Autoantibody signatures as biomarkers to distinguish prostate cancer from benign prostatic hyperplasia in patients with increased serum prostate specific antigen

BACKGROUND

Serum prostate specific antigen (PSA) concentrations lack the specificity to differentiate prostate cancer from benign prostate hyperplasia (BPH), resulting in unnecessary biopsies. We identified 5 autoantibody signatures to specific cancer targets which might be able to differentiate prostate cancer from BPH in patients with increased serum PSA.

METHODS

To identify autoantibody signatures as biomarkers, a native antigen reverse capture microarray platform was used. Briefly, well-characterized monoclonal antibodies were arrayed onto nanoparticle slides to capture native antigens from prostate cancer cells. Prostate cancer patient serum samples (n=41) and BPH patient samples (collected starting at the time of initial diagnosis) with a mean follow-up of 6.56 y without the diagnosis of cancer (n=39) were obtained. One hundred micrograms of IgGs were purified and labeled with a Cy3 dye and incubated on the arrays. The arrays were scanned for fluorescence and the intensity was quantified. Receiver operating characteristic curves were produced and the area under the curve (AUC) was determined.

RESULTS

Using our microarray platform, we identified autoantibody signatures capable of distinguishing between prostate cancer and BPH. The top 5 autoantibody signatures were TARDBP, TLN1, PARK7, LEDGF/PSIP1, and CALD1. Combining these signatures resulted in an AUC of 0.95 (sensitivity of 95% at 80% specificity) compared to AUC of 0.5 for serum concentration PSA (sensitivity of 12.2% at 80% specificity).

CONCLUSION

Our preliminary results showed that we were able to identify specific autoantibody signatures that can differentiate prostate cancer from BPH, and may result in the reduction of unnecessary biopsies in patients with increased serum PSA.

Native antigen fractionation protein microarrays for biomarker discovery

In this protocol, we used the T24 human bladder cancer cell line as a source of native antigens to construct fractionated lysate microarrays. Subsequently, these microarrays were used to compare the autoantibody responses of individuals with interstitial cystitis/painful bladder syndrome (IC/PBS) to those of normal female controls. To accomplish this, T24 cells were lysed under nondenaturing conditions to obtain native antigens. These native antigens were then fractionated in 2D using a PF-2D liquid chromatography; the first dimension separated the proteins by their isoelectric points, and the second separated them according to hydrophobicity. The resulting protein fractions were printed onto nitrocellulose-coated glass slides (PATH slides) to create a set of fractionated lysate microarrays. To compare the autoantibody responses of IC/PBS patients with normal controls, the fractionated lysate arrays were competitively hybridized with fluorescently labeled IgG samples purified from both IC/PBS and control sera. This protocol presents a detailed description of the creation and use of native antigen fractionated lysate microarrays for autoantibody profiling.

Autoantibody profiling to identify biomarkers of key pathogenic pathways in mucinous ovarian cancer

Mucinous epithelial ovarian cancers are clinically and morphologically distinct from the other histopathologic subtypes of ovarian cancer. Unlike other ovarian subtypes, epidemiologic studies have indicated that tobacco exposure is a significant risk factor for developing mucinous ovarian cancer. Detection of autoantibody reactivity is useful in biomarker discovery and for explaining the role of important pathophysiologic pathways in disease. In order to study if there are specific antibody biomarkers in the plasma samples of mucinous ovarian cancer patients, we have initiated a screen by employing a 'reverse capture antibody microarray' platform that uses native host antigens derived from mucinous ovarian tissues as 'baits' for the capture of differentially labelled patient and control autoantibodies. Thirty-five autoantibodies that were significantly elevated in the cancer plasma samples compared with healthy controls, and six autoantibodies that segregated smoking and non-smoking patients were identified. Functional annotation of the antibody targets has identified nine target antigens involved in integrin and Wnt signalling pathways. Immunohistochemistry of archived ovarian specimens showed significant overexpression of eight of the nine target antigens in mucinous ovarian tumour tissues, suggesting that plasma autoantibodies from mucinous ovarian cancer patients might have heightened reactivities with epitopes presented by these overexpressed antigens. Autoantibody profiling may have an unexpected utility in uncovering key signalling pathways that are dysregulated in the system of interest.

Microelectrical sensors as emerging platforms for protein biomarker detection in point-of-care diagnostics

Current methods used to measure protein expression on microarrays, such as labeled fluorescent imaging, are not well suited for real-time, diagnostic measurements at the point of care. Studies have shown that microelectrical sensors utilizing silica nanowire, impedimetric, surface acoustic wave, magnetic nanoparticle and microantenna technologies have the potential to impact disease diagnosis by offering sensing characteristics that rival conventional sensing techniques. Their ability to transduce protein binding events into electrical signals may prove essential for the development of next-generation point-of-care devices for molecular diagnostics, where they could be easily integrated with microarray, microfluidic and telemetry technologies. However, common limitations associated with the microelectrical sensors, including problems with sensor fabrication and sensitivity, must first be resolved. This review describes governing technical concepts and provides examples demonstrating the use of various microelectrical sensors in the diagnosis of disease via protein biomarkers.

Identifying autoantigens as theranostic targets: antigen arrays and immunoproteomics approaches

The development of theranostics holds great potential for the advancement of personalized medical treatments for human diseases. The theranostic approach combines diagnostic testing with predicted treatment outcomes in order to specifically tailor medical treatments for individual patients. As such, theranostic tests have the potential to revolutionize the diagnosis, treatment and monitoring of human diseases. Currently, the search is underway to identify novel targets for use in the development of theranostic approaches. The aims of this review are: to briefly describe the current capabilities of antigen-autoantibody microarrays, including our native antigen 'reverse-capture' autoantibody microarray platform to identify theranostic targets; to detail other immunoproteomics approaches for the identification of theranostic targets, including serological analysis of antigens by recombinant complementary (c)DNA expression cloning, serological proteome analysis, and autoantibody-mediated identification of antigens; and to provide examples of their use in theranostic discoveries.

Autoantibody microarrays for biomarker discovery

Identification of autoantigens and the detection of autoantibody reactivity are useful in biomarker discovery and for explaining the role of important biochemical pathways in disease. Despite all of their potential advantages, the main challenge to working with autoantibodies is their sensitivity. Nevertheless, proteomics may hold the key to overcoming this limitation by providing the means to multiplex. Clearly, the ability to detect multiple autoantigens using a platform such as a high-density antigen microarray would improve sensitivity and specificity of detection for autoantibody profiling. The aims of this review are to: briefly describe the current status of antigen-autoantibody microarrays; provide examples of their use in biomarker discoveries; address current limitations; and provide examples and strategies to facilitate their implementation in the clinical setting.