Predicting tumour mutational burden from histopathological images using multiscale deep learning

Abstract

Tumour mutational burden (TMB) is an important biomarker for predicting response to immunotherapy in cancer patients. Gold-standard measurement of TMB is performed using whole exome sequencing (WES), which is not available at most hospitals owing to its high cost, operational complexity, and long turnover times. We developed a machine learning algorithm, Image2TMB, which can predict TMB from readily available lung adenocarcinoma histopathological images. Image2TMB integrates the predictions of three deep learning models that operate at different resolution scales (5X, 10X, and 20X magnification) to determine if the TMB of a cancer is high or low. On a held-out set of patients, Image2TMB achieves an area under the precision recall curve of 0.92, an average precision of 0.89, and has the predictive power of a targeted sequencing panel of approximately 100 genes. This study demonstrates that it is possible to infer genomic features from histopathology images, and potentially opens avenues for exploring genotype-phenotype relationships.