WO2021215986A1

WO2021215986A1 - Cancer biomarkers

Info

Publication number: WO2021215986A1
Application number: PCT/SE2021/050362
Authority: WO
Inventors: Tobias SJÖBLOM; Joakim EKSTRÖM
Original assignee: Sjoeblom Tobias; Ekstroem Joakim
Priority date: 2020-04-21
Filing date: 2021-04-20
Publication date: 2021-10-28

Abstract

A respective amount of OPN, PRL and one to three of GDF15, EGFR, PECAM-1, IL-8, OPG, CEA, Fas, ErbB2, leptin, AFP, IL-6, KLK6, Cyfra21-1, NSE, CA-125, ENG, MSLN, TIMP1, MDK, CA15-3 and CD44 is measured in a body sample taken from a subject and compared with a respective threshold value. The comparison is then used to determine whether the subject is suffering from cancer.

Description

CANCER BIOMARKERS

TECHNICAL FIELD

The present invention generally relates to biomarkers for cancer prediction, and in particular to methods and kits for predicting whether a subject is suffering from cancer.

BACKGROUND

Cancer is a group of diseases involving abnormal cell growth with the potential to invade or spread to other parts of the body. In 2015 over 90 million people had cancer, with an estimated annual number of new cancer patients of about 17 million. Worldwide about 10 million people die every year from various cancer diseases. The survival rate of cancer is highly dependent on an early diagnosis.

Today, most cancers are initially recognized either because of the appearance of signs or symptoms or through screening using, for instance, blood tests, tissue biopsy, X-ray, computed tomography (CT) scans and/or endoscopy.

Several cancer prediction tests have been proposed in the art including COLOGUARD®, which is an invasive multitarget stool DNA test for colorectal cancer that includes quantitative molecular assays for V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations, aberrant N-Myc Downstream-Regulated Gene 4 (NDRG4) and Bone Morphogenetic Protein 3 (BMP3) methylation, and b-actin, plus a hemoglobin immunoassay. The test has a receiver operating characteristic (ROC) area under curve (AUC) of 0.93. The blood-based cancer test on the market with the highest ROC AUC of 0.82 is Epi proColon® for detection of colorectal cancer. Epi proColon® is a qualitative assay for the real-time polymerase chain reaction (PCR) detection of methylated Septin 9 DNA in bisulfite converted DNA. Another liquid biopsy test under development is CancerSEEK that evaluates levels of eight proteins and the presence of mutations in 1933 distinct genomic positions in cell free DNA (cfDNA). CancerSEEK has a preliminary ROC AUC of 0.91. A further test under development is denoted “Stockholm3” that is based on three forms of prostate-specific antigen (PSA), kallikrein-2 and information of family history (U.S. patent application no. 2005/0094221) with a preliminary ROC AUC of 0.84. Another prostate cancer test is the so called Beckman-Coulter Prostate Health Index (BC-phi) that uses three forms of PSA and has a ROC AUC of 0.71.

WO 2019/067092 discloses methods for identifying presence of cancer in a subject. The methods involve detecting the presence of one or more genetic markers in selected genes of circulating tumor DNA (ctDNA) in a first biological sample obtained from the subject. The methods also comprise detecting a level of one or more protein markers in a second biological sample obtained from the subject and comparing the levels to one or more reference levels. The presence of cancer in the subject is then identified when the presence of the one or more genetic biomarkers is detected and when the detected levels of the one or more protein biomarkers are higher than the reference levels.

A biomarker, or biological marker, is a measurable indicator of some biological state or condition. Biomarkers are often measured and evaluated to examine normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.

The various prior art cancer prediction tests are marred by shortcoming. In general, the reliability of the tests is too low. In addition, several tests are too expensive to be used in large scale within the healthcare community. A reason for the high cost is that many of the tests are combined tests of different analysis methods, such as immunoassays for protein analysis and sequencing of cfDNA. Furthermore, for practical reasons liquid biopsies in terms of blood samples are more preferred as compared to feces samples.

There is therefore still a need for a cancer prediction test and method that overcomes at least some of the above mentioned shortcoming of the prior art.

SUMMARY

It is a general objective to provide a cancer prediction test and method that overcomes at least some of the above mentioned shortcoming of the prior art.

This and other objectives are solved by embodiments as disclosed herein.

The present invention is defined in the independent claims. Further embodiments of the invention are defined in the dependent claims.

An aspect of the invention relates to a method of determining whether a subject is suffering from cancer. The method comprises measuring, in a body sample taken from the subject, a respective amount of osteopontin (OPN), prolactin (PRL) and at least one protein but not more than three proteins selected from the group consisting of growth/differentiation factor 15 (GDF15), epidermal growth factor receptor (EGFR), platelet endothelial cell adhesion molecule (PECAM-1), interleukin 8 (IL-8), osteoprotegerin (OPG), carcinoembryonic antigen (CEA), Fas, receptor tyrosine-protein kinase erbB-2 (ErbB2), leptin, alpha-fetoprotein (AFP), interleukin 6 (IL-6), kallikrein-6 (KLK6), Cyfra21-1, neuron specific enolase (NSE), cancer antigen 125 (CA-125), endoglin (ENG), mesothelin (MSLN), TIMP metallopeptidase inhibitor 1 (TIMP1), midkine (MDK), carcinoma antigen 15-3 (CA15-3) and CD44. The method also comprises comparing the respective amount with a respective threshold value and determining whether the subject is suffering from cancer based on the comparison.

Another aspect of the invention relates to a kit for determining whether a subject is suffering from cancer. The kit comprises an antibody specifically binding to OPN and an antibody specifically binding to PRL. The kit also comprises one to three antibodies specifically binding to a respective protein selected from the group consisting of GDF15, EGFR, PECAM-1, IL-8, OPG, CEA, Fas, ErbB2, leptin, AFP, IL-6, KLK6, Cyfra21-1, NSE, CA-125, ENG, MSLN, TIMP1, MDK, CA15-3 and CD44. The kit further comprises data defining a respective threshold value and instructions for measuring, in a body sample taken from the subject, an amount of OPN using the antibody that binds specifically to OPN, an amount of PRL using the antibody that binds specifically to PRL and a respective amount of the one to three proteins selected from the group using the one to three antibodies specifically binding to the respective protein. The kit also comprises instructions for comparing the respective amount with the respective threshold value and determining whether the subject is suffering from cancer based on the comparison.

A further aspect of the invention relates to a computer program comprising instructions, which when executed by at least one processor, cause the at least one processor to compare a respective amount of, as measured in a body sample taken from the subject, osteopontin (OPN), prolactin (PRL) and at least one protein but not more than three proteins selected from the group consisting of growth/differentiation factor 15 (GDF15), epidermal growth factor receptor (EGFR), platelet endothelial cell adhesion molecule (PECAM-1), interleukin 8 (IL-8), osteoprotegerin (OPG), carcinoembryonic antigen (CEA), Fas, receptor tyrosine-protein kinase erbB-2 (ErbB2), leptin, alpha-fetoprotein (AFP), interleukin 6 (IL-6), kallikrein-6 (KLK6), Cyfra21-1, neuron specific enolase (NSE), cancer antigen 125 (CA-125), endoglin (ENG), mesothelin (MSLN), TIMP metallopeptidase inhibitor 1 (TIMP1), midkine (MDK), carcinoma antigen 15-3 (CA15-3) and CD44 with a respective threshold value. The at least one processor is also caused to generate, based on the comparison, information representative of whether the subject is suffering from cancer. A related aspect of the invention defines a computer-readable storage medium comprising a computer program according to above.

The present invention provides biomarkers for identifying subjects suffering from cancer at a high accuracy (ROC AUC) and where such biomarkers are cancer or tumor type specific, i.e., has favorable cross-reactivity relative to cancer or tumor types other than the intended one.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments, together with further objects and advantages thereof, may best be understood by making reference to the following description taken together with the accompanying drawings, in which:

Figs. 1A to 1 D illustrate ROC curves for the protein panels OPN, PRL, GDF-15 and EGFR (1A), OPN, PRL, GDF-15 and PECAM-1 (1 B), OPN, PRL, leptin and OPG (1C) and OPN, PRL, GDF-15 and leptin (1 D) together with AUC for breast cancer (BrC), lung cancer (Lung), ovarian cancer (OvCa), liver cancer (Liver), esophageal cancer (Eso), stomach cancer (Stom), colorectal cancer (CRC) and pancreatic cancer (PanC);

Figs. 2A and 2B illustrate ROC curves for the protein panels OPN, PRL, CEA and Fas (2A), and OPN, PRL, IL-8 and Fas (2B) together with AUC for breast cancer (BrC), lung cancer (Lung), ovarian cancer (OvCa), liver cancer (Liver), esophageal cancer (Eso), stomach cancer (Stom), colorectal cancer (CRC) and pancreatic cancer (PanC);

Figs. 3A to 3C illustrate ROC curves for the protein panels OPN, PRL, GDF-15 and EGFR (3A), OPN, PRL, GDF-15 and ErbB2 (3B) and OPN, PRL, MDK and ErbB2 (3C) together with AUC for breast cancer (BrC), lung cancer (Lung), ovarian cancer (OvCa), liver cancer (Liver), esophageal cancer (Eso), stomach cancer (Stom), colorectal cancer (CRC) and pancreatic cancer (PanC);

Figs. 4A to 4C illustrate ROC curves for the protein panels OPN, PRL, AFP and IL-6 (4A), OPN, PRL, AFP and KLK6 (4B) and OPN, PRL, AFP and Cyfra21-1 (4C) together with AUC for breast cancer (BrC), lung cancer (Lung), ovarian cancer (OvCa), liver cancer (Liver), esophageal cancer (Eso), stomach cancer (Stom), colorectal cancer (CRC) and pancreatic cancer (PanC); Fig. 5 illustrates ROC curves for the protein panel OPN, PRL, GDF-15 and NSE together with AUC for breast cancer (BrC), lung cancer (Lung), ovarian cancer (OvCa), liver cancer (Liver), esophageal cancer (Eso), stomach cancer (Stom), colorectal cancer (CRC) and pancreatic cancer (PanC);

Figs. 6A to 6E illustrate ROC curves for the protein panels OPN, PRL, CA-125 and ENG (6A), OPN, PRL, MSLN and TIMP1 (6B), OPN, PRL, Cyfra21-1 and TIMP1 (6C), OPN, PRL, EGFR and TIMP1 (6D) and OPN, PRL and CA-125 (6E) together with AUC for breast cancer (BrC), lung cancer (Lung), ovarian cancer (OvCa), liver cancer (Liver), esophageal cancer (Eso), stomach cancer (Stom), colorectal cancer (CRC) and pancreatic cancer (PanC);

Figs. 7A to 7D illustrate ROC curves for the protein panels OPN, PRL, CA-125 and ErbB2 (7A), OPN, PRL, GDF-15 and MDK (7B), OPN, PRL, CA15-3 and MDK (7C) and OPN, PRL, MSLN and MDK (7D) together with AUC for breast cancer (BrC), lung cancer (Lung), ovarian cancer (OvCa), liver cancer (Liver), esophageal cancer (Eso), stomach cancer (Stom), colorectal cancer (CRC) and pancreatic cancer (PanC); and

Fig. 8 illustrates ROC curves for the protein panel OPN, PRL, CD44 and GDF-15 together with AUC for breast cancer (BrC), lung cancer (Lung), ovarian cancer (OvCa), liver cancer (Liver), esophageal cancer (Eso), stomach cancer (Stom), colorectal cancer (CRC) and pancreatic cancer (PanC).

Fig. 9 schematically illustrates a computer according to an embodiment.

DETAILED DESCRIPTION

Many prior composite biomarkers are designed to produce a score through a linear combination of input parameters, including constituent proteins. As an example, Stockholm3 defines a score function SCORE = 0.806743 - 0.000112063*AGE + 0.0541963*FAMILYHISTORY + 0.000537xltPSA + 0.0605211 xfPSA - 0.0218285x (IF/TPSA) + 0.624642*hK2, where a score greater than 0.5 produces a positive test result for prostate cancer (AUC 0.836). Another example is Beckman-Coulter Prostate Health Index (BC-phi), which has score function phi = (-2 ^c proPSA / free PSA) ^c PSA^1/2), where a score of phi greater than 55.0 produces a positive test result for prostate cancer (AUC 0.708). Composite biomarkers that produce a score through a function as exemplified above need precise measurements of the constituent proteins throughout the whole range of values that may occur within the tested population. However, analysis of proteins through immunoassay and similar techniques is typically relatively precise in the center of the standard curve, while more uncertain toward extremes of the standard curve. Moreover, measurement observations outside the upper and lower limit of detection constitute a problem.

The present invention has taken a radically different approach by using individual threshold values or cut-offs for each constituent protein. Thus, if it can be established that a measurement observation of a constituent protein is above or below its threshold value or cut-off, then upper and lower limits of detection present no issues. Further, uncertainties in proteomic analysis at the extremes of the standard curve will generally not adversely affect the composite biomarker since the uncertainty is collapsed into the dichotomized levels above the threshold value or cut-off or below the threshold value or cut-off. As a further advantage, the constituent proteins can be analyzed using only one standard for each constituent protein, set to equal their respective threshold values or cut-offs, which hold economic and practical benefits.

Furthermore, proteomic multiplexing is possible for combinations of constituent proteins, such as using antibodies binding specifically to osteopontin (OPN), prolactin (PRL), cancer antigen 125 (CA-125), carcinoma antigen 15-3 (CA 15-3), leptin, interleukin 6 (IL-6), IL-8, carcinoembryonic antigen (CEA), alpha-fetoprotein (AFP), Fas, Cyfra21-1, respectively, which is an additional economic and practical advantage of the present invention. From practical and economic points of view, the composite biomarkers benefit from the fact that all constituent proteins can be analyzed using immunoassay proteomics, such as enzyme-linked immunosorbent assay (ELISA).

Prior art tests are also marred by testing a large number of mutations in genes and/or a comparatively a large number of proteins. For instance, CancerSeek is disclosed in WO 2019/067092, as evaluating levels of ten proteins and mutations in 16 genes in ctDNA. Although, this high number of genes and proteins were investigated the average sensitivity was only about 70 % for eight common cancer types and ranging from 33 to 98 %.

In general, it is desirable from a practical point of view to limit the number of constituent proteins in a composite biomarker to a handful, as opposed to numerous proteins, while maintaining a high diagnostic accuracy. The composite biomarkers of the present invention based on OPN and PRL can achieve high diagnostic accuracy but still contain a limited number of constituent proteins. This was highly surprising given that prior art tests, such as CancerSeek, use a comparatively high number of proteins in addition to mutation analysis in several genes. In fact, the present invention is able to achieve higher ROC AUC as compared to CancerSeek by using a composite biomarker with a lower number of constituent proteins and without any analysis of mutations in ctDNA.

The composite biomarkers of the present invention have favorable cross-reactivity receiver operating characteristic (ROC) curves and area under curve (AUC) values relative to cancer or tumor types other than the one intended, i.e., the composite biomarkers of the present invention are cancer or tumor type specific, which is a property that meets an existing clinical need. While some cancer or tumor types have distinct symptoms, such as for instance skin melanoma, many cancer or tumor types manifest themselves through vague symptoms, for instance, abdominal pain, that are common for many cancer or tumor types. If diagnostic cancer biomarkers are not cancer or tumor type specific, any diagnostic cancer test will tend to produce a positive test result, whether or not the tested individual has developed cancer of the tested cancer or tumor type or a separate cancer or tumor type. With vague symptoms, a positive test result of the wrong cancer or tumor type can lead to many difficulties and intricacies when the confirmatory method of diagnosis, such as endoscopy, subsequently produces a negative test result. Because the composite biomarkers of the present invention are sensitive to their intended cancer tumor type, i.e., a high percentage of individuals with the cancer or tumor type obtain a positive test result, but have limited cross-reactivity relative to other cancer or tumor types, the composite biomarkers will tend to produce a positive cancer test result only for the existing cancer or tumor type, which simplifies confirmatory diagnostic testing and, hence, is highly desirable.

Hence, the biomarkers disclosed herein can be used to identify subjects suffering from cancer in an efficient and reliable manner, using a method and kit at a comparatively low cost as compared to prior art biomarkers.

As used herein “biomarker” or “composite biomarker” denotes a panel of analytes that can be measured in a body sample taken from a subject and compared to a respective threshold value or cutoff in order to determine whether the subject is suffering from cancer, preferably of a particular cancer type. The panel of analytes in such a biomarker or composite biomarkers is also referred herein as “constituent proteins” of that particular biomarker or composite biomarker. Biomarkers can be diagnostic, prognostic and/or predictive biomarkers. Generally, a prognostic biomarker provides information about the subject’s overall cancer outcome, regardless of therapy, whilst a predictive biomarker gives information about the effect of a therapeutic intervention. Diagnostic biomarkers are used to diagnose subjects, i.e., can be used to detect or confirm presence of a disease or condition of interest or to identify subjects with a subtype of the disease.

The present invention is based on the discovery that OPN and PRL can be used together with one to three other proteins selected from the above presented list as a biomarker for determining whether a subject is suffering from cancer by comparing the amounts of the constituent proteins in the biomarker with a respective threshold value and thereby based on whether the amount of constituent proteins exceed or fall below the threshold values.

The method measures the amount of the respective constituent protein in a body sample taken from the subject. In a preferred embodiment, the body sample is a body fluid sample.

In a particular embodiment, the body fluid sample is selected from the group consisting of blood sample, a blood serum sample, a blood plasma sample, a cerebrospinal fluid sample, a peritoneal fluid sample, a pleural fluid sample, an amniotic fluid sample, a lymph sample, a saliva sample, a synovial fluid sample, a pericardial fluid sample, a urine sample, a sputum sample, a bronchioalveolar lavage sample and an otic fluid sample. In a preferred embodiment, the body fluid sample is a blood sample, a blood plasma sample or a blood serum sample. Although, the body sample is preferably a body fluid sample, the embodiments are not limited thereto but can also be applied to measuring the amount of the constituent proteins in a solid sample, such as a tissue sample or biopsy.

In an embodiment, measuring the respective amount of the constituent proteins, i.e., OPN, PRL and one to three proteins selected from the listed group, is performed in an immunoassay, i.e., using antibodies, or antigen-binding fragments thereof, that bind specifically to the respective constituent proteins. Also other proteomic assays could be used to measure the respective amounts of the constituent proteins including, but not limited to, mass spectrometry (MS), nuclear magnetic resonance (NMR), affinity proteomics assays and microarrays.

The specificity of an antibody or an antigen-binding fragment thereof can be determined based on affinity and/or avidity. The affinity, represented by the equilibrium constant for the dissociation of an antigen with the antibody or the antigen-binding fragment thereof (Kd), is a measure for the binding strength between an antigenic determinant and an antigen-binding site on the antibody or the antigenbinding fragment thereof. The lesser the value of Kd, the stronger the binding strength between the antigenic determinant and the antibody or the antigen-binding fragment thereof. Alternatively, the affinity can also be expressed as the affinity constant (Ka), which is 1/Kd. As will be clear to the skilled person, affinity can be determined in a manner known per se, depending on the specific antigen of interest.

Avidity is the measure of the strength of binding between an antibody or an antigen-binding fragment thereof and the pertinent antigen. Avidity is related to both the affinity between an antigenic determinant and its antigen binding site on the antibody or the antigen-binding fragment thereof and the number of pertinent binding sites present on the antibody or the antigen-binding fragment thereof.

Typically, antibodies will bind to their antigen with a dissociation constant (Kd) of 10⁵ to 10-¹² moles/liter (M) or less, and preferably 10 to 10-¹² M or less and more preferably 10⁸ to 10-¹² M, i.e. with an association constant (Ka) of 10⁵ to 10¹² M ¹ or more, and preferably 10⁷ to 10¹² M ¹ or more and more preferably 10⁸ to 10¹² M ¹.

Generally, any Kd value greater than 10 M (or any Ka value lower than 10⁴ M ¹) is considered to indicate non-specific binding. Preferably, an antibody or an antigen-binding fragment thereof will bind to a constituent protein with an affinity less than 500 nM, preferably less than 200 nM, more preferably less than 10 nM, such as less than 5 nM or even lower, such as 1 nM or lower.

Specific binding of an antibody or an antigen-binding fragment thereof to an antigen or antigenic determinant can be determined in any suitable manner known per se, including, for example, Scatchard analysis and/or competitive binding assays, such as radioimmunoassay (RIA), enzyme immunoassays (EIA) and sandwich competition assays, and the different variants thereof known per se in the art.

In an embodiment, the antibody is a monoclonal antibody. In another embodiment, the antibody is a polyclonal antibody. It is also a possible to use a combination of monoclonal and polyclonal antibodies. For instance, the antibody used to measure a first constituent protein of a biomarker is a monoclonal, whereas the antibody used to measure a second constituent protein of the biomarker is a polyclonal antibody.

An antigen-binding fragment of an antibody as used herein can be selected from a group consisting of a single chain antibody, a variable fragment (Fv), a single-chain variable fragment (scFv), an antigenbinding fragment (Fab), a F(ab’)2 fragment, a Fab’ fragment, a Fd fragment, a single-domain antibody (sdAb), a scFv-Fc fragment, a di-scFv fragment and a complementarity-determining region (CDR).

Examples of immunoassays that can be used according to the embodiments include enzyme-linked immunosorbent assay (ELISA), LUMINEX®, SINGULEX® and GYROLAB® immunoassays. For instance, the MILLIPLEX® MAP Human Circulating Cancer Biomarker Magnetic Bead Panels HCCBP1MAG-58K, HCCBP3MAG-58K and/or HCCBP4MAG-58K, MILLIPLEX® MAP Human Cancer/ Metastasis Biomarker Magnetic Bead Panel HCMBMAG-22K, MILLIPLEX® MAP Human Angiogenesis/Growth Factor Magnetic Bead Panel HAGP1 MAG-12K, MILLIPLEX® MAP Human Angiogenesis Panel HANG2MAG-12K, and/or MILLIPLEX® MAP Human TIMP Magnetic Bead Panel HTMP1MAG-54K could be used together with ELISA, LUMINEX®, SINGULEX® or GYROLAB® immunoassays to detect the constituent proteins in biomarker from, for instance, serum or plasma samples. Other examples of immunoassays that can be used according to the embodiments include lateral flow assays (LFA), chemiluminescence assays and electrochemiluminescence assays.

In an embodiment, determining the amount of a constituent protein of a biomarker in the body sample comprises contacting the body sample with the antibody or the antigen-binding fragment thereof. This embodiment also comprises measuring an amount of antibody or antigen-binding fragment thereof bound to the constituent protein.

Contacting the body sample with the antibody or the antigen-binding fragment thereof may be achieved by adding the antibody or the antigen-binding fragment thereof to the body sample and incubating the body sample with the antibody or the antigen-binding fragment thereof. The antibody or the antigenbinding fragment thereof thereby binds to the constituent protein forming a complex between the antibody or the antigen-binding fragment thereof and the constituent protein. In such an embodiment, measuring the amount of antibody or antigen-binding fragment bound to the constituent protein can include measuring or quantifying the complex between the antibody or the antigen-binding fragment thereof and the constituent protein to thereby measure or quantify the amount of antibody or antigenbinding fragment bound to the constituent protein.

In an embodiment, the method also comprises correlating the measured amount of antibody or antigenbinding fragment bound to the constituent protein to an amount of that constituent protein. This may be performed by using a pre-defined correlation between measured amount of antibody or antigen-binding fragment bound to a reference protein and concentration of the reference protein. A typical reference protein that can be used when generating such a pre-defined correlation is recombinant human form of the constituent protein.

The pre-defined correlation may be generated by adding the antibody or the antigen-binding fragment thereof to different samples comprising different concentrations of the reference protein. The amount of antibody or antigen-binding fragment bound to the reference protein is then measured in the different samples to thereby get a standard curve, function or relationship between concentration of reference protein and the measured amount of antibody or antigen-binding fragment bound to the reference protein.

This pre-defined correlation, such as standard curve, function or relationship, can then be used to map or convert the measured amount of antibody or antigen-binding fragment bound to the constituent protein in the body sample to a concentration of the constituent protein in the body sample.

Osteopontin (OPN), also known as bone sialoprotein I (BSP-1 or BNSP), early T-lymphocyte activation (ETA-1), secreted phosphoprotein 1 (SPP1), 2ar and Rickettsia resistance (Ric), is a protein that in humans is encoded by the SPP1 gene (secreted phosphoprotein 1). Prolactin (PRL), also known as luteotropic hormone or luteotropin, is a protein best known for its role in enabling mammals to produce milk.

Growth/differentiation factor 15 (GDF15) was first identified as macrophage inhibitory cytokine-1 (MIC- 1) and is a protein belonging to the transforming growth factor beta superfamily.

Epidermal growth factor receptor (EGFR), also denoted ErbB-1 or FIERI, is a transmembrane protein that is a receptor for members of the epidermal growth factor family (EGF family) of extracellular protein ligands.

Platelet endothelial cell adhesion molecule (PECAM-1), also known as cluster of differentiation 31 (CD31), is a protein that in humans is encoded by the PECAM1 gene and plays a key role in removing aged neutrophils from the body.

Interleukin 8 (IL-8), also referred to as chemokine (C-X-C motif) ligand 8 (CXCL8), is a chemokine produced by macrophages and other cell types such as epithelial cells, airway smooth muscle cells and endothelial cells.

Osteoprotegerin (OPG), also known as osteoclastogenesis inhibitory factor (OCIF) or tumor necrosis factor receptor superfamily member 11 B (TNFRSF11 B), is a cytokine receptor of the tumor necrosis factor (TNF) receptor superfamily encoded by the TNFRSF11B gene.

Carcinoembryonic antigen (CEA) describes a set of highly related glycoproteins involved in cell adhesion.

Fas or FasR, also known as apoptosis antigen 1 (APO-1 or APT), cluster of differentiation 95 (CD95) or tumor necrosis factor receptor superfamily member 6 (TNFRSF6), is a protein that in humans is encoded by the FAS gene.

Receptor tyrosine-protein kinase (ErbB-2), also known as cluster of differentiation 340 (CD340), protooncogene Neu, or ERBB2, is a protein that in humans is encoded by the ERBB2 gene. Leptin is a hormone predominantly made by adipose cells and enterocytes in the small intestine that helps to regulate energy balance by inhibiting hunger, which in turn diminishes fat storage in adipocytes. Alpha-fetoprotein (AFP), also referred to as alpha-1 -fetoprotein, alpha-fetoglobulin, or alpha fetal protein, is a protein that in humans is encoded by the AFP gene.

Interleukin 6 (IL-6) is an interleukin that acts as both a pro-inflammatory cytokine and an antiinflammatory myokine. In humans, it is encoded by the IL6 gene.

Kallikrein-6 (KLK6) is a protein that in humans is encoded by the KLK6 gene.

Cyfra 21-1, also referred to as keratin, type I cytoskeletal 19 or cytokeratin-19 (CK-19) or keratin-19 (K19), is a 40 kDa protein that in humans is encoded by the KRT19 gene.

Neuron specific enolase (NSE), also referred to as gamma-enolase or enolase 2 (EN02), is an enzyme that in humans is encoded by the EN02 gene.

Cancer antigen 125 (CA-125), also referred to as carcinoma antigen 125, carbohydrate antigen 125, mucin 16 (MUC16), is a protein that in humans is encoded by the MUC16 gene.

Endoglin (ENG), also referred to as cluster of differentiation 105 (CD105), END, FLJ41744, HHT 1 , ORW and ORW1, is a type I membrane glycoprotein located on cell surfaces and is part of the TGF beta receptor complex.

Mesothelin (MSLN) is a protein that in humans is encoded by the MSLN gene.

TIMP metallopeptidase inhibitor 1 (TIMP1) is glycoprotein acting as a tissue inhibitor of metalloproteinases.

Midkine (MDK), also known as neurite growth-promoting factor 2 (NEGF2), is a protein that in humans is encoded by the MDK gene. Carcinoma Antigen 15-3 (CA15-3) is derived from MUC1 and is a tumor marker for many types of cancer, most notably breast cancer.

CD44, also referred to as CD44 antigen, is a cell-surface glycoprotein involved in cell-cell interactions, cell adhesion and migration and is encoded in humans by the CD44 gene

In an embodiment, measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and at least two but not more than three proteins selected from the group consisting of GDF15, EGFR, PECAM-1, IL-8, OPG, CEA, Fas, ErbB2, leptin, AFP, IL-6, KLK6, Cyfra21-1, NSE, CA-125, ENG, MSLN, TIMP1, MDK, CA15-3 and CD44. Hence, in this embodiment, the biomarker comprises four or five constituent proteins.

In a particular embodiment, measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and two proteins selected from the group consisting of GDF15, EGFR, PECAM-1, IL-8, OPG, CEA, Fas, ErbB2, leptin, AFP, IL-6, KLK6, Cyfra21-1, NSE, CA-125, ENG, MSLN, TIMP1, MDK, CA15-3 and CD44. Hence, in this particular embodiment, the biomarker comprises four constituent proteins.

In an embodiment, the method also comprises determining the respective threshold value based on a selected or pre-defined trade-off between sensitivity and specificity. Hence, the threshold value used for a constituent protein in the biomarker can be determined based on a selected or pre-defined trade-off between sensitivity, i.e., the proportion of actual positives that are correctly identified as such, and specificity, i.e., the proportion of actual negatives that are correctly identified as such.

In an embodiment, cut-off values can be evaluated at every observed measurement value, which corresponds to evaluating cut-off values at every real number due to an intrinsic property of the ROC curve.

For instance, assume a biomarker comprising OPN, PRL, GDF15 and NSE that is used to determine whether a subject is suffering from lung cancer, see Fig. 5. For a positive determination of lung cancer, each of these protein measurements needs to be simultaneously either elevated relative to the respective cut-off associated with OPN, PRL or GDF-15, or depressed relative to the cut-off associated with NSE. Cut-off values may be evaluated at every observed measurement value. Assume a data set comprising 1817 sample donors as is shown in the Example section. Thus, with four proteins, the total number of sets of cut-offs equal, in this example, 1817⁴ « 10.9x10¹² for this particular biomarker consisting of four proteins. Once all sets of cut-offs have been evaluated for true positive fraction and false positive fraction, a ROC curve is obtained through partial maximization, and thereafter the particular sets of cut-offs that yielded the favorable pairs of true positive fraction (sensitivity) and false positive fraction (specificity) can be obtained by processing the resulting ROC curve. The ROC curve as shown in Fig. 5 comprises multiple steps, wherein each step represents a new partial maximum arising from a pair of true positive fraction (sensitivity) and false positive fraction (specificity) yielded by a set of cut-off values. Hence, every step corresponds to one or more sets of cut-off values. For example, there are 209 distinct sets of cut-off values that yield the pair of true positive fraction and false positive fraction of, respectively, 99.0% (sensitivity) and 33.1% (specificity). In such a case, any of the 209 sets of cut-off values can be used toward a test intended for determination of whether a subject is suffering from lung cancer for a sensitivity of 99.0% and a specificity of 33.1 %.

Table 1 presents examples of cut-off values that give the 10 left-most steps in the ROC curve of Fig. 5. Table 1 - Example of cut-off values

The value for OPN, PRL, GDF15 and NSE are in pg/ml. This concept of determining or selecting threshold or cut-off values based on a selected trade-off between specificity and sensitivity can be applied to any of the biomarkers as disclosed herein.

In an embodiment, the cancer is selected from the group consisting of breast cancer, colorectal cancer, esophageal cancer, liver cancer, lung cancer, ovarian cancer, pancreatic cancer and stomach cancer.

In an embodiment, the cancer is breast cancer. In such an embodiment, measuring the respective amount preferably comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and at least two but not more than three proteins selected from the group consisting of GDF15, EGFR, PECAM-1, leptin and OPG. In a preferred embodiment, measuring the respective amount preferably comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and two proteins selected from the group consisting of GDF15, EGFR, PECAM-1, leptin and OPG

In a particular embodiment, the cancer is breast cancer and measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and GDF15. In an embodiment, the method optionally, but preferably, also comprises measuring, in the body sample taken from the subject, the amount of one or two proteins, preferably one protein, selected from the group consisting of EGFR, PECAM-1 and leptin.

Experimental data as presented herein, see Figs. 1A to 1 D, identifies four biomarkers each with four constituent proteins that are useful in determining whether a subject is suffering from breast cancer. These four biomarkers are OPN, PRL, GDF-15 and EGFR; OPN, PRL, GDF-15 and PECAM-1; OPN, PRL, leptin and OPG; and OPN, PRL, GDF-15 and leptin. Hence, in such an embodiment, measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of i) OPN, PRL, GDF-15 and EGFR; ii) OPN, PRL, GDF-15 and PECAM-1; iii) OPN, PRL, leptin and OPG; and/or iv) OPN, PRL, GDF-15 and leptin.

In an embodiment, determining whether the subject is suffering from cancer comprises determining that the subject is suffering from breast cancer if the amount of OPN is above an OPN threshold value, the amount of PRL is above a PRL threshold value, the amount of GDF-15 is above a GDF-15 threshold value and the amount of EGFR is equal to or below an EGFR threshold value. In another embodiment, determining whether the subject is suffering from cancer comprises determining that the subject is suffering from breast cancer if the amount of OPN is above an OPN threshold value, the amount of PRL is above a PRL threshold value, the amount of GDF-15 is above a GDF-15 threshold value and the amount of PECAM-1 is equal to or below an PECAM-1 threshold value. In a further embodiment, determining whether the subject is suffering from cancer comprises determining that the subject is suffering from breast cancer if the amount of OPN is above an OPN threshold value, the amount of PRL is above a PRL threshold value, the amount of leptin is above a leptin threshold value and the amount of OPG is equal to or below an OPG threshold value. In yet another embodiment, determining whether the subject is suffering from cancer comprises determining that the subject is suffering from breast cancer if the amount of OPN is above an OPN threshold value, the amount of PRL is above a PRL threshold value, the amount of GDF-15 is above a GDF-15 threshold value and the amount of leptin is above a leptin threshold value.

The above described embodiments relating to the four different biomarkers useful in determining or predicting whether a subject is suffering from breast cancer can either be used alone or may be combined. In the latter case, multiple, i.e., at least two, of the biomarkers for breast cancer may be used to determine whether a subject is suffering from breast cancer. However, each of these biomarkers can be used alone and provide sufficient sensitivity and specificity in determining whether a subject is suffering from breast cancer.

In an embodiment, the cancer is colorectal cancer. In such an embodiment, measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and at least two but not more than three proteins selected from the group consisting of CEA, Fas and IL-8. In a particular embodiment, measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and two proteins selected from the group consisting of CEA, Fas and IL-8

In a particular embodiment, the cancer is colorectal cancer and measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and Fas. In an embodiment, the method optionally, but preferably, also comprises measuring, in the body sample taken from the subject, the amount of one or both proteins, preferably one protein, selected from the group consisting of CEA and II-8.

Experimental data as presented herein, see Figs. 2A and 2B, identifies two biomarkers each with four constituent proteins that are useful in determining whether a subject is suffering from colorectal cancer. These two biomarkers are OPN, PRL, CEA and Fas; and OPN, PRL, IL-8 and Fas. Flence, in such an embodiment, measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of i) OPN, PRL, CEA and Fas; and/or ii) OPN, PRL, IL-8 and Fas.

In an embodiment, determining whether the subject is suffering from cancer comprises determining that the subject is suffering from colorectal cancer if the amount of OPN is above an OPN threshold value, the amount of PRL is above a PRL threshold value, the amount of CEA is above a CEA threshold value or the amount of Fas is equal to or below a Fas threshold value. In another embodiment, determining whether the subject is suffering from cancer comprises determining that the subject is suffering from colorectal cancer if the amount of OPN is above an OPN threshold value, the amount of PRL is above a PRL threshold value, the amount of IL-8 is above an IL-8 threshold value or the amount of Fas is equal to or below a Fas threshold value.

The above described embodiments relating to the two different biomarkers useful in determining or predicting whether a subject is suffering from colorectal cancer can either be used alone or may be combined. In the latter case, both of the biomarkers for colorectal cancer may be used to determine whether a subject is suffering from colorectal cancer. However, each of these biomarkers can be used alone and provide sufficient sensitivity and specificity in determining whether a subject is suffering from colorectal cancer.

In an embodiment, the cancer is esophageal cancer. In such an embodiment, measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and at least two but not more than three proteins selected from the group consisting of GDF- 15, EGFR, ErbB2 and MDK. In a particular embodiment, measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and two proteins selected from the group consisting of GDF-15, EGFR, ErbB2 and MDK.

In a particular embodiment, the cancer is esophageal cancer and measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and GDF15. In an embodiment, the method optionally, but preferably, also comprises measuring, in the body sample taken from the subject, the amount of one or both proteins, preferably one protein, selected from the group consisting of EGFR and ErbB2. In another particular embodiment, the cancer is esophageal cancer and measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and ErbB2. In an embodiment, the method optionally, but preferably, also comprises measuring, in the body sample taken from the subject, the amount of one or both proteins, preferably one protein, selected from the group consisting of EGFR and MDK.

Experimental data as presented herein, see Figs. 3A to 3C, identifies three biomarkers each with four constituent proteins that are useful in determining whether a subject is suffering from esophageal cancer. These three biomarkers are OPN, PRL, GDF-15 and EGFR; OPN, PRL, GDF-15 and ErbB2; and OPN, PRL, MDK and ErbB2. Hence, in such an embodiment, measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of i) OPN, PRL, GDF-15 and EGFR; ii) OPN, PRL, GDF-15 and ErbB2; and/or iii) OPN, PRL, MDK and ErbB2.

In an embodiment, determining whether the subject is suffering from cancer comprises determining that the subject is suffering from esophageal cancer if the amount of OPN is above an OPN threshold value, the amount of PRL is above a PRL threshold value, the amount of GDF-15 is above a GDF-15 threshold value and the amount of EGFR is equal to or below an EGFR threshold value. In another embodiment, determining whether the subject is suffering from cancer comprises determining that the subject is suffering from esophageal cancer if the amount of OPN is above an OPN threshold value, the amount of PRL is above a PRL threshold value, the amount of GDF-15 is above a GDF-15 threshold value and the amount of ErbB2 is equal to or below an ErbB2 threshold value. In a further embodiment, determining whether the subject is suffering from cancer comprises determining that the subject is suffering from esophageal cancer if the amount of OPN is above an OPN threshold value, the amount of PRL is above a PRL threshold value, the amount of MDK is above a MDK threshold value or the amount of ErbB2 is equal to or below an ErbB2 threshold value.

The above described embodiments relating to the three different biomarkers useful in determining or predicting whether a subject is suffering from esophageal cancer can either be used alone or may be combined. In the latter case, multiple of the biomarkers for esophageal cancer may be used to determine whether a subject is suffering from esophageal cancer. However, each of these biomarkers can be used alone and provide sufficient sensitivity and specificity in determining whether a subject is suffering from esophageal cancer. In an embodiment, the cancer is liver cancer. In such an embodiment, measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and at least two but not more than three proteins selected from the group consisting of AFP, IL-6, KLK6 and Cyfra21-1. In a particular embodiment, measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and two proteins selected from the group consisting of AFP, IL-6, KLK6 and Cyfra21-1.

In a particular embodiment, the cancer is liver cancer and measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and AFP. In an embodiment, the method optionally, but preferably, also comprises measuring, in the body sample taken from the subject, the amount of one or two proteins, preferably one protein, selected from the group consisting of IL-6, KLK6 and Cyfra21-1.

Experimental data as presented herein, see Figs. 4A to 4C, identifies three biomarkers each with four constituent proteins that are useful in determining whether a subject is suffering from liver cancer. These three biomarkers are OPN, PRL, AFP and IL-6; OPN, PRL, AFP and KLK6; and OPN, PRL, AFP and Cyfra21-1. Hence, in such an embodiment, measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of i) OPN, PRL, AFP and IL-6; ii) OPN, PRL, AFP and KLK6; and/or iii) OPN, PRL, AFP and Cyfra21-1.

In an embodiment, determining whether the subject is suffering from cancer comprises determining that the subject is suffering from liver cancer if the amount of OPN is above an OPN threshold value, the amount of PRL is above a PRL threshold value, the amount of AFP is above an AFP threshold value or the amount of IL-6 is above an IL-6 threshold value. In another embodiment, determining whether the subject is suffering from cancer comprises determining that the subject is suffering from liver cancer if the amount of OPN is above an OPN threshold value, the amount of PRL is above a PRL threshold value, the amount of AFP is above an AFP threshold value or the amount of KLK6 is above an KLK6 threshold value. In a further embodiment, determining whether the subject is suffering from cancer comprises determining that the subject is suffering from liver cancer if the amount of OPN is above an OPN threshold value, the amount of PRL is above a PRL threshold value, the amount of AFP is above an AFP threshold value or the amount of Cyfra21-1 is above a Cfra21-1 threshold value.

The above described embodiments relating to the three different biomarkers useful in determining or predicting whether a subject is suffering from liver cancer can either be used alone or may be combined. In the latter case, multiple of the biomarkers for liver cancer may be used to determine whether a subject is suffering from liver cancer. However, each of these biomarkers can be used alone and provide sufficient sensitivity and specificity in determining whether a subject is suffering from liver cancer.

In an embodiment, the cancer is lung cancer. In such an embodiment, measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL, GDF-15 and NSE.

Experimental data as presented herein, see Fig. 5, identifies a biomarker with four constituent proteins that are useful in determining whether a subject is suffering from lung cancer. This biomarker is OPN, PRL, GDF-15 and NSE. Hence, in such an embodiment, measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL, GDF-15 and NSE.

In an embodiment, determining whether the subject is suffering from cancer comprises determining that the subject is suffering from lung cancer if the amount of OPN is above an OPN threshold value, the amount of PRL is above a PRL threshold value, the amount of GDF-15 is above a GDF-15 threshold value and the amount of NSE is equal to or below a NSE threshold value.

In an embodiment, the cancer is ovarian cancer. In such an embodiment, measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and at least one protein but not more than three proteins, preferably at least two but not more than three proteins, selected from the group consisting of CA-125, ENG, MSLN, TIMP1, Cyfra21- 1 and EGFR. In a particular embodiment, measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and one or two proteins, preferably two proteins, selected from the group consisting of CA-125, ENG, MSLN, TIMP1, Cyfra21-1 and EGFR.

In a particular embodiment, the cancer is ovarian cancer and measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and CA-125. In an embodiment, the method optionally, but preferably, also comprises measuring, in the body sample taken from the subject, the amount of ENG. In another particular embodiment, the cancer is ovarian cancer and measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and TIMP1. In an embodiment, the method optionally, but preferably, also comprises measuring, in the body sample taken from the subject, the amount of one or two proteins, preferably one protein, selected from the group consisting of MSLN, Cyfra21-1 and EGFR.

Experimental data as presented herein, see Figs. 6A to 6E, identifies five biomarkers each with three or four constituent proteins that are useful in determining whether a subject is suffering from ovarian cancer. These five biomarkers are OPN, PRL, CA-125 and ENG; OPN, PRL, MSLN and TIMP1 ; OPN, PRL, Cyfra21-1 and TIMP1; OPN, PRL, EGFR and TIMP1; and OPN, PRL and CA-125. Hence, in such an embodiment, measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of i) OPN, PRL, CA-125 and ENG; ii) OPN, PRL, MSLN and TIMP1; iii) OPN, PRL, Cyfra21-1 and TIMP1; iv) OPN, PRL, EGFR and TIMP1; and/or v) OPN, PRL and CA-125.

In an embodiment, determining whether the subject is suffering from cancer comprises determining that the subject is suffering from ovarian cancer if the amount of OPN is above an OPN threshold value, the amount of PRL is above a PRL threshold value, the amount of CA-125 is above a CA-125 threshold value or the amount of ENG is equal to or below an ENG threshold value. In another embodiment, determining whether the subject is suffering from cancer comprises determining that the subject is suffering from ovarian cancer if the amount of OPN is above an OPN threshold value, the amount of PRL is above a PRL threshold value, the amount of MSLN is above a MSLN threshold value or the amount of TIMP1 is above a TIMPI threshold value. In a further embodiment, determining whether the subject is suffering from cancer comprises determining that the subject is suffering from ovarian cancer if the amount of OPN is above an OPN threshold value, the amount of PRL is above a PRL threshold value, the amount of Cyfra21-1 is above a Cyfra21-1 threshold value or the amount of TIMP1 is above a TIMP1 threshold value. In yet another embodiment, determining whether the subject is suffering from cancer comprises determining that the subject is suffering from ovarian cancer if the amount of OPN is above an OPN threshold value, the amount of PRL is above a PRL threshold value, the amount of EGFR1 is equal to or below an EGFR1 threshold value and the amount of TIMP1 is above a TIMP1 threshold value. In an embodiment, determining whether the subject is suffering from cancer comprises determining that the subject is suffering from ovarian cancer if the amount of OPN is above an OPN threshold value, the amount of PRL is above a PRL threshold value or the amount of CA-125 is above an CA-125 threshold value. The above described embodiments relating to the five different biomarkers useful in determining or predicting whether a subject is suffering from ovarian cancer can either be used alone or may be combined. In the latter case, multiple of the biomarkers for ovarian cancer may be used to determine whether a subject is suffering from ovarian cancer. However, each of these biomarkers can be used alone and provide sufficient sensitivity and specificity in determining whether a subject is suffering from ovarian cancer.

In an embodiment, the cancer is pancreatic cancer. In such an embodiment, measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and at least two but not more than three proteins selected from the group consisting of CA- 125, ErbB2, GDF-15, MDK, CA15-3, and MSLN. In a particular embodiment, measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and two proteins selected from the group consisting of CA-125, ErbB2, GDF-15, MDK, CA15-3, and MSLN.

In a particular embodiment, the cancer is pancreatic cancer and measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and MDK. In an embodiment, the method optionally, but preferably, also comprises measuring, in the body sample taken from the subject, the amount of one or two proteins, preferably two proteins, selected from the group consisting of GDF-15, CA15-3 and MSLN.

Experimental data as presented herein, see Figs. 7A to 7D, identifies four biomarkers each with four constituent proteins that are useful in determining whether a subject is suffering from pancreatic cancer. These four biomarkers are OPN, PRL, CA-125 and ErbB2; OPN, PRL, GDF-15 and MDK; OPN, PRL, CA15-3 and MDK; and OPN, PRL, MSLN and MDK. Hence, in such an embodiment, measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of i) OPN, PRL, CA-125 and ErbB2; ii) OPN, PRL, GDF-15 and MDK; iii) OPN, PRL, CA15-3 and MDK; and/or iv) OPN, PRL, MSLN and MDK.

In an embodiment, determining whether the subject is suffering from cancer comprises determining that the subject is suffering from pancreatic cancer if the amount of OPN is above an OPN threshold value, the amount of PRL is above a PRL threshold value, the amount of CA-125 is above a CA-125 threshold value or the amount of ErbB2 is above an ErbB2 threshold value. In another embodiment, determining whether the subject is suffering from cancer comprises determining that the subject is suffering from pancreatic cancer if the amount of OPN is above an OPN threshold value, the amount of PRL is above a PRL threshold value, the amount of GDF-15 is above a GDF-15 threshold value and the amount of MDK is above a MDK threshold value. In a further embodiment, determining whether the subject is suffering from cancer comprises determining that the subject is suffering from pancreatic cancer if the amount of OPN is above an OPN threshold value, the amount of PRL is above a PRL threshold value, the amount of CA15-3 is above a CA15-3 threshold value and the amount of MDK is above a MDK threshold value. In yet another embodiment, determining whether the subject is suffering from cancer comprises determining that the subject is suffering from pancreatic cancer if the amount of OPN is above an OPN threshold value, the amount of PRL is above a PRL threshold value, the amount of MSLN is equal to or below an MSLN threshold value and the amount of MDK is above a MDK threshold value.

The above described embodiments relating to the four different biomarkers useful in determining or predicting whether a subject is suffering from pancreatic cancer can either be used alone or may be combined. In the latter case, multiple of the biomarkers for pancreatic cancer may be used to determine whether a subject is suffering from pancreatic cancer. However, each of these biomarkers can be used alone and provide sufficient sensitivity and specificity in determining whether a subject is suffering from pancreatic cancer.

In an embodiment, the cancer is stomach cancer. In such an embodiment, measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL, CD44 and GDF-15.

Experimental data as presented herein, see Fig. 8, identifies a biomarker with four constituent proteins that is useful in determining whether a subject is suffering from stomach cancer. This biomarker is OPN, PRL, CD44 and GDF-15. Hence, in such an embodiment, measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL, CD44 and GDF-15.

In an embodiment, determining whether the subject is suffering from cancer comprises determining that the subject is suffering from stomach cancer if the amount of OPN is above an OPN threshold value, the amount of PRL is above a PRL threshold value, the amount of CD44 is equal to or below a CD44 threshold value and the amount of GDF-15 is above a GDF-15 threshold value. In an embodiment, the method of the invention comprises predicting whether a subject is suffering from cancer based on the comparison of the respective amount of the constituent proteins in a biomarker with a respective threshold value. This means that the invention can identify the subjects having a high risk of suffering from cancer, or expressed differently, having a comparatively high likelihood of suffering from cancer. Hence, the invention can be used to identify subjects predicted to have high likelihood of suffering from cancer from other subjects having a comparatively lower likelihood of suffering from cancer.

The method of the present invention may find applicability in the form of a decision support method, i.e., a non-diagnostic method. This means that the decision support method will result in decision support information e.g. as exemplified by respective amounts of the constituent proteins or information of whether the determined amounts are above or below their respective threshold values, which is merely interim results. Additional data and the competence of a physician are typically required for providing a final diagnosis. Thus, the invention gives a decision support upon which a physician can base his/her decision about which measures that should be taken.

The present invention enables diagnosis of cancer patients, and preferably the particular type of cancer the patient may be suffering from. Such identified cancer patients can then be selected for a cancer treatment and/or a surveillance schedule.

Hence, in an embodiment, the method further comprises selecting a cancer treatment for the subject based on the comparison. Thus, an optimal or at least suitable anti-cancer treatment is selected for subjects determined to be suffering from cancer and as identified based on the biomarker(s) of the present invention. The biomarkers of the present invention can also be used to identify the particular cancer type that the subject is likely to suffer from. In such a case, the cancer treatment can be selected based on that particular cancer type. Examples of cancer treatments that can be selected include one or more of surgery, radiation therapy, chemotherapy, targeted therapies, cancer immunotherapy, hormonal therapy, and angiogenesis inhibitor treatment.

In an embodiment, the method further comprises selecting a patient surveillance schedule for the subject based on the comparison. Thus, an optimal or at least suitable patient surveillance schedule or scheme is selected for subject based on the biomarker(s) of the present invention. For instance, further diagnostic investigations can be selected for the subject, optionally based on the particular type of cancer as identified by the biomarker(s) of the present invention. Non-limiting, but illustrative examples, of such further diagnostic investigations include X-rays, computed tomography (CT) scan, biopsy, endoscopy, cytogenetics and immunohistochemistry.

This means that the biomarkers of the present invention can be used in cancer risk stratification when selecting optimal treatment and surveillance schedules for cancer patients.

As disclosed herein, there may be more than one biomarker, i.e., panel of proteins, that can be used to determine whether a subject is likely to suffer from a particular cancer, such as the four biomarker i) OPN, PRL, GDF-15 and EGFR; ii) OPN, PRL, GDF-15 and PECAM-1; iii) OPN, PRL, leptin and OPG; and iv) OPN, PRL, GDF-15 and leptin for breast cancer. The reason being that for different subjects, the cancer may express itself through one or more elevated (or reduced) concentrations among a small group of certain proteins. The existence of distinct expressions of this small group of proteins is that the cancer is pathologically a set of distinct sub-types of cancer that all produce tumors in the same body organ. Therefore, the sub-types of cancer, sub-types of breast cancer in this particular case, can express themselves through different combinations of proteins, such as the four different panels of proteins or biomarkers for four different sub-types of breast cancer.

The present invention can therefore not only be used to determine whether a subject is suffering from a cancer but may also provide an indication of which particular sub-type of that cancer the subject is likely to suffer from dependent on which particular biomarker, i.e., panel of proteins, that indicates that the subject has a high likelihood of suffering from cancer.

The information of which particular sub-type a subject is likely to suffer from as determined according to the invention can be used to select appropriate treatment and/or patient surveillance. For instance, for a first sub-type of breast cancer a first anti-cancer treatment is most appropriate whereas for a second sub-type of breast cancer a second anti-cancer treatment is deemed to be best. Hence, the particular biomarker, i.e., panel of proteins, that indicate that a subject is suffering from a cancer can also be used to define the particular sub-type of cancer and therefore also the most appropriate anti-cancer treatment and/or patient surveillance that is adapted to the particular sub-type of cancer.

In an embodiment, the subject is a human subject or patient. The present invention is advantageously used for human diagnosis and for identifying human subjects suffering from or predicted to be suffering from cancer. The present invention may, however, also be used for veterinary purposes and for determining whether an animal is suffering from cancer. In such a case, the animal is preferably a mammal, such as selected among dog, cat, horse, cow, goat, sheep, mouse, rat and rabbit.

An advantage of the present invention is that the determination of whether a subject is suffering from cancer is made based on measuring a respective amount of three to five proteins of a composite biomarker. Hence, the method of the present invention is preferably based solely on protein measurements. This is a major difference to and advantage over several of the prior art cancer prediction tests that are instead based on mutation analysis alone or in combination with protein measurements. Such mutation analyses are generally more complex and expensive as compared to immunoassays that can be used according to the present invention.

In an embodiment, determining whether the subject is suffering from cancer is performed without any information of any genetic markers or nucleic acid mutations detected in the body sample from the subject. Hence, in this embodiment, the determination whether the subject is suffering from cancer is not based on any information of genetic markers and/or nucleic acid mutations, such as gene mutations, mutations in cfDNA and/or mutations in ctDNA.

Another aspect of the invention relates to a kit for determining whether a subject is suffering from cancer. The kit comprises an antibody specifically binding to osteopontin (OPN) and an antibody specifically binding to prolactin (PRL). The kit also comprises one to three antibodies specifically binding to a respective protein selected from the group consisting of growth/differentiation factor 15 (GDF15), epidermal growth factor receptor (EGFR), platelet endothelial cell adhesion molecule (PECAM-1), interleukin 8 (IL-8), osteoprotegerin (OPG), carcinoembryonic antigen (CEA), Fas, receptor tyrosine-protein kinase erbB-2 (ErbB2), leptin, alpha-fetoprotein (AFP), interleukin 6 (IL-6), kallikrein-6 (KLK6), Cyfra21-1, neuron specific enolase (NSE), cancer antigen 125 (CA-125), endoglin (ENG), mesothelin (MSLN), TIMP metallopeptidase inhibitor 1 (TIMP1), midkine (MDK), carcinoma antigen 15- 3 (CA15-3) and CD44. The kit further comprises data defining a respective threshold value for each of the constituent proteins. The kit comprises instructions for measuring, in a body sample taken from the subject, an amount of OPN using the antibody that binds specifically to OPN, an amount of PRL using the antibody that binds specifically to PRL and a respective amount of the one to three proteins selected from the group using the one to three antibodies specifically binding to the respective protein. The kit also comprises instructions for comparing the respective amount with the respective threshold value and determining whether the subject is suffering from cancer based on the comparison. The kit may be in the form of an ELISA kit, a LUMINEX® kit, a SINGULEX® kit or a GYROLAB® kit as an illustrative, but non-limiting, example. For instance, the HCCBP1MAG-58K, HCCBP3MAG-58K, HCCBP4MAG-58K, HCMBMAG-22K, HAGP1 MAG-12K, HANG2MAG-12K, and/or HTMP1MAG-54K panel could be included in the kit.

Antibodies specifically binding to the proteins in the biomarkers of the present invention are commercially available, such as from the above mentioned magnetic bead panels.

Fig. 9 is a schematic block diagram of a computer 200 comprising a processor 210 and a memory 220 that can be used to determining whether a subject is suffering from cancer according to the embodiment. In such an embodiment, the determination could be implemented in a computer program 240, which is loaded into the memory 220 for execution by processing circuitry including one or more processors 210 of the computer 200. The processor 210 and the memory 220 are interconnected to each other to enable normal software execution. An input and output (I/O) unit 230 is preferably connected to the processor 210 and/or the memory 220 to enable reception of measurement data for the constituent proteins.

The term processor should be interpreted in a general sense as any circuitry, system or device capable of executing program code or computer program instructions to perform a particular processing, determining or computing task. The processing circuitry including one or more processors 210 is, thus, configured to perform, when executing the computer program 240, well-defined processing tasks such as those described herein.

The processor 210 does not have to be dedicated to only execute the above-described steps, functions, procedure and/or blocks, but may also execute other tasks.

In a particular embodiment, the computer program 240 comprises instructions, which when executed by at least one processor 210, cause the at least one processor 210 to compare a respective amount of, as measured in a body sample taken from the subject, osteopontin (OPN), prolactin (PRL) and at least one protein but not more than three proteins selected from the group consisting of growth/differentiation factor 15 (GDF15), epidermal growth factor receptor (EGFR), platelet endothelial cell adhesion molecule (PECAM-1), interleukin 8 (IL-8), osteoprotegerin (OPG), carcinoembryonic antigen (CEA), Fas, receptor tyrosine-protein kinase erbB-2 (ErbB2), leptin, alpha-fetoprotein (AFP), interleukin 6 (IL- 6), kallikrein-6 (KLK6), Cyfra21-1, neuron specific enolase (NSE), cancer antigen 125 (CA-125), endoglin (ENG), mesothelin (MSLN), TIMP metallopeptidase inhibitor 1 (TIMP1), midkine (MDK), carcinoma antigen 15-3 (CA15-3) and CD44 with a respective threshold value. The at least one processor 210 is also caused to generate, based on the comparison, information representative of whether the subject is suffering from cancer.

In an embodiment, the I/O unit 230 is configured to receive input data representing measured amount of antibody or antigen-binding fragment bound to the constituent proteins. In such an embodiment, the at least one processor 210 is caused to correlate the measured amount of antibody or antigen-binding fragment bound to the constituent protein to an amount of that constituent protein. This may be performed by using a pre-defined correlation between measured amount of antibody or antigen-binding fragment bound to a reference protein and concentration of the reference protein stored in the memory 220.

The memory 220 is preferably configured to store respective threshold values. In an embodiment, the memory 220 is configured to store, for a given biomarker of constituent proteins, multiple sets of threshold values for the constituent proteins and where each such set of threshold values is selected or adapted for a selected trade-off between sensitivity and specificity. For instance, the memory 220 could store a first set of threshold valued Tn, T21, T31 and T41 for a biomarker with four constituent proteins and for a first combination of sensitivity and specificity and a second set of threshold values T 12, T22, T32 and T42 for a second combination of sensitivity and specificity, and so on. In such an embodiment, the at least one processor 210 may be configured to select the particular set of threshold values to use based on an input signal as received from the I/O unit 230. Such input signal may then be generated based on a user input where the user or operator of the computer has selected a suitable combination of sensitivity and specificity.

The proposed technology also provides a computer-readable storage medium 250 comprising the computer program 240. By way of example, the software or computer program 240 may be realized as a computer program product, which is normally carried or stored on a computer-readable medium 250, in particular a non-volatile medium. The computer-readable medium 250 may include one or more removable or non-removable memory devices including, but not limited to a Read-Only Memory (ROM), a Random Access Memory (RAM), a Compact Disc (CD), a Digital Versatile Disc (DVD), a Blu-ray disc, a Universal Serial Bus (USB) memory, a Hard Disk Drive (HDD) storage device, a flash memory, a magnetic tape, or any other conventional memory device. The computer program 240 may, thus, be loaded into the operating memory 220 of the computer for execution by the processor 210 thereof. EXAMPLES

Publicly available data were downloaded from website https://sciencemaq.org (DOI: 10.1126/science.aar3247). The downloaded data comprised protein measurements from plasma samples from 1817 sample donors. Of the 1817 sample donors, 812 were healthy, 318 had colorectal cancer, 209 breast cancer, 104 lung cancer, 93 pancreatic cancer, 68 stomach cancer, 54 ovarian cancer, 45 esophageal cancer, and 44 liver cancer. The cancers constituted a distribution of stages I through III, and the healthy sample donors and the cancer sample donors had comparable distributions of age and gender.

The data included protein measurements of 39 proteins: AFP (Uniprot ID P02771), Angiopoietin-2 (015123), AXL (P30530), CA-125 (Q8WXI7), CA15-3 (P15941), CA19-9 (P21217), CD44 (P16070), CEA (P06731), CYFRA21-1 (P08727), DKK1 (094907), Endoglin (P17813), FGF2 (P09038), Follistatin (P19883), Galectin-3 (P17931), G-CSF (P09919), GDF15 (Q99988), HE4 (Q14508), HGF (P14210), IL- 6 (P05231), IL-8 (P10145), Kallikrein-6 (Q92876), Leptin (P41159), Mesothelin (Q13421), Midkine (P21741), Myeloperoxidase (P05164), NSE (P09104), OPG (000300), OPN (P10451), PAR (Q03405), Prolactin (P01236), sEGFR (P00533), sFas (P25445), SHBG (P04278), sHER2/sEGFR2/sErbB2 (P04626), sPECAM-1 (P16284), TGFa (P01135), Thrombospondin-2 (P35442), TIMP-1 (P01033), and TIMP-2 (P16035). Protein measurements were collected using LUMINEX®-200™ immunoassay microsphere technology with reagents from Millipore through panels HCCBP1MAG-58K, HCCBP3MAG-58K, HCCBP4MAG-58K, HCMBMAG-22K, HAGP1 MAG-12K, HANG2MAG-12K, and HTMP1MAG-54K.

The data was processed into 9 comma-separated data text files. The first comma-separated data text file contained all protein measurements across all sample donors, i.e., healthy sample donors and all cancer sample donors. The second comma-separated data text file contained all protein measurements across the healthy donors and all colorectal cancer sample donors. The third through ninth comma- separated data text file contained all protein measurements across the healthy donors and, respectively, the breast, lung, pancreatic, stomach, ovarian, liver and esophageal cancer sample donors.

Firstly, Receiver Operating Characteristic (ROC) computation was performed for all unitary biomarkers, i.e., non-proper composite biomarkers that each possesses only one constituent protein. The number of unitary biomarkers equals the number of proteins, i.e., 39. For each cut-off value, the number of sample donors with cancer who had measurement values above the cut-off were counted and registered as a true positive count, and the number of sample donors without cancer who had measurement values above the cut-off were counted and registered as a false positive count. By dividing the true positive count by the total number of sample donors with cancer, a true positive fraction was obtained, and by dividing the false positive count by the total number of sample donors without cancer, a false positive fraction was obtained. The pair of true positive fraction and false positive fraction constituted a ROC point, and by computing the ROC points for all cut-offs a ROC plot or curve was obtained. In more detail, the ROC curve was obtained from the ROC points by constructing the function that had value equal to the greatest true positive fraction of the subset of ROC points that had false positive fraction less than or equal to the function argument; hence a partial maximization procedure. Because it is conceivable that some proteins are related to cancer so that the protein concentration on average is lower for individuals with cancer than for individuals without cancer, ROC points were also computed such that for each sample donor with cancer who had measurement values below the cut-off were counted and registered as a true positive count, and sample donors without cancer who had measurement values below the cut-off were counted and registered as a false positive count; thus yielding additional ROC points. That is, ROC points were computed both when sample donors with elevated protein concentration values were viewed as test positives, and when sample donors with reduced protein concentration values were viewed as test positives.

Secondly, ROC computation was performed for all composite biomarkers with two constituent proteins. The number of composite biomarkers with two constituent proteins equaled 741. For each composite biomarker, the number of pairs of cut-off values equaled the product of the number of cut-off values for the two constituent proteins. As an example, the composite biomarker with constituent proteins AFP and Angiopoietin-2 had 1,303,666 pairs of cut-off values. For each pair of cut-off values, eight classification regions were evaluated: (1) both proteins above their respective cut-off values, (2) both proteins below their cut-off values, (3) the first protein above its cut-off value and the other protein below its cut-off value, (4) the first protein below its cut-off value and the other protein above its cut-off value, (5) one or two of the two proteins above their cut-off value, (6) one or two of the proteins below their cut-off value, (7) the first protein above its cut-off value and/or the other below its cut-off value, (8) the first protein below its cut-off value and/or the other above its cut-off value. Flence, for the composite biomarker with constituent proteins AFP and Angiopoietin-2, to continue the example, 10,429,328 classification regions were evaluated for true positive fraction and false positive fraction. The 10,429,328 pairs of true positive fraction and false positive fraction represent an identical number of ROC points, from which a ROC curve was obtained by partial maximization as discussed for ROC computation for unitary biomarkers.

Thirdly, ROC computation was performed for all composite biomarkers with three constituent proteins. The number of composite biomarkers with three constituent proteins equaled 9,139. For each such composite biomarker, triples of cut-off values were formed. For each biomarker, the number of triples of cut-off values equaled the product of the numbers of cut-off values for each of the three constituent proteins. As an example, the composite biomarker with constituent proteins AFP, Angiopoietin-2 and AXL had 2,295,755,826 triples of cut-off values. For each triple, 16 classification regions were evaluated for true positive fraction and false positive fraction. In the example with the composite biomarker with constituent proteins AFP, Angiopoietin-2 and AXL, 36,732,093,216 ROC points were thus obtained, from which a ROC curve was obtained by partial maximization as discussed above.

Fourthly and fifthly, ROC computation was performed for all composite biomarkers with four and five constituent proteins, respectively. The number of composite biomarkers with four and five constituent proteins equaled 82,251 and 575,751 respectively. For each such composite biomarker, n-tuples of four and five cut-off values were formed, respectively. For each n-tuple, the number of classification regions evaluated was 32 and 64, respectively. For each composite biomarker, all ROC points were computed analogously to the discussions above, and for each composite biomarker ROC curves were obtained by partial maximization as previously discussed.

The ROC computation was repeated for all nine data files, yielding ROC curves for the composite biomarkers of one through five constituent proteins toward one or all of the eight tumor types.

For each ROC curve obtained, Area Under Curve (AUC) and partial Area Under Curve (pAUC) in the interval of false positive fractions from 0 to 0.2 were computed. It is known in the field that the AUC values summarize, in a numeric value, the highness of the ROC curves, and thus a higher AUC value represents a higher ROC curve, on average, which is desirable. Further, a high pAUC value represents a ROC curve that is high in the part of the ROC curve that is most relevant to a diagnostic screening application.

When evaluating all computed ROC curves and AUC values, the biomarkers of the present invention, distinguished themselves through their high AUC value performance toward their respective tumor types. By ranking all composite biomarkers by AUC value, the top biomarkers were selected, including the biomarkers of this invention, and for each selected composite biomarker, the classification regions that produced the ROC points along the ROC curves were recorded. In practice, the recording was conducted by recounting all classification regions, and comparing the counts with the previously obtained ROC curves to determine whether the classification regions produced a ROC point that attained the ROC curve, and then recorded the classification region accordingly. Using those recorded classification regions, the performance of the selected composite biomarkers toward other tumor types were evaluated by counting the number of sample donors of other tumor types that were in those classification regions. In this way, a biomarker cross-reactivity analysis was achieved. By maximizing cross-tumor type counts relative to the counts of healthy sample donors, cross-reactivity ROC curves and associated AUC values were obtained.

Ideally, a composite biomarker should have high AUC value for its intended tumor type, while having a relatively low AUC value for other tumor types. This means that such a composite biomarker should be highly sensitive for the intended tumor type while not producing positive test results for tumor types beyond that or those intended.

The biomarkers of this invention had particularly favorable cross-reactivity ROC curves and AUC values. Seemingly, OPN and PRL provided a strong foundational level of diagnostic accuracy towards all eight tumor types, and by augmenting the composite biomarker with the additional proteins as per the present invention, improved diagnostic accuracy toward a specific tumor type is achieved while reducing cross-reactivity AUC.

Figs. 1A to 1 D illustrate ROC curves for the protein panels OPN, PRL, GDF-15 and EGFR (1A), OPN, PRL, GDF-15 and PECAM-1 (1 B), OPN, PRL, leptin and OPG (1C) and OPN, PRL, GDF-15 and leptin (1 D) together with AUC for breast cancer (BrC), lung cancer (Lung), ovarian cancer (OvCa), liver cancer (Liver), esophageal cancer (Eso), stomach cancer (Stom), colorectal cancer (CRC) and pancreatic cancer (PanC).

Figs. 2A and 2B illustrate ROC curves for the protein panels OPN, PRL, CEA and Fas (2A), and OPN, PRL, IL-8 and Fas (2B) together with AUC for breast cancer (BrC), lung cancer (Lung), ovarian cancer (OvCa), liver cancer (Liver), esophageal cancer (Eso), stomach cancer (Stom), colorectal cancer (CRC) and pancreatic cancer (PanC). Figs. 3A to 3C illustrate ROC curves for the protein panels OPN, PRL, GDF-15 and EGFR (3A), OPN, PRL, GDF-15 and ErbB2 (3B) and OPN, PRL, MDK and ErbB2 (30) together with AUC for breast cancer (BrC), lung cancer (Lung), ovarian cancer (OvCa), liver cancer (Liver), esophageal cancer (Eso), stomach cancer (Stom), colorectal cancer (CRC) and pancreatic cancer (PanC).

Figs. 4A to 40 illustrate ROC curves for the protein panels OPN, PRL, AFP and IL-6 (4A), OPN, PRL, AFP and KLK6 (4B) and OPN, PRL, AFP and Cyfra21-1 (4C) together with AUC for breast cancer (BrC), lung cancer (Lung), ovarian cancer (OvCa), liver cancer (Liver), esophageal cancer (Eso), stomach cancer (Stom), colorectal cancer (CRC) and pancreatic cancer (PanC).

Fig. 5 illustrates ROC curves for the protein panel OPN, PRL, GDF-15 and NSE together with AUC for breast cancer (BrC), lung cancer (Lung), ovarian cancer (OvCa), liver cancer (Liver), esophageal cancer (Eso), stomach cancer (Stom), colorectal cancer (CRC) and pancreatic cancer (PanC).

Figs. 6A to 6E illustrate ROC curves for the protein panels OPN, PRL, CA-125 and ENG (6A), OPN, PRL, MSLN and TIMP1 (6B), OPN, PRL, Cyfra21-1 and TIMP1 (6C), OPN, PRL, EGFR and TIMP1 (6D) and OPN, PRL and CA-125 (6E) together with AUC for breast cancer (BrC), lung cancer (Lung), ovarian cancer (OvCa), liver cancer (Liver), esophageal cancer (Eso), stomach cancer (Stom), colorectal cancer (CRC) and pancreatic cancer (PanC).

Figs. 7A to 7D illustrate ROC curves for the protein panels OPN, PRL, CA-125 and ErbB2 (7A), OPN, PRL, GDF-15 and MDK (7B), OPN, PRL, CA15-3 and MDK (7C) and OPN, PRL, MSLN and MDK (7D) together with AUC for breast cancer (BrC), lung cancer (Lung), ovarian cancer (OvCa), liver cancer (Liver), esophageal cancer (Eso), stomach cancer (Stom), colorectal cancer (CRC) and pancreatic cancer (PanC).

A representative example of a sequentially enlarged composite biomarkers, constructed as per the present application, is discussed in the following using the lung cancer biomarker of Figure 5. Individually, OPN and PRL have AUC values 0.796 and 0.900 respectively, which is insufficient for most common applications. As a pair, OPN and PRL have AUC value 0.947 which is considered sufficient for most common applications. However, since OPN and PRL constitute a common denominator for composite biomarkers used to detect each of breast cancer, lung cancer, ovarian cancer, liver cancer, esophageal cancer, stomach cancer, colorectal cancer and pancreatic cancer, this composite biomarker possesses no intrinsic tumor type specificity. Hence, a biomarker consisting of only OPN and PRL has a high AUC value but low tumor type specificity.

When the composite biomarker of OPN and PRL is enlarged with NSE, the AUC value yielded is 0.955, which is a notable increment, and when the composite biomarker is further enlarged with GDF-15, into the composite biomarker of Figure 5, the AUC value yielded is 0.973. As discussed in this application, the composite biomarker of Figure 5 possesses both a highly desirable diagnostic accuracy while also achieving a desirable tumor type specificity. When the composite biomarker of Figure 5 is further sequentially enlarged, the AUC value increment is relatively insignificant; the highest AUC values obtainable are yielded when the aforementioned composite biomarker is sequentially enlarged with Galectin-3 and then further with DKK1, yielding AUC values 0.976 and 0.979. Hence, the composite biomarkers of the present invention as designed to give high sensitivity, i.e., high AUC values, and can, in addition, be designed to have specificity, i.e., tumor type specificity. Increasing the number of proteins in the composite biomarkers of the invention do not significantly improve the specificity or sensitivity of the biomarkers but increases the complexity in using the biomarkers and determining whether a subject is suffering from cancer.

REFERENCES

1. Imperiale et al., Multitarget Stool DNA Testing for Colorectal-Cancer Screening, The New England Journal of Medicine 2014, 370: 1287-1297

2. U.S. Patent No. 5,741,650

Claims

1. A method of determining whether a subject is suffering from cancer, the method comprising: measuring, in a body sample taken from the subject, a respective amount of osteopontin (OPN), prolactin (PRL) and at least one protein but not more than three proteins selected from the group consisting of growth/differentiation factor 15 (GDF15), epidermal growth factor receptor (EGFR), platelet endothelial cell adhesion molecule (PECAM-1), interleukin 8 (IL-8), osteoprotegerin (OPG), carcinoembryonic antigen (CEA), Fas, receptor tyrosine-protein kinase erbB-2 (ErbB2), leptin, alpha- fetoprotein (AFP), interleukin 6 (IL-6), kallikrein-6 (KLK6), Cyfra21-1, neuron specific enolase (NSE), cancer antigen 125 (CA-125), endoglin (ENG), mesothelin (MSLN), TIMP metallopeptidase inhibitor 1 (TIMP1), midkine (MDK), carcinoma antigen 15-3 (CA15-3) and CD44; comparing the respective amount with a respective threshold value; and determining whether the subject is suffering from cancer based on the comparison.

2. The method according to claim 1, wherein measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and at least two but not more than three proteins selected from the group consisting of GDF15, EGFR, PECAM-1, IL-8, OPG, CEA, Fas, ErbB2, leptin, AFP, IL-6, KLK6, Cyfra21-1, NSE, CA-125, ENG, MSLN, TIMP1, MDK, CA15-3 and CD44.

3. The method according to claim 2, wherein measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and two proteins selected from the group consisting of GDF15, EGFR, PECAM-1, IL-8, OPG, CEA, Fas, ErbB2, leptin, AFP, IL-6, KLK6, Cyfra21-1, NSE, CA-125, ENG, MSLN, TIMP1, MDK, CA15-3 and CD44.

4. The method according to any of the claims 1 to 3, wherein the body sample is a body fluid sample taken from the subject, preferably selected from the group consisting of a blood sample, a blood serum sample, a cerebrospinal fluid sample, a peritoneal fluid sample, a pleural fluid sample, a urine sample, a sputum sample, a bronchioalveolar lavage sample and an amniotic fluid sample, more preferably selected from the group consisting of a blood sample, a blood plasma sample and a blood serum sample.

5. The method according to any of the claims 1 to 4, further comprising determining the respective threshold value based on a selected trade-off between sensitivity and specificity.

6. The method according to any of the claims 1 to 5, wherein the cancer is selected from the group consisting of breast cancer, colorectal cancer, esophageal cancer, liver cancer, lung cancer, ovarian cancer, pancreatic cancer and stomach cancer.

7. The method according to claim 6, wherein the cancer is breast cancer; and measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and at least two but not more than three proteins, preferably two proteins, selected from the group consisting of GDF15, EGFR, PECAM-1, leptin and OPG.

8. The method according to claim 7, wherein measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of

OPN, PRL, GDF-15 and EGFR; OPN, PRL, GDF-15 and PECAM-1 ;

OPN, PRL, leptin and OPG; and/or OPN, PRL, GDF-15 and leptin.

9. The method according to claim 6, wherein the cancer is colorectal cancer; and measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and at least two but not more than three proteins, preferably two proteins, selected from the group consisting of CEA, Fas and IL-8.

10. The method according to claim 9, wherein measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of

OPN, PRL, CEA and Fas; and/or OPN, PRL, IL-8 and Fas.

11. The method according to claim 6, wherein the cancer is esophageal cancer; and measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and at least two but not more than three proteins, preferably two proteins, selected from the group consisting of GDF-15, EGFR, ErbB2 and MDK.

12. The method according to claim 11, wherein measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of

OPN, PRL, GDF-15 and EGFR;

OPN, PRL, GDF-15 and ErbB2; and/or OPN, PRL, MDK and ErbB2.

13. The method according to claim 6, wherein the cancer is liver cancer; and measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and at least two but not more than three proteins, preferably two proteins, selected from the group consisting of AFP, IL-6, KLK6 and Cyfra21 -1.

14. The method according to claim 13, wherein measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of

OPN, PRL, AFP and IL-6;

OPN, PRL, AFP and KLK6; and/or OPN, PRL, AFP and Cyfra21-1.

15. The method according to claim 6, wherein the cancer is lung cancer; and measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL, GDF-15 and NSE.

16. The method according to claim 6, wherein the cancer is ovarian cancer; and measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and at least one protein but not more than three proteins, preferably at least two but not more than three proteins, and more preferably two proteins, selected from the group consisting of CA-125, ENG, MSLN, TIMP1, Cyfra21-1 and EGFR.

17. The method according to claim 16, wherein measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of

OPN, PRL, CA-125 and ENG;

OPN, PRL, MSLN and TIMP1; OPN, PRL, Cyfra21-1 and TIMP1;

OPN, PRL, EGFR and TIMP1; and/or OPN, PRL and CA-125.

18. The method according to claim 6, wherein the cancer is pancreatic cancer; and measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL and at least two but not more than three proteins, preferably two proteins, selected from the group consisting of CA-125, ErbB2, GDF-15, MDK, CA15-3, and MSLN.

19. The method according to claim 18, wherein measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of

OPN, PRL, CA-125 and ErbB2; OPN, PRL, GDF-15 and MDK;

OPN, PRL, CA15-3 and MDK; and/or OPN, PRL, MSLN and MDK.

20. The method according to claim 6, wherein the cancer is stomach cancer; and measuring the respective amount comprises measuring, in the body sample taken from the subject, the respective amount of OPN, PRL, CD44 and GDF-15.

21. The method according to any of the claims 1 to 20, wherein determining whether the subject is suffering from cancer comprises determining whether the subject is suffering from cancer based on the comparison but not based on any nucleic acid mutations, such as gene mutations, mutations in cell free deoxyribonucleic acid (cfDNA) and/or mutations in circulating tumor DNA (ctDNA), detected in a body sample from the subject.

22. The method according to any of the claims 1 to 21, further comprising selecting a cancer treatment for the subject based on the comparison.

23. The method according to any of the claims 1 to 22, further comprising selecting a patient surveillance schedule for the subject based on the comparison.

24. A kit for determining whether a subject is suffering from cancer, the kit comprises: an antibody specifically binding to osteopontin (OPN); an antibody specifically binding to prolactin (PRL); one to three antibodies specifically binding to a respective protein selected from the group consisting of growth/differentiation factor 15 (GDF15), epidermal growth factor receptor (EGFR), platelet endothelial cell adhesion molecule (PECAM-1), interleukin 8 (IL-8), osteoprotegerin (OPG), carcinoembryonic antigen (CEA), Fas, receptor tyrosine-protein kinase erbB-2 (ErbB2), leptin, alpha- fetoprotein (AFP), interleukin 6 (IL-6), kallikrein-6 (KLK6), Cyfra21-1, neuron specific enolase (NSE), cancer antigen 125 (CA-125), endoglin (ENG), mesothelin (MSLN), TIMP metallopeptidase inhibitor 1 (TIMP1), midkine (MDK), carcinoma antigen 15-3 (CA15-3) and CD44; data defining a respective threshold value; and instructions for measuring, in a body sample taken from the subject, an amount of OPN using the antibody that binds specifically to OPN, an amount of PRL using the antibody that binds specifically to PRL and a respective amount of the one to three proteins selected from the group using the one to three antibodies specifically binding to the respective protein; comparing the respective amount with the respective threshold value; and determining whether the subject is suffering from cancer based on the comparison.

25. A computer program (240) comprising instructions, which when executed by at least one processor (210), cause the at least one processor to (210): compare a respective amount of, as measured in a body sample taken from the subject, osteopontin (OPN), prolactin (PRL) and at least one protein but not more than three proteins selected from the group consisting of growth/differentiation factor 15 (GDF15), epidermal growth factor receptor (EGFR), platelet endothelial cell adhesion molecule (PECAM-1), interleukin 8 (IL-8), osteoprotegerin (OPG), carcinoembryonic antigen (CEA), Fas, receptor tyrosine-protein kinase erbB-2 (ErbB2), leptin, alpha-fetoprotein (AFP), interleukin 6 (IL-6), kallikrein-6 (KLK6), Cyfra21-1, neuron specific enolase (NSE), cancer antigen 125 (CA-125), endoglin (ENG), mesothelin (MSLN), TIMP metallopeptidase inhibitor 1 (TIMP1), midkine (MDK), carcinoma antigen 15-3 (CA15-3) and CD44 with a respective threshold value; and generate, based on the comparison, information representative of whether the subject is suffering from cancer.

26. A computer-readable storage medium (250) comprising a computer program according to claim 25.