WO2012100339A1

WO2012100339A1 - Methods and compositions for the detection of pancreatic cancer

Info

Publication number: WO2012100339A1
Application number: PCT/CA2012/000078
Authority: WO
Inventors: Eleftherios P. Diamandis; Shalini MAKAWITA
Original assignee: University Health Network
Priority date: 2011-01-27
Filing date: 2012-01-27
Publication date: 2012-08-02

Abstract

The disclosure includes a method of screening for, diagnosing or detecting pancreatic cancer in a subject, the method comprising: a. determining a level of one or more biomarkers in a sample from the subject, wherein the biomarker(s) is/are selected from the proteins listed in any one of Tables 1 to 4, 7-13, and b. comparing the level of each biomarker in the sample with a control; wherein an increased level of any one of the biomarkers compared to the control is indicative that the subject has pancreatic cancer.

Description

Title: Methods and Compositions for the Detection of Pancreatic Cancer

Field of the Disclosure

The disclosure relates to pancreatic cancer biomarkers and specifically to methods, compositions and kits for detecting serum pancreatic cancer biomarkers.

Background of the Disclosure

Pancreatic cancer is the 4^th leading cause of cancer-related death and one of the most highly aggressive and lethal of all solid malignancies [1]. Worldwide, over 200,000 individuals are diagnosed with pancreatic cancer each year, and due to the asymptomatic nature of its early stages, coupled with inadequate methods for early detection, the majority of patients (>75%) present with locally advanced and inoperable forms of the cancer at the time of diagnosis [1]. At these advanced stages, available chemotherapy, radiation and combinatorial therapies are largely anecdotal, and less than 5% of patients survive up to five-years post diagnosis [1 ,2].

Methods relating to screening for pancreatic cancer are described for example in, WO2004/031412, WO2008/063479 and WO2008/064670.

One way to aid in the clinical management of cancer patients is through the use of serum biomarkers. Biomarkers are measurable indicators of a biological state or condition, and in the context of cancer, serum biomarkers present a non-invasive and relatively cost effective means to aid in detection, monitor tumor progression and response to therapy, and for other measurable outcomes of disease [3,4]. The most widely used biomarker in the clinic for pancreatic cancer is carbohydrate antigen 19.9 (CA19.9), a sialylated Lewis A antigen found on the surface of proteins [5,6]. While CA19.9 is elevated in late stage disease, it is also elevated in benign and inflammatory diseases of the pancreas and in other malignancies of the gastrointestinal tract [7]. As well, for early-stage pancreatic cancer detection, CA- 19.9 has a reported sensitivity of ~55% and is often undetectable in many asymptomatic individuals [5,8]. Other tumor markers such as members of the carcinoembryonic antigen (CEA) [9, 10] and mucin (MUC) [1 1 , 12] families have also been associated with pancreatic cancer. When used in combination, with or without CA-19.9, some of these markers have shown enhanced sensitivity and specificity; however none have become a constant fixture in the clinic. The lack of a single highly specific and sensitive marker has led to a growing consensus in the field towards the development of multiparametric panels of biomarkers, where-by the combinatorial assessment of multiple molecules car! likely achieve increased sensitivity and specificity for disease detection and management [13-15].

Protein-based biomarkers that can be detected in circulation are typically proteins that are secreted, shed or cleaved from tumor cells, or ones that may leak out due to local tissue destruction during disease progression [4], As such, biological fluids in close proximity to tumor cells likely serve as enriched sources of potential biomarkers before they enter circulation and become vastly diluted and potentially masked by proteins of high abundance [16-19]. With respect to pancreatic cancer, proteomic analysis of biological fluids such as pancreatic juice, cyst fluids and bile have been conducted [20-27]. Protein numbers ranging from 22 to 170 have been identified in six pancreatic juice studies using a variety of different MS-based approaches [20-25], as well as over 460 proteins recently identified in a cyst fluid study [26], and 127 proteins in the bile proteome from patients with bile duct stenosis [27]; subsequent verification of candidate biomarkers in serum or plasma has been minimal.

Tissue culture supernatants or conditioned media (CM) is another relevant fluid, the utility of which, for the identification of novel biomarkers, has been demonstrated in multiple cancer sites [28-35]. For pancreatic cancer, Gronborg et al. analyzed differential protein secretion between the CM of a pancreatic cancer cell line in comparison to a normal ductal epithelial cell line and identified 195 proteins, of which 145 showed >1.5 fold-change [36], and Mauri et al. had identified 46 proteins from the supernatant of a pancreatic cancer cell line (SUIT2) [37]. In a more recent study, Wu et al. performed proteomic analysis of 23 cell lines from 1 1 cancer sites, of which two cell lines were of pancreatic cancer origin [33], This group utilized the Human Protein Atlas database and the absence of proteins in other cancer cell lines to delineate candidate biomarkers for the various cancer sites. More recently, another group analyzed the conditioned media of 5 pancreatic cell lines to identify deregulated pathways [38]. What is lacking in the field is integrative analysis and mining of the proteomes from different biological sources pertaining to a disease type for biomarker discovery. The utility of using an integrative approach to biomarker discovery has been described recently [16, 19]. Given that cancer is a highly heterogeneous disease, through integration and comparison of proteomes from multiple biological sample types, the advantages of one source may account for the shortcomings of others, resulting in more relevant and stronger candidates for verification in plasma.

Summary of the Disclosure

An aspect of the disclosure includes a method of screening for, diagnosing or detecting pancreatic cancer in a subject, the method comprising:

a) determining a level of one or more biomarkers in a sample from the subject, wherein the biomarker(s) is/are selected from the proteins listed in Table 4, and

b) comparing the level of each biomarker in the sample with a control; wherein an increased level of any one of the biomarkers compared to the control is indicative that the subject has pancreatic cancer.

Another aspect of the disclosure includes a method of monitoring response to treatment comprising:

a) determining a base-line level of one or more biomarkers in a sample from the subject, wherein the biomarker(s) is/are selected from the proteins listed in Table 4;

b) determining a level of the one or more biomarkers in a post- treatment sample from the subject; and

c) comparing the level of each biomarker in the post-treatment sample with the base-line level;

wherein an increase in the biomarker level in the post-treatment sample compared to the baseline level is indicative the subject is not responding or is responding poorly to treatment, and a decrease in the biomarker level in the post treatment sample compared to the base-line level is indicative that the subject is responding to treatment.

A further aspect includes a method of monitoring disease progression comprising:

a) determining a base-line level of one or more biomarkers in a sample from the subject, wherein the biomarker(s) is/are selected from the proteins listed in Table 4; b) - -determining a level of the one or more biomarkers in a sample taken subsequent to the base-line sample from the subject; and

c) comparing the level of each biomarker in the subsequent sample with the base-line level;

wherein an increase in the biomarker level in the subsequent sample compared to the base-line level is indicative the disease is progressing, and a decrease in the biomarker level in the subsequent sample compared to the base-line level is indicative that the disease is not progressing.

In another embodiment, the one or biomarkers are selected from and/or consist of REG1 B, LOXL2, AGR2, OLFM4, PIGR, SYCN and COL6A1.

In another embodiment, the one or biomarkers are selected from and/or consist of REG1 B, LOXL2, AGR2, PIGR and SYCN.

In an embodiment, the one or more biomarkers is selected from Table 7. In yet another embodiment, the one or more biomarkers is selected from Table 8. In an embodiment, the one or biomarkers further comprise a biomarker selected from any one of Tables 1 to 3.

In an embodiment, the method further comprises determining a level of CA19.9 in a sample from the subject, and comparing the level of CA 9.9 to a control, wherein said level of CA19.9 is increased compared to the control.

In an embodiment, the method comprises determining the level of a combination of biomarkers, wherein the combination is selected from Table 1 or 12.

In an embodiment, the combination comprises CA19.9, REG1 B and SYCN.

In an embodiment, the method is for screening for, diagnosing or detecting early stage pancreatic cancer in a subject, and/or monitoring response to treatment or monitoring disease progression in a subject with early stage pancreatic cancer. In an embodiment, the one or more markers for screening for, diagnosing or detecting early stage pancreatic cancer in a subject, and/or monitoring treatment efficacy or disease progression in a subject with early stage pancreatic cancer is a combination of biomarkers, the combination for example selected from the combination of biomarkers listed in Table 3. · In an embodiment, the biomarker level determined is a soluble or serum biomarker level.

Also included are immunoassays, compositions and kits for use with the

methods described herein.

Other features and advantages of the present disclosure will become

apparent from the following detailed description. It should be understood, however,

that the detailed description and the specific examples while indicating preferred

embodiments of the disclosure are given by way of illustration only, since various

changes and modifications within the spirit and scope of the disclosure will become

apparent to those skilled in the art from this detailed description.

Brief description of the drawings

An embodiment of the present disclosure will now be described in relation to

the drawings in which: Figure 1. Total non-redundant proteins identified. (A) Venn diagrams depicting

total proteins identified with >2 peptides in the three replicates of each cell line

conditioned media (CM) and pancreatic juice pool. (B) Overlap of 3479 total non- redundant proteins identified in the conditioned media and pancreatic juice analysis

is also depicted. HPDE, (normal) human pancreatic ductal epithelial cell line.

• Figure 2. Hierarchical clustering analysis in the seven cell lines and two

pancreatic juice pools based on normalized emPAI values for the 3479 total

non-redundant proteins identified. Clustering analysis using Pearson and Ward's

minimum variance methods for distance and aggregation depicting the seven cell

lines and two pancreatic juice pools on the x-axis and proteins on the y-axis. Shown

is a segment of the resulting dendrogram depicting two clusters of proteins (their

corresponding gene names and the z-score values provided in the table) found

highly expressed in the cancer cell lines and pancreatic juice (low expression in the

normal HPDE cell line), emPAI, exponentially modified protein abundance index; EC,

extracellular; CS, cell surface. Figure 3. Verification of (A) Anterior Gradient Homolog 2 (AGR2), (B) Olfactomedin-4 (OLF 4), (C) Syncollin (SYCN), (D) Collagen alpha-l(VI) chain (COL6A1), and (E) Polymeric Immunoglobulin Receptor (PIGR) in plasma from pancreatic cancer patients and healthy controls of similar age and sex. Plasma concentrations of the proteins were measured through ELISA. Mean values are indicated by a horizontal line and p-values were calculated using the Mann-Whitney U-test. Twenty cases were analyzed for pancreatic cancer and 20 for healthy controls.

Figure 4. Receiver Operating Characteristic (ROC) curve analysis for CA19.9 and candidates and scatter plot of CA19.9 and AGR2. Area under curve (AUC) is given at 95% confidence intervals. (A) AUC of CA19.9 (i), AGR2 (ii), OLFM4 (iii), SYCN (iv), COL6A1 (v) and PIGR (vi) is depicted individually. CA19.9 performs best individually in this sample set of 20 cancer and 20 controls (AUC of 0.97). (B) The combination of SYCN (i), OLFM4 (ii), COL6A1 (iii) and PIGR (iv) with CA19.9 shows improved AUC to CA19.9 alone. The ROC curve model for AGR2 and CA19.9 produced a complete separation of cases from controls in this sample set and was not modeled. Instead, the combination of AGR2 and CA19.9 is depicted in the log- transformed scatter plot showing complete separation of cases (circles) from controls (crosses) (v) in comparison to the separation of cases from controls for CA19.9 alone (vi). Note that two samples which had been missed by CA19.9 had elevated levels of AGR2. (C) The combination of AGR2, OLFM4, SYCN, COL6A1 and PIGR (5 biomarkers) shows an improvement to the AUC of CA19.9 alone. All markers together with CA19.9 demonstrate an AUC of 1.0 and the ability to perfectly discriminate between pancreatic cancer and controls in this sample set.

Figure 5. Boxplots of the distribution of markers as a factor of the sample group (Gallinger (plasma) or Haun (serum)) and disease status (pancreatic cancer or healthy control). Concentrations were truncated to a maximum value, dependent on the marker, to effectively show the distribution. A significant difference between the plasma and serum samples was seen in healthy controls for CA19.9, AGR2 and PIGR, and for all markers except AGR2 OIFM4 and Col6A1 between plasma and serum samples in pancreatic cancer patients. Figure 6. Marker distribution across sample groups. Box-plots of concentration of candidates (ug/L for SYCN, AGR2, REG1 B, LOXL2 and PIGR, and units/mL for CA19.9) in healthy ( 1 ), pancreatic cancer (PDAC, 2), benign diseases (pancreatic and other gastrointestinal adenomas, 3), pancreatitis (4), Intraductal papillary mucinous neoplasms (5), other gastrointestinal malignancies (6) and pancreatic endocrine tumors (7) are shown. Concentrations were truncated to a maximum value, dependent on the marker, to effectively show the distribution.

Figure 7. Receiver operating characteristic (ROC) curves of each marker to assess performance in discriminating between pancreatic cancer cases (N=183) and healthy control (N=165). AUC with 95% confidence intervals of curves are provided in Table 10.

Figure 8. Distribution of markers in healthy and stage-specific pancreatic cancer samples are shown. Markers with high concentrations were capped to a maximum value. Corresponding marker characteristics, significance tests and AUCs are presented in Table 13 for early stage (stage l/l I) pancreatic cancer and healthy controls.

Detailed description of the Disclosure

I. Abbreviations

[0001] CA19.9, carbohydrate antigen 19.9; CEA, carcinoembryonic antigen; MUC, mucin; CM, conditioned media; 2D-LC-MS/MS, two dimensional liquid chromatography tandem mass spectrometry; AGR2, anterior gradient homolog 2; PIGR, Polymeric immunoglobulin receptor; OLFM4, Olfactomedin-4; SYCN, Syncollin; COL6A1 , Collagen alpha-l (VI) chain; REG1 B, Regenerating islet-derived 1 beta; LOXL2, lysyl oxidase-like 2; CDCHO, Chinese hamster ovary serum-free medium; SCX, strong cation exchange; LTQ, linear ion trap; IPI, international protein index; FDR, false discovery rate; GO, gene ontology; emPAI, exponentially modified protein abundance index; IPA, Ingenuity Pathway Analysis; ANOVA, analysis of variance; ROC, receiver operating characteristic; AUC, area under curve; KEGG, Kyoto Encyclopedia of Genes and Genomes; HCA, hierarchical clustering analysis; KLK, kallikrein; RNASE1 , pancreatic ribonuclease; CV, coefficient of variation; TiSGeD, Tissue-Specific Genes Database; TiGER, Tissue-specific and Gene Expression and Regulation; HE4, WAP four-disulfide core domain protein 2 precursor; PSA, prostate specific antigen; CA125, carbohydrate antigen 125.

II. Definitions

[0002] The term "pancreatic cancer" as used herein includes exocrine pancreatic tumors such as pancreatic ductal adenocarcinomas as well as early-stage and late-stage pancreatic cancers.

[0003] The phrase "screening for, diagnosing or detecting pancreatic cancer" refers to a method or process that aids in the determination of whether a subject has or does not have pancreatic cancer involving detecting the level of one or more biomarkers described herein. For example, detection of increased levels of biomarker(s) selected from Table 4, and/or for example of REG1 B, LOXL2, AGR2, , PIGR, and/or SYCN , or any combination thereof, alone or in combination with CA19.9 compared to a control is indicative that the subject has pancreatic cancer or has an increased likelihood of having pancreatic cancer. For example, further tests, such as biopsies and scans may be warranted in subjects with an increased level of one or more biomarkers in Table 4.

[0004] The term "subject" as used herein refers to any member of the animal kingdom, preferably a human being including for example a subject that has or is suspected of having pancreatic cancer.

[0005] The term "level" as used herein refers to an amount (e.g. relative amount or concentration) of biomarker that is detectable or measurable in a sample. For example, the level can be a concentration such as pg/L or a relative amount such as 1 .2, 1 .3, 1.4, 1.5, 1.6, 1 .7, 1 .8, 1.9, 2.0, 2.2, 2.4, 2.6, 2.8, 3.0, 3.2, 3.4, 3.6, 3.8, 4.0, 4.2, 4.4, 4.6, 4.8, 5.0, 10, 15, 20, 25, 30, 40, 60, 80 and/or 100 times a control level, where for example, the control level is the level such as the average or median level in a normal sample (e.g. serum from a subject without pancreatic cancer). The level of biomarker can be, for example, the level of soluble (e.g. cleaved, secreted, released, or shed biomarker) biomarker.

[0006] The term "cut-off level" as used herein refers to a value corresponding to a level of a biomarker in a sample above which a subject is likely to have pancreatic cancer for a particular specificity and sensitivity and which is used for determining if a subject has or does not have pancreatic cancer. For example, the cut-off level can be the- highest value associated with a panel of controls (e.g. 100% specificity). In a further example, the cut-off level can be a relative amount of a biomarker in comparison to a control, such as 1.2, 1.3, 1 .4, 1.5, 1.6, 1 .7, 1.8, 1 .9, 2.0, 2.1 , 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3.0, 3.1 , 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5, 10, 15, 20, and 40 times a control level.

[0007] The term "specificity" as used herein refers to the percentage of subjects without pancreatic cancer that are identified as not having pancreatic cancer based on a biomarker level that is, for example, at or below a control level and/or a cut-off level.

[0008] The term "sensitivity" as used herein refers to the percentage of subjects with pancreatic cancer that are identified as having pancreatic cancer based on a biomarker level that is, for example, above a control level and/or a cut-off level.

[0009] The term "control" as used herein refers to a sample from an individual or a group of individuals who are known as not having pancreatic cancer or to a biomarker level or value, such as a cut-off value corresponding to such a sample, wherein subjects with a biomarker level at or below such value are likely to belong to a pancreatic cancer free class and subjects with a biomarker level above such value have or are likely to have pancreatic cancer. In another example, the control can be a value that corresponds to the median level of the biomarker in a set of samples from subjects without pancreatic cancer. In addition, the control is for example derived from tissue of the same type as the sample of the subject being tested. For example, both the control and the sample (e.g. test sample) can comprise plasma or serum.

[0010] The term "baseline level" as used herein refers to a level that is used for comparison to a sample taken at a later time point. For example, in methods related to monitoring response to treatment or disease progression, "base-line level" can refer to a level of a biomarker in a sample taken prior to a subsequent sample, e.g. base-line sample is taken before treatment, comparison to which provides an indication of response to treatment. [0011] The term "biomarker" as used herein refers to a polypeptide antigen, such as an expression product or fragment thereof, of a gene listed in any one of Tables 1 to 4, also referred to as "biomarkers of the disclosure", the level of which can be used to distinguish subjects with or without pancreatic cancer or likely to be with or without pancreatic cancer. For example, REG1 B, LOXL2, AGR2, PIGR, and SYCN and/or any combination thereof are biomarkers whose levels can distinguish subjects with or without pancreatic cancer or which have a greater likelihood of having pancreatic cancer. The term "biomarker" can also include CA19.9 when referring to combinations of biomarkers. The term polypeptide biomarker includes without limitation, soluble biomarkers and/or serum biomarkers.

The term "biomarker specific detection agent" refers to an agent that selectively binds its cognate biomarker compared to another molecule and which can be used to detect a level and/or the presence of the biomarker. Similarly, an antibody or fragment (e.g. binding fragment) thereof that specifically binds a biomarker refers to an antibody or fragment that selectively binds its cognate biomarker compared to another molecule. "Selective" is used contextually, to characterize the binding properties of an antibody. An antibody that binds specifically or selectively to a given biomarker or epitope thereof will bind to that biomarker and/or epitope either with greater avidity or with more specificity, relative to other, different molecules. For example, the antibody can bind 3-5 fold, 5-7 fold, 7-10, 10-15, 5-15, or 5-30 fold more efficiently to its cognate biomarker compared to another molecule.

[0012] The term "polypeptide biomarker" refers to a polypeptide expression product and/or fragment thereof of a biomarker of the present disclosure and includes polypeptides translated from the RNA transcripts of biomarkers described herein. Polypeptide biomarkers include for example soluble biomarkers such as secreted, cleaved, released, and/or shed polypeptide products and serum biomarkers which are soluble biomarkers present in blood or blood fractions.

[0013] The terms "polypeptide" and "protein" are intended to be used interchangeably.

[0014] The term "soluble biomarker" as used herein refers to a biomarker, that is detectable in a biological fluid, such as blood, serum, plasma, pancreatic juice, cyst fluid, biological fluid in close proximity to tumor cells and/or in a fraction thereof. The soluble biomarker can for example be a shed polypeptide or a carbohydrate antigen as in the case of CA19.9. For example, without wishing to be bound to theory, a soluble biomarker can be cleaved, secreted, or shed from a cell, e.g. a tumour cell. Such antigens can become elevated, for example in biological fluid such as serum, through several possible mechanisms. Molecules may be released into the circulation through aberrant shedding and secretion from tumour cells or through destruction of tissue architecture and angiogenesis as the tumour invades. Proteins and other molecules can also be cleaved from the extracellular surface of tumour cells by proteases and subsequently make their way into the circulation. To this end, it is hypothesized that novel candidate biomarkers can be identified through extensive proteomic analysis of (a) supernatants of human cancer cell lines grown in vitro and/or (b) relevant biological fluids collected from cancer patients. Due to the close proximity of these fluids to tumor cells, it is hypothesized that they are highly enriched sources of proteins secreted, shed, or cleaved from the tumor cells.

[0015] The term "serum biomarker" as used herein refers to a soluble biomarker detectable in blood or a blood fraction such as plasma and or serum.

[0016] The term "sample" as used herein refers to any biological fluid, cell or tissue sample from a subject (e.g. test subject), which can be assayed for biomarkers (e.g. carbohydrate antigen, and/or polypeptide expression products), such as soluble biomarkers. For example the sample is or can comprise blood, or a fraction thereof such as serum or plasma, pancreatic juice, cyst fluid, or a biological fluid in close proximity to tumor cells. The sample can for example comprise pancreas tissue such as a biopsy, including a needle biopsy, a brush biopsy and/or a laparoscopic biopsy. The sample can for example be a "post-treatment" sample wherein the sample is obtained after one or more treatments, or a "base-line sample" which is for example used as a base line for assessing disease progression.

[0017] The term "biological fluid" as used herein refers to any body fluid, which can comprise cells or be substantially cell free, which can be assayed for biomarkers, including for example blood, serum, plasma, pancreatic juice, cyst fluid, or biological fluid in close proximity to tumor. For example, the fluid may be a non- invasively obtained biological fluid, e.g. such as serum/plasma. [0018] The term "antibody" as used herein is intended to include monoclonal antibodies, polyclonal antibodies, and chimeric antibodies. The antibody may be from recombinant sources and/or produced in transgenic animals. Antibodies can be fragmented using conventional techniques. For example, F(ab')2 fragments can be generated by treating the antibody with pepsin. The resulting F(ab')2 fragment can be treated to reduce disulfide bridges to produce Fab' fragments. Papain digestion can lead to the formation of Fab fragments. Fab, Fab' and F(ab')2, scFv, dsFv, ds- scFv, dimers, minibodies, diabodies, bispecific antibody fragments and other fragments can also be synthesized by recombinant techniques. Antibody fragments mean binding fragments.

[0019] Antibodies having specificity for a specific protein, such as the protein product of a biomarker of the disclosure, may be prepared by conventional methods. A mammal, (e.g. a mouse, hamster, or rabbit) can be immunized with an immunogenic form of the peptide which elicits an antibody response in the mammal. Techniques for conferring immunogenicity on a peptide include conjugation to carriers or other techniques well known in the art. For example, the peptide can be administered in the presence of adjuvant. The progress of immunization can be monitored by detection of antibody titers in plasma or serum. Standard ELISA or other immunoassay procedures can be used with the immunogen as antigen to assess the levels of antibodies. Following immunization, antisera can be obtained and, if desired, polyclonal antibodies isolated from the sera.

[0020] To produce monoclonal antibodies, antibody producing cells (lymphocytes) can be harvested from an immunized animal and fused with myeloma cells by standard somatic cell fusion procedures thus immortalizing these cells and yielding hybridoma cells. Such techniques are well known in the art, (e.g. the hybridoma technique originally developed by Kohler and Milstein (Nature 256:495- 497 (1975)) as well as other techniques such as the human B-cell hybridoma technique (Kozbor et al., Immunol. Today 4:72 (1983)), the EBV-hybridoma technique to produce human monoclonal antibodies (Cole et al., Methods Enzymol, 121 140-67 (1986)), and screening of combinatorial antibody libraries (Huse et al. , Science 246: 1275 (1989)). Hybridoma cells can be screened immunochemically for production of antibodies specifically reactive with the peptide and the monoclonal antibodies can be isolated.

[0021] The term "detection agent" as used herein refers to any molecule or compound that can bind to a biomarker product described herein, including polypeptides such as antibodies, nucleic acids and peptide mimetics. For example, a suitable antibody for detecting the level of a biomarker that is a transmembrane protein includes an antibody that binds an extracellular portion of the protein. The "detection agent" can for example be coupled to or labeled with a detectable marker. The label is preferably capable of producing, either directly or indirectly, a detectable signal. For example, the label may be radio-opaque or a radioisotope, such as ³H, ¹⁴C, ³²P, ³⁵S, ¹²³l, ¹²⁵l, ³¹l; a fluorescent (fluorophore) or chemiluminescent (chromophore) compound, such as fluorescein isothiocyanate, rhodamine or luciferin; an enzyme, such as alkaline phosphatase, beta-galactosidase or horseradish peroxidase; an imaging agent; or a metal ion.

[0022] The term "AGR2" means Anterior Gradient Homolog 2 and includes without limitation, all known AGR2 molecules, including naturally occurring variants, and including those deposited in Genbank, and/or for example, with Unigene accession number NP_006399.1 and/or International Protein Indexh accession number IPI00007427 each of which is herein incorporated by reference.

[0023] The term "OLFM4" means Olfactomedin-4 and includes without limitation, all known OLFM4 molecules, including naturally occurring variants, and including those deposited in Genbank, and/or for example, with Unigene accession number NP 006409.3 and/or International Protein Index accession number IPI00022255, each of which is herein incorporated by reference. Other names/symbols for OLFM4 known in the art include GC1 , GW1 12 and OlfD (Hugo Gene Nomenclature Committee Gene Symbol Report).

[0024] The term "PIGR" means Polymeric Immunoglobulin Receptor and includes without limitation, all known PIGR molecules, including naturally occurring variants, and including those deposited in Genbank, and/or for example, with Unigene accession number NP 002635.2 and/or International Protein Index accession number IPI00004573, each of which is herein incorporated by reference. [0025] The term "SYCN" means Syncollin and includes without limitation, all known SYCN molecules, including naturally occurring variants, and including those deposited in Genbank, and/or for example, with Unigene accession number NP 1 10413.2 and/or International Protein Index accession number IPI00397717, each of which is herein incorporated by reference. Other names/symbols known in the art for SYCN include INSSA1 , insulin synthesis associated 1 FLJ27441 and SYL (Hugo Gene Nomenclature Committee Gene Symbol Report).

[0026] The term "COL6A1 " means Collagen alpha-1 (VI) chain or collagen, type VI, alpha 1 and includes without limitation, all known COL6A1 molecules, including naturally occurring variants, and including those deposited in Genbank, or for example, with Unigene accession number NP_006498.1 or International Protein Index accession number I PI00291 136, each of which is herein incorporated by reference.

[0027] The term "REG1 B" means regenerating islet-derived 1 beta and includes without limitation, all known REG1 B molecules, including naturally occurring variants, and including those deposited in Genbank, or for example with Unigene accession number NP 006498.1 and/or International Protein Index accession number IPI00009197, each of which is herein incorporated by reference Other names/symbols for REG1 B known in the art include lithostathine-1-beta precursor, lithostathine 1 beta, PSPS2, REGH, REGI-BETA, REGL and secretory pancreatic stone protein 2 (Hugo Gene Nomenclature Committee Gene Symbol Report).

[0028] The term "LOXL2" means lysyl oxidase-like 2 and includes without limitation, all known LOXL2 molecules, including naturally occurring variants, and including those deposited in Genbank, or for example with Unigene accession number NP 002309.1 and/or International Protein Index IPI00294839, each of which is herein incorporated by reference. Other names for LOXL2 known in the art include WS9-14(Hugo Gene Nomenclature Committee Gene Symbol Report)..

[0029] The term "CA19.9" means carbohydrate antigen 19.9, a sialylated Lewis A antigen (e.g. a sialylated lacto-N-Fucopentaose II molecule) found on the surface of proteins such as mucin glycoproteins. [0030] The term "internal normalization control" or "internal' control" as used herein means a non-biomarker normalization control such as a polypeptide that is present in the sample being assayed, for example a house keeping gene protein, such as beta-actin, glyceraldehyde-3-phosphate dehydrogenase, or beta-tubulin, or total protein, which is relatively constant between subjects for a given volume and can be used to adjust for assay or technical differences between samples.

[0031] In understanding the scope of the present disclosure, the term "comprising" and its derivatives, as used herein, are intended to be open ended terms that specify the presence of the stated features, elements, components, groups, integers, and/or steps, but do not exclude the presence of other unstated features, elements, components, groups, integers and/or steps. The foregoing also applies to words having similar meanings such as the terms, "including", "having" and their derivatives. Finally, terms of degree such as "substantially", "about" and "approximately" as used herein mean a reasonable amount of deviation of the modified term such that the end result is not significantly changed. These terms of degree should be construed as including a deviation of at least ±5% of the modified term if this deviation would not negate the meaning of the word it modifies.

[0032] In understanding the scope of the present disclosure, the term "consisting" and its derivatives, as used herein, are intended to be close ended terms that specify the presence of stated features, elements, components, groups, integers, and/or steps, and also exclude the presence of other unstated features, elements, components, groups, integers and/or steps.

[0033] The recitation of numerical ranges by endpoints herein includes all numbers and fractions subsumed within that range (e.g. 1 to 5 includes 1 , 1.5, 2, 2.75, 3, 3.90, 4, and 5). It is also to be understood that all numbers and fractions thereof are presumed to be modified by the term "about." Further, it is to be understood that "a," "an," and "the" include plural referents unless the content clearly dictates otherwise. The term "about" means plus or minus 0.1 to 50%, 5-50%, or 10- 40%, preferably 10-20%, more preferably 10% or 15%, of the number to which reference is being made.

[0034] Further, the definitions and embodiments described in particular sections are intended to be applicable to other embodiments herein described for which they are suitable as would be understood by a person skilled in the art. For example, in the following passages, different aspects of the invention are defined in more detail. Each aspect so defined may be combined with any other aspect or aspects unless clearly indicated to the contrary. In particular, any feature indicated as being preferred or advantageous may be combined with any other feature or features indicated as being preferred or advantageous.

III. Methods

[0035] Disclosed herein is a proteomic analysis of cell line conditioned media and pancreatic juice. An in-depth shotgun proteomic analyses, integrating and comparing the proteomes of conditioned media from pancreatic cancer cell lines, as well as pancreatic juice samples was performed to identify novel pancreatic cancer biomarkers. Seven cell lines and six pancreatic juice samples in two pools were analyzed in triplicate for a total of 27 experiments using 2D-LC-MS/MS. Through label-free protein quantification between the cancer and normal cell lines and integration of the pancreatic juice proteome with that of the cell lines, candidate biomarkers were delineated for verification in plasma. Within these candidates were numerous proteins known to be upregulated in pancreatic cancer, and proteins previously studied in serum as pancreatic cancer biomarkers, which helps to provide credence to this approach. Of the derived candidates, verification in plasma and/or serum samples from patients with established pancreatic cancer and controls was conducted for Regenerating islet-derived 1 beta (REG1 B), lysyl oxidase-like 2 (LOXL2), Anterior Gradient Homolog 2 (AGR2), Polymeric Immunoglobulin Receptor (PIGR), Olfactomedin-4 (OLFM4), Syncollin (SYCN) and Collagen alpha-l (VI) chain (COL6A1 ). Combinations with CA 9.9 were also assessed.

[0036] The biomarkers are listed in Tables 1 , 2, 3, and 4 as well as subsets in Table 7 and 8 and combinations in Tables 1 1 and 12. Table 1 provides a list candidate biomarkers identified by performing label-free protein quantification between cancer cell lines and the normal HPDE cell line, and selecting for extracellular and cell surface proteins with at least a 5-fold increase in at least three pancreatic cancer cell lines in comparison to HPDE, as described under "Label-free Protein Quantification" in the Materials and Methods section. Table 2 provides candidate biomarkers generated by selecting for extracellular and cell surface proteins common to multiple biological fluids (i.e. proteins common to cancer cell lines, cancer pancreatic juice and the proteome of ascites fluid from 3 patients with pancreatic adenocarcinoma) and which were not identified in the HPDE normal cell line. Table 3 provides candidate biomarkers selected based on their selective or strong expression in the pancreas and Table 4 provides a selected subset of Table 1 , 2 and 3 biomarkers. Table 4 does not include for example proteins found in high abundance in serum/plasma of healthy individuals. Also not included in Table 4 are acute-phase reactant proteins as levels of these proteins fluctuate in serum/plasma due to a variety of conditions/inflammation. Table 7 provides data for a subset of markers, COL6A1 , OLFM4, CA19.9, AGR2, SYCN, REG1 B, LOXL2 and PIGR and Table 8 provides data for CA19.9, AGR2, SYCN, REG1 B, LOXL2 and PIGR, in serum and blood separately. Table 9 provides validation data for Table 7 biomarkers using combined serum and blood data. Table 10 provides data to assess the correlation between AGR2, SYCN, REG1 B, LOXL2 and PIGR and CA19.9. Table 1 1 looks at combinations of biomarkers (with and without CA 9.9) and Table 12 lists a subset of the combinations in Table 12. Table 13 provides data for COL6A1 , OLFM4, AGR2, SYCN, REG1 B, LOXL2, PIGR and CA19.9 according to disease stage.

[0037] Accordingly, the present disclosure discloses for example methods for screening for, diagnosing and/or detecting pancreatic cancer, screening for the need of follow up pancreas cancer screening, monitoring response to treatment and monitoring disease progression using biomarkers, which are differentially present, including differentially expressed, secreted, released or shed (e.g. soluble and/or serum biomarkers) between individuals having or not having pancreatic cancer. In an embodiment, the one or more biomarkers comprises one or more proteins listed in any one of Tables 1 to 4 and/or 7 to 13. In an embodiment, the one or more biomarkers is/are selected from the proteins listed in Table 4.

[0038] An aspect of the disclosure includes a method of screening for, diagnosing and/or detecting pancreatic cancer in a subject, the method comprising: a. determining a level of one or more biomarkers in a sample from the subject, wherein the one or more biomarkers are selected from the biomarkers listed in Table 4, and

b. comparing the level of each biomarker in the sample with a control; wherein an increased level of any one of the one or more biomarkers compared to the control is indicative that the subject has pancreatic cancer and/or an increased likelihood of pancreas cancer.

[0039] An increased level of any one of the one or more biomarkers is indicative for example of an increased likelihood of pancreas cancer and therefore a need of follow up testing.

[0040] Accordingly in another aspect, the disclosure includes a method of screening for a need of follow up pancreas cancer testing, the method comprising: a. determining a level of one or more biomarkers in a sample from the subject, wherein the one or more biomarkers are selected from the biomarkers listed in Table 4, and

b. comparing the level of each biomarker in the sample with a control; wherein an increased level of any one of the one or more biomarkers compared to the control is indicative that the subject is need of follow up pancreas cancer testing.

[0041] In an embodiment, the follow up testing includes a biopsy and/or imaging.

[0042] As it is predictable that increases in tumour burden will correspond to increases in biomarker expression, the biomarkers disclosed herein are useful for monitoring response to treatment and/or monitoring disease progression.

[0043] Accordingly, another aspect of the disclosure includes a method of monitoring response to treatment comprising:

wherein an increase in the biomarker level in the post-treatment sample compared to the baseline level is indicative the subject is not responding or is responding poorly to treatment, and a decrease in the biomarker level in the post treatment sample compared to the base-line level is indicative that the subject is responding to treatment. .

[0044] In an embodiment, the treatment is surgery and the sample is taken after surgical resection.

[0045] A further aspect includes a method of monitoring disease progression comprising:

b) determining a level of the one or more biomarkers in a sample taken subsequent to the base-line sample from the subject; and

[0046] In an embodiment, the one or more biomarkers is/are selected from and/or comprise one or more of the proteins listed in Tables 7 and/or 8. In another embodiment, the one or more biomarkers comprise(s) a combination listed in Tables 1 1 or 12.

[0047] In another embodiment, the one or more biomarkers further include a biomarker selected from the biomarkers listed in Table 1 , 2, and/or 3.

[0048] In an embodiment, the one or more biomarkers comprise 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14 or 15 biomarkers. In an embodiment, the one or more biomarkers comprises 15 or more, 20 or more, 25 or more, 30 or more, 35 or more, 40 or more biomarkers. [0049] In another embodiment, the one or more biomarkers comprises and/or is/are selected from REG1 B, LOXL2, AGR2, OLFM4, PIGR, SYCN, and/or COL6A1 and/or any combination thereof.

[0050] In another embodiment, the one or more biomarkers comprises and/or is selected from REG1 B, LOXL2, AGR2, PIGR and/or SYCN and/or any combination thereof.

[0051] In yet another embodiment, the one or more biomarkers comprises and/or is selected from REG1 B, LOXL2, AGR2, and/or SYCN and/or any combination thereof.

[0052] In an embodiment, the one or more biomarkers comprises and/or is selected from REG1 B, PIGR and/or SYCN and/or any combination thereof.

[0053] In an embodiment, at least one of the one or more biomarkers is selected from REG1 B, LOXL2, AGR2, SYCN, OLFM4, PIGR and/or COL6A1.

[0054] In another embodiment, at least one of the one or more biomarkers is selected from REG1 B, LOXL2, AGR2 and/or SYCN.

[0055] In another embodiment, at least one of the one or more biomarkers is selected from REG1 B, PIGR, AGR2 and/or SYCN.

[0056] In an embodiment, the one or more biomarkers comprises and/or is REG1 B.

[0057] In an embodiment, the one or more biomarkers comprises and/or is LOXL2.

[0058] In an embodiment, the one or more biomarkers comprises and/or is AGR2.

[0059] In an embodiment, the one or more biomarkers comprises and/or is SYCN.

[0060] In an embodiment, the one or more biomarkers comprises and/or is PIGR.

[0061] It is also demonstrated herein that a significant correlation exists for REG1 B, AGR2 and PIGR with CA19.9. [0062] CA19.9 as mentioned is the most widely used biomarker in the clinic for pancreatic. While CA19.9 is elevated in late stage disease, it is also elevated in benign and inflammatory diseases of the pancreas and in other malignancies of the gastrointestinal tract [7]. As well, for early-stage pancreatic cancer detection, CA- 19.9 has a reported sensitivity of -55% and is often undetectable in many asymptomatic individuals [5,8].

[0063] In an embodiment, the one or more biomarkers comprises a combination of at least two biomarkers.

[0064] In an embodiment, the biomarkers consist of AGR2, OLFM4, PIGR, SYCN, and COL6A1.

[0065] Tables 1 1 and 12 list two biomarker combinations (e.g. each row is a two biomarker combination) and their AUC in the datasets analyzed. In an embodiment, the combination of biomarkers is selected from Table 1 1. In another embodiment, the combination is selected from Table 12. In an embodiment, the combination comprises and/or is SYCN and AGR2 SYCN and REG1 B, SYCN and LOXL2, SYCN and PIGR, AGR2 and REG1 B, AGR2 and LOXL2 AGR2 and PIGR REG1 B and LOXL2 REG1 B and PIGR and/or LOXL2 and PIGR.

[0066] In an embodiment, the one or more biomarkers comprises CA19.9 in combination with one or more biomarkers disclosed herein.

[0067] It is also demonstrated that detection of biomarker CA19.9 in combination with one or more of REG1 B, LOXL2, AGR2, PIGR, OLFM4, SYCN and/or COL6A1 improves biomarker performance compared to CA19.9 alone.

[0068] CA19.9 is currently the most widely used biomarker in the clinic for pancreatic cancer. It is demonstrated for example in Figure 4B and Tables 1 1 and 12 that the combination of CA19.9 and one or more of the candidate biomarkers can improve the performance of CA19.9 alone Further Figure 4C and Tables 1 1 and 12 demonstrate that the tested candidates in combination with CA19.9, improves the AUC compared to CA19.9 alone. For example, Table 12 AUC calculations demonstrate that the combination of CA19.9 + SYCN , CA19.9 + REG1 B and CA19.9 + REG1 B + SYCN significantly improve the performance of CA19.9 alone. Accordingly, in an embodiment, the combination of biomarkers comprises and/or is a combination disclosed in Table 1 1 or 12, for example CA19.9 + SYCN, CA19.9 + REG1 B and/or CA19.9 -+ REG1 B + SYCN.

[0069] In another embodiment, the method further comprises determining a level of CA19.9 in a sample from the subject, and comparing the level of CA19.9 to a control, wherein said level of CA19.9 is increased compared to the control. In an embodiment, the one or more biomarkers comprise and/or are selected, from CA19.9 and one or more of REG1 B, AGR2, OLFM4, PIGR, SYCN and COL6A1 , for example two or more, three or more or four or more of REG1 B, LOXL2, AGR2, OLFM4, PIGR, SYCN and COL6A1. In an embodiment, the two or more biomarkers comprise and/or are CA19.9 + SYCN , CA19.9 + REG1 B and/or CA19.9 + REG1 B + SYCN. . In an embodiment wherein CA19-9 is detected, at least one of the one or more biomarkers is selected from OLFM4, PIGR and/or COL6A1 . In another embodiment, at least one of the one or more biomarkers is selected from REG1 B, LOXL2, SYCN, AGR2 and PIGR.

[0070] In another embodiment, the one or more biomarkers consist of AGR2, OLFM4, PIGR, SYCN, COL6A1 and CA19.9.

[0071] In yet another embodiment, the one or more biomarkers consist of CA19.9 + SYCN , CA19.9 + REG1 B and/or CA19.9 + REG1 B + SYCN.

[0072] In an embodiment, the biomarker is a soluble biomarker, detectable for example in a biological fluid.

[0073] In an embodiment, the sample and/or control is or comprises a biological fluid, selected from blood, serum, plasma, pancreatic juice, cyst fluid, bile and/or biological fluid in close proximity to tumor cells. In an embodiment, the sample and/or control comprises serum or plasma.

[0074] In an embodiment, the sample and/or control is blood or a fraction thereof such as serum or plasma.

[0075] In an embodiment, the one or more biomarkers comprise and/or is/are selected from REG1 B, PIGR and/or SYCN and/or any combination thereof and the sample comprises and/or is serum and/or plasma. In an embodiment, the one or more biomarkers comprise and/or is AGR2 and the sample comprises and/or is serum. In another embodiment, the one or more biomarkers comprise and/or is LOXL2 and the sample comprises and/or is plasma.

[0076] A person skilled in the art is familiar with the techniques for obtaining a serum sample. For example, the sample can be collected in EDTA-containing vacutainer tubes, centrifuged at 3000 rotations per minute for 15 minutes within one hour of collection, and for example stored at -80 degrees Celsius.

[0077] In certain embodiments, the samples are processed prior to detecting the biomarker level. For example, a sample may be fractionated (e.g. by centrifugation or using a column for size exclusion), concentrated or proteolytically processed such as trypsinized, depending on the method of determining the level of biomarker employed.

[0078] In an embodiment, the sample and control are the same or similar tissue type, e.g. both comprise blood and/or serum. Alternatively, the control is a value that corresponds to a level of biomarker derived from a control sample wherein the control sample is the same or similar type (e.g. tissue) as the sample (e.g. the test sample).

[0079] In an embodiment, the control is a value that corresponds to a biomarker level in a control subject or population of control subjects. For example, the control can be a predetermined cut-off level or threshold wherein subjects with an amount of biomarker greater than the cut-off level have a greater likelihood and/or have pancreas cancer. For example, the median polypeptide levels of AGR2, , PIGR, SYCN, REG1 B and/or LOXL2were demonstrated to be significantly increased in subjects with pancreas cancer compared to subjects with subjects without pancreas cancer (e.g. control subjects). Selecting a value for the control (e.g. a cutoff value) wherein subjects having an increased level of one of more biomarkers disclosed herein is useful for identifying subjects as having pancreas cancer and/or needing follow-up testing. The value selected will vary with the desired specificity and sensitivity.

[0080] In another embodiment, the level of biomarker indicative of pancreatic cancer is the median level in a population of subjects with pancreatic cancer. For example, described herein are methods of determining the median level of a biomarker of the disclosure in subjects with or without pancreatic cancer. In an embodiment, the level of biomarker in the sample is at least the median level of the biomarker in subjects with pancreatic cancer.

[0081] In an embodiment, the level of biomarker(s) in the sample indicative of pancreatic cancer is at least the median level.

[0082] In an embodiment, the relative biomarker e.g. polypeptide level, compared to control is calculated, for example the relative polypeptide level of a biomarker of Table 4 is compared to a level in a control subject or predetermined value and the relative increase calculated. In another embodiment, the absolute biomarker e.g. polypeptide level, is compared to a control, wherein the control is for example a predetermined value such as a cut-off value, and subjects having a biomarker level above the control are more likely and/or have pancreatic cancer.

[0083] In an embodiment, the one or more biomarkers is or comprises AGR2 and the level of AGR2 in the sample relative to the control is at least 1 .5, 1.6, 1 .7, 1.8, 1 .9, 2.0, 2.1 , 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3.0, 3.1 , 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5, 10, 15, 20, or 25 fold increased.

[0084] In an embodiment, the one or more biomarkers is or comprises AGR2 and the level of AGR2 in the sample is at least 50 ^ig/L, 60 ug/L, 70 ^ig/L, 80 ^ig/L, 90 μg/L, 100 μg/L, 150 μg/L, 200 μg/L, 250 ug/L, 300 μρ/ί, 320 g/L, 350 μρ/ί, and/or 400 μg/L, 5.

[0085] In an embodiment, the control comprises less than 60 μg L 50 μg/L, 40 μg/L, or less than 30 μg/L of AGR2. In an embodiment, the control comprises between about 60 μg L to about 30 μg/L of AGR2.

[0086] In an embodiment, the one or more biomarkers is or comprises OLFM4 and the level of OLFM4 in the sample relative to the control is at least 1 .3, 1.4, 1 .5, 1.6, 1 .7, 1 .8, 1.9, 2.0, 2.1 , 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3.0, 3.1 , 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5, 10, or 15 fold increased. [0087] In an embodiment, the one or more biomarkers is or comprises OLFM4 and the level of OLFM4 in the sample is at least 70

80 μ Ι, 90 μς/ί, 100 ug/L, 1 10 μοίΐ, 120

140 μg/L, 150 μg/L, 160 μg/L, 170 μς/ί, 180 μg/L, 190 μg/L, or 200 μ9/Ι_.

[0088] In an embodiment, the control comprises less than 70 μg^ , 65 μg/L, 60 μ9/\-, 50 μ$/^ 45 ug/L, or 40 μg/L of OLFM4. In an embodiment, the control comprises between about 70 μ9/Ι_ and 40 μ9/Ι_ of OLFM4.

[0089] In another embodiment, the biomarker is or comprises SYCN and the level of SYCN in the sample relative to the control is at least 1.5, 1.6, 1 .7, 1 .8, 1 .9, 2.0, 2.1 , 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3.0, 3.1 , 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4.0, 4.2, 4.4, 4.6, 4.8, 5, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 9.0, or 10 fold increased.

[0090] In an embodiment, the one or more biomarkers is or comprises SYCN and the level of SYCN in the sample is at least 1.4 μg/L, 2 μg/L, 3 μg/L, 4 μg/L, 5 μg/L, 6 μg/L, 7 μς/ί, 8 μς/Ι, 9.0 μg/L 10 μς/ί, 1 1 μg/L, 12 μg/L, 13 μ ΐ, 14 μg/L, 15 μς/ί, 16 μ ΐ, 17 μς/ί, 18 μg/L, 19 μg/L 20 μg/L, 21 μ Ι, 22 μg/L, 23 μ$/1-, 24 μg/L, 0Γ 25 μ9/ί..

[0091] In yet another embodiment, the control comprises less than 9.0 μg/ -, 8.0 μ9/Ι_, 7 μg/L, 6 μg/L, 5 μ9/Ι_, 4 μg/L, 3 μ^Ι, 2 μg/L, 1 .5 μg/L, or less than 1 μ9/Ι_, of SYCN. In an embodiment, the control comprises between about 9.0 μg/L and about 1.0 μg/L of SYCN or between about 3.0 μg/L and about 1 .0 μ9/Ι_ of SYCN.

[0092] In another embodiment, the one or more biomarkers is or comprises PIGR and the level of PIGR in the sample relative to the control is at least 1 .2, 1 .3, 1.4, 1 .5, 1.6, 1.7, 1 .8, 1 .9, 2.0, 2.1 , 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3.0, 3.2, 3.4, 3.6, 3.8, 4.0, 4.5, 5.0, 6.0, 8.0, or 10 fold increased.

[0093] In an embodiment, the one or more biomarkers is or comprises PIGR and the level of PIGR in the sample is at least 1 1 mg/L, 1 1.5 mg/L, 12 mg/L, 12.5 mg/L, 13 mg/L, 13.5 mg/L, 14 mg/L, 14.5 mg/L, 15 mg/L, 16 mg/L, 16.5 mg/L, 17 mg/L, 18 mg/L, 19 mg/L, or 20 mg/L.

[0094] In yet another embodiment, the control comprises less than 12 mg/L, 1 1 mg/L, 10 mg/L, 9.8 mg/L, 9.6 mg/L, 9.4 mg/L, 9.2 mg/L, 9 mg/L, 8.8mg/L, 8.6 mg/L, 8.4 mg/L, 8.2 mg/L, or 8 mg/L of PIGR. In an embodiment the control comprises between about 12 mg/L and 8 mg/L of PIGR.

[0095] In an embodiment, the one or more biomarkers is or comprises COL6A1 and the level of COL6A1 in the sample is at least 1 .8 mg/L, 1.9 mg/L, 2 mg/L, 2.1 mg/L, 2.2 mg/L, 2.3 mg/L, 2.4 mg/L, 2.5 mg/L, 2.6 mg/L, 2.7 mg/L, 2.8 mg/L, 2.9 mg/L, 3 mg/L, 3.1 mg/L, 3.2 mg/L, 3.4 mg/L, 3.6 mg/L, 4 mg/L, 4.5 mg/L, or 5 mg/L.

[0096] In another embodiment, the control comprises less than 1.8 mg/L, 1.7 mg/L, 1.6 mg/L, 1.5mg/L, 1 .4 mg/L, 1.2 mg/L, 1 mg/L, 0.9 mg/L, 0.8 mg/L, or 0.7 mg/L of COL6A1. In an embodiment the control comprises between about 1.8 mg/L and 0.7 mg/L of COL6A1 .

[0097] In another embodiment, the one or more biomarkers is or comprises REG1 B and the level of REG1 B in the sample relative to the control is at least 1 .5, 1.6, 1.7, 1 .8, 1.9, 2.0, 2.1 , 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3.0, 3.2, 3.4, 3.6, 3.8, 4.0, 4.5, 5.0, 6.0, 8.0, or 10 fold increased.

[0098] In an embodiment, the one or more biomarkers is or comprises REG1 B and the level of REG1 B in the sample is at least 2 mg/L, 3 mg/L, 4 mg/L, 5 mg/L, 6 mg/L, 7 mg/L, 8 mg/L, 9 mg/L, 10 mg/L, 1 1 mg/L, 12 mg/L, 13 mg/L, 14 mg/L, 15 mg/L, 16 mg/L, 17 mg/L, 18 mg/L, 19 mg/L, 20 mg/L, 21 mg/L, 22 mg/L, 23 mg/L, or 24 mg/L. In another embodiment, the range of REG1 B associated with disease, progression, need for follow pancreas cancer testing is from about 2 mg/L to about 30 mg/L, from about 5 mg/L to about 30 mg/L. from about 9 mg/L to about 30 mg/L, or from about 12 mg/L to about 30 mg/L.

[0099] In yet another embodiment, the control comprises less than 9 mg/L, 8 mg/L, 7 mg/L, 6.5 mg/L, 6 mg/L, 5.5 mg/L, 5 mg/L, 4.5 mg/L, 4.0 mg/L, 3.5 mg/L, 3 mg/L, 2.5 mg/L or 2 mg/L or less than 2 mg/L of REG1 B. In an embodiment the control comprises between about 9 mg/L and mg/L of REG1 B, or from about 2 mg/L to 0 mg/L or REG1 B.

[00100] In an embodiment, the one or more biomarkers is or comprises LOXL2 and the level of LOXL2 in the sample relative to the control is at least 1.3, 1.4, 1.5, 1 .6, 1.7, 1 .8, 1.9, 2.0, 2.1 , 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 or 10 fold increased. [00101] In an embodiment, the one or more biomarkers is or comprises LOXL2 and the level of LOXL2 in the sample is at least, 120 ug/L, 130 μg/L, 140

150

180 _txg/L, 190 μς/ί, or 200 μg/L.

[00102] In an embodiment, the control comprises less than 140 μg L, 130 μg/L, 120 μg/L, 1 15 μg/L, 1 10 μg/L, or 100 μg/L of LOXL2. In an embodiment, the control comprises between about 140 μg/L and 100

of LOXL2.

[00103] In embodiments wherein CA19.9 is also determined, the relative level of CA19.9 is determined. In an embodiment, the relative increase of CA19.9 indicative of an increased likelihood of pancreatic cancer is at least In healthy individuals CA19.9 is -35 units/mL or less (La'ulu SL, Roberts WL. (2007) Performance Characteristics of Five Automated CA 19-9 Assays. Am J Clin Pathol 127:436-440).

[00104] A person skilled in the art will appreciate that a number of methods can be used to determine the amount of a biomarker, including a soluble biomarker of the disclosure, including mass spectrometry approaches, such as multiple reaction monitoring (MRM) and product-ion monitoring (PIM), and also including antibody based methods such as immunoassays such as Western blots and enzyme-linked immunosorbant assay (ELISA). In certain embodiments, the step of determining the biomarker level comprises using immunohistochemistry and/or an immunoassay. In certain embodiments, the immunoassay is an ELISA. In yet a further embodiment, the ELISA is a sandwich type ELISA.

[00105] The level of two or more markers can be determined for example using mass spectrometry-based methods such as single or multiple reaction monitoring assays. An example of such an assay is the "Product-ion monitoring" PIM assay. This method is a hybrid assay wherein an antibody for a biomarker is used to extract and purify the biomarker from a sample e.g. a biological fluid, the biomarker is then trypsinized in a microtitre well and a proteolytic peptide is monitored with a triple- quadrapole mass spectrometer, during peptide fragmentation in the collision cell. More technical details can be found in Kulasingam V, Smith CR, Batruch I, Buckler A, Jeffery DA, Diamandis EP (2008) "Product ion monitoring" assay for prostate- specific antigen in serum using a linear ion-trap. J of Proteome Res 7: 640-647. Biomarker levels for a model biomarker has been quantified as low as 0.1 ng/mL with CVs less than 20%.

[00106] Alternatively, it is also possible to quantify analytes present at relatively higher concentration in a biological fluid such as serum (e.g. > 100 ng/mL) without antibody enrichment. In this case, the biological fluid (e.g. serum) is digested with trypsin and selected proteotypic peptides are monitored for various transitions during fragmentation, as described above. With such assays, multiplexing 5 or more biomarkers is possible.

[00107] In an embodiment, antibodies or antibody fragments are used to determine the level of polypeptide of one or more biomarkers of the disclosure and/or CA19.9. In an embodiment, the antibody or antibody fragment is labeled with a detectable marker. In a further embodiment, the antibody or antibody fragment is, or is derived from, a monoclonal antibody. A person skilled in the art will be familiar with the procedure for determining the level of a biomarker by using said antibodies or antibody fragments, for example, by contacting the sample from the subject with an antibody or antibody fragment labeled with a detectable marker, wherein said antibody or antibody fragment forms a complex with the biomarker.

[00108] The label is preferably capable of producing, either directly or indirectly, a detectable signal. For example, the label may be radio-opaque or a radioisotope, such as ³H, ⁴C, ³²P, ³⁵S, ²³l, ¹²⁵l, ¹³¹|; a fluorescent (fluorophore) or chemiluminescent (chromophore) compound, such as fluorescein isothiocyanate, rhodamine or luciferin; an enzyme, such as alkaline phosphatase, beta- galactosidase or horseradish peroxidase; an imaging agent; or a metal ion.

[00109] In another embodiment, the level of biomarker of the disclosure is detectable indirectly. For example, a secondary antibody that is specific for a primary antibody that is in turn specific for a biomarker of the disclosure wherein the secondary antibody contains a detectable label can be used to detect the target polypeptide biomarker.

[00110] In certain embodiments, for example, when using Western blot analysis, the level of the biomarker is normalized to an internal control. For example, the level of a biomarker may be normalized to an internal normalization control such as a polypeptide that is present in the sample type being assayed, for example a house keeping gene protein, such as beta-actin, glyceraldehyde-3-phosphate dehydrogenase, or beta-tubulin, or total protein, e.g. any level which is relatively constant between subjects for a given volume.

[00111] In an embodiment, the method is used in addition to traditional diagnostic techniques for pancreatic cancer, for example contrast-enhanced Doppler ultrasound (US), helical computed tomography (CT), enhanced magnetic resonance imaging (MRI), and endoscopic US (EUS).

[00112] In another embodiment, the method further comprises before step a) obtaining a sample from the subject.

[00113] In an embodiment, the pancreatic cancer is a late-stage pancreatic cancer. In an embodiment, the pancreatic cancer is an early-stage pancreatic cancer.

[00114] In an embodiment, the pancreatic cancer being screened for, diagnosed and/or detected, and/or monitored is an early-stage pancreatic cancer and the one or more biomarkers comprise and/or is/are selected from the biomarkers described in Table 13. In an embodiment, the one or more biomarkers comprise and/or is SYCN. In another embodiment, the one or more biomarkers comprise and/or is REG1 B..

IV. Compositions

[00115] A further aspect of the disclosure includes a composition comprising at least two biomarker specific detection agents, each of which binds a biomarker selected from CA19.9 and/or the biomarkers listed in Table 4 and/or subsets thereof such as in Table 7 and 8, and/or a combination described herein. In an embodiment, the composition includes at least two biomarker specific detection agents, each of which binds a biomarker selected from CA19.9, REG1 B, LOXL2, AGR2, OLFM4, PIGR, SYCN and COL6A1. In an embodiment, the composition is for use in a method described herein. In another embodiment, the two or more biomarker specific agents comprise agents that bind CA19.9 + SYCN , CA19.9 + REG1 B and/or CA19.9 + REG1 B + SYCN. [00116] In an embodiment, the biomarker specific detection agent comprises an antibody or fragment thereof.

[00117] In an embodiment, the composition comprises a suitable carrier, diluent or additive as are known in the art. For example, wherein the detection agent comprises an antibody or fragment thereof, the suitable carrier can be a protein such as BSA.

[00118] In an embodiment, the biomarker specific detection agent further comprises a detectable label. A person skilled in the art will appreciate that the detection agents can be labeled. The label is preferably capable of producing, either directly or indirectly, a detectable signal. For example, the label may be radio- opaque or a radioisotope, such as ³H, ¹⁴C, ³²P, ³⁵S, ¹²³l, ¹²⁵l, ¹³¹l; a fluorescent (fluorophore) or chemiluminescent (chromophore) compound, such as fluorescein isothiocyanate, rhodamine or luciferin; an enzyme, such as alkaline phosphatase, beta-galactosidase or horseradish peroxidase; an imaging agent; or a metal ion.

V. Immunoassays and Kits

[00119] Another aspect of the disclosure provides an immunoassay comprising at least two antibodies e.g. capture antibodies, wherein each antibody can be for example, immobilized on a solid support, wherein each capture antibody selectively binds a biomarker selected from the biomarkers listed in Table 4 and/or subsets thereof such as in Table 7 and 8. In an embodiment, each capture antibody selectively binds a biomarker selected from the biomarkers listed in Table 4 and/or subsets thereof such as in Table 7 and 8. In an embodiment, the immunoassay further comprises a biomarker selected from the biomarkers listed in any one of Tables 1 to 3. In another embodiment each capture antibody selectively binds a biomarker selected from the biomarkers selected from REG1 B, LOXL2, CA19.9, AGR2, OLFM4, PIGR, SYCN and COL6A1 . In another embodiment, at least one of the capture antibodies is selected from an antibody that selectively binds REG1 B, LOXL2, AGR2PIGR and/or SYCN. In an embodiment, the immunoassay further comprises a detection antibody directed to the biomarker of each of the at least two capture antibodies, wherein each detection antibody can be for example labeled, for example fluorescently labeled. In an embodiment, the immunoassay comprises 3 or more capture antibodies and in another embodiment 3 or more detection antibodies. In another embodiment, the immunoassay comprises 4, 5 or 6 or more capture antibodies and in another embodiment 4, 5, or 6 or more detection antibodies, which can for example be labeled, for example as previously described. In an embodiment, the immunoassay is a multiplex assay for detecting two or more biomarkers listed in Table 4 and/or subsets thereof such as in Table 7 and 8 and/or any combination described herein. In an embodiment, the two or more biomarkers are selected from CA19.9, REG1 B, LOXL2, AGR2, OLFM4, PIGR, SYCN and COL6A1. In another embodiment, the two or more biomarkers are selected from CA19.9, REG1 B, LOXL2, AGR2, PIGR and SYCN. In a further embodiment, the two or more biomarkers are a combination listed in Tables 1 1 and/or 12. In an embodiment, the two or more biomarkers comprise and/or are SYCN and AGR2 SYCN and REG1 B, SYCN and LOXL2, SYCN and PIGR, AGR2 and REG1 B, AGR2 and LOXL2 AGR2 and PIGR REG1 B and LOXL2 REG1 B and PIGR and/or LOXL2 and PIGR. In another embodiment, the two or more biomarkers comprise and/or are CA19.9 + SYCN, CA19.9 + REG 1 B and/or CA19.9 + REG1 B + SYCN.

[00120] Another aspect of the disclosure is a kit for detecting one or more biomarkers selected from the biomarkers listed in Table 4 and/or subsets thereof such as in Table 7 and 8.. In an embodiment, the kit is for screening for, detecting, or diagnosing pancreatic cancer and/or an increased likelihood of pancreas cancer in a subject. In another embodiment, the kit is for determining the need for follow up pancreas cancer screening, monitoring disease progression and/or monitoring response to treatment. In an embodiment, the kit comprises two or more biomarker specific detection agents, each which is specific for a biomarker listed in Table 4. In an embodiment, the kit further comprises a biomarker specific detection agent specific for a biomarker listed in any one of Tables 1 to 3. In another embodiment, the kit comprises a quantity of at least one standard and/or instructions for use. In yet a further embodiment, the kit comprises one or more, e.g. 1 , 2, 3, 4 5 or 6, biomarker specific agents.

[00121] In an embodiment, the kit further comprises a biomarker specific detection agent specific for CA19.9. [00122] In an embodiment, the biomarker specific detection agent comprises an antibody or fragment thereof that is specific for a biomarker listed in any one of Tables 1 to 4, and/or biomarkers listed in Tables 7 and/or 8. In an embodiment, the kit comprises two or more biomarker specific detection agents, each which is for a biomarker selected from CA 9.9, REG1 B, LOXL2, AGR2, OLFM4, PIGR, SYCN and/or COL6A1 and in an embodiment, instructions for use.

[00123] In another embodiment, the kit comprises two or more biomarker specific detection agents, each which is for a biomarker selected from CA19.9, REG1 B, LOXL2, AGR2, PIGR, and/or SYCN and in an embodiment, instructions for use.

[00124] In a further embodiment, the two or more biomarkers are a combination listed in Tables 1 1 and/or 12. In an embodiment, the two or more biomarkers comprise and/or are SYCN and AGR2 SYCN and REG 1 B, SYCN and LOXL2, SYCN and PIGR, AGR2 and REG1 B, AGR2 and LOXL2 AGR2 and PIGR REG1 B and LOXL2 REG1 B and PIGR and/or LOXL2 and PIGR. In another embodiment, the two or more biomarkers comprise and/or are CA19.9 + SYCN, CA19.9 + REG1 B and/or CA19.9 + REG1 B + SYCN.

[00125] In an embodiment, at least one of biomarker specific detection agents is selected from OLFM4, PIGR, and/or COL6A1.

[00126] In an embodiment, at least one of biomarker specific detection agents is selected from REG1 B, LOXL2, AGR2, PIGR, and/or SYCN.

[00127] In an embodiment, the kit comprises a composition or immunoassay described herein.

[00128] The kit can also include a control or reference standard. In addition, the kit can include ancillary agents such as vessels for storing or transporting the detection agents and/or buffers or stabilizers.

[00129] In an embodiment, the biomarker specific detection agent is an antibody or fragment thereof (e.g. binding fragment thereof). In another embodiment, the kit comprises at least two antibodies or fragments thereof each which selectively bind a biomarker selected from REG1 B, LOXL2, AGR2, OLFM4, PIGR, SYCN and COL6A1 and in an embodiment CA19.9; and a quantity of a purified standard, such as a known quantity of biomarker polypeptide. In an embodiment, at least one of the two antibodies or fragments thereof selective for a biomarker is selected from OLFM4, PIGR, and/or COL6A1. In another embodiment, at least one of the two antibodies or fragments thereof selective for a biomarker is selected from REG1 B, LOXL2, AGR2 and SYCN.

[00130] In an embodiment, the kit for detecting a biomarker comprises:

(a) a biomarker specific detection agent that specifically binds a biomarker selected from the biomarkers listed in Table 4, preferably selected from REG1 B, LOXL2, AGR2,, PIGR and SYCN; and

(b) instructions for use, and/or

(c) a quantity of at least one purified standard.

[00131] In an embodiment the kit further comprises a CA19.9 biomarker specific detection agent. In an embodiment, the standard comprises a purified amount of REG1 B, LOXL2, AGR2, OLFM4, PIGR, SYCN, and/or COL6A1 polypeptide and/or CA19.9 antigen. In an embodiment, at least one of the biomarkers is OLFM4, PIGR, and/or COL6A1

[00132] In another embodiment, the kit comprises a CA19.9 biomarker specific detection agent and a second biomarker specific detection agent that binds to a biomarker selected from the biomarkers listed in Table 4, for example selected from AGR2, OLFM4, PIGR, SYCN and/or COL6A1 . In an embodiment, at least one of the biomarkers is OLFM4, PIGR, and/or COL6A1 . In an embodiment, at least one of the biomarkers is selected from REG1 B, LOXL2, AGR2 and SYCN.

[00133] In yet another embodiment, the kit comprises biomarker specific detection agents, for CA19.9, AGR2, OLFM4, PIGR, SYCN, and COL6A1 .

[00134] The above disclosure generally describes the present application. A more complete understanding can be obtained by reference to the following specific examples. These examples are described solely for the purpose of illustration and are not intended to limit the scope of the application. Changes in form and substitution of equivalents are contemplated as circumstances might suggest or render expedient. Although specific terms have been employed herein, such terms are intended in a descriptive sense and not for purposes of limitation. [00135] The following non-limiting examples are illustrative of the present disclosure:

Examples Example 1

Increasing Protein Yield

[00136] Once the cell lines were grown and CM collected, the samples were subjected to a 2D-LC-MS/MS analysis which combined strong cation exchange

(SCX) liquid chromatography on a High Pressure Liquid Chromatography (HPLC) system, followed by LC-MS/MS. Guided by previous studies [29-32], SCX fractions were initially collected at 5 minute intervals during peptide elution, resulting in approximately 8 fractions that were analyzed using a ~2 hour reverse-phase method on the LTQ-Orbitrap mass spectrometer. This resulted in the identification of 1305, 1468, and 1749 proteins (>1 peptide) in the triplicate analysis of the BxPc3, HPDE6 and MIA-PaCa2 cell lines, respectively. In some of the individual 5 min fractions analyzed (specifically the fractions that contained the highest absorbance readings during SCX peptide elution) >700 proteins were identified per fraction. Based on previous experience, this was a very large number of proteins to have been identified in individual fractions. Consequently, a different fraction collection and pooling strategy was used. By collecting fractions every minute from SCX and pooling fractions based on the intensity of peaks eluting on the SCX chromatogram (as described in the 'Experimental Procedures'), we identified 2017 proteins for the BxPc3 cell line, 2297 for HPDE, and 2756 for the MIA-PaCa2 cell line subjected to the same growth and sample processing conditions, in triplicate. In order to ensure that this increase in protein yield was not due to variation in cell growth/sample collection, an additional replicate using MIA-PaCa2 CM left over from the initial analysis, which had been stored in -80°C, was also run and 2348 proteins were identified (a 52-56% increase from the individual replicates of the first run of MIA- PaCa2).

[00137] Over 90% of the proteins from the first analysis were re-identified and the new pooling strategy resulted in approximately a 54-58% increase in protein yield across the cell lines. This improved strategy was utilized for proteomic analysis of the remaining cell lines and the pancreatic juice samples. Protein Identification through LC-MS/MS

[00138] Six human pancreatic cancer cell lines, one 'near normal' human pancreatic ductal epithelial cell line (HPDE) and six pancreatic juice samples from ductal adenocarcinoma patients (in two pools) were analyzed in triplicate (Figure 1).

Using both MASCOT and X!Tandem search engines, between 2017 to 3250 proteins were identified in the 7 cell lines and 1014 and 956 proteins were identified from pool A and B of pancreatic juice, respectively. These numbers represent proteins identified in the three replicates combined, with 1 or more peptides and with protein false discovery rates (FDR) of <1 .0%. For protein identifications, the human forward and reverse IPI3.62 database which contains 167,894 forward and reverse protein sequences was used, and FDR was calculated as [2XFP/(TP+FP)]100, where FP (false positive) is the number of proteins that were identified based on sequences in the reverse database component and TP (true positive) is the number of proteins that were identified based on sequences in the forward database component [48-50]. For increased stringency and assurance of protein identification, only proteins identified with two or more peptides were included in the remainder of the analysis, resulting in between 1261 and 2171 proteins for each of the cell lines and a total of 648 non-redundant proteins from the pancreatic juice analysis.

Protein Overlap Between Samples

[00139] From combined analysis, a total of 12,805 proteins were identified with >2 peptides. The majority of these proteins were common to multiple biological samples and resulted in the identification of 3479 non-redundant proteins (3324 in the cell lines and 648 in the pancreatic juice analysis). Six-hundred and forty-four proteins (of 3324; 19.4%) were common to all cell lines and an average of 143 proteins were unique to each. From our preliminary studies of the three cell lines described in the 'increasing protein yield' section above, 83 additional non-redundant proteins (≥2 peptides) were identified; however these were not included in the remainder of the analyses.

[00140] Significant overlap was noted between the pancreatic juice and CM proteins. Approximately 76% (493 of 648) of proteins identified in the pancreatic juice samples were also identified in the cell line analysis (Figure 1 B), which indicates much similarity in the proteomes between these biological fluids; however many proteins that are largely associated with exocrine pancreatic function were unique to the pancreatic juice and not identified in the cell lines. Analysis of overrepresented KEGG pathways through Protein Center, calculated through hypergeometric test analysis, further revealed the KEGG pancreatic secretion pathway (KEGG pathway - hsa04972) to be one of three pathways overrepresented in the pancreatic juice proteome in comparison to the combined cell line proteome (p=3.61 1 E-5).

Gene Ontology - Function, Process and Cell Localization Classifications

[00141] Gene ontology classifications, which include function, process and cell localization, were obtained for all identified proteins. Proteins that are secreted into the extracellular milieu or cleaved from the plasma membrane of cells have the highest chance of entering the circulation and serving as serological biomarkers. Between 34.1 %-42.6% of proteins in each of the cell lines and 57% of proteins in the pancreatic juice samples were annotated as belonging to the extracellular and cell surface compartments. In total, 1376 (40%) of 3479 proteins contained these two annotations. The cytoplasm received the greatest number of annotations in both biological fluids and approximately 2.9% of the total contingent of proteins did not contain cell localization information and are unannotated. It is important to note that proteins can be classified as belonging to multiple cellular localizations, processes and functions and as a result, the categories for each are non-exclusive and the sum of the percentages can be >100%.

[00142] The top three molecular functions for the cell lines and the pancreatic juice were the same: protein binding (-80.2%, 79.9%), catalytic activity (69.4%, 70.2%) and metal ion binding (45.7%, 49.7%), respectively. Both fluids also shared the top two biological processes - metabolic process (81 .2%, 83.5%) and regulation of biological process (61.9%, 63.1 %), respectively. In a comparison between the cell line and pancreatic juice proteomes as a whole, several molecular functions related to enzyme activity and biological processes related to inflammatory responses were found overrepresented in the pancreatic juice proteome. The only Genome Ontology (GO) category overrepresented in the cell line proteome was the biological process 'macromolecule metabolic process' (GO:0043170; p=5.338E-7; FDR p=3.308E-3). No GO terms were over or underrepresented in a comparison between the cancer cell lines and HPDE. Hierarchical Clustering [00143] One of the difficulties in dealing with large datasets is visualizing the proteomes as a whole and identifying subsets of proteins that may be of importance within certain biological contexts. In an initial attempt to mine and explore the CM and pancreatic juice proteomes, unsupervised two-way hierarchical clustering analysis (HCA) was performed using average emPAI values of the three replicates of each sample, normalized through Z-scores. Through these means, proteins were clustered based on abundance within each sample. The concentrations of two proteins (KLK6 and KLK10) were assessed in the CM through ELISA to determine if Z-scores of emPAI values are a suitable indicator of protein abundance. Good correlation was seen between Z-scores of ELISA concentrations and Z-scores of emPAIs (r-squared = 0.7362). Additionally, the lowest ELISA concentration measured was 0.80 pg/L for KLK10 in CAPAN1 CM, which indicates the sensitivity of the mass spectrometry analysis in general, to be at least in the low pg/L range for the CM analysis.

[00144] HCA was performed on the entire dataset of 3479 proteins and based on normalized emPAI values, the pancreatic juice samples were distinctly clustered separately from the cell lines, and within the cell lines, the three derived from metastatic sites (SU.86.86, CFPAC1 and CAPAN1 ) were clustered together. MIA- PaCa2, PANC1 and BxPc3 are cell lines derived from the primary tumor site of three patients [40], The MIA-PaCa2 and PANC1 proteomes were clustered together, as were the BxPc3 and HPDE cell lines. Heat-map visualization facilitated a first exploration of the dataset and the identification of several regions or protein clusters of interest. Among them, two clusters containing 34 proteins were shown to be highly expressed in multiple cancer cell lines and the pancreatic juice samples, all with minimal expression in HPDE (Figure 2). This included proteins such as MUC1 (Mucin-1) [12] and RNASE1 (pancreatic ribonuclease) [51] which have been shown to be elevated in pancreatic cancer and studied previously as pancreatic cancer biomarkers in serum. This prompted further examination of proteins that are differentially expressed between the cancer cell lines and the HPDE cell line.

Differential Expression of Proteins in Cancer vs. Normal Cell Lines

[00145] Normalized spectral counts of the cancer cell lines were compared with those of the normal HPDE cell line as described in the 'Experimental Procedures' section. The Pearson correlation coefficient was evaluated for all pairs of the 21 replicates from the 7 cell lines using normalized spectral count values. With the exception of replicate 2 from CFPAC1 , which showed 0.727 and 0.851 correlation with CFPAC1 replicates 1 and 3. good correlation (ranging from 0.944-0.993) was seen between replicates of each cell line (including CFPAC1 replicates 1 and 3) indicating good reproducibility.

[00146] Analysis of variance (ANOVA) testing identified 1293 proteins (each with a minimum number of 10 spectra in at least one cell line), with a statistically significant difference amongst the seven cell lines (p<0.05). Based on the criteria described in the 'Experimental Procedures', 491 of these proteins showed > 5-fold increase in at least one cancer cell line. One-hundred and nineteen proteins further demonstrated≥ 5-fold increase in at least three cancer cell lines in comparison to HPDE, of which 53 proteins showed over 10-fold increase and 18 showed over a 20- fold increase in at least three cancer cell lines. Sixty-three of the 1 19 proteins were extracellular and cell surface-annotated and are listed in Table 1 . Pathway analysis shows cellular movement, cancer and cell-to-cell signalling and interaction as the top pathways assigned to this set of proteins. Additionally, 17 of these proteins have been previously shown to be upregulated in pancreatic cancer in at least four studies [52], and 10 have been shown to be elevated in pancreatic cancer serum in comparison to controls [51 , 53-62] (Table 1 ). The unstudied proteins may yield promising new candidate biomarkers for pancreatic cancer.

[00147] Many of the proteins were also identified in a comprehensive database of human plasma proteins [63] and five proteins, COPS4 (COP9 signalosome complex subunit 4), PXN (Paxillin), MY01 C (Myosin-l c), GBA (protein similar to Glucosylceramidase) and LMAN2 (Vesicular integral-membrane protein VIP36), were also identified in a recent global genomic analysis of pancreatic cancer [64] as overexpressed in the large majority of pancreatic cancer cases studied. Examination of underexpressed proteins revealed 19 proteins consistently decreased at least 5- fold in all six cancer cell lines and 18 consistently decreased in five cancer cell lines in comparison to HPDE. Further Prioritization of Candidates through Integration of Biofluids and Tissue

Specificity

[00148] Recent evidence suggests the integration and combining of different biological fluids may also yield strong candidates for verification phases of biomarker discovery [16, 19], As such, a set of filtering criteria based on overlap of proteins between different biological sources, the cellular localization of proteins and tissue specificity for the generation of further candidates was applied. Of the 488 proteins common to the pancreatic juice and cell lines (Figure 1 B), 235 had been annotated as belonging to the extracellular and cell surface compartments. One-hundred and nine of these proteins were also identified in the proteome of ascites fluid from 3 patients with pancreatic adenocarcinoma and of these, 43 were not identified in the HPDE normal cell line (Table 2).

[00149] Because there may be pertinent proteins in the pancreatic juice that may not be identified in the CM and vice versa, proteins identified in either proteome that were shown to be highly specific to/expressed in the pancreas were also included. To examine tissue specificity, the proteins identified in the CM and pancreatic juice datasets were compared to proteins shown highly specific to the pancreas based on microarray, EST and immunohistochemistry data using TiSGeD [65], TiGER [66], Unigene [67], and the Human Protein Atlas [68], respectively. These are publicly available databases that have been described in detail previously [65-68]. Specifically, the proteins identified herein were compared to 150 proteins reported as specific to pancreas tissue using TiSGeD specificity measure >0.90 [65], 55 pancreas-specific proteins from Unigene, 205 proteins preferentially expressed in the pancreas based on the TiGER database and 198 proteins showing 'strong' pancreatic exocrine cell staining and annotated on the Human Protein Atlas. Twenty proteins were common to at least three or more of the databases, of which 2 proteins, PRSS1 and SPINK1 , were identified in the cell line CM as meeting these criteria and 15 proteins from the pancreatic juice proteome (including PRSS1 and SPINK1 ) met the same criteria (Table 3). Twelve of these proteins have been previously shown to be elevated in serum/plasma of patients with pancreatitis or pancreatic cancer [69-81 ], leaving CTRC (chymotrypsin C), SYCN (syncollin) and REG1 B (Lithostathine-1-beta) (Table 3). Candidate Verification in Plasma

[00150] Two main strategies were used for identifying potential candidate pancreatic cancer biomarkers to test in serum. The first was based on label-free protein quantification data reported above, in which each cancer cell line was compared to the normal HPDE cell line to determine proteins overexpressed by at least 5-fold. The second method was based on studying the proteins common to the pancreatic cancer cell lines, cancer pancreatic juice samples and an ascites proteome (Table 2). As well, proteins shown to be expressed very specifically only in the pancreas was also a criteria for generation of candidates that was a part of the 'integrated strategy' as described herein (Table 3). In both approaches (label-free protein quantification and the integrated strategy), focus was placed on proteins that were annotated as extracellular or cell surface proteins as these proteins are the most likely to be secreted and shed by tumor cells and enter circulation. Additionally, within the label-free protein quantification approach, focus was placed on proteins that were overexpressed in at least 3 cancer cell lines in comparison to the HPDE cell line (i.e. Table 1 ).

[00151] Proteins previously studied in serum and shown already to have diagnostic utility were eliminated from further study (e.g. 9 proteins marked in Table 1 were eliminated). The biomarker candidates previously not studied in serum/plasma are listed in Table 4. Of note, AGR2 was also marked in Table 1 as previously studied but was included in this analysis. The reason for this is discussed further in the next section. Of the remaining candidates, not all proteins have commercially available ELISA kits for testing in serum/plasma and five candidates with commercially available enzyme-linked immunosorbent assays - Anterior Gradient Homolog 2 (AGR2), Olfactomedin-4 (OLFM4), Syncollin (SYCN), Collagen alpha-l (VI) chain (COL6A1 ), and Polymeric Immunoglobulin Receptor (PIGR) (Figure 3A-E) - were verified in plasma samples.

[00152] In the CM analysis, AGR2 showed over 10-fold increase in the BxPc3, CAPAN1 , CFPAC1 and SU-86-86 cell lines compared to the near normal HPDE cell line (Table 1 ). As well, AGR2 was common to the CM and pancreatic juice proteomes and was identified in the cluster of proteins highly expressed in many cancer cell lines and pancreatic juice in comparison to HPDE (Figure 2). In plasma, AGR2 levels were significantly increased in pancreatic cancer patients (p<0.0001 ) in comparison to controls (Figure 3A). Mean and median plasma levels in the pancreatic cancer patients were 8.8 pg/L and 2.1 pg/L while mean and median levels in controls were 0.33 pg/L and 0.28 pg/L).

[00153] OLFM4 was a protein identified based on the integrated method (Table 2), and as well it was identified in the cluster shown in Figure 2. In the plasma samples, OLFM4 also showed a significant elevation (p<0.0001 ) in cancer (mean = 161 pg/L, median = 90 pg/L) in comparison to controls (mean = 51 pg/L, median = 38 pg/L) (Figure 3B). SYCN was a protein identified solely in the pancreatic juice samples. It is monospecific to the pancreas based on TiGER, TiSGeD and Unigene databases (data was unavailable in the Human Protein Atlas) (Table 3). This protein is a part of the secretory granule membranes of the exocrine pancreas, and due to its tissue specificity, it was selected for the verification phases. In the plasma samples, SYCN also showed a significant increase in pancreatic cancer patients (p=0.001 1 ; mean cancer = 18.2 pg/L, median cancer = 13.5 pg/L; mean controls = 5.1 pg/L, median controls = 2.9 pg/L) (Figure 3C).

[00154] COL6A1 was expressed over 20-fold in all of the cancer cell lines except for the BxPc3 cell line in comparison to the HPDE cell line. Similarly, PIGR was expressed over 20-fold in three of the cancer cell lines (Table 1 ). Both proteins showed a significant increase in pancreatic cancer plasma in a preliminary analysis (p=0.0098; mean cancer = 3.3 mg/L, median cancer = 2.1 mg/L; mean controls = 1.5 mg/L, median controls = 0.73 mg/L for COL6A1 and p<0.0001 ; mean cancer = 16.8 mg/L, median cancer = 12.3 mg/L; mean controls = 9.2 mg/L, median controls = 8.96 mg/L for PIGR) (Figure 3D,E).

[00155] At present, CA19.9 is the most widely used pancreatic cancer biomarker and CA19.9 levels were also assessed in the screening set of plasma samples. While none of the candidate biomarkers identified herein shows enhanced performance compared to CA19.9 individually (Figure 4A), preliminary assessment of each of the five candidate biomarkers in combination with CA19.9 shows an improvement compared to the performance of CA19.9 alone (Figure 4B,D). Additionally, the combination of AGR2, OLFM4, PIGR, SYCN and COL6A1 show increased AUC to CA19.9 alone, with the combination of the five biomarkers and CA19.9 showing complete separation of cancer from controls (Figure 4C).

Discussion

[00156] Deregulated molecular pathways are a hallmark of cancer and the resultant secretion, shedding and aberrant cleavage of proteins by tumor cells and their microenvironment present a way in which to detect and track tumor development and progression [4,82]. With the advent of high throughput protein profiling techniques, at the centre of which lies mass spectrometry analysis, various approaches have been taken for the identification of novel protein biomarkers and novel biomarker candidates. Serum or plasma is the desired diagnostic fluid in the clinic; however initial discovery studies in serum are hampered by the high complexity of the fluid and its large dynamic range [83]. To overcome these limitations and others posed by MS analysis of serum, researchers have turned to the characterization of proteomes of less complex biological fluids that are 'upstream' of plasma. Due to the proximity of selected biological fluids to tumor cells and tissues, their proteomes likely represent a reservoir of proteins enriched in potential biomarkers prior to dilution upon entering the circulation [16,18, 19].

[00157] Although many notable differences exist, the genomic and transcriptional make-up of cancer cell lines have been shown to recapitulate salient aspects of primary tumors [39,40, 84-86]. In addition, the identification of known biomarkers in the conditioned media of cancer cell lines for numerous cancer sites, make it a viable source to mine [28,87]. Previously, the CM of breast, ovarian, prostate and lung cancer-related cell lines has been characterized using 3-4 cell lines per cancer site [29-32]. Using an LTQ mass spectrometer, 1 139, 1830, 2124 and 2039 proteins were identified with at least one peptide in the breast [29], lung [32], prostate [30] and ovarian cancer [31] analyses, respectively. Given the vast heterogeneity of the disease it was concluded that a larger number of cell lines per cancer site, as well as the incorporation and integration of proximal biological fluids from patients may provide a more complete picture of disease heterogeneity and the tumor-host interface, there-by facilitating the identification of stronger candidates for verification. [00158] As described herein, such an approach to pancreatic cancer was applied. By utilizing 2D LC-MS/MS, the proteomes of conditioned media from six pancreatic cancer cell lines, one near normal pancreatic ductal epithelial cell line and six pancreatic juice samples in two pools were characterized. All experiments were performed in triplicate and multiple search engines (MASCOT and X!Tandem), which employ different search algorithms, were utilized for protein identification. Previously it has been reported that use of multiple search engines results in increased confidence in the proteins identified [88,89]. Additionally, only proteins identified with multiple peptides (≥ 2 peptides) were used in the analysis. Through these means 3324 non-redundant proteins in the CM of the seven cell lines and 648 proteins in the pancreatic juice were identified. In total, 3479 non-redundant proteins were identified. This may represent one of the largest and most comprehensive proteomes to date for pancreatic cancer-related biological fluids in a single study.

[00159] An increase in protein yield of ~50% was achieved by applying a pre- fractionation strategy that was tailored to the SCX elution profile. SCX was the first dimension of fractionation in the multidimensional approach. Different modes of fractionation from isoelectric focusing (IEF) to SDS-PAGE fractionation and SCX have been previously compared, with different studies reporting different methods as the most effective when coupled with MS analysis [90-93], Fractionation of complex samples prior to MS analysis is a technique used to minimize sample complexity and penetrate deeper into the proteome, there-by achieving increased coverage of proteins. Indeed, more proteins (including those known to be of low abundance such as various interleukins) were identified through these means. A corollary of increased fractionation is typically decreased throughput. Reduced gradient times during the second dimension of separation (reverse-phase) helped to keep any increase in analysis time to a minimum.

[00160] Not all proteins identified in shotgun proteomics-driven discovery approaches will be suitable for study as serological biomarkers, and one of the challenges in the field is in the selection of the most promising candidates for further investigation. As disclosed herein, two strategies were used: (1 ) semi-quantitative analysis through label-free protein quantification between the cancer and normal cell lines and (2) integrative analysis of cell line CM and pancreatic juice. Label-free approaches typically employ chromatographic ion intensity-based methods or spectral count-based means to obtain relative quantification of proteins between LC- MS/MS run samples [94,95]. Further approximations of absolute protein abundance can be obtained through reported indices such as emPAI and absolute protein expression (APEX) [96,97]. Normalized spectral counts have been reported previously to be reliable indicators of protein abundance in studies comparing different label-free methods, and strong correlation between spectral counts and protein abundance have been shown [98]. When restricting analysis to proteins identified with five or more spectra, results comparable to label-based approaches have been shown to be obtainable [99], This method was utilized for relative quantification between the cancer cell lines and the HPDE cell line.

[00161] Using the criteria outlined in the 'Experimental Procedures', 1 19 proteins were found to be expressed over 5-fold consistently in at least three cancer cell lines. Included in this list were many proteins previously shown to be upregulated in pancreatic cancer. For instance, the protein GDF15, also known as macrophage inhibitory cytokine 1 (MIC1 ), showed > 10-fold increase in the CAPAN1 , CFPAC1 , PANC1 and SU.86.86 cell lines. Increased GDF15 mRNA and protein levels have been shown previously in pancreatic tissue in comparison to adjacent normal controls [60] and evaluation of this protein in serum has also shown it to have diagnostic potential [61 ]. Similarly, neutrophil gelatinase-associated lipocalin (LCN2) [62], matrix metalloproteinase 7 (MMP7) [58], complement component 3 (C3) [55,57] and leucine-rich alpha-2-glycoprotein (LRG1) [100] have been reported to be elevated in serum of pancreatic cancer patients, while mesothelin (MSLN) [101], tissue-type plasminogen activator (PLAT) [102], C-X-C motif chemokine 5 (CXCL5) [103] and other proteins highlighted in Table 1 have been shown to be upregulated in pancreatic cancer or pancreatic neoplasia at the level of tissue and/or mRNA.

[00162] Identification of these proteins provides some credence to the label- free discovery approach; however proteomic comparisons between non-malignant and malignant biological sources are limited by the possibility that the observed differences may be due to many factors, not solely due to differences in tumorigenic potential alone. As a result, another three proteins, AGR2, PIGR and COL6A1 , were investigated in human plasma. Except possibly for AGR2, neither of these proteins have previously been studied in sera/plasma of pancreatic cancer patients. AGR2 is an orthologue of the Xenopus laevis protein XAG-2, which is a protein shown to play a role in ectodermal patterning [104]. The function of AGR2 in normal human states is largely unknown; however in human cancers, AGR2 has been associated with several cancer types [105-107] and recently, increased AGR2 levels were reported in pancreatic juice [59]. In this latter study, Chen et al., utilized quantitative proteomics to profile pancreatic juice samples from pancreatic intraepithelial neoplasia (PanIN) patients in comparison to controls and AGR2 was one of the proteins this group found to show over 2-fold increase in PanlN-stage III. While Chen et al. found diagnostic relevance for AGR2 in pancreatic juice, their analysis in 6 paired serum and pancreatic juice samples from PanIN patients found no correlation between serum and pancreatic juice AGR2 levels. Further analysis by this group in serum of 9 pancreatic cancer and 9 cancer-free controls showed no significant difference in AGR2 levels as well [59]. Despite this, given that AGR2 was highly elevated in the majority of cancer cell line CM based on spectral counting as provided herein, as well as its identification in pancreatic juice, its levels were tested in the screening set of plasma samples and a significant elevation in AGR2 levels in pancreatic cancer plasma versus controls was found (Figure 3A). AGR2 has been previously shown to play a role in invasion and metastasis [107-109], and it may be that elevated levels of this protein occur in blood in the later stages of pancreatic cancer.

[00163] PIGR has been shown previously through multiple reaction monitoring (MRM) to be increased in endometrial cancer tissue homogenates [1 10]; however it has not been studied in clinical samples from many other cancer sites. In the present study we demonstrate its significant increase in pancreatic cancer plasma. COL6A1 is an important component of microfibrillar network formation, associating closely with basement membranes in many tissues. It is an extracellular matrix protein and also found in stromal tissue [1 1 1]. Mutations in this gene play a role in muscular disorders and differential COL6A1 gene expression has been associated with astrocytomas [1 12, 1 13]; however it has not been studied in pancreatic cancer and was found to be significantly increased in our preliminary assessment in plasma. Taken together, the increased levels of these proteins in pancreatic cancer plasma demonstrate the utility of our label-free differential protein quantification approach to identify proteins relevant for study as potential serological biomarkers of pancreatic cancer and warrants the preliminary verification of the remaining candidates in serum/plasma, as well as the further investigation of these three proteins in larger sample sizes.

[00164] The identification of cancer-derived protein alterations through integration of different biological sources is also an area of interest in cancer proteomics and the integrative mining of multiple biological fluids may result in the identification of relevant candidates [16]. For instance, in a recent analysis [19], which compared the proteins/genes identified in six publications chosen arbitrarily to represent various biological sources and both proteomic and genomic data pertaining to ovarian cancer (2 cell line CM studies, 2 ascites, 1 tissue proteomics study and 1 microarray study), no proteins were found common to all 6; however two proteins were found common to four of the studies. The proteins identified were WAP four-disulfide core domain protein 2 precursor (HE4) and GRN (granulin). Both have been implicated in ovarian cancer and HE4 is a recently FDA-approved ovarian cancer biomarker [1 14]. In this respect, we looked at proteins common to the cancer CM and pancreatic juice for identification of further candidates. These proteins were also compared to a pancreatic cancer ascites proteome for additional filtering. Most, if not all, current biomarkers, such as PSA for prostate cancer, CA125 for ovarian cancer, hCG for testicular cancer, etc. are secreted and shed proteins and focus was given to extracellular and cell surface proteins as they have the highest likelihood of entering into the circulation [4,1 15]. Focus was also given to proteins highly or specifically expressed in the pancreas. If a protein is only expressed in one tissue in healthy individuals, that tissue is likely the only contributor to endogenous serum levels of that protein. As such, increasing serum contributions of such a protein due to the presence of a growing tumor may be more easily detected. Furthermore, many current biomarkers, such as PSA and hCG mentioned above are specific to one tissue [1 16].

[00165] Interestingly, the great majority of pancreas-specific proteins (as denoted by several databases) were unique to the pancreatic juice and not identified in the cell lines. Similarly, the KEGG pancreatic secretion pathway was overrepresented in the pancreatic juice proteome. In the exocrine pancreas, acinar cells are responsible for secretion of enzymes (zymogens) while ductal cells secrete primarily an alkaline fluid [1 17, 1 18]. While the majority of pancreatic cancers are ductal adenocarcinomas with pancreatic ductal cell-like properties, the cell of origin of these cancers is still unclear [1 19-120], Previously it has been shown that acinar cells, once having undergone a transformation to duct-like cells show a reduced secretion of zymogens [120], The lack of pancreas specific proteins (enzymes, zymogens, etc.) in the cell line CM may likely reflect the ductal-like nature of the cell lines, while the presence of such proteins in the pancreatic juice may be reflective its acinar cell contributions.

[00166] Among the proteins common to all three biological fluids, were several proteins shown previously to be increased in the serum of pancreatic cancer patients and studied as pancreatic cancer biomarkers, such as MUC1 (mucin 1 ) and CEACAM5 (CEA) [12, 121 , 122]. Two proteins not previously assessed in the serum/plasma of pancreatic cancer patients, OLFM4 and SYCN, were selected for verification as candidate biomarkers of pancreatic cancer. OLFM4 has been shown to promote proliferation in the PANC1 cell line by Kobayashi et al [123], and its mRNA levels were shown to be elevated in 5 cancerous, versus non-cancerous pancreatic tissue samples in the same study. OLFM4 serum protein levels have shown potential diagnostic utility for gastric cancer [124]; however this protein has not been studied in serum/plasma of pancreatic cancer patients. As disclosed herein, OLFM4 showed over 5-fold expression in the CAPAN1 cell line in comparison to the HPDE cell line. It was also identified in the pancreatic juice and ascites and preliminary assessment shows that it is significantly increased in plasma from pancreatic cancer patients (Figure 3B). Syncollin is a zymogen granule protein specific to the pancreas and is believed to play a role in the concentration and/or efficient maturation of zymogens [125]. Syncollin has been previously identified through mass spectrometry in human pancreatic juice and in the proteomic analysis of plasma from a murine pancreatic cancer model [20, 126]; however little is known about the role of this pancreas-specific protein in pancreatic cancer and other pathologies. Our data show that it is significantly elevated in human pancreatic cancer plasma through ELISA (Figure 3C). [00167] The growing consensus in this field is towards the development of panels of biomarkers, as the combined assessment of multiple molecules can result in increased sensitivity and specificity, in comparison to the assessment of molecules individually. The combination of each candidate, individually, with CA19.9 showed improved AUC to CA19.9 alone, as did the combined assessment of AGR2, OLFM4, PIGR, SYCN, and COL6A1 (Figure 4). CA19.9 has reported sensitivity and specificity values between 70%-90% (median -79%) and 68%-91 % (median -82%), respectively, for detection of pancreatic cancer (note: sensitivity decreases to ~55% in early-stage disease and CA19.9 is often undetectable in many asymptomatic individuals; specificity decreases with benign disease) [5]. CA19.9 showed a very high AUC (0.97) likely because the cancer plasma samples utilized were from patients with established (primarily late-stage) pancreatic cancer.

[00168] Three of the five proteins verified in plasma (AGR2, PIGR and OLFM4) were also identified in relevant clusters through hierarchical clustering analysis. emPAI is another means of label-free protein quantification [96], and the identification of these three proteins through emPAI-based quantification, and several other proteins, that were also identified through spectral counting, is not unexpected. Recently, Wu et al. [33], utilized emPAI values of proteins normalized through z-scores for pathway-based biomarker discovery as a part of their study of 23 human cancer cell lines. As disclosed herein, normalized emPAI values of proteins were used to gain a preliminary understanding of the dataset through hierarchical clustering analysis. The six cancer cell lines chosen for analysis are well characterized and highly studied cell lines. They contain many of the major genetic aberrations present in pancreatic cancer such as mutations in Kras, SMAD4, CD16 and TP53 [39,40]. Interestingly, the cancer cell lines derived from metastatic sites (SU-86-86, CFPAC1 and CAPAN1 ) were clustered together, while MIA-PaCa2 and PANC1 , which are cell lines derived from a primary tumor site were clustered together, as were the BxPc3 and HPDE cell lines. BxPc3 is a cancer cell line derived from a primary tumor site and HPDE is a widely used surrogate for normal pancreatic ductal epithelial cells. Incidentally these two cell lines were the only ones with wild-type Kras expression [40], a gene that is mutated in the vast majority

(>90%) of pancreatic cancers; however firm conclusions cannot be drawn regarding the clustering without further investigation. None-the-less, identification of three of the five proteins verified as candidate biomarkers of pancreatic cancer render the proteins identified in relevant clusters through normalized emPAI values a potentially viable means for the generation of biologically relevant leads.

[00169] Pancreatic cancer bodes one of the lowest five-year survival rates (<5%) of all cancer types [1 ]. This is largely associated with the existence of locally advanced or metastatic disease in the majority of patients at the time of diagnosis. Genomic sequencing studies reveal that a broad window may exist for the detection of pancreatic cancer between the initial stages of tumour development and dissemination to secondary sites [127]. Disclosed herein is the proteomic analysis of pancreatic cancer-related cell lines and pancreatic juice for the identification of novel diagnostic leads. Label-free protein quantification methods revealed a group of proteins differentially expressed in pancreatic cancer. Contained within this group were numerous proteins previously studied as pancreatic cancer biomarkers and associated with pancreatic cancer pathology. Further candidates were generated through integrative analysis of multiple biological fluids. Through a preliminary assessment, five proteins (AGR2, OLFM4, PIGR, SYCN and COL6A1 ) were shown to be significantly increased in plasma from pancreatic cancer patients. The combination of each with CA19.9 resulted in an improvement of CA19.9 alone, as did the combination of the five proteins (without CA19.9). Perfect separation of cancer from controls was achieved through the combination of CA19.9 with AGR2 and CA19.9 with the five proteins in this small sample set. The biomarker panel will be further validated with samples that have low/normal CA19.9 values and include patients with early-stage disease, as well as with benign abdominal pathologies.

Experimental Procedures

Cell Lines

[00170] Six pancreatic cancer cell lines (MIA-PaCa2 (CRL-1420), PANC1 (CRL-1469), BxPc3 (CRL-1687), CAPAN1 (HTB-79), CFPAC-1 (CRL-1918) and SU.86.86 (CRL-1837)) were obtained from the American Type Culture Collection (ATCC, Manassas, VA). The cell lines were derived from pancreatic ductal adenocarcinomas, which account for approximately 85-90% of all pancreatic cancers. The cell lines originated from primary tumors of the head or body of the pancreas (MIA-PaCa2, PANC1 , BxPc3), or from metastatic sites (CAPAN1 , CFPAC- 1 , SU.86.86) [39,40]. The cell lines were derived from individuals of similar ethnic background and age group (with the exception of CFPAC-1 ), and all of the cancer cell lines, except for BxPc3, are positive for K-ras mutations, which is found in 85- 90% of pancreatic cancers. An HPV transfected 'normal' human pancreatic ductal epithelial ceil line (HPDE) [41], provided by Dr. Ming-Sound Tsao at Princess Margaret Hospital, Toronto, Ontario, Canada was also analyzed. Apart from a slightly aberrant expression of p53, molecular profiling of this cell line has shown that expression of other proto-oncogenes and tumour suppressor genes are normal [41 ].

[00171] Cell culture media specified by ATCC for each of the six pancreatic cancer cell lines was used and are as follows: DMEM (Catalog No. 30-2002 from ATCC) with 10% fetal bovine serum (Catalog No.10091 -148; Invitrogen) was used for MIA-PaCa2 and Panel ; RPMI-1640 medium modified to contain 2 mM L- glutamine, 10 mM HEPES, 1 mM sodium pyruvate, 4500 mg/L glucose, 1500 mg/L sodium bicarbonate (ATCC Catalog No. 30-2001 ) with 10% FBS was used for SU.86.86 and BxPc3; IMDM (Catalog No. 30-2005) with 10% and 20% FBS was used for the CFPAC-1 and Capanl cell lines, respectively. The HPDE cell line was grown in keratinocyte serum free media (Catalog No.17005-042; Invitrogen) supplemented with bovine pituitary extract and recombinant epidermal growth factor. All cells were cultured in an atmosphere of 5% CO2 in air in a humidified incubator at 37°C. Cell Culture

[00172] An optimal seeding density and incubation period which supported maximal protein secretion with minimal cell death was selected for each of the cell lines, as described previously [29]. Cells were cultured in T-175 cm² flasks at determined optimal seeding densities of - 10 X 10⁶, for MIA-PaCa2 and Panel , 14 X 10⁶ for BxPc3, 3 X 10⁶ for HPDE, 10 X 10⁶ for Capanl , 13 X 10⁶ for CFPAC1 and 4 X 10⁶ for Su.86.86 in three replicates per cell line. Cells were first cultured for 48 hours in 40ml_ of their respective growth media to obtain adherence to culture flasks. The media was then removed and the cells/flasks were subjected to two gentle washes with 30ml_ of PBS (Invitrogen). Forty milliliters of chemically defined Chinese hamster ovary (CDCHO) serum-free medium (Invitrogen) supplemented with 8 mM glutamine (Invitrogen) was then added and the cells were left to culture for determined optimal incubation periods of 72 hours for Capanl , CFPAC1 and SU.86.86, 96 hours for BxPc3 and HPDE and 144 hours for MIA-PaCa2. The CDCHO media that the cells were grown in were subsequently collected and centrifuged at 1500 rpm for 10 minutes to remove cellular debris. Total protein concentration (as determined through a Coomassie (Bradford) total protein assay [42]) was measured in each of the three replicates and a volume corresponding to 1 mg of total protein from each of the replicates was subjected to the sample preparation protocol below.

Pancreatic Juice

[00173] Pancreatic juice samples were provided by Dr. Felix Ruckert, Dresden, Germany. Approximately 50-500 μΙ_ of pancreatic juice was collected from the main pancreatic duct of patients undergoing pancreatic surgery. Upon collection, the samples were stored in -80°C until further use. Samples from patients with clinically confirmed cases of pancreatic ductal adenocarcinoma that contained no visible signs of blood were selected for analysis. Six pancreatic juice samples met these criteria. The samples were centrifuged at 16,000 rpm for 10 minutes at 4°C to remove tissue debris. Total protein concentration of each sample was measured using the Biuret method [43]. Keeping in line with the cell line conditioned media analysis, it was desirable to use a total protein amount of 1 mg for analysis of each of the three replicates per sample. As a result, two pools of pancreatic juice (pool A and B) were made, containing three samples each, with total protein concentrations of 2.65 mg/mL and 2.32 mg/mL for pool A and B, respectively. A volume corresponding to 1 mg of total protein was retrieved from each pool, in triplicate, and subjected to the standardized sample preparation protocol below (with the exception of dialysis).

Sample Preparation

[00174] Samples were processed as described previously [29]. Briefly, samples were dialyzed using a 3.5 kDa molecular weight cut-off membrane (Spectrum Laboratories, Inc., Compton, CA) in 5 L of 1 mM NH₄HC0₃ buffer solution at 4°C overnight and subsequently frozen and lyophilized to dryness to concentrate proteins using a ModulyoD Freeze Dryer (Thermo Electron Corporation). Proteins in each lyophilized replicate were denatured using 8 M urea and reduced with the addition of 200 mM dithiothreitol (final concentration of 13 mM) in 1 M NH₄HC0₃ at 50°C for 30 minutes. Samples were then alkylated with the addition of 500 mM iodoacetamide and incubated in the dark, at room temperature, for 1 hour. Each replicate was then desalted using a NAP5 column (GE Healthcare), frozen and lyophilized. Lastly, samples were trypsin-digested (Promega, sequencing-grade modified porcine trypsin) through an overnight incubation at 37°C using a ratio of 1 :50 trypsin to protein concentration. Tryptic peptides were frozen in solution at -80°C to inhibit trypsin function and lyophilized.

Strong Cation Exchange (SCX) on a High Pressure Liquid Chromatography (HPLC) System

[00175] The tryptic peptides were resuspended in 510 pL of mobile phase A (0.26 M formic acid in 10% acetonitrile; pH 2-3) and loaded directly onto a 500 pL loop connected to a PolySULFOETHYL A™ column (The Nest Group, Inc.). The column has a silica-based hydrophilic, anionic polymer (poly-2-sulfoethyl aspartamide) with a pore size of 200 A and a diameter of 5 pm. The SCX chromatography and fractionation was performed on an HPLC system (Agilent 1 100) using a 1 -hour procedure with a linear gradient of mobile phase A. For elution of peptides, an elution buffer which contained all components of mobile phase A with the addition of 1 M ammonium formate was introduced at 20 min in the 60 min method. The eluent was monitored at a wavelength of 280 nm and fractions were collected every minute from the 20 minute time point onwards. This resulted in the collection of 40 one-minute fractions. Collected fractions were left unpooled or subsequently combined into 2, 3 or 5 min pools, according to the elution profile of the resulting SCX chromatogram. As a general strategy, where the absorbance reading of the elution profile was greater (typically the first 10-15 min of elution), fractions were left unpooled or pooled every two minutes to keep sample complexity at a minimum. Where the absorbance readings were lower (towards the end of the method), fractions were pooled in 3 or 5 min pools. The same pooling method was utilized for all three replicates of the CM from each cell line and for the pancreatic juice pools.

Mass spectrometry (LC-MS/MS)

[00176] The SCX fractions/pools were purified through OMIX Pipette Tips C18

(Varian Inc.) to further remove impurities and salts and eluted in 4 pL of 70% MS Buffer B (90% ACN, 0.1 % formic acid, 10% water, 0.02% TFA ) and 30% MS Buffer A (95% water, 0.1 % formic acid, 5% ACN, 0.02% TFA). Eighty microlitres of MS Buffer A was added to the eluent, and 40 μΙ_ of sample was loaded onto a 3 cm Ci₈ trap column (with an inner diameter of 150 pm; New Objective), packed in-house with 5 pm Pursuit C18 (Varian Inc.). A 96-well microplate autosampler was utilized for sample loading. Eluted peptides from the trap column were subsequently loaded onto a resolving analytical PicoTip Emitter column, 5 cm in length (with an inner diameter of 75 μιη and 8 pm tip, New Objective) and packed in-house with 3 pm Pursuit C18 (Varian Inc.). The trap and analytical columns were operated on the EASY-nLC system (Proxeon Biosystems, Odense, Denmark), and this liquid chromatography setup was coupled online to an LTQ-Orbitrap XL hybrid mass spectrometer (Thermo Fisher Scientific, San Jose, California) using a nano-ESI source (Proxeon Biosystems, Odense, Denmark). Samples were analyzed using a gradient of either 54 or 90 minutes (for 5 min pools, a 90 minute gradient was used, and for 2min, 3min and non-pooled samples, a 54 minute gradient was used). Samples were analyzed in data dependent mode and while full MS1 scan acquisition from 450-1450 m/z occurred in the Orbitrap mass analyzer (resolution 60,000), MS2 scan acquisition of the top six parent ions occurred in the linear ion trap (LTQ) mass analyzer. The following parameters were enabled: monoisotopic precursor selection, charge state screening and dynamic exclusion. In addition, charge states of +1 , >4 and unassigned charge states were not subjected to MS2 fragmentation.

Protein Identification

[00177] XCalibur software was utilized to generate RAW files of each MS run. The RAW files were subsequently used to generate Mascot Generic Files (MGF) through extract_msn on Mascot Daemon (version 2.2). Once generated, MGFs were searched with two search engines, Mascot (Matrix Science, London, UK; version 2.2) and X!Tandem (Global Proteome Machine Manager; version 2006.06.01), to confer protein identifications. Searches were conducted against the non-redundant Human IPI database (v.3.62) which contains a total of 167,894 forward and reverse protein sequences and using the following parameters: fully tryptic cleavages, 7 ppm precursor ion mass tolerance, 0.4 Da fragment ion mass tolerance, allowance of one missed cleavage, fixed modifications of carbamidomethylation of cysteines, and variable modification of oxidation of methionines. The files generated from MASCOT (DAT files) and X!Tandem (XML files) for the three replicates of each biological source were then integrated through Scaffold 2 software (version 2.06; Proteome Software Inc., Portland, Oregon) resulting in a non-redundant list of identified proteins per sample. Results were filtered using the X!Tandem LogE filter and Mascot ion-score filters on Scaffold to achieve a protein false discovery rate (FDR) <1.0%.

Data Analysis

[00178] Scaffold prot-XML reports were generated and uploaded onto Protein Center (Proxeon) to facilitate comparisons between cell line CM and pancreatic juice proteomes, and to obtain gene ontology (GO) information. Cellular localization, function and process annotations were extracted by Protein Center from the Gene Ontology (GO) Consortium (http://www.geneontologv.org/GO.tools.shtml). Due to the large number of different GO annotations per localization, function and process, Protein Center reduces terms to approximately 20 high-level terms that are used for filtering. Details can be found at http://tgh.proteincenter.proxeon.com/ProXweb/Help/Manual/apd.html. A Microsoft Excel Macro developed in-house by Dr. Irv Bromberg, Mount Sinai Hospital, was also utilized for comparison of protein lists based on accession number or gene name. Hierarchical clustering analysis of proteomic data was performed using PermutMatrix, available freely online at http://www.lirmm.fr/~caraux/PermutMatrix/EN/index.html. PermutMatrix was a software originally developed for gene expression analysis [44]. More recently it has been utilized and validated for proteomics [45]. For clustering analysis, average emPAI values from the triplicate analysis of the samples were exported from Protein Center into a space delimited Microsoft Excel file. For visualization, comparison and data analysis purposes, cell line or pancreatic juice samples with missing emPAI values for a particular protein were assigned half the minimum emPAI value for that protein in the data set. The emPAI values were imported into PermutMatrix and transformed to Z score values for normalization. Two-way hierarchical clustering analysis was performed using the Pearson and Ward's minimum variance methods for distance and aggregation, respectively. Resultant dendograms with cell lines and pancreatic juice samples on the x-axis and gene name on the y-axis were exported. Lastly, Ingenuity Pathway Analysis (IPA, Ingenuity Systems, www.ingenuity.com), which uses a knowledge-base from literature, was used to obtain disease associations of groups of proteins and their associated pathway interactions.

Label-free Protein Quantification

[00179] Semi-quantitative analysis was conducted between the cancer cell lines and the HPDE normal pancreatic ductal epithelial cell line to ascertain proteins over or under-expressed in the cancer cell lines based on spectral counting. The

'Quantitative Value' function of Scaffold 2.06 software, which provides normalized spectral counts based on the total number of spectra identified in each sample was utilized. One file containing all of the normalized spectral counts of each of the three replicates from the 7 cell lines was generated for proteins identified with 2 or more peptides. One-way ANOVA was conducted to determine proteins that show a significant difference amongst the seven cell lines (p < 0.05). For proteins that showed a p value <0.05, the average spectral count for the three replicates was calculated and fold-change was determined by dividing the average counts from each of the cancer cell lines with that of the normal HPDE cell line and vice versa. Not all proteins were identified in all of the cell lines; however all proteins had to have been identified by ten or more spectra in at least one biological sample to be included. Proteins with ambiguous peptides were searched individually to ensure normalization of spectral counts did not significantly alter values. Unidentified proteins or missing values in a particular biological sample were assigned a normalized spectral count of 1 to keep from dividing by zero and to prevent overestimation of fold-changes.

Plasma Samples

[00180] Blood samples were collected from pancreatic cancer patients at the Princess Margaret Hospital Gl Clinic in Toronto, Canada, or from kits sent directly to consented patients recruited from the Ontario Pancreas Cancer Study at Mount Sinai Hospital following a standardized protocol (age range 55-86; median age 68; 10 female and 10 male). Samples were collected with informed consent, and with the approval of the institutional ethics board. Samples from healthy controls were obtained from the Familial Gastrointestinal Cancer Registry (FGICR). The controls are non-blood relatives of patients in FGICR studies (age range 46-84; median age 60; 9 female and 1 1 male). Blood was collected in ACD (anticoagulant) vacutainer tubes and plasma samples were processed within 24 hours of blood draw. To pellet the cells, blood samples were centrifuged at room temperature for 10 minutes at 913 X g. Immediately after centrifugatlon, the plasma samples were aliquoted into 250 μΐ. cryotubes and stored in -80°C or liquid nitrogen until further use.

ELISAs and Immunoassays

[00181] Enzyme-linked immunosorbent assays for AGR2, SYCN, OLFM4, COL6A1 and PIGR were purchased commercially and performed according to the manufacturer's instructions. The five ELISA kits were purchased from USCN LifeSciences (AGR2: Catalogue # E2285Hu, SYCN: Catalogue # E93879Hu, OLFM4: Catalogue # E90162Hu, COL6A1 : Catalogue # E92150Hu; PIGR: Catalogue # E91074Hu). The ELECSYS CA 19-9 immunoassay by Roche was utilized to measure CA 9.9 levels in plasma and kallikrein 6 and 10 internal control proteins were measured in CM using in-house developed ELISA assays, as described previously [46,47].

Statistical Analysis

[00182] Mann-Whitney U-tests were applied to verification experiments in plasma to determine if differences in the medians were significant between cancer and control groups using Graph Pad Prism 4 Software. The five candidates that showed a statistically significant difference (p < 0.05) were then assessed in combination in comparison to CA19.9 through ROC curve analysis. The area under the curves (AUC) were calculated using ROCR software and the corresponding variances were calculated with a bootstrap method.

EXAMPLE 2

[00183] Appropriate validation of potential biomarkers requires the use of clearly defined clinical specimen, appropriate controls and a large number of samples (preclinical, early and late-stage, benign disease, healthy controls) [128]. Further validation of these five proteins assessed in Example 1 is conducted in larger cohorts of samples (early and late-stage pancreatic cancer, benign disease and healthy controls). These proteins are also considered in the development of biomarker panels for pancreatic cancer. [00184] The biomarkers will be validated in a larger number of samples. Specifically, concentrations of the five proteins (AGR2, SYCN, PIGR, COL6A1 and OLFM4) will be measured in pancreatic cancer serum samples from patients with early and late-stage disease, benign controls, and healthy controls. CA19.9 will also be assessed in these samples and appropriate statistical analysis will be performed to assess the utility of the candidates for detection of pancreatic cancer (individually and in combination). It is expected that with addition of early-stage cancer and benign disease controls, the AUC of CA19.9 will decrease (to -0.70-0.75 which are levels described in literature).

[00185] Additionally, other Table 1 , 2, 3, and 4 biomarkers will be assessed for their levels in serum/plasma samples from subjects with pancreatic cancer for diagnostic utility.

[00186] Pre and post treatment (surgery, chemotherapy, radiation therapy) serum samples are used to assess levels of these proteins.

EXAMPLE 3

[00187] Pancreatic cancer is a devastating disease for which clinically useful serum biomarkers are urgently needed. Pancreatic cancer is the tenth most common cancer type; however it is the fourth leading cause of cancer-related deaths [130]. Due to the absence of specific symptoms in the early stages of pancreatic cancer development, it is most often diagnosed in the later stages, once the malignancy has progressed to surrounding structures or distant sites. Such locally advanced and metastatic disease is refractory to standard chemotherapy / radiation regiments; it carries with it a median survival time of 8-12 or 5-8 months for locally advanced and metastatic disease, respectively [131 , 132].

[00188] Diagnosis of very small, early-stage tumors that can be surgically resected offers patients the best chances for survival and can increase five-year survival rates from 5% to 20-30% [130, 133]. Unfortunately, given the asymptomatic nature of the early stages, the aggressive nature of pancreatic cancer, and limitations of current detection technologies, fewer than 10% of patients are diagnosed early. Additionally, a large number of early diagnoses are due to incidental findings during abdominal imaging procedures [134]. [00189] Currently, detection of pancreatic cancer is largely based on imaging techniques, targeting pancreatic masses or suspected cancerous lesions in individuals who either present with nonspecific abdominal complaints, or symptoms suggestive of pancreatic cancer, such as painless jaundice and weight loss [135]. There are conflicting reports as to which imaging method shows superiority for the clinical assessment of pancreatic cancer and a combination of techniques may be utilized, based on the clinical question and practice preferences [3, 135-139]. High- resolution, contrast-enhanced cross sectional computed tomography (CT), in particular, which enables the acquisition of thin image slices (5mm) from the base of the lungs to the pelvis, is a widely used technique for pancreatic cancer detection [136], By examining contour abnormalities within the pancreas and surrounding ducts and arteries, CT can also facilitate assessment of staging, tumor resectibility, and post-operative follow-up in patients with established pancreatic cancer [135,136], Endoscopic ultrasound (EUS) has also emerged as a sensitive means for the detection of pancreatic tumor masses [137, 138]. Through a combination of real- time endoscopy and high-frequency ultrasound, EUS is used to image the pancreas through the gastric and duodenal walls. The close proximity at which images are obtained has enabled EUS to overcome confounding effects caused by gaseous and boney structures overlying the pancreas. In this respect, EUS has been shown useful for the detection and evaluation of small (minimum size 2-3mm) focal lesions [137, 138]. Other techniques for detection and assessment of pancreatic cancer include magnetic resonance imaging (MRI) and positron emission tomography (PET), the latter of which is used largely for the detection of metastasis and the former, providing better imaging of cystic lesions and the benefit of no radiation exposure [139]. Certain definitive diagnoses of pancreatic cancer may require more invasive means such as endoscopic retrograde cholangiopancreatography (ERCP) which enables tissue sampling, acquisition of a computed tomography (CT)-guided biopsy or endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) [3, 139].

[00190] The major drawback of all of these methods for the optimal management of pancreatic cancer patients is that they are primarily utilized after the onset of symptoms (i.e. predominantly after the onset of late stage disease). In addition, given the rarity of the disease (with an incidence of —1 1.9 per 100,000 per year in the United States) [140], screening the general population for pancreatic cancer is not recommended [8, 141 -143]. At many institutions, imaging methods have been implemented for routine screening of asymptomatic individuals within high risk populations. Screening is recommended for individuals with a relative risk >10-fold compared to the general population [144]. This includes individuals with Peutz- Jegher's syndrome (STK11 mutations; > 100-fold increased risk of pancreatic cancer development), hereditary pancreatitis (PRSS1 mutations; ~50-67-fold increased risk), familial atypical multiple mole melanoma (FAMMM, p 16 mutations; -13-39-fold increased risk), individuals with multiple family members with pancreatic cancer, and individuals with hereditary breast or ovarian cancer, and comprises approximately 10% of all pancreatic cancer cases [142], In general, studies using different imaging modalities have shown screening in such groups to be efficacious in detecting pancreatic cancer and precancerous lesions; although in certain patients, accelerated development of metastasis may still prevent detection of early-stage tumors [143]. Additionally, imaging methods have associated high operating costs, and can be relatively time-consuming or invasive. In this regard, the identification of highly sensitive and specific serum biomarkers for pancreatic cancer can greatly offset such limitations and likely enhance current screening and detection protocols.

[00191] In addition to detection, tumor markers to aid in the determination of operability of established pancreatic cancer, the identification of disease recurrence, and for monitoring response to treatment would also be highly clinically beneficial [145], Currently, carbohydrate antigen 19.9 (CA19.9) is the most widely used marker in the clinic for pancreatic cancer. CA-19.9 is a sialylated lewis A antigen found on the surface of proteins [5,6]. It has a reported diagnostic sensitivity of 70%-90% (median -79%) and specificity of 68%-91 % (median -82%) [5]. While elevated CA- 19.9 levels have been associated with the advanced stages of the disease, they have also been associated with benign and inflammatory diseases such as obstructive jaundice, pancreatitis, as well as other malignancies of the gastrointestinal system [6,7, 146, 147]. For early-stage pancreatic cancer detection, CA-19.9 has a reported sensitivity of -55% for early stage disease and is often undetectable in many asymptomatic individuals [5,8], In addition, CA-19.9 is associated with Lewis antigen status and is absent in individuals with blood group a b^" (-10% of the general population) [148]. Taken together, CA19.9 lacks the necessary sensitivity and specificity for early pancreatic cancer detection and according to the American Society of Clinical Oncology Tumor Markers Expert Panel, CA19.9 is recommended only for monitoring response to treatment in patients who had elevated levels prior to treatment [145].

[00192] Extensive proteomic analysis of conditioned media (CM) from six pancreatic cancer cell lines, one normal pancreatic ductal epithelial cell line and six pancreatic juice samples using two dimensional LC-MS/MS is described above [149]. Specifically, our triplicate analysis of the BxPc3, MIA-PaCa2, PANC1 , CAPAN1 , CFPAC1 , SU.86.86 and HPDE cell line CM, and pancreatic juice samples resulted in the identification of 3479 non-redundant proteins with two or more peptides. Through subsequent examination of differential protein expression between the cancer and normal cell lines using relative label-free protein quantification and integrative analysis, focusing on the overlap of proteins between the multiple biological fluids, cellular localization and tissue specificity, candidate biomarkers for verification were elucidated. Preliminary verification of 5 candidate proteins, anterior gradient homolog 2 (AGR2), syncollin (SYCN), olfactomedin-4 (OLFM4), polymeric immunoglobulin receptor (PIGR), and collagen alpha-l (VI) chain (COL6A1 ) in 20 plasma samples from pancreatic cancer patients and 20 healthy individuals of similar age/sex using enzyme-linked immunosorbent assays (ELISAs) showed a significant elevation of these proteins in plasma from pancreatic cancer patients [149]. The present study details a more extended validation in larger sample sets of SYCN, AGR2 and PIGR, along with two other candidates generated from our previous research, regenerating islet-derived 1 beta (REG1 B) and lysyl oxidase-like 2 (LOXL2).

MATERIALS & METHODS

Serum and Plasma Samples

[00193] This retrospective study population consisted of 480 individuals which was comprised of the following: 183 patients with established pancreatic ductal adenocarcinomas (PDAC or pancreatic cancer), 165 healthy controls (either non- blood relatives of patients in the Familial Gastrointestinal Cancer Registry, Toronto, Canada, or from pancreatic-disease free volunteer donors at the University of Arkansas Cancer Research Center), 44 benign disease patients (which included 10 intraductal papillary mucinous neoplasms (IPMNs), 14 adenomas of the pancreas and of other gastrointestinal (Gl) regions such as the duodenum, and 20 pancreatitis samples (18 chronic and 2 acute)), as well as 88 patients with other Gl malignancies (primarily colon, but also liver, stomach and ampullary cancer, and 18 pancreatic endocrine tumors) (Table 5). All samples were kindly provided by Dr. Randy Haun at the University of Arkansas Cancer Research Center and Dr. Steven Gallinger at the University Health Network, Toronto, Canada. Blood samples consisted of both serum and plasma. Plasma samples (100 PDAC and 92 healthy controls that are non-blood relatives of pancreatic cancer patients) were provided by Dr. Gallinger's group and collected from pancreatic cancer patients at the Princess Margaret Hospital Gl Clinic in Toronto, Canada, or from kits sent directly to consented patients recruited from the Ontario Pancreas Cancer Study at Mount Sinai Hospital following a standardized protocol. The remainder of the samples were serum samples provided by Dr. Haun's group. Blood was collected in ACD (anticoagulant) vacutainer tubes and plasma samples were processed within 24 hours of blood draw. To pellet the cells, blood samples were centrifuged at room temperature for 10 minutes at 913 X g. Immediately after centrifugation, the plasma samples were aliquoted into 250uL cryotubes and stored in -80°C or liquid nitrogen until further use. Measurement of CA19.9, AGR2, REG1 B, SYCN, LOXL2 and PIGR levels in serum/plasma

[00194] CA19.9 levels were measured using a commercially available immunoassay (ELECSYS by Roche) and performed according to manufacturer's instructions. Enzyme linked- immunosorbent assay kits were purchased for AGR2, REG1 B, SYCN, LOXL2 and PIGR from USCN LifeSciences (AGR2: Catalogue # E2285Hu; SYCN: Catalogue # E93879Hu; REG1 B: Catalogue # E94674Hu; PIGR: Catalogue # E91074Hu; LOXL2: Catalogue # E95552Hu). ELISAs were performed according to manufacturer's instructions with slight modifications. Briefly, 100uL of sample was incubated in pre-coated 96-well plates for 2 hours in 37 °C, along with standards. Samples were diluted in phosphate buffered saline as instructed, using a 1 in10 dilution for SYCN and AGR2, 1 in 100 dilution for LOXL2 and 1 in 2000 dilution for REG1 B and PIGR. Plates were washed 2 times using the wash buffer provided in the kits (where-as manufacturer's instructions indicate no washing needed at this stage). A biotin-conjugated polyclonal secondary antibody specific for each of the proteins (detection reagent A from USCN kit) was prepared and incubated for 1 hour in 37 °C. Following 4 washes, horseradish peroxidase (HRP) conjugated to avidin (detection reagent B from USCN kit) was prepared and incubated for 30 min at 37 °C. The plates were washed 4 times and GOLIL of tetramethylbenzidine (TMB) substrate was added to each well Wells were protected from light and incubated in 37 °C for 10-15 min or until the two highest standards were not saturated (based on visual examination of colour change). Fifty microlitres of stop solution (sulphuric acid solution provided in USCN kit) was added and the colour change was measured spectrophotometrically using the Perkin-Eimer Envision 2103 multilabel reader at a wavelength of 450 nm (540nm measurements were used to determine background).

Statistical analysis

[00195] All comparisons of medians between serum/plasma and normal/healthy groups were performed by the Mann-Whitney U test, as the distribution of concentrations deviated from normality. Correlation of markers with age was evaluated by a linear regression model, fitting age to the log₂ transformed marker concentration of healthy control data (n=165). The diagnostic value of the proteins was further assessed using receiver operating characteristic (ROC) curve analysis and area under the curve (AUC) calculations. Confidence intervals (95%) for AUC were calculated by taking 2000 stratified bootstrap samples. DeLong's test for two correlated ROC curves was used to assess differences of the AUC between two ROC curves. Additionally, Spearman's rank correlation rho statistic was calculated to evaluate the significance of correlation between CA19.9 and each of the tested candidates.

[00196] Multi-parametric models for combinations of markers were evaluated using a logistic regression model. The log₂ transformed marker concentrations were used as predictors on a logistic regression model against the outcome (healthy vs PDAC). The estimated coefficients of the model were used to construct a composite score for each observation which was used for the construction of the ROC curves and subsequent analysis.

[00197] All parts of the statistical analysis were performed in the R environment (version 2.14.0). The library pROC was used for ROC curve construction and analysis (http://web.expasy.org/pROC/). RESULTS · ^. · ^{. ,} .

Association of biomarkers with age

[00198] To assess the effect of age on marker levels, we used a linear model to examine the correlation of marker concentrations with age in healthy controls (n=165), as described in the statistical analysis section above. Of the five validated candidates (SYCN, AGR2, REG1 B, LOXL2 and PIGR), AGR2 and PIGR displayed a significant correlation with age; however the percentage of explained variance is small and does not exceed 5% (Table 6).

Plasma-serum differences

[00199] Since the samples utilized in this study consisted of both serum and plasma (serum from Dr. R. Haun's group and plasma from Dr. S. Gallinger's group), to determine if the serum and plasma data were comparable, a Wilcoxon two-sample test was performed. Healthy controls between Dr. Gallinger's samples and Dr. Haun's samples were compared for each marker, as were pancreatic cancer samples between the two sample-sets for each marker (Table 7, Figure 5). A significant difference between the plasma and serum samples was seen in healthy controls for CA19.9, AGR2, OIFM4 and PIGR., A significant difference between the plasma and serum samples was seen for all markers except AGR2, OIFM4 and Col6A1 in pancreatic cancer patients. Due to these differences, significance tests in the plasma sample set and serum sample set were conducted both separately, and then together.

Distribution of AGR2, SYCN, REG1 B, LOXL2, PIGR and CA19.9 in pancreatic cancer and healthy controls - separate analysis of serum samples and plasma samples

[00200] Cases and controls were analyzed separately in the serum and plasma sample sets (Table 8). SYCN, REG1 B and PIGR showed a significant difference between pancreatic cancer and healthy controls in both the serum and plasma sample sets. AGR2 and LOXL2 showed a significant difference in only one of the sample sets (between the pancreatic cancer and healthy serum sample set for

AGR2, and between the two groups in the plasma sample set for LOXL2). Levels of CA19.9 were also assessed for comparison purposes and a significant difference between pancreatic cancer and healthy controls was noted in both serum and plasma for CA19.9 (Table 8). To assess biomarker performance ROC curve analysis was performed. GA19.9 was shown to have the greatest AUC in both sample-sets (AUC = 0.82 in- both, 95% confidence intervals (C!) ), with REG1 B showing the next highest AUC (0.74 with 95%. CI of 0 67,-0.81 in the plasma samples and AUC of 0.77 , with 95%· CI of 0.69-0.·84 in the serum samples) (Table 8).

[00201] A combined analysis was also performed, combining the serum and plasma samples (Table 9, Figure 6). As shown in Table 5, with regards to cancer and healthy control samples, the plasma sample set consisted of plasma (Gallinger) from 92 healthy individuals and 100 pancreatic cancer patients, and the serum (R. Haun) sample set consisted of serum from 73 healthy individuals and 83 pancreatic cancer patients. Although it was shown above that a significant difference between the plasma and serum samples can be noted for certain candidates, since CA19.9 was also analyzed in these samples, a combined analysis (including both serum and plasma data) was also performed and used for comparison of candidates with CA19.9 (Table 9). Combining samples increases the sample sizes of each group and provides tighter bounds for confidence intervals.

[00202] In the combined analysis, a significant difference between pancreatic cancer and healthy controls was noted for SYCN, AGR2, REG1 B and PIGR, as well as for CA19.9 (Table 9). The AUC of calculated ROC curves was highest for CA19.9 (AUC = 0.82; 95% CI of 0.77-0.86), followed by REG1 B (AUC = 0.75; 95% CI of 0.70-0.80) and SYCN (AUC = 0.73; 95% CI of 0.68-79) (Table 9; Figure 7). Correlation of markers

[00203] Spearman's rank correlation coefficient was evaluated to assess correlation between each of the candidates and CA19.9 (Table 10). A significant correlation was noted for REG1 B, AGR2 and PIGR with CA19.9.

Biomarker Panel Modeling

[00204] Combinations of biomarkers (with and without CA19.9) were modeled in combinations of two markers (Table 1 1 lists all two marker combinations for the candidates and respective AUC values). The top five combinations are presented in

Table 12 (this also includes one combination of a three marker panel of CA 9.9,

REG1 B and SYCN). The combinations of CA19.9 + REG1 B (AUC 0.853; p=0.0055), CA19.9 + SYCN (AUC 0.865; p=5.21 E-04) and CA19.9 + REG1 B + SYCN (AUC 0.884; p= 3.58E-05) showed a significant improvement to the AUC of CA19.9 alone (0.815). The combination of CA19.9 + SYCN also afforded the greatest sensitivity (65.1 %) at 95% specificity (Table 12).

Distribution of AGR2, SYCN. REG1 B, LOXL2 and CA19.9 in early stage PDAC and controls

[00205] Of the samples for which clinical staging information was available, there were 60 early stage (stage I and II) PDAC cases and we further assessed the ability of our candidates to distinguish early stage PDAC from healthy controls (n=165) (Table 13). While CA19.9 performed the best (AUC of 0.80; 95% CI of 0.72-

0.87), SYCN also showed similar performance in the early-stage versus healthy controls analysis (AUC of 0.78; 95% CI of 0.71 -0.84). AUCs of all candidates are presented in Table 13 for this analysis. Distribution of markers is presented in box- plot form across all stages, including stage l/ll and healthy controls, in Figure 8.

[00206] Due to the lack of one single highly sensitive and specific marker for many diseases, including for various measurable outcomes of pancreatic cancer, research has shifted to the identification of panels of markers to achieve enhanced performance [150-152]. In the current study, five pancreatic cancer biomarker candidates (SYCN, REG1 B, AGR2, LOXL2 and PIGR) were validated in 480 serum/plasma samples. Individually, these markers do not outperform CA19.9 in this sample set (although the performance of SYCN was comparable to CA19.9 for early stage detection); however the combination of SYCN+CA19.9 and REG1 B+CA19.9 and SYCN+REG1 B+CA19.9 significantly improved the performance of CA19.9 alone, from 0.82 to 0.88 for the best of the tested combinations. These candidates were initially identified through a comprehensive proteomics-based discovery approach described in our recent publication [149]. It is highly likely, that with the addition of several of other candidates presented in for example Tables 4 or Tables

1 , 2 or 3], a further improvement in performance of this panel can be achieved.

[00207] The levels of SYCN, AGR2, REG1 B, LOXL2, PIGR, OLFM4,COL6A1 in figures 5,6 and 8 and tables 7,8,9,13 are reported in ug/L(micrograms per liter). The levels of CA19.9 are reported in U/ml (Units per milliliter).

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a Proteome of ascites samples from pancreatic cancer patients (Makawita et al., unpublished).

b Identification in 12,787 protein containing plasma proteome database [63].

FC, fold change between cancer cell line and HPDE; %CV, percent coefficient of variation in normalized spectral counts for triplicates of cell line; PJ, pancreatic juice

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a Ascites fluid proteome from 3 pancreatic cancer patients

b Identification in 12,787 protein containing plasma proteome database [63]. PDAC, pancreatic ductal adenocarcinoma

Table 3. List of 15 Pancreas Specific Proteins³ (>3 databases) Identified in CM and Pancreatic Juice

^a Pancreas-specific proteins as it applies to this table indicates proteins identified in at least three out of the four databases queried as highly/preferentially expressed in the normal pancreas.

HPA, Human Protein Atlas; TiSGeD, Tissue-Specific Genes Database; TiGER, Tissue-specific and Gene Expression and Regulation

Table 4. Selected candidates from label-free quantitative analysis and integrated analysis

High abundance serum proteins and acute-phase reactant proteins commonly upregulated in inflammation have also been removed.

Table 5. Sample demographics.

Table 6. Correlation of candidate biomarkers and CA19.9 with age. A linear model to examine the correlation of marker concentrations with Age, in healthy controls (n=165). The log2 transformed concentration of each marker was fitted into a linear model using age as a predictor. Overall, only AGR2 and PIGR displayed a significance correlation with age, however the percentage of explained variance is small and doesn't exceed 5%.

Table 7. Comparison between concentration of markers in serum and plasma.

The median value of each group is shown and a Wilcoxon two-sample test was performed to assess difference in medians between serum and plasma samples. The Gallinger group consisted of plasma samples and the R. Haun group consisted of serum samples.

Table 8. Sample characteristics, significance tests and AUC values for AGR2, SYCN, REG1 B, LOXL2, PIGR and CA19.9 analyzed separately in the serum sample set and plasma sample set. The p-value refers to a comparison between PDAC and Healthy subgroups (Mann-Whitney non-parametric test). Sample sizes are provided in table 5.

AUC, area under the receiver operating characteristic curve; PDAC, pancreatic ductal adenocarcinoma (analogous to use of the term pancreatic cancer elsewhere herein).

Table 9. Characteristics, significance tests and AUC values for AGR2, SYCN, REG1 B, LOXL2, PIGR and CA19.9 in a combined analysis of the serum and plasma sample sets. The p-values were calculated using a Mann-Whitney significance test. Confidence intervals for AUC are calculated by taking 2000 stratified bootstrap samples.

Table 10. Spearman correlation between CA19.9 and 5 candidate biomarkers.

Spearman's rank correlation rho is shown along with the associated p-value. REG B, AGR2 and PIGR are significantly correlated with CA19.9

Table 11. Area under the curve (AUC) for two marker combinations. Biomarker panel modeling was conducted using a logistic regression model.

Table 12. Biomarker panel modeling using a logistic regression model. The combination of CA19.9 + SYCN, CA19.9 + REG1 B and CA19.9 + REG1 B + SYCN significantly improve the performance of CA19.9 alone.

Table 13. Sample characteristics, significance tests and AUC values for AGR2, SYCN, REG1B, LOXL2, PIGR and CA19.9 in early-stage (stage I and II) pancreatic cancer (n=60) and healthy controls (n=165). The p-values were calculated using a Mann-Whitney significance test. Confidence intervals for AUC are calculated by taking 2000 stratified bootstrap samples.

[00208] While the present application has been described with reference to what are presently considered to be the preferred examples, it is to be understood that the application is not limited to the disclosed examples. To the contrary, the application is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

[00209] All publications, patents and patent applications are herein incorporated by reference in their entirety to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated by reference in its entirety. Specifically, the sequences associated with each accession numbers provided herein including for example accession numbers and/or biomarker sequences ( e.g. protein and/or nucleic acid) provided in the Tables or elsewhere, are incorporated by reference in its entirely. CITATIONS FOR REFERENCES REFERRED TO IN THE SPECIFICATION

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Claims

1 . A method of screening for, diagnosing or detecting pancreatic cancer in a subject, the method comprising:

a. determining a level of one or more biomarkers in a sample from the subject, wherein the biomarker(s) is/are selected from the proteins listed in Table 4, and

b. comparing the level of each biomarker in the sample with a control; wherein an increased level of any one of the biomarkers compared to the control is indicative that the subject has pancreatic cancer.

2. The method of claim 1 where the one or biomarkers are selected from REG1 B, LOXL2, AGR2, PIGR and SYCN.

3. The method of claim 1 or 2, further comprising determining a level of CA19.9 in a sample from the subject, and comparing the level of CA19.9 to a control, wherein said level of CA19.9 is increased compared to the control.

4. The method of any one of claim 3, wherein the one or more biomarkers comprise and/or are CA19.9, REG1 B and SYCN; CA19.9 and REG1 B; and/or CA19.9 and.

5. The method according to any one of claims 1 to 4, wherein the biomarker level determined is or comprises a soluble or serum biomarker level.

6. The method of any one of claims 1 to 5, wherein the sample and/or control comprises a biological fluid, selected from blood, serum, plasma, pancreatic juice, cyst fluid, bile and/or biological fluid in close proximity to tumor cells.

7. The method of claim 6, wherein the biological fluid is plasma or serum.

8. The method of any one of claims 1 to 7, wherein the control is a value corresponding to a control subject.

9. The method of any one of claims 1 to 8, wherein the one or more biomarkers is or comprises AGR2 and the level of AGR2 in the sample relative to the control is at least 1 .5, 1.6, 1.7, 1 .8, 1.9, 2.0, 2.1 , 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3.0, 3.1 , 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5, 10, 15, 20, or 25 fold increased.

10. The method of any one of claims 1 to 8, wherein the one or more biomarkers is or comprises AGR2 and the level of AGR2 in the sample is at least 0.5 ng/L, 1.0 μg/L, 1.5 μο/L, 2.0

4.0 μρ/ί, 5.0 μθ/L, 6.0

8.0 |.ig/L, 9.0 μg/L, or 10 μg L.

1 1 . The method of claims 9 or 10, wherein the control comprises less than 0.6 μg/L 0.5

0.4 μg/L 0.3 μg/L, of AGR2.

12. The method of any one of claims 1 to 11 , wherein the one or more biomarkers is or comprises REG1 B and the level of REG1 B in the sample relative to the control is at least 1.5, 1.6, 1.7, 1 .8, 1 .9, 2.0, 2.1 , 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3.0, 3.2, 3.4, 3.6, 3.8, 4.0, 4.5, 5.0, 6.0, 8.0, or 10 fold increased.

13. The method of any one of claims 1 to 1 1 , wherein the one or more biomarkers is or comprises REG1 B and the level of REG1 B in the sample is at least 9 mg/, 10 mg/, 1 1 mg/L, 12 mg/L, 13 mg/L, 14 mg/L, 15 mg/L, 16 mg/L, 17 mg/L, 18 mg/L, 19 mg/L, 20 mg/L, 21 mg/L, 22 mg/L, 23 mg/L, or 24 mg/L.

14. The method of claim 12 or 13, wherein the control comprises less than 9 mg/L, 8 mg/L, 7 mg/L, 6.5 mg/L, 6 mg/L, 5.5 mg/L, 5 mg/L, 4.5 mg/L or 4.0 of

REG1 B. In an embodiment the control comprises between about 9 mg/L and 4 mg/L of REG1 B.

15. The method of any one of claims 1 to 14, wherein the biomarker is or comprises SYCN and the level of SYCN in the sample relative to the control is at least 1 .5, 1 .6, 1.7, 1 .8, 1 .9, 2.0, 2.1 , 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9,

3.0, 3.1 , 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4.0, 4.2, 4.4, 4.6, 4.8, 5, 5.5, 6, 6.5, 7, 7.5, 8, 9, or 10 fold increased.

16. The method of any one of claims 1 to 14, wherein the one or more biomarkers is or comprises SYCN and the level of SYCN in the sample is at least 9.0 μg/L 10 μg/L, 1 1 μg/L, 12 μg/L, 13 ug/L, 14 μg/L, 15 μg/L·, 16 μg/L,

17 μg/L, 18 μg/L, 19 μ9/Ι_ 20 μg/L, 21 μg/L, or 22 μ9.

17. The method of claim 15 or 16, wherein the control comprises less than 9.0 g/L, 8.0 μg/L, 7 μ Ι, 6 μg L, 5 μg/L, 4 μg/L, or 3 μg/L of SYCN.

18. The method of any one of claims 1 to 17, wherein the one or more biomarkers is or comprises PIGR and the level of PIGR in the sample relative to the control is at least 1.2, 1.3, 1.4, 1.5, 1.6, 1 .7, 1.8, 1.9, 2, 2.1 , 2.2, 2.3,

2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3, 3.2, 3.4, 3.6, 3.8, 4, 4.5, 5, 6, 8, or 10 fold increased.

19. The method of any one of claims 1 to 17, wherein the one or more biomarkers is or comprises PIGR and the level of PIGR in the sample is at least 1 1 mg/L, 1 1.5 mg/L, 12 mg/L, 12.5 mg/L, 13 mg/L, 13.5 mg/L, 14 mg/L, 14.5 mg/L, 15 mg/L, 16 mg/L, 16.5 mg/L, 17 mg/L, 18 mg/L, 19 mg/L, or 20 mg/L.

20. The method of claim 18 or 19, wherein the control comprises less than 12 mg/L, 1 1 mg/L, 10 mg/L, 9.8 mg/L, 9.6 mg/L, 9.4 mg/L, 9.2 mg/L, 9 mg/L, 8.8 mg/L, 8.6 mg/L, 8.4 mg/L, 8.2 mg/L, or 8.0 mg/L of PIGR.

21 . The method of any one of claims 1 to 20, wherein the one or more biomarkers is or comprises LOXL2 and the level of LOXL2 in the sample relative to the control is at least 1.3, 1.4, 1.5, 1 .6, 1 .7, 1 .8, 1.9, 2.0, 2.1 , 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 or 10 fold increased.

22. The method of any one of claims 1 to 20, wherein the one or more biomarkers is or comprises LOXL2 and the level of LOXL2 in the sample is at least, 120 μg/L, 130

160 ng/L, 170 μg/L, 180 μ ΐ, 190 μg/L, or 200 μg/L.

23. The method of claim 21 or 22, wherein the control comprises less than 140 μg/L, 130 μg/L, 120 μg/L, 1 15 μg/L, 1 10 μg/L, or 100 μg/L of LOXL2. In an embodiment, the control comprises between about 140 μg/L and 100 μg/L of LOXL2.

24. The method of any one of claims 3 to 21 wherein the level of CA19.9 in the sample is greater than 35 Units/mL.

25. The method according to any one of claims 1 to 24, wherein the level of polypeptide biomarker of one or more of the biomarkers is determined by contacting the sample with a detection agent such as an antibody, such as a monoclonal antibody, or antibody fragment wherein the detection agent forms a complex with the biomarker.

26. The method of claim 25 wherein the detection agent is labeled with a detectable marker.

27. The method of claim 25 or 26, wherein the level of polypeptide biomarker of one or more of the biomarkers is determined using immunohistochemistry or an immunoassay, for example an enzyme-linked immunosorbant assay (ELISA), such as a sandwich type ELISA.

28. The method of any one of claims 1 to 27, wherein the level of biomarker in the sample and/or control is normalized to an Internal normalization control.

29. The method according to any one of claims 1 to 28, wherein the method is used in addition to traditional diagnostic techniques for pancreatic cancer selected from contrast-enhanced Doppler ultrasound (US), helical computed tomography (CT), enhanced magnetic resonance imaging (MRI), and endoscopic US (EUS).

30. The method of any one of claims 1 to 29, wherein the method further comprises before step a) obtaining a sample from the subject.

31 . The method of any one of claims 1 to 16, wherein the pancreatic cancer is an early-stage pancreatic cancer, optionally wherein the one or more biomarkers comprise and/or is selected from Table 13, preferably comprising SYCN.

32. A method according to claim 1 for monitoring response to treatment, the method comprising:

a) determining a base-line level of one or more biomarkers in a sample from the subject, wherein the biomarker(s) is/are selected from the proteins listed in Table 4

33. A method of monitoring response to treatment according to claim 30, wherein the biomarker(s) is/are selected from REG1 B, LOXL2, AGR2, PIGR and/or SYCN.

34. A method according to the method of claim 1 for monitoring disease progression comprising:

b) determining a level of the one or more biomarkers in a sample taken subsequent to the base-line sample from the subject; and c) comparing the level of each biomarker in the subsequent sample with the base-line level;

35. The method of monitoring diseases progression according to claim 34, wherein the biomarker(s) is/are selected from REG1 G, LOXL2, AGR2, PIGR and/or SYCN.

36. The method according to any one of claims 32 to 35, wherein the level of CA19.9 is also determined and compared with the base-line level.

37. The method of any one of claims 1 to 36, further comprising further comprising determining a level of a biomarker selected from any one of Tables 1 to 3 in a sample from the subject, and comparing the level of the biomarker selected from any one of Tables 1 to 3 to a control, wherein said level of the biomarker selected from any one of Tables 1 to 3 is increased compared to the control.

38. An immunoassay for detecting two or more biomarkers comprising at least two antibodies or fragments thereof, wherein each antibody specifically binds a biomarker, the two or more biomarkers selected from Table 4, or subsets thereof in Tables 7, 8, 1 1 , 12 and 13, for example selected from REG1 B, LOXL2, AGR2, PIGR and/or SYCN, and/or CA19.9, for use in a method of any one of claims 1 to 30.

39. A composition comprising at least two biomarker specific detection agents, each of which specifically binds a biomarker selected from CA19.9 and the biomarkers- listed in any one of Tables 1 to 4 and/or 7 to 13, preferably selected from CA19.9, REG1 B, LOXL2, AGR2, PIGR, and/or SYCN for use in a method of any one of claims 1 to 30.

40. The composition of claim 39, wherein the biomarker specific detection agents are antibodies.

41 . A kit for detecting a biomarker for use in a method of any one of claims 1 to 30 comprising:

a) at least two biomarker specific detections agents, each of which binds a biomarker selected from CA19.9 and/or the biomarkers listed in Table 4, preferably selected from REG1 B, LOXL2, AGR2, PIGR and/or SYCN; and/or

b) one or more of instructions for use, a purified standard and vessel for containing a biomarker specific detection agent.

42. A kit according to claim 41 further comprising a quantity of at least one purified standard.

43. The kit of claim 42 wherein the standard is selected from REG1 B polypeptide, LOXL2 polypeptide, AGR2 polypeptide, PIGR polypeptide, SYCN polypeptide, and CA19.9 carbohydrate.