What question did this study set out to answer?

The research aims to investigate if metabolic states can be predicted from routine H&E-stained slides for patient stratification.

April 5, 2026

Abstract 6726: A translational deep learning platform predicts metabolic pathway activity from routine H&E slides to enable patient stratification.

Key Points

The research aims to investigate if metabolic states can be predicted from routine H&E-stained slides for patient stratification.
Developed a multi-omics platform integrating histomorphology with transcriptomic data.
Derived binary biomarkers for 32 pathways using personalized metabolic models.
Applied statistical tests to evaluate pathway activity from gene expression profiles.
Trained deep learning models on morphological features from 41 to 887 WSIs.
Identified robust predictive morphological patterns for various metabolic pathways.
Achieved high predictability (AUC = 0.85) for nucleotide metabolism in testicular germ cell tumors.
Observed strong signals of nuclear transport in multiple cancer types with AUCs ranging from 0.71 to 0.79.
Consistent predictability for fatty acid beta-oxidation across several cancer types.

Abstract

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.

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