Abstract Next generation sequencing technologies enable precise personalized medicine. Thus, patients with oncogene driven tumors are currently treated with targeted therapeutics such as EGFR inhibitors. However, drug interactions with other activated signaling pathways in treated tumors often alter predicted therapeutic response. Therefore, bioinformatics algorithms are needed to infer unanticipated molecular interactions from anticipated molecular response to targeted therapeutics in diverse genetic backgrounds. To model heterogeneous genetic backgrounds in HNSCC, we use HaCaT cells with forced overexpression of EGFR, HRAS, and PIK3CA. Previously, the CoGAPS matrix factorization algorithm was shown to infer the specific signaling pathways that were activated in these HaCaT knock-in constructs from gene expression data. In this study, we evaluated whether CoGAPS could also delineate unanticipated signaling changes from anticipated cellular signaling response caused by targeted therapeutic in diverse genetic backgrounds. To delineate these signaling responses, we measured gene expression after treating the modified HaCaT cells with three EGFR targeted agents (gefitinib, cetuximab and afatinib) for 24 hours. The CoGAPS matrix factorization algorithm distinguished a gene expression signature associated with the anticipated silencing of the EGFR network and a signature associated with unanticipated transcriptional feedback in HaCaT constructs that were sensitive to EGFR inhibitors. Notably, the feedback signature showed that EGFR gene expression itself increased in cells that were responsive to EGFR inhibitors. The CoGAPS algorithm further associated such feedback with increased expression of several growth factor receptors by the AP-2 family of transcription factors. Once transcribed, these growth factor receptors may ultimately compensate for EGFR inhibition in these sensitive cells. Our data suggest, that CoGAPS gene expression signatures delineate on target and feedback effects of drugs related to therapeutic sensitivity in diverse genetic backgrounds. Citation Format: Elana J. Fertig, Hiroyuki Ozawa, Manjusha Thakar, Jason Howard, Gabriel Krigsfeld, Alexander V. Favorov, Daria A. Gaykalova, Michael F. Ochs, Christine H. Chung. The CoGAPS matrix factorization algorithm infers feedback mechanisms from therapeutic inhibition of EGFR that increases expression of growth factor receptors. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 777.