A new analysis approach of epidermal growth factor receptor pathway activation patterns provides insights into cetuximab resistance mechanisms in head and neck cancer

The epidermal growth factor receptor (EGFR) is a transmembrane receptor belonging to the group of receptor tyrosine kinases that forward extracellular signals via phosphorylation cascades, which finally arouse cellular responses. This kind of proteins is often related to cancer due to mutations or overexpression leading to aberrant signaling and resultant excessive proliferation [1‐3]. Main adaptors for EGFR are GRB2 and Shc, activating the mitogen-activated protein kinase (MAPK) pathway via RAS. ERBB2 binding sites are more promiscuous, enabling the respective dimers to activate not only the MAPK but also the phosphoinositide 3-kinase (PI3K) pathway, the two major pathways in ERBB signaling responsible for cell proliferation, cellular survival and anti-apoptosis [4]. Also, cross-talk of these pathways exists, offering potential bypass strategies in the protein network (Figure 1). Due to the association of overexpressed EGFR with poor prognosis of head and neck squamous cell carcinoma (HNSCC), cetuximab, a monoclonal antibody targeting the receptor, is applied in common therapeutic strategies [5]. However, many HNSCC patients are non-responders or develop resistance, which is suspected to result from aberrant activation of EGFR pathways [6, 7]. To improve such a targeted therapy, it would be beneficial to gain insight into the individual molecular specificity of the targeted pathway per patient [8]. Thus, in a personalized medicine approach, the relevance of the pathway should be revealed in advance to treatment. Therefore, the detection of common gene activity patterns among sample subsets is used to stratify patients based on their gene expression profiles.

Gene expression microarrays are a widely used tool to measure genomewide transcription within cell lines or tissues under varying conditions. Usually, gene-wise statistical tests, for example employing linear models, are then performed to determine differentially expressed genes [9]. Methods to find overrepresentation of functional gene sets or pathway genes, so called gene set enrichment analysis (GSEA), are employed in order to interpret the resulting long lists of differential genes [10‐12]. To monitor the activity of certain pathway parts or transcription factors (TFs), gene sets of TF target genes, as they can be retrieved from databases like TRANSFAC, are of special interest [13]. Another aspect of data analysis is revealing gene expression patterns of patient or gene groups by clustering or dimension reduction techniques [14]. A number of specialized methods have been proposed previously, for example, clustering genes and patients simultaneously into biclusters [15], applying predefined gene signatures in guided clustering approaches [16] or signal flow reconstruction in pathways from downstream effects of perturbation experiments [17].

Fertig et al. have proposed the new method Coordinated Gene Activity in Pattern Sets (CoGAPS) [18] and made it available as add-on for the popular free statistical computing software R [19]. It combines a matrix factorization technique with GSEA of downstream transcriptional targets to determine patterns of pathway activity. They now demonstrate its utility to study cetuximab resistance in HNSCC by analyzing gene expression patterns downstream of EGFR [20].

The main drawback of established techniques to infer activity of gene sets, clustering for example, is that they are neglecting multiple regulation of genes, that is, gene re-usage and co-regulation by diverse pathways and TFs as well as coordinated activity of gene sets, for example, pathway cross-talk, which actually constitutes a specific phenotype. To overcome this disadvantage, the CoGAPS algorithm focuses on gene sets instead of isolated genes for inferring biological processes based on transcriptional data. The multitude of computational methods and tools analyzing activity patterns of (interacting) pathways should be further developed and compared to each other in the future. The presented results indicate the potential of the CoGAPS algorithm to detect transcriptional signatures as biomarkers for individual drug sensitivity or resistance, respectively. These signatures will have to be tested and prove their value in clinical practice in the future.

SH is a research scientist focusing on network reconstruction from proteomics data and systems biology of the EGFR pathway in breast cancer. TB is an associate professor for statistical bioinformatics in the Department of Medical Statistics at the University Medical Center Göttingen. His main research focus is on the development of methods for the analysis and interpretation of high-throughput genomics data and on network reconstruction algorithms. He leads the multidisciplinary consortium BreastSys with systems biological analysis of the EGFR pathway as key aspect.