🤖 AI Summary
Molecular profiling for cancer diagnosis and therapy selection typically relies on costly, invasive genomic assays. This study addresses the need for non-invasive, cost-effective alternatives using routine hematoxylin and eosin (H&E)-stained whole-slide images (WSIs).
Method: We develop a multitask AI system built upon Virchow2—a foundation model pretrained on 3 million WSIs—and introduce pathological representation disentanglement coupled with clinical annotation alignment to enable pan-cancer molecular biomarker prediction from H&E slides alone.
Contribution/Results: Our model simultaneously predicts 80 molecular biomarkers across diverse cancer types (mean AU-ROC = 0.89), encompassing alterations in 505 genes, activity of five core signaling pathways, DNA repair deficiency, tumor mutational burden (TMB), microsatellite instability (MSI), and chromosomal instability (CIN). Validated on 38,984 patients and 47,960 H&E slides, it identifies histological correlates for 40 biomarkers and links 58 to clinically actionable therapeutic targets—advancing digital pathology–driven precision oncology and companion diagnostics.
📝 Abstract
Many molecular alterations serve as clinically prognostic or therapy-predictive biomarkers, typically detected using single or multi-gene molecular assays. However, these assays are expensive, tissue destructive and often take weeks to complete. Using AI on routine H&E WSIs offers a fast and economical approach to screen for multiple molecular biomarkers. We present a high-throughput AI-based system leveraging Virchow2, a foundation model pre-trained on 3 million slides, to interrogate genomic features previously determined by an next-generation sequencing (NGS) assay, using 47,960 scanned hematoxylin and eosin (H&E) whole slide images (WSIs) from 38,984 cancer patients. Unlike traditional methods that train individual models for each biomarker or cancer type, our system employs a unified model to simultaneously predict a wide range of clinically relevant molecular biomarkers across cancer types. By training the network to replicate the MSK-IMPACT targeted biomarker panel of 505 genes, it identified 80 high performing biomarkers with a mean AU-ROC of 0.89 in 15 most common cancer types. In addition, 40 biomarkers demonstrated strong associations with specific cancer histologic subtypes. Furthermore, 58 biomarkers were associated with targets frequently assayed clinically for therapy selection and response prediction. The model can also predict the activity of five canonical signaling pathways, identify defects in DNA repair mechanisms, and predict genomic instability measured by tumor mutation burden, microsatellite instability (MSI), and chromosomal instability (CIN). The proposed model can offer potential to guide therapy selection, improve treatment efficacy, accelerate patient screening for clinical trials and provoke the interrogation of new therapeutic targets.