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Antibody microarray technology has begun to play a significant role for detection and quantification of proteins in complex biological samples. The ability of antibodies to perform

highly specific protein capture makes this approach particularly well suited for detecting rare analytes in highly heterogeneous mixtures, like biomarkers in serum. Hence, antibody microarray could offer the ability to identify new panels of TAAs produce in cancer [167]. Haab and co-workers [98] tested multiple antibody-antigen interactions by localizing specific components of the complex mixtures on the antibody microarray to defined cognate spots. The analytical performance demonstrates that 20 % of the arrayed antibodies provided specific and accurate measurements of their cognate ligands at or below concentrations of 1.6µg/ml. In particular, some pairs of antibody-antigen allowed detection of the cognate ligands at absolute concentrations below 1 ng/ml, indicating that the sensitivities are sufficient for measurement of many clinically important proteins in patient blood samples.

Recent works have been looked into the use of antibody microarrays to identify tumour- related antigens and serum protein profiling [168, 169]. Multiple antigen expression patterns would be more specifically dysregulated in the different human neoplasms than individual protein profiles, because of the inherent complexity and heterogeneity of the mechanisms involved in neoplastic cell development [170]. Therefore, it would be more informative for characterisation of cancer biomarkers to design wide panels of antigens. Antibody microarrays provide sufficient density and high throughput for parallel screening of many biological samples to discover antigens across large patient populations. Orchekowski et al [171] used antibody microarrays to probe the associations of multiple serum proteins with pancreatic cancer and to explore the use of combined measurements for sample classification. A logistic-regression algorithm distinguished cancer samples from healthy donors with a 90 % and 93 % sensitivity and a 90 % and 94 % specificity in duplicate experiment sets. Cancer samples were distinguished from benign disease samples with 95 % and 92 % sensitivity, and 88 % and 74 % specificity in duplicate experiment sets. The classification accuracies were significantly improved over those achieved using individual antibodies, which demonstrates an effective strategy with antibody microarrays to profile proteins and identify candidate biomarkers.

In another report [172], protein markers of colorectal carcinogenesis and progression were identified with protein microarrays and validated on tissue microarrays. Using cancer and adjacent normal samples from 10 patients with early and 6 with advanced colorectal cancer, 67 differentially expressed genes were identified between normal and cancer samples. A marker set containing 6 proteins (CCNA1, AR, TOP1, TGFB, HSP60, ERK1) was developed which could differentiate normal specimen, early and late stage of colorectal cancer with high sensitivity and specificity. The results shows that mRNA and protein expression of 143 genes showed strong positive correlations (R(2) > 0.8), while a negative correlation (R(2) > 0.9) was found in case of 95 genes. Therefore, a correlation could be established between transcriptome and antibody microarray results. Thus, the former may be

used as a high-capacity screening method before applying antibody microarrays containing already planned targets. It was suggested that antibody microarrays may have a fundamental importance in screening marker combinations and in future applications in diagnostics of cancer.

Boehm et al. [173] immobilized 23 antibodies on nitrocellulose slides to determine the levels of acute phase proteins, interleukins and complement factors in 101 participants sera (49 women with primary breast cancer and 52 healthy age-matched controls). Statistical analysis of reaction intensities identified six proteins (interleukine (IL)-6, Hsp60, Hsp70, C3/3b, glial fibrillary acid proteins (GFAP) and ionized calcium binding adaptor molecule (IBA) 1) that showed significantly (p < 0.05) different levels in breast cancer patients vs. healthy donors. The neural network distinguished cancer patients from controls with a sensitivity of 69 % and a specificity of 76 %. Sanchez-Carbayo et al [84] selected antibodies against targets differentially expressed in bladder tumors and designed antibody microarrays for detection of bladder cancer. Serum protein profiles measured by an antibody microarray containing 254 antibodies discriminated bladder cancer patients from controls (n = 95) with a correct classification rate of 93.7 %. A second independent antibody microarray containing 144 antibodies revealed that protein profiles provide predictive information by stratifying patients with bladder tumors (n = 37) based on their overall survival (P = 0.0479). Besides, serum proteins were associated with pathological stage, tumor grade and survival, which also be validated by immunohistochemistry of tissue microarrays, demonstrating to contain bladder tumors (n = 173). The results provide experimental evidence for the use of several integrated technologies strengthening the process of biomarker discovery. Serum protein profiles obtained by antibody microarrays proved to be comprehensive means for bladder cancer diagnosis and clinical outcome stratification. This could potentially assist in screening of cancer patients who would benefit from early, individualized therapeutic intervention.

Consequently, antibody microarray analysis could be used as a powerful tool for the development of improved diagnostics and biomarker discovery for cancer patients. However, a plethora of parameters such as surface chemistry, composition and pH of the spotting buffer, blocking reagents, antibody concentration, storage procedures etc, have effects on analytical performances (sensitivity, specificity, limit of detections) of antibody microarray [101, 110]. These parameters should also be taken into account for detection and identification of cancer biomarkers. Since early diagnosis of cancer implies biomarkers have to be detected at low concentration, high sensitivity assay is desirable for cancer biomarker discovery and validation from clinical samples like serum. Miller and co-workers [174] developed a practical strategy for profiling prostate cancer sera by identifying differential fluorescently labeled protein expression patterns. Protein abundances from 33 prostate cancer and 20 control serum samples were compared to abundances from a common reference pool.

Compared to the traditional chemically derivatised silica surface, the detection limit of antigens were improved by 6-fold, down to 200 ng/ml, by using a three-dimensional acrylamide gel surface. Moreover, most abundant antigens (such as PSA, C-reactive proteins, serum amyloid A and α-1-anti-trypsin, among others) have been detected in serum samples using this approach. In a recent report, Luo et al [175] optimized parameters of antibody microarray with Taguchi design for detection of five breast cancer biomarkers: CA15-3, CEA, HER2, MMP9, and uPA. Two successive optimization rounds with each 16 experimental trials were performed, in which three factors (capture antibody, detection antibody, and analyte) at four different levels (concentrations) in the first round and seven factors (including buffer solution, streptavidin-Cy5 dye conjugate concentration, and incubation times for five assay steps) with two levels each in the second round, as well as five two-factor interactions between selected pairs of factors were tested. The concentration of capture antibody, streptavidin-Cy5, and buffer composition were identified as the most significant factors for all assays; analyte incubation time and detection antibody concentration were significant only for MMP9 and CA15-3, respectively. Interactions between pairs of factors were less influential compared with single factor effects. Under the Taguchi optimal conditions, the assay sensitivity was improved between 7 and 68 times but depending on the analyte, reaching 640 fg/mL for uPA.

In conclusion, antibody microarrays have been successfully used for protein profiling of biological samples for screening tumor-associated antigens [168]. Highly parallel protein profiling using antibody microarrays does not only facilitate more rapid biomarker discovery, but also enable the direct observation of relationships between proteins. Furthermore, one could examine combinations of multiple markers that might increase the statistical significance of a diagnosis from single data sets [174]. However, validation test are required to rule out cross-reactivity or lack of specificity of antibodies and well established antibody pairs for most antigens have not yet been developed. Accordingly, there is a noteworthy lack of highly specific antibodies or alternative high-affinity capture reagents that can be functional in a protein microarray format.