Dientsmann, R.

Abstract INTRODUCTION: Prostate cancer (PC) shows marked heterogeneity in clinical outcomes, from indolent to highly aggressive disease. Alterations in TP53, PTEN, RB1, and DNA damage repair genes are linked to poor patient outcomes, yet how the interaction and co-occurrence of these alterations impact prognosis remains unclear. We leveraged the AACR Project GENIE prostate cancer cohort to assess the prognostic impact of individual and concomitant gene alterations, focusing on key PC drivers (BRCA2, TP53, PTEN, RB1). METHODS: the study was conducted in two stages: (i) identification of candidate alterations at individual gene level through a systematic literature review, followed by analyzing the prevalence and co-occurrence of genes in the AACR Project GENIE Public Release (PR) dataset. (ii) Prognostic analysis of the pre-selected biomarkers in the AACR Project GENIE Biopharma Collaborative (BPC) cohort. Analyses were performed in three clinical settings (entire BPC cohort, n=1114; subgroup of patients with localized disease at diagnosis, n=837; and subgroup of patients with metastatic PC with a biopsy collected before or at the time of mPC, n=455) using two statistical approaches: univariate analysis (UA) and inverse probability weighting using propensity scores (PS). Cox models were applied to estimate overall survival hazard ratios (HRs). RESULTS: after integrating the findings from the systematic review with the analysis of the GENIE PR (n=5684), 107 individual genes and 122 co-occurring alterations were selected for study. In the GENIE BPC (n=1114 patients) 75% were M0 at diagnosis, and 67% presented metastatic PC at any time. Alteration prevalence was: TP53, 25%; PTEN, 15%; BRCA2, 4%; RB1, 4%; co-alterations included, PTEN-TP53 (6.4%), RB1-TP53 (2.1%), PTEN-RB1 (1.3%), and BRCA2-TP53 (1.2%). All four key genes showed a statistically significant prognostic impact in the entire cohort with UA (BRCA2, HR:1.9; TP53, HR:2.6; PTEN, HR:2.4; RB1, HR:4.1, all p<0.001). Results were consistent in the subgroup analysis and when using PS. Additional genes such as FANCA, PIK3CA, and MYC were also associated with poor outcomes. Among co-alterations, BRCA2-TP53 (HR:2.0, p=0.05), RB1-TP53 (HR:3.6, p<0.001), PTEN-TP53 (HR:2.4, p<0.001); PTEN-RB1 (HR:5.5, p<0.001), and PTEN-TP53-RB1 (HR:9.0, p<0.001) were the strongest predictors of poor survival, with consistent effects across subgroups and analytical methods. Other combinations including MYC-TP53 (HR 2.2, p<0.001) and PIK3CA-TP53 (HR 3.2, p<0.001), also showed independent prognostic value. CONCLUSIONS: Genomic alterations in TP53, PTEN, RB1, and BRCA2, alone and in combination, independently associate with poor prognosis. Co-alteration patterns such as PTEN-TP53-RB1 define aggressive molecular subsets. This evidence supports integrating genomic interaction patterns for enhancing clinical risk models and guiding precision patient management. Furthermore, this study highlights the potential of AACR Project GENIE to enable data-driven development of biomarker-informed patient care strategies. Citation Format: Pablo Cresta Morgado, Victor Navarro, Guillermo Vilacampa, Haitham Alatoom, Manuel Ramos del Rio, Irbaz Riaz, Shawn Sweeney, Nikolaus Schultz, Ken Kehl, Gregory Riely, Wasim Abida, Katherine Panageas, Deborah Schrag, Philippe Bedard, Christine Micheel, Xindi Guo, Chelsea Nayan, Rodrigo Dientsmann, Joaquin Mateo. Genomic prognostic biomarkers in prostate cancer from the AACR GENIE cohort [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Innovations in Prostate Cancer Research and Treatment; 2026 Jan 20-22; Philadelphia PA. Philadelphia (PA): AACR; Cancer Res 2026;86(2_Suppl):Abstract nr A013.

Abstract Comprehensive interpretation of a patient's cancer genome is a cross-disciplinary process requiring the expertise of oncologists, molecular pathologists, genetic counselors, genomic scientists, and bioinformaticians. The Variant Interpretation for Cancer Consortium (VICC) has created a non-clinical forum for the variant interpretation process by connecting experts from multiple academic institutions and industry to create a Virtual Molecular Tumor Board (VMTB). During VICC-VMTB sessions, experts retrospectively discuss de-identified, challenging patient cases in the context of genomic interpretation guidelines to evaluate a patient's profile across several distinct components: genomic variants; disease context; therapeutic history; family history; demographic information; genomic assay information; and variant allele frequency. These components are processed through the prism of publically available knowledge and expertise of VMTB members, resulting in a broad spectrum of clinically relevant assertions about the patient's disease that may be of prognostic, diagnostic, or predictive relevance. Through these evaluations, the VICC-VMTB primarily works as a means of illuminating challenges in genomic interpretation and advancing the objectives of domain-specific VICC working groups. Examples of challenges that have been highlighted by the VICC-VMTB forum include: 1) inconsistent disease and therapy ontologies across resources are being addressed by Disease Harmonization & Drug Harmonization working groups, respectively; 2) use of machine-learning and natural language processing into clinical-grade variant interpretation are being addressed by the AI-Assisted Curation working group; 3) aggregation and harmonization of knowledgebase data into a centralized resource is being addressed by the Data Licensing and Variant Harmonization working groups; 4) inefficiencies in knowledgebase search functions are being addressed by the Search working group; 5) determination of accurate prediction of biological consequences of rare variants is being addressed by the In Silico Interpretation working group; and 6) standardization of genomic variant curation is being addressed by the Knowledge Curation and Interpretation Standards working group. These VICC working groups provide dedicated expert assemblies intended to specifically tackle these challenges, further increasing the breadth for which genomic information can be made useful in cancer variant interpretation. The VICC-VMTB has been a pertinent link between cancer genomes and clinical interpretation. We are confident VICC-VMTB will continue to be a gateway of acceleration and progress needed in the field of cancer interpretation to provide accurate and efficient answers for our patients in the future. Citation Format: Beth A. Pitel, Shruti Rao, Catherine Del Vecchio Fitz, Subha Madhavan, Rodrigo Dientsmann, Peter Horak, Ian King, Susan M. Mockus, Gordana Raca, Damian T. Rieke, Peter Rogan, Dmitriy Sonkin, David Tamborero, Ioannis S. Vlachos, Brian Walsh, Jeremy L. Warner, Malachi Griffith, Obi L. Griffith, Debyani Chakravarty, Alex H. Wagner. The Virtual Molecular Tumor Board of the Variant Interpretation for Cancer Consortium: A systematic gateway connecting cancer genome interpretation and progress in genomic knowledgebases in cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3222.