Interpretable multi-stage attention network to predict cancer subtype, microsatellite instability, TP53 mutation and TMB of endometrial and colorectal cancer

Mismatch repair deficiency (dMMR), also known as high-grade microsatellite instability (MSI-H), is a well-established biomarker for predicting the immunotherapy response in endometrial cancer (EC) and colorectal cancer (CRC). Tumor mutational burden (TMB) has also emerged as an important quantitative genomic biomarker for assessing the efficacy of immune checkpoint inhibitors. Although next-generation sequencing (NGS) can be used to assess MSI and TMB, the high costs, low sample throughput, and significant DNA requirements make NGS impractical for routine clinical screening. In this study, an interpretable, multi-stage attention deep learning (DL) network is introduced to predict pathological subtypes, MSI, TP53 mutations, and TMB directly from low-cost, routinely used histopathological whole slide images of EC and CRC slides. Experimental results showed that this method consistently outperformed seven state-of-the-art approaches in cancer subtyping and molecular status prediction across EC and CRC datasets. Fisher’s Least Significant Difference test confirmed a strong correlation between model predictions and actual molecular statuses (MSI, TP53, and TMB) ($p<0.001$). Furthermore, Kaplan–Meier disease-free survival analysis revealed that CRC patients with model-predicted high TMB had significantly longer disease-free survival than those with low TMB ($p<0.05$). These findings demonstrate that the proposed DL-based approach holds significant potential for directly predicting immunotherapy-related pathological diagnoses and molecular statuses from routine WSIs, supporting personalized cancer immunotherapy treatment decisions in EC and CRC.