CoGAPS matrix factorization algorithm identifies transcriptional changes in AP-2alpha target genes in feedback from therapeutic inhibition of the EGFR network
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Elana J. Fertig1,*, Hiroyuki Ozawa1,2,*, Manjusha Thakar1, Jason D. Howard1, Luciane T. Kagohara1, Gabriel Krigsfeld1, Ruchira S. Ranaweera1,3, Robert M. Hughes1, Jimena Perez1, Siân Jones4, Alexander V. Favorov1,5,6, Jacob Carey7, Genevieve Stein-O’Brien8,9, Daria A. Gaykalova10, Michael F. Ochs11, Christine H. Chung1,3
1Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA
2Department of Otorhinolaryngology-Head and Neck Surgery, Keio University School of Medicine, Tokyo, Japan
3Department of Head and Neck-Endocrine Oncology, Moffitt Cancer Center, Tampa, FL, USA
4Personal Genome Diagnostics, Baltimore, MD, USA
5Vavilov Institute of General Genetics, Moscow, Russia
6Research Institute for Genetics and Selection of Industrial Microorganisms, Moscow, Russia
7Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
8Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
9Lieber Institute for Brain Development, Baltimore, MD, USA
10Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
11Department of Mathematics and Statistics, The College of New Jersey, Ewing Township, NJ, USA
*These two authors contributed equally to this project as co-first authors
Elana J. Fertig, email: firstname.lastname@example.org
Keywords: EGFR, targeted therapeutics, cell signaling, genomics, crosstalk
Received: May 11, 2016 Accepted: September 02, 2016 Published: September 16, 2016
Patients with oncogene driven tumors are treated with targeted therapeutics including EGFR inhibitors. Genomic data from The Cancer Genome Atlas (TCGA) demonstrates molecular alterations to EGFR, MAPK, and PI3K pathways in previously untreated tumors. Therefore, this study uses bioinformatics algorithms to delineate interactions resulting from EGFR inhibitor use in cancer cells with these genetic alterations. We modify the HaCaT keratinocyte cell line model to simulate cancer cells with constitutive activation of EGFR, HRAS, and PI3K in a controlled genetic background. We then measure gene expression after treating modified HaCaT cells with gefitinib, afatinib, and cetuximab. The CoGAPS algorithm distinguishes a gene expression signature associated with the anticipated silencing of the EGFR network. It also infers a feedback signature with EGFR gene expression itself increasing in cells that are responsive to EGFR inhibitors. This feedback signature has increased expression of several growth factor receptors regulated by the AP-2 family of transcription factors. The gene expression signatures for AP-2alpha are further correlated with sensitivity to cetuximab treatment in HNSCC cell lines and changes in EGFR expression in HNSCC tumors with low CDKN2A gene expression. In addition, the AP-2alpha gene expression signatures are also associated with inhibition of MEK, PI3K, and mTOR pathways in the Library of Integrated Network-Based Cellular Signatures (LINCS) data. These results suggest that AP-2 transcription factors are activated as feedback from EGFR network inhibition and may mediate EGFR inhibitor resistance.
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