Abstract 861: Integrative multi-omics analysis enables a comprehensive characterization of prostate cancer and unveils metastasis-associated candidate biomarkers
Authors: Villa Matteo , Cazzaniga Giorgio , Bolognesi Maddalena , Crippa Valentina , Malighetti Federica , Aroldi Andrea , Bozzini Giorgio , Pagni Fabio , Piazza Rocco , Mologni Luca , Ramazzotti Daniele 
Published: March 2024

Abstract
Prostate cancer (PCa), a common malignancy in men, presents a complex and variable disease landscape, requiring precise evaluation for personalized treatment decisions. Urologists employ a range of assessments, including clinical staging, PSA levels, imaging, Gleason scores, and emerging biomarkers. Nevertheless, the need for additional biomarkers to enhance precision medicine continues. This study utilizes extensive omics datasets to thoroughly profile PCa. We conducted an in-depth analysis of primary PCa data from the multi-omics dataset from The Cancer Genome Atlas studies to identify molecular subtypes showing significant prognostic differences. We conducted integrative clustering analysis on a dataset comprising 341 primary prostate adenocarcinomas with comprehensive multi-omics data, including substitutions, small insertions/deletions, copy number alterations, DNA methylation, gene expression profiles, micro-RNA expression, reverse-phase protein arrays, and microbiome data. The analysis revealed seven distinct clusters (C1 to C7) based on multi-omics features. At the genomic level, recurrent copy number losses and DNA methylation/demethylation were the most prominent alterations across clusters. When looking at survival curves, the 7 clusters could be clearly partitioned into two groups. Therefore, to further investigate multi-omics features specifically linked to prognosis, we grouped clusters with similar prognoses into two macro-clusters. Clusters C1 to C4 formed MC1 (favorable prognosis), while C5 to C7 constituted MC2 (poor prognosis). A comprehensive comparison between MC1 and MC2 revealed molecular differences associated with survival. We performed computational validation of candidate prognostic biomarkers using two external datasets. By utilizing these selected features, we could stratify patients in both the validation datasets into groups with significant differences in survival. The analysis identified six features consistently associated with prognosis across both datasets, including CCNB1, PCNA, RAD51, FOXM1, miR-503-5p (positively associated with risk), and miR-7704 (negatively associated with risk). The practical applicability of these biomarkers was explored through immunohistochemical analysis on biopsy samples obtained at diagnosis and from metastatic sites. Based on their potential relevance according to the literature, we investigated three candidate biomarkers derived from the multi-omics analysis: FOXM1, CCNB1 and RAD51. The expression of these proteins was found to correlate with PCa progression and mark the metastatic stage. In summary, this study highlights the potential of translating comprehensive multi-omics bioinformatic analyses into practical approaches for understanding the molecular mechanisms of tumors and identifying prognostic markers.
Citation Format: Matteo Villa, Giorgio Cazzaniga, Maddalena Bolognesi, Valentina Crippa, Federica Malighetti, Andrea Aroldi, Giorgio Bozzini, Fabio Pagni, Rocco Piazza, Luca Mologni, Daniele Ramazzotti. Integrative multi-omics analysis enables a comprehensive characterization of prostate cancer and unveils metastasis-associated candidate biomarkers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 861.