A large-scale, multi-target deep learning model for virtual genomic and molecular risk profiling in colorectal cancer.

3526 Background: Molecular profiling of tumor biopsies is central to precision oncology, informing treatment selection, prognostication, and disease monitoring. Computational analysis of routine H n = 422 CRC patients). Results: Individual models predicted 379 mutated genes with an internal area under the receiver operating curve (AUROC) > 0.7. We validated 254 of these genes within TCGA with an AUROC > 0.7. Training a multi-task learning model to predict all genes simultaneously improved the AUROC for 85% of the genes. Based on NCCN guidelines in CRC, we further trained individual models to predict MSI status, BRAF V600E, KRAS G12D/V/G13D, and POLE/POLD1 exonuclease mutations with AUROCs of 0.96, 0.93, 0.84, and 0.86, respectively. Lastly, we developed an image-based molecular recurrence risk model trained directly on longitudinal ctDNA outcomes. The model stratified patients into low, medium, and high risk groups with a concordance index of 0.67. Compared to low-risk patients, the high-risk group exhibited a hazard ratio (HR) of 4.67, while the medium-risk group showed an HR of 1.98 (p-values < < 1E-5). Conclusions: This unified framework demonstrates that virtual genomics from routine H&E histopathology can enable scalable, cost-efficient inference of hundreds of clinically relevant genomic alterations and molecular recurrence risk in CRC. By leveraging universally available diagnostic slides, virtual genomics has the potential to expand access to precision oncology, optimize molecular testing strategies, and support earlier risk stratification.