Abstract 2333: Possible prediction of tumor specific checkpoint inhibitor response based on TCGA somatic mutation load

Abstract

Abstract Immune checkpoint inhibitor therapy has been shown to be effective in a subset of patients with melanoma, non-small cell lung cancer (NSCLC) and kidney cancer. Recent studies have suggested that the number of non-synonymous mutations (NsM) can be used to select melanoma and NSCLC patients most likely to benefit from immune checkpoint inhibitor treatment. It is hypothesized that NsMs generate novel epitopes and gene products which can be detected by the immune system. The aim of this study is to apply prior information on NsM count and immune checkpoint inhibitor treatment to a range of tumor subtypes in the TCGA database to evaluate the proportion cases that could be possible responders. In our analysis of published studies of melanoma and NSCLC, the receiver operator characteristic (ROC) curve suggests a cutoff of 192 NsM would have a maximum combination of sensitivity (74%) and specificity (59.3%) for potential clinical benefit for patients. We conducted an analysis of 7,757 samples from 26 different TCGA tumor types and observed that approximately 16.2% of TCGA samples harbor more than 192 NsM, and thus could be in the category associated with a higher likelihood of response to immune checkpoint inhibitors. Based on NsM count, bladder, colon, gastric, and endometrial cancers each contained more than 30% of tumors with a high NsM and thus, could be possible candidates for checkpoint immune therapy. Although our model estimates clinical response to immune checkpoint treatment from NsM count is preliminary and needs future validation, information on NsM count can be useful for selecting tumor types most likely to benefit in clinical trials of immune checkpoint inhibitor treatment. Citation Format: Leandro Machado Colli, Mitchell J. Machiela, Timothy Myers, Lea Jessop, Kai Yu, Stephen J. Chanock. Possible prediction of tumor specific checkpoint inhibitor response based on TCGA somatic mutation load. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2333.