Predicting TP53 Biomarkers from Whole Slide Images across Human Solid Tumors Using Weakly Supervised Learning

Accurate molecular profiling from routine histopathology slides could transform clinical oncology. A Vision Transformer (ViT)-based model was developed to jointly predict the TP53 biomarker, detect 32 solid tumor types, and predict survival directly from whole slide images (WSIs). Over 11,000 primary tumor data were retrieved from the Pan-Cancer Atlas, along with corresponding somatic mutation, RNA-sequencing, and clinical outcome data. WSIs underwent tissue masking, quality control, stain normalization, patch extraction, and feature embedding using a ViT encoder. Seven task heads were developed to generate predictions for cancer type, TP53 mutation status, TP53 RNA expression levels, overall survival, progression-free interval, and their corresponding event times. Model training proceeded in two stages: initial training on tumor-only patches at multiple magnifications, followed by fine-tuning on WSIs using a content-aware strategy. Model performance was evaluated on an independent validation set of 1729 slides using evaluation metrics, including the area under the receiver operating characteristic curve, regression metrics, and the concordance index. An area under the receiver operating characteristic curve of 0.766 was found for TP53 mutation detection on an independent validation set across 32 human solid tumors. In conclusion, the ViT-based model could simultaneously infer TP53 mutation status, TP53 RNA expression levels, and tumor taxonomy directly from WSIs, supporting the existence of reproducible morphologic correlates of TP53 alterations across human cancers, whereas prognostic risk prediction remained limited.