Abstract 2678: A multimodal model to forecast prostate cancer metastasis from primary tumor transcriptomics and histopathology morphology.

Abstract Prostate cancer (PCa) is the most commonly diagnosed non-cutaneous malignancies in men in the United States, and metastatic dissemination remains the principal driver mortality. While advanced disease is typically managed with androgen deprivation therapy, most patients eventually progress to castration-resistant PCa, underscoring the need for early identification of tumors predisposed to metastasis. Clinicopathological metrics, including PSA, tumor stage, and Gleason grade, provide only coarse estimates of risk and fail to capture the molecular and spatial heterogeneity underlying disease progression. To address this limitation, we developed Met-Score, a transcriptomic signature derived from meta-analysis of primary tumor expression profiles from 1,239 PCa patients. Using a rigorous training (n=1000) and independent validation (n=239) design, we computed gene-level Hedges’ effect sizes across cohorts, pooled them using a random-effects model, integrated evidence across datasets using Fisher’s and log-sum methods, and applied false discover rate correction to identify genes most strongly associated with metastatic progression. In the validation cohort, Met-Score achieved an AUC of 0.72 for predicting metastasis, and maintained independent prognostic value for overall, metastasis-free, and progression-free survival across two independent datasets, even after adjustment for Gleason score, demonstrating its clinical relevance beyond standard pathology. To investigate whether Met-Score reflects morphological phenotypes encoded in routine histopathology, we quantified morphometric features from digitized H Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2678.