Prediction of response to alkylator-based chemotherapy in metastatic melanoma (MM) using gene expression and promoter methylation signatures.

9009 Background: Temozolomide and dacarbazine (TMZ and DTIC) remain the mainstay of alkylator-based chemotherapy for MM, despite response rates of 10-15% and the absence of any impact on survival. Classification of patients according to responsiveness can guide the individualization of therapy and inform approaches to abrogate mechanisms of chemotherapy resistance. Epigenetic mechanisms play an important role in regulation of genes associated with resistance and were evaluated in tandem with gene expression profiling in biological samples from MM patients (pts) to refine our understanding of the epigenomic-genomic-phenotypic interplay. METHODS We examined promoter methylation and gene expression in tumor tissues of 21 pts with MM treated with TMZ or DTIC, using high throughput technologies (Illumina Inc). The cases were divided into responder (R) and non-responder (NR) groups based on clinical response. The data were analyzed using Prediction Analysis of Microarrays (PAM) from BRB array tools. RESULTS Differential promoter methylation analysis revealed that 63.6% of promoter sites were hypomethylated in tumors obtained from R pts (p<0.0001). PAM analysis of gene expression data revealed that a classifier set consisting of 82 genes was able to predict NRs from Rs with 83% sensitivity and 89% specificity. Promoter methylation profiling did not independently correlate with R status. A simultaneous analysis of the promoter methylation and gene expression values first stratified into 3 data-driven categories and then combined into a 3 by 3 matrix allowed us to identify a common gene expression/methylation signature of 15 genes that classified both NR and R groups accurately 100% of the time. CONCLUSIONS Gene expression signatures independently predict response to chemotherapy in MM, however promoter methylation profiling alone does not. Analysis of combined gene expression and promoter methylation in a well- annotated clinical data set dichotomized according to response identified a highly predictive signature. The findings from this study are qualified by the relatively small sample size and are currently being validated in an expanded sample set. Supported in part by the ECOG Paul Carbone, MD, Fellowship Award. No significant financial relationships to disclose.