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Int J Cancer. 2009 Sep 15;125(6):1257-65. doi: 10.1002/ijc.24535.

A comprehensive catalogue of functional genetic variations in the EGFR pathway: protein-protein interaction analysis reveals novel genes and polymorphisms important for cancer research.

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1
Division of Applied Molecular Oncology, Department of Medical Biophysics, Ontario Cancer Institute, Toronto, Ontario, Canada. savas@mun.ca

Abstract

The EGFR pathway is a critical signaling pathway deregulated in many solid tumors. In addition to the initiation and progression of cancer, the EGFR pathway is also implicated in variable treatment responses and prognoses. Genetic variation in the form of Single Nucleotide Polymorphisms (SNPs) can affect the function/expression of the EGFR pathway genes. Here, we applied a systematic and comprehensive approach utilizing diverse public databases and in silico analysis tools to select putative functional genetic variations from 244 genes involved in the EGFR pathway. Our data comprises 649 SNPs. Three hundred sixty SNPs are predicted to have biological consequences (functional SNPs). These SNPs can be directly used in further studies to test their association with risk, treatment response and prognosis in cancer. To systematically cover the EGFR pathway, we also performed a network-based analysis to further select putative functional SNPs from the genes whose protein products physically interact with the EGFR pathway proteins. We utilized protein-protein interaction information and focused on 14 proteins that have a high degree of connectivity (interacting with > or = 10 proteins) with the EGFR pathway genes identified to have functional SNPs (f-EGFR genes). Two of these proteins (FYN and LCK) had interactions with 17 of the f-EGFR genes, yet both lacked any putative functional SNP. However, our analysis indicated the presence of potentially functional SNPs in 9 other highly interactive proteins. The genes and their SNPs identified in the network-based analysis represent potential candidates for gene-gene and SNP-SNP interaction studies in cancer research.

PMID:
19499547
DOI:
10.1002/ijc.24535
[Indexed for MEDLINE]
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