Abstract Molecular profiling of tumor biopsies is the cornerstone of precision oncology, guiding therapy selection and prognostic assessment. Computational analysis of routine H0.7. We validated 254 of these genes within TCGA with an AUROC0.7. 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, 0.92, 0.83, and 0.83, respectively. For the BRAF, KRAS, and POLE/POLD1 mutations, we observed continued benefit from increasing cohort size from as small as 10 mutated cases up to the maximum positivity within our cohort, suggesting that we have not reached the full performance potential for these mutations. Finally, virtually-imputed BRAF mutations stratified minimal residual disease (MRD) risk better than the WES-derived genomic mutation status revealing that the model was capturing MAPK pathway activation rather than single mutation status. The virtual genomic algorithm advances virtual genomic profiling in CRC, demonstrating scalable and potentially generalizable prediction of hundreds of genomic alterations from routine histology. Beyond serving as a low-cost pre-screening tool to guide molecular testing, the virtually imputed biomarkers capture pathway-level activity that better correlates with clinical outcomes such as MRD risk, underscoring its potential to transform precision oncology through image-based genomic insight. Citation Format: Erik N. Bergstrom, Tinghui Wu, Michail Chatzianastasis, Thinh Tran, Aaron Rosenfeld, Robert Burns, Adham Jurdi, Helio Costa, Frank Zhang, . A large-scale, multi-target deep learning model for virtual genomic profiling in colorectal cancer abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 4000.
Bergstrom et al. (2026) studied this question.