Tumour mutational burden (TMB) is an important biomarker for predicting the response to immunotherapy in patients with cancer. Gold-standard measurement of TMB is performed using whole exome sequencing (WES), which is not available at most hospitals because of its high cost, operational complexity and long turnover times. We have 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 (×5, ×10 and ×20 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 ~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.

肿瘤突变负担（TMB）是预测癌症患者对免疫治疗反应的重要生物标志物。TMB的金标准测量是使用全外显子组测序（WES）进行的，由于成本高，操作复杂且周转时间长，因此大多数医院无法使用。我们已经开发了一种机器学习算法Image2TMB，可以从易于获得的肺腺癌组织病理学图像中预测TMB。Image2TMB集成了三种深度学习模型的预测，这些模型在不同的分辨率等级（放大倍数为5，×10和×20）下运行，以确定癌症的TMB是高还是低。对于一组病人，Image2TMB在精确召回曲线下的面积为0.92，平均精确度为0.89，并具有约100个基因的靶向测序小组的预测能力。这项研究表明，有可能从组织病理学图像中推断出基因组特征，并为探索基因型与表型之间的关系开辟了道路。