Abstract Cancer effect size quantifies the strength of evolutionary selection acting on somatic mutations within tumors, providing insights into which genetic alterations are being actively selected during tumor evolution. While histopathology has long been the gold standard for cancer diagnosis and classification, the relationship between evolutionary selection pressures on specific mutations and observable tumor morphology remains unexplored. We investigated whether combinations of somatic mutations, weighted by their cancer effect sizes, could serve as genomic proxies for pathological features in breast cancer. Using deep learning-based feature extraction from pre-trained histopathology models, we extracted high-dimensional morphological representations from H Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2729.
Asefon et al. (Fri,) studied this question.