Construction of mutation burden estimation model for predicting efficacy of cancer immunotherapy

• Main Authors: Guan-Yi Lyu, 呂冠毅
• Other Authors: Yu-Chao Wang
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/83795893168676353125

碩士 === 國立陽明大學 === 生物醫學資訊研究所 === 105 === Abstract Cancer is the leading cause of human death worldwide. Many researches are dedicated to finding the therapeutics of the disease, and immunotherapy is one of the new therapeutic approaches. The most popular drugs of immunotherapy is the immune checkpoints inhibitors, such as anti-PD-1 and anti-CTLA-4. Although the efficacy of these drugs have been demonstrated, there are still some patients who do not respond to them. Therefore, how to identify the patients potentially respond to the drugs would be an essential question. Literature evidences have shown that mutation burden (the total number of nonsynonymous point mutations) might be a useful predictive biomarker for treatment responses. However, whole-exome sequencing is needed to be performed to unravel the mutation burden of the tumor, which is not cost and time-effective for standard clinical test. Therefore, focused on lung adenocarcinoma, the objective of this study is to construct a mutation burden estimation model of a small set of genes for predicting efficacy of cancer immunotherapy. With the somatic mutation data downloaded from The Cancer Genome Atlas (TCGA) database, a computational framework was developed to construct the mutation burden estimation model. Candidate genes were selected based on mutation frequency, CDS length, and the association between mutation status with mutation burden. Subsequently, least squares parameter estimation and Bayesian information criterion were used for model construction. The constructed estimation model consisted of 24 genes, which was applied to two independent data to test the performance. R2 between predicted and actual mutation burden is 0.7626. Since there are treatment response information for immunotherapy in the second independent data, the predicted mutation burden were also employed to classify the samples as durable clinical benefit (DCB) or no durable benefit (NDB) with 85% sensitivity, 93% specificity, and 89% accuracy. Based on the constructed estimation model, we can design a customized panel of targeted sequencing of these selected genes instead of whole-exome sequencing. Consequently, the cost and time needed for assessing mutation burden would be significantly decreased and the efficacy prediction of cancer immunotherapy would be more feasible for standard clinical examination. Keywords: mutation burden, immunotherapy, mathematical model, cancer genomics, next generation sequencing