Abstract A1-23: Deciphering the cancer methylome with Boolean implications to find novel drivers of aberrant DNA methylation and actionable drug targets

Aberrant changes in DNA methylation are known to play a major role in the evolution of multiple cancers, but the molecular events responsible for perturbing methylated genomic landscapes have not been completely characterized. In particular, many tumors with dysregulated DNA methylation landscapes do not bear mutations in known regulators of DNA CpG methylation such as IDH1/2, TET2 and DNMT3A. Nevertheless, identification of molecular drivers of aberrant DNA methylation in cancer is essential for directing epigenetic targeted therapy. Thus far, this has proven to be challenging due to various factors including high levels of variation in methylation and multiple co-occurring mutations within a tumor cohort. In order to systematically find mutations that may drive DNA hyper- or hypo- methylation, we analyzed mutation and methylation data from TCGA using a novel computational method based on Boolean implications (if-then rules). The distribution of points in a scatterplot of two variables in a Boolean implication is L-shaped instead of linear, facilitating the discovery of subset (containment) and mutual exclusion relationships between samples with genetic lesions and samples with hypermethylation or hypomethylation of specific CpG sites. We applied the algorithm to recurrent mutations in acute myeloid leukemia (AML), bladder, breast, head & neck, renal clear cell, glioma, lung, ovarian and uterine cancer. Consistent with previous findings, our algorithm identified mutations in IDH2 as a genetic driver of DNA hypermethylation in AML and glioma, and mutation in DNMT3A to be associated with DNA hypomethylation in AML. However, we also found many unexpected associations between somatic mutation and DNA hyper- and hypo- methylation that had not been reported. First, we noted that mutation in the Wilms9 Tumor 1 (WT1mut) gene was associated with a high degree of CpG hypermethylation but virtually no hypomethylation. Introduction of a mutant version of WT1 into wildtype AML cells induced consistent DNA hypermethylation in the same set of genes, confirming WT1mut to be causally associated with DNA hypermethylation in AML. We also identified several new associations between somatic mutations and aberrant DNA methylation in several other cancers, including associations between DNA hypomethylation and mutations in (i) STAG2 (a member of the cohesin complex) in bladder cancer and AML, (ii) DNMT3B (an alternative de-novo DNA methyltransferase) in lung adenocarcinomas and (iii) KDM5C (a histone demethylase) clear cell cancer. These data suggest that induction of aberrant DNA methylation by somatic mutation may be an important but relatively under-studied mechanism of cancer evolution. To find potential mutation-specific drug targets, we analysed the genes associated with the hypermethylated CpG sites for each recurrent mutation in AML. Methylated genes in WT1mut samples were highly enriched for polycomb repressor complex 2 (PRC2) targets (q=7.1E-84). Expression of mutant WT1 in normal cord blood stem/progenitor cells induced myeloid differentiation block and treatment of AML primary cells with GSK-126, a small molecule inhibitor of the PRC2/EZH2 complex, promoted myeloid differentiation of WT1mut+ leukemic blasts but not WT1mut- blasts in cell culture. In summary, we present a general method to identify mutation-specific DNA methylation signatures in cancer. This method found a new causal association between WT1mut and DNA hypermethylation in AML that was empirically validated. The WT1mut-specific methylation pattern identified by Boolean implications also revealed a potential treatment strategy for WT1mut AML. The method also found several new candidate genetic drivers of aberrant DNA methylation in other cancers. Our results highlight a strong association between specific mutations and aberrant hyper- or hypo- methylation in cancer and demonstrate that deciphering mutation-specific methylation patterns can lead to therapeutic insights. Citation Format: Subarna Sinha, Daniel Thomas, Ravindra Majeti, David L. Dill. Deciphering the cancer methylome with Boolean implications to find novel drivers of aberrant DNA methylation and actionable drug targets. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B2-20.