Whole Slide Imaging-Based Prediction of TP53 Mutations Identifies an Aggressive Disease Phenotype in Prostate Cancer

• Published in: Cancer research (Chicago, Ill.) Ill.), 2023-09, Vol.83 (17), p.2970-2984
• Main Authors: Pizurica, Marija, Larmuseau, Maarten, Van der Eecken, Kim, de Schaetzen van Brienen, Louise, Carrillo-Perez, Francisco, Isphording, Simon, Lumen, Nicolaas, Van Dorpe, Jo, Ost, Piet, Verbeke, Sofie, Gevaert, Olivier, Marchal, Kathleen
• Format: Article
• Language: English
• Subjects: Computational Cancer Biology and Technology; Humans; Male; Mutation; Phenotype; Prognosis; Prostate - pathology; Prostatic Neoplasms - genetics; Prostatic Neoplasms - pathology; Tumor Suppressor Protein p53 - genetics
• ISSN: 0008-5472; 1538-7445; 1538-7445
• DOI: 10.1158/0008-5472.CAN-22-3113

In prostate cancer, there is an urgent need for objective prognostic biomarkers that identify the metastatic potential of a tumor at an early stage. While recent analyses indicated TP53 mutations as candidate biomarkers, molecular profiling in a clinical setting is complicated by tumor heterogeneity. Deep learning models that predict the spatial presence of TP53 mutations in whole slide images (WSI) offer the potential to mitigate this issue. To assess the potential of WSIs as proxies for spatially resolved profiling and as biomarkers for aggressive disease, we developed TiDo, a deep learning model that achieves state-of-the-art performance in predicting TP53 mutations from WSIs of primary prostate tumors. In an independent multifocal cohort, the model showed successful generalization at both the patient and lesion level. Analysis of model predictions revealed that false positive (FP) predictions could at least partially be explained by TP53 deletions, suggesting that some FP carry an alteration that leads to the same histological phenotype as TP53 mutations. Comparative expression and histologic cell type analyses identified a TP53-like cellular phenotype triggered by expression of pathways affecting stromal composition. Together, these findings indicate that WSI-based models might not be able to perfectly predict the spatial presence of individual TP53 mutations but they have the potential to elucidate the prognosis of a tumor by depicting a downstream phenotype associated with aggressive disease biomarkers. Deep learning models predicting TP53 mutations from whole slide images of prostate cancer capture histologic phenotypes associated with stromal composition, lymph node metastasis, and biochemical recurrence, indicating their potential as in silico prognostic biomarkers. See related commentary by Bordeleau, p. 2809.