GB2541712A - Autoantibody biomarkers for gastric cancer - Google Patents

Abstract

This invention provides an improvement for the early diagnosis of GC and development of an effective non-invasive diagnostic and/or prognostic test for gastric cancer. The set goal is achieved by identifying a set of antigens with relevance for diagnosis of GC by using a high-throughput technological platform (i.e., microarray) to analyze autoantibodies in sera from healthy individuals and cancer patients against all of the antigens simultaneously. Eleven antigens are disclosed in SEQ ID NOs 1-11.

Description

AUTOANTIBODY BIOMARKERS FOR GASTRIC CANCER BACKGROUND OF THE INVENTION AND PRIOR ART Technical field

The invention generally relates to biomarkers associated with cancer and methods and compositions for the detection, diagnosis, prognosis, prediction of therapy outcome, and monitoring of the progression of cancer, in particular, stomach cancer.

Background art

Despite the decrease in incidence in many Western countries, gastric cancer (GC) with 952,000 new cases (6.8% of total) and 723,000 deaths (8.8% of total) in year 2012 remains the fifth most common type of cancer and the third most common cause of cancer-related death worldwide [1]. The incidence rates of GC vary significantly across the world - almost half of the GC cases occurs in Eastern Asia, and it is followed by Eastern Europe, Southern Europe and Balkan countries, and Central and South America [2]. Generally, GC rates are about twice as high in males as in females. The high mortality rate from GC is mostly due to its detection at advanced stage - over 80% of GC cases are detected at stage IIIA-IV, when the estimated 5-year survival rate ranges from 4 to 20%. On the contrary, GC is curable by complete resection of the tumour or by endoscopic mucosa dissection if detected at an early localized stage [3]. However, the early detection of GC is hampered by the lack of specific symptoms before it has spread beyond the original site. Currently, the diagnosis is based on endoscopic examination followed by the histological analysis of a gastric biopsy, which is an invasive technique not applicable for the regular screening of asymptomatic patients.

There have been attempts to develop various cancer diagnostic and/or prognostic tests based on the quantitative analyses of serum markers like CEA, CA 19-9, CA 72-4, pepsinogen and others. Currently, the only non-invasive test used for GC detection, particularly that of intestinal type is the pepsinogen (PG) test, which measures the serum PGI and PGM levels, which are indicative for chronic atrophic gastritis, the premalignant lesion of GC. This test has been used for more than 20 years in opportunistic screening settings, and its diagnostic performance, evaluated by numerous studies, has shown to be limited - with the widely accepted cut-off values of PGI ^70 ng/ml and PGI/PGIl ratio <3 ng/ml, this test has sensitivity in ranges 58-85%, specificity 70-74% and positive predictive value of 0.7-2.6% [4]. Our data suggest that only about 1/3 of GCs can be identified by the pepsinogen test. Hence, the development of a reliable and simple non-invasive biomarker assay for the early detection of GC among high-risk individuals is of paramount importance for reducing the disease burden and mortality and improving the management of patients with gastrointestinal complaints.

Autoantibodies against tumour-associated antigens (TAAs) have been detected by various approaches in all cancer types analyzed so far [5;6]. Due to their specificity, high affinity and stability in the sera, they represent attractive targets for the development of multiplex serological tests for the detection of cancer. Furthermore, several lines of evidence suggest that the immune system can detect the presence of cancer even years before the clinical manifestation of disease [7;8] clearly demonstrating their potential for the early detection of cancer.

However, so far the exploitation of tumour-associated autoantibodies for cancer diagnosis has been hampered by the facts that the frequency of antibodies against any individual antigen is generally low, typically ranging from 1 to ~15%, hence the sensitivity of a diagnostic test that is based on the detection of a single antibody (as in the case of infectious diseases) is very low. Besides, the autoantibody repertoire is heterogeneous and to some extent resembles the response to tissue damage by viral infections or autoimmune diseases.

DISCLOSURE OF THE INVENTION

The aim of the invention is to offer an improvement for the early diagnosis of GC and to develop an effective non-invasive diagnostic and/or prognostic test for gastric cancer.

The set goal is achieved by identifying a set of antigens with relevance for diagnosis of GC by using a high-throughput technological platform (i.e., microarray) to analyze autoantibodies in sera from healthy individuals and cancer patients against all of the antigens simultaneously.

To address these issues, in our previous studies we applied the T7 phage display-based SEREX approach and the T7 phage displayed tumour antigen microarray technique to systematically search for antigens that elicit humoral responses in patients with GC, but not in healthy controls and/or various inflammatory conditions in stomach [9], (unpublished results). As a result from these studies, a set of 105 distinct serologically active antigen clones with most promising diagnostic value for GC diagnosis were chosen. Among these, there are well-known TAAs, such as Cancer-Testis antigens, previously uncharacterized antigens and novel artificial peptides.

Next, to obviate the biological limitations of the used T7 phage display system, this set of antigens was produced as His-Helo tagged recombinant proteins in E.coli and purified. The set of antigens were directly used for the generation of recombinant TAA microarrays that were further applied for the systematic survey of autoantibody responses against the TAAs in an independent GC patient and matched control cohorts - i.e., validation set comprising 159 GC patients and 156 matched healthy donors.

To select for top GC-associated autoantibody biomarkers, our developed microarray data analysis algorithm was applied - it included ranking of each individual antigen basing on the frequency of specific autoantibody detection in the two groups and the intensity of autoantibody signal, and assigning a score to each individual serum. A set of 26 top ranked antigens were identified as most relevant for GC diagnosis. From these, 15 TAAs represented known immunogenic proteins and included Cancer-Testis and universal cancer antigens while 11 antigens were discovered as novel antigens relevant for GC detection. The autoantibodies reactive to the novel antigenic determinants were capable of increasing the diagnostic value of the established markers in GC by 8.7 % and thus can be used for developing in vitro immunoassays for measuring the presence or concentration of autoantibodies against these antigens in biological samples.

The optimal type of biological sample to be used for the in vitro analysis of the specific autoantibody presence is peripheral blood, from which serum or plasma sample is collected and a small volume (usually not more than 2 μΙ) subsequently used for the immunoassay for screening or diagnostic purposes. Antibodies are very stable molecules, which have been used for years in clinical immunoassays and new tests focusing on antibody detection can easily be adapted for the use in clinical practice.

In general, our studies allowed to demonstrate the presence of an autoantibody biomarker signature associated to GC, and in particular, defined new specific biomarkers of the disease with a diagnostic potential for GC by using blood sample from patients having GC, which in combination can achieve greater diagnostic specificity and sensitivity than known autoantibody biomarkers alone.

Explanation of the terms used in the description

By “biomarker” a biochemical characteristic that can be used to diagnose, or to measure the progress of a disease or condition, or the effects of treatment of a disease or condition is meant. A biomarker can be, for example, the presence of a nucleic acid, protein, or antibody associated with the presence of cancer or another disease in an individual. The present invention provides biomarkers for stomach cancer that are antibodies present in the body fluids of subjects diagnosed with stomach cancer. The biomarker antibodies in the present invention are the autoantibodies displaying increased reactivity in individuals with stomach cancer. The autoantibodies can be detected with autoantigens, human proteins that are specifically bound by the antibodies. As used herein, the word “protein” refers to a full-length protein, a portion of a protein, or a peptide. The term protein includes antibodies. Proteins can be produced via fragmentation of larger proteins, or chemically synthesized. As used herein, the term “peptide,” “oligopeptide,” and "polypeptide" are used interchangeably with protein herein and refer to a sequence of contiguous amino acids linked by peptide bonds. As used herein, the term “protein” refers to a polypeptide that can also include post-translational modifications that include the modification of amino acids of the protein and may include the addition of chemical groups or biomolecules that are not amino acid-based. The terms apply to amino acid polymers in which one or more amino acid residue is an analogue or mimetic of a corresponding naturally occurring amino acid, as well as to naturally occurring amino acid polymers. "Antibody" refers to a polypeptide ligand substantially encoded by an immunoglobulin gene or immunoglobulin genes, or fragments thereof, which specifically recognizes and binds a molecule or a region or domain of a molecule (an epitope). The recognized immunoglobulin genes include the kappa and lambda light chain constant region genes, the alpha, gamma, delta, epsilon and mu heavy chain constant region genes, and the myriad immunoglobulin variable region genes. Antibodies exist, e.g., as intact immunoglobulins or as a number of well characterized fragments produced by digestion with various peptidases. The term "antibody," as used herein, also includes antibody fragments either produced by the modification of whole antibodies or those synthesized de novo using recombinant DNA methodologies. It also includes polyclonal antibodies, monoclonal antibodies, chimeric antibodies, humanized antibodies, or single chain antibodies. An “autoantibody” is an antibody present in an individual that specifically recognizes a biomolecule present in the individual or its structural mimetic (mimotope - the mimetic of a specific epitope). Typically an autoantibody specifically binds a protein expressed by the individual, or a modified form thereof present in a sample from the individual. Autoantibodies are generally IgG antibodies that circulate in the blood of an individual, although the invention is not limited to IgG autoantibodies or to autoantibodies present in the blood.

As used herein, a “biomarker detection panel” or “biomarker panel” refers to a collection of biomarkers that are provided together for detection, diagnosis, prognosis, staging, or monitoring of a disease or condition, based on detection values for the set (panel) of biomarkers. The set of biomarkers is physically associated, such as by being packaged together, or by being reversibly or irreversibly bound to a solid support. For example, the biomarker detection panel can be provided, in separate tubes that are sold and/or shipped together, for example as part of a kit, or can be provided on a chip, membrane, strip, filter, or beads, particles, filaments, fibers, or other supports, in or on a gel or matrix, or bound to the wells of a multi-well plate. A biomarker detection panel can in addition or in the alternative be associated by a list, table, or program provided to a user or potential user that provides an internet address that provides computer-based linkage of the biomarker identities and information stored on a web site. A computer-based program can provide links between biomarker identities, information, and/or purchasing functions for a collection of biomarkers that make up a biomarker detection panel, based on the user’s entered selections.

Brief description of drawings

Fig. 1 shows serum scores in the study groups. Comparison of the known 15 TAA set vs full set of 26 biomarkers comprising 11 novel antigens. The mean serum score value of the 26-biomarker set in GC patient and healthy donor cohorts were 83.1 and 3.7, respectively, while that of the 15 known TAA set - 66.9 and 1.7, respectively. Abbreviations: GC - gastric cancer patients, FID - healthy donors, Ag - antigen.

Fig. 2 shows comparison of the area under the curve (AUC) of the known 15 TAA set (black line, AUC=0.6051) vs full set of 26 biomarkers comprising 11 novel GC associated antigens (red line, AUC=0.6575). Abbreviations: GC - gastric cancer patients, HD - healthy donors, Ag - antigen.

Detailed description of invention

In particular, the inventors have identified 105 different GC-associated antigen clones by applying the phage display SEREX method, developed a microarray-based autoantibody profiling approach (antigen microchip) and a system for data processing and analyses [9]. The obtained 105 antigen microarray was screened with sera from an independent validation cohort of 159 GC patients and 156 matched healthy donors to establish its diagnostic value. As a result, 11 antigens were discovered as novel antigens relevant for GC detection. These antigens included 9 artificial peptides and a protein fragment encoding AKAP12, and a short peptide from the C-terminus of a protein RNF14. These peptides can be used for developing immunoassays for measuring the presence or concentration of autoantibodies against these antigens in biological samples. The amino acid sequences of all antigens identified to be relevant for GC diagnosis are provided in Table 1.

Table 1. The tumour-associated antigen clones represented on the TAA microarray with the diagnostic value in gastric cancer.

Adding the 11-antigen panel to the previously known GC-associated antigen panel improved the diagnostic value (i.e. AUC) of the biomarker assay by 8.7 % as well as substantially increases its sensitivity - from 23.3% to 38.4% (Figure 2, Table 2).

Table 2. Diagnostic parameters of the identified diagnostic biomarker sets

There were the following steps in the identification of antigens having diagnostic value for GC: 1. obtaining mRNA from cancer tissues, 2. construction of cDNA expression library using the T7 phage display expression system, 3. immunoscreening of the recombinant phage library with patients' sera, isolation of serum positive clones and identification of the inserts (discovery study). 4. production of Τ7 phage displayed antigen microarray and screening with sera from cancer patients and healthy donors (training study), 5. microarray data processing and statistical analyses to determine the most significant cancer associated autoantibodies, 6. expression of the GC-associated TAAs with the most promising diagnostic value as recombinant proteins and development of a recombinant GC antigen array prototype, 7. testing of the developed TAA microarray with a different set of GC patients' sera and sera from healthy donors (validation study). * The methodological approaches used for the discovery of GC-associated antigens and selection of the most promising biomarker candidates (steps 1-5 above) have been described in detail in Zayakin etal, 2013 [9],

Serum samples. All cancer patients’ serum samples were obtained before treatment and stored at -80°C till processing. Serum samples of 159 GC patients and 156 age and gender matched cancer-free healthy individuals were provided by German Cancer Research Center, Heidelberg, Germany. All serum specimens were collected after the patients’ informed consent was obtained in accordance with the regulations of the ethical committee of the clinical partner organization.

Production and processing of recombinant antigen microarrays. Expression of the GC associated antigens was accomplished by cloning the identified antigencoding sequences in pFN19A (HaloTag®7) T7 SP6 Flexi expression vector and expressing them as His-HaloTag fusion proteins in E. coll. Expressed antigens were purified by using Protino® Ni-TED 150 Ni^"" columns, quantified spectrophotometrically, and probed in Western blot with anti-HaloTag-HRP antibody to assess antigen purity and evaluate solubility. After expression, the purified protein stocks are mixed with glycerine and stored in aliquots at -80°C in ready-to-print format. Throughout experiments, each aliquot is used once, avoiding repeated thaw-freeze cycles.

For microarray generation, the expressed antigens in line with 10 non-recombinant controls were printed in duplicate by Q-Array Mini microarray printer (Genetix) onto nickel chelate glass slides (Xenopore), and the protein amount in each spot was determined by using 1:500 diluted rabbit Anti-HaloTag® polyclonal antibody (Promega) and 1:2000 diluted Anti-rabbit-Cy3 (Jackson Immunoresearch) secondary antibody. For 1:100 diluted serum autoantibody detection, 1:1500 diluted Alexa Fluor®647-AffiniPure Goat Anti-Fluman IgG antibody (Jackson Immunoresearch) was used. The arrays were scanned at 10 pm resolution in PowerScanner (Tecan) with 532 and 635 nm lasers, the results were recorded as TIFF files and the data were extracted using GenePixPro software. The obtained data were further analysed by using an ad hoc program composed in R language.

Microarray data processing and statistical analysis. For each spot, after background subtraction the median of Alexa Fluor®647 and Cy3 signal ratios for each antigen was calculated and averaged between replicates. Empty spots were excluded from the analysis. A normalization strategy was used for the fluorescent signal ratios in order to eliminate variations introduced by the custom production of microarrays and variable background intensities of different sera. At first, the values for each area were centered by the median of all signal ratios and equated to 1. Next, 70 percentile of all signal ratios in all areas were equated to 2. Cut-offs distinguishing the two groups were defined for each antigen individually.

Next, the antigens were ranked, taking into account the signal intensity and frequency of reactivity with GC patient sera compared to healthy donor sera, using the following formula:

Finally, a score for each serum was calculated as follows:

The non-parametric Mann-Whitney U test was used to compare the serum scores between two independent groups of samples. The receiver operating characteristic (ROC) curve was constructed and the area under the curve (AUC) was calculated to evaluate the diagnostic performance of the serum scores.

To define cut-off points on the ROC curves with the maximal sum of sensitivity and specificity, minimal misclassification cost term (MCT) approach was used as follows:

where the cost(FN)/cost(FP) was set at 0.5.

Selection of most significant antigens for GC diagnosis. In order to determine the diagnostic value of the selected 105 TAAs for GC, the recombinant protein microarray was tested with an independent serum set (i.e., the validation set) including sera from 159 GC patients and 156 age and gender matched healthy individuals, and the microarray data was processed and analyzed as described above. The obtained results showed that a set of 26 analyzed antigens could discriminate the cancer patients’ sera from healthy control sera with AUC of 0.6575 Sn=38.4 %, Sp=92.3 %, P=5.3e-10 (Table 2).

Description of the antigen set applicable for the diagnosis and/or prognosis of gastric cancer. The inventors have used a custom made antigen database and clone collection, antigen microarray technology and an original approach of data statistical processing and have identified a set of antigens that is applicable for the detection of antibodies in human serum for the diagnosis, prognosis and prediction of immunotherapy outcome of GC. Among the 26 antigens identified as diagnostically relevant for GC (Table 1), there are 15 known gene products that have been previously described as immunogenic in cancer patients: these are proteins from the CTAG2, HORMAD1, DDX53, TP53, TERT, MTA1, SPAG6 and IMP and MAGE families, however, their individual use for autoantibody detection for GC diagnosis either doesn’t reach an adequate sensitivity or has not been studied in GC. For example, the most frequent spontaneous humoral response has been shown against CTAG2 (highly cross-reactive with CTAG1B/NYES01) -observed in patients with melanoma, gastric, breast, lung, ovarian, liver and other cancers with a frequency of 2-20% [10-13], but this is not sufficient for the development of a cancer diagnosis test based on the detection of anti-CTAG2 antibodies.

Apart from the known TAAs, inventors have identified 11 novel immunogenic peptides relevant to the GC diagnosis capable to improve the diagnostic performance of the existing biomarker set. In particular, the phage display SEREX approach, which has originally be used for the biomarker identification, doesn’t select for the correct open reading frame (ORE) of a cloned cDNA and can yield immunogenic peptides generated due to the translation of alternative ORFs and inserts of genomic DNA that most likely represent mimotopes of cancer antigens whose identity is elusive. The inventors describe here the identification of 9 novel artificial peptides that form as a results of inappropriate translation of cDNA clones, as well as two novel TAAs, AKAP12 and RNF14, in gastric cancer (see Table 1 for corresponding amino acid sequences).

The diagnostic vaiue of the autoantibody test The diagnostic value of the 26 antigen set was determined by the analysis of the recombinant antigen microarray using an independent validation set of sera that comprised sera from 159 various stage GC patients and 156 healthy individuals and all the analyses and calculations were performed as described above. Results showed that the mean serum score value of the 26-biomarker set in GC patient and healthy donor cohorts were 83.1 and 3.7, respectively, while that of the 15 known TAA set - 66.9 and 1.7, respectively (Figure 1). The differences in the diagnostic performance of the two biomarker sets are shown in Figure 2 and Table 2. Overall, the addition of the 11 novel biomarkers to the known TAA set was able to increase the AUC by 6.25%, sensitivity from 23.3% to 38.4%.

Use of the offered antigen set The offered antigen set can be used for the detection of GC specific autoantibodies in biological samples for the diagnosis of GC. Any kind of high sensitivity antibody detection system like ELISA, Western blot, any technology based on beads or microarrays or any other material that is appropriate for a multiplex test can be used for this purpose. The identified antigens can be obtained either by chemical synthesis or by using any biotechnological approach for the generation and purification of recombinant proteins. The antigens can be bound to the above substrates that are marked with a fluorescent or any other informative signal that ensures the information about the identity of the bound antigen. The binding of antigens to substrates can be achieved through direct conjugation or through intermediate elements for example but not limited to StrepTag®, HaloTag® or other tags. The presence of a reactive antibody specific for the antigens can be detected by incubating the antigen-bound substrate with the serum sample of interest, washing away the unbound antibodies, and using an anti-human IgG secondary antibody that is marked with a reporter molecule that emits a signal detectable by appropriate machines.

Reference List 1. Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 2015; 136(5):E359-E386. 2. Ferlay J, Parkin DM, Steliarova-Foucher E. Estimates of cancer incidence and mortality in Europe in 2008. Eur J Cancer 2010; 46(4):765-781. 3. Rosati G, Ferrara D, Manzione L. New perspectives in the treatment of advanced or metastatic gastric cancer. World J Gastroenterol 2009; 15(22):2689-2692. 4. Yanaoka K, Oka M, Mukoubayashi C, Yoshimura N, Enomoto S, Iguchi M et al. Cancer high-risk subjects identified by serum pepsinogen tests: outcomes after 10-year follow-up in asymptomatic middle-aged males. Cancer Epidemiol Biomarkers Prev 2008; 17(4):838-845. 5. Chen YT. Identification of human tumor antigens by serological expression cloning: an online review on SEREX. Cancer Immun 2004. 6. Kalnina Z, Silina K, Line A. Autoantibody profiles as biomarkers for response to therapy and early detection of cancer. Curr Cane Ther Rev 2008; 4(2). 7. Zhong L, Coe SP, Stromberg AJ, Khattar NH, Jett JR, Hirschowitz EA. Profiling tumor-associated antibodies for early detection of non-small cell lung cancer. J Thorac Oncol 2006; 1(6):513-519. 8. Pedersen JW, Gentry-Maharaj A, Fourkala EO, Dawnay A, Burnell M, Zaikin A et al. Early detection of cancer in the general population: a blinded case-control study of p53 autoantibodies in colorectal cancer. Br J Cancer 2013; 108(1):107-114. 9. Zayakin P, Ancans G, Silina K, Meistere I, Kalnina Z, Andrejeva D et al. Tumor-associated autoantibody signature for the early detection of gastric cancer. Int J Cancer 2013; 132(1):137-147. 10. Reuschenbach M, von Knebel DM, Wentzensen N. A systematic review of humoral immune responses against tumor antigens. Cancer Immunol Immunother 2009; 58(10):1535-1544. 11. Sugita Y, Wada H, Fujita S, Nakata T, Sato S, Noguchi Y et al. NY-ESO-1 expression and immunogenicity in malignant and benign breast tumors. Cancer Res 2004; 64(6):2199-2204. 12. Fujita S, Wada H, Jungbluth AA, Sato S, Nakata T, Noguchi Y et al. NY-ESO-1 expression and immunogenicity in esophageal cancer. Clin Cancer Res 2004; 10(19):6551-6558. 13. Gnjatic S, Nishikawa H, Jungbluth AA, Gure AO, Ritter G, Jager E et al. NY-ESO-1: review of an immunogenic tumor antigen. Adv Cancer Res 2006; 95:1-30.

Claims (6)

1. An isolated antigenic peptide selected from the group consisting of SEQ ID No. 1, SEQ ID No. 2, SEQ ID No. 3, SEQ ID No. 4, SEQ ID No. 5, SEQ ID No. 6, SEQ ID No. 7, SEQ ID No. 8, SEQ ID No. 9, SEQ ID No. 10 and SEQ ID No. 11 or variant, homologue or fragment thereof.

2. A method for in vitro detection of antibodies against a peptide according to claim 1 comprising the steps: a) providing of isolated body fluid and a peptide according to claim 1 b) bringing in isolated body fluid and peptide of step a) and c) determining bounding of antibodies from isolated body fluid to the peptide.

3. The method according to claim 2, wherein said isolated body fluid is blood, plasma, or serum.

4. A kit comprising peptide according to claim 1 which is suitable for detecting, diagnosing, prognosing, staging or monitoring gastric cancer.

5. A kit according to claim 4, containing isolated antigenic peptide selected form the group consisting of SEQ ID No. 1, SEQ ID No. 2, SEQ ID No. 3, SEQ ID No. 4, SEQ ID No. 5, SEQ ID No. 6, SEQ ID No. 7, SEQ ID No. 8, SEQ ID No. 9, SEQ ID No. 10 and SEQ ID No. 11.

6. Use of the kit according to any one of claims 4 or 5 for the detecting, diagnosing, prognosing, staging or monitoring gastric cancer.