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

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 ≥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. © 2009 UICC

Differential Effects of Myopathy-Associated Caveolin-3 Mutants on Growth Factor Signaling

“…41 In conclusion, our novel finding of various effects of caveolin-3 mutants on different RTKs might contribute to understanding the puzzling observation that neighboring caveolin-3 point mutations cause diverse phenotypes in patients. Moreover, individual genetic differences caused by single nucleotide polymorphisms in genes for growth factor receptors or cholesterol-synthesis associated genes could play an additional role in the pathogenesis of caveolinopathies, 42 contributing to the puzzling differences between clinical phenotypes. 43 …”

Integrative computational biology for cancer research

“…For example, proteins encoded by cancer genes tend to be central in interaction networks [they have high degree and betweenness centrality (Jonsson and Bates 2006; Rambaldi et al 2008; Syed et al 2010)], have high clustering coefficients (Li et al 2009), and are overrepresented in network motifs (Rambaldi et al 2008). Several methods use the topological characteristics of known cancer genes, in combination with other features (such as Gene Ontology categories, protein domains, biological pathways, and sequence features), to predict new cancer genes (Aragues et al 2008; Rambaldi et al 2008) or functional SNPs (Savas et al 2009). Many algorithms identify modules in interaction networks, or groups of densely interconnected genes that can be highly functionally related (King et al 2004; Newman 2006; Palla et al 2005; Spirin and Mirny 2003).…”

“…For instance, while biological pathway databases may disagree on individual pathway definitions, by combining them we can diminish their false positives and false negatives; by integrating pathways with protein interactions, we can improve their coverage and relevance (Radulovich et al 2010; Savas et al 2009). Having all of these data available from a single source or at least in a standardized format simplifies integrative analyses and reduces the error and database incompatibility (arising from, e.g., inconsistent gene nomenclature).…”

Reply to T. Valerius

“…4 Polymorphisms in the CYP3A4/5 genes may be involved in the metabolism of EGFR TKIs, but to date, such polymorphisms have not been shown to have a significant effect on efficacy outcomes. Genetic polymorphisms may affect outcome by several mechanisms.…”