Tumor mutational burden (TMB) is a potential genomic biomarker of immunotherapy. However, TMB detected through whole exome sequencing lacks clinical penetration in low-resource settings. In this study, we proposed a multi-scale deep learning framework to address the detection of TMB status from routinely used whole slide images for a multiple cancer TMB prediction model (MC- TMB). The MC-TMB achieved a mean area under the curve (AUC) of 0.818 (0.804-0.831) in the cross-validation cohort, which showed superior performance to each single-scale model. The improvements of MC-TMB over the single-tumor models were also confirmed by the ablation tests on x10 magnification, and the highly concerned regions typically correspond to dense lymphocytic infiltration and heteromorphic tumor cells. MC-TMB algorithm also exhibited good generalization on the external validation cohort with an AUC of 0.732 (0.683-0.761), and better performance when compared to other methods. In conclusion, we proposed a deep learning-based approach to reveal tumor mutational burden status from routinely used pathological slides across multiple cancer types.
@article{arxiv.2204.03257,
title = {Deep learning-based approach to reveal tumor mutational burden status from whole slide images across multiple cancer types},
author = {Siteng Chen and Jinxi Xiang and Xiyue Wang and Jun Zhang and Sen Yang and Junzhou Huang and Wei Yang and Junhua Zheng and Xiao Han},
journal= {arXiv preprint arXiv:2204.03257},
year = {2023}
}