treatment) for the subject. Clinical action or decision can include advising the subject to undergo a secondary clinical test to confirm the increased risk of colorectal cancer. This secondary clinical test 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 biocytology, a FIT test, a FOBT test, or a combination thereof.

[0516] In some embodiments, the difference in the quantitative measurement of the protein of the data set on the colorectal cancer associated protein panel (including the quantitative measurement of the colorectal cancer associated protein panel determined between two or more time points) can indicate that the risk of the subject suffering from colorectal cancer is reduced. For example, if the object is detected with colorectal cancer at both an earlier time point and a later time point, and if the difference is a negative difference (for example, the quantitative measurement of the protein of the data set on the colorectal cancer associated protein panel, including the quantitative measurement of the colorectal cancer associated protein panel from an earlier time point to a later time point is reduced), then the difference can indicate that the risk of the subject suffering from colorectal cancer is reduced. Clinical action or decision can be made based on this indication of reduced risk of colorectal cancer for the object (for example, to continue or end the current therapeutic intervention). Clinical action or decision can include advising the object to undergo a secondary clinical test to confirm the reduced risk of suffering from colorectal cancer. This secondary clinical test 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 biocytology, a FIT test, a FOBT test, or a combination thereof.

[0517] In some embodiments, the difference in the quantitative measure of the protein of the data set on the colorectal cancer associated protein panel (including the quantitative measure of the colorectal cancer associated protein panel determined between two or more time points) can indicate that the treatment process of colorectal cancer of the treatment object is effective. For example, if the object detects colorectal cancer at an earlier time point, but not at a later time point, the difference can indicate that the treatment process of colorectal cancer of the treatment object is effective. Clinical action or decision can be made based on this indication that the treatment process of colorectal cancer of the treatment object is effective, for example, to continue or end the current therapeutic intervention for the object. Clinical action or decision can include suggesting that the object undergo a secondary clinical test to confirm that the treatment process of colorectal cancer is effective. This secondary clinical test 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 biological cytology, FIT tests, FOBT tests or combinations thereof.

[0518] In some embodiments, the difference in the quantitative measure of the protein of the data set on the colorectal cancer associated protein panel (including the quantitative measure of the colorectal cancer associated protein panel determined between two or more time points) can indicate that the treatment process of the colorectal cancer of the treatment object is invalid. For example, if the object is detected with colorectal cancer at both the earlier time point and the later time point, and if the difference is positive or zero difference (for example, the quantitative measure of the protein of the data set on the colorectal cancer associated protein panel, including the quantitative measure of the colorectal cancer associated protein panel, is increased or maintained at a constant level from the earlier time point to the later time point), and if effective treatment is indicated at the earlier time point, the difference can indicate that the treatment process of the colorectal cancer of the treatment object is invalid. Clinical action or decision can be made based on this indication that the treatment process of the colorectal cancer of the treatment object is invalid, for example, to end the current therapeutic intervention for the object and / or convert (for example, prescribe) a new different therapeutic intervention. Clinical action or decision can include suggesting that the object undergo a secondary clinical test to confirm that the treatment process for colorectal cancer is invalid. This secondary clinical test 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 biocytology, a FIT test, a FOBT test, or a combination thereof.

[0519] Reagent test kit

[0520] The present disclosure provides a kit for identifying or monitoring a cancer in a subject. The kit may include antibodies, probes, or primers for identifying a quantitative measure of protein (e.g., indicating presence, absence, or relative amount) on each of a plurality of cancer-associated proteins in a cell-free biological sample of the subject. The quantitative measure (e.g., indicating presence, absence, or relative amount) of a small group of proteins in a cell-free biological sample may indicate one or more cancers. The probe may be selective for proteins in a cell-free biological sample. The kit may include instructions for processing a cell-free biological sample using a probe to generate a data set, and the data set indicates a quantitative measure (e.g., indicating presence, absence, or relative amount) of a protein in a cell-free biological sample of the subject.

[0521] The probes in the kit may be selective for proteins or protein-encoding sequences on multiple cancer-associated proteins in a cell-free biological sample. The probes in the kit may be configured to selectively enrich protein molecules corresponding to multiple cancer-associated proteins. The probes in the kit may be antibodies recognized by proteins and labeled to allow separation after binding to proteins in a biological sample.

[0522] The instructions in the kit may include instructions for assaying the acellular biological sample using a probe that is selective for a cancer-associated protein in the acellular biological sample. A quantitative measure of a protein or sequence encoding a protein (e.g., indicating presence, absence, or relative amount) on each of a plurality of cancer-associated proteins in the acellular biological sample may indicate one or more cancers.

[0523] The instructions in the kit may include instructions for measuring and interpreting an assay readout that can be quantified at one or more of a plurality of cancer-associated proteins to generate a dataset indicating a quantitative measure (e.g., indicating presence, absence, or relative amount) of a protein or a sequence encoding the protein at each of a plurality of cancer-associated proteins in a cell-free biological sample.

[0524] Example

[0525] Example 1: Protein analysis in patient plasma samples.

[0526] In cancer, proteins (whether cancer neoantigens or canonical proteins) represent a source of potential early diagnostic biomarkers for colorectal cancer. Protein signatures can be determined from plasma by assessing protein overexpression, deletion or mutation in cancer patients. Some proteins associated with breast cancer, prostate cancer, colorectal cancer, lung cancer and ovarian cancer have been identified.

[0527] In order to identify proteins that are informative for the methods and classifiers described herein, plasma from patients with colon cell proliferative disorders and plasma from subjects without colon cell proliferative disorders (control plasma or reference plasma) have been examined to identify a characteristic panel of proteins produced by patients with colon cell proliferative disorders in response to colon cell proliferative disorders and corresponding reactive proteins. To this end, plasma from patients with colon cell proliferative disorders and control plasma were analyzed using proteomic-based mass spectrometry and immunoaffinity assays to identify and quantify circulating plasma proteins. Prior to characterization, the plasma may or may not have been depleted of high abundance proteins (e.g., albumin, immunoglobulins).

[0528] The panel of proteins identified by this analysis allows differentiation of plasma from subjects with cell proliferative disorders of the colon from healthy subjects.

[0529] method

[0530] Sample classification

[0531] To detect proteins in plasma samples, immunoassays and mass spectrometry were performed using plasma samples drawn from subjects subsequently identified as having colorectal cancer (CRC), advanced adenoma (AA), benign polyps (NAA), or none of these (NEG).

[0532] Plasma samples were obtained using a standardized blood collection and processing protocol and subsequently stored at -80°C until use. Written consent was obtained from all subjects with Institutional Review Board approval.

[0533] Table 1 provides a description of the study cohort, which shows the number of healthy and cancer samples used in the classification model (by sex and age) for the CRC experiments.

[0534] Table 1

[0535]

[0536] The goal of this study is to identify serum protein biomarkers that distinguish colorectal cancer from advanced adenomas, benign disease, and healthy controls to improve the sensitivity of current biomarkers and guide clinical decisions

[0537] Plasma was isolated from subject samples representing NEG, CRC, AA, and NAA subject populations and screened on protein arrays. A total of 1,472 features were identified in the NEG, CRC, AA, and NAA subject populations and queried for differential expression in plasma from subjects with colon cell proliferative disorders and plasma from healthy subjects.

[0538] All protein quantification data were normalized and reported on a relative or absolute scale.

[0539] Filter feature values:

[0540] The ability of each protein to distinguish patients with and without a specific condition was assessed by calculating indices for discriminating three groups. The discriminating groups were: disease negative vs colorectal cancer (NEG vs CRC), disease negative vs advanced adenoma (NEG vs AA), and disease negative vs advanced adenoma and non-advanced adenoma (NEG vs AA+NAA). Indices calculated for this comparison included the Hedges'G effect size metric, the Wasserstein distance metric, feature weights in single-measurement linear logistic regression with elastic net regularization, feature importance in single-measurement nonlinear random forests, and feature weights in multi-measurement logistic regression models.

[0541] For each protein, the percentile of that protein in the per-metric distribution over all proteins was calculated. Proteins were ranked by the percentile in the per-metric distribution. Proteins were retained for further consideration if they met at least one of the following criteria:

[0542] A) The maximum index percentile for protein analysis only is 95 or higher and the median index percentile is 90 or higher;

[0543] B) ranked in the top 25 in terms of maximum indicator percentile or median indicator percentile; and

[0544] C) Top 25 ranked by feature weight in a multiple determination logistic regression model.

[0545] Some additional features were also included for consideration. Features that met the above criteria were further explored for reagent availability, including additional features, and features were selected.

[0546] result

[0547] NEG, NAA, AA, CRC

[0548] Table 2 provides a list of identified proteins of the protein biomarker panel for CRC identification.

[0549] Table 2

[0550]

[0551]

[0552] ROC cross-validation test fold average metric. The average performance metric is determined across 20 discovery data folds where all data is used for feature selection but the model weights are defined in cross-validation.

[0553] For the metric "target 0.9 specificity", predictions were made based on prediction probabilities (scores) and labels. A threshold was chosen that maximized sensitivity while achieving a specificity just above 0.9. For the metric "empirical", predictions were made by using the default prediction threshold from the classifier, not targeting any particular specificity or based on test samples. Confidence intervals are reported in square brackets and are the average of the confidence intervals per fold.

[0554] Illustration of classification performance of the 53-feature CRC vs. NEG on the discovery data, where all data was used for feature selection but model weights were defined in cross-validation (no real holdout in this performance).

[0555] Although preferred embodiments of the present invention have been shown and described herein, it is obvious to those skilled in the art that such embodiments are provided by way of example only. The present invention is not intended to be limited to the specific embodiments provided in this specification. Although the present invention has been described with reference to the above specific description, the description and example illustration of the embodiments herein are not intended to be interpreted in a restrictive sense. Without departing from the present invention, those skilled in the art will now think of various variations, changes and alternatives. In addition, it should be understood that all aspects of the present invention are not limited to the specific depiction, configuration or relative proportions set forth herein, which depend on various conditions and variables. It should be understood that various alternatives to the embodiments of the present invention described herein can be used to practice the present invention. Therefore, it is envisioned that the present invention should also cover any such alternatives, modifications, variations or equivalents. The appended claims are intended to define the scope of the present invention and thus cover methods and structures and their equivalents within the scope of these claims.

Claims

1. A panel of predetermined proteins characteristic of a cell proliferative disorder of the colon, 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, 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.

2. The panel of claim 1, 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 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.

3. The panel of claim 1, wherein the panel comprises CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, IL-8, or a combination thereof.

4. The panel of claim 1, wherein 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, NTproBNP, total NTproBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or a combination thereof.

5. The panel of claim 1, wherein 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.

6. The panel of claim 1, wherein the panel is configured to distinguish between healthy subjects, subjects with benign colon polyps, subjects with advanced adenomas, or subjects with colorectal cancer.

7. The panel of claim 1, wherein the panel is configured to indicate 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.

8. The panel of claim 1, wherein the panel is configured to be indicative of advanced adenoma 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, 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.

9. The panel of claim 1, wherein the panel is configured to indicate 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.

10. The panel of claim 1, wherein the panel is configured to be indicative of 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, 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.

11. A panel as described in claim 1, wherein the cell proliferative disorder of the colon is selected from: adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma and sarcoma.

12. A classifier configured to distinguish between a population of healthy subjects and subjects suffering from a cell proliferative disorder of the colon, the classifier comprising: a set of measurements representing proteins from a predetermined panel of proteins characteristic of the colon cell proliferative disorder, wherein the set of measurements is obtained from protein expression data from samples from healthy subjects and samples from subjects with a cell proliferative disorder of the colon, wherein the measurements are used to generate a feature set corresponding to characteristics of the protein expression data, wherein the feature set is computer processed using a machine learning or statistical model, and wherein the machine learning or statistical model provides a feature vector that can be used as a classifier capable of distinguishing between a population of healthy subjects and subjects suffering from the colon cell proliferative disorder.

13. The classifier of claim 12, wherein the predetermined panel of proteins 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.

14. The classifier of claim 12, wherein 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 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.

15. The classifier of claim 12, wherein the panel comprises CXCL13, CYFRA 21-1, FGF23, GDF-15, HE4, IL-8, or a combination thereof.

16. A classifier as described in claim 12, wherein the panel includes 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 NT-proBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40 or a combination thereof.

17. A classifier as described in claim 12, wherein the panel 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.

18. The classifier of claim 12, wherein the classifier is configured to distinguish between a healthy subject, a subject with benign colon polyps, a subject with advanced adenoma, or a subject with colorectal cancer.

19. The classifier of claim 12, wherein the panel is configured to indicate 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.

20. The classifier of claim 12, wherein the panel is configured to be indicative of advanced adenoma 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.

21. The classifier of claim 12, wherein the panel is configured to be indicative of 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.

22. The classifier of claim 12, wherein the panel is configured to be indicative of 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.

23. A classifier as described in claim 12, wherein the predetermined protein panel includes at least 4 proteins selected from the following: AFP, AFP-L3, anti-p53, CA125II, CA15-3, CA72-4, CEA, CXCL13, CYFRA 21-1, ferritin, FGF23, gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNPII, sFlt-1 and YKL.

24. A classifier as described in claim 12, wherein the predetermined protein 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 following: 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.

25. The classifier of claim 12, wherein the predetermined panel of proteins 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.

26. A classifier as described in claim 12, wherein the predetermined protein 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 following: AFP, AFP-L3, anti-p53, CEA, CXCL13, CYFRA 21-1, ferritin, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE and sFlt-1.

27. The classifier of claim 12, wherein the predetermined panel of proteins 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.

28. A classifier as described in claim 12, wherein the predetermined protein 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 following: AFP, AFP-L3, hGH, IL8, NSE, CEA, anti-p53, sFlt-1, ferritin, CYFRA 21-1, CA125, IGF-1, CXCL-13 and YKL.

29. The classifier of claim 12, wherein the predetermined panel of proteins 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.

30. The classifier of claim 12, wherein the predetermined panel of proteins 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.

31. A classifier as described in claim 12, wherein the predetermined protein panel includes at least 4 proteins selected from the following: AFP, AFP-L3, Ang-2, CA125II, 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.

32. A classifier as described in claim 12, wherein the predetermined panel of proteins includes at least 5, at least 6, at least 7, at least 8, at least 9 or at least 10 proteins selected from the following: AFP, AFP-L3, Ang-2, CA125II, 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.

33. A classifier as described in claim 12, wherein the predetermined protein panel includes at least 4 proteins selected from the following: 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.

34. A classifier as described in claim 12, wherein the predetermined protein 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 following: 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.

35. A classifier as described in claim 12, wherein the cell proliferative disorder of the colon is selected from the group consisting of: adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma and sarcoma.

36. A system comprising a machine learning model classifier for detecting a colon cell proliferative disorder, the system comprising: A computer-readable medium comprising a classifier operable to classify an object based on a predetermined panel of proteins; as well as One or more processors for executing instructions stored on the computer-readable medium.

37. The system of claim 36, comprising the classifier of claim 9 loaded into a memory of a computer system, wherein the machine learning model is trained using training data obtained from training biological samples, wherein a first subset of the training biological samples are identified as corresponding to subjects having a cell proliferative disorder of the colon, and wherein a second subset of the training biological samples are identified as corresponding to subjects not having a cell proliferative disorder of the colon.

38. The system of claim 36, wherein the panel 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.

39. The system of claim 36, 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 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.

40. The system of claim 36, wherein the predetermined panel of proteins 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, proBNPII, sFlt-1, and YKL.

41. The system of claim 36, wherein 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, CA125II, 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.

42. The system of claim 36, wherein the predetermined panel of proteins 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.

43. The system of claim 36, wherein 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.

44. The system of claim 36, wherein the predetermined panel of proteins 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.

45. The system of claim 36, wherein 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, CYFRA 21-1, CA125, IGF-1, CXCL-13 and YKL.

46. ​​The system of claim 36, wherein the predetermined panel of proteins 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.

47. The system of claim 36, wherein the predetermined panel of proteins 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.

48. The system of claim 36, wherein the predetermined panel of proteins 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.

49. The system of claim 36, wherein 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, 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.

50. The system of claim 36, wherein the predetermined panel of proteins 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.

51. The system of claim 36, wherein 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, CYFRA 21-1, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, PRL2, sFlt-1 and YKL.

52. The system of claim 36, wherein 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 classifier, a linear kernel support vector machine classifier, a first-order 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.

53. A method for determining a protein profile of a biological sample from a subject, the method comprising: a) obtaining the biological sample containing the protein from the subject; as well as b) measuring the amount of proteins from a predetermined panel of proteins in the biological sample, thereby providing the protein profile of the subject, wherein the predetermined panel of proteins includes 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.

54. The method of claim 53, wherein 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 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.

55. The method of claim 53, wherein the predetermined panel of proteins comprises CXCL13, CYFRA21-1, FGF23, GDF-15, HE4, IL-8, or a combination thereof.

56. The method of claim 53, wherein the predetermined panel of proteins 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-NTproBNP, sICAM-1, sTREM-1, TSH, VEGF-A, YKL40, or a combination thereof.

57. The method of claim 53, wherein the predetermined panel of proteins 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.

58. The method of claim 53, wherein the protein profile is associated with a cell proliferative disorder of the colon and provides a classification of the subject as having the cell proliferative disorder of the colon.

59. The method of claim 53, wherein the biological sample from the subject is selected from the group consisting of body fluids, feces, colon discharge, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

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

61. The method of claim 53, further comprising: c) treating said subject with surgery and / or therapeutic agents based on said protein profile of said subject.

62. A method for detecting a colon cell proliferative disorder in a subject, the method comprising: a) obtaining a biological sample containing protein from the subject; b) measuring the amount of proteins from a predetermined panel of proteins in the biological sample, thereby providing a protein profile of the subject, wherein the predetermined panel of proteins includes 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-NTproBNP, proGRP, PRL, PSA, S100, SCC, sFlt-1, SHBG, sHLA-G, sICAM-1, sTREM-1, TK1, TSH, VEGF-A, and YKL40; as well as c) computer processing the protein profile using a machine learning model that is trained to be able to distinguish between healthy subjects and subjects suffering from the colon cell proliferative disorder to provide an output value associated with the presence or absence of the colon cell proliferative disorder, thereby indicating the presence or absence of the colon cell proliferative disorder in the subject.

63. The method of claim 62, wherein 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 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.

64. The method of claim 62, wherein the predetermined panel of proteins comprises CXCL13, CYFRA21-1, FGF23, GDF-15, HE4, IL-8, or a combination thereof.

65. The method of claim 62, wherein the predetermined panel of proteins 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.

66. The method of claim 62, wherein the predetermined panel of proteins 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.

67. The method of claim 62, wherein the protein profile is associated with a cell proliferative disorder of the colon and provides a classification of the subject as having the cell proliferative disorder of the colon.

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

69. The method of claim 62, wherein the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

70. The method of claim 62, further comprising: d) treating said subject with surgery and / or therapeutic agents based on said protein profile of said subject.

71. A predetermined panel of proteins characteristic of a cell proliferative disorder of the colon, 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.

72. A panel as described in claim 71, wherein 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 following: 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.

73. The panel of claim 71, wherein the panel is configured to distinguish between healthy subjects, subjects with benign colon polyps, subjects with advanced adenomas, or subjects with colorectal cancer.

74. A panel as described in claim 71, wherein the cell proliferative disorder of the colon is selected from: adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma and sarcoma.

75. A method for determining a protein profile of a biological sample from a subject, the method comprising: a) obtaining the biological sample containing the protein from the subject; as well as b) measuring the amount of protein from a predetermined panel of proteins in the biological sample, the predetermined panel of proteins 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.

76. The method of claim 75, 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.

77. The method of claim 75, wherein the protein profile is associated with a cell proliferative disorder of the colon and provides a classification of the subject as having the cell proliferative disorder of the colon.

78. The method of claim 75, wherein the biological sample from the subject is selected from the group consisting of body fluids, feces, colon discharge, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

79. The method of claim 75, wherein the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

80. The method of claim 75, further comprising: c) treating said subject with surgery and / or therapeutic agents based on said protein profile of said subject.

81. A method for detecting a colon cell proliferative disorder in a subject, the method comprising: a) obtaining a biological sample containing protein from the subject; b) measuring the amount of proteins from a predetermined panel of proteins in the biological sample, thereby providing a protein profile of the subject, wherein the predetermined panel of proteins includes 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, proBNPII, sFlt-1, and YKL; as well as c) computer processing the protein profile using a machine learning model that is trained to be able to distinguish between healthy subjects and subjects suffering from the colon cell proliferative disorder to provide an output value associated with the presence or absence of the colon cell proliferative disorder, thereby indicating the presence or absence of the colon cell proliferative disorder in the subject.

82. The method of claim 81, wherein 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, CA125II, 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.

83. The method of claim 81, wherein the protein profile is associated with a cell proliferative disorder of the colon and provides a classification of the subject as having the cell proliferative disorder of the colon.

84. The method of claim 81, wherein the biological sample from the subject is selected from the group consisting of body fluids, feces, colon discharge, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

85. The method of claim 81, wherein the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

86. The method of claim 81, further comprising: d) treating said subject with surgery and / or therapeutic agents based on said protein profile of said subject.

87. A predetermined panel of proteins characteristic of a cell proliferative disorder of the colon, the 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.

88. A panel as described in claim 87, wherein 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 following: AFP, AFP-L3, anti-p53, CEA, CXCL13, CYFRA 21-1, ferritin, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE and sFlt-1.

89. The panel of claim 87, wherein the panel is configured to distinguish between healthy subjects, subjects with benign colon polyps, subjects with advanced adenomas, or subjects with colorectal cancer.

90. A panel as described in claim 87, wherein the cell proliferative disorder of the colon is selected from: adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma and sarcoma.

91. A method for determining a protein profile of a biological sample from a subject, the method comprising: a) obtaining the biological sample containing the protein from the subject; as well as b) measuring the amount of protein from a predetermined panel of proteins in the biological sample, wherein the predetermined panel of proteins includes 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.

92. The method of claim 91, 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.

93. The method of claim 91, wherein the protein profile is associated with a cell proliferative disorder of the colon and provides a classification of the subject as having the cell proliferative disorder of the colon.

94. The method of claim 91, wherein the biological sample from the subject is selected from the group consisting of body fluids, feces, colon discharge, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

95. The method of claim 91, wherein the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

96. The method of claim 91, further comprising: c) treating said subject with surgery and / or therapeutic agents based on said protein profile of said subject.

97. A method for detecting a colon cell proliferative disorder in a subject, the method comprising: a) obtaining a biological sample containing protein from the subject; b) measuring the amount of proteins from a predetermined panel of proteins in the biological sample, thereby providing a protein profile of the subject, wherein the predetermined panel of proteins includes 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; as well as c) computer processing the protein profile using a machine learning model that is trained to be able to distinguish between healthy subjects and subjects suffering from the colon cell proliferative disorder to provide an output value associated with the presence or absence of the colon cell proliferative disorder, thereby indicating the presence or absence of the colon cell proliferative disorder in the subject.

98. The method of claim 97, wherein 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.

99. The method of claim 97, wherein the protein profile is associated with a cell proliferative disorder of the colon and provides a classification of the subject as having the cell proliferative disorder of the colon.

100. The method of claim 97, wherein the biological sample from the subject is selected from the group consisting of body fluids, feces, colon discharge, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

101. The method of claim 97, wherein the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

102. The method of claim 97, further comprising: d) treating said subject with surgery and / or therapeutic agents based on said protein profile of said subject.

103. A predetermined panel of proteins characteristic of a cell proliferative disorder of the colon, 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, CYFRA 21-1, CA125, IGF-1, CXCL-13 and YKL.

104. A panel as described in claim 103, wherein 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 following: AFP, AFP-L3, hGH, IL8, NSE, CEA, anti-p53, sFlt-1, ferritin, CYFRA 21-1, CA125, IGF-1, CXCL-13 and YKL.

105. The panel of claim 103, wherein the panel is configured to distinguish between healthy subjects, subjects with benign colon polyps, subjects with advanced adenomas, or subjects with colorectal cancer.

106. A panel as described in claim 103, wherein the colon cell proliferative disorder is selected from: adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma and sarcoma.

107. A method for determining a protein profile of a biological sample from a subject, the method comprising: a) obtaining the biological sample containing the protein from the subject; as well as b) measuring the amount of a protein from a predetermined panel of proteins in the biological sample, the predetermined panel of proteins comprising 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.

108. The method of claim 107, 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.

109. The method of claim 107, wherein the protein profile is associated with a cell proliferative disorder of the colon and provides a classification of the subject as having the cell proliferative disorder of the colon.

110. The method of claim 107, wherein the biological sample from the subject is selected from the group consisting of body fluids, feces, colon discharge, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

111. The method of claim 107, wherein the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma and sarcoma.

112. The method of claim 107, further comprising: c) treating said subject with surgery and / or therapeutic agents based on said protein profile of said subject.

113. A method for detecting a colon cell proliferative disorder in a subject, the method comprising: a) obtaining a biological sample containing protein from the subject; b) measuring the amount of proteins from a predetermined panel of proteins in the biological sample, thereby providing a protein profile of the subject, wherein the predetermined panel of proteins includes 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; as well as c) computer processing the protein profile using a machine learning model that is trained to be able to distinguish between healthy subjects and subjects suffering from the colon cell proliferative disorder to provide an output value associated with the presence or absence of the colon cell proliferative disorder, thereby indicating the presence or absence of the colon cell proliferative disorder in the subject.

114. The method of claim 113, wherein 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, CYFRA 21-1, CA125, IGF-1, CXCL-13 and YKL.

115. The method of claim 113, wherein the protein profile is associated with a cell proliferative disorder of the colon and provides a classification of the subject as having the cell proliferative disorder of the colon.

116. The method of claim 113, wherein the biological sample from the subject is selected from the group consisting of body fluids, feces, colon discharge, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

117. The method of claim 113, wherein the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma and sarcoma.

118. The method of claim 113, further comprising: d) treating said subject with surgery and / or therapeutic agents based on said protein profile of said subject.

119. A predetermined panel of proteins characteristic of a cell proliferative disorder of the colon, said panel comprising 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.

120. The panel of claim 119, wherein 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, CYFRA 21-1, IL-6, IL-6hs, OPN and total PSA.

121. The panel of claim 119, wherein the panel is configured to distinguish between healthy subjects, subjects with benign colon polyps, subjects with advanced adenomas, or subjects with colorectal cancer.

122. A panel as described in claim 119, wherein the colon cell proliferative disorder is selected from: adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma and sarcoma.

123. A method for determining a protein profile of a biological sample from a subject, the method comprising: a) obtaining the biological sample containing the protein from the subject; as well as b) measuring the amount of protein from a predetermined panel of proteins in the biological sample, the predetermined panel of proteins 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.

124. The method of claim 123, 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.

125. The method of claim 123, wherein the protein profile is associated with a cell proliferative disorder of the colon and provides a classification of the subject as having the cell proliferative disorder of the colon.

126. The method of claim 123, wherein the biological sample from the subject is selected from the group consisting of body fluids, feces, colon discharge, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

127. The method of claim 123, wherein the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

128. The method of claim 123, further comprising: c) treating said subject with surgery and / or therapeutic agents based on said protein profile of said subject.

129. A method for detecting a colon cell proliferative disorder 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 panel of proteins in the biological sample, thereby providing a protein profile of the subject, wherein the predetermined panel of proteins includes 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, proBNPII, sFlt-1, and YKL; as well as c) computer processing the protein profile using a machine learning model that is trained to be able to distinguish between healthy subjects and subjects suffering from the colon cell proliferative disorder to provide an output value associated with the presence or absence of the colon cell proliferative disorder, thereby indicating the presence or absence of the colon cell proliferative disorder in the subject.

130. The method of claim 129, wherein 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, CYFRA 21-1, ferritin, FGF23, gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1 and YKL.

131. The method of claim 129, wherein the protein profile is associated with a cell proliferative disorder of the colon and provides a classification of the subject as having the cell proliferative disorder of the colon.

132. The method of claim 129, wherein the biological sample from the subject is selected from the group consisting of body fluids, feces, colon discharge, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

133. The method of claim 129, wherein the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

134. The method of claim 129, further comprising: d) treating said subject with surgery and / or therapeutic agents based on said protein profile of said subject.

135. A predetermined panel of proteins characteristic of a cell proliferative disorder of the colon, 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.

136. A panel as described in claim 135, wherein 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 following: 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.

137. The panel of claim 135, wherein the panel is configured to distinguish between healthy subjects, subjects with benign colon polyps, subjects with advanced adenomas, or subjects with colorectal cancer.

138. A panel as described in claim 135, wherein the colon cell proliferative disorder is selected from: adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma and sarcoma.

139. A method for determining a protein profile of a biological sample from a subject, the method comprising: a) obtaining the biological sample containing the protein from the subject; as well as b) measuring the amount of protein from a predetermined panel of proteins in the biological sample, the predetermined panel of proteins 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.

140. The method of claim 139, 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.

141. The method of claim 139, wherein the protein profile is associated with a cell proliferative disorder of the colon and provides a classification of the subject as having the cell proliferative disorder of the colon.

142. The method of claim 139, wherein the biological sample from the subject is selected from the group consisting of body fluids, feces, colon discharge, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

143. The method of claim 139, wherein the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

144. The method of claim 139, further comprising: c) treating said subject with surgery and / or therapeutic agents based on said protein profile of said subject.

145. A method for detecting a colon cell proliferative disorder in a subject, the method comprising: a) obtaining a biological sample containing protein from the subject; b) measuring the amount of proteins from a predetermined panel of proteins in the biological sample, thereby providing a protein profile of the subject, wherein the predetermined panel of proteins includes 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, proBNPII, sFlt-1, and YKL; as well as c) computer processing the protein profile using a machine learning model that is trained to be able to distinguish between healthy subjects and subjects suffering from the colon cell proliferative disorder to provide an output value associated with the presence or absence of the colon cell proliferative disorder, thereby indicating the presence or absence of the colon cell proliferative disorder in the subject.

146. The method of claim 145, wherein 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, CYFRA 21-1, ferritin, FGF23, gastrin-17, GDF-15, HCT, hGH, IGF-1, IL-8, NSE, PRL2, proBNP II, sFlt-1 and YKL.

147. The method of claim 145, wherein the protein profile is associated with a cell proliferative disorder of the colon and provides a classification of the subject as having the cell proliferative disorder of the colon.

148. The method of claim 145, wherein the biological sample from the subject is selected from the group consisting of body fluids, feces, colon discharge, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

149. The method of claim 145, wherein the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

150. The method of claim 145, further comprising: d) treating said subject with surgery and / or therapeutic agents based on said protein profile of said subject.

151. A predetermined panel of proteins characteristic of a cell proliferative disorder of the colon, the panel comprising 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.

152. A panel as described in claim 151, wherein 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 following: 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.

153. The panel of claim 151, wherein the panel is configured to distinguish between healthy subjects, subjects with benign colon polyps, subjects with advanced adenomas, or subjects with colorectal cancer.

154. A panel as described in claim 151, wherein the colon cell proliferative disorder is selected from: adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma and sarcoma.

155. A method for determining a protein profile of a biological sample from a subject, the method comprising: a) obtaining the biological sample containing the protein from the subject; as well as b) measuring the amount of protein from a predetermined panel of proteins in the biological sample, wherein the predetermined panel of proteins includes 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.

156. A method as described in claim 155, wherein 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, CA125 II, CA15-3, CEA, CXCL13, CYFRA 21-1, FGF23, GDF-15, hGH, IGF-1, IL-8, NSE, PRL2, sFlt-1 and YKL.

157. The method of claim 155, wherein the protein profile is associated with a cell proliferative disorder of the colon and provides a classification of the subject as having the cell proliferative disorder of the colon.

158. The method of claim 155, wherein the biological sample from the subject is selected from the group consisting of body fluids, feces, colon discharge, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

159. The method of claim 155, wherein the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

160. The method of claim 155, further comprising: c) treating said subject with surgery and / or therapeutic agents based on said protein profile of said subject.

161. A method for detecting a colon cell proliferative disorder in a subject, the method comprising: a) obtaining a biological sample containing protein from the subject; b) measuring the amount of proteins from a predetermined panel of proteins in the biological sample, thereby providing a protein profile of the subject, wherein the predetermined panel of proteins includes 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; as well as c) computer processing the protein profile using a machine learning model that is trained to be able to distinguish between healthy subjects and subjects suffering from the colon cell proliferative disorder to provide an output value associated with the presence or absence of the colon cell proliferative disorder, thereby indicating the presence or absence of the colon cell proliferative disorder in the subject.

162. A method as described in claim 161, wherein the predetermined protein 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 following: 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.

163. The method of claim 161, wherein the protein profile is associated with a cell proliferative disorder of the colon and provides a classification of the subject as having the cell proliferative disorder of the colon.

164. The method of claim 161, wherein the biological sample from the subject is selected from the group consisting of body fluids, feces, colon discharge, urine, plasma, serum, whole blood, isolated blood cells, cells isolated from blood, tissue biopsy, and combinations thereof.

165. The method of claim 161, wherein the cell proliferative disorder of the colon is selected from the group consisting of adenoma (adenomatous polyp), polyposis, Lynch syndrome, sessile serrated adenoma (SSA), advanced adenoma, colorectal dysplasia, colorectal adenoma, colorectal cancer, colon cancer, rectal cancer, colorectal epithelial carcinoma, colorectal adenocarcinoma, carcinoid tumor, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), lymphoma, and sarcoma.

166. The method of claim 161, further comprising: d) treating said subject with surgery and / or therapeutic agents based on said protein profile of said subject.

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