Predicting Tumor Mutation Burden of TNBC Based on Nuclei Scores of Histopathological Images

Tumor mutation burden(TMB) is a biomarker for predicting immunotherapy responses, which can be used to filter out Triple-Negative Breast Cancer(TNBC) patients who benefit from immunotherapy. It is generally measured by whole-exome sequencing (WES) in clinical practice. However, WES has the disadvantage of being expensive, time-consuming, and operational complexity so that it is not available in most hospitals. To solve these issues, we developed a machine learning algorithm that predicts the TMB of TNBC based on the nuclei score of histopathological images, which can obtain high accuracy without manually labeling tumor regions. We verify the effectiveness of patches filtered by nuclei score to TMB classifier by comparing the performance of the model that trained with all patches and trained with selected patches. Experiments results show that the accuracy of the model trained by patches selected with nuclei score is 87.5% and F1 is 80%, which are much higher than training with all patches(87.5% vs 56.25%, 80% vs 58.82%). The time of testing a sample using our approach is only 1 in 26, compared with the test time with all patches. To the best of our knowledge, this is the first research to predict TMB from TNBC histopathological images. The proposed approach has the potential to provide immunotherapy to a much broader subset of patients with TNBC.