# A multimodal foundation model linking histopathology and DNA methylation

Preprint · Wang D, Zhang J, Chen C, Zhang W, Wang S, Meng Y, Sonpavde G, Horbinski C, Tian Y

- Archive id: doi:10.64898/2026.06.11.731518
- Published: 2026-06-14
- Venue: bioRxiv
- DOI: https://doi.org/10.64898/2026.06.11.731518
- Citations: 0
- Source URL: https://doi.org/10.64898/2026.06.11.731518
- Provenance: europepmc

## Abstract

Hematoxylin and eosin (H&E) slides are routinely available in cancer care, but molecular profiling often requires additional tissue processing and turnaround time. We introduce HistoMethyl, a DNA methylation-aware pathology foundation model that aligns whole-slide histopathology with matched genome-scale methylation beta-value profiles during pre-training while requiring only an H&E slide at inference. We evaluated HistoMethyl across four task groups: gene mutation prediction, morphology-associated classification, overall survival prediction, and direct DNA methylation beta-value recovery. Evaluation spanned TCGA cross-validation, a disease-held-out lower-grade glioma cohort, and external cohort validation in CPTAC glioblastoma and SurGen rectal adenocarcinoma, totaling 14 cancer cohorts and 81 gene mutation tasks. The best-performing configuration improved mean mutation AUROC by 5.03 percentage points. By converting DNA methylation supervision into an H&E-only representation, His-toMethyl could support an early molecular triage layer that helps prioritize cases for confirmatory sequencing, methylation profiling, immunohistochemistry, and molecular tumor board review. These image-only predictions are intended to accelerate downstream molecular testing and tissue allocation while leaving final diagnosis and treatment selection anchored in validated molecular assays.

_Abstract reproduced under CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)._

Topics: [AI in Clinical Medicine](https://space-kitty.com/topics/ai-medicine)

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