Predicting tumor mutation burden and VHL mutation from renal cancer pathology slides with self‐supervised deep learning

Abstract

AbstractBackgroundTumor mutation burden (TMB) and VHL mutation play a crucial role in the management of patients with clear cell renal cell carcinoma (ccRCC), such as guiding adjuvant chemotherapy and improving clinical outcomes. However, the time‐consuming and expensive high‐throughput sequencing methods severely limit their clinical applicability. Predicting intratumoral heterogeneity poses significant challenges in biology and clinical settings. Our aimed to develop a self‐supervised attention‐based multiple instance learning (SSL‐ABMIL) model to predict TMB and VHL mutation status from hematoxylin and eosin‐stained histopathological images.MethodsWe obtained whole slide images (WSIs) and somatic mutation data of 350 ccRCC patients from The Cancer Genome Atlas for developing SSL‐ABMIL model. In parallel, 163 ccRCC patients from Clinical Proteomic Tumor Analysis Consortium cohort was used as independent external validation set. We systematically compared three different models (Wang‐ABMIL, Ciga‐ABMIL, and ImageNet‐MIL) for their ability to predict TMB and VHL alterations.ResultsWe first identified two groups of populations with high‐ and low‐TMB (cut‐off point = 0.9). In two independent cohorts, the Wang‐ABMIL model achieved the highest performance with decent generalization performance (AUROC = 0.83 ± 0.02 and 0.8 ± 0.04 in predicting TMB and VHL, respectively). Attention heatmaps revealed that the Wang‐ABMIL model paid the highest attention to tumor regions in high‐TMB patients, while in VHL mutation prediction, non‐tumor regions were also assigned high attention, particularly the stromal regions infiltrated by lymphocytes.ConclusionsOur results indicated that SSL‐ABMIL can effectively extract histological features for predicting TMB and VHL mutation, demonstrating promising results in linking tumor morphology and molecular biology.