Abstract A14: Convergence analysis of regulatory mutations into immuno-modulatory pathways across 14 tumor types

Evasion of immune system surveillance is a hallmark of cancer. Cancer cells may secrete cytokines like TGF-β which hampers cytotoxic T-cells and natural killer cells in addition to recruiting tumor-infiltrating regulatory T-cells endowed with immunosuppressive potential. Little is known about the genomic basis for immune evasion especially in the context of dysregulation and rewiring of the immune-related circuitry of tumor cells. A vast majority of mutations in cancer frequently occur in non-coding regions. The functional impact of these mutations in mediating interactions with the tumor microenvironment have largely been unexplored. Given that recent efforts have implicated non-coding elements in various disease association studies, it can be expected that a significant number of recurrent non-coding mutations in cancer have a regulatory effect. Pan-Cancer analysis has improved the discovery and analysis of these regulatory mutations while avoiding the type I and type II errors made in several tissue-specific cancer projects. By leveraging recent pan-cancer whole-genome sequencing efforts, we have been able to characterize the non-coding mutational profiles of 505 samples, spread across 14 tumor types. These methods amplify the power to detect heterogeneous signals of positive selection thereby enhancing our ability to distinguish ‘driver’ from ‘passenger’ alterations. This study aims at applying a pan-cancer genome-wide approach towards identifying regulatory mutations that potentially impact immune modulation and evasion. We first infer the genome-wide position- and sample-specific probabilities of mutation from somatic mutations calls. A multinomial logistic regression model describes the relationship between the mutation rate and a set of explanatory variables such as the sample ID, replication timing, the genomic context and the local mutation rate. The predicted site-specific probabilities, when overlaid with tissue-specific annotations from the Roadmap Epigenomics consortium and GENCODE, allow us to derive a test statistic for each cis non-coding region that yields information regarding the likelihood that the region of interest is a “driver region”. By utilizing published databases on enhancer-gene links, we extend this framework to comprehensively characterize distal enhancer regions mapped with their target genes. We then systematically identify key immunomodulatory networks enriched for non-coding mutations and evaluate these in a pan-cancer context. A number of immune-related genes are found to harbor an excess of non-coding mutations suggesting higher-order regulatory convergence. We assign a significance score to this convergence by factoring the proximity to the target gene as well as the genomic instability modeled through local copy number changes. Therefore, we integrate individual low-frequency alterations into high-frequency recurrent events across different tumor types. We believe that the proposed model presents an unbiased method towards characterizing the impact of regulatory mutations towards immunomodulation, immune suppression and evasion. We anticipate that this approach will serve as a template for future functional non-coding mutational dissections in tumor-related studies.

Citation Format: Karthik Murugadoss, Malene Rasmussen, Alvin Shi, Manolis Kellis. Convergence analysis of regulatory mutations into immuno-modulatory pathways across 14 tumor types. [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2016 Oct 20-23; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2017;5(3 Suppl):Abstract nr A14.