Estimating tumor mutational burden from RNA-sequencing without a matched-normal sample

Abstract

The identification of somatic point mutations in tumor samples is of high clinical value, such as for the development of targeted therapies. Here the authors develop a machine learning pipeline for detecting somatic point mutations from RNA sequencing without a matched-normal sample, and utilize the model's prediction for computing the tumor mutational burden.