Method for diagnosing pancreatic ductal adenocarcinoma

Abstract

A method of diagnosing pancreatic ductal adenocarcinoma (PDAC) is disclosed The method comprises detecting the presence of autoantibodies in a biological sample from a subject which bind to LEMD1 and MAGEB1 antigens. The method optionally also comprises detecting autoantibodies from one or more additional antigens selected from ACVR2B, GAGE1, TSGA10, PAGE1, BAGE4, AURKA, MAGEA10, PLEKHA5, PLK4 and XAGE3Av1. The autoantibodies can be detected by contacting the sample with immobilized LEMD1 and MAGEBB1, and optionally any other antigens listed above, so that any autoantibodies in the sample bind to the antigens. Detection antibodies, e.g. labeled anti-human IgG or IgA antibodies, can then be used to detect the bound autoantibodies, for example in a sandwich assay. Antigen complexes, a kit and a computer-implemented method of diagnosing PDAC are also disclosed.

Description

[0001]METHOD FOR DIAGNOSING PANCREATIC DUCTAL ADENOCARCINOMA FIELD This invention relates to a method for diagnosing pancreatic ductal adenocarcinoma based on the detection of autoantibodies in a sample, and to a device and kit for performing the method. BACKGROUND Pancreatic ductal adenocarcinoma (PDAC), also known as pancreatic cancer, is a heterogeneous cancer which is both difficult to diagnose and difficult to treat, and the overall survival rate of pancreatic cancer is reported to be less than 5%. A lack of druggable targets has hindered the development of effective drugs for treating the disease and surgery, when possible, remains the most effective treatment. Early detection is therefore vital. However, due to the absence of early symptoms or the symptoms being associated with other abdominal diseases such as dyspepsia and pancreatitis, most cases of pancreatic cancer are only diagnosed at a late state when curative surgery is almost impossible. Traditionally, the diagnosis of PDAC has been based on advanced radiological methodologies, such as computed tomography (CT), magnetic resonance imaging (MRI), endoscopic ultrasound (EUS) and 18- Fluorodeoxyglucose positron emission tomography (FDG-PET). However, such imaging methods not only have low diagnostic accuracy, but they are expensive, require instrumentation that is not readily available in developing countries and are low throughput. These methods therefore require some other indication of risk of PDAC to justify their use. Percutaneous needle biopsies have also emerged as an adjunct diagnostic approach, representing a minimally-invasive method to obtain tissue which can then be used to confirm the presence of PDAC through histology. However, inherent sampling bias means that this is not a realistic early detection method on its own. Serum marker detection would be superior to the abovementioned traditional methods in terms of high reproducibility, good patient compliance, easy follow-up, and low cost. However, to date no serum biomarkers with sufficient specificity and selectivity have been identified. Carbohydrate antigen 19-9 (CA19-9) is regarded as the best serological biomarker available so far in the diagnosis of PDAC, but it is not specific enough to be used for screening because of its elevation in other malignancies, including colorectal cancer, cholangiocarcinoma, hepatocarcinoma, gastric cancer, and even benign diseases such as obstructive jaundice, cirrhosis, cholangitis and other gastrointestinal diseases. Also, a high CA19-9 level usually suggests advanced PDAC instead of early-stage PDAC. It thus remains clear that despite advancements in imaging and sampling technologies, the challenge of early stage PDAC diagnosis remains formidable, especially in resource limiting settings. SUMMARY According to a first aspect, there is provided a method of diagnosing pancreatic ductal adenocarcinoma (PDAC) in a subject, the method comprising detecting the presence of autoantibodies in a biological sample from the subject which recognize LEMD1 and MAGEB1 antigens. The method may comprise detecting the presence of autoantibodies which recognize LEMD1 and MAGEB1 and also detecting an autoantibody or autoantibodies in the biological sample which recognize at least one of ACVR2B, GAGE1, TSGA10, PAGE1, BAGE4, AURKA, MAGEA10, PLEKHA5, PLK4 and XAGE3Av1. More particularly, the method may comprise detecting autoantibodies in the sample which recognize LEMD1 and MAGEB1 and at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or ten of the other antigens. The method may comprise detecting autoantibodies in the biological sample which recognize LEMD1, MAGEB1 and TSGA10. The method may comprise detecting autoantibodies in the biological sample which recognize LEMD1, MAGEB1, TSGA10 and either or both of ACVR2B and GAGE1. The method may comprise detecting autoantibodies in the biological sample which recognize LEMD1, MAGEB1 and at least one other antigen selected from the group consisting of ACVR2B, GAGE1 and TSGA10. The method may comprise detecting autoantibodies in the biological sample which recognize LEMD1, MAGEB1, TSGA10, ACVR2B and GAGE1. The method may comprise detecting autoantibodies in the biological sample which recognize LEMD1, MAGEB1 and at least two, at least three or at least four other antigens selected from the group consisting of ACVR2B, GAGE1, TSGA10 and PAGE1. The method may comprise detecting autoantibodies in the biological sample which recognize: i) LEMD1 and MAGEB1; ii) LEMD1, MAGEB1 and TSGA10; iii) LEMD1, MAGEB1 and GAGE1; iv) LEMD1, MAGEB1 and ACVR2B; v) LEMD1, MAGEB1 and PAGE1; vi) LEMD1, MAGEB1, TSGA10 and GAGE1; vii) LEMD1, MAGEB1, TSGA10 and ACVR2B; viii) LEMD1, MAGEB1, GAGE1 and ACVR2B; ix) LEMD1, MAGEB1, ACVR2B and PAGE1; or x) LEMD1, MAGEB1, TSGA10, ACVR2B and GAGE1. The autoantibodies may be IgG antibodies. The method may further comprise detecting IgA autoantibodies in the biological sample which recognize GAGE1 and / or MAGEA10. The method may further comprise detecting IgA autoantibodies in the biological sample which recognize GAGE1 and / or MAGEA10 and at least one other antigen selected from the group consisting of AURKA, MAGEB1, BAGE1, PLEKHA5, PLK4, XAGE3Av1, GAGE1 and MAGEA10. The method may comprise detecting IgA autoantibodies in the biological sample which recognize GAGE1 and / or MAGEA10 and at least two, at least three, at least four, at least five, or at least six other antigens selected from the group consisting of AURKA, MAGEB1, BAGE1, PLEKHA5, PLK4, XAGE3Av1, GAGE1 and MAGEA10. The method may comprise detecting IgA autoantibodies in the biological sample which recognize: i) GAGE1, MAGEA10, AURKA,and MAGEB1; ii) MAGEA10, AURKA, MAGEB1 and PAGE1; iii) MAGEA10, AURKA, MAGEB1 and PLEKHA5; iv) GAGE1, MAGEA10, AURKA, MAGEB1 and PLK4; or v) GAGE1, MAGEA10, AURKA, PLEKHA5 and XAGE3Av1. The method may comprise detecting IgA autoantibodies in the biological sample which recognize GAGE1, MAGEA10, AURKA, PLEKHA5 and XAGE3Av1. The method may comprise detecting any one of the combinations of IgG autoantibodies and any one of the combinations of IgA autoantibodies listed above. The autoantibodies may be detected by: - contacting the antigens with the biological sample; and - detecting binding of autoantibodies to the antigens. Binding of the autoantibodies to the antigens may be detected using one or more labeling reagents capable of binding to the autoantibodies. The one or more labeling reagents may be labeled anti-human IgG antibodies, labeled anti-human IgA antibodies or a combination thereof. The antibodies may be fluorescently labeled. The method may further comprise: - quantifying the levels of each autoantibody detected in the biological sample; and - comparing the level of each autoantibody to a level of that autoantibody associated with a healthy subject, or to a level associated with chronic pancreatitis or PDAC. The antigens may be biotinylated. The antigens may be bound to a solid support. The solid support may be a bead, a membrane, a slide, a plate, a well, a tube, a filter, a dipstick or the like. The biological sample may be a blood sample. The method may further comprise the step of treating a subject diagnosed as having PDAC, by surgery or by administering a suitable therapeutic agent. The method may further comprise the step of referring a subject diagnosed as having PDAC for a confirmatory test. According to a second aspect, there is provided an antigen complex comprising an antigen attached to a solid support for use in the method described above, wherein: the antigen is selected from the group consisting of LEMD1, MAGEB1, ACVR2B, GAGE1, TSGA10, PAGE1, BAGE4, AURKA, MAGEA10, PLEKHA5, PLK4 and XAGE3Av1; and the solid support is a bead, a membrane, a slide, a plate, a well, a tube, a filter or a dipstick and is coated with streptavidin. According to a third aspect, there is provided an immune reaction analysis device comprising LEMD1 and MAGEB1 antigens, each antigen being attached to a solid support. The device may further comprise one or more other antigens attached to a solid support, the one or more other antigens being selected from the group consisting of ACVR2B, GAGE1, TSGA10, PAGE1, BAGE4, AURKA, MAGEA10, PLEKHA5, PLK4 and XAGE3Av1. The solid support may be a bead, a membrane, a slide, a plate, a well, a tube, a filter or a dipstick. In particular, the solid support may be a bead. The antigens may be in the same chamber of the device or may be kept apart in different chambers. The device may be a microplate having a plurality of wells. According to a further aspect, there is provided a kit comprising: - LEMD1 and MAGEB1 antigens; - optionally one or more other antigens selected from the group consisting of ACVR2B, GAGE1, TSGA10, PAGE1, BAGE4, AURKA, MAGEA10, PLEKHA5, PLK4 and XAGE3Av1; or - a device as described above. Each antigen in the kit may be attached to a solid support. The kit may further include: - one or more labeling reagents; - reagents for washing and removing unbound antibodies; - reference levels of autoantibodies; and / or - instructions, in written or computer-implementable form, for performing the method described above. The one or more labeling reagents may be labeled anti-human IgG antibodies and / or labeled anti-human IgA antibodies. According to yet a further aspect, there is provided a computer implemented method of diagnosing PDAC, the computer performing steps including: - receiving inputted subject data comprising the number of autoantibodies detected in a biological sample from a patient, wherein the antibodies are autoantibodies bound to LEMD1, MAGEB1 and optionally also one or more other antigens selected from the group consisting of ACVR2B, GAGE1, TSGA10, PAGE1, BAGE4, AURKA, MAGEA10, PLEKHA5, PLK4 and XAGE3Av1; - comparing the data obtained from the sample to reference data for the same autoantibodies associated with a subject without PDAC or associated with a patient having PDAC and thereby determining whether the subject has, or possibly has, PDAC; and - displaying a diagnosis. According to yet a further aspect, there is provided a use of any of the immobilised antigens, a device or a kit as described above in a method of diagnosing PDAC. According to yet a further aspect, there is provided a use of any of the immobilised antigens described above in the manufacture of a diagnostic composition, device or kit for use in diagnosing PDAC. BRIEF DESCRIPTION OF THE FIGURES Figure 1: Scheme of a method for diagnosing PDAC based on a panel of autoantibody biomarkers described herein. Figure 2: Autoantibody response against tumour antigens in PDAC serum and tissue samples. Tissue analysis for (A) IgG and (B) IgA were used as surrogate indicators of antibody-isotype abundance between the three tissue types. (C) The proportion of antibody-positive antigens was also compared for IgG and IgA across the three tissue types. Forest plots showing the antibody subclass abundance for IgG and IgA antibody-isotypes in tissue. (D) Antibody subclass abundance determined by calculating the mean difference between antigen ratios (mean isotype antigen ratio – mean subclass antigen ratio), and the 95% and 5% confidence interval (CI) and represented on a forest plot. The four IgG subclasses show IgG4 as the predominant antibody response in PDAC tissue, IgG1 and IgG3 have similar abundance. (E) IgA2 is the predominant IgA subclass, and IgA1 is comparably elevated in PDAC tumour tissue. Serum analysis showing bar graphs for differences in signal intensity values between cohorts with the pooled DYS+CP cohort segregated into two groups of Dyspepsia (DYS) and chronic pancreatitis (CP) for IgG and IgA. (F) IgG response shows that both confounding groups (CP and DYS) have no significant difference to the control group. (G) The CP group has a significantly different response to the control group for IgA, while the DYS group has a similar response to the control group. (H) Forest plot comparing abundant isotype distribution of antibody-positive antigens, showing that more antigens are IgA-positive. (I) Column graph showing the distribution of IgG subclasses based on signal RFU in the pooled DYS+CP cohort. (J) IgA subclass reactivity for the pooled DYS+CP group, indicating abundant IgA1 compared to IgA2. Signal intensity values for normal-adjacent, tumour, and chronic pancreatitis (CP) tissue, and serum samples for PDAC, CP,DYS, and LTBI were set at a threshold > mean+2SD of the normal-adjacent antibody-positive antigen response and the scatter plots showing the relative fluorescent units (RFU) of the mean ±SEM for Statistical analysis by one-way ANOVA, pairwise comparison of each group against the control group, graph represents mean values ± SEM (*P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001). Figure 3: Comparing glycosylation features of serum autoantibodies against CTAs in PDAC. (A) The total glycosylation distribution for the PDAC, pooled DYS+CP, and control cohort for galactosylation, fucosyaltion, and sialylation. Column graphs showing the mean ± SEM relative fluorescent units (RFU) of antigen-bound autoantibodies (threshold set ≥ mean+2SD of the control RFU) that have glycans recognized by (A) RCA (β1,4-Gal), (B) SNA (α2,6-Sal), (C) LCA(α1,6-Fuc), and (D) ECL(β1,4-Gal) as a surrogate marker for assessing the detection of glycosylation moieties in serum autoantibodies using the Sengenics CT262 microarray platform. (E) Glycosylation features assessed in lectin-positive antigens observed in the PDAC cohort compared to the pooled DYS+CP group, showing the variable presence of different carbohydrate moieties. (F) A heatmap and dendogram of log2transformed median normalised microarray data for each antigen-positive serum autoantibody feature and the associated detection of cancer testis antigens. Hierarchical clustering has grouped serum features and positive antigens by similarity. Figure 4: Combinatorial analysis showing a Receiver Operating Characteristics (ROC) curve for the combination of IgG autoantibodies in a GSH (‘GSH’) discovery cohort against the antigens LEMD1 & MAGEB1. Figure 5: Combinatorial analysis showing a ROC curve for the combination of IgG autoantibodies in the GSH discovery cohort against the antigens LEMD1, MAGEB1 & TSGA10. Figure 6: Combinatorial analysis showing a ROC curve for different panels of IgG autoantibodies in the GSH discovery cohort against the antigens LEMD1, MAGEB1, TSGA10, ACVR2B, GAGE1 & PAGE1. Figure 7: Combinatorial analysis showing a ROC curve for the combination of IgG autoantibodies in the NHLS validation cohort against the antigens LEMD1 & MAGEB1. Figure 8: Combinatorial analysis showing a ROC curve for different panels of IgG autoantibodies in the NHLS validation cohort against the antigens LEMD1, MAGEB1, TSGA10, GAGE1 & ACVR2B. Figure 9: Combinatorial analysis showing a ROC curve for the combination of IgG autoantibodies against the antigens LEMD1, MAGEB1, TSGA10 & ACVR2B. Figure 10: ELISA data for CA19-9 in the discovery (‘GSH’), validation (‘NHLS’) and chronic pancreatitis control (‘CP’) cohorts. Relative CA19-9 concentration are reported in kU / L. Figure 11: Combinatorial analysis showing a ROC curve for different panels of IgG autoantibodies in the GSH discovery cohort against the antigens LEMD1, MAGEB1, TSGA10, ACVR2B & GAGE1 in combination with CA19-9. DETAILED DESCRIPTION Although the present disclosure is further described in more detail below, it is to be understood that this disclosure is not limited to the particular methodologies, protocols and reagents described herein as these may vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the present disclosure which will be limited only by the appended claims. Unless defined otherwise, all technical and scientific terms used herein have the same meanings as commonly understood by one of ordinary skill in the art. The use of the word “a” or “an” when used in conjunction with the term “comprising” in the claims and / or the specification may mean “one,” but it is also consistent with the meaning of “one or more,” “at least one,” and “one or more than one.” The word “about” means plus or minus 5% of the stated number. Unless the context requires otherwise, the word “comprise” or variations such as “comprises” or “comprising” will be understood to imply the inclusion of a stated integer or group of integers but not the exclusion of any other integer or group of integers. The practice of the present disclosure will employ, unless otherwise indicated, conventional chemistry, biochemistry, cell biology, immunology, and recombinant DNA techniques which are explained in the literature in the field. The use of any and all examples, or exemplary language (e.g., "such as"), provided herein is intended merely to better illustrate the present disclosure and does not pose a limitation on the scope of the present disclosure otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the present disclosure. The term "optional" or "optionally" as used herein means that the subsequently described event, circumstance or condition may or may not occur, and that the description includes instances where said event, circumstance, or condition occurs and instances in which it does not occur. Where used herein, "and / or" is to be taken as specific disclosure of each of the two specified features or components with or without the other. For example, "X and / or Y" is to be taken as specific disclosure of each of (i) X, (ii) Y, and (iii) X and Y, just as if each is set out individually herein. The term "at least one" as used herein in the context of "at least one antigen" means 1 or more, e.g., 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, or 8 or more. In some embodiments, the term "at least one" refers to 1, 2, 3, 4, 5, 6, 7, 8, and so forth. The term "antibody" as used herein, refers to an immunoglobulin molecule, which is able to specifically bind to an epitope on an antigen. In particular, the term "antibody" refers to a glycoprotein comprising at least two heavy (H) chains and two light (L) chains inter-connected by disulfide bonds. An “autoantibody”, as used herein, refers to an antibody produced by the immune system that is directed against one or more of the individual's own proteins. The term “antigen”, as used herein, refers to a molecule capable of being recognized by an antibody. An antigen can be, for example, a full-length protein or a fragment thereof comprising one or more epitopes. The term “epitope”, as used herein, refers to an antigenic determinant in a molecule such as an antigen, i.e., to a part in, portion of or fragment of the molecule that is recognized by the immune system, for example, that is recognized by antibodies. An epitope of a protein may comprise a continuous or discontinuous portion of the protein and, e.g., may be between about 5 and about 100, between about 5 and about 50, between about 8 and about 30, or between about 10 and about 25 amino acids in length. Additional terms shall be defined, as required, in the detailed description that follows. A method of diagnosing pancreatic ductal adenocarcinoma (PDAC) is disclosed. The method comprises detecting autoantibodies in a sample from a subject that recognize at least two antigens selected from LEMD1, MAGEB1, ACVR2B, GAGE1, TSGA10, PAGE1, BAGE4, AURKA, MAGEA10, PLEKHA5, PLK4 and XAGE3Av1. The autoantibodies can be detected by contacting the sample with the at least two antigens or antigenic fragments thereof, so that any autoantibodies in the sample that recognize these antigens will bind to the antigens. Detection antibodies, e.g. labeled anti-human IgG or IgA antibodies, can then be used to detect the bound autoantibodies, for example in a sandwich assay. Antigen complexes, a device, a kit and a computer-implemented method of diagnosing PDAC are also disclosed. Details of the proteins mentioned above are: Protein Protein Name UniProt SEQ ID Symbol Accession NO: ACVR2B Human Activin receptor type-2B Q13705 1 AURKA Human Aurora kinase A O14965 2 BAGE1 Human B melanoma antigen family, member 1 Q13072 3 Human B melanoma antigen 4 (Cancer / testis antigen 2.4) 4 BAGE4 Q86Y28 (CT2.4) GAGE1 Human G antigen 1 Q13065 5 LEMD1 Human LEM domain containing protein 1 Q68G75 6 MAGEA10 Human Melanoma-associated antigen 10 P43363 7 MAGEB1 Human Melanoma-associated antigen B1 P43366 8 PLEKHA5 Human Pleckstrin homology domain-containing family Q9HAU0 9 member 5 PLK4 Human Serine / threonine-protein kinase PLK4 O00444 10 PAGE1 Human P antigen family member 1 O75459 11 TSGA10 Human Testis-specific gene 10 proein Q9BZW7 12 XAGE3Av1 Human X antigen family member 3 variant 1 Q8WTP9 13 Similarly to autoimmune disorders, cancer produces autoantibodies. An autoantibody is an antibody produced by the immune system that is directed against one or more of the individual's own proteins. Typically, these autoantibodies are raised early in disease against mutated or aberrantly expressed / modified proteins. Numerous studies have identified the presence of autoantibodies in cancer, but the immunological importance of those antibodies in terms of whether they contribute to, or hamper cancer progression remains a matter of on-going debate. Autoantibody-based tumor biomarkers have been studied as potential prognostic, diagnostic, and monitoring agents of therapeutic response in breast, prostate, and lung cancers amongst others. The common challenge with these biomarker studies is that the identified targets individually typically lack specificity and sensitivity, and some are only applicable to a specific tumor subset. As a result, the clinical application of antibodies in cancer to date has largely focused on their use as targeted immunotherapies. Of note, there are five human antibody isotypes, yet current therapeutics are based on the IgG isotype, particularly IgG1, with other antibody isotypes having not been explored or developed for mAb therapies. Similarly, the aforementioned studies on autoantibody-based biomarkers were based solely on detection of IgG antibodies. Tumour antigens can be defined as tumour associated (TA) antigens, which are antigens similar to proteins found in normal cells but are modified or aberrantly expressed. Amongst the subset of proteins that are more likely to elicit an autoantibody response in cancers, the cancer-testis (CT) antigens are a family of ca.500 tumor-specific antigens with highly restricted expression in normal adult somatic tissues and aberrant expression in various cancers as a result of disrupted gene regulation. As the testis is an immune-privileged site, aberrant expression of these antigens in cancer typically triggers a spontaneous cellular (T cell) and humoral (B cell) immune response to the relevant CT antigen. The latter includes the maturation of B cells against specific antigens to produce cognate antibodies which are detectable in the circulation. An assay of >3000 healthy individuals conducted by the Applicant showed no detectable anti- CT antigen autoantibody titre. Antibody production is compartmentalised, with bone marrow-derived B-cells and tissue resident B-cells having distinct lineages and producing different antibody isotypes, potentially against different target antigens, which can confuse interpretation of the physiological significance of autoantibody production in cancers. In this study, an immunoproteomic approach was applied to investigate autoantibody responses against cancer-testis and tumour-associated antigens in PDAC using a high-throughput multiplexed protein microarray platform, comparing humoral immune responses in serum and at the site of disease. Serum or tissue IgG and IgA antibody isotypes and subclasses in a cohort of PDAC, disease control and healthy patients were simultaneously quantified. Moreover, because different subclasses of each antibody isotype have varying effector functions, which are also dependent on the glycan composition of the Fc region, their characterization would better reflect the antibody effector roles that are responsible for immune regulation in PDAC carcinogenesis. Thus, this study also aimed to identify the glycan moieties associated with the antigen-specific autoantibodies identified in serum. In order to provide more detailed characterization of the humoral response at the site of disease in PDAC patients, quantitative autoantibody profiling was carried out against 262 cancer-testis and tumour- associated antigens, utilizing a multiplexed, reproducible high through-put microarray platform to determine the isotype, subclass, and sialylation of antigen-specific autoantibodies in serum and matched tumour tissue from PDAC patients and controls. Amongst others, the data from this study revealed significant differences in anti-CT / TA antigen IgG and IgA autoantibody titres between PDAC patients and controls that are measurable in serum and which may provide the basis for early detection of PDAC. The data from this study also revealed significant differences in anti-CT / TA antigen isotype and subclass utilization in tissue biopsies at the site of disease compared to that found in matched sera or in matched adjacent normal biopsies, which argues against simple infiltration of antibodies from blood into the diseased tissue and instead argues for local autoantibody production at the site of disease. Subclass utilization in tumor tissue samples was observed to be predominantly immune suppressive IgG4 and inflammatory IgA2, contrasting with predominant IgG3 and IgA1 subclass utilization in matched sera and implying local autoantibody production at the site of disease in an immune-tolerant environment. By comparison, serum autoantibody subclass profiling for the disease controls identified IgG4, IgG1, and IgA1 as the abundant subclasses. The identified anti-CT / TA antigen autoantibody responses were shown to predominantly reflect a tolerogenic antibody response at the site of disease. The predominant anti-CT / TA antigen IgG subclass was found to be the non-activating IgG4, whereas IgG1 and IgG3 – which can induce antibody-dependent cellular cytotoxicity (ADCC) and complement dependent cytotoxicity (CDC) through their effector functions - were found to be the least abundant IgG subclasses in tissue, implying that these functions are not effectively activated to drive tumour clearance; and the predominant anti-CT / TA antigen IgA subclass was found to be the pro-inflammatory IgA2. Antibodies are produced in the adaptive phase of an immune response and increased affinity for antigens is achieved through somatic hypermutations and isotype switching. Previously, studies have largely focused on IgG profiling in cancer and have demonstrated elevated expression levels of IgG in cancer cells. By simultaneously measuring IgG and IgA responses in PDAC, this study showed that whilst signal intensities were higher for IgG compared to IgA in serum, IgA has a broader selectivity for CT / TA antigens compared to IgG in both serum and tissue. Isotype switching increases the functional diversity of antibodies as the immune response proceeds. Indeed, the type of antibody activated during an immune response is important in determining downstream effector signaling and interaction with other immune cells. Previous studies have demonstrated that antibody isotype and subclass abundance fluctuate through the course of infectious diseases and it is plausible therefore that the same phenomenon occurs in cancers. Whilst the cohort studied here was cross-sectional, not longitudinal, in design, it is nonetheless interesting that in the serum analysis of this study, patient IgG subclass evaluation revealed that IgG1 and IgG2 reactivity was absent in some patients, which may be an indication of temporal changes that occur during cancer progression being reflected in the systemic immune response. Aberrant glycosylation has been a key feature in the acquisition and sustenance of hallmark characteristics that have been implicated in cancer development. Furthermore the glycobiology of antibodies and their respective receptors have been suggested to be an important factor in mediated effector responses and therapeutic development and activity. Additionally, research has shown that 2,6 sialic acid expression is associated with chemoresistance in PDAC, albeit that data was based on altered sialylation of tumour antigens, not of tumour-associated autoantibodies as observed here. The presence of specific glycan moieties on cancer antigen-specific serum autoantibodies was determined herein using a microarray platform, focused on three possible glycan sites by using a panel of four lectins: SNA, LCA, RCA, and ECL, in order to respectively investigate sialylation (α2,6-Sal), fucosylation (α1,6-Fuc), and galactosylation (β1,4-Gal) of antigen-bound serum autoantibodies. Assessment of antigen-specific serum autoantibody glycoforms revealed abundant sialylation on IgA in PDAC, consistent with an immune suppressive IgA response to disease. Combinatorial analysis of serum autoantibody responses identified panels of candidate biomarkers that include LEMD1, MAGEB1, ACVR2B, GAGE1, TSGA10, PAGE1, BAGE4, BAGE1, AURKA, MAGEA10, PLEKHA5, PLK4 and XAGE3Av1. As discussed above, pancreatic ductal adenocarcinoma (PDAC) is a disease with minimal response to therapeutic intervention if diagnosed at a late stage. Early stage detection of the disease is crucial for the patient’s survival. It is envisaged that by using the autoantibodies described herein for cancer diagnosis, increased autoantibody levels could be detected at early stages of the disease (e.g. stage 1A, 1B or 2A), making it possible to diagnose PDAC several months or years before any clinical signs of tumor development are presented. To the best of the inventors’ knowledge, this is the first study to demonstrate the diagnosis of PDAC based on the detection of autoantibodies in a sample from a human patient. These and other aspects of the disclosure are described in detail below. Importantly, whilst the individual autoantigen targets of these autoantibodies are known in the art from, for example, genome sequencing and proteomics, the presence of autoantibodies against specific autoantigens in peripheral blood from PDAC patients cannot be predicted simply from knowledge of the existence of the autoantigen, since expression of an autoantigen in a given tissue is well known to not automatically result in generation of a cognate autoantibody against that autoantigen. Moreover, the specific combinations of autoantibody biomarkers that are able to distinguish PDAC from CP patients (as described herein) could not have been predicted from prior genomic, proteomic or histology data. In one embodiment, the method includes detecting the presence of autoantibodies in a biological sample from a subject which recognize (i.e. bind to) at least LEMD1 and MAGEB1. The method can also include further detecting the presence of autoantibodies in the sample which recognize LEMD1, MAGEB1 and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten or all eleven of ACVR2B, GAGE1, TSGA10, PAGE1, BAGE4, BAGE1, AURKA, MAGEA10, PLEKHA5, PLK4 and XAGE3Av1. More particularly, the method can include detecting the presence of autoantibodies in the sample which recognize LEMD1, MAGEB1 and at least two, at least three or at least four of ACVR2B, GAGE1, TSGA10 and PAGE1. For example, the method can comprise detecting autoantibodies to any one of the following panels of antigens: i) LEMD1-MAGEB1; ii) LEMD1-MAGEB1-ACVR2B; iii) LEMD1-MAGEB1-GAGE1; iv) LEMD1-MAGEB1-PAGE1; v) LEMD1-MAGEB1-TSGA10; vi) LEMD1-MAGEB1-ACVR2B-GAGE1; vii) LEMD1-MAGEB1-ACVR2B- PAGE1; viii) LEMD1-MAGEB1-ACVR2B-TSGA10; ix) LEMD1-MAGEB1-GAGE1-TSGA10; x) LEMD1-MAGEB1-GAGE1-PAGE1; xi) LEMD1-MAGEB1-ACVR2B-GAGE1-TSGA10; xii) LEMD1-MAGEB1-ACVR2B-GAGE1-TSGA10-BAGE4. In another embodiment, the method includes detecting the presence of autoantibodies in a biological sample from a subject which recognize GAGE1 and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven or all 12 of ACVR2B, TSGA10, PAGE1, BAGE4, BAGE1, AURKA, MAGEA10, PLEKHA5, PLK4, LEMD1, MAGEB1 and XAGE3Av1. In yet another embodiment, the method includes detecting the presence of autoantibodies in a biological sample from a subject which recognize MAGEA10 and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven or all twelve of AURKA, ACVR2B, TSGA10, PAGE1, BAGE4, BAGE1, PLEKHA5, PLK4, LEMD1, MAGEB1, GAGE1 and XAGE3Av1. For example, the method can comprise detecting IgA autoantibodies to any one of the following panels of antigens: xiii) GAGE1, AURKA, MAGEA10 and MAGEB1; xiv) AURKA, MAGEA10, MAGEB1 and PAGE1; xv) AURKA, MAGEA10, MAGEB1 and PLEKHA5; xvi) GAGE1, AURKA, MAGEA10, MAGEB1 and PLK4; xvii) GAGE1, AURKA, MAGEA10, PLEKHA5 and XAGE3Av1. It will be apparent to a person of skill in the art that autoantigens to other antigens can be added to the panels described above. The autoantibodies are typically IgG and or IgA antibodies. In one embodiment, only IgG autoantibodies are detected. In another embodiment, a combination of IgG and IgA autoantibodies are detected. In an alternative embodiment, only IgA autoantibodies are detected. The IgG and / or IgA antibodies may be directed to any of the antigens described herein. The top IgG panels include ACVR2B, GAGE1, LEMD1, MAGEB1, TSGA10 and / or PAGE1 and the top IgA panels include AURKA, GAGE1, MAGEA10, BAGE1, MAGEB1, PLEKHA5, PLK4 and / or XAGE3aV1. For example, in one embodiment, the method includes detecting IgG autoantibodies in the sample which recognize the antigens of any one of panels (i)-(xii) listed above. Optionally, the method can also include detecting IgA autoantibodies in the sample which recognize GAGE1 and / or MAGEA10. In one embodiment, the method includes detecting IgA autoantibodies which recognize GAGE1 and / or MAGEA10 and at least one of AURKA, MAGEB1, BAGE1, PLEKHA5, PLK4, XAGE3Av1, GAGE1 and MAGEA10. More particularly, the method can comprise detecting IgA autoantibodies in the sample which recognize GAGE1 and / or MAGEA10 and at least two, at least three, at least four, at least five, or at least six of AURKA, MAGEB1, BAGE1, PLEKHA5, PLK4, XAGE3Av1, GAGE1 and MAGEA10. For example, the method can include detecting IgA autoantibodies in the biological sample which recognize any one of the panels (xiii)-(xvii) listed above. In particular, the method can include detecting autoantibodies which recognize GAGE1, MAGEA10, AURKA, PLEKHA5 and XAGE3Av1 (xvii). The method can further include detecting the amount of a known cancer biomarker in the sample, such as CA 19-9, CA 125 or CEA. The method optionally further includes a step of determining the sialylation, fucosylation and / or galactosylation of the autoantibodies. The pattern of antibody Fc glycosylation has a significant impact on the interaction of antigen-specific antibodies with other components of the innate immune system and modulates the associated effector functions, which are a key component of adaptive immune response control of disease. In this study, it was observed that the patterns of antigen-specific antibody glycoslation differ in PDAC patients compared to confounding disease controls. Thus, determining the sialylation, fucosylation and / or galactosylation of the antigen-specific autoantibodies serves to further increase the discriminatory power of the antigen-specific autoantibody diagnostic panels. The autoantibodies can be detected by contacting the biological sample with the antigens, and detecting binding of autoantibodies from the sample to the antigens. One or more labeling reagents capable of binding to the autoantibodies can be used for this purpose, such as anti-human IgG antibodies and / or anti-human IgA antibodies conjugated to a detectable label. The antigens are typically biotinylated and attached to a solid support, such as a bead, a membrane, a slide, a plate, a well, a tube, a filter, a dipstick, etc. The amount of each autoantibody detected in the sample can be quantified. This can then be compared to a previously determined amount or level of the same autoantibody associated with a healthy subject, or to a level associated with chronic pancreatitis or PDAC. A diagnosis of the patient having PDAC or possibly having PDAC can be made when the levels of the detected autoantibodies are higher than a typical level of the same autoantibodies in subjects without PDAC. Cut-off or threshold values can be determined based on levels of the same autoantibodies which are typically found in patients without PDAC. The levels referred to herein may be the concentration of the antibody in the sample. In one embodiment, the biological sample is blood, which may be whole blood, serum or plasma. The blood may be fresh or dried blood (e.g. a dried blood spot). For simple, low-cost sample collection, and clinical implementation for patient access from resource limiting sites to a centralised laboratory, the use of dried blood spots collected on blood cards can be easily implemented. As autoantibody signals can be quantified after storage on blood cards for up to 3 months at room temperature, the blood cards can be posted or couriered at room temperature and at low cost, from remote sites to a central laboratory. Alternatively, the biological sample could be a different liquid sample, such as urine. Other examples of suitable biological samples are a tissue biopsy from the site of disease, a tissue biopsy from an adjacent lymph node, exosomes prepared from blood, or stool. The method may be an initial diagnostic test, i.e. in the case of a positive diagnosis, the patient will be sent for a further test to confirm the diagnosis. Alternatively, or in addition, a patient with a positive result can be treated for PDAC. For example, surgery may be performed (i.e. surgical resection of the pancreas) followed by administration of postoperative chemotherapy, radiation and / or immunotherapy. Alternatively, the patient may be treated with chemotherapy, radiation, immunotherapy or a combination thereof in cases where surgery is not advised. When the cancer is advanced, palliative treatment may be administered, e.g. treatment to relieve pain or nausea. The method can also be as a companion diagnostic, i.e. to monitor a patient’s response to PDAC treatment. For example, the method can be performed prior to or at a particular point in the treatment, and performed again at a later date. If the autoantibody levels have not decreased, this is an indication that the treatment is not effective. Given the paucity of clinical symptoms in early disease that might trigger a request for a specific test, a regular screening program amongst at-risk populations can be provided, as is done for a number of other cancers. An immunoassay can be used to perform the method described herein. In one embodiment, the immunoassay is a multiplexed “indirect” or “antigen-down” assay in which antigens of a target antigen panel are immobilised on a solid support, such as a bead, a membrane, a slide, a plate, a well, a tube, a filter or a dipstick. When a biological sample from a patient is brought into contact with the antigens, any autoantibodies in the sample which recognize the antigens (“primary antibodies”) will bind to the antigens and become immobilised on the solid support. After a washing step to remove unbound and non- specifically bound autoantibodies, differentially labeled species-specific antibodies (i.e. anti-human IgG and / or anti-human IgA) (“secondary” or “detection” antibodies) which bind to the primary autoantibodies are then added. The label is typically a fluorophore but could also be any other type of detectable label, such as an enzyme. Using methods and devices known in the art (such as a microarray scanner or a Bioplex® or xMAP® system (Luminex)), the presence of antigen-specific autoantibodies in the sample are then detected and quantified, with pg / ml limits of detection. Based on this data, a diagnosis can be made. An algorithm can be provided to compare the sample data to an autoantibody signature, to previously calculated reciprocal titres for the autoantibodies and / or to antigen ratios of median antibody titres in PDAC and non-cancer patients or patients with a confounding disease of the pancreas such as chronic pancreatitis (CP), and to discriminate between PDAC patients and other confounding diseases. Figure 1 shows such a method. An antigen complex for use in the method described herein is also provided. The antigen complex comprises an antigen selected from any one of LEMD1, MAGEB1, ACVR2B, GAGE1, TSGA10, PAGE1, BAGE4, AURKA, MAGEA10, PLEKHA5, PLK4 and XAGE3Av1 immobilized on a solid support. The solid support is a bead, a membrane, a slide, a plate, a well, a tube, a filter or a dipstick. In one embodiment, the solid support is a bead. The solid support can be coated with streptavidin and / or a distinguishable dye. The antigens can be biotin carboxyl carrier protein (BCCP)-tagged. This can be done by, for example, expressing the antigens as fusions to a BCCP tag in vivo in insect cells, e.g. using a baculoviral system as described in Beeton-Kempen et al. (Beeton-Kempen, N., et al., Development of a novel, quantitative protein microarray platform for the multiplexed serological analysis of autoantibodies to cancer-testis antigens. Int J Cancer, 2014.135(8): p.1842-51). A device for performing the method described herein is further provided. The device is typically an immune reaction analysis device containing the target antigens attached to a solid support. The target antigens are at least LEMD1 and MAGEB1, but are optionally also ACVR2B, GAGE1, TSGA10, PAGE1, BAGE4, AURKA, MAGEA10, PLEKHA5, PLK4 and XAGE3Av1, or antigenic fragments thereof. For example, the target antigens may comprise any one of the panels described above. The solid support can be a bead, a membrane, a slide, a plate, a well, a tube, a filter, a dipstick or the like. In one embodiment, the solid support is one or more beads. The antigens may be combined together, i.e. with antigens of different types in the same chamber or tube, or may be separated, i.e. the different antigens will be in separate chambers or wells. The device may be a microtiter plate with a plurality of wells. A kit is also provided. The kit can include LEMD1 and MAGEB1 antigens, and optionally also one or mor of ACVR2B, GAGE1, TSGA10, PAGE1, BAGE4, AURKA, MAGEA10, PLEKHA5, PLK4 and XAGE3Av1, or antigenic fragments thereof attached to a solid support. Alternatively, the kit can include a device as described above. The kit can further include one or more labeling reagents (e.g. labeled anti-human IgG antibodies and / or labeled anti-human IgA antibodies), reagents for washing and removing unbound antibodies, reference autoantibody levels for the target antigens; and / or instructions, in written or computer-implementable form, for performing the method described herein. A computer implemented method of diagnosing PDAC is also provided, the computer performing steps including: - receiving inputted subject data comprising the number of autoantibodies detected in a biological sample from a patient, wherein the antibodies are autoantibodies bound to LEMD1, MAGEB1 and optionally also one or more other antigens selected from the group consisting of ACVR2B, GAGE1, TSGA10, PAGE1, BAGE4, AURKA, MAGEA10, PLEKHA5, PLK4 and XAGE3Av1; - comparing the data obtained from the sample to reference data for the same autoantibodies associated with a subject without PDAC or associated with a patient having PDAC and thereby determining whether the subject has, or possibly has, PDAC; and - displaying a diagnosis. Use of the antigens or antigenic fragments or the panels of antigens described above are also provided for the manufacture of a kit, composition, complex, medicament, solid support or device for use in diagnosing PDAC. The invention will now be described in more detail by way of the following non-limiting examples. Example 1: Identification of autoantibody biomarkers and biomarker panels for PDAC Materials and Methods Sample collection for study cohort This study was approved (HREC 654 / 2017 & HREC 802 / 2020) by the Human Research Ethics Committee of the Faculty of Health Sciences, University of Cape Town. Blood samples were obtained from patients with early-stage (stage 1A, 1B or 2A) pancreatic ductal adenocarcinoma (PDAC) (n=30) who were diagnosed and underwent tumour resection surgery (pancreaticoduodenectomy) at Groote Schuur Hospital (GSH), Cape Town, South Africa. Informed consent was obtained from all patients involved in the study. Additionally, serum from patients with confounding diseases of the pancreas, chronic pancreatitis (CP) (n=16), and non-ulcer dyspepsia (n=13) were used as the disease controls, and serum from patients characterised as having a latent tuberculosis infection (LTBI) (n=30) but who were otherwise healthy, were used as ‘healthy’ controls. Notably, in a South African context, public health data suggests that ca. 80% of adults carry a latent tuberculosis infection, thus making the LTBI group an appropriate control in the present study. Tissue matched from a subset of PDAC patients in the serum cohort was collected for assessment of local autoantibody production. Here, paired tissue sections of ~3mm2for PDAC tumour (n=8), normal adjacent (n=8), and chronic pancreatitis (n=8) were lysed for antibody extraction and antibody presence in tissue lysates was confirmed by an immunoglobulin affinity purification method using magnetic Protein A and Protein G microbeads (MagReSyn®), as per to the manufacturer’s protocol. Serum and tissue samples were stored at -80℃ until assays were performed. Serum and tissue antibody assays Antibody assays were performed as described in Smith et al. (Smith, M. et al., Age, Disease Severity and Ethnicity Influence Humoral Responses in a Multi-Ethnic COVID-19 Cohort. Viruses, 2021.13(5)), with the following modifications: A dual colour format was adapted for antibody detection with simultaneous incubation of AF647-labelled anti-human IgG, and AF555-labelled anti-human IgA detection antibody (both at 10µg / ml) for 30 min. Similarly, dual-colour microarray assays were performed for the respective antibody subclasses. The serum autoantibody and lectin microarray assays were carried out using commercial cancer-testis antigen arrays comprising 213 CT plus 49 TA antigens (Sengenics), whereas microarray assays on tissue extracts were performed on a custom CT100+platform as previously described in Beeton-Kempen et al. Both arrays contained triplicate spots of each antigen, and individual arrays were isolated using ProPlate 4-plex multi-well chambers. Detection antibody subclasses (anti- human IgG1, IgG2, IgG3, IgG4, IgA1, and IgA2) and anti-human IgA were derivatised in-house with either Alexa Fluor (AF)647 or AF555. The assays were performed with a serum or tissue lysate dilution of 1:200 in phosphate-buffered saline with Tween20 (PBST), with minimal light, at room temperature, and all incubations were performed on a shaker at 100 rpm unless stated otherwise. Reagents are listed in Table 1. Table 1: Reagents used for antibody and lectin derivatisation, and for microarray assays. Reagent Stock Supplier Catalogue number concentration α-hIgG 2 mg / ml ThermoFisher A21145 α-hIgA 2.4mg / ml ThermoFisher 31140 AF647-NHS ester 10mg / ml ThermoFisher AF647-NHS ester AF555-NHS ester 10mg / ThermoFi ester α- 0.5mg / Southern α- 0.5mg / Southern α- 0.5mg / Southern α- 0.5mg / Southern α- 0.5mg / Southern α- 0.5mg / Southern RCA 10 mg / Vector lab SNA 5 mg / m Vector lab LCA 10 mg / Vector lab ECL 10 mg / Vector lab 50 mM Sigma® CaCl2 500 m Glentham 50% Sigma® 2.5 M Glentham KCl 1 M Sigma® Milk Powde Sigma® 1000 m Glentham 100 m Glentham PBS 10x Gibco 70011-036 Tris 500 m Glentham GP7166 Triton X-100 100% Sigma® 93443-100ML Tween 20 100 % Glentham GK2245 Sodium bicarbonate 0.1M Glentham GE8414 Biomarker antigens which had been biotinylated and BCCP-tagged such that oriented immobilisation and in situ purification of each antigen was obtained, were bound to a solid support, in this case glass slides with a hydrogel layer coated with covalently bound streptavidin tetramers. The microarray slides were stored at -20℃. Microarray slides were removed from -20℃ storage and blocked with ice-cold blocking buffer for one hour at room temperature, and then then washed 2x in PBST and 2X PBS for five minutes at room temperature (100rpm). Thereafter, the slides were washed and dried by centrifugation at 1200 Xg for 2 minutes. Subsequently, slides were assembled in 4-plex gaskets and incubated with patient sera (1:200) for one hour at room temperature (100rpm). Thereafter, the slides were briefly rinsed with PBST, removed from the gaskets, and washed 3x for five minutes with PBST. Following this, fluorescently labelled anti-human IgG and IgA detection antibody (10µg / ml) were added and incubated for 30min at room temperature on a shaker (100rpm). Thereafter, the slides were washed 2x with PBST, and 2X with PBS for 5min at 100rpm at room temperature. Finally, the slides were dried at 1200xg for 3 minutes at 23℃. Serological lectin assays Microarray slides were washed with PBST and incubated with gentle agitation for 3x 5 min, then washed with 2x 5min with PBS and dried by centrifugation at 1200X g for 2min. Individual arrays were incubated with the patient serum for 1hr with gentle agitation. Thereafter, the slides were briefly rinsed 3x with Tris- buffer saline (TBS) and incubated with 3% deglycosylated BSA for 1hr. Subsequently, each lectin was diluted to 1ug / ml in lectin binding buffer (20 mM Tris (pH 8.0), 0.1 mM CaCl2, 0.1 mM MgCl2, 0.1m M MnCl2, 0.2% Tween 20), added to the slide, and incubated with gentle agitation for 30 min. Finally, the slides were washed 2x for 5 min each with TBST and TBS. Bioinformatic analysis The statistical estimation of power and sample size were calculated using power calculations and performed using G*Power version 3.1.9.4. Post hoc power calculations of matched tumour and normal- adjacent tissue was performed using the R package “ssize.fdr”. Microarray image analysis and raw data extraction Microarray slides were scanned at a fixed gain setting using an InnoScan 710 (Innopsys, Carbonne, France) fluorescence microarray scanner, generating a 16-bit TIFF file. A visual quality control check was conducted and any arrays showing artifacts were re-assayed. A GAL (GenePix Array List) file containing information regarding the location and identity of all antigen spots was used for image analysis. Automatic extraction and quantification of each spot were performed using Mapix software (Innopsys) to obtain the median foreground and local background pixel intensities for each spot. Data pre-processing and statistical analysis The mean net fluorescence intensity of each spot was calculated as the difference between the raw mean pixel intensity and its local background using in-house developed software (Protein Microarray Analyser; Da Gama Duarte, J., et al., PMA: Protein Microarray Analyser, a user-friendly tool for data processing and normalization. BMC Res Notes, 2018. 11(1): p. 156). The output files contained the relative fluorescent unit (RFU) and coefficient of variation for all antigens and controls spotted on the array. The R studio and R packages were used to perform clustering analysis, and the OptimalCutPoints package and receiver operating curves (ROC) were used to determine antigen specificity and sensitivity. Combinatorial ROC analysis (www.combiroc.eu) was used to determine the potential antigen biomarker panels. Other statistical analyses and graphical representations were generated using GraphPad Prism (v 9.5.1; GraphPad Software, San Diego, CA, USA). Results Quantifying autoantibody responses against cancer testis antigens To determine the specific autoantibody reactivity against the cancer antigens on the microarray platforms, a signal intensity threshold was set to distinguish true antibody-antigen binding from non-specific binding. After data normalization, the threshold for each protein was set as the mean plus 2SD for the normal- adjacent tissue samples (for tissue extracts) or the control group for serum; proteins with signals above this threshold were considered true autoantibody binding. It is well understood from ligand binding theory that the relative fluorescent units (RFU) measured for autoantibodies bound to individual autoantigens on a protein microarray depends on the density of the immobilised autoantigen, the concentration of the autoantibody in solution and the affinity of interaction between the autoantigen and autoantibody. Furthermore, the fluorophore labelling efficiency will vary between different isotype-specific detecting antibodies. Thus, whilst it is meaningful to compare RFU values for a given isotype bound to the same autoantigen across different samples, it is generally not considered meaningful to directly compare RFU values between the same isotype bound to different autoantigens on a microarray, or between different isotypes bound to the same autoantigen. However, RFU values for each autoantigen-bound autoantibody are nonetheless linearly related to antibody concentrations (titres). Given moreover that, when comparing autoantibody profiles in PDAC vs chronic pancreatitis (CP) and other controls, in serum and in tumour or normal-adjacent samples, different autoantigens were identified in the different disease and sample types, all antigen-specific RFU values for each autoantibody isotype (IgG; or IgA) in each sample type (tumour-; CP-; or normal-adjacent tissue) were plotted in order to provide a measure of relative autoantibody isotype abundance (Figures 2A & B). An increased number of autoantibody-positive antigens for a specific isotype (Figure 2C) should result in an increased mean autoantibody RFU value for that isotype. Thus, Figures 2A-C should be read together since they provide two different dimensions of relative autoantibody isotype abundance: number of autoantigens per isotype and mean titres of antigen-specific autoantibodies per isotype. For the tissue samples, post-hoc power calculations indicate that a single comparison of the paired PDAC tumour (n=8) and normal-adjacent (n=8) provides >99% power to detect a 5-fold change in IgG / IgA ratios either direction (FDR 0.05). Thus, the microarray results show that whilst there was little difference observed for the IgG levels between CP tissue and normal-adjacent tissue, significant differences were found in IgG levels between normal-adjacent and tumour tissue (p<0.0001) (Figure 2A). The autoantibody levels for IgA also showed a significant difference for both tumour and CP tissue compared to the normal- adjacent samples (Figure 2B). Furthermore, IgA had a higher RFU compared to IgG in the tissue samples, with a 1:5 IgG / IgA ratio. These results thus provide an estimation of the abundant autoantibody isotype in pancreatic tissue and indicate an IgA-dominant cancer tissue microenvironment. In accordance with autoantibody abundance, the proportion of autoantibody-positive antigens showed significantly higher (p<0.0001) levels of IgA-positive antigens in tumour samples compared to IgG-positive antigens, but no statistically significant difference was observed for the normal-adjacent and the CP tissue samples (Figure 2C). Together, these results indicate that in tissue, IgA-based responses against cancer antigens predominate in PDAC, but isotype-based specificity is not established in CP. Furthermore, the abundance of the respective subclasses for each antibody isotype was investigated, by calculating the antigen ratio (number of antibody-positive antigens ÷ number of antibody-negative antigens) for each subclass and corresponding isotype in each sample and then representing the mean difference between the two for each sample type (mean isotype antigen ratio – mean subclass antigen ratio), together with the 95% and 5% confidence interval (CI), for each subclass in a forest plot, in order to enable comparisons to be made independent of differences in isotype abundance between patients. The smaller and likely more negative the resultant mean difference, the greater the abundance of the subclass within the respective isotype. In tissue, the majority of IgG antigen-bound autoantibodies were found to be of the IgG4 subclass (Figure 2D). Moreover, the complement activating subclasses, IgG1 and IgG3 were observed to be the least abundant in the tumour environment of PDAC. The IgG subclass data thus indicated a tolerogenic immune response because the dominant antibody, IgG4, is considered a non-activating antibody. IgA subclass autoantibodies targeting tumour antigens in PDAC had a similar abundance, but with higher IgA2 than IgA1, as expected (Figure 2E). Although IgA1 and IgA2 mediate their effector functions by binding to the FcαRI as monomers, they are considered to be regulatory and pro-inflammatory respectively, with IgA2 typically dominant in mucosal tissue. Moreover, in the tissue extracts, it is likely that dimeric sIgA is being detected, which is produced by mucosal plasma cells, rather than monomeric IgA (produced by bone marrow-derived B-cells). Together, the antibody subclass data from tissue samples indicates an antibody response that is pro-inflammatory, and not optimal for the activation of effector functions, thus promoting a tolerogenic tumour environment in PDAC. By contrast, when the mean signal intensities of IgG and IgA in serum were quantified, the overall mean intensity values were ~5-fold higher for IgG (Figure 2F) than for IgA (Figure 2G), as expected. Thereafter, the abundance of autoantibody isotypes between IgG and IgA were assessed by calculating the antigen ratio for each isotype (number of antibody-positive antigens ÷ number of antibody-negative antigens), and the 95% and 5% confidence interval (CI) and represented these ratios as a forest plot. The results showed that IgA had a high abundance of positive anti-CT / TA antigen autoantibody signals compared to IgG (Figure 2H), albeit IgG had a higher RFU signal overall. Furthermore, serum antibody subclass profiles - obtained by enumerating the number of antigens that had a positive response for a specific subclass in each patient - indicated that the most abundant isotypes in serum were IgG3 and IgG4, followed by IgG1, and the least abundant isotype was IgG2. Generally, in serum the overall pattern for all the patients indicated a dominant IgA subclass reactivity against the cancer antigens, with variation on the preferred subclass, indicating the significance of an IgA directed anti-CT / TA antigen response in PDAC. A reactivity pattern was observed that showed that patients (n=4 / 30; 13%) who had a dominant IgA subclass reactivity that was strong or very strong for both subclasses, had no IgG1 reactivity. The serum autoantibody subclass profiling of each patient is summarised in Table 2. By contrast, the serum autoantibody subclass profiling for the pooled DYS+CP group indicated IgG1, IgG4 and IgA1 as the abundant subclasses (Figure 2I and Figure 2J). Table 2: A summary of individual autoantibody subclass reactivity in serum outlining the presence and frequency of antibody-positive antigen frequency per patient. Patient (Px) ID Antibody subclass IgG1 IgG2 IgG3 IgG4 IgA1 IgA2 Px1 + +++ ++ ++ +++ + Px2 - + ++ ++ ++ ++ Symbol key: PSA (Polyspecific antibody “sticky phenotype” i.e., antigen frequency > n=80) ++++ (very strong; n= 64-80) +++(strong; n=48-63) ++ (moderate; n=30-47) + (weak; n=1-29) – (absent; n=0) Identification of candidate autoantibody-based serum biomarkers The minimal invasive nature of acquiring liquid biopsies makes them attractive candidates for cancer diagnosis, monitoring, and characterization. Thus, the most significant cancer antigen-specific autoantibodies from this study were identified and a subset of those autoantibodies that could be used as a panel of candidate serum biomarkers was determined. Here, 15% of IgG-positive significant antigens were obtained, compared to 61% for IgA-positive antigens, and 24% of the identified significant antigens were shared between the two antibody isotypes. Thereafter, a false discovery rate (FDR) of 1% was applied using the Benjamini-Hochberg method (Tong, T. and H. Zhao, Practical guidelines for assessing power and false discovery rate for a fixed sample size in microarray experiments. Stat Med, 2008.27(11): p. 1960-72), after which no IgG-reactive antigens were retained individually as candidates. However, there were 12 IgA-reactive antigens retained as candidates after applying a 1% FDR. Subsequently, multiplex analysis of the data was performed by combinatorial ROC analysis on all antigens that were identified as being significantly different, with comparisons between the PDAC group and the pooled DYS+CP group. This allowed the determination of a subset of antigen combinations with the best specificity and sensitivity as potential PDAC biomarkers, thus creating a panel of antigens instead of single biomarkers. The top five antigen combinations for IgG and IgA are summarised in Table 3. The top IgG combinations had AUC, sensitivity, and specificity values of 0.906, 0.933, and 0.767, respectively. The top IgA combination showed AUC, sensitivity, and specificity values of 0.968, 1.00, and 0.833, respectively. Table 3: Combinatorial ROC analysis of top 5 antigen combinations for serum IgG and IgA PDAC classifiers showing the area under the curve (AUC), sensitivity and specificity values for each antigen combination. Autoantibody Combination Antigens AUC Sensitivity Specificity IgG I GAGE1-LEMD1-MAGEB1- 0.824 0.800 0.767 PAGE1 III GAGE1-LEMD1-MAGEB1- 0.833 0.833 0.800 TSGA10 XXIX ACVR2B-BAGE4-GAGE1- 0.899 0.800 0.800 LEMD1-MAGEB1-TSGA10 VII ACVR2B-GAGE1- LEMD1- 0.901 0.900 0.733 MAGEB1-TSGA10 VI ACVR2B-GAGE1- LEMD1- 0.906 0.933 0.767 MAGEB1-PAGE1 IgA X AURKA-GAGE1-MAGEA10- 0.956 1.000 0.833 MAGEB1 XXIX AURKA -MAGEA10-MAGEB1- 0.940 0.900 0.867 PAGE1 XXX AURKA -MAGEA10-MAGEB1- 0.968 1.000 0.833 PLEKHA5 LXXV AURKA -GAGE1-MAGEA10- 0.963 0.967 0.933 MAGEB1-PLK4 LXXVII AURKA -GAGE1-MAGEA10- 0.954 1.000 0.800 PLEKHA5-XAGE3Av1 Antigen-specific autoantibody glycosylation patterns differ between PDAC and confounding cohort Having confirmed autoantibody reactivity against specific cancer-testis antigens on the microarray platform, whether there were differential glycan moieties on the antigen-specific autoantibodies in PDAC and controls was determined. This was achieved by adapting a fluorescently-labelled lectin-based protocol from published bead-based assays for detecting antibody glycosylation patterns (Li, C., et al., A multiplexed bead assay for profiling glycosylation patterns on serum protein biomarkers of pancreatic cancer. Electrophoresis, 2011. 32(15): p. 2028-35), profiling the glycoforms present on antigen-bound autoantibodies, detecting with 1µg / ml of RCA (β1,4-Gal), ECL(β1,4-Gal), SNA (α2,6-SA), or LCA(α1,6- Fuc), for the presence of each respective glycoform. As above, a threshold was set and a true positive signal was defined as any relative fluorescent unit (RFU) that was above the mean+2SD of the control RFU. Thereafter, the mean ± standard error of the mean (SEM) for all the positive antigens was used as a measure of the total galactosylation, sialylation, and fucosylation levels in each cohort. The investigated glycoforms had uniform low signal intensities across all the lectin-specific glycans for the control group (Figures 3A-D), further validating the specificity of the assay on a cancer antigen microarray platform. Moreover, the data shows relatively high sialylation (Figure 3B), and fucosylation (Figure 3C) for the PDAC cohort compared to the pooled DYS+CP cohort. These results suggest that although the antigen- bound autoantibodies in the pooled DYS+CP cohort may be sialylated and fucosylated, the carbohydrate content varies between the two disease conditions, favoring increased α2,6-Sal and α1,6-Fuc in PDAC. However, the galactosylation levels show contrasting outcomes respectively, showing high (Figure 3A) and low (Figure 3D) galactosylation for PDAC compared to the pooled DYS+CP group. Although both RCA and ECL have selectivity for β1,4-Gal, their respective binding affinity is impacted by the presence of other carbohydrate groups, particularly sialylation for ECL, suggesting that the glycosylation features between PDAC and pooled DYS+CP diseases of the pancreas are notably distinct. Furthermore, the relationship between the observed glycan motifs between PDAC and the pooled DYS+CP group was assessed. Significance testing was followed by pairwise testing, adjusting the antigen significance level based on probability value (p-value) rank order by employing the stringent Bonferroni correction to limit the risk of a type I error from multiple pairwise tests performed on the data. Subsequently, each disease condition was plotted against the corrected significant antigen count for each lectin (Figure 3E). The results show a marginal increase for RCA binding (p-value =0.553), and a significant increase for SNA (p-value <0.0001) and LCA binding (p-value<0.0001) for PDAC. In agreement with the above galactosylation data, a significant (p-value <0.0001) decrease in the ECL (β1,4-Gal) motif in the PDAC cohort was observed. Therefore, there are differences in the carbohydrate composition and motifs of serum autoantibodies identified for PDAC and pooled DYS+CP diseases of the pancreas, and these may be indicative of differential Fc region glycosylation that exists between serum autoantibodies present between the two different disease states. Thereafter, based on the lectin assay data of glycan-positive autoantibodies, and the data generated from autoantibody subclass assays, unsupervised hierarchical clustering was performed to determine whether the serum autoantibody signatures of the PDAC cohort could be associated with the observed glycosylation features (Figure 3F). To ensure comparability and avoid clustering driven by technical variation between signals for the same autoantigen in different samples, the log2 transformed data was scaled to the standard deviation. Antibody and lectin features form distinct clusters, with IgA subclasses showing shared characteristic antigen binding with the SNA (α2,6-Sal) lectin, indicating that both interactions are similar, and represent the serum autoantibody profile of the PDAC cohort. This data thus suggests that increased sialylation in both IgA1 and IgA2 is an immune feature of PDAC. Example 2: Bead-based diagnostic method directed to IgG and IgA serum biomarker panels A Luminex bead-based assay, in which an individual patient sample is added to a mixture of streptavidin- coated, dye-encoded xMap bead types, was designed to diagnose PDAC according to the method described herein. Each unique bead type is encoded by a unique dye combination as known in the art and is derivatised with a unique BCCP-tagged CT antigen drawn from the IgG and / or IgA diagnostic panels described herein. Each antigen in the diagnostic panel is thus encoded by the xMAP bead type, as will be understood by a person skilled in the art. During incubation, autoantibodies in the patient sample bind to the cognate analytes of interest on the xMAP beads. Following a wash step, anti-human IgG and anti-human IgA detection antibodies – each labelled with a different fluorophore as known in the art - are added and allowed to bind to the patient autoantibodies captured on the xMAP beads. The beads are thereafter read, for example on a dual-laser Luminex Bioplex 200® for signal quantification. Sample preparation: A patient blood sample can be collected fresh, thawed from storage at -80℃ or from blood cards. Sample concentrations can be prepared to at least 1:200 in wash buffer (PBST). Sample concentration can be varied but should be within dynamic range of the assay. Bead preparation: Each xMAP bead type is individually coated with streptavidin and is non-covalently bound to a unique enzymatically biotinylated, BCCP-tagged CT antigen from the diagnostic panel via high affinity streptavidin-biotin interactions. Post binding of the BCCP-tagged CT antigen, the bead surface is blocked with blocking buffer containing free biotin for ~30 min, with gentle shaking at RT. The beads are washed in PBST prior to performing the capture assay. Hybridisation / capture assay: Technical duplicates of each patient sample are added to the mixture of antigen-derivatised xMAP beads and incubated at room temperature to allow PDAC-specific autoantibodies to form an antigen-antibody complex with their cognate bead-bound antigen. Thereafter, a wash step is performed to remove unbound- and non-specifically bound antibodies. Differentially fluorophore-labelled anti-human IgG and anti-human IgA detection antibodies are added (at µg / ml concentrations) to the bead-bound antigen- antibody complexes, incubated for ca.1hr at room temperature, then washed with PBST and with PBS. The beads are then read on, for example, a Bioplex 200™ or Bioplex or xMAP system (Luminex), according to manufacturer’s protocols. The signal readings are exported for data analysis. Example 3: Validation of autoantibody biomarkers and biomarker panels for PDAC The 12 BCCP-tagged autoantigen targets of the autoantibody biomarkers identified in Example 1 (GAGE1; ACVR2B; LEMD1; MAGEB1; PAGE1; TSGA10; BAGE4; AURKA; MAGEA10; PLEKHA5; PLK4; and XAGE3Av1) were re-expressed in Sf9 insect cells using a baculoviral expression system and replica protein microarrays were fabricated as previously described [Beeton-Kempen, N., et al., Development of a novel, quantitative protein microarray platform for the multiplexed serological analysis of autoantibodies to cancer-testis antigens. Int J Cancer, 2014. 135(8): p. 1842-51], creating 16-plex custom protein microarrays comprising only those autoantigens (each printed in technical triplicate on each replica array) plus assay controls. Serological assays were performed and raw microarray data was recorded and extracted as described in Example 1, using the original discovery cohort (in order to verify the technical performance of the custom protein microarrays) as well as using an independent validation cohort, in order to evaluate autoantibody biomarker performance in the validation cohort. The discovery cohort comprised patients with confirmed pancreatic ductal adenocarcinoma (PDAC) diagnosis from the Groote Schuur Hospital (GSH) in Cape Town, South Africa. The validation cohort PDAC samples were obtained from the National Health Laboratory Services (NHLS), Johannesburg, South Africa. A cohort of chronic pancreatitis (CP) patients from GSH were included as a study control for a confounding disease of the pancreas. Cohort characteristics are summarised in Table 4 Table 4: Cohort characteristics for the discovery cohort (GSH), validation cohort (NHLS) and disease control (CP). Variable GSH cohort NHLS cohort CP cohort n=30 n=180 n=16 Demographic and clinical characteristics Age 58.5±10.5 49.05±27.0 51.5 ±13.5 sex (%) Male 44 60 87.5 Female 56 40 12.5 Race (%) Black 25 96.11 80.9 Mixed ancestry 56.25 3.89 14.29 White 18.75 4.76 Data from the validation cohort was analysed separately to data from the discovery cohort. Diagnostic performance of the autoantibody biomarkers and biomarker panels was evaluated in each cohort using Receiver Operating Characteristics (ROC) curves, based on the input signal intensity values for each antigen in each PDAC or CP sample that was measured on the custom protein microarrays in each cohort. Optimal autoantibody biomarker combinations were identified and selected based on selectivity and specificity thresholds above 0.80, in accordance with area under the curve (AUC) considerations. Some of the identified combinations are listed below: • LEMD1-MAGEB1 • LEMD1-MAGEB1-ACVR2B • LEMD1-MAGEB1-GAGE1 • LEMD1-MAGEB1-PAGE1 • LEMD1-MAGEB1-TSGA10 • LEMD1-MAGEB1-ACVR2B-GAGE1 • LEMD1-MAGEB1-ACVR2B- PAGE1 • LEMD1-MAGEB1-ACVR2B-TSGA10 • LEMD1-MAGEB1-GAGE1-TSGA10 • LEMD1-MAGEB1-ACVR2B-GAGE1-TSGA10 A. Verification of the technical performance of the custom protein microarrays The ROC curve analysis of the GSH discovery PDAC cohort compared to the confounding disease chronic pancreatitis (CP) cohort demonstrated that the data from the custom protein microarrays was consistent with the data generated on the commercial cancer-testis antigen arrays used in Example 1, with the autoantibody biomarker panels reported in Table 3 being re-confirmed on the custom protein microarray, thus verifying the technical performance and reproducibility of the custom protein microarrays. For example, a 2-autoantibody panel comprising autoantibodies against the antigens LEMD1 & MAGEB1 showed an AUC, sensitivity, and specificity of 0.879, 0.931, and 0.938, respectively in distinguishing PDAC from CP in the discovery cohort, with an optimal cut-point of 0.634 (Figure 4, Table 5). In addition, a 3-autoantibody panel comprising autoantibodies against the antigens LEMD1, MAGEB1 & TSGA10 showed an AUC, sensitivity, and specificity of 0.905, 0.862, and 0.938, respectively in distinguishing PDAC from CP in the discovery cohort, with an optimal cut-point of 0.708 (Figure 5, Table 5). The performance of several other top-performing autoantibody biomarker combinations from Example 1 in the GSH discovery cohort is shown in Figure 6 and Table 5. Table 5: Combinatorial analysis showing the combination of IgG autoantibodies in the GSH discovery cohort against different panels of antigens. Combination AUC SE SP Optimal Cutoff LEMD1-MAGEB1 0.879 0.931 0.938 0.634 LEMD1-MAGEB1-PAGE1 0.895 0.966 0.875 0.420 LEMD1-MAGEB1-TSGA10 0.905 0.862 0.938 0.708 ACVR2B-LEMD1-MAGEB1-PAGE1 0.892 0.931 0.875 0.502 ACVR2B-LEMD1-MAGEB1-TSGA10 0.903 0.897 0.938 0.692 B. Analysis of the independent validation cohort ROC curve analysis of the NHLS validation PDAC cohort compared to the confounding disease chronic pancreatitis (CP) cohort identified optimal autoantibody biomarker panels that validated those reported for the GSH discovery cohort in Examples 1 and 3 (A) above. For example, a 2-autoantibody panel comprising autoantibodies against the antigens LEMD1 & MAGEB1 showed an AUC, sensitivity, and specificity of 0.879, 0.931, and 0.938, respectively in distinguishing PDAC from CP in the validation cohort, with an optimal cut-point of 0.634 (Figure 7, Table 6). In addition, a 3-autoantibody panel comprising autoantibodies against the antigens LEMD1, MAGEB1 & TSGA10 showed an AUC, sensitivity, and specificity of 0.887, 0.886, and 0.812, respectively in distinguishing PDAC from CP in the validation cohort, with an optimal cut-point of 0.864 and an improved AUC compared to the LEMD1-MAGEB1 panel (Combo I, Figure 8; Table 6). Furthermore, a 4-autoantibody panel comprising autoantibodies against the antigens LEMD1, MAGEB1, TSGA10 & ACVR2B showed an AUC, sensitivity, and specificity of 0.886, 0.886, and 0.812, respectively in distinguishing PDAC from CP in the validation cohort (Combo II; Figure 9), with an optimal cut-point of 0.864, whilst a related 4-autoantibody panel comprising autoantibodies against the antigens LEMD1, MAGEB1, TSGA10 & GAGE1 showed an AUC, sensitivity, and specificity of 0.885, 0.899, and 0.812, respectively in distinguishing PDAC from CP in the validation cohort (Combo III, Figure 8), with an optimal cut-point of 0.858 and an improved AUC compared to the LEMD1-MAGEB1 panel. Moreover, a 5-autoantibody panel comprising autoantibodies against the antigens LEMD1, MAGEB1, TSGA10, GAGE1 & ACVR2B showed an AUC, sensitivity, and specificity of 0.883, 0.892, and 0.812, respectively in distinguishing PDAC from CP in the validation cohort, with an optimal cut-point of 0.856 and an improved AUC compared to the LEMD1-MAGEB1 panel (Combo IV, Figure 8; Table 6). Table 6: Combinatorial analysis showing the selectivity (SE) and specificity (SP) for the combinations of IgG autoantibodies in the NHLS validation cohort against different panels of antigens. Combination AUC SE SP Optimal Cutoff LEMD1-MAGEB1 0.879 0.931 0.938 0.634 LEMD1-MAGEB1-TSGA10 0.887 0.886 0.812 0.864 ACVR2B-LEMD1-MAGEB1-TSGA10 0.886 0.886 0.812 0.864 GAGE1-LEMD1-MAGEB1-TSGA10 0.885 0.889 0.812 0.858 ACVR2B-GAGE1-LEMD1-MAGEB1-TSGA10 0.883 0.892 0.812 0.856 Thus, the data from the NHLS validation cohort validates specific autoantibody biomarker panels reported in Example 1 and also unexpectedly demonstrates a minimal autoantibody biomarker panel of LEMD1 & MAGEB1, with incremental benefit being provided by addition of autoantibodies against TSGA10, GAGE1 and / or ACVR2B into the minimal autoantibody biomarker panel. Example 4: Incremental benefit of combining autoantibody biomarkers and biomarker panels for PDAC with CA19-9 scores Carbohydrate antigen 19-9 (CA19-9) is a widely used serum biomarker for pancreatic cancer, particularly for diagnosis. While elevated CA19-9 levels are commonly associated with PDAC, the CA19-9 marker alone lacks specificity, as it may also be elevated in gastrointestinal malignancies, and so is well known to have limited diagnostic utility as a single marker. An enzyme-linked immunosorbent assay (ELISA) was carried out on serum samples from the discovery and validation cohorts, utilising a commercial ELISA kit for CA19-9 (Cat. No. EHCA199, ThermoFisher Scientific). The ELISA kit utilizes a capture antibody specific to CA19-9, followed by a detection system (an enzyme-linked secondary antibody and substrate reaction) to produce a measurable signal. The intensity of the signal correlates with CA19-9 concentration, allowing for the measurement of CA19-9 biomarker levels in each patient sample (Figure 10). The CA19-9 ELISA data was combined with the autoantibody biomarker data from Example 3 and combinatorial ROC analyses were performed for the discovery and, separately, the validation cohort. The results of the ROC analyses demonstrated an incremental increase in specificity can be gained through combining the CA19-9 data with the autoantibody data. For example, a 2-biomarker panel comprising autoantibodies against the antigens LEMD1 & MAGEB1 showed an AUC, sensitivity, and specificity of 0.879, 0.931 and 0.938, respectively in distinguishing PDAC from CP in the GSH discovery cohort (Figure 4), whereas a 3-biomarker panel comprising autoantibodies against the antigens LEMD1 & MAGEB1 plus CA19-9 unexpectedly showed an AUC, sensitivity, and specificity of 0.912, 0.724 and 1.000, respectively in distinguishing PDAC from CP in the GSH discovery cohort (Combo II, Figure 11; Table 7), with an optimal cut-point of 0.808. Table 7: Combinatorial analysis for various panels of IgG autoantibodies in the GSH discovery cohort. Optimal Combination AUC SE SP Cutoff CA19.9-LEMD1-MAGEB1 0.912 0.724 1.000 0.808 ACVR2B-CA19.9-LEMD1-MAGEB1 0.903 0.793 0.938 0.752 CA19.9-GAGE1-LEMD1-MAGEB1 0.916 0.724 1.000 0.815 CA19.9-LEMD1-MAGEB1-TSGA10 0.925 0.862 0.875 0.630 ACVR2B-CA19.9-GAGE1-LEMD1-MAGEB1 0.907 0.828 0.938 0.722 ACVR2B-CA19.9-LEMD1-MAGEB1-TSGA10 0.929 0.897 0.812 0.535 CA19.9-GAGE1-LEMD1-MAGEB1-TSGA10 0.950 0.862 1.000 0.762 ACVR2B-CA19.9-GAGE1-LEMD1-MAGEB1-TSGA10 0.955 0.862 1.000 0.765

Claims

CLAIMS:

1. A method of diagnosing pancreatic ductal adenocarcinoma (PDAC) in a subject, the method comprising detecting the presence of autoantibodies in a biological sample from the subject which recognize LEMD1 and MAGEB1 antigens.

2. The method of claim 1, which comprises detecting the presence of autoantibodies in the biological sample which recognize LEMD1 and MAGEB1 antigens and also detecting an autoantibody or autoantibodies in the biological sample which recognize at least one other antigen selected from the group consisting of ACVR2B, GAGE1, TSGA10, PAGE1, BAGE4, AURKA, MAGEA10, PLEKHA5, PLK4 and XAGE3Av1.

3. The method of claim 2, which comprises detecting autoantibodies which recognize LEMD1 and MAGEB1 antigens and at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or ten of the other antigens.

4. The method of any one of claims 1 to 3, which comprises detecting autoantibodies in the biological sample which recognize LEMD1 and MAGEB1 antigens and at least one other antigen selected from the group consisting of ACVR2B, GAGE1 and TSGA10.

5. The method of any one of claims 1 to 4, which comprises detecting autoantibodies in the biological sample which recognize LEMD1, MAGEB1 and TSGA10 antigens.

6. The method of any one of claims 1 to 5, which comprises detecting autoantibodies in the biological sample which recognize LEMD1, MAGEB1 and TSGA10 antigens and either or both of ACVR2B and GAGE1 antigens.

7. The method of any one of claims 1 to 6, which comprises detecting autoantibodies in the biological sample which recognize LEMD1, MAGEB1, TSGA10, ACVR2B and GAGE1 antigens.

8. The method of any one of claims 1 to 3, which comprises detecting autoantibodies in the biological sample which recognize LEMD1 and MAGEB1 antigens and at least two, at least three or at least four other antigens selected from the group consisting of ACVR2B, GAGE1, TSGA10 and PAGE1.

9. The method of any one of claims 1 to 8, which comprises detecting autoantibodies in the biological sample which recognize: i) LEMD1 and MAGEB1; ii) LEMD1, MAGEB1 and TSGA10; iii) LEMD1, MAGEB1 and GAGE1;iv) LEMD1, MAGEB1 and ACVR2B; v) LEMD1, MAGEB1 and PAGE1; vi) LEMD1, MAGEB1, TSGA10 and GAGE1; vii) LEMD1, MAGEB1, TSGA10 and ACVR2B; viii) LEMD1, MAGEB1, GAGE1 and ACVR2B; ix) LEMD1, MAGEB1, ACVR2B and PAGE1; or x) LEMD1, MAGEB1, TSGA10, ACVR2B and GAGE1.

10. The method of any one of claims 4 to 9, wherein the autoantibodies are IgG antibodies.

11. The method of claim 10, which comprises detecting IgA autoantibodies in the biological sample which recognize GAGE1 and / or MAGEA10 antigens.

12. The method of claim 11, which further comprises detecting IgA autoantibodies in the biological sample which recognize GAGE1 and / or MAGEA10 antigens and at least one other antigen selected from the group consisting of AURKA, MAGEB1, BAGE1, PLEKHA5, PLK4, XAGE3Av1, GAGE1 and MAGEA10.

13. The method of claim 12, which comprises detecting IgA autoantibodies in the biological sample which recognize GAGE1 and / or MAGEA10 antigens and at least two, at least three, at least four, at least five, or at least six other antigens selected from the group consisting of AURKA, MAGEB1, BAGE1, PLEKHA5, PLK4, XAGE3Av1, GAGE1 and MAGEA10.

14. The method of either of claims 12 or 13, which comprises detecting IgA autoantibodies in the biological sample which recognize: xi) GAGE1, MAGEA10, AURKA,and MAGEB1; xii) MAGEA10, AURKA, MAGEB1 and PAGE1; xiii) MAGEA10, AURKA, MAGEB1 and PLEKHA5; xiv) GAGE1, MAGEA10, AURKA, MAGEB1 and PLK4; or xv) GAGE1, MAGEA10, AURKA, PLEKHA5 and XAGE3Av1.

15. The method of claim 14, which comprises detecting IgA autoantibodies in the biological sample which recognize GAGE1, MAGEA10, AURKA, PLEKHA5 and XAGE3Av1.

16. The method of claim 1, which comprises detecting any one of the combinations of IgG autoantibodies listed in claims 4 to 9 and any one of the combinations of IgA autoantibodies listed in claims 11 to 15.

17. The method of any one of claims 1 to 16, wherein the autoantibodies are detected by: - contacting the antigens with the biological sample; and - detecting binding of autoantibodies to the antigens.

18. The method of claim 17, wherein binding of the autoantibodies to the antigens is detected using one or more labeling reagents capable of binding to the autoantibodies.

19. The method of claim 18, wherein the one or more labeling reagents are labeled anti-human IgG antibodies and / or labeled anti-human IgA antibodies.

20. The method of any one of claims 17 to 19, which further comprises: - quantifying the levels of each autoantibody detected in the biological sample; and - comparing the levels of the autoantibodies to a level of the same autoantibodies associated with a healthy subject, or to a level associated with chronic pancreatitis or PDAC.

21. The method of any one of claims 17 to 20, wherein the antigens are biotinylated.

22. The method of any one of claims 17 to 21, wherein the antigens are bound to a solid support.

23. The method of claim 22, wherein the solid support is a bead, a membrane, a slide, a plate, a well, a tube, a filter or a dipstick.

24. The method of any one of claims 1 to 23, wherein the biological sample is a blood sample.

25. The method of any one of claims 1 to 24, which further comprises the step of treating a subject diagnosed as having PDAC, by surgery or by administering a suitable therapeutic agent.

26. The method of any one of claims 1 to 24, which further comprises the step of referring a subject diagnosed as having PDAC for a confirmatory test.

27. An antigen complex comprising a biotin carboxyl carrier protein (BCCP)-tagged antigen attached to a solid support for use in the method of any one of claims 1 to 26, wherein: the antigen is selected from the group consisting of LEMD1, MAGEB1, ACVR2B, GAGE1, TSGA10, PAGE1, BAGE4, AURKA, MAGEA10, PLEKHA5, PLK4 and XAGE3Av1; and the solid support is a bead, a membrane, a slide, a plate, a well, a tube, a filter or a dipstick and is coated with streptavidin.

28. An immune reaction analysis device comprising LEMD1 and MAGEB1 antigens, each antigen being attached to a solid support.

29. The device of claim 28, which further comprises one or more other antigens attached to a solid support, the one or more other antigens being selected from the group consisting of ACVR2B, GAGE1, TSGA10, PAGE1, BAGE4, AURKA, MAGEA10, PLEKHA5, PLK4 and XAGE3Av1.

30. The device of either of claims 28 or 29, wherein the solid support is a bead, a membrane, a slide, a plate, a well, a tube, a filter or a dipstick.

31. The device of any one of claims 28 to 30, wherein the antigens are attached to beads.

32. The device of claim 31, wherein the antigens are in the same chamber.

33. The device of claim 31, wherein the antigens are in different chambers.

34. The device of any one of claims 28 to 33, which is a microtiter plate having a plurality of wells.

35. A kit comprising: - LEMD1 and MAGEB1 antigens; - optionally one or more other antigens selected from the group consisting of ACVR2B, GAGE1, TSGA10, PAGE1, BAGE4, AURKA, MAGEA10, PLEKHA5, PLK4 and XAGE3Av1.

36. The kit of claim 35, wherein each antigen is attached to a solid support.

37. The kit of either of claims 35 or 36, which includes a device according to any of claims 28 to 34.

38. The kit of any one of claims 35 to 37, which further comprises: - one or more labeling reagents; - reagents for washing and removing unbound antibodies; and / or - instructions, in written or computer-implementable form, for performing the method of any one of claims 1 to 26.

39. The kit of claim 38, wherein the one or more labeling reagents are labeled anti-human IgG antibodies, labeled anti-human IgA antibodies or a mixture thereof.

40. A computer implemented method of diagnosing PDAC, the computer performing steps including: - receiving inputted subject data comprising the number of autoantibodies detected in abiological sample from a subject, wherein the antibodies are autoantibodies bound to LEMD1, MAGEB1 and optionally also one or more other antigens selected from the group consisting of ACVR2B, GAGE1, TSGA10, PAGE1, BAGE4, AURKA, MAGEA10, PLEKHA5, PLK4 and XAGE3Av1; - comparing the data obtained from the sample to reference data for the same autoantibodies associated with a subject without PDAC or associated with a patient having PDAC and thereby determining whether the subject has, or possibly has, PDAC; and - displaying a diagnosis.

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