CoGAPS matrix factorization algorithm identifies AP-2alpha as a feedback mechanism from therapeutic inhibition of the EGFR network

Patients with oncogene driven tumors are currently treated with targeted therapeutics such as epidermal growth factor receptor (EGFR) inhibitors. The inhibited oncogenic pathway often interacts with other signaling pathways and alters predicted therapeutic response. Genomic data from The Cancer Genome Atlas (TCGA) demonstrates pervasive molecular alterations to EGFR, MAPK, and PI3K signaling in previously untreated tumors. Therefore, this study uses bioinformatics algorithms to infer the complex pathway interactions that result from EGFR inhibitor use in cancer cells that contain these these common EGFR network genetic alterations. To do this, we modified the HaCaT keratinocyte cell line model of premalignancy to simulate cancer cells with constitutive activation of EGFR, HRAS, and PI3K in a controlled genetic background. We then measured gene expression after treating modified HaCaT cells with three EGFR targeted agents (gefitinib, afatinib, and cetuximab) for 24 hours. The study has a total of 96 samples in 4 experimental batches. 89 of the samples are from HaCaT constructs with a control empty vector (HaCaT-Mock), overexpression of EGFR (HaCaT-Mock), of PIK3CA-HR (HaCaT-PIK3CA), and of HRASVal12D (HaCaT-HRAS) treated with siEGFR, afatinib, gefitinib, cetuximab, or appropriate controls. All experiments are run in triplicate unless otherwise specified. In addition, the design includes 1 FaDu cell line, 1 HaCaT parental cell line, 2 UMSCC1, and 2 UMSCC25 cell lines to facilitate normalization across batches.