TY - JOUR T1 - Untangling the gene-epigenome networks: Timing of epigenetic regulation of gene expression in acquired cetuximab resistance JF - bioRxiv DO - 10.1101/136564 SP - 136564 AU - Genevieve Stein-O’Brien AU - Luciane T Kagohara AU - Sijia Li AU - Manjusha Thakar AU - Ruchira Ranaweera AU - Hiroyuki Ozawa AU - Haixia Cheng AU - Michael Considine AU - Alexander V Favorov AU - Ludmila V Danilova AU - Joseph A Califano AU - Evgeny Izumchenko AU - Daria A Gaykalova AU - Christine H Chung AU - Elana J Fertig Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/06/01/136564.abstract N2 - Targeted cancer therapeutics induce transcription changes in hundreds of genes simultaneously due to the mechanism of action of the treatment, changes in cellular proliferation, and development of resistance. While almost all patients treated with targeted therapeutics develop resistance, the timing and interplay among regulatory pathways responsible for acquired resistance remain unknown. Here we developed a robust combination of experimental and bioinformatics tools to measure genomic changes during the development of resistance. Using the targeted therapeutic cetuximab, an EGFR blocking agent, on an in vitro model of head and neck squamous cell carcinoma (HNSCC), we characterized the genetic and epigenetic alterations that occur while cells acquired cetuximab resistance. We confirmed that gene expression signatures from previous gold-standard studies comparing only pre and post resistance samples conflate genes associated with early therapeutic response and genes associated with acquired therapeutic resistance. In contrast, analysis of the time course data with CoGAPS non-negative matrix factorization found substantial gene expression changes uniquely associated with acquired therapeutic resistance. Specifically, analysis of DNA methylation data in our time course identified patterns of gene expression anti-correlated with DNA methylation that changed over time with the acquisition of resistance. Further, this novel experimental and bioinformatics approach identified a robust temporal delay between gene expression changes and DNA methylation, suggestive of an epigenetic mechanism to stabilize the critical alterations for the resistant phenotype. Together this method is generalizable to cross-platform time course analysis of high-throughput genomics data in other dynamic systems where development of resistance leads to recurrent disease. ER -