Deep Learning Platform Predicts Metabolic Pathway Activity from H&E Slides for Patient...

Background: Metabolic reprogramming is a hallmark of cancer and a potential therapeutic target, yet clinical assessment remains challenging. We hypothesized that functional metabolic states could be inferred from routinely collected H&E-stained whole-slide images (WSIs), offering a scalable approach to biomarker discovery and patient stratification. We developed a multi-omics platform integrating histomorphology with transcriptomic and metabolic modelling data, applying it across 21 cancer types to identify digital biomarkers predictive of pathway-specific metabolic activity. Methods: We derived binary biomarkers for 32 pathways using personalized genome-scale metabolic models and transcriptomic data from The Cancer Genome Atlas (TCGA). Pathway activity was inferred by comparing sample-specific gene expression profiles to a generalized metabolic network, with statistical significance determined by t-tests and Benjamini-Hochberg correction (FDR < 0.05). Deep learning models were trained to learn the morphological features predictive of these metabolic states, with cohort sizes ranging from 41 to 887 WSIs per biomarker. Model performance was validated using three-fold cross-validation and evaluated by the area under the curve (AUC), with ± showing standard deviation across folds. Results: The approach identified robust morphological patterns predictive of key metabolic pathways across diverse tumor types. High predictability was achieved for nucleotide metabolism in testicular germ cell tumors (AUC = 0.85 ±0.13) and pancreatic adenocarcinoma (AUC = 0.79 ±0.08). Strong morphological signals of nuclear transport were observed in colon adenocarcinoma (AUC = 0.79 ±0.05), skin cutaneous melanoma (AUC = 0.75 ±0.06), and lung squamous cell carcinoma (AUC = 0.71 ±0.07). Pathways related to fatty acid beta-oxidation showed consistent predictability (AUC > 0.65) across thyroid carcinoma (AUC = 0.73 ±0.07), liver hepatocellular carcinoma (AUC = 0.70 ±0.05), pancreatic adenocarcinoma (AUC = 0.69 ±0.02), stomach adenocarcinoma (AUC = 0.68 ±0.04), and ovarian serous cystadenocarcinoma (AUC = 0.67 ±0.05). Conclusions: This study demonstrates the potential of a multi-omics platform for decoding functional metabolic states from routine pathology slides. The platform can identify patient populations with specific metabolic dependencies (e.g., nucleotide metabolism) or pathway dysregulations (e.g., nuclear transport), thereby enabling targeted patient stratification for clinical trials. By translating complex histomorphology from standard H&E slides into actionable molecular insights, this technology offers a scalable and cost-effective solution to accelerate biomarker-driven drug discovery in precision oncology. Citation Format: Salim Arslan, Julian Schmidt, Cher Bass, Foivos Ntelemis, Oscar Maiques, Vishali Sharma, Jakob Nikolas Kather, Pahini Pandya. A translational deep learning platform predicts metabolic pathway activity from routine H&E slides to enable patient stratification [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 6726.