Zhang, Sheng

Blood is a readily accessible biofluid containing a plethora of important proteins, nucleic acids, and metabolites that can be used as clinical diagnostic tools in diseases, including cancer. Like the on-going efforts for cancer biomarker discovery using the liquid biopsy detection of circulating cell-free and cell-based tumor nucleic acids, the circulatory proteome has been underexplored for clinical cancer biomarker applications. A comprehensive proteome analysis of human serum/plasma with high-quality data and compelling interpretation can potentially provide opportunities for understanding disease mechanisms, although several challenges will have to be met. Serum/plasma proteome biomarkers are present in very low abundance, and there is high complexity involved due to the heterogeneity of cancers, for which there is a compelling need to develop sensitive and specific proteomic technologies and analytical platforms. To date, liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics has been a dominant analytical workflow to discover new potential cancer biomarkers in serum/plasma. This review will summarize the opportunities of serum proteomics for clinical applications; the challenges in the discovery of novel biomarkers in serum/plasma; and current proteomic strategies in cancer research for the application of serum/plasma proteomics for clinical prognostic, predictive, and diagnostic applications, as well as for monitoring minimal residual disease after treatments. We will highlight some of the recent advances in MS-based proteomics technologies with appropriate sample collection, processing uniformity, study design, and data analysis, focusing on how these integrated workflows can identify novel potential cancer biomarkers for clinical applications.

104 Background: Currently there are no serum predictive markers of response to AA. We used a proteomic based analytic approach to identify candidates. Methods: Serum from three non-localized prostate cancer cohorts was analyzed. The first included15 paired untreated hormone-sensitive “pre-AA” and 3-month “post-AA” specimens; the second included 10 “early AA failure” (median time to AA failure:11 months) and the third included 10 “late AA failure” specimens (median time to AA failure: 95 months). Proteomic analysis was performed with isobaric mass tags for relative and absolute quanititation (iTRAQ) analyzed by reverse-phase liquid chromatography coupled to tandem mass spectrometry (LC/MS/MS). Differentially expressed candidate proteins were identified by comparisons of (i) paired pre/post-AA proteomes and (ii) post-AA proteome with the combined AA failure cohorts at a False Discovery Rate of 0.2. ELISA assays were used to verify candidate markers in a second stored aliquot of first cohort specimens. This cohort was followed for AA failure. Association of post-AA ELISA levels of candidate markers with time to AA failure was performed using Cox proportional hazards regression, summarized as relative risk (RR) for AA failure. Results: Median PSA in pre/post-AA first cohort were 3.15 ng/ml and 0.29 ng/ml. Median PSA in the second and third cohorts were 27.3 and 4.3 ng/ml. Between post-AA and AA failure cohorts, 149 proteins were differentially expressed. Between early and late AA failure 98 proteins were differentially expressed; 47 proteins were common in both comparisons. ELISA assays verified expression levels of 2/47 proteins in the first cohort; zinc alpha-2 macroglobulin (ZAG), and Neuropilin-2 (NPL2). Median change in ZAG decreased by 2073.5 ng/ml (post versus pre-AA) while median change in NPL2 levels increased by 2.9 ng/ml. After a median follow-up of 43 months from the post-AA time-point, 4/15 first cohort subjects had failed AA. The RR of AA failure for ZAG levels below the median change was 3.8 (95% CI: 0.4-37) and 3.0 (95% CI: 0.3-29) for NPL2. Conclusions: A global proteomic analysis identified ZAG and NPL2 as candidate serum predictive markers of AA response which needs further validation.