Abstract Somatic noncoding mutations are abundant in cancer genomes, yet their regulatory functional consequences remain poorly understood. To address this gap, we generated a pan-cancer atlas of variant regulatory effects by profiling 35M SNVs within +/-20 kb of transcription start sites across more than 8,000 TCGA samples. Chromatin impact was predicted with the Sei deep-learning model (20,000 features across 40 sequence classes), and clonal architecture was inferred with PyClone-VI, enabling genome-wide, evolution aware annotation of proximal regulatory alterations. We then aggregated these variant-level functional predictions into gene-level and patient-level representations of regulatory dysregulation. Despite comparable mutation counts near COSMIC Tier1/2 and non-COSMIC genes, the atlas revealed substantially higher predicted regulatory impact in promoter-proximal mutations, uncovering a noncoding functional dimension not detectable from burden alone. Aggregating these predicted effects into a personalized somatic dysregulation representation across COSMIC Hallmark genes produced a patient-level promoter-proximal regulatory effect score. In a multivariable Cox model of progression-free interval stratified by cancer type and adjusted for log1p(FGA), HRD score, non-silent mutations per Mb, and age at diagnosis, this regulatory score was a strong and independent predictor of adverse progression (HR = 1.10, 95% CI 1.04-1.15, P 0.005), whereas noncoding mutation count contributed no additional information. Partitioning regulatory burden by evolutionary timing revealed distinct processes: clonal promoter-proximal disruption consistently predicted worse pan-cancer outcomes and showed stronger within-cancer associations, whereas subclonal burden was prognostic only in specific cancers. For example, subclonal promoter-proximal disruption within Hallmark genes was significantly associated with progression in Uterine Corpus Endometrial Carcinoma (HR = 1.36, 95% CI 1.10-1.70, P = 0.01), possibly indicating late-arising regulatory programs contributing to tumor progression. Finally, deep-learning regulatory sequence classes mapped to tissue-specific vulnerabilities: lower-grade glioma showed disproportionate impact from the E3 brain/melanocyte enhancer class, whereas sarcoma was most strongly associated with PC4 polycomb/bivalent stem-cell-linked disruption. Together, these results show that functional promoter-proximal disruption, not mutational load, captures clinically relevant noncoding dysregulation, shaped by context-specific and evolution-linked regulatory programs. This atlas provides a scalable foundation for integrating noncoding regulatory functions into prognostic modeling and will be made available to the research community. Citation Format: Ksenia Sokolova, Vessela N. Kristensen, Olga G. Troyanskaya. Deep-learning profiling of regulatory somatic variants across clonal cancer evolution 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 1295.
Sokolova et al. (Fri,) studied this question.