Biomarkers for Colorectal Cancer

Abstract

The present invention relates to the identification of biomarkers associated with a higher risk of advanced colorectal neoplasms (ACN), such as colorectal cancer (CRC), in a subject. Detection and measurement of these biomarkers in biological samples can be used to inform clinicians as to whether further invasive procedures, including colonoscopy or sigmoidoscopy, are required to provide a definitive diagnosis of colorectal cancer in a subject.

Description

Related Applications

[0001] This application claims priority to AU2021 / 901164, filed April 20, 2021, the entire contents of which are incorporated herein by reference.

[0002] All documents cited or referenced herein, and all documents cited or referenced in the documents cited herein, are hereby incorporated by reference in their entirety, together with any manufacturer's instructions, descriptions, product specifications, and product sheets for any products referred to herein or in any document incorporated by reference herein.

[0003] Reference to sequence listing The entire electronic sequence listing submission is incorporated by reference in its entirety for all purposes. [Technical field]

[0004] The present invention relates to the identification of biomarkers associated with a higher risk of advanced colorectal neoplasms (ACN), such as colorectal cancer (CRC), in a subject. Detection and measurement of these biomarkers in biological samples can be used to inform clinicians as to whether further invasive procedures, including colonoscopy or sigmoidoscopy, are required to provide a definitive diagnosis of colorectal cancer in a subject. [Background technology]

[0005] Colorectal cancer (CRC), also referred to as colon or intestinal cancer, is the third most common cause of cancer in men and the second most common cause of cancer in women worldwide. In 2018, there were over 1.8 million new cases of CRC, with Australia ranking 11th highest in the world, with an age-standardized rate of approximately 37 cases per 100,000. Unfortunately, 30-50% of patients have occult or overt metastases at presentation, and once the tumor has metastasized, the prognosis is very poor, with a 5-year survival rate of less than 10% (Etzioni et al., (2003) Nat Rev Cancer 3:243-252). In contrast, over 90% of patients who present while the tumor is still localized are still alive after 5 years and can be considered cured. Thus, early detection of colorectal lesions significantly reduces the impact of colon cancer.

[0006] Current widely used screening assays for the diagnosis of colorectal cancer are fecal occult blood test (FOBT), flexible sigmoidoscopy, and colonoscopy (Lieberman, (2010) Gastroenterology 138:2115-2126). Although the specificity of FOBT for colorectal cancer is very high (92-95%), the percentage of FOBT-positive subjects found at colonoscopy to have colorectal cancer is low (approximately 3-4%). ​​Therefore, all positive FOBT must be followed up with colonoscopy. Specimen collection is performed at home by the individual and requires that at least two consecutive fecal samples be analyzed to achieve optimal sensitivity. Some versions of FOBT also require dietary restriction prior to specimen collection. FOBT also lacks sensitivity for early cancerous lesions, as they do not bleed into the intestine as frequently as more advanced cancers, but these early lesions are the ones most successfully treated.

[0007] FOBT screening results in a reduction in mortality due to colorectal cancer, but suffers from low compliance rates (30-40%) due in part to the unpleasant nature of the test, which limits its usefulness as a screening tool. Colonoscopy is the current gold standard, with a specificity of over 90%, but is invasive and expensive, and has a small but finite risk of complications (2.1 per 1000 procedures) (Levin, (2004) Gastroenterology 127:1841-1844). The development of a rapid, specific, and inexpensive blood-based assay would overcome compliance issues commonly seen with other screening tests and capture many more subjects at risk for CRC. Summary of the Invention

[0008] The present disclosure is based on the identification of blood-based biomarkers that are associated with a higher risk of colorectal cancer in subjects. The inventors have also identified biomarker combinations that are gender-specific for males and females. The present invention relates to specific combinations of biomarkers and to methods for diagnosing and detecting colorectal cancer, as well as methods for identifying subjects at risk for colorectal cancer.

[0009] Thus, in a first aspect, the disclosure provides a method for diagnosing colorectal cancer and / or for identifying a subject suspected of having or at greater risk of having colorectal cancer, the method comprising: determining measurements for a panel of biomarkers in a biological sample obtained from the subject, the panel including at least brain derived neurotropic factor (BDNF) and tumor M2-PK; Methods are provided in which measuring comprises measuring the level of each of the biomarkers in the panel.

[0010] In one example according to the first aspect, determining the measurement includes detecting at least BDNF and M2PK (and one or more other biomarkers) in the biological sample by contacting the sample with a detectable binding agent that specifically binds to the biomarker. In a further example, the method includes detecting specific binding between the specific binding agent and the biomarker using a detection assay. In one example, the detection assay in an ELISA assay. In a further example, determining the measurement includes measuring the concentration of the biomarker in the biological sample. In a further example, determining the measurement includes performing a statistical analysis. In another example, the method includes interpolating the biomarker concentration to an algorithm described herein.

[0011] In one example according to any aspect herein, the method further comprises determining a measurement of one or more additional biomarkers selected from the group consisting of DKK3, TGFβ1, IGFBP2, TIMP1, IL6, IL8, TNFα, IGFII, lipocalin, M30, M65, Mac2BP, MMP1, MMP7, MIP1B, and IL13. In another example, the one or more additional biomarkers are selected from the group consisting of DKK3, TGFβ1, IGFBP2, TNFα, TIMP1, IL8, MIP1B, and Mac2BP.

[0012] In one example according to the first aspect, the method comprises determining measurements for a panel of at least three biomarkers, the panel comprising at least BDNF and M2PK.

[0013] In a further example, the at least three biomarkers include BDNF, M2PK, and an additional biomarker selected from the group consisting of DKK-3, TNFα, IL-8, MAC2BP, and IGFBP2.

[0014] In one example according to the first aspect, the three biomarker panel comprises: i) BDNF, M2PK, DKK-3; ii) BDNF, M2PK, TNFα; iii) BDNF, M2PK, IL-8; iv) BDNF, M2PK, MAC2BP; or v) BDNF, M2PK, IGFBP2.

[0015] In another example according to the first aspect, the method comprises determining measurements for a panel of at least four biomarkers, the panel comprising at least BDNF and M2PK, hi a further example, the at least four biomarkers comprise BDNF, M2PK and two biomarkers selected from the group consisting of DKK3, TNFα, IGFBP2, TIMP1, MIP1β, MMP7, MMP1, IGFII, and IL-8.

[0016] In a further example, the at least four biomarkers include BDNF, M2PK, and two biomarkers selected from the group consisting of DKK3, IGFBP2, TIMP1, and IL-8. In a further example, the at least four biomarkers include BDNF, M2PK, and two biomarkers selected from the group consisting of DKK3, IGFBP2, and TIMP1. In one example according to the first aspect, the four biomarkers include DKK3, M2PK, IGFPB2, and BDNF.

[0017] In another example according to the first aspect, the four biomarkers are: i) BDNF, M2PK, IL8, TNFα; ii) BDNF, M2PK, IL8, MIP1B; iii) BDNF, M2PK, IL8, DKK3; iv) BDNF, M2PK, MMP1, DKK3; v) BDNF, M2PK, IL8, IGFBP2; vi) BDNF, M2PK, IL8, TIMP1; vii) BDNF, M2PK, MMP7, TNFα; viii) BDNF, M2PK, IL8, MMP1; ix) BDNF, M2PK, IGFBP2, TNFα; or x) BDNF, M2PK, IL8, IGFII.

[0018] In another example according to the first aspect, the method comprises determining measurements for a panel of at least four biomarkers, the panel comprising at least BDNF, M2PK, IL-8 and a further biomarker selected from the group consisting of DKK3, TNFα, IGFBP2, TIMP1, MIP1β, MMP7, MMP1, and IGFII.

[0019] In another example according to the first aspect, the method comprises determining measurements for a panel of at least five biomarkers, the panel comprising at least BDNF, M2PK, and three or more biomarkers selected from the group consisting of DKK3, TNFα, TGFBETA1, lipocalin, IGFBP2, MAC2BP, MIP1β, MMP7, and IL-8.

[0020] In one example, a five biomarker panel is i) BDNF, M2PK, IL8, TNFα, MIP1β; ii) BDNF, M2PK, IL8, lipocalin, DKK3; iii) BDNF, M2PK, IL8, TGFβ1, DKK3; iv) BDNF, M2PK, IL8, TNFα, TGFβ1; v) BDNF, M2PK, IGFBP2, TNFα, DKK3; vi) BDNF, M2PK, IL8, MMP7, DKK3; vii) BDNF, M2PK, IL8, TNFα, DKK3; viii) BDNF, M2PK, IL8, IGFBP2, MIP1β; ix) BDNF, M2PK, IL8, MAC2BP, DKK3; or x) BDNF, M2PK, IL8, TNFα, MAC2BP, or consisting of them.

[0021] In another example according to the first aspect, the method comprises determining measurements for a panel of at least five biomarkers, the panel comprising at least BDNF, M2PK, and three or more biomarkers selected from the group consisting of TIMP1, DKK3, TNFα, TGFBETA1, lipocalin, IGFBP2, MAC2BP, MIP1β, MMP7, and IL-8. In one example, the panel comprises at least BDNF, M2PK, and three or more biomarkers selected from the group consisting of TIMP1, DKK3, IGFBP2, MAC2BP, IL13, and IL-8.

[0022] In one example, the five biomarker panel includes PKM2 (also called M2PK), BDNF, DKK3, IGFBP2, and TIMP1.

[0023] In one example, the five biomarker panel includes DKK3, M2PK, Mac2BP, IGFBP2, and BDNF.

[0024] In another example according to the first aspect, the method comprises determining measurements for a panel of at least six biomarkers, the panel comprising at least BDNF, M2PK, and four or more biomarkers selected from the group consisting of DKK3, TNFα, IGFBP2, MIP1β, TGFβ1, MMP1, MAC2BP, IGFII, lipocalin, IL6, M30, and IL-8.

[0025] In one example according to the first aspect, the six biomarker panel comprises: i) BDNF, M2PK, MIP1β, DKK3, IGFBP2, TNFα; ii) BDNF, M2PK, MIP1β, DKK3, IGFBP2, MMP1; iii) BDNF, M2PK, MAC2BP , DKK3, IGFBP2, TNFα;iv)BDNF, M2PK, Lipocalin, DKK3, IGFBP2, TNFα;v)BDNF, M2PK, IL6, DKK3, IGFBP2, TNFα;vi)B vii) BDNF, M2PK, IL8, DKK3, lipocalin, TGFβ1; viii) BDNF, M2PK, IL8, MIP1B, TGFβ1, TNFα; viii) BDNF, M2PK, IL8, MIP1B, IGFII, TNFα; ix) BDNF, M2PK, IL8, M30, TGFβ1, TNFα; or x) BDNF, M2PK, IL8, DKK3, IGFII, TNFα.

[0026] In another example according to the first aspect, the method comprises determining measurements for a panel of at least seven biomarkers, the panel comprising at least five or more biomarkers selected from the group consisting of BDNF, M2PK, TNFα, DKK3, MIP1β, IL8, MMP1, IGFBP2, lipocalin, MAC2BP, IL6, MMP7, IGFII, M65, TIMP1, and TGFβ1. In one example, the method comprises determining measurements for a panel of at least seven biomarkers, the panel comprising at least four or more biomarkers selected from the group consisting of BDNF, M2PK, TNFα, DKK3, MIP1β, IL8, MMP1, IGFBP2, lipocalin, MAC2BP, IL6, MMP7, IGFII, M65, TIMP1, and TGFβ1. In one example, the method includes determining measurements for a panel of at least seven biomarkers, the panel including at least three or more biomarkers selected from the group consisting of BDNF, M2PK, TNFα, DKK3, MIP1β, IL8, MMP1, IGFBP2, lipocalin, MAC2BP, IL6, MMP7, IGFII, M65, TIMP1, and TGFβ1.

[0027] In another example according to the first aspect, the method comprises determining measurements for a panel of at least eight biomarkers, the panel comprising at least six or more biomarkers selected from the group consisting of BDNF, M2PK, DKK3, TNFα, MIP1β, IL8, MMP1, IGFBP2, lipocalin, MAC2BP, IL6, MMP7, IGFII, M65, TIMP1, TGFβ1, and IL13. In one example, the panel comprises five or more biomarkers selected from the group consisting of BDNF, M2PK, DKK3, TNFα, MIP1β, IL8, MMP1, IGFBP2, lipocalin, MAC2BP, IL6, MMP7, IGFII, M65, TIMP1, TGFβ1, and IL13. In one example, the panel includes four or more biomarkers selected from the group consisting of BDNF, M2PK, DKK3, IGFBP2, and TNFα, MIP1β, IL8, MMP1, lipocalin, MAC2BP, IL6, MMP7, IGFII, M65, TIMP1, TGFβ1, and IL13.

[0028] In another example according to the first aspect, the method comprises determining measurements for a panel of at least four biomarkers, the panel comprising at least BDNF, DKK3, M2PK, and one or more biomarkers selected from the group consisting of TNFα, MIP1β, IL8, MMP1, IGFBP2, lipocalin, MAC2BP, IL6, MMP7, IGFII, M65, TIMP1, TGFβ1, and IL13.

[0029] In another example according to the first aspect, the method comprises determining measurements for a panel of at least five biomarkers, the panel comprising at least one biomarker selected from the group consisting of DKK3, IGFBP2, BDNF, M2PK, and one or more biomarkers selected from the group consisting of TNFα, MIP1β, IL8, MMP1, lipocalin, MAC2BP, IL6, MMP7, IGFII, M65, TIMP1, TGFβ1, and IL13.

[0030] In another example according to the first aspect, the method comprises determining measurements for a panel of biomarkers present in Tables 7, 8, 9, 10, 11, 12, 13, 14, or 15.

[0031] In some examples, the methods of the present disclosure also contemplate including the age and / or sex of a subject as a biomarker in the biomarker panels described herein.

[0032] In some examples, the methods of the present disclosure also contemplate including the age of a subject as a biomarker in the biomarker panel described herein. In one example, the age of a subject is the age of the subject in years. In another example according to the first aspect, the method comprises determining a measurement for a panel of biomarkers present in Tables 16, 17, 18, 19, 20, 21, 22, 23, or 24.

[0033] In some examples, the method of the present disclosure also contemplates including the gender of the subject as a biomarker in the biomarker panel described herein.Gender can be included in the method as a factor by either separating samples from males and females and analyzing them separately.Alternatively, gender can be included in the logistic regression algorithm as a factor by assigning an arbitrary value for females and a different arbitrary value for males.In one example, gender of the subject can be included in the logistic regression algorithm as a factor by assigning an arbitrary value for males and females (e.g., 1.1 for females and 1.0 for males).

[0034] The inventors have also determined biomarker panels that are particularly relevant for men and women.

[0035] In a second aspect, the disclosure provides a method for diagnosing colorectal cancer and / or for identifying a subject suspected of having or at greater risk of having colorectal cancer, wherein the subject is a female, and the method comprises: determining measurements for a panel of biomarkers in a biological sample obtained from the subject, the panel including at least brain-derived neurotrophic factor (BDNF) and tumor M2-PK; Methods are provided in which measuring comprises measuring the level of each of the biomarkers in the panel.

[0036] In one example according to the second aspect, the method comprises determining measurements for a panel of at least three biomarkers, the panel comprising at least BDNF, M2PK and a further biomarker selected from the group consisting of TNFα, IGFBP2, TIMP1, MIP1β, MMP7, MMP1, IGFII, M65, M30, lipocalin, IL8, IL13, MAC2BP, TGFβ1 and IL6, preferably a further biomarker selected from the group consisting of MIP1β, MMP1, lipocalin, IL13, IL8, MAC2BP and IL6.

[0037] In another example according to the second embodiment, the three biomarker panel comprises: (i) BDNF, M2PK, IL8; ii) BDNF, M2PK, MAC2BP; iii) BDNF, M2PK, MMP1; iv). BDNF, M2PK, lipocalin; v) BDNF, M2PK, IL13; vi) BDNF, M2PK, MIP1β; or vii) BDNF, M2PK, IL6.

[0038] In another example according to the second aspect, the method comprises determining measurements for a panel of at least four biomarkers, the panel comprising at least BDNF, M2PK and two biomarkers selected from the group consisting of TNFα, IGFBP2, TIMP1, MIP1β, MMP7, MMP1, IGFII, M65, M30, lipocalin, IL8, IL13, MAC2BP, TGFBETA1 and IL6, preferably two biomarkers selected from the group consisting of TNFα, MIP1β, MMP1, IGFII, lipocalin, IL8, IL13, MAC2BP, TGFβ1 and IL6.

[0039] In another example according to the second embodiment, the four biomarker panel comprises: i) BDNF, M2PK, IL8, MMP1; ii) BDNF, M2PK, IL8, lipocalin; iii) BDNF, M2PK, IL8, IL6; iv) BDNF, M2PK, IL8, MIP1β; v) BDNF, M2PK, IL8, MAC2BP; vi) BDNF, M2PK, IL8, TGFβ; vii) BDNF, M2PK, IL8, IL13; viii) BDNF, M2PK, IL8, IGFII; ix) BDNF, M2PK, IL8, TNFα; or x). BDNF, M2PK, TGFβ1, MMP1, or consisting of them.

[0040] In another example according to the second aspect, the method comprises determining measurements for a panel of at least five biomarkers, the panel comprising at least BDNF, M2PK, and three biomarkers selected from the group consisting of TNFα, IGFBP2, TIMP1, MIP1β, MMP7, MMP1, IGFII, M65, M30, lipocalin, IL13, MAC2BP, TGFβ1, and IL6, preferably three biomarkers selected from the group consisting of TNFα, MIP1β, MMP1, IGFII, IL8, IL13, MAC2BP, TGFβ1, and IL6.

[0041] In another example according to the second aspect, the method comprises determining measurements for a panel of at least five biomarkers, the panel comprising at least BDNF, M2PK, IL-8 and two biomarkers selected from the group consisting of TNFα, IGFBP2, TIMP1, MIP1β, MMP7, MMP1, IGFII, M65, M30, lipocalin, IL13, MAC2BP, TGFβ1 and IL6, preferably two biomarkers selected from the group consisting of TNFα, MIP1β, MMP1, IGFII, lipocalin, IL13, MAC2BP, TGFβ1 and IL6.

[0042] In another example according to the second embodiment, the five biomarker panel comprises: i) BDNF, M2PK, IL8, TGFβ1, TNFα; ii) BDNF, M2PK, IL8, TNFα, MMP1; iii). BDNF, M2PK, IL8, TGFβ1, MMP1; iv) BDNF, M2PK, IL8, TGFβ1, MAC2BP; v) BDNF, M2PK, IL8, TGFβ; vi) BDNF, M2PK, IL8, TGFβ1, IL13; vii) BDNF, M2PK, IL8, TGFβ1, IGFII; viii) BDNF, M2PK, IL8, TGFβ1, MIP1β; ix) BDNF, M2PK, IL8, MMP1, MIP1β; or x) BDNF, M2PK, IL8, MAC2BP, MIP1β.

[0043] In another example according to the second aspect, the method comprises determining measurements for a panel of at least six biomarkers, the panel comprising at least BDNF, M2PK, and four biomarkers selected from the group consisting of TNFα, IGFBP2, TIMP1, MIP1β, MMP7, MMP1, IGFII, M65, M30, lipocalin, IL13, MAC2BP, TGFβ1, and IL6, preferably four biomarkers selected from the group consisting of DKK3, TNFα, MIP1β, MMP1, M65, M30, IL8, IL13, MAC2BP, TGFβ1, and IL6.

[0044] In another example according to the second embodiment, the six biomarker panel comprises: i).BDNF, M2PK, IL8, IL13, M65, M30; ii) BDNF, M2PK, IL8, MMP1, TGFβ1, TNFα; iii) BDNF, M2PK, IL8, MAC2BP, TGFβ1, TNFα; iv) BDNF, M2PK, IL8, IL6, TGFβ1, TNFα; v) BDNF, M2PK, IL8, MMP1, MAC2BP, TNFα; vi) BDNF, M2PK, vii) BDNF, M2PK, IL8, MMP1, TGFβ1, DKK3; viii) BDNF, M2PK, IL8, IL6, TGFβ1, MAC2BP; ix). BDNF, M2PK, IL8, MMP1, DKK3, MAC2BP; or x) BDNF, M2PK, IL8, IL13, TGFβ1, MIP1β.

[0045] In another example according to the second aspect, the method comprises determining measurements for a panel of at least seven biomarkers, the panel including at least BDNF, M2PK, and five or more biomarkers selected from the group consisting of BDNF, M2PK, IL8, and four ... In one example, a method includes determining measurements for a panel of at least seven biomarkers, the panel including at least three or more biomarkers selected from the group consisting of BDNF, M2PK, M65, IL8, MMP1, IL13, IGFBP2, TNFα, MIP1β, lipocalin, MAC2BP, IL6, MMP7, IGFII, TGFβ1, and M30. In one example, a method includes determining measurements for a panel of at least seven biomarkers, the panel including at least two or more biomarkers selected from the group consisting of BDNF, M2PK, M65, MMP1, IL8, IL13, IGFBP2, TNFα, MIP1β, lipocalin, MAC2BP, IL6, MMP7, IGFII, TGFβ1, and M30.

[0046] In one example, according to the second embodiment, the method comprises determining measurements for a panel of biomarkers present in Table 28.

[0047] In another example according to the second aspect, the method comprises determining measurements for a panel of at least eight biomarkers, the panel including at least six or more biomarkers selected from the group consisting of BDNF, M2PK, IL8, M65, MMP1, TNFα, MIP1β, lipocalin, MAC2BP, IL6, MMP7, IGFII, TIMP1, TGFβ1, M30, and DKK3. In one example, the method comprises determining measurements for a panel of at least eight biomarkers, the panel including at least five or more biomarkers selected from the group consisting of BDNF, M2PK, IL8, M65, MMP1, TNFα, MIP1β, lipocalin, MAC2BP, IL6, MMP7, IGFII, TIMP1, TGFβ1, M30, and DKK3. In one example, a method includes determining measurements for a panel of at least eight biomarkers, the panel including at least four or more biomarkers selected from the group consisting of BDNF, M2PK, IL8, M65, MMP1, TNFα, MIP1β, lipocalin, MAC2BP, IL6, MMP7, IGFII, TIMP1, TGFβ1, M30, and DKK3. In one example, a method includes determining measurements for a panel of at least eight biomarkers, the panel including at least three or more biomarkers selected from the group consisting of BDNF, M2PK, IL8, M65, MMP1, TNFα, MIP1β, lipocalin, MAC2BP, IL6, MMP7, IGFII, TIMP1, TGFβ1, M30, and DKK3.

[0048] In one example, according to the second embodiment, the method comprises determining measurements for a panel of biomarkers present in Tables 27, 26, or 25.

[0049] In another example according to the second aspect, the method further comprises age of the subject as an additional biomarker. In one example, the method comprises determining a measurement for a panel of biomarkers present in Tables 34, 35, 36, 37, 38, 39, 40, 41, or 42.

[0050] In a third aspect, the disclosure provides a method for diagnosing colorectal cancer and / or identifying a subject suspected of having or at greater risk of having colorectal cancer, wherein the subject is male, and the method comprises: determining measurements for a panel of biomarkers in a biological sample obtained from the subject, the panel including at least brain-derived neurotrophic factor (BDNF) and tumor M2-PK; Methods are provided in which measuring comprises measuring the level of each of the biomarkers in the panel.

[0051] In one example according to the third aspect, the method comprises determining measurements for a panel of at least three biomarkers, the panel comprising at least BDNF, M2PK and a further biomarker selected from the group consisting of DKK3, TNFα, IGFBP2, TIMP1, MIP1β, MMP7, MMP1, IGFII, M65, M30, lipocalin, IL8, IL13, MAC2BP and TGFβ1, preferably a further biomarker selected from the group consisting of TNFα, IGFBP2, TIMP1, MMP7, IGFII, lipocalin, IL8, IL13, MAC2BP and TGFβ1.

[0052] In another example according to the third aspect, the three biomarker panel comprises: i) BDNF, M2PK, TNFα; ii) BDNF, M2PK, TIMP1; iii) BDNF, M2PK, IL8; iv) BDNF, M2PK, TGFβ; v) BDNF, M2PK, MMP7; vi) BDNF, M2PK, IL13; vii) BDNF, M2PK, IGFII; viii) BDNF, M2PK, IGFBP2; ix) BDNF, M2PK, MAC2BP; or x) BDNF, M2PK, lipocalin.

[0053] In another example according to the third aspect, the method comprises determining measurements for a panel of at least four biomarkers, the panel comprising at least BDNF, M2PK and two biomarkers selected from the group consisting of DKK3, TNFα, IGFBP2, TIMP1, MIP1β, MMP7, MMP1, IGFII, M65, M30, lipocalin, IL8, IL13, MAC2BP and TGFβ1, preferably two biomarkers selected from the group consisting of TNFα, IGFBP2, TIMP1, MIP1B, MMP7, M65 and IL8.

[0054] In another example according to the third aspect, the four biomarker panel comprises: i) BDNF, M2PK, DKK3, TIMP1; ii) BDNF, M2PK, IL8, TNFα; iii) BDNF, M2PK, IGFBP2, TNFα; iv) BDNF, M2PK, IL8, IGFBP2; v) BDNF, M2PK, DKK3, TNFα; vi) BDNF, M2PK, IL8, MMP7; vii) BDNF, M2PK, DKK3, M65; viii) BDNF, M2PK, DKK3, IL8; ix) BDNF, M2PK, DKK3, IGFBP2; or x) BDNF, M2PK, MIP1β, TIMP1.

[0055] In another example according to the third aspect, the method comprises determining measurements for a panel of at least five biomarkers, the panel comprising at least BDNF, M2PK, and three biomarkers selected from the group consisting of DKK3, TNFα, IGFBP2, TIMP1, MIP1β, MMP7, MMP1, IGFII, M65, M30, lipocalin, IL8, IL13, MAC2BP, and TGFβ1, preferably three biomarkers selected from the group consisting of DKK3, TNFα, IGFBP2, TIMP1, MIP1β, IGFII, M65, lipocalin, IL8, and MAC2BP.

[0056] In another example according to the third aspect, the five biomarkers are: i) BDNF, M2PK, DKK3, IGFBP2, TNFα; ii) BDNF, M2PK, DKK3, lipocalin, TIMP1; iii) BDNF, M2PK, DKK3, M65, TNFα; iv) BDNF, M2PK, DKK3, IGFBP2, TIMP1; v) BDNF, M2PK, DKK3, IL8, IGFII; vi) BDNF, M2PK, IL13, IL8, TNFα; vii) BDNF, M2PK, DKK3, MAC2BP, TIMP1; viii) BDNF, M2PK, DKK3, M65, TIMP1; ix) BDNF, M2PK, DKK3, MIP1B, TNFα; or x) BDNF, M2PK, DKK3, MIP1B, TIMP1.

[0057] In another example according to the third aspect, the method comprises determining measurements for a panel of at least five biomarkers, the five biomarkers comprising BDNF, M2PK, DKK-3 and two biomarkers selected from TNFα, IGFBP2, TIMP1, MIP1β, MMP7, MMP1, IGFII, M65, M30, lipocalin, IL8, IL13, MAC2BP or TGFBETA1, preferably two biomarkers selected from TNFα, IGFBP2, TIMP1, MIP1β, IGFII, M65, lipocalin, IL8 or MAC2BP.

[0058] In one example according to the third aspect, the five biomarkers are: i) BDNF, M2PK, DKK3, IGFBP2, TNFα; ii) BDNF, M2PK, DKK3, lipocalin, TIMP1; iii) BDNF, M2PK, DKK3, M65, TNFα; iv) BDNF, M2PK, DKK3, IGFBP2, TIMP1; v) BDNF, M2PK, DKK3, IL8, IGFII; vi) BDNF, M2PK, DKK3, MAC2BP, TIMP1; vii) BDNF, M2PK, DKK3, M65, TIMP1; viii). BDNF, M2PK, DKK3, MIP1β, TNFα; or ix) BDNF, M2PK, DKK3, MIP1β, TIMP1.

[0059] In another example according to the third aspect, the method comprises determining measurements for a panel of at least six biomarkers, the six biomarkers including BDNF, M2PK, and four biomarkers selected from the group consisting of DKK3, TNFα, IGFBP2, TIMP1, MIP1β, MMP7, MMP1, IGFII, M65, M30, lipocalin, IL8, IL13, MAC2BP, and TGFβ1, preferably four biomarkers selected from the group consisting of DKK3, TNFα, IGFBP2, TIMP1, MIP1β, MMP7, MMP1, IGFII, M65, M30, lipocalin, IL8, and IL13.

[0060] In another example according to the third aspect, the biomarkers comprise BDNF, M2PK, DKK3, TNFα, and two biomarkers selected from the group consisting of IGFBP2, TIMP1, MIP1β, MMP7, MMP1, IGFII, M65, M30, lipocalin, IL8, IL13, MAC2BP, and TGFβ1, preferably two biomarkers selected from the group consisting of IGFBP2, TIMP1, MIP1β, MMP7, MMP1, IGFII, M65, M30, lipocalin, IL8, and IL13.

[0061] In one example according to the third aspect, the six biomarkers are: i) BDNF, M2PK, DKK3, TNFα, TIMP1, IL8; ii) BDNF, M2PK, DKK3, TNFα, IGFBP2, MMP1; iii) BDNF, M2PK, DKK3, TNF α, IGFBP2, M30; iv) BDNF, M2PK, DKK3, TNFα, MIP1B, M65; v) BDNF, M2PK, DKK3, TNFα, IGFBP2, MMP7; vi) BDNF, M2 vii) BDNF, M2PK, DKK3, TNFα, IGFBP2, lipocalin; viii) BDNF, M2PK, DKK3, TNFα, IGFII, IL8; ix) BDNF, M2PK, DKK3, TNFα, M65, M30; or x) BDNF, M2PK, DKK3, TNFα, IGFBP2, IL13.

[0062] In another example according to the third aspect, the method comprises determining measurements for a panel of at least seven biomarkers, the panel comprising at least five or more biomarkers selected from the group consisting of BDNF, M2PK, DKK3, TNFα, MIP1β, MMP1, IGFBP2, lipocallin, TIMP1, M30, IGII, IL8, MMP7, and M65. In one example, the method comprises determining measurements for a panel of at least seven biomarkers, the panel comprising at least four or more biomarkers selected from the group consisting of BDNF, M2PK, DKK3, TNFα, MIP1β, MMP1, IGFBP2, lipocallin, TIMP1, M30, IGII, IL8, MMP7, and M65. In one example, a method includes determining measurements for a panel of at least seven biomarkers, the panel including at least three or more biomarkers selected from the group consisting of BDNF, M2PK, DKK3, TNFα, MIP1β, MMP1, IGFBP2, lipocallin, TIMP1, M30, IGII, IL8, MMP7, and M65. In one example, a method includes determining measurements for a panel of at least seven biomarkers, the panel including at least two or more biomarkers selected from the group consisting of BDNF, M2PK, DKK3, TNFα, IGFBP2, MIP1β, MMP1, lipocallin, TIMP1, M30, IGII, IL8, MMP7, and M65.

[0063] In another example, according to the third embodiment, a method comprises determining measurements for a panel of biomarkers present in Table 46.

[0064] In another example according to the third aspect, the method comprises determining measurements for a panel of at least eight biomarkers, the panel comprising at least six or more biomarkers selected from the group consisting of BDNF, M2PK, DKK3, TNFα, MIP1β, MMP1, IGFBP2, lipocallin, TIMP1, TGFβ1, M30, IL13, IGII, IL8, MMP7, and IL13. In one example, the method comprises determining measurements for a panel of at least eight biomarkers, the panel comprising at least five or more biomarkers selected from the group consisting of BDNF, M2PK, DKK3, TNFα, MIP1β, MMP1, IGFBP2, lipocallin, TIMP1, TGFβ1, M30, IL13, IGII, IL8, MMP7, and IL13. In one example, a method includes determining measurements for a panel of at least eight biomarkers, the panel including at least four or more biomarkers selected from the group consisting of BDNF, M2PK, DKK3, TNFα, MIP1β, MMP1, IGFBP2, lipocallin, TIMP1, TGFβ1, M30, IL13, IGII, IL8, MMP7, and IL13. In one example, a method includes determining measurements for a panel of at least eight biomarkers, the panel including at least three or more biomarkers selected from the group consisting of BDNF, M2PK, DKK3, TNFα, MMP7, MIP1β, MMP1, IGFBP2, lipocallin, TIMP1, TGFβ1, M30, IL13, IGII, IL8, and IL13.

[0065] In another example, according to the third embodiment, a method comprises determining measurements for a panel of biomarkers present in Table 45.

[0066] In another example, the method according to the third aspect comprises determining measurements for at least nine or at least ten biomarkers, the biomarkers including at least BDNF and M2PK. In one example, the method comprises determining measurements for a panel of biomarkers present in Table 44 or 43.

[0067] In further examples, biomarkers include PKM2 (also called M2PK), BDNF, DKK3, IGFBP2, and TIMP1.

[0068] In another example according to the third aspect, the method further comprises age of the subject as an additional biomarker. In one example, the method comprises determining a measurement for a panel of biomarkers present in Tables 52, 53, 54, 55, 56, 57, 58, 59, or 60.

[0069] In further examples, the methods described herein may use one or more or all of the biomarker combinations provided in Tables 7-15, Tables 16-24, Tables 25-33, Tables 34-42, Tables 43-51, Tables 52-60, or Tables 61-66.

[0070] In a fourth aspect, a biomarker combination described in any one of Tables 7-15, Tables 16-24, Tables 25-33, Tables 34-42, Tables 43-51, Tables 52-60, or Tables 61-66 is provided.

[0071] It will be understood that the present disclosure encompasses additional known colorectal cancer biomarkers not specifically described herein, examples of which include one or more of IGF-I, amphiregulin, VEGFA, VEGFD, MMP2, MMP3, MMP9, TIMP2, ENA-78, MCP-1, IFN-γ, IL10, IL-1β, IL4, OPN, CEACAM6, VEGF alpha, and VEGFpan.

[0072] In addition to age and / or sex, it will be understood that the present disclosure encompasses additional demographic or morphometric factors not specifically described herein. Examples of these other demographic or morphometric factors include, but are not limited to, smoking history, body mass index (BMI), and hip-to-waist ratio.

[0073] In one example according to any embodiment described herein, determining the measurement includes measuring the concentration of a biomarker in the biological sample. In one example according to any embodiment, determining the measurement includes detecting the biomarker in the biological sample by contacting the sample with a detectable binding agent that specifically binds to the biomarker. In a further example according to any embodiment, the method includes detecting specific binding between the specific binding agent and the biomarker using a detection assay. In a further example according to any embodiment, determining the measurement includes performing a statistical analysis.

[0074] In one example according to any embodiment described herein, the method includes a capture antibody. In one example, the capture antibody is immobilized, for example, on a plate. In one example, the plate is an ELISA plate. In one example according to any embodiment, the method includes a labeled antibody for detecting binding of the biomarker to the capture antibody.

[0075] In one example according to any embodiment, the level of at least one biomarker in the panel of biomarkers is increased or decreased relative to the level of the same biomarker in a reference panel. More specifically, the measurement of a biomarker is relative to a reference concentration for that biomarker, which is determined in known cases and / or control samples of CRC by an algorithm trained on case and control samples.

[0076] In one example according to any aspect, the method of the present disclosure includes: (i) performing measurements of the concentration of each biomarker in a biomarker panel described herein and deriving statistics; (ii) comparing the value obtained in step (i) with a statistical value obtained from concentrations of the same biomarker in a corresponding reference panel of biomarkers; (iii) correlating the values ​​obtained in step (ii) to derive a definitive value for the subject's risk of colorectal cancer.

[0077] In some examples, the biomarker is a protein biomarker. In one example, the biomarker is a polynucleotide biomarker.

[0078] In some examples, the disclosed method includes contacting the biological sample with an antibody that specifically binds to a biomarker protein. Preferably, there is at least one antibody that individually binds to each biomarker that is sought to be detected in the biological sample. Preferably, an antibody specifically binds to a given biomarker. In some examples, two or more antibodies may bind to a single biomarker, for example, in a "sandwich" format.

[0079] In some examples, the measurement format is an immunoassay. In another example, the immunoassay is an ELISA, which typically has a capture antibody bound to the surface of an ELISA plate and a detection antibody for detecting the binding of the biomarker to the capture antibody. In one example, the antibody can be detectably labeled. In one example, the capture antibody can be the same antibody as the detection antibody or can be a different antibody. Methods for labeling antibodies are known in the art.

[0080] In some examples, where the biomarkers are polynucleotides, analytical methods may include measuring gene transcripts corresponding to the individual biomarkers. Such methods are familiar to those of skill in the art.

[0081] Methods for performing model building and statistical analysis are known to those skilled in the art. In some instances, linear or non-linear regression is performed. The method may also utilize the Baezian probability algorithm.

[0082] In some instances, analysis of a biomarker panel can be used to determine a treatment regimen for a subject. For example, statistics derived from the measurement of biomarkers can be used to inform further treatment by colonoscopy or sigmoidoscopy to provide a definitive diagnosis of colorectal cancer.

[0083] In a fifth aspect, there is provided a method of treating a subject suspected of having colorectal cancer, comprising: (i) determining measurements for a panel of biomarkers in a biological sample obtained from a subject by a method described herein; (ii) treating the subject with colonoscopy or sigmoidoscopy.

[0084] In another example according to any embodiment, the method further recites obtaining a biological sample from the subject. In a further example, the biological sample is a blood sample. In another example, the sample is a serum or plasma sample. Methods for obtaining a biological sample are known to those skilled in the art. For example, for extraction of the blood sample, a venous blood draw is preferably performed.

[0085] In a sixth aspect, a composition is provided that comprises a labeled antibody that specifically binds to a biomarker in a biomarker panel described herein.

[0086] In a seventh aspect, (i) a labeled antibody that binds to a biomarker in a biomarker panel described herein; (ii) instructions for carrying out detection of a biomarker in the biomarker panel.

[0087] In some examples, the kit also includes a surface, on which a capture antibody that binds to each biomarker in the biomarker panel is immobilized. In some examples, the kit also includes an ELISA plate, on which a capture antibody that binds to each biomarker in the biomarker panel is immobilized. In some examples, the kit also includes beads (e.g., microbeads or magnetic beads), on which a capture antibody that binds to each biomarker in the biomarker panel is immobilized. In some examples, the kit also provides instructions for analysis of the detected biomarkers by a computer-generated algorithm. In further examples, a clinical report is generated.

[0088] In an eighth aspect, there is provided a kit as described herein together with a software package, wherein the software package comprises an algorithm, the algorithm for generating statistics based on measurements of biomarkers in a biomarker panel as described herein.

[0089] In a ninth aspect, there is provided a system for determining a subject suspected of having or at greater risk of having colorectal cancer, comprising: (i) determining a measurement for a panel of biomarkers described herein in a biological sample obtained from a subject, the measurement being the concentration of each biomarker in the panel; (ii) inputting the concentration of each biomarker in the panel into a computer, the computer including an input device, the input device operatively connected to the computer to receive the concentration of each biomarker in the panel; (iii) an output device connected to the computer to provide information to a user; (iv) an algorithm executed by a computer, the algorithm executed based on the data received by the input device, the algorithm calculating a risk score. [Brief description of the drawings]

[0090] [Figure 1-1](A) shows the frequency with which each biomarker appears in the top 100 10-biomarker combinations that distinguish between sera from cancer patients and healthy controls with a sensitivity range of 85.6-82.2% at 95% specificity. (B) shows the frequency with which each biomarker appears in the top 100 9-biomarker combinations that distinguish between sera from cancer patients and healthy controls with a sensitivity range of 84.7-81.7% at 95% specificity. (C) shows the frequency with which each biomarker appears in the top 100 8-biomarker combinations that distinguish between sera from cancer patients and healthy controls with a sensitivity range of 84.2-80.6% at 95% specificity. (D) shows the frequency with which each biomarker appears in the top 100 7-biomarker combinations that distinguish between sera from cancer patients and healthy controls with a sensitivity range of 83.2-79.2% at 95% specificity. (E) The frequency with which each biomarker appears in the top 100 6-biomarker combinations that distinguish sera from cancer patients from healthy controls with a sensitivity ranging from 80.4-77.0% at 95% specificity. (F) The frequency with which each biomarker appears in the 74 5-biomarker combinations that distinguish sera from cancer patients from healthy controls with a sensitivity of >75% (range 80.5-75.0%) at 95% specificity. [Figure 1-2](A) shows the frequency with which each biomarker appears in the top 100 10-biomarker combinations that distinguish between sera from cancer patients and healthy controls with a sensitivity range of 85.6-82.2% at 95% specificity. (B) shows the frequency with which each biomarker appears in the top 100 9-biomarker combinations that distinguish between sera from cancer patients and healthy controls with a sensitivity range of 84.7-81.7% at 95% specificity. (C) shows the frequency with which each biomarker appears in the top 100 8-biomarker combinations that distinguish between sera from cancer patients and healthy controls with a sensitivity range of 84.2-80.6% at 95% specificity. (D) shows the frequency with which each biomarker appears in the top 100 7-biomarker combinations that distinguish between sera from cancer patients and healthy controls with a sensitivity range of 83.2-79.2% at 95% specificity. (E) The frequency with which each biomarker appears in the top 100 6-biomarker combinations that distinguish sera from cancer patients from healthy controls with a sensitivity ranging from 80.4-77.0% at 95% specificity. (F) The frequency with which each biomarker appears in the 74 5-biomarker combinations that distinguish sera from cancer patients from healthy controls with a sensitivity of >75% (range 80.5-75.0%) at 95% specificity. [Figure 2-1](A) shows the frequency with which each biomarker appears in the top 100 10-biomarker combinations (plus age) that distinguish sera from cancer patients from healthy controls with a sensitivity range of 86.8%-83.5% at 95% specificity. (B) shows the frequency with which each biomarker appears in the top 100 9-biomarker combinations (plus age) that distinguish sera from cancer patients from healthy controls with a sensitivity range of 87.0%-82.9% at 95% specificity. (C) shows the frequency with which each biomarker appears in the top 100 8-biomarker combinations (plus age) that distinguish sera from cancer patients from healthy controls with a sensitivity range of 85.1%-81.7% at 95% specificity. (D) The frequency with which each biomarker appears in the top 100 seven-biomarker combinations (plus age) that distinguish sera from cancer patients from healthy controls with a sensitivity range of 84.2%-79.9% at 95% specificity. (E) The frequency with which each biomarker appears in the top 100 six-biomarker combinations (plus age) that distinguish sera from cancer patients from healthy controls with a sensitivity range of 83.2%-77.5% at 95% specificity. (F) The frequency with which each biomarker appears in the 73 five-biomarker combinations (plus age) that distinguish sera from cancer patients from healthy controls with a sensitivity of >75% (range 80.0-75.0%) at 95% specificity. [Figure 2-2](A) shows the frequency with which each biomarker appears in the top 100 10-biomarker combinations (plus age) that distinguish sera from cancer patients from healthy controls with a sensitivity range of 86.8%-83.5% at 95% specificity. (B) shows the frequency with which each biomarker appears in the top 100 9-biomarker combinations (plus age) that distinguish sera from cancer patients from healthy controls with a sensitivity range of 87.0%-82.9% at 95% specificity. (C) shows the frequency with which each biomarker appears in the top 100 8-biomarker combinations (plus age) that distinguish sera from cancer patients from healthy controls with a sensitivity range of 85.1%-81.7% at 95% specificity. (D) The frequency with which each biomarker appears in the top 100 seven-biomarker combinations (plus age) that distinguish sera from cancer patients from healthy controls with a sensitivity range of 84.2%-79.9% at 95% specificity. (E) The frequency with which each biomarker appears in the top 100 six-biomarker combinations (plus age) that distinguish sera from cancer patients from healthy controls with a sensitivity range of 83.2%-77.5% at 95% specificity. (F) The frequency with which each biomarker appears in the 73 five-biomarker combinations (plus age) that distinguish sera from cancer patients from healthy controls with a sensitivity of >75% (range 80.0-75.0%) at 95% specificity. [Figure 3-1](A) The frequency with which each biomarker appears in the top 100 10 biomarker combinations that distinguish sera from female cancer patients from healthy controls with 95% specificity. (B) The frequency with which each biomarker appears in the top 100 9 biomarker combinations that distinguish sera from female cancer patients from healthy controls with 95% specificity. (C) The frequency with which each biomarker appears in the top 100 8 biomarker combinations that distinguish sera from female cancer patients from healthy controls with 95% specificity. (D) The frequency with which each biomarker appears in the top 100 7 biomarker combinations that distinguish sera from female cancer patients from healthy controls with 95% specificity. (E) The frequency with which each biomarker appears in the top 100 6 biomarker combinations that distinguish sera from female cancer patients from healthy controls with 95% specificity. (F) Frequency with which each biomarker appears in the top 100 5-biomarker combinations that distinguish sera from female cancer patients from those of healthy controls with 95% specificity. [Figure 3-2] (A) The frequency with which each biomarker appears in the top 100 10 biomarker combinations that distinguish sera from female cancer patients from healthy controls with 95% specificity. (B) The frequency with which each biomarker appears in the top 100 9 biomarker combinations that distinguish sera from female cancer patients from healthy controls with 95% specificity. (C) The frequency with which each biomarker appears in the top 100 8 biomarker combinations that distinguish sera from female cancer patients from healthy controls with 95% specificity. (D) The frequency with which each biomarker appears in the top 100 7 biomarker combinations that distinguish sera from female cancer patients from healthy controls with 95% specificity. (E) The frequency with which each biomarker appears in the top 100 6 biomarker combinations that distinguish sera from female cancer patients from healthy controls with 95% specificity. (F) Frequency with which each biomarker appears in the top 100 5-biomarker combinations that distinguish sera from female cancer patients from those of healthy controls with 95% specificity. [Figure 4-1] (A) The frequency with which each biomarker appears in the top 100 10 biomarker combinations (plus age) that distinguish sera from female cancer patients from healthy controls with 95% specificity. (B) The frequency with which each biomarker appears in the top 100 9 biomarker combinations (plus age) that distinguish sera from female cancer patients from healthy controls with 95% specificity. (C) The frequency with which each biomarker appears in the top 100 8 biomarker combinations (plus age) that distinguish sera from female cancer patients from healthy controls with 95% specificity. (D) The frequency with which each biomarker appears in the top 100 7 biomarker combinations (plus age) that distinguish sera from female cancer patients from healthy controls with 95% specificity. (E) The frequency with which each biomarker appears in the top 100 6 biomarker combinations that distinguish sera from female cancer patients from healthy controls with 95% specificity. (F) Frequency with which each biomarker appears in the top 100 5-biomarker combinations that distinguish sera from female cancer patients from those of healthy controls with 95% specificity. [Figure 4-2](A) The frequency with which each biomarker appears in the top 100 10 biomarker combinations (plus age) that distinguish sera from female cancer patients from healthy controls with 95% specificity. (B) The frequency with which each biomarker appears in the top 100 9 biomarker combinations (plus age) that distinguish sera from female cancer patients from healthy controls with 95% specificity. (C) The frequency with which each biomarker appears in the top 100 8 biomarker combinations (plus age) that distinguish sera from female cancer patients from healthy controls with 95% specificity. (D) The frequency with which each biomarker appears in the top 100 7 biomarker combinations (plus age) that distinguish sera from female cancer patients from healthy controls with 95% specificity. (E) The frequency with which each biomarker appears in the top 100 6 biomarker combinations that distinguish sera from female cancer patients from healthy controls with 95% specificity. (F) Frequency with which each biomarker appears in the top 100 5-biomarker combinations that distinguish sera from female cancer patients from those of healthy controls with 95% specificity. [Figure 5-1](A) The frequency with which each biomarker appears in the top 100 10 biomarker combinations that distinguish sera from male cancer patients from healthy controls with 95% specificity. (B) The frequency with which each biomarker appears in the top 100 9 biomarker combinations that distinguish sera from male cancer patients from healthy controls with 95% specificity. (C) The frequency with which each biomarker appears in the top 100 8 biomarker combinations that distinguish sera from male cancer patients from healthy controls with 95% specificity. (D) The frequency with which each biomarker appears in the top 100 7 biomarker combinations that distinguish sera from male cancer patients from healthy controls with 95% specificity. (E) The frequency with which each biomarker appears in the top 100 6 biomarker combinations that distinguish sera from male cancer patients from healthy controls with 95% specificity. (F) Frequency with which each biomarker appears in the top five biomarker combinations that distinguish sera from female cancer patients from those of healthy controls with a sensitivity of >75% at a specificity of 95%. [Figure 5-2] (A) The frequency with which each biomarker appears in the top 100 10 biomarker combinations that distinguish sera from male cancer patients from healthy controls with 95% specificity. (B) The frequency with which each biomarker appears in the top 100 9 biomarker combinations that distinguish sera from male cancer patients from healthy controls with 95% specificity. (C) The frequency with which each biomarker appears in the top 100 8 biomarker combinations that distinguish sera from male cancer patients from healthy controls with 95% specificity. (D) The frequency with which each biomarker appears in the top 100 7 biomarker combinations that distinguish sera from male cancer patients from healthy controls with 95% specificity. (E) The frequency with which each biomarker appears in the top 100 6 biomarker combinations that distinguish sera from male cancer patients from healthy controls with 95% specificity. (F) Frequency with which each biomarker appears in the top five biomarker combinations that distinguish sera from female cancer patients from those of healthy controls with a sensitivity of >75% at a specificity of 95%. [Figure 6-1] (A) The frequency with which each biomarker appears in the top 100 10 biomarker combinations (plus age) that distinguish sera from male cancer patients from healthy controls with 95% specificity. (B) The frequency with which each biomarker appears in the top 100 9 biomarker combinations (plus age) that distinguish sera from male cancer patients from healthy controls with 95% specificity. (C) The frequency with which each biomarker appears in the top 100 8 biomarker combinations (plus age) that distinguish sera from male cancer patients from healthy controls with 95% specificity. (D) The frequency with which each biomarker appears in the top 100 7 biomarker combinations (plus age) that distinguish sera from male cancer patients from healthy controls with 95% specificity. (E) The frequency with which each biomarker appears in the top 100 6 biomarker combinations (plus age) that distinguish sera from male cancer patients from healthy controls with 95% specificity. (F) Frequency with which each biomarker appears in the top five biomarker combinations (plus age) that distinguish sera from female cancer patients from those of healthy controls with a sensitivity of >75% at a specificity of 95%. [Figure 6-2](A) The frequency with which each biomarker appears in the top 100 10 biomarker combinations (plus age) that distinguish sera from male cancer patients from healthy controls with 95% specificity. (B) The frequency with which each biomarker appears in the top 100 9 biomarker combinations (plus age) that distinguish sera from male cancer patients from healthy controls with 95% specificity. (C) The frequency with which each biomarker appears in the top 100 8 biomarker combinations (plus age) that distinguish sera from male cancer patients from healthy controls with 95% specificity. (D) The frequency with which each biomarker appears in the top 100 7 biomarker combinations (plus age) that distinguish sera from male cancer patients from healthy controls with 95% specificity. (E) The frequency with which each biomarker appears in the top 100 6 biomarker combinations (plus age) that distinguish sera from male cancer patients from healthy controls with 95% specificity. (F) Frequency with which each biomarker appears in the top five biomarker combinations (plus age) that distinguish sera from female cancer patients from those of healthy controls with a sensitivity of >75% at a specificity of 95%. [Figure 7] FIG. 1 shows the training and testing structure for Study 9. This figure represents an example of a training-test configuration (i.e., cohort 2 was also used to train the algorithm, and cohort 1 was used to test). DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0091] General Techniques and Definitions Unless specifically defined otherwise, all technical and scientific terms used herein are intended to have the same meaning as commonly understood by one of ordinary skill in the art (e.g., in cell culture, molecular genetics, immunology, immunohistochemistry, protein chemistry, and biochemistry).

[0092] Unless otherwise indicated, the recombinant protein, cell culture, and immunological techniques utilized in the present invention are standard procedures, well known to those skilled in the art. Such techniques are described in the literature, e.g., J. Perbal, A Practical Guide to Molecular Cloning, John Wiley and Sons (1984); J. Sambrook et al., Molecular Cloning: A Laboratory Manual, 3rd edn, Cold Spring Harbor Laboratory Press (2001); R. Scopes, Protein Purification-Principals and Practice, 3rd edn, Springer (1994); TA Brown (editor), Essential Molecular Biology: A Practical Approach, Volumes 1 and 2, IRL Press (1991); D. M. Glover and B. D. Hames (editors), DNA Cloning: A Practical Approach, Volumes 1-4, IRL Press (1995 and 1996); and FM. Ausubel et al. (editors), Current Protocols in Molecular Biology, Greene Pub. Associates and Wiley-Interscience (1988, including all current updates), Ed Harlow and David Lane (editors), Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory, (1988), and JE Coligan et al. (editors), Current Protocols in Immunology, John Wiley & Sons (including all current updates).

[0093] As used herein, "colorectal cancer (CRC)" refers to cancer that begins in the colon or rectum. These cancers can also be referred to separately as colon cancer or rectal cancer, depending on where they begin. Colon cancer and rectal cancer have many common characteristics. More than 95% of colorectal cancers are of a type known as adenocarcinoma. These cancers begin in cells derived from glands that allow mucus to lubricate the inside of the colon and rectum. In most cases, these cells first form benign proliferations of the colorectal epithelium called adenomas, and more than 90% of colorectal cancers first appear as small foci of highly dysplastic tissue within these otherwise benign adenomas. Other less common types of tumors can also begin in the colon and rectum. These include carcinoid tumors, gastrointestinal stromal tumors (GISTs), lymphomas, and sarcomas. In a preferred example, the colorectal cancer is an adenocarcinoma. Adenocarcinomas are staged to help guide clinical management. The most frequently used staging system for colorectal cancer is the American Joint Committee on Cancer (AJCC) TNM system (https: / / www.cancer.org / cancer / colon-rectal-cancer / detection-diagnosis-staging / staged.html), which is based on three key pieces of information: ●T-tumor size (level 0-4): ○ How far has the cancer grown within the wall of the colon or rectum: Is it still confined to the colon epithelium? Has it invaded the thin layer of muscle just below the epithelium? Has it invaded the fibrous tissue below this thin muscle layer? Has it invaded the thicker muscle layer below? Has it invaded the layer of connective tissue below the main muscle layer? ● N - Level of lymph node involvement (Level 0-2): Has the cancer spread to nearby lymph nodes? If so, how many? M - Level of metastatic spread (Level 0-1): Has the cancer spread to distant lymph nodes or distant organs such as the liver or lungs?

[0094] As used herein, the term "biomarker" refers to any biological compound that can be measured as an indicator of the physiological state of a biological system. In some examples, a biomarker is a polynucleotide or a nucleic acid. In some examples, a biomarker is a polypeptide or a protein. A biomarker can also be the age, sex, and / or BMI of a subject, as further described herein.

[0095] As used herein, the term "measuring" refers to assessing the presence, absence, quantity, or amount of a given substance in a sample, including deriving a qualitative or quantitative concentration level of such substance. The term "measuring" refers to methods that include detecting the presence or absence of a biomarker in a sample, quantifying the amount of a biomarker in a sample, and / or assigning an aptitude to a type of biomarker. Measuring may be accomplished by methods known in the art and further described herein, including, but not limited to, mass spectrometry and immunoassay methods (e.g., ELISA), or any suitable method may be used to detect and measure one or more of the markers described herein. References to biomarker sequences may be found in Table 2, which provides UniProt (uniprot.org) and NCBI / Genbank accession numbers (ncbi.nlm.nih.gov / genbank).

[0096] The term "detect" refers to identifying the presence, absence, or amount of a detected biomarker. Non-limiting examples include, but are not limited to, detection of a protein, peptide, or nucleic acid.

[0097] The term "report" refers to the printed results provided to a physician from the methods of the present invention. The report can indicate the presence, nature, or risk of a pathological condition. The report can also indicate which treatment is most appropriate: no treatment, surgery, further testing, or administration of a therapeutic agent.

[0098] As used herein, the term "biological sample" refers to a cell or population of cells, or a volume of tissue or fluid from a subject. Most often, the sample has been taken from a subject, but the term "biological sample" can also refer to cells or tissues that are analyzed in vivo, i.e., without being taken from a subject. Preferably, "biological sample" refers to the non-cellular fraction of blood, saliva, or urine. Biological samples include, but are not limited to, whole blood, plasma, serum, lymph, or urine.

[0099] As used herein, the term "control reference" refers to a known steady state molecule or non-disease healthy state that is used as a relative marker to study variations or to compare a non-steady state molecule or normal non-disease healthy state, or it may also be used to calibrate or normalize values. In some examples, the control reference value is a calculated value, such as a combination of biomarker concentrations or a combination of concentration ranges.

[0100] The term "immunoassay" is an assay that uses an antibody to specifically bind to an antigen (e.g., a marker). Immunoassays are characterized by the use of the specific binding properties of a particular antibody to isolate, target, and / or quantitate the antigen.

[0101] The term "antibody" refers to a polypeptide ligand substantially encoded by one or more immunoglobulin genes or fragments thereof, which specifically binds to and recognizes an epitope. Antibodies exist, for example, as intact immunoglobulins or as a number of well-characterized fragments produced by digestion with various peptidases. This includes, for example, Fab" and F(ab)"2 fragments. As used herein, the term "antibody" also includes antibody fragments produced by either the modification of whole antibodies or those synthesized de novo using recombinant DNA methods. Antibodies also include polyclonal, monoclonal, chimeric, humanized, or single chain antibodies. The "Fc" portion of an antibody refers to that portion of an immunoglobulin heavy chain that includes one or more heavy chain constant region domains, but does not include the heavy chain variable region.

[0102] As used herein, the term "subject" refers to any animal that may develop colorectal cancer, including animals, e.g., mammals, e.g., humans, or non-human mammals, e.g., cats and dogs, laboratory animals, e.g., mice, rats, rabbits, or guinea pigs, and livestock animals. In a preferred embodiment, the subject is a human.

[0103] As used herein, the term "sample" or "biological sample" refers to a sample of biological fluid, tissue, or cells in a healthy and / or pathological state obtained from a subject. Preferably, the term "sample" refers to a blood sample, more preferably a serum sample.

[0104] overview The present disclosure provides methods for analyzing a biological sample from a subject using an assay, where the assay is coupled with a computer-executable algorithm for determining biomarkers indicative of colorectal cancer. Generally, the methods use proteins present in the subject's biological sample to identify biomarkers or a biomarker profile, thus identifying subjects who have or are at higher risk of colorectal cancer, and who may require further screening, such as a colonoscopy or sigmoidoscopy.

[0105] The present disclosure also provides commercially available diagnostic kits, which generally include compositions used for detection of the biomarkers provided herein.

[0106] Biomarkers The present disclosure utilizes a panel of biomarkers that are measured in a biological sample obtained from a subject to identify subjects having or suspected of having colorectal cancer.

[0107] As used herein, the term "biomarker" refers to any biological compound that can be measured as an indicator of the physiological state of a biological system. In some examples, the biomarker is a protein biomarker. In other examples, the biomarker is a nucleic acid biomarker.

[0108] This study demonstrates the specific role of brain-derived neurotrophic factor (BDNF) as an informative biomarker for identifying subjects at increased risk of colorectal cancer. BDNF has been observed to be elevated in solid tumors, including colorectal cancer (Yang X et al., (2013) Exp Ther Med 6(6):1475-1481). Furthermore, when this biomarker is combined with M2PK, a known marker in CRC, the sensitivity of detection is comparable to or exceeds that achieved with fecal occult blood testing (FOBT). According to the Cancer Council of Australia, the sensitivity of FOBT for advanced adenomas ranges from 16 to 64% with a specificity of approximately 93% (see https: / / wiki.cancer.org.au / policy / Bowel_cancer / Screening).

[0109] In some examples, the biomarker panel may include two, three, four, five, six or more biomarkers selected from the group consisting of DKK3, M2PK, TGFβ, IGFBP2, TIMP1, BDNF, IL6, IL8, TNFα, IGFII, lipocalin, M30, M65, Mac2BP, MMP1, MMP7, MIP1β, and IL13.

[0110] Reference to any of these biomarkers includes reference to all polypeptide and polynucleotide variants, such as isoforms and transcript variants, known to those of skill in the art. NCBI accession numbers of representative sequences for each of the biomarkers are provided in Table 1 in the Examples.

[0111] It will be appreciated that in some instances, demographic or morphometric factors may also be included as factors in the analysis, e.g., a logistic regression algorithm, including, but not limited to, age, sex, smoking history, body mass index (BMI), and hip-to-waist ratio.

[0112] In some examples, the method of the present disclosure also contemplates including the gender of a subject as a biomarker in the biomarker panel described herein. Without wishing to be bound by theory, the gender of a subject can be included as a factor in the logistic regression algorithm by assigning an arbitrary value for females and a different arbitrary value for males. As will be understood by those skilled in the art, the numerical value of the arbitrary value is not important, but it is important that different arbitrary values ​​are assigned for males and females. In one example, the gender of a subject can be included as a factor in the logistic regression algorithm by assigning an arbitrary value of 1 for females and 0 for males. In one example, the gender of a subject can be included as a factor in the logistic regression algorithm by assigning an arbitrary value of 1.1 for females and 1 for males.

[0113] In some examples, the methods of the present disclosure also contemplate including subject age as a biomarker in the biomarker panels described herein.

[0114] Sample preparation and processing Prior to analyzing a biological sample, it may be desirable to perform one or more sample preparation operations on the sample. Generally, these sample preparation operations may include operations such as extraction and isolation of intracellular material from cells or tissues, e.g., extraction of nucleic acids, proteins, or other macromolecules from the sample.

[0115] Sample preparation that can be used with the methods of the present disclosure include, but are not limited to, centrifugation, affinity chromatography, magnetic separation, fractionation, precipitation, and combinations thereof.

[0116] Sample preparation can further include dilution, with appropriate solvents and amounts to ensure that an appropriate range of concentration levels is detected by a given assay.

[0117] Accessing nucleic acids and macromolecules from the intercellular spaces of a sample can generally be performed by either physical methods, chemical methods, or a combination of both. In some applications of the methods, following isolation of the crude extract, it is often desirable to separate nucleic acids, proteins, cell membrane particles, and the like. In some examples of the methods, it is desirable to retain the nucleic acids together with their proteins and cell membrane particles.

[0118] In some examples of the methods provided herein, nucleic acids and proteins may be extracted from biological samples and then analyzed using the methods of the present disclosure. Extraction can be by means including, but not limited to, the use of detergent lysates, sonication, or vortexing with glass beads.

[0119] In some examples, molecules may be isolated using any technique suitable in the art, including, but not limited to, the use of liquid extraction with gradient centrifugation (e.g., cesium chloride gradients, sucrose gradients, glucose gradients, etc.), centrifugation protocols, boiling, techniques using purification kits, and drug extraction methods, such as those using Trizol or DNAzol.

[0120] Depending on the desired detection method, the sample may be prepared according to standard biological sample preparation. For example, for mass spectrometry detection, the biological sample obtained from the patient may be subjected to centrifugation, filtration, processing with an immunoaffinity column, separation into fractions, partial digestion, and combinations thereof. The various fractions may be resuspended in a suitable carrier, such as a buffer or other type of loading solution for detection and analysis, including LCMS loading solution.

[0121] Biomarker measurements Measurement of a biomarker panel relates to the quantitative measurement of multiple biomarkers. The present disclosure provides a method for detecting biomarkers in a biological sample. Biomarkers include, but are not limited to, proteins, DNA molecules, and RNA molecules. More specifically, the present disclosure is based on the discovery of protein biomarkers that are differentially expressed in subjects at increased risk of developing or having colorectal cancer. Thus, detection of one or more of these differentially expressed biomarkers in a biological sample provides useful information about whether a subject is at risk of or suffering from colorectal cancer, and what type of nature or status of the condition is. Any suitable method known to those skilled in the art can be used to detect one or more of the biomarkers described herein.

[0122] Useful analyte capture agents that may be used with the present disclosure include, but are not limited to, antibodies, such as crude serum containing antibodies, purified antibodies, monoclonal antibodies, polyclonal antibodies, synthetic antibodies, antibody fragments (e.g., Fab fragments); antibody interactors, such as protein A, carbohydrate binding proteins, and other interactors; protein interactors (e.g., avidin and its derivatives); peptides; and small chemical entities, such as enzyme substrates, cofactors, metal ions / chelates, and haptens. Antibodies may be modified or chemically treated to optimize binding to targets or solid surfaces (e.g., biochips and columns).

[0123] In one particular example of the present disclosure, biomarkers can be detected in biological samples using immunoassays. Immunoassays are assays that use antibodies that specifically bind to or recognize an antigen (e.g., a site on a protein or peptide, a biomarker target). The method includes contacting the biological sample with the antibody, allowing the antibody to form a complex with the antigen in the sample, washing the sample, and detecting the antibody-antigen complex with a detection reagent. In one example, antibodies that recognize the biomarkers can be commercially available. In another example, antibodies that recognize the biomarkers can be generated by known methods of antibody production.

[0124] Alternatively, the marker in the sample can be detected using an indirect assay, in which, for example, a second labeled antibody is used to detect the bound marker-specific antibody. Exemplary detectable labels include magnetic beads (e.g., DYNABEADS™), fluorescent dyes, radiolabels, enzymes (e.g., horseradish peroxide, alkaline phosphatase, and other commonly used), and colorimetric labels, such as colloidal gold or colored glass or plastic beads. The marker in the sample can be detected using and / or in a competitive or inhibition assay, in which, for example, monoclonal antibodies that bind to different epitopes of the marker are incubated simultaneously with the mixture.

[0125] Conditions for detecting an antigen using an immunoassay depend on the particular antibody used, and incubation times depend on the assay format, marker, solution volume and concentration, etc. Generally, immunoassays are performed at room temperature, although they can be performed over a range of temperatures, such as 10 degrees Celsius to 40 degrees Celsius, depending on the antibody used.

[0126] There are various types of immunoassays known in the art that can be used as a starting point to tailor an assay for detection of the biomarkers of the present disclosure. Useful assays can include enzyme immunoassays (EIAs), such as ELISAs, including sandwich enzyme-linked immunosorbent assays (ELISAs). There are many variations of these approaches, but they are based on a similar idea. For example, if an antigen can be bound to a solid support or surface, it can be detected by reacting it with a specific antibody, and the antibody can be quantified either by reacting it with a secondary antibody or by directly incorporating a label into the primary antibody. Alternatively, an antibody can be bound to a solid surface and the antigen can be added. Then a second antibody that recognizes a different epitope on the antigen can be added and detected. This is often referred to as a "sandwich assay" and can be frequently used to avoid problems with high background or non-specific reactions. These types of assays are sensitive and reproducible enough to measure low concentrations of antigens in biological samples.

[0127] Immunoassays can be used to determine the presence or absence of a marker in a sample, and the amount of a marker in a sample. Methods for measuring the amount or presence of an antibody-marker complex include, but are not limited to, fluorescence, luminescence, chemiluminescence, absorbance, reflectance, transmittance, birefringence or refractive index (e.g., surface plasmon resonance, ellipsometry, resonant mirror methods, grating coupler waveguide methods, or interferometry). Generally, these reagents are used with optical detection methods, such as various forms of microscopy, imaging methods, and non-imaging methods. Electrochemical methods include voltammetry, amperometry, and electrochemiluminescence methods. Radio frequency methods include multipolar resonance spectroscopy.

[0128] In one example, the present disclosure provides that antibodies can be used for the detection of biomarkers. Antibodies that specifically bind to the biomarkers of the present assay can be made and prepared using standard methods known in the art. For example, polyclonal antibodies can be produced for large amounts of antibodies by injecting antigen into a mammal, such as a mouse, rat, rabbit, goat, sheep, or horse. Blood isolated from these animals contains polyclonal antibodies, which are multiple antibodies that bind to the same antigen. Alternatively, polyclonal antibodies can be produced by injecting antigen into chickens to produce polyclonal antibodies in the egg yolk. In addition, modified forms of the biomarkers can be made, such as antibodies that specifically recognize the phosphorylated form of the biomarker, i.e., antibodies that recognize tyrosine or serine after phosphorylation but not in the absence of phosphate. In this way, antibodies can be used to determine the phosphorylation state of a particular biomarker.

[0129] Antibodies may be obtained commercially or produced using well-established methods. To obtain antibodies specific to a single epitope of an antigen, antibody-secreting lymphocytes are isolated from an animal and immortalized by fusing them with a cancer cell line. The fused cells are called hybridomas and continue to grow and secrete antibodies in culture. Single hybridoma cells are isolated by dilution cloning to generate cell clones that all produce the same antibody, which are called monoclonal antibodies.

[0130] Polyclonal and monoclonal antibodies can be purified in several ways. For example, antibodies can be isolated using antigen affinity chromatography, linked to bacterial proteins such as Protein A, Protein G, Protein L, or recombinant fusion protein Protein A / G, followed by detection of eluate fractions at 280 nm absorbance via ultraviolet light to determine which fractions contain the antibody. Protein A / G binds to all subclasses of human IgG, making it useful for purifying polyclonal or monoclonal IgG antibodies whose subclass has not been determined. In addition, Protein A / G binds to IgA, IgE, IgM, and (to a lesser extent) IgD. Protein A / G also binds to all subclasses of mouse IgG, but does not bind to mouse IgA, IgM, or serum albumin. This feature allows Protein A / G to be used to purify and detect mouse monoclonal IgG antibodies without interference from IgA, IgM, and serum albumin.

[0131] Antibodies can come from different classes or isotypes of molecules, such as IgA, IgA, IgD, IgE, IgM, and IgG. The most useful antibodies in biological studies are of the IgG class, which are protein molecules that are made and secreted and can recognize specific antigens. IgG is composed of two subunits, which contain two "heavy" chains and two "light" chains. These are assembled in a symmetrical structure, with each IgG having two identical antigen recognition domains. The antigen recognition domain is a combination of amino acids from both the heavy and light chains. The molecule is roughly shaped like a "Y", with the arms / tips of the molecule containing the antigen recognition regions or Fab (fragment, antigen binding) regions, and the stem of the Fc (fragment, crystallizable) region not involved in recognition and fairly constant. The constant regions are identical in all antibodies of the same isotype, but different in antibodies of different isotypes.

[0132] It is also possible to use antibodies to detect proteins by Western blotting after fractionation. In one example, the present disclosure can use Western blotting for the detection of biomarkers. Western blot (protein immunoblot) is an analytical technique used to detect specific proteins in a given sample or protein extract from a sample. Western blots use gel electrophoresis, SDS-PAGE, to separate native proteins by their three-dimensional structure, or Western blots can be performed under denaturing conditions to separate proteins by their length. After separation by gel electrophoresis, the proteins are then transferred to a membrane (typically nitrocellulose or PVDF). The proteins transferred to the membrane from SDS-PAGE can then be incubated with a specific antibody under gentle agitation, rinsed to remove non-specific binding, and the protein-antibody complex bound to the blot can be detected using either a one-step or two-step detection method. The one-step method includes a probe antibody, which recognizes the protein of interest and contains a probe that is a detectable label, and the probe is often available for a known protein tag. The two-step detection method involves a secondary antibody that has a reporter enzyme or reporter attached to it. With appropriate reference controls, this approach can be used to measure protein abundance.

[0133] In one example, the disclosed method can use flow cytometry. Flow cytometry is a laser-based biophysical technique that can be used for biomarker detection, quantification (cell counting), and cell isolation. This technique is used regularly in the diagnosis of health disorders, especially blood cancers. In general, flow cytometry works by suspending single cells in a fluid flow, a beam of light of a single wavelength (usually laser light) is directed onto the liquid flow, and the scattered light caused by the passing cells is detected by an electronic detection device. Fluorescence-activated cell sorting (FACS) is a specialized type of flow cytometry that often uses the aid of fluorescently labeled antibodies to detect antigens on cells of interest. This additional feature of using antibody labels in FACS provides simultaneous multiparameter analysis and quantification based on the specific light scattering and fluorescence characteristics of each cell fluorescently labeled cells, providing physical separation of populations of cells of interest as well as traditional flow cytometry.

[0134] In another example, flow cytometry is combined with a bead system, where the target antigen is bound to beads. Such systems are known to those skilled in the art.

[0135] A wide range of fluorophores can be used as labels in flow cytometry. Fluorophores are typically conjugated to antibodies that recognize target features on or within cells. Examples of suitable fluorescent labels include, but are not limited to, fluorescein (FITC), 5,6-carboxymethylfluorescein, Texas Red, nitrobenz-2-oxa-1,3-diazol-4-yl (NBD), and the cyanine dyes Cy3, Cy3.5, Cy5, Cy5.5, and Cy7. Other fluorescent labels, such as Alexa Fluor® dyes, DNA quantity dyes such as DAPI, Hoechst dyes, are well known in the art and all can be readily obtained from a variety of commercial sources. Each fluorophore has a characteristic peak excitation and emission wavelength, and the emission spectra often overlap. The absorption and emission maxima for these fluorophores are FITC (490 nm; 520 nm), Cy3 (554 nm; 568 nm), Cy3.5 (581 nm; 588 nm), Cy5 (652 nm: 672 nm), Cy5.5 (682 nm; 703 nm), and Cy7 (755 nm; 778 nm), respectively, so choosing ones that do not have much spectral overlap allows for their simultaneous detection. Fluorescent labels can be obtained from a variety of commercial sources. The maximum number of distinguishable fluorescent labels is thought to be roughly about 17 or 18 different fluorescent labels. This level of complex readout requires laborious optimization to limit artifacts and complex deconvolution algorithms to separate overlapping spectra. Quantum dots are sometimes used instead of traditional fluorophores due to their narrower emission peaks. Other methods that can be used for detection include isotopically labeled antibodies, such as lanthanide isotopes. However, this technique ultimately destroys the cells, preventing their recovery for further analysis.

[0136] In one example, the method of the present disclosure can use immunohistochemistry to detect the expression levels of the biomarkers of the present disclosure. Thus, antibodies specific for each marker are used to detect the expression of the claimed biomarkers in biological samples. The antibodies can be detected by directly labeling the antibody itself, for example, with a radiolabel, a fluorescent label, a hapten label such as biotin, or an enzyme such as horseradish peroxidase or alkaline phosphatase. Alternatively, an unlabeled primary antibody is used with a labeled secondary antibody, which comprises an antiserum, a polyclonal antiserum, or a monoclonal antibody specific for the primary antibody. Immunohistochemistry protocols are well known in the art, and protocols and antibodies are commercially available. Alternatively, antibodies can be made against the biomarkers disclosed herein or modified versions or binding partners of the biomarkers that are useful for determining expression levels in biological samples.

[0137] In one example, the method of the present disclosure can use a biochip. The biochip can be used to screen a large number of macromolecules. In this technology, the macromolecules are attached to the surface of the biochip in an aligned array format. The grid pattern of test areas allows the individual analytes at their predefined locations (addresses) to be analyzed by imaging software for rapid and simultaneous quantification. The CCD camera is a highly sensitive and high-resolution sensor that can accurately detect and quantify very low levels of light on the chip.

[0138] Biochips can be designed with immobilized nucleic acid molecules, full-length proteins, antibodies, affibodies (small molecules engineered to mimic monoclonal antibodies), aptamers (nucleic acid-based ligands), or chemical compounds. Chips can be designed to detect multiple macromolecule types on one chip. For example, chips can be designed to detect nucleic acid molecules, proteins, and metabolites on one chip. Biochips are used and designed to simultaneously analyze panel biomarkers in a single sample to generate a subject profile for these biomarkers. The use of biochips allows multiple analyses to be performed, reducing overall processing time and the amount of sample required.

[0139] Protein microarrays are a particular type of biochip that can be used with the present disclosure. The chip consists of a support surface, such as a glass slide, a nitrocellulose membrane, beads, or a microtiter plate, to which an array of capture proteins is attached on the immobilized surface in an array format. Protein array detection methods must give high signals and low background. Typically, detection probe molecules labeled with fluorescent dyes are added to the array. Any reaction between the probe and the immobilized protein releases a fluorescent signal, which is read by a laser scanner. Such protein microarrays are rapid, can be automated, and provide high sensitivity of protein biomarker readout for diagnostic testing. However, those skilled in the art will readily appreciate that there are a variety of detection methods that can be used with this technology.

[0140] There are at least three types of protein microarrays currently used to study the biochemical activity of proteins: analytical microarrays (also known as capture arrays), functional protein microarrays (also known as target protein arrays), and reverse phase protein microarrays (RPA).

[0141] The present disclosure provides for the detection of biomarkers using analytical protein microarrays such as Luminex xMAP technology. Analytical protein microarrays are constructed using libraries of antibodies, aptamers, or affibodies. The arrays are probed with complex protein solutions such as blood, serum, or cell lysates, which function by capturing protein molecules to which they specifically bind. Analysis of the resulting binding reactions using various detection systems can provide information regarding the expression levels of specific proteins in the sample, as well as measurements of binding affinity and specificity. This type of protein microarray is particularly useful for comparing protein expression in different samples.

[0142] In one example, the disclosed method can use functional protein microarrays. These are constructed by immobilizing a large number of purified full-length functional proteins or protein domains and are used to identify protein-protein, protein-DNA, protein-RNA, protein-phospholipid, and protein-small molecule interactions, to assay enzyme activity, and to detect antibodies and demonstrate their specificity. These protein microarray biochips can be used to study the biochemical activity of the entire proteome in a sample.

[0143] In one example, the disclosed method can use reverse phase protein microarrays (RPA). Reverse phase protein microarrays are constructed from tissue and cell lysates that are arrayed on the microarray and probed with antibodies against target proteins of interest. These antibodies are typically detected with chemiluminescent, fluorescent, or colorimetric assays. In addition to the proteins in the lysate, reference control peptides are printed on the slide to allow for protein quantification. RPA allows for the determination of the presence of altered proteins or other substances that may be due to disease and may be present in diseased cells.

[0144] In some instances, detection of biomarkers utilizes the ARCHITECT system (Abbott).

[0145] The present disclosure provides for the detection of biomarkers using mass spectrometry (alternatively referred to as mass spectrometry). Mass spectrometry (MS) is an analytical technique that measures the mass-to-charge ratio of charged particles. MS is primarily used for the determination of the elemental composition of a sample or molecule, and for elucidating the chemical structure of molecules such as peptides and other chemical compounds. MS works by ionizing chemical compounds to produce charged molecules or molecular fragments and measuring their mass-to-charge ratios. MS instruments typically consist of three modules: (1) an ion source that can convert gas-phase sample molecules into ions (or in the case of electrospray ionization, can transfer ions present in solution into the gas phase), (2) a mass analyzer that sorts the ions by their mass by applying an electromagnetic field, and (3) a detector that measures the value of an indicator quantity, thus providing data for calculating the abundance of each ion present.

[0146] Suitable mass spectrometry methods for use with the present disclosure include, but are not limited to, one or more of electrospray ionization mass spectrometry (ESI-MS), ESI-MS / MS, ESI-MS / (MS)n, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption / ionization time-of-flight mass spectrometry (SELDI-TOF-MS), tandem liquid chromatography-mass spectrometry (LC-MS / MS) mass spectrometry, desorption / ionization on silicon (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS / MS, APCI-(MS), atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS / MS and APPI-(MS)n, quadrupole mass spectrometry, Fourier transform mass spectrometry (FTMS), and ion trap mass spectrometry, where n is an integer greater than zero.

[0147] LC-MS is commonly used to resolve components of complex mixtures to gain insight into the underlying proteomics of a sample. LC-MS methods generally include protease digestion and denaturation (usually including a protease, e.g., trypsin, and a denaturant, e.g., urea, to denature tertiary structures, and iodoacetamide to cap cysteine ​​residues), followed by LC-MS with peptide mass fingerprinting to derive the sequences of individual peptides, or LC-MS / MS (tandem MS). LC-MS / MS is most commonly used for proteomic analysis of complex samples, where peptide masses may overlap even in high-resolution mass spectrometers. Samples of complex biofluids, such as human serum, can first be separated in SDS-PAGE gels or HPLC-SCX, and then subjected to LC-MS / MS, which allows the identification of over 1000 proteins.

[0148] Although multiple mass spectrometry techniques can be used with the methods of the present disclosure provided herein, in some applications, it may be desirable to quantify proteins in biological samples from a selected subset of proteins of interest.One such MS technique that can be used with the present disclosure is multiple reaction monitoring mass spectrometry (MRM-MS), or alternatively referred to as selected reaction monitoring mass spectrometry (SRM-MS).

[0149] The MRM-MS technique uses a triple quadrupole (QQQ) mass spectrometer to select positively charged ions from a peptide of interest, fragment the positively charged ions, and then measure the abundance of the selected positively charged fragment ions, commonly referred to as transitions.

[0150] In some applications, MRM-MS is coupled to high pressure liquid chromatography (HPLC) and more recently ultra-high pressure liquid chromatography (UHPLC), in other applications MRM-MS is coupled to UHPLC with a QQQ mass spectrometer to provide the desired LC-MS transition measurements for all of the peptides and proteins of interest.

[0151] In some applications, the use of a quadrupole time-of-flight (qTOF) mass spectrometer, a time-of-flight (TOF-TOF) mass spectrometer, an orbitrap mass spectrometer, a quadrupole orbitrap mass spectrometer, or any quadrupole ion trap mass spectrometer, can be used to select positively charged ions from one or more proteins of interest. The fragmented positively charged ions can then be measured to determine the abundance of the positively charged ions to quantify the peptides or proteins of interest.

[0152] In some applications, the use of a time-of-flight (TOF), quadrupole time-of-flight (qTOF) mass spectrometer, time-of-flight (TOF-TOF) mass spectrometer, orbitrap mass spectrometer, or quadrupole orbitrap mass spectrometer can be used to measure the mass and abundance of positively charged peptide ions from a protein of interest without fragmentation for quantification. In this application, the accuracy of the analyte amount measurement can be used as a selection criterion for the assay. Isotopically labeled internal standards of known composition and concentration can be used as part of the mass spectrometry quantification method.

[0153] In some applications, a time-of-flight (TOF), quadrupole time-of-flight (qTOF) mass spectrometer, time-of-flight (TOF-TOF) mass spectrometer, orbitrap mass spectrometer, or quadrupole orbitrap mass spectrometer may be used to measure the mass and abundance of the protein of interest for quantification. In this application, the accuracy of the analyte amount measurement may be used as the selection criteria for the assay. Optionally, this application may use proteolytic digestion of the protein prior to analysis by mass spectrometry. Isotopically labeled internal standards of known composition and concentration may be used as part of the mass spectrometry quantification method.

[0154] In some applications, various ionization techniques can be coupled to the mass spectrometers provided herein to generate the desired information. Non-limiting exemplary ionization techniques that can be used with the present disclosure include, but are not limited to, matrix-assisted laser desorption ionization (MALDI), desorption electrospray ionization (DESI), direct assisted real-time (DART), surface-assisted laser desorption ionization (SALDI), or electrospray ionization (ESI).

[0155] In some applications, HPLC and UHPLC may be coupled to a mass spectrometer. Several other protein separation techniques may be performed prior to analysis by mass spectrometry. Some exemplary separation techniques that may be used to separate the desired analyte (e.g., peptide or protein) from the matrix background include, but are not limited to, reversed-phase liquid chromatography (RP-LC) of proteins or peptides, off-line liquid chromatography (LC) prior to MALDI, first-dimensional gel separation, second-dimensional gel separation, strong cation exchange (SCX) chromatography, strong anion exchange (SAX) chromatography, weak cation exchange (WCX), and weak anion exchange (WAX). One or more of the above techniques may be used prior to analysis by mass spectrometry.

[0156] In one example of the present disclosure, biomarkers can be detected in biological samples using microarrays. Differential gene expression can also be identified or confirmed using microarray technology. Thus, expression profile biomarkers can be measured in either fresh or fixed tissues using microarray technology. In this method, polynucleotide sequences of interest (including cDNA and oligonucleotides) are plated or arrayed on a microchip substrate. The arrayed sequences are then hybridized with specific DNA probes from cells or tissues of interest. The source of mRNA is typically total RNA isolated from biological samples, and corresponding normal tissues or cell lines can be used to determine differential expression.

[0157] In a particular embodiment of the microarray technique, PCR-amplified inserts of cDNA clones are applied to a substrate in a high-density array. Preferably, at least 10,000 nucleotide sequences are applied to the substrate. The microarrayed genes are immobilized on the microchip with 10,000 elements each, and are suitable for hybridization under stringent conditions. Fluorescently labeled cDNA probes can be generated through the incorporation of fluorescent nucleotides by reverse transcription of RNA extracted from tissues of interest. The labeled cDNA probes applied to the chip hybridize specifically to each spot of DNA on the array. After stringent washing to remove non-specifically bound probes, the microarray chip is scanned by a device such as confocal laser microscopy or by another detection method such as a CCD camera. Quantification of the hybridization of each arrayed element allows the assessment of the corresponding mRNA abundance. With dual-color fluorescence, separately labeled cDNA probes generated from two sources of RNA hybridize to the array in pairs. Thus, the relative abundance of the transcripts from the two sources corresponding to each specified gene is determined simultaneously.Microarray analysis can be performed by commercially available equipment following the manufacturer's protocols.

[0158] In one example of the present disclosure, biomarkers can be detected in biological samples using qRT-PCR, which can be used to compare mRNA levels in different sample populations, in normal tissues and in drug-treated or drug-untreated tumor tissues, to characterize patterns of gene expression, to distinguish between closely related mRNAs, and to analyze RNA structure. The first step in gene expression profiling by RT-PCR is to extract RNA from a biological sample, followed by reverse transcription of the RNA template into cDNA and amplification by a PCR reaction. The reverse transcription reaction step is generally primed using specific primers, random hexamers, or oligo-dT primers, depending on the goal of expression profiling. Two commonly used reverse transcriptases are aviromeloblastosis virus reverse transcriptase (AMV-RT) and Moloney murine leukemia virus reverse transcriptase (MLV-RT).

[0159] The PCR step can use a variety of thermostable DNA-dependent DNA polymerases, but typically uses Taq DNA polymerase, which has 5'-3' nuclease activity but lacks 3'-5' proofreading endonuclease activity. Thus, TaqMan™ PCR typically uses the 5' nuclease activity of Taq or Tth polymerase to hydrolyze hybridization probes bound to the target amplicon, although any enzyme with equivalent 5' nuclease activity can be used. Two oligonucleotide primers can be used to generate an amplicon typical of a PCR reaction. A third oligonucleotide or probe is designed to detect the nucleotide sequence located between the two PCR primers. The probe is not extendible by the Taq DNA polymerase enzyme and is labeled with a reporter fluorescent dye and a quencher fluorescent dye. Any laser-induced emission from the reporter dye is quenched by the quenching dye when the two dyes are positioned close together as they are on the probe. During the amplification reaction, the Taq DNA polymerase enzyme cleaves the probe in a template-dependent manner. The resulting probe fragments dissociate in solution, and the signal from the released reporter dye is free of the quenching effect of the second fluorophore. One molecule of reporter dye is liberated for each new molecule synthesized, and detection of the unquenched reporter dye provides the basis for quantitative interpretation of the data.

[0160] TaqMan™ RT-PCR can be performed using commercially available instruments, such as the ABI PRISM 7700 Sequence Detection System™ (Perkin-Elmer-Applied Biosystems, Foster City, Calif., USA), or the Lightcycler (Roche Molecular Biochemicals, Mannheim, Germany). In a preferred embodiment, the 5' nuclease procedure is performed in a real-time quantitative PCR device, such as the ABI PRISM 7700™ Sequence Detection System™. The system consists of a thermocycler, a laser, a charge-coupled device (CCD), a camera, and a computer. The system includes software for running the instrument and for analyzing the data. The 5' nuclease assay data is initially expressed as Ct or threshold cycle. As discussed above, fluorescence values ​​are recorded during every cycle and represent the amount of product amplified at that point in the amplification reaction. The point at which the fluorescent signal is first recorded as statistically significant is the threshold cycle (Ct).

[0161] To minimize errors and the effects of sample-to-sample variation, RT-PCR is usually performed using an internal standard. An ideal internal standard is expressed at a constant level among different tissues and is not affected by the experimental treatment. The RNAs most frequently used to normalize patterns of gene expression are the mRNAs for the housekeeping genes glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and beta-actin.

[0162] A more recent variation of the RT-PCR technique is real-time quantitative PCR, which measures PCR product accumulation via a dual-labeled fluorogenic probe (i.e., TaqMan™ probe). Real-time PCR is compatible with both quantitative competitive PCR, in which an internal competitor for each target sequence is used for normalization, and quantitative comparative PCR, which uses normalization genes contained within the sample or housekeeping genes for RT-PCR. For further details, see, for example, Held et al., Genome Research 6:986-994 (1996). Other quantitative methods include digital droplet PCR.

[0163] Typically, the quantification of biomarkers carried out in the present disclosure includes a reference control sample. In some examples, the control reference is determined from the measurement of the biomarkers in the corresponding panel of biomarkers from a population of healthy individuals. As used herein, the term "healthy individual" refers to a person or a population of people who are known not to have colorectal cancer, such knowledge being derived from clinical data on the individual that may have been determined from colonoscopy or sigmoidoscopy. In some examples, the control reference is determined from the measurement of the corresponding biomarkers in a "typical population". Preferably, the "typical population" represents the spectrum of colorectal cancer at different stages of disease progression. It is particularly preferred that the "typical population" represents the expression characteristics of a cohort of subjects described herein.

[0164] In another example, the control reference is 1. A dataset comprising measurements of biomarkers for a population of subjects known to have colorectal cancer; 2. A dataset that includes measurements of biomarkers for a subject being tested, the measurements having been made previously, e.g., when the subject was known to be healthy, or, if the subject has colorectal cancer, when the subject was diagnosed or is earlier in the progression of the disease; and / or 3. It may be derived from an established dataset that includes one or more of the datasets that include measurements of biomarkers for a healthy individual or population of healthy individuals.

[0165] Data analysis In some examples, the methods for determining whether a subject has, or is otherwise at increased risk for developing, colorectal cancer are based on biomarker panel measurements compared to a reference profile, which may be generated along with statistical analysis.

[0166] The quantitative score may be determined by application of a specific algorithm. The algorithm used to calculate the quantitative score in the methods disclosed herein may group the expression level values ​​of a biomarker or a group of biomarkers. In addition, the formation of a specific group of biomarkers can facilitate the mathematical weighting of the contributions (e.g., classifiers) of various expression levels of a biomarker or a subset of biomarkers to the quantitative score.

[0167] In some examples, SPSS software can be used for statistical analysis. In some examples, binary logistic regression analysis can be used to predict the diagnostic efficiency of selected biomarkers. In some examples, a statistical algorithm can be used with a computer to implement the statistical algorithm. In some examples, the statistical algorithm is a learning statistical classifier system. Examples of such systems include random forests, interactive trees, classification and regression tree classifiers, or neural networks.

[0168] A fair evaluation of a test requires its evaluation using "out-of-sample" subjects, i.e., subjects not included in the construction of the initial predictive model. This is achieved by evaluating test performance using n-fold cross-validation.

[0169] Tests for statistical significance include linear and non-linear regression, including ANOVA, Kruskal-Wallis, Wilcoxon, Mann-Whitney and odds ratios, Bayesian probability algorithms. However, as the number of biomarkers measured increases, it may generally be more expedient to use more sophisticated techniques, such as random forests, simple logistic, or Bayesian nets, to name a few.

[0170] In some examples, Bayesian probability may be employed. In this situation, 10-fold cross-validation may be used to estimate the "out-of-sample" performance of the model of interest. For each combination of biomarkers under consideration, the data may be randomly split into 10 sub-samples, each with a similar proportion of healthy subjects and subjects at each stage of the disease. Each sub-sample may then be left out, and a logistic model may be constructed using the remaining 90% of the subjects. This model may then be used to estimate the probability of cancer for the left-out sub-sample to provide an estimate of the "out-of-sample" performance. By repeating this for the remaining 9 sub-samples, the "out-of-sample" performance may be estimated from the study data itself. These "out-of-sample" predicted probabilities may then be compared to the actual disease state of the subject, a receiver operating characteristic (ROC) curve may be created, and the cross-validated sensitivity at a given specificity (e.g., 95% specificity) may be estimated from the ROC curve.

[0171] Each estimate of "out-of-sample" performance using cross-validation (or any other method) is unbiased but has an element of variability to it. Thus, a ranking of models (based on biomarker combinations) can only indicate the relative performance of such models. However, as demonstrated through "out-of-sample" performance evaluation, a set of biomarkers that can be used in multiple combinations to generate a diagnostic test will almost certainly contain within itself combinations of biomarkers that will stand up to repeated evaluation.

[0172] In one example, the biomarkers are measured using the following algorithm:

number

[0173] Other non-linear or linear logistic algorithms that are equally applicable include random forests, linear models for microarray data (LIMMA) and / or significance analysis of microarray data (SAM), best first, greedy stepwise, naive Bayes, linear forward selection, scatter search, linear discriminant analysis (LDA), stepwise logistic regression, receiver operating characteristic, and classification trees (CT).

[0174] Those skilled in the art are familiar with determining the coefficient values ​​in regression algorithms.

[0175] The algorithms described herein can be used to derive a cancer likelihood score. In some instances, a quantitative score can be derived that indicates an increased likelihood of poor clinical outcome, good clinical outcome, high risk of CRC, or low risk of CRC. The score can then inform treatment management.

[0176] In some instances, the biomarker panel can detect CRC with sensitivity and specificity comparable to or better than FOBT. Sensitivity refers to the proportion of actual positives in a diagnostic test that are correctly identified as having colorectal cancer, as known to those skilled in the art. Specificity measures the proportion of negatives that are correctly identified as not having colorectal cancer.

[0177] In some examples, the biomarker panel has a sensitivity of at least 50%, 60%, or 65%, or at least 70%, 80%, 83%, 85%, 86%, 87%, 88%, 89%, 90%, 93%, or at least 95%.

[0178] In some examples, the biomarker panel has a specificity of at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, or at least 95%.

[0179] Data Processing It will be apparent from the discussion herein that knowledge-based computer software and hardware for implementing the algorithms also form part of the present disclosure, such computer software and / or hardware being useful for carrying out the method of detecting colorectal cancer according to the present disclosure.

[0180] The values ​​from the assays described herein may be calculated and stored manually. Alternatively, the statistical analysis steps may be performed in whole or in part by a computer program product. Thus, the present disclosure provides a computer program product including a computer readable storage medium, in which a computer program is stored. The program, when read by a computer, can perform relevant calculations based on values ​​obtained from the analysis of one or more biological samples from a subject (e.g., gene or protein expression levels, normalization, standardization, thresholding, and conversion of values ​​from the assay to clinical outcome scores, and / or text or graphical depictions of clinical conditions or stages and related information). In the computer program product, a computer program for performing the calculations is stored.

[0181] The present disclosure also provides a system for performing the above data collection and processing or for computing the above software program, which generally includes: a) a central computing environment; b) an input device operatively connected to the computing environment to receive patient data, where the patient data may include, for example, gene or protein expression levels or other values ​​obtained from an assay using a biological sample from a subject, or mass spectrometry data or data for any of the assays provided by the present disclosure; c) an output device connected to the computing environment to provide information to a user (e.g., a medical professional); and d) an algorithm executed by the central computing environment (e.g., a processor), where the algorithm is executed based on the data received by the input device, where the algorithm calculates an expression score, thresholding, or other function described herein. The methods provided by the present disclosure may also be fully or partially automated. In some examples, the methods include a combination of experimental and computer-based methods.

[0182] In one example, the methods of the present disclosure may be used in existing knowledge-based architectures or platforms associated with pathology services. For example, results from the methods described herein may be transmitted via a communication network (e.g., the Internet) to a processing system where algorithms are stored and used to generate predicted posterior probability values, which are converted to disease probability scores, which are then transmitted to an end user in the form of a diagnosis or prognosis report.

[0183] Thus, the methods of the present disclosure may be in the form of a kit or computer-based system that includes the necessary reagents for detecting the concentration of the biomarkers and computer hardware and / or software to facilitate the determination of a report and its transmission to a clinician.

[0184] The assays described herein can be integrated into existing or newly developed pathology architectures or platform systems. For example, the present disclosure provides a method that allows a user to determine a subject's risk for colorectal cancer, comprising: (a) receiving subject data, where the subject data is obtained from determining a measurement of each biomarker in a biomarker panel described herein; (b) processing the data via multivariate analysis (e.g., regression analysis) to provide a disease score; (c) determining the subject's status according to the disease score result compared to a predetermined value; (d) transmitting, via a communications network, the indication of the subject's condition to the user, wherein references to multivariate analysis include algorithms, and wherein the algorithms perform the multivariate analysis functions.

[0185] kit The present invention provides kits for the detection of biomarkers. Such kits may be suitable for the detection of nucleic acid species, or alternatively may be for the detection of proteins or polypeptides.

[0186] For detection of polypeptides, antibodies are most typically used as a component of the kit. However, any agent capable of specifically binding to a biomarker gene product is useful. Other components of the kit typically include labels, secondary antibodies, inhibitors, cofactors, and control gene or protein product preparations, which allow the user to quantify expression levels and / or evaluate whether the measurement worked correctly. Enzyme-linked immunosorbent assay-based (ELISA) tests and competitive ELISA tests are particularly suitable assays, and these tests can be easily performed by those skilled in the art using the kit components.

[0187] In some examples, the kit may include a microtiter plate on which a capture antibody corresponding to the biomarker being measured is immobilized.

[0188] In some examples, the kit may include beads on which a capture antibody corresponding to the biomarker being measured is immobilized.

[0189] Optionally, the kit further comprises a means for detection of binding of the antibody to the biomarker polypeptide. Such means include a reporter molecule, such as an enzyme (e.g., horseradish peroxidase or alkaline phosphatase), a dye, a radionucleotide, a luminescent material, a chemiluminescent material, a fluorescent material, biotin, or a colloidal particle, such as colloidal gold or selenium. Preferably, such a reporter molecule is directly attached to the antibody.

[0190] In one example, the kit may additionally include a reference sample. In one embodiment, the reference sample includes the polypeptide detected by the antibody. Preferably, the polypeptide is of known concentration. Such a polypeptide is particularly useful as a standard. Thus, various known concentrations of such a polypeptide can be detected using the diagnostic assay described herein.

[0191] For detection of nucleic acids, such kits may contain a first container, such as a vial or plastic tube or a microtiter plate that contains an oligonucleotide probe. The kit may optionally contain a second container that holds a primer. The probe may be hybridizable to DNA, the altered expression of which is associated with colorectal cancer, and the primer is useful for amplifying the DNA. Kits containing oligonucleotide probes immobilized on a solid support may be developed, for example, using arrays (see supplement to issue 21(1) Nature Genetics, 1999).

[0192] For PCR amplification of nucleic acid, nucleic acid primers can be included in the kit, and the nucleic acid primers are complementary to at least a portion of the biomarker genes described herein. A set of primers typically includes at least two oligonucleotides, preferably four oligonucleotides, which are capable of specific amplification of DNA. Fluorescently labeled oligonucleotides that allow quantitative PCR determination can be included (e.g., TaqMan chemistry, molecular beacon). Also included are enzymes suitable for amplifying DNA.

[0193] A control nucleic acid may be included for comparison or validation purposes. Such controls can be either RNA / DNA isolated from healthy tissue or healthy individuals, or housekeeping genes such as beta-actin or GAPDH, whose mRNA levels are unaffected by colorectal cancer.

[0194] It will be understood by those skilled in the art that numerous variations and / or modifications may be made to the above-described embodiments without departing from the broad general scope of the present disclosure, and the present embodiments are therefore to be considered in all respects as illustrative and not restrictive. EXAMPLES

[0195] Materials and Methods ethics All research protocols used in this study were approved by the relevant human research ethics committees. Written informed consent was obtained from each participant prior to blood sample collection.

[0196] Participant samples A collection of plasma and serum samples was collected and processed from a cohort of 96 colorectal cancer patients (Dukes stages I-IV) treated at several hospitals. Samples were obtained from the Victorian Cancer Biobank.

[0197] Blood was collected and processed from a group of approximately 50 healthy volunteers (controls) aged over 50 years and between 50 and 85 years.

[0198] Subjects who had previously received chemotherapy and / or radiation therapy were excluded from the analysis. Subject characteristics are summarized in Table 1 below. [Table 1]

[0199] Blood Collection and Processing Serum samples from subjects were collected using standard operating procedures as previously described (Brierley GV, et al. (2013) Cancer Biomark. 13:67-73). Blood was collected into serum gel tubes (Scientific Specialties Inc., USA) and each sample was left at room temperature for at least 30 minutes before being centrifuged (1,200g, 10 minutes, room temperature). The serum fraction was then transferred to a clean 15 mL tube and centrifuged again (1,800g, 10 minutes, room temperature) before being aliquoted (250 μL) and stored (-80°C). All samples were processed and stored within 2 hours after collection. Serum samples were thawed only once before use.

[0200] Blood Biomarkers and Biomarker Analysis A total of 18 biomarkers were evaluated and are summarized in Table 2 below. [Table 2]

[0201] Blood Biomarker Analysis Sandwich ELISA assays were used to quantitate biomarker levels in serum samples from patients using standard protocols. Biomarker analysis was performed with commercially available kits and procured antibodies. Details of the biomarkers evaluated and the antibodies / ELISA kits used are shown in Table 3. [Table 3-1] [Table 3-2]

[0202] For each assay, samples were run in duplicate and in-house quality control (QC) samples were included, which consisted of pooled normal and pooled CRC patient serum samples.

[0203] For standard ELISA, absorbance or fluorescence signals were detected using a Wallac Victor3 1420 multilabel counter (Perkin Elmer, USA). Biomarker concentrations were derived from the corresponding standard curves using WorkOut software (Qiagen, Hilden Germany).

[0204] Sensitivity and specificity determination The diagnostic potential of any given test is typically expressed in terms of its sensitivity and specificity for a given disease. The results for a given case / control study can be assigned to one of four quadrants shown in Table 4. [Table 4]

[0205] Sensitivity is a measure of the ability of a test to accurately detect people with colorectal cancer using the diagnosis from colonoscopy and histopathology as a reference, and is determined by the following equation: Test sensitivity (%) = 100% × TP / (TP+FN)

[0206] Specificity is a measure of the ability of a test to accurately detect people without colorectal cancer using the colonoscopy / histopathology results as a reference and is determined by the following equation: Test specificity (%) = 100% × TN / (FP+TN)

[0207] A threshold concentration of a given biomarker (serum concentration of the biomarker protein, as determined by ELISA) must be selected to define which patient results are considered positive or negative according to the ELISA test. The sensitivity and specificity of the test can be determined at any threshold concentration value and over the range of concentrations observed in the experiment. There is an inverse relationship between sensitivity and specificity.

[0208] Statistical Evaluation and Modeling To find the combination of biomarkers that best separates controls from colorectal cancer patients, logistic regression based on the following equation was utilized as shown in the equation below. Yi=β0+β1[M1]+β2[M2]……+ε i During the ceremony, ● Yi is a binary indicator of the presence or absence of CRC, as determined by colonoscopy in the experimental cohort. ●β0 is the regression intercept value. M1, etc. are the base 10 logarithms of the concentration of biomarker 1, measured in specific units. M2, M3, etc. represent each individual biomarker. • β1 etc. are coefficients multiplied by the logged biomarker concentrations. ε i is the error term associated with the model.

[0209] To find the combination of biomarkers plus age that best separates controls from colorectal cancer patients, logistic regression based on the following equation was utilized as shown in the equation below. Yi=β0+β1[M1]+β2[M2]………+β 年齢 (age)+ε i During the ceremony, ● Yi is a binary indicator of the presence or absence of CRC, as determined by colonoscopy in the experimental cohort. ●β0 is the regression intercept value. M1, etc. are the base 10 logarithms of the concentration of biomarker 1, measured in specific units. M2, M3, etc. represent each individual biomarker. • β1 etc. are coefficients multiplied by the logged biomarker concentrations. ●Age is the target age in years. ●β 年齢 is a coefficient multiplied by the subject's age in years. ε i is the error term associated with the model.

[0210] The models were analyzed using different combinations of biomarkers and associated 0~10 By constructing using varying values ​​for the coefficients, models that most closely approximate the true values ​​Yi can be determined for panels of biomarkers of different sizes.

[0211] To address problems such as overfitting or selection bias that are often encountered in statistical and machine learning processes, and to provide insight into how any given model generalizes to independent data sets, the data for each marker was reanalyzed using 10-fold cross-validation.In brief, the entire data set for any marker was divided into 10 equally sized sub-samples.One sub-sample was kept as a validation data set, and the remaining 9 sub-samples were used as training data.This process was repeated 10 times, and each sub-sample was used exactly once as validation data.The data presented in the following table was obtained using this cross-validation procedure.

[0212] The results for each assay were analyzed using the statistical software packages Prism and "R." Individual performance of the markers was assessed using the non-parametric Mann-Whitney t-test, and individual receiver operator characteristic (ROC) curves were generated.

[0213] Example 1 Analysis of Individual Biomarkers Clinical characteristics for the subjects analyzed in this study are shown in Table 1. A total of 95 subjects with a colonoscopy-confirmed diagnosis of colorectal cancer (CRC) were analyzed along with 50 healthy controls. Among the CRC subjects, the median age was 67 years. The ratio of males to females was approximately equal.

[0214] Individual biomarkers were assessed by ELISA assay, and statistical differences between the median values ​​for cases and controls for each biomarker were assessed using a two-tailed Mann-Whitney T-test. Biomarkers that individually distinguished colorectal cancer subjects from control subjects at p<0.05 were BDNF, TIMP1, TNFα, MAC2BP, MMP1, MMP7, IGFII, M65, TGFβ1, IL6, IL8, VEGFA, IGFBP2, DKK3, and M2PK.

[0215] The biomarkers M2PK, TIMP1, IGFBP2, BDNF, IL6, and IL8 appeared to provide the greatest discrimination between colorectal cancer subjects and controls, according to Table 5.

[0216] Each biomarker (with or without age) was examined for its ability to distinguish colorectal cancer subjects from control subjects across all individuals and when the data was separated based on male / female. The cross-validated sensitivity for each individual biomarker (with or without age) is presented in Table 5. The cross-validated sensitivity was derived from applying logistic regression to the concentration values ​​for each biomarker considered individually. The cross-validated sensitivity represents the best sensitivity achievable at 95% specificity by differentially weighting the log concentration values ​​for a particular biomarker and is applied to all case and control samples. [Table 5]

[0217] Considered individually, biomarkers of greatest diagnostic potential combine high sensitivity with high specificity for the detection of a given disease. No individual biomarker, either alone or in combination with age, distinguished between cancer and normal controls with a sensitivity at 95% specificity, sufficient to be useful as a stand-alone biomarker for screening applications.

[0218] Example 2 Analysis of Biomarker Combinations To identify the combination of biomarkers that provides the best performance for colorectal cancer detection, we measured the levels of the 18 top biomarkers (identified in Table 3) in serum samples from a new cohort of subjects with the characteristics described in Table 6. [Table 6]

[0219] Serum samples from subjects listed in Table 6 were obtained from the Victorian Cancer Biobank. Samples were prepared according to strict standard operating protocols as described at https: / / viccancerbiobank.org.au / for-researchers / quality-assurance / . Biomarker concentrations were measured in each serum sample using commercially available ELISA kits as listed in Table 3 above.

[0220] Combinations of 2-10 biomarkers were identified using logistic regression as described above, differentially weighted to provide the best possible resolution between case and control samples for each combination. Sensitivity values ​​in Tables 7-15 below are 10-fold cross-validated. The top 10 performing biomarkers for combinations of 2-10 biomarkers are shown in Tables 7-15 below. [Table 7] [Table 8] [Table 9] [Table 10] [Table 11] [Table 12] [Table 13] [Table 14] [Table 15]

[0221] From Tables 7-15, it is clear that the highest sensitivity was obtained when more biomarkers were included in the panel. For panels within the 4-10 biomarker panel size class, the sensitivity reduction observed within the top 10 models for any particular biomarker panel number class was very slight, ranging from 1.2% for the 8 biomarker panel to 3.8% for the 4 biomarker panel. For biomarker panels containing 7-10 biomarkers and biomarker panels containing 4-6 biomarkers, the range of sensitivity values ​​observed for the top 10 panels overlapped between adjacent biomarker panel size classes. The highest sensitivity recorded (for mixed male and female gender) was 85.6% at 95% specificity for a 10 biomarker panel including BDNF, M2PK, DKK3, TGFBETA, IGFBP2, lipocalin, TNFα, MIP1β, M65, and IGFII.

[0222] BDNF and M2PK were present in the top performing panels in each biomarker panel size class. Other prominently observed biomarkers in the top 10 6-10 biomarker panel size classes included IGFBP2, DKK3, and TNFα. Also, in the 4-8 biomarkers, IL8 appeared to be well represented in the top 10 models.

[0223] To identify biomarkers found most frequently in a broader set of biomarker panels that showed strong resolution between sera from individuals with advanced colorectal neoplasia (males and females combined) and those without (males and females combined), we plotted the frequency with which individual biomarkers were represented in the top 100 models (or the top model yielding a sensitivity of 75% or greater at 95% specificity) for each of the 10 to 3 biomarker panels.

[0224] The results obtained for both gender combinations, where age was not included in the modeling, are shown in Figures 1-1 and 1-2. For all biomarker panels, M2PK and BDNF were present.

[0225] For panels of 8-10 biomarkers, other biomarkers (present in over 50% of this broader selection of high performing panels) included DKK3, IGFBP2, and TNFα. For panels of 4-7 biomarkers, IL8 emerged as a prominent marker, with TNFα and IGFBP2 beginning to decline but still contributing to a significant proportion of strong performing biomarker panels.

[0226] Without wishing to be bound by theory, the inventors have found that including BDNF in a biomarker panel provides an unexpected improvement in test sensitivity at 95% specificity and / or cross-validated sensitivity at 95% specificity compared to previously disclosed biomarker combinations. For example, in WO2012 / 006681, a method for diagnosing or detecting colorectal cancer in a subject is disclosed. In this application, the top three biomarker combination was DKK3, M2PK, and IGFBP2, with a test sensitivity at 95% specificity of 72.9% and a cross-validated sensitivity at 95% specificity of 70.8%. The inventors have demonstrated that including BDNF in this biomarker panel (i.e., BDNF, DKK3, M2PK, and IGFBP2) improves the test sensitivity at 95% specificity and the cross-validated sensitivity to 73.9% and 74.5%, respectively. Similarly, the top-ranked four-biomarker combination in WO2012 / 006681 was DKK3, M2PK, IGFBP2, and Mac2BP, with a test sensitivity of 68.8% at 95% specificity and a cross-validated sensitivity of 69.8% at 95% specificity. The inventors demonstrated that including BDNF in this biomarker panel (BDNF, DKK3, M2PK, IGFBP2, and Mac2BP) improved the test sensitivity and cross-validated sensitivity at 95% specificity to 77.7% and 76.6%, respectively. Finally, in WO2012 / 006681, the four-biomarker combination of DKK3, M2PK, IGFBP2, and TIMP1 was reported to have a test sensitivity of 61.5% at 95% specificity and a cross-validated sensitivity of 53.1% at 95% specificity. The inventors demonstrated that including BDNF in this biomarker panel (i.e., BDNF, DKK3, M2PK, IGFBP2, and TIMP1) significantly improved the test sensitivity and cross-validated sensitivity at 95% specificity to 77% and 76%, respectively.

[0227] The enhanced performance resulting from the inclusion of BDNF over the biomarker combinations described in WO2012 / 006681 was observed when the top performing biomarker combinations were considered in various biomarker panels. For example, the top performing 7-marker panel identified when BDNF was included in the modeling process showed an internal cross-validated sensitivity of 83.2% at 95% specificity for all stages of CRC (without age and / or gender included) compared to an internal cross-validated sensitivity of 69% for the top performing 7-biomarker panel model described in WO2012 / 006681 that contained BDNF and did not include BDNF. Similarly, for the 3-, 4-, and 5-biomarker panels that included BDNF, the top performing models in each class showed sensitivities of 74.6%, 77.8%, and 80.5% at 95% specificity, respectively, compared to sensitivities of 70.8%, 69.8%, and 70.8% for the top performing models that did not include BDNF. Indeed, surprisingly, BDNF was included in all or most of the 100 top performing models (or models resulting in greater than 75% sensitivity at 95% specificity) based on panels of 5-10 biomarkers (see Figures 2-1 and 2-2).

[0228] Example 3. The effect of adding demographic data, age Age is the highest risk factor for CRC. The risk of developing colorectal cancer increases dramatically from age 50 years. Therefore, we used logistic regression analysis to evaluate whether including age with multiple biomarker combinations can modify the sensitivity for detection of CRC achieved with biomarker combinations. Tables 16-24 below show the sensitivity at 95% specificity of the top 10 performing 2-10 biomarker panels when age in weighted years is added to the analysis as an additional biomarker panel. Examples of the top 10 performing 2-10 biomarker panel combinations plus age are shown in Tables 16-24. [Table 16] [Table 17] [Table 18] [Table 19] [Table 20] [Table 21] [Table 22] [Table 23] [Table 24]

[0229] Similar to biomarker combinations in the absence of age, when age was included, the highest sensitivity was obtained when there were more biomarkers in the panel. Within the top 10 4-10 biomarker panels plus age, the sensitivity decline observed within any particular biomarker panel size class was again slight, ranging from 1.2% for the 10 biomarker panel size class to 3.3% for the 4 and 6 biomarker panel size classes. For 4-10 biomarker panels plus age, the range of sensitivity values ​​observed for the top 10 biomarker panels overlapped between adjacent panel size classes. The highest sensitivity recorded at 95% specificity for biomarker plus age, both genders, was 87% with the 9 biomarker panel.

[0230] For panels of 6-10 biomarkers, the top models that included age showed slightly higher sensitivity values ​​than their counterparts that did not include age. The biomarker composition for the top performing panels in each biomarker panel size class differed slightly between models that included age and those that did not, with the exception of the five biomarker panels. Importantly, however, those biomarkers that were most prominent in the top 10 biomarker panel size classes in the absence of age were preserved in the equivalent panels in which age was included. Notably, BDNF and M2PK (tumor morphology) were present in all models. DKK3 and IGFBP2 were particularly prominent in the 6-10 biomarker panel size class.

[0231] IL8, TNFα, and MMP1 also appeared frequently in many of the top biomarker panels. IGFBP2 and MMP1 were more commonly represented in panels of 6-10 biomarkers, while IL8 and TNFα were prominent in all biomarker panels. MIP1β was strongly represented in the top models for 8-10 biomarker panels, but its frequency decreased somewhat in top models containing 6 or fewer biomarkers.

[0232] The frequency with which individual biomarkers were represented in the top 100 biomarker models (or top models resulting in 75% or greater sensitivity at 95% specificity) for 10-5 biomarker panels based on data from both gender combinations where age was included in the modeling, respectively, is shown in Figures 2-1 and 2-2. For nearly all biomarker panels, M2PK and BDNF were present in all panels in this broader selection of top performing models for gender combinations when age was included. The exception was the 5 biomarker panel, where 8% of the top 100 models did not contain BDNF. For the 8-10 biomarker panels, other prominent biomarkers (present in more than 50% of this broader selection of high performing models) included DKK3, IGFBP2, MMP1, and TNFα. For the 4-7 biomarker panels, IL8 was less prominent than M2PK and BDNF, but still prominent. Panel inclusion of TNFα, DKK3, MMP1, and IGFBP2 dropped below 50% in these panels, but continued to be included in a higher percentage of strong performing panels than other biomarkers.

[0233] Those skilled in the art will understand that there is an inverse relationship between sensitivity and specificity. Thus, when specificity is reduced, the sensitivity of a test generally increases. There are situations where higher sensitivity may be required operationally and lower specificity is acceptable. For example, there is no two-marker panel plus age that distinguishes cancer serum samples from control serum samples with a sensitivity of more than 75% at 95% specificity, but when specificity is reduced to 90%, a model that includes M2PK, BDNF, and age has a cross-validated sensitivity of 77.8%.

[0234] Example 4. Effect of Gender (separate analyses for males and females) It is known that men are more likely to develop and die from colorectal cancer. In 2015, of the 15,604 new cases of colorectal cancer diagnosed in Australia, 7,031 were in women and 8,573 were in men. In 2019, of the estimated 5,597 deaths from colorectal cancer that year, 2,588 were in women and 3,009 were in men. Also, in 2019, the risk of an individual Australian being diagnosed with colorectal cancer by their 85th birthday was estimated to be 1 in 14 (1 in 12 men and 1 in 17 women) (https: / / bowel-cancer.canceraustralia.gov.au / statistics). These gender biases in colorectal cancer risk statistics are also reflected in the positivity rates by gender when assessed by FIT in the Australian National Bowel Cancer Screening Program based on 2017 data (8.8% for men and 7.1% for women) (Australian Institute of Health and Welfare 2019. National Bowel Cancer Screening Program: monitoring report 2019. Cancer series no. 125. Cat. no. CAN 125. Canberra: AIHW). These patterns of increased frequency of colorectal cancer in men versus women were also reflected in a study of a new diagnostic methylation test for colorectal cancer, although sex was not a positive predictor for the test itself (Pedersen et al. Evaluation of an assay for methylated BCAT1 and IKZF1 in plasma for detection of colorectal neoplasia. BMC Cancer (2015) 15:654 DOI 10.1186 / s12885-015-1674-2).

[0235] The best performing 10, 9, 8, 7, 6, 5, 4, 3, and 2 biomarker panels at 95% specificity, including BDNF but not age, for both gender combinations, are shown in Tables 7-15 above, with top observed sensitivities of 85.6%, 84.7%, 84.2%, 83.2%, 80.4%, 80.5%, 77.8%, 74.6%, and 63.8%, respectively.

[0236] To understand whether these panels performed equally well for both genders, the same core biomarker concentration data from the 193 colorectal cancer / 149 healthy control cohort was separated into male-only and female-only case / control cohorts. Tables 25-60 below show the best performing 2-10 biomarker panels, identified by logistic regression and 10-fold cross-validation run independently on the biomarker data derived from these female or male cohorts.

[0237] Examples of 2-10 biomarker panels for women that include BDNF are shown in Tables 25-33. Examples of 2-10 biomarker panels for women that include BDNF when age is also taken into account are shown in Tables 34-42. The top 10 biomarker combinations are shown in each table below. [Table 25] [Table 26] [Table 27] [Table 28] [Table 29] [Table 30] [Table 31] [Table 32] [Table 33] [Table 34] [Table 35] [Table 36] [Table 37] [Table 38] [Table 39] [Table 40] [Table 41] [Table 42]

[0238] Considering Tables 25-33 and 34-42 above, it becomes apparent that, in general, the top performing sets of biomarkers developed from female-only participants achieved higher sensitivity at 95% specificity when the panels contained progressively higher numbers of biomarkers, regardless of whether age was included in the algorithm. For the six to nine biomarker panels, the maximum observed sensitivity achieved with the female-only data increases slightly when age is included in the model.

[0239] Comparing Tables 7-15 (gender combination, biomarkers only) with Tables 25-33 (female, biomarkers only), it is striking to observe that cross-validated models with significantly higher sensitivity at 95% specificity were identified when modeled on data from females alone than when modeled on data from both genders combined. Differences as high as 10% were observed across all biomarker panel size classes and whether or not age was included in the modeling (compare Tables 16-24 with Tables 34-42 for modeling where age was included).

[0240] For models built on the combined male and female data, and for models developed on females only, the biomarker composition for the top performing panels in each biomarker panel differed slightly between models that included age and models that did not include age (the exceptions were the 3 and 4 biomarker panel size classes, where the top models in each class had the same biomarker composition in the presence and absence of age). However, biomarkers that were prominent in the top models for each biomarker value class in the absence of age were preserved in the equivalent models in which age was included. Notably, BDNF and M2PK were present in all models. IL8 and MMP1 were prominent across many classes. IGFBP2 and lipocalin did not appear in any of the top models in any of the biomarker panels derived from female-only data in the absence of age, but both of these markers were prominent in the 6-10 biomarker panels when age was included in the analysis.

[0241] Figures 3-1 and 3-2 show graphs showing the frequency with which individual biomarkers (when age is not taken into account) are represented in the top 100 models (or the top models that provide 75% or greater sensitivity at 95% specificity) developed on female-only data for biomarker panels containing 5-10 biomarkers. Figures 4-1 and 4-2 show graphs showing the frequency with which individual biomarkers (when age is taken into account) are represented in the top 100 models (or the top models that provide 75% or greater sensitivity at 95% specificity) developed on female-only data for biomarker panels containing 5-10 biomarkers.

[0242] For models based only on female data, M2PK was present in all high-performing models in all value classes, whether or not age was included. BDNF was present in over 80% of high-performing biomarker panels in 5-10 biomarker combinations, whether or not age was included in the modeling. In 3 and 4 biomarker panels, BDNF remained the second most frequent biomarker, whether or not age was included. For female-only biomarker combinations of 8-10 biomarkers, other prominent biomarkers (present in over 50% of this broader selection of high-performing models), independent of whether age was included, included MMP1, M65, and IL8. When age was included, lipocalin and IGFBP2 were also represented in 50% of these high-performing panels. For 4-7 biomarker panels, IL8 remained prominent. MMP1 was also among the more commonly included biomarkers, but its frequency dropped to less than 50% in panels of 3-5 biomarkers. IL6 was represented in greater than 20% of high performance panels in both the 3 and 4 biomarker panels, regardless of whether age was included in the modeling.

[0243] Examples of the top 10 biomarker combinations for panels of 2-10 biomarkers developed on male-only data are shown in Tables 43-51. Also, examples of the top 10 biomarker combinations for panels of 2-10 biomarkers developed on male-only data when age is also taken into account are shown in Tables 52-60. [Table 43] [Table 44] [Table 45] [Table 46] [Table 47] [Table 48] [Table 49] [Table 50] [Table 51] [Table 52] [Table 53] [Table 54] [Table 55] [Table 56] [Table 57] [Table 58] [Table 59] [Table 60]

[0244] Comparing Tables 7-15 (gender combination, biomarkers only) with Tables 43-51 (male, biomarkers only), the differences in performance between models developed on data from both gender combinations and those developed on male-only data were minimal. This pattern was also observed when modeling included age (compare Tables 16-24 with Tables 52-60). These results for males contrasted sharply with similar results obtained with models built on female-only data.

[0245] Consideration of Tables 43-51 and 52-60 reveals that, in general, the top performing sets of models developed on data from male-only subjects achieved higher sensitivity values ​​at 95% specificity when the panel contained a higher number of biomarkers, regardless of whether age was included in the calculation. As observed for the gender combinations, there was an overlap in the sensitivity achieved by the top performing models between adjacent biomarker size classes in the range of 2 to 10 obtained with the male-only data. No improvement in sensitivity was observed when age was included in the modeling performed with the male-only data.

[0246] For models built on the combined male and female data, for models developed in males only, the biomarker composition for the top performing panels in each biomarker value class differed slightly between models that included age and models that did not. The only exception was the five biomarker panel, where the top models had the same biomarker composition in the presence and absence of age. However, there was still strong conservation of key biomarkers in the top performing models plus and minus age in each biomarker value class. In particular, BDNF and M2PK were present in all top models. DKK3 and TNFα were prominent across most biomarker combinations, while IL8 and TIMP1 were also fairly commonly represented. IGFBP2 was only represented in the top biomarker-only models of the five biomarker combinations, but was present in the top models including age of the four, seven, nine, and ten biomarker combinations.

[0247] Figures 5-1 and 5-2 show graphs showing the frequency with which individual biomarkers are represented in the top 100 models (or the top models that provide 75% or greater sensitivity at 95% specificity) developed on male-only data for a panel of 5-10 biomarkers. Figures 6-1 and 6-2 show graphs showing the frequency with which individual biomarkers (when age is taken into account) are represented in the top 100 models (or the top models that provide 75% or greater sensitivity at 95% specificity) developed on male-only data for a biomarker panel containing 5-10 biomarkers.

[0248] For models based on male data only, M2PK was present in all high-performing models in all biomarker value classes, whether or not age was included. Unlike models built in similar gender combinations and female-only data sets, in male-only models, DKK3 was the next most frequent marker, present in all panels of 7-10 biomarker combinations in this broader selection of high-performing models, and in over 80% of all models in 4-6 biomarker combinations, whether or not the models included age. BDNF was present in all models in 8-10 biomarker combinations, present in over 90% of the high-performing 7 biomarker panels, and present in over 50% of the 6 biomarker combinations. Although its frequency dropped below 50% in the 4 and 5 biomarker combinations, BDNF was still firmly within the second tier of biomarker frequency. TNFα was frequent in the high-performing 6-10 biomarker combinations, and remained a robust second tier marker in the 5 biomarker combinations. IGFBP2 was also frequent among high-performing models of 7-10 biomarker combinations, but its representation dropped substantially in combinations of 3-6 biomarkers. In contrast, TGFβ1, which contributed less to the top performing models in 7-10 biomarker panels, contributed significantly to the high performing 4-6 biomarker panels, becoming the third most frequently represented marker in 4 and 5 biomarker panels.

[0249] For men, no two biomarker combinations (with or without age) distinguished case and control sera from men with greater than 75% sensitivity at 95% specificity. The best performing pairwise combination including BDNF for men (when age was not considered) was M2PK+BDNF with a sensitivity of 52.3%. The best performing pairwise combination including BDNF for men (when age was included) was M2PK+BDNF with a sensitivity of 57.9%.

[0250] Example 5. Other high performance biomarker combinations The methods and uses etc. described herein may also use one or more or all of the biomarker combinations provided in Tables 61 to 66. In these tables, Sens. = cross-validated average sensitivity. [Table 61-1] [Table 61-2] [Table 61-3] [Table 61-4] [Table 61-5] [Table 61-6] [Table 61-7] [Table 62-1] [Table 62-2] [Table 62-3] [Table 62-4] [Table 62-5] [Table 62-6] [Table 62-7] [Table 63-1]

Table 63-2

Table 63-3

Table 63-4

Table 63-5

Table 63-6

Table 64-1

Table 64-2

Table 64-3

Table 64-4

Table 64-5

Table 64-6

Table 64-7

Table 65-1

Table 65-2

Table 65-3

Table 65-4

Table 65-5

[0251] Example 6 Performance of the Five Biomarker Panel (Study 9) To investigate the potential utility of a blood-based five-biomarker panel for early detection of CRC and to explore its potential application in CRC screening of asymptomatic individuals at normal risk of developing CRC, a case / control study was first performed to identify the performance characteristics of an exemplary five-biomarker panel including M2PK, BDNF, IGFBP2, TIMP1, and DKK3.

[0252] Such a panel may be useful in several situations: as an adjunct to current FIT or colonoscopy screening, to provide an alternative test for people who cannot or will not test for colorectal cancer using a stool test, as an add-on test to facilitate triaging those with a positive FIT result for colonoscopy, or potentially as an alternative to FIT for first-line CRC screening applications. Thus, a comparison of the performance of the five biomarker panel with that of FIT is important, particularly the relative positive and negative predictive values ​​of the tests when applied to asymptomatic normal-risk screening populations.

[0253] method Study population [Table 67]

[0254] Samples and quantification Serum samples for this study, collected between 2018 and 2021, were procured from the Victorian Cancer Biobank or commercially from ProteoGenex (ProteoGenex, Inc., Englewood, CA, USA). The study protocol was approved by the Cancer Council Victoria Human Research Ethics Committee (HREC-1803) and the Russian Oncological Research Center Ethics Committee (IRB PG-ONC 2003 / 1) via ProteoGenex.

[0255] The concentrations of M2PK, BDNF, IGFBP2, TIMP1, and DKK3 were quantified in serum samples from two independent CRC case / healthy normal control cohorts. The characteristics of these cohorts are described in Table 67. ELISA was used to measure the concentrations of biomarkers. Biomarker concentrations were combined with age, sex, or BMI data via the algorithm described herein to obtain colorectal cancer likelihood scores. In this example, women were assigned an arbitrary value of 1.1, and men were assigned an arbitrary value of 1. It is expected that those with scores above the defined threshold will be advised by medical professionals to proceed to colonoscopy for definitive diagnosis. Those with scores below the threshold will be advised to screen again within a time frame of 2 years.

[0256] Biomarker Panel Training and Testing Sample concentrations from both cohorts, in addition to age and sex, were used in the training and testing of logistic regression-based algorithms. In the derivation of these equations, protein biomarker concentrations were log-transformed to base 10. Women were assigned a biomarker value of 1.0 and men a value of 1.1. Age was expressed in years. Body mass index values ​​were calculated from participants' height and weight measurements in the derivation of these algorithms, where BMI was included. Train-test split (split ratios of 60:40, 70:30, 80:20, 90:10, and 100:100) and K-fold cross-validation (K=5, 10) methods were applied to the shuffled data with 100 resamples and 1000 iterations to generate algorithms with 5-8 parameter panels under consideration. The training, testing, and validation methods were applied in two different ways (Figure 7). One cohort was split using a training-test split or k-fold cross-validation to create training and test subsets. The resulting algorithm was then validated on a second cohort. ● One entire cohort was used to train the algorithm, and then the algorithm was tested on a second cohort.

[0257] Logistic regression and fuzzy logic analysis were performed on the data using the python-based integrated development environment PyCharm (version X, JetBrains, Prague 4, Prague, Czech Republic) and the numerical computing environment MATLAB (version 2021b, MathWorks, Natick, MA, USA).

[0258] Algorithm Selection Algorithms trained and within-sample tested in cohort 1 were tested in cohort 2. Top performing algorithms were selected based on performance (training and testing sensitivity exceeding 73% performance goal at 95% specificity), reliability in performance, robustness (transferability between datasets), and training dataset size. Wilson score intervals with 95% confidence were calculated manually for the top performing algorithm sensitivity with the number of true positives (sensitivity) expressed as a binomial distribution (E.B. Wilson. “Probable inference, the law of succession, and statistical inference,” Journal of the American Statistical Association, vol. 22, no. 158, pp. 209-212, 1927). A performance target of 73% was selected using a cutoff value of 20 μg Hb / g based on a meta-analysis of FIT (K. Selby et al., “Effect of sex, age, and positivity threshold on fecal immunochemical test accuracy: a systematic review and meta-analysis,” Gastroenterology, vol. 157, no. 6, pp. 1494-1505, 2019). Results for the top performing algorithms are shown below.

[0259] result Performance of individual biomarkers measured in serum of colorectal cancer (CRC) and control patients Considered individually, the levels of each of the five biomarkers differed significantly between CRC and control serum samples in cohort 1 (Table 68). Levels of M2PK, IGFBP2, and TIMP1 were elevated in serum from CRC patients versus controls, while levels of BDNF and DKK3 were reduced versus controls. In the smaller cohort 2, significant differences in biomarker concentrations between cancer and control serum were observed for M2PK, IGFBP2, and TIMP1, while DKK3 approached significance. [Table 68]

[0260] When considered individually, M2PK best discriminated between cancer and controls, with 8.30e in cohorts 1 and 2, respectively. -06 and 5.46e -009 IGFBP2 was the second best, with a P value of 4.68e in cohorts 1 and 2, respectively. -023 and 1.03e -006 The P values ​​of 0.001 and 0.002 were the lowest in cohort 1 and 2, respectively. Discrimination between cases and controls was lowest for DKK3 (P=0.0049) and BDNF (P=0.3041) in cohort 2, which may have been due to the small size of the cohorts. However, based on ROC analysis, none of these markers individually discriminated between cases and controls with sufficient accuracy to be used clinically for early detection of CRC.

[0261] Biomarker Panel Performance To determine whether these five (M2PK, DKK3, IGFBP2, TIMP1, and BDNF) biomarkers, when combined with considerations of age, sex, or body mass index (BMI), could usefully distinguish samples from CRC patients and controls, we applied logistic regression and receiver operator characteristic (ROC) curve analysis.

[0262] A high-performance algorithm that combines biomarker concentrations with additional considerations for age and sex and discriminates between case and control samples with high sensitivity and specificity was trained on data from cohort 1 as described herein. The winning algorithm was then intra-sample cross-validated using multiple iterations of an 80 / 20 split training / testing approach. The algorithm that yielded the highest cross-validated sensitivity and specificity (the point closest to the (0,1) corner in the ROC plane) in the ROC analysis was selected and locked. A threshold was included in these locked parameters, above which the test result is considered positive, and below which the test is scored as negative. This algorithm was then tested on a completely independent dataset (cohort 2). For each analysis, the area under the ROC curve was determined. Sensitivity and specificity values ​​at the locked threshold were determined, along with positive and negative predictive values.

[0263] The results were as follows: [Table 69]

[0264] These small apparent variations in sensitivity values ​​across different training and testing regimens were not statistically significant. Furthermore, the sensitivity and specificity values ​​are highly competitive with those observed for FIT in the Australian NBCSP pilot study conducted in an asymptomatic normal CRC risk population aged 50-69 years.

[0265] Mapping the sensitivity and specificity values ​​for the above five protein plus age and sex classifiers to a theoretical normal CRC risk screening population of 1 million participants with a CRC prevalence of 0.00264 (Australian Institute of Health and Welfare 2014. Analysis of bowel cancer outcomes for the National Bowel Cancer Screening Program. Cat. no. CAN 87. Canberra: AIHW) allows the calculation of the theoretical positive predictive value (PPV) and negative predictive value (NPV) expected in the screening population. A comparison of these values ​​to the equivalents observed in the FIT population screening in the Australian National Bowel Cancer Screening program is shown in Table 69. [Table 70]

[0266] The results in Table 69 predict substantial improvements in performance parameters for a five biomarker panel including BDNF, M2PK, IGFBP2, TIMP1, and DKK3 plus age and gender versus FIT, suggesting strong potential utility when applied to asymptomatic normal CRC risk screening populations.

[0267] As will be appreciated by those skilled in the art, although this example was achieved with a five protein biomarker panel (M2PK, BDNF, IGFBP2, TIMP1, and IGFBP2) plus quantification of demographic indicators for age and sex, similar robust performance can also be expected using the five protein biomarkers alone, with age alone, and with sex alone. Furthermore, those skilled in the art will appreciate that adding one or more demographics or replacing one or more demographics with other demographics or morphometrics, including but not limited to smoking history, body mass index (BMI), and hip-to-waist ratio, is also expected to provide robust performance tests highly competitive with FIT.

[0268] The following results table supports this understanding. The results table describes sensitivity and specificity data for five protein biomarkers alone, including BDNF, M2PK, IGFBP2, TIMP1, and DKK3, and for these protein biomarkers together with additional demographic or morphometric biomarkers, including subject age, sex, and BMI (body mass index values ​​were calculated from participants' height and weight measurements). Taking sex into account, women were assigned an arbitrary value of 1.1, and men were assigned an arbitrary value of 1.

[0269] Referring to the table below, BM1 refers to the PKM2 tumor form, BM2=TIMP1, BM3=IGFBP2, BM4=DKK3, and BM5=BDNF. [Table 71]

[0270] All "test" results consider all samples (i.e., no samples were excluded). Values ​​for sensitivity and specificity in the second and third columns were measured at the point that minimized the Euclidean distance between the ROC curve and the (0,1) point. Sensitivity, presented in the fourth, fifth, and sixth columns, was determined at the threshold that resulted in a specificity of 95%.

[0271] summary All biomarker panel categories investigated showed strong cancer / healthy control discrimination performance. Combining the five protein biomarkers with BMI appears to perform best with average specificity. However, statistically, panels including the five protein biomarkers alone, the five protein biomarkers plus age, the five protein biomarkers plus sex, the five protein biomarkers plus age plus sex, the five protein biomarkers plus age plus sex plus BMI, and the five protein biomarkers plus BMI appear to be comparable.

[0272] It should be noted that the effects of age and sex may have been underestimated in this particular study. Case and control samples are more closely matched in these cohorts than might be expected to occur in either prospectively recruited clinically symptomatic patients or asymptomatic normal-risk CRC screening populations aged 50-74 years. Importantly, however, these results indicate that useful biomarker panels and algorithms can be developed using this five protein biomarker panel, either alone or in combination with a range of additional demographic and / or morphometric parameters.

[0273] It will be appreciated by those skilled in the art that numerous variations and / or modifications may be made to the invention as shown in the specific embodiments without departing from the scope of the invention as broadly described, and the present embodiments are therefore to be considered in all respects as illustrative and not restrictive.

[0274] All publications discussed and / or referenced herein are incorporated herein in their entirety.

[0275] Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present specification is solely for the purpose of providing a context for the present invention and is not to be construed as an admission that any or all of such matters form part of the prior art or were common general knowledge in the field relevant to the present invention as they existed prior to the priority date of each claim of this application.

Claims

1. A method for identifying a subject suspected of having colorectal cancer, the method comprising: determining measurements for a panel of biomarkers in a biological sample obtained from the subject, the panel including at least brain derived neurotropic factor (BDNF) and tumor M2-PK; The method, wherein said measuring comprises measuring the level of each of said biomarkers in said panel.

2. 2. The method of claim 1, further comprising determining a measurement of one or more additional biomarkers selected from the group consisting of DKK3, TGFβ1, IGFBP2, TIMP1, IL6, IL8, TNFα, IGFII, lipocalin, M30, M65, Mac2BP, MMP1, MMP7, MIP1B, and IL13.

3. The method of claim 1 or 2, wherein the biomarkers include M2PK, BDNF, DKK3, and IGFBP2.

4. 3. The method of claim 1 or 2, wherein the biomarkers include M2PK, BDNF, DKK3, TIMP1, and IGFBP2.

5. 2. The method of claim 1, further comprising the subject's age and / or sex and / or body mass index (BMI) as additional biomarkers.

6. determining a measurement for a panel of at least three biomarkers, said panel of biomarkers comprising: i) BDNF, M2PK, DKK-3; ii) BDNF, M2PK, TNFα; iii) BDNF, M2PK, IL-8; iv) BDNF, M2PK, MAC2BP; or v) The method according to claim 1 or 2, comprising or consisting of BDNF, M2PK, IGFBP2. (1) determining measurements for a panel of at least four biomarkers, said panel of biomarkers including at least BDNF, M2PK, IL-8, and an additional biomarker selected from the group consisting of DKK3, TNFα, IGFBP2, TIMP1, MIP1β, MMP7, and IGFII; or, (2) determining a measurement for a panel of at least four biomarkers, wherein the panel of biomarkers is i) BDNF, M2PK, IL8, TNFα; ii) BDNF, M2PK, IL8, MIP1β; iii) BDNF, M2PK, IL8, DKK3; iv) BDNF, M2PK, MMP1, DKK3; v) BDNF, M2PK, IL8, IGFBP2; vi) BDNF, M2PK, IL8, TIMP1; vii) BDNF, M2PK, MMP7, TNFα; viii) BDNF, M2PK, IL8, MMP1; ix) BDNF, M2PK, IGFBP2, TNFα; or x) comprising or consisting of BDNF, M2PK, IL8, IGFII; The method according to claim 1 or 2.

8. determining measurements for a panel of at least five biomarkers, said panel of biomarkers comprising: i) BDNF, M2PK, IL8, TNFα, MIP1β; ii) BDNF, M2PK, IL8, lipocalin, DKK3; iii) BDNF, M2PK, IL8, TGFβ, DKK3; iv) BDNF, M2PK, IL8, TNFα, TGFβ; v) BDNF, M2PK, IGFBP2, TNFα, DKK3; vi) BDNF, M2PK, IL8, MMP7, DKK3; vii) BDNF, M2PK, IL8, TNFα, DKK3; viii) BDNF, M2PK, IL8, IGFBP2, MIP1β; ix) BDNF, M2PK, IL8, MAC2BP, DKK3; or The method of claim 1 or 2, comprising or consisting of x) BDNF, M2PK, IL8, TNFα, MAC2BP.

9. determining measurements for a panel of at least six biomarkers, said panel of biomarkers comprising: i) BDNF, M2PK, MIP1β, DKK3, IGFBP2, TNFα; ii) BDNF, M2PK, MIP1β, DKK3, IGFBP2, MMP1; iii) BDNF, M2PK, MAC2BP, DKK3, IGFBP2, TNFα; iv) BDNF, M2PK, lipocalin, DKK3, IGFBP2, TNFα; v) BDNF, M2PK, IL6, DKK3, IGFBP2, TNFα; vi) BDNF, M2PK, IL8, DKK3, lipocalin, TGFβ; vii) BDNF, M2PK, IL8, MIP1B, TGFβ, TNFα; viii) BDNF, M2PK, IL8, MIP1B, IGFII, TNFα; ix) BDNF, M2PK, IL8, M30, TGFβ, TNFα; or The method of claim 1 or 2, comprising or consisting of x) BDNF, M2PK, IL8, DKK3, IGFII, TNFα.

10. 3. The method of claim 1 or 2, wherein the panel further comprises the age of the subject as an additional biomarker.

11. 10. The method of claim 1, wherein determining a measurement comprises contacting the biological sample with a detectable binding agent that specifically binds to each of the biomarkers.

12. The method of claim 11 , wherein the method comprises detecting specific binding between the specific binding agent and the biomarker using a detection assay.

13. 10. The method of claim 1, wherein determining a measurement comprises measuring a concentration of a biomarker in the biological sample.

14. The method of claim 11 , wherein the binding agent is an antibody.

15. The method of claim 1 , wherein the biological sample is a blood sample.

16. The method of claim 1, further comprising confirming the diagnosis by colonoscopy or sigmoidoscopy.

17. (i) a labeled antibody that binds to the biomarkers in the panel of biomarkers of claim 1; (ii) instructions for carrying out detection of said biomarkers in said panel of biomarkers.

18. 20. The kit of claim 17, further comprising instructions for analysis of the detected biomarkers by a computer-generated algorithm.

19. A composition comprising a labeled antibody that specifically binds to the biomarker in the panel of biomarkers of claim 1.

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