A multimodal foundation model linking histopathology and DNA methylation
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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.
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dding variants, colored by cancer type. b, Image-to-DNAmretrieval across GBM, LGG, HNSC, and BRCA using matched slide and methylation embeddings,shown as Recall@1, Recall@5, and Recall@10. These analyses use pre-trained embeddings beforesupervised downstream task adaptation, providing a direct
In figure 2(a) why are the Gastric points so disparate/spread out for HistoMethylGigaPath? Same for Lung ADC?
I think either this is a t-SNE artifact or implies something abt the model.
Then again for 2(a) one could argue with the four panels => four separate times get embeddings => four independent optimizations with their own initialization, seed, and convergence. I wouldn't expect there to be a shared coordinate system. Even the same embedding re-run at a different seed will redistribute cluster compactness.... If so though the figure doesn't …
dding variants, colored by cancer type. b, Image-to-DNAmretrieval across GBM, LGG, HNSC, and BRCA using matched slide and methylation embeddings,shown as Recall@1, Recall@5, and Recall@10. These analyses use pre-trained embeddings beforesupervised downstream task adaptation, providing a direct
In figure 2(a) why are the Gastric points so disparate/spread out for HistoMethylGigaPath? Same for Lung ADC?
I think either this is a t-SNE artifact or implies something abt the model.
Then again for 2(a) one could argue with the four panels => four separate times get embeddings => four independent optimizations with their own initialization, seed, and convergence. I wouldn't expect there to be a shared coordinate system. Even the same embedding re-run at a different seed will redistribute cluster compactness.... If so though the figure doesn't imply much at all / is lowkey useless.
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istoMethyl demonstrates that DNA methylation can be used not only as a molecularendpoint, but also as a biologically structured supervision signal for learning clini-cally useful whole-slide representations.
Important contribution!
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toMethyl overview. a, Cancer cohorts from TCGA [14] used for HistoMethyl pre-training.b, Pre-training inputs, including WSIs divided into tiles and paired DNAm beta values correspondingto each case. c, Pre-training with contrastive alignment. WSI and DNAm embeddings are extractedby their respective encoders, projected by MLPs, and aligned with a contrastive loss. d, Downstreamtask adaptation. HistoMethyl uses task-specific supervised models to adapt aligned slide embeddingsto downstream tasks, including gene mutation, morphology, survival analysis, and DNAm pr
Methodologically its really smart to split the WSI branch from the DNAm branch and then use a contrastive alignment on the embeddings. What does the joint space look like? Any structure that falls out? Have the authors investigated any patterns in the joint embedding space? I …
toMethyl overview. a, Cancer cohorts from TCGA [14] used for HistoMethyl pre-training.b, Pre-training inputs, including WSIs divided into tiles and paired DNAm beta values correspondingto each case. c, Pre-training with contrastive alignment. WSI and DNAm embeddings are extractedby their respective encoders, projected by MLPs, and aligned with a contrastive loss. d, Downstreamtask adaptation. HistoMethyl uses task-specific supervised models to adapt aligned slide embeddingsto downstream tasks, including gene mutation, morphology, survival analysis, and DNAm pr
Methodologically its really smart to split the WSI branch from the DNAm branch and then use a contrastive alignment on the embeddings. What does the joint space look like? Any structure that falls out? Have the authors investigated any patterns in the joint embedding space? I would expect some rather clear boundaries given the inputs.
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