Abstract 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 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 Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6726.
Arslan et al. (Fri,) studied this question.