Abstract 90: Histologic stratification of hepatocellular carcinoma using deep learning informed by spatial transcriptomics.

Abstract Introduction: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality. Although multiple classification systems have been developed, their clinical utility remains limited. With the increasing use of tissue biopsies in targeted therapy trials, there is an opportunity to advance both molecular and histologic approaches for HCC stratification. Methods: Publicly available spatial transcriptomics data with paired hematoxylin and eosin (H n=340) as well as an in-house validation cohort (n=48). Tile-level predictions were aggregated to generate patient-level histologic scores, which were then clustered into three subclasses (A, B, and C), which were then assessed for unique clinical and molecular characteristics. Results: Models achieved holdout AUROCs of 0.93 (S1), 0.92 (S2), and 0.94 (S3). In TCGA, subclasses predicted overall survival (A vs B, p0.0001; A vs C, p0.0001), disease-free interval (A vs B, p0.001; A vs C, p0.0001), and progression-free interval (A vs B, p0.01; A vs C, p0.0001). Histologic subtypes were independently prognostic when considered alongside clinical variables via stepwise Cox proportional hazards (A vs B, p=0.008; A vs C, p=0.001). Each cluster associated with distinct clinical features (e.g. cluster A with early pathologic stage and HBV etiology, and cluster B with late stage), mutations, and enriched pathways (cluster A with metabolic pathways, cluster B with cell cycle pathways, and cluster C with immune pathways). Cluster C was also enriched for a signature of anti-PD-1 response in HCC (p1x10-10). In the validation cohort, overall survival trends were maintained (A vs B, p=0.121, A vs C, p=0.005). Conclusions: Using a deep learning model which predicts spatial subtype signatures from H Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 90.