Establishment of a novel gene panel as a biomarker of immune checkpoint inhibitor response

• Main Authors: Yi‐Ru Pan, Chiao‐En Wu, Yu‐Chao Wang, Yi‐Chen Yeh, Meng‐Lun Lu, Yi‐Ping Hung, Yee Chao, Da‐Wei Yeh, Chien‐Hsing Lin, Jason Chia‐Hsun Hsieh, Ming‐Huang Chen, Chun‐Nan Yeh
• Format: Article
• Language: English
• Published: Wiley 2020-01-01
• Series: Clinical & Translational Immunology
• Subjects: cell‐free DNA; DNA repair gene; gene panel; immune checkpoint inhibitor; tumor mutational burden
• Online Access: https://doi.org/10.1002/cti2.1145

Abstract Objective Immune checkpoint inhibitors (ICIs) have become the standard of care in various cancers, although the predictive tool is still unknown. Methods This study aimed to develop a novel gene panel by selecting DNA damage response (DDR) genes from the Catalogue of Somatic Mutations in Cancer (COSMIC) databank and validating them in previously reported cohorts. This association between DDR gene mutations and tumor mutation burden or microsatellite status was analysed from The Cancer Genome Atlas (TCGA) databank. Furthermore, we made the gene panel clinically accessible and predicted the response in clinical patients receiving ICIs by using cell‐free DNA. Results The top 20 mutated DDR genes in various cancers (total 37 genes) were taken from the COSMIC databank, and the DDR genes found to individually predict a response rate > 50% in Van Allen's cohort were selected (Science, 350, 2015 and 207). Eighteen DDR genes were selected as the gene panel. The prevalence and predicted response rate were validated in the other three reported cohorts. Tumor mutational burden‐high was positively associated with mutations of the 18 DDR genes for most cancers. We used cell‐free DNA to test the DDR gene panel and validated by our patients receiving ICIs. This DDR gene panel accounted for approximately 30% of various cancers, achieving a predicted response rate of approximately 60% in patients with a mutated gene panel receiving ICIs. Conclusion This gene panel is a novel and reliable tool for predicting the response to ICIs in cancer patients and guides the appropriate administration of ICIs in clinical practice.