#4826 Cancer/testis antigen expression pattern is a potential biomarker for prostate cancer aggressiveness Luciane T. Kagohara1, Prakash Kulkarni1, Takumi Shiraishi1, Guangjing Zhu1, Robert Vessella2, Robert W. Veltri1 1Johns Hopkins University-The Brady Urological Institute, Baltimore, MD; 2University of Maryland-Institute for Bioscience & Biotechnology Research, Rockville, MD; 3Univeristy of Washington - Department of Urology, Seattle, WA email: ltsukam1@jhmi.edu INTRODUCTION RESULTS Prostate cancer (PCa) is the most common cancer and the second leading cause of cancer-related deaths among men in the US. The introduction of prostate-specific antigen (PSA) test has greatly aided for the early detection of PCa and also for recurrence after radical prostatectomy (RP). However, it is estimated that 35% of patients submitted to RP that progress with detectable PSA levels will never present metastatic disease. Thus, in men that have undergone RP, there is a need to distinguish localized curable disease from a subset which may be at risk for progression. Currently, the D'Amico Stratification System defines high risk PCa as any combination of the following factors: a PSA score >20 ng/ml, a Gleason score of 8–10, or clinical stage T2c or greater. The central hypothesis of this study is that a cancer/testis antigen (CTA)-based biomarker can be used to discern PCa patients with aggressive disease, that do not fit the D’Amico criteria, from those with less aggressive curable tumors. The CTAs are a unique group of genes whose expression is normally confined to germ cells in normal testis and placenta, but aberrantly expressed in several types of cancers. Since non-tumor tissues rarely present detectable levels of CTAs expression they constitute ideal potential cancer biomarkers. Another relevant fact regarding CTAs is their immunogenic capacity that might be used in increasing response to therapy when associated with current chemotherapeutic agents. Until the present date, little is known about the association of CTAs and prognostic factors in PCa. 1. CTA expression by Nanostring and qPCR validation in Localized PCa vs. Metastatic PCa 2. CTA expression in paired benign cancer-adjacent tissue and PCa cases Figure 6 – ROC curve and predictive probability for PAGE4 to discriminate tumor from non-tumor tissue. PAGE4 expression profile is a good candidate for prostate tumors, since it correctly classified 93.75% of the cases. FIGURE 1 - Nanostring gene expression analysis approach schematic diagram. Different combinations for the color coded reporter probe allows the detection of different targets making Nanostring a multiplex approach for expression analysis of ~800 mRNA targets. FIGURE 2 – Relative gene expression levels in localized and metastatic PCa obtained by Nanostring. The scatter plots represent relative expression levels for LPCa samples (green) and MPCa samples (blue). Receiver operator characteristic (ROC) curves were used to identify a cutoff ratio (red line) for each gene to set specificity at the percentage that maximizes the number of samples correctly classified. Only those CTAs with significant expression changes are represented. FIGURE 5 – Relative gene expression levels (∆Ct) in tumor and benign adjacent tissue from the same PCa patient obtained by qRT-PCR. The scatter plots represent relative expression levels for benign samples (green) and tumor samples (blue). Receiver operator characteristic (ROC) curves were used to identify a cutoff ratio (red line) for each gene to set specificity at the percentage that maximizes the number of samples correctly classified. Only those CTAs previously selected by Nanostring and validated by qRT-PCR are shown here. Figure 7 – ROC curve analysis to identify a panel of biomarkers to separate tumor from benign adjacent tissue and to identify CTA expression profiles associated with Gleason Score 3+3/3+4 vs. >4+3. Using stepwise backwards logistic regression model (pr=0.05), a panel including the CTAs CEP55, NUF2 and TTK was capable of discriminating tumor from benign tissue (A). PAGE4 (B) and SSX2 (C) expression profiles discriminated the tumor cases according to their Gleason Score. OBJECTIVES Identify CTAs that are differentially expressed in local vs. aggressive (metastatic) disease. Based on CTAs expression pattern build a panel model that is capable to discriminate aggressive from indolent disease. Validate the expression profile of the selected candidates in a cohort of tumor and benign adjacent tissue paired cases. MATERIAL AND METHODS Using Nanostring multiplex approach, we screened the expression of 22 CTA genes (CEP55, CSAG2, CTAG1B, JARID1B, MAGEA1, MAGEA2, MAGEA6, MAGEA12, NOL4, NUF2, PAGE4, PBK, PLAC1, RQCD1, SEMG1, SPAG4, SSX2, SSX4, TMEFF2, TMEM108, TPTE and TTK) in 20 localized (LPCa) and 20 metastatic prostate cancer (MPCa) samples, obtained from University of Washington. Using ROC curve analysis we ranked the CTAs capable of discriminating LPCa and MPCa. Differences in CTA expression were considered significant when AUC>0.70. qRT-PCR was used to validate CTA expression pattern in the same set of samples and to identify the candidate CTA biomarkers. After the best candidates were selected, CTA expression was evaluated in new a cohort of 23 paired samples of PCa and benign adjacent tissue by qRT-PCR. Association between CTA expression pattern and clinical-pathological fetures was evaluated. Stepwise logistic regression model was used to build biomarker panels capable of discriminating MPCa vs. LPCa and benign vs. PCA cases. Figure 4 – ROC curve and predictive probability for a panel of biomarkers to differentiate LPCa from MPCa. Using stepwise backwards logistic regression model (pr=0.05), a panel including the CTAs CEP55 and RQCD1 was capable of discriminating LPCa from MPCa. FIGURE 3 - ROC curve analysis and predictive probability for CTAs expression profile by qRT-PCR. All CTAs candidates selected by Nanostring were capable of discriminating LPCa vs. MPCa (AUC>0.7). PAGE4 expression profile per se is a good candidate for prostate aggressive tumors, since it correctly classified 92.5% of the cases. Table 1 – Summary of ROC curve analysis for the 8 best CTA candidates. Analysis for the two cohorts (LPCa vs. MPCa and Tumor vs. Benign adjacent tissue) are listed in the table. Using ROC curve analysis specificity and sensitivity were chosen at the percentage that maximizes the number of samples correctly classified. CONCLUSION Acknowledgements PAGE4 expression pattern (down-regulation) is a potential biomarker for PCa aggressive disease and also to discriminate tumor from non-tumor cases. Combined expression patterns of CEP55 and RQCD1, both up-regulated in MPCa, consititutes a panel capable of separating with accuracy LPCa vs. MPCa. Combined expression patterns of CEP55, NUF2 and TTK, all up-regulated in tumor samples, consititutes a panel capable of separating with accuracy tumor vs. benign adjacent. All the CTA shown to be aberrantly expressed in PCa vs. benign tissue and also in aggressive cases (MPCa) will be evaluated by quantitative immunohistochemistry in larger cohorts organized in TMAs. This project is supported by:DOD W81XWH-12-1-0535