Mutation load estimation model as a predictor of the response to cancer immunotherapy

The determination of the mutation load, a total number of nonsynonymous point mutations, by whole-exome sequencing was shown to be useful in predicting the treatment responses to cancer immunotherapy. However, this technique is expensive and time-consuming, which hampers its application in clinical practice. Therefore, the objective of this study was to construct a mutation load estimation model for lung adenocarcinoma, using a small set of genes, as a predictor of the immunotherapy treatment response. Using the somatic mutation data downloaded from The Cancer Genome Atlas (TCGA) database, a computational framework was developed. The estimation model consisted of only 24 genes, used to estimate the mutation load in the independent validation cohort precisely (R2 = 0.7626). Additionally, the estimated mutation load can be used to identify the patients with durable clinical benefits, with 85% sensitivity, 93% specificity, and 89% accuracy, indicating that the model can serve as a predictive biomarker for cancer immunotherapy treatment response. Furthermore, our analyses demonstrated the necessity of the cancer-specific models by the constructed melanoma and colorectal models. Since most genes in the lung adenocarcinoma model are not currently included in the sequencing panels, a customized targeted sequencing panel can be designed with the selected model genes to assess the mutation load, instead of whole-exome sequencing or the currently used panel-based methods. Consequently, the cost and time required for the assessment of mutation load may be considerably decreased, which indicates that the presented model is a more cost-effective approach to cancer immunotherapy response prediction in clinical practice.Cancer genetics: Predicting patient response to immunotherapyEstimating patients’ mutation load from a small set of genes can accurately predict their response to cancer immunotherapy. Harnessing patients’ immune response to target tumor cells is an effective treatment approach in some cases but not others. A patient’s number of deleterious genetic mutations across all their protein-coding genes has been shown to correlate with their responsiveness to immunotherapy. However, whole-exome sequencing is time-consuming and costly. Yu-Chao Wang at the National Yang-Ming University, Taiwan, and colleagues have developed cancer-specific mutation load estimation models for adenocarcinoma, melanoma and colorectal cancer that require sequencing only a small number of genes. They show that the mutation load in lung adenocarcinoma patients can be estimated from 24 genes and that they can predict immunotherapy responsiveness with similar accuracy to that obtained using whole-exome sequencing.