Description
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.