Open Access Research article

Gene expression signatures modulated by epidermal growth factor receptor activation and their relationship to cetuximab resistance in head and neck squamous cell carcinoma

Elana J Fertig1*, Qing Ren2, Haixia Cheng1, Hiromitsu Hatakeyama3, Adam P Dicker2,4, Ulrich Rodeck2,5, Michael Considine1, Michael F Ochs1,6 and Christine H Chung1

Author Affiliations

1 Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, Baltimore, MD, USA

2 Department of Radiation Oncology, Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA

3 Department of Otolaryngology, Hokkaido University, Sapporo, Hokkaido, Japan

4 Department of Pharmacology and Experimental Therapeutics, Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA

5 Department of Dermatology and Cutaneous Biology, Thomas Jefferson University, Philadelphia, PA, USA

6 Department of Health Science Informatics, School of Medicine, Johns Hopkins University, Baltimore, MD, USA

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BMC Genomics 2012, 13:160 doi:10.1186/1471-2164-13-160

Published: 1 May 2012

Abstract

Background

Aberrant activation of signaling pathways downstream of epidermal growth factor receptor (EGFR) has been hypothesized to be one of the mechanisms of cetuximab (a monoclonal antibody against EGFR) resistance in head and neck squamous cell carcinoma (HNSCC). To infer relevant and specific pathway activation downstream of EGFR from gene expression in HNSCC, we generated gene expression signatures using immortalized keratinocytes (HaCaT) subjected to ligand stimulation and transfected with EGFR, RELA/p65, or HRASVal12D.

Results

The gene expression patterns that distinguished the HaCaT variants and conditions were inferred using the Markov chain Monte Carlo (MCMC) matrix factorization algorithm Coordinated Gene Activity in Pattern Sets (CoGAPS). This approach inferred gene expression signatures with greater relevance to cell signaling pathway activation than the expression signatures inferred with standard linear models. Furthermore, the pathway signature generated using HaCaT-HRASVal12D further associated with the cetuximab treatment response in isogenic cetuximab-sensitive (UMSCC1) and -resistant (1CC8) cell lines.

Conclusions

Our data suggest that the CoGAPS algorithm can generate gene expression signatures that are pertinent to downstream effects of receptor signaling pathway activation and potentially be useful in modeling resistance mechanisms to targeted therapies.