RT Journal Article
SR Electronic
T1 Untangling the gene-epigenome networks: Timing of epigenetic regulation of gene expression in acquired cetuximab resistance
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 136564
DO 10.1101/136564
A1 Genevieve Stein-O’Brien
A1 Luciane T Kagohara
A1 Sijia Li
A1 Manjusha Thakar
A1 Ruchira Ranaweera
A1 Hiroyuki Ozawa
A1 Haixia Cheng
A1 Michael Considine
A1 Alexander V Favorov
A1 Ludmila V Danilova
A1 Joseph A Califano
A1 Evgeny Izumchenko
A1 Daria A Gaykalova
A1 Christine H Chung
A1 Elana J Fertig
YR 2017
UL http://biorxiv.org/content/early/2017/06/01/136564.abstract
AB 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.