Abstract 229: Methylation as a surrogate for mutations to identify therapeutic targets

Background: Patients whose tumors exhibit an inflammatory "hot” phenotype tend to show initial efficacy toward immune checkpoint inhibitors but develop resistance over time, becoming “cold” tumors. Understanding the biology involved in this transition is of interest, especially for the identification of genes where a mutation may be facilitating the transition. As the occurrence of mutations are often rare, identifying these markers in an affected population of patients can be challenging. Here we describe a method using hyper- and hypomethylation, which can mimic the loss and gain of function caused by mutations, as a surrogate to identify mutations potentially involved in the transition of tumors from hot to cold. Methods: Breast cancer methylation and corresponding gene expression data were downloaded from TCGA. Samples were classified as immune hot or cold tumors, the latter being either an immunosuppressive state or immune inert state, by a previously defined gene algorithm [1]. Genes that contributed to immune state classification were compared between gene expression and hypermethylation status by creating a new logistic regression model for the latter. A candidate list of genes that were hypermethylated in cold tumors was created. As a validation step, Cancer Cell Line Encyclopedia (CCLE) expression and mutation data were downloaded from Depmap. Cell lines were classified as hot or cold by the same algorithm and were curated for damaging mutations within the derived gene list. For each candidate gene, the number of damaging mutations in cold cell lines was compared to the number of wild type “hot” cell lines (Chi-squared analysis) and significance was determined by comparing observed values to the distribution of 1000 randomly selected gene sets. Results: Of the final 1562 candidate genes, 420 genes had at least a 5% difference in hot and cold phenotypes between cell lines with and without a damaging mutation in that gene when compared to the methylation data (Z=10.24 , p