Antibody profiling with protein antigen microarrays in early stage cancer

Modeling the sequence dependence of differential antibody binding in the immune response to infectious disease

Past studies have shown that incubation of human serum samples on high density peptide arrays followed by measurement of total antibody bound to each peptide sequence allows detection and discrimination of humoral immune responses to a variety of infectious diseases. This is true even though these arrays consist of peptides with near-random amino acid sequences that were not designed to mimic biological antigens. This "immunosignature" approach, is based on a statistical evaluation of the binding pattern for each sample but it ignores the information contained in the amino acid sequences that the antibodies are binding to. Here, similar array-based antibody profiles are instead used to train a neural network to model the sequence dependence of molecular recognition involved in the immune response of each sample. The binding profiles used resulted from incubating serum from 5 infectious disease cohorts (Hepatitis B and C, Dengue Fever, West Nile Virus and Chagas disease) and an uninfected cohort with 122,926 peptide sequences on an array. These sequences were selected quasi-randomly to represent an even but sparse sample of the entire possible combinatorial sequence space (~1012). This very sparse sampling of combinatorial sequence space was sufficient to capture a statistically accurate representation of the humoral immune response across the entire space. Processing array data using the neural network not only captures the disease-specific sequence-binding information but aggregates binding information with respect to sequence, removing sequence-independent noise and improving the accuracy of array-based classification of disease compared with the raw binding data. Because the neural network model is trained on all samples simultaneously, a highly condensed representation of the differential information between samples resides in the output layer of the model, and the column vectors from this layer can be used to represent each sample for classification or unsupervised clustering applications.

Novel potential serological prostate cancer biomarkers using CT100+ cancer antigen microarray platform in a multi-cultural South African cohort

There is a growing need for high throughput diagnostic tools for early diagnosis and treatment monitoring of prostate cancer (PCa) in Africa. The role of cancer-testis antigens (CTAs) in PCa in men of African descent is poorly researched. Hence, we aimed to elucidate the role of 123 Tumour Associated Antigens (TAAs) using antigen microarray platform in blood samples (N = 67) from a South African PCa, Benign prostatic hyperplasia (BPH) and disease control (DC) cohort. Linear (fold-over-cutoff) and differential expression quantitation of autoantibody signal intensities were performed. Molecular signatures of candidate PCa antigen biomarkers were identified and analyzed for ethnic group variation. Potential cancer diagnostic and immunotherapeutic inferences were drawn. We identified a total of 41 potential diagnostic/therapeutic antigen biomarkers for PCa. By linear quantitation, four antigens, GAGE1, ROPN1, SPANXA1 and PRKCZ were found to have higher autoantibody titres in PCa serum as compared with BPH where MAGEB1 and PRKCZ were highly expressed. Also, p53 S15A and p53 S46A were found highly expressed in the disease control group. Statistical analysis by differential expression revealed twenty-four antigens as upregulated in PCa samples, while 11 were downregulated in comparison to BPH and DC (FDR = 0.01). FGFR2, COL6A1and CALM1 were verifiable biomarkers of PCa analysis using urinary shotgun proteomics. Functional pathway annotation of identified biomarkers revealed similar enrichment both at genomic and proteomic level and ethnic variations were observed. Cancer antigen arrays are emerging useful in potential diagnostic and immunotherapeutic antigen biomarker discovery.

Development of recombinant antigen array for simultaneous detection of viral antibodies

Protein microarrays have been developed to study antibody reactivity against a large number of antigens, demonstrating extensive perspective for clinical application. We developed a viral antigen array by spotting four recombinant antigens and synthetic peptide, including glycoprotein G of herpes simplex virus (HSV) type 1 and 2, phosphoprotein 150 of cytomegalovirus (CMV), Rubella virus (RV) core plus glycoprotein E1 and E2 as well as a E1 peptide with the optimal concentrations on activated glass slides to simultaneously detect IgG and IgM against HSV1, HSV2, CMV and RV in clinical specimens of sera and cerebrospinal fluids (CSFs). The positive reference sera were initially used to measure the sensitivity and specificity of the array with the optimal conditions. Then clinical specimens of 144 sera and 93 CSFs were tested for IgG and IgM antibodies directed against HSV1, HSV2, CMV and RV by the antigen array. Specificity of the antigen array for viral antibodies detection was satisfying compared to commercial ELISA kits but sensitivity of the array varied relying on quality and antigenic epitopes of the spotting antigens. In short, the recombinant antigen array has potential to simultaneous detect multiple viral antibodies using minute amount (3 µl) of samples, which holds the particularly advantage to detect viral antibodies in clinical CSFs being suspicious of neonatal meningitis and encephalitis.