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Prostate cancer (PCa) metastases are highly enriched with genomic alterations including a gain at the 16p13.3 locus, recently shown to be associated with disease progression and poor clinical outcome. ECI1, residing at the 16p13.3 gain region, encodes Δ3, Δ2-Enoyl-CoA Delta Isomerase 1 (ECI1), a key mitochondrial fatty acid β-oxidation enzyme. Although deregulated mitochondrial fatty acid β-oxidation is known to drive PCa pathogenesis, the role of ECI1 in PCa is still unknown. We investigated the impacts of ECI1 on PCa phenotype in vitro and in vivo by modulating its expression in cell lines and assessed the clinical implications of its expression in human prostate tissue samples. In vitro, ECI1 overexpression increased PCa cell growth while ECI1 deficiency reduced its growth. ECI1 also enhanced colony formation, cell motility, and maximal mitochondrial respiratory capacity. In vivo, PCa cells stably overexpressing ECI1 injected orthotopically in nude mice formed larger prostate tumors with higher number of metastases. Immunohistochemistry analysis of the human tissue microarray representing 332 radical prostatectomy cases revealed a stronger ECI1 staining in prostate tumors compared to corresponding benign tissues. ECI1 expression varied amongst tumors and was higher in cases with 16p13.3 gain, high Gleason grade, and advanced tumor stage. ECI1 overexpression was a strong independent predictor of biochemical recurrence after adjusting for known clinicopathologic parameters (hazard ratio: 3.65, P 0.001) or the established CAPRA-S score (hazard ratio: 3.95, P 0.001). ECI1 overexpression was also associated with significant increased risk of distant metastasis and reduced overall survival. Overall, this study demonstrates the functional capacity of ECI1 in PCa progression and highlights the clinical implication of ECI1 as a potential target for the management of PCa.

Identifying tumors with high metastatic potential is key to improving the clinical management of prostate cancer. Recently, we characterized a chromosome 16p13.3 gain frequently observed in prostate cancer metastases and now demonstrate the prognostic value of this genomic alteration in surgically treated prostate cancer. Dual-color FISH was used to detect 16p13.3 gain on a human tissue microarray representing 304 primary radical prostatectomy (RP) cases with clinical follow-up data. The results were validated in an external dataset. The 16p13.3 gain was detected in 42% (113/267) of the specimens scorable by FISH and was significantly associated with clinicopathologic features of aggressive prostate cancer, including high preoperative PSA (P = 0.03) levels, high Gleason score (GS, P < 0.0001), advanced pathologic tumor stage (P < 0.0001), and positive surgical margins (P = 0.009). The 16p13.3 gain predicted biochemical recurrence (BCR) in the overall cohort (log-rank P = 0.0005), and in subsets of patients with PSA ≤10 or GS ≤7 (log-rank P = 0.02 and P = 0.006, respectively). Moreover, combining the 16p13.3 gain status with standard prognostic markers improved BCR risk stratification and identified a subgroup of patients with high probability of recurrence. The 16p13.3 gain status was also associated with an increased risk of developing distant metastases (log-rank P = 0.03) further substantiating its role in prostate cancer progression. Implications: This study demonstrates the prognostic significance of the 16p13.3 genomic gain in primary prostate tumors, suggesting potential utility in the clinical management of the disease by identifying patients at high risk of recurrence who may benefit from adjuvant therapies. Mol Cancer Res; 16(1); 115–23. ©2017 AACR.

109 Background: Current practice stratifies men with prostate cancer into risk groups based primarily on Gleason grade. When applied to biopsy samples, the Gleason grading is inaccurate due to sampling error and inter-observer variation. The result is that men either receive unnecessary surgical treatment, or they don’t receive adequate treatment, leading to worse outcomes. Previously published genomic tests have not successfully distinguished indolent low grade (G6 or GG1) cancers from their more aggressive intermediate grade (G7 or GG2 and 3) counterparts. PRONTO is specifically aimed at creating a multi-modal risk stratification tool to improve treatment stratification following a core biopsy diagnosis. Methods: PRONTO links 7 projects, each with novel diagnostic assays for risk stratification that focus on analysis of copy number variations (CNV), DNA hypermethylation, trans-differentiation, cancer metabolism, or the tumor microenvironment. We merged the best transcripts from each project into a single NanoString gene expression assay, measuring 393 transcripts, in a cohort of 365 cases of radical prostatectomy from low-to-intermediaterisk patients. To minimize sampling error, we took multiple samples, and obtained high grade, low grade and benign areas for each radical prostatectomy case. Results: Our primary goal was to develop a multivariate molecular classifier of grade that distinguished G6 from G7 (3+4 or 4+3). Cases were randomly partitioned into five equally sized groups. A supervised machine learning algorithm (random forests) was trained on samples from four of the groups, and then evaluated by testing on the fifth group. This process was repeated for each of the five groups, yielding a combined clinical and molecular classifier. DNA methylation profiles and CNV profiles are currently being integrated into our classifier Conclusions: We have developed a multivariate classifier that distinguishes low grade from intermediate grade prostate cancer. It will be clinically validated in biopsy samples from large cohorts of early prostate cancer patients.