Abstract Introduction and Aim: Prostate cancer (PCa) is the most frequent malignancy detected in males and the second leading cause of death from cancer in males. Despite the current diagnostic standards, mainly based on PSA detection, histologic grading, and tumor-lymph nodes-metastases (TNM) staging, there is still an urgent need for the identification of new prognostic markers to help in the definition of an individualized and personalized treatment for patients. Metabolites reflect gene expression and protein function and perturbed metabolism is considered a novel “hallmark of cancer.” Therefore, the analysis of the metabolomic profiling of high-risk and lethal PCa might help in the characterization of the molecular characteristics of this subgroup of patients. In this study, we aimed to identify the metabolic signature for lethal prostate cancer, in order to characterize those patients at highest risk of metastatic progression. Experimental Procedure: We employed high-resolution matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry imaging (MALDI-FT-ICR MSI) to detect and visualize metabolites in formalin-fixed, paraffin-embedded (FFPE) PCa tissue microarrays (TMAs) in a cohort of 211 PCa patients who underwent radical prostatectomy between 1994 and 2001. To calculate the prognostic power of measured metabolites, we separated the patients’ cohort into good and poor survivor groups by the application of intensity cut-offs optimized to the clinical endpoint (e.g., PSA-free survival). Statistical differences in patient survival were measured using the Kaplan-Meier log-rank test and multivariate survival analysis performed using Cox proportional hazards regression models. Results: We employed a metabolic profiling approach to distinguish lethal from nonlethal disease in a subset of lethal PCa patients (PCa-related death within 5 years following radical prostatectomy) vs. nonlethal PCa patients (survival > 10 years following radical prostatectomy, without evidence of recurrence). By virtual microdissection molecular signatures were selected from tumor regions and subsequently statistically compared, leading to 172 small molecules significantly different between lethal and nonlethal PCa patients (p<0.05). Visualization of these m/z species displayed clear differences in the intensity patterns between lethal vs. non-lethal disease. Next, we used metabolite MSI data to address patient survival outcome between lethal and nonlethal PCa cases. Statistical analysis showed that different m/z species significantly correlated with patients’ outcome (e.g., PSA-free survival and disease-free survival). Preliminary pathway enrichment analysis displayed a significant upregulation of purine and pyrimidine metabolism KEGG pathway in lethal vs. nonlethal disease. Strikingly, these networks have been previously associated with PCa progression in RNA-based studies. Conclusion: In this study we identified a metabolic pattern able to discriminate between lethal and nonlethal disease. Further validation of an independent cohort is needed to validate the novel findings of metabolites and might lead to the identification of novel biologic markers involved in PCa progression. Citation Format: Eugenio Zoni, Achim Buck, Annette Feuchtinger, Martin Spahn, Axel Walch, Marianna Kruithof-de Julio. Metabolic signature in lethal vs. nonlethal prostate cancer [abstract]. In: Proceedings of the AACR Special Conference: Prostate Cancer: Advances in Basic, Translational, and Clinical Research; 2017 Dec 2-5; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(16 Suppl):Abstract nr A068.
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