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NPJ Genom Med. 2018 Apr 30;3:12. doi: 10.1038/s41525-018-0051-x. eCollection 2018.

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

Author information

1
1Institute of Biomedical Informatics, National Yang-Ming University, Taipei, 11221 Taiwan.
2
2Department of Life Sciences and Institute of Genome Sciences, National Yang-Ming University, Taipei, 11221 Taiwan.
3
3Department of Pathology and Laboratory Medicine, Taipei Veterans General Hospital, Taipei, 11217 Taiwan.
4
4School of Medicine, National Yang-Ming University, Taipei, 11221 Taiwan.
5
5Center for Systems and Synthetic Biology, National Yang-Ming University, Taipei, 11221 Taiwan.

Abstract

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.

Conflict of interest statement

The authors declare that they have no competing interests.

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