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

BMC Medicine

Tim Beissbarth¹  Silvia von der Heyde¹ 

[1] Department of Medical Statistics, University Medical Center Göttingen, Humboldtallee 32, Göttingen, 37073, Germany

关键词: pathway signature;    GSEA;    matrix factorization;    drug resistance;    cetuximab;    EGFR;    HNSCC;   

Others  :  1126210
DOI  :  10.1186/1741-7015-10-43

 received in 2012-04-11, accepted in 2012-05-01,  发布年份 2012

【 摘 要 】

The pathways downstream of the epidermal growth factor receptor (EGFR) have often been implicated to play crucial roles in the development and progression of various cancer types. Different authors have proposed models in cell lines in which they study the modes of pathway activities after perturbation experiments. It is prudent to believe that a better understanding of these pathway activation patterns might lead to novel treatment concepts for cancer patients or at least allow a better stratification of patient collectives into different risk groups or into groups that might respond to different treatments. Traditionally, such analyses focused on the individual players of the pathways. More recently in the field of systems biology, a plethora of approaches that take a more holistic view on the signaling pathways and their downstream transcriptional targets has been developed. Fertig et al. have recently developed a new method to identify patterns and biological process activity from transcriptomics data, and they demonstrate the utility of this methodology to analyze gene expression activity downstream of the EGFR in head and neck squamous cell carcinoma to study cetuximab resistance. Please see related article: http://www.biomedcentral.com/1471-2164/13/160 webcite

【 授权许可】

   
2012 von der Heyde and Beissbarth; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

【 参考文献 】

• [1]Hynes NE, Lane HA: ERBB receptors and cancer: the complexity of targeted inhibitors. Nat Rev Cancer 2005, 5(5):341-354.
• [2]Sahin O, Fröhlich H, Löbke C, Korf U, Burmester S, Majety M, Mattern J, Schupp I, Chaouiya C, Thieffry D, Poustka A, Wiemann S, Beissbarth T, Arlt D: Modeling ERBB receptor-regulated G1/S transition to find novel targets for de novo trastuzumab resistance. BMC Syst Biol 2009, 3:1.
• [3]Lai SY, Johnson FM: Defining the role of the JAK-STAT pathway in head and neck and thoracic malignancies: implications for future therapeutic approaches. Drug Resist Updat 2010, 13(3):67-78.
• [4]Jones RB, Gordus A, Krall JA, MacBeath G: A quantitative protein interaction network for the ErbB receptors using protein microarrays. Nature 2006, 439(7073):168-174.
• [5]Vermorken JB, Mesia R, Rivera F, Remenar E, Kawecki A, Rottey S, Erfan J, Zabolotnyy D, Kienzer HR, Cupissol D, Peyrade F, Benasso M, Vynnychenko I, Raucourt DD, Bokemeyer C, Schueler A, Amellal N, Hitt R: Platinum-based chemotherapy plus cetuximab in head and neck cancer. N Engl J Med 2008, 359(11):1116-1127.
• [6]Uribe P, Gonzalez S: Epidermal growth factor receptor (EGFR) and squamous cell carcinoma of the skin: molecular bases for EGFR-targeted therapy. Pathol Res Pract 2011, 207(6):337-342.
• [7]Hatakeyama H, Cheng H, Wirth P, Counsell A, Marcrom SR, Wood CB, Pohlmann PR, Gilbert J, Murphy B, Yarbrough WG, Wheeler DL, Harari PM, Guo Y, Shyr Y, Slebos RJ, Chung CH: Regulation of heparin-binding EGF-like growth factor by miR-212 and acquired cetuximab-resistance in head and neck squamous cell carcinoma. PLoS One 2010, 5(9):e12702.
• [8]Freudlsperger C, Burnett JR, Friedman JA, Kannabiran VR, Chen Z, Waes CV: EGFR-PI3K-AKT-mTOR signaling in head and neck squamous cell carcinomas: attractive targets for molecular-oriented therapy. Expert Opin Ther Targets 2011, 15:63-74.
• [9]Smyth GK: Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004, 3:Article3.
• [10]Beissbarth T: Interpreting experimental results using gene ontologies. Methods Enzymol 2006, 411:340-352.
• [11]Wu D, Lim E, Vaillant F, Asselin-Labat ML, Visvader JE, Smyth GK: ROAST: rotation gene set tests for complex microarray experiments. Bioinformatics 2010, 26(17):2176-2182.
• [12]Bauer S, Robinson PN, Gagneur J: Model-based gene set analysis for Bioconductor. Bioinformatics 2011, 27(13):1882-1883.
• [13]Wingender E: The TRANSFAC project as an example of framework technology that supports the analysis of genomic regulation. Brief Bioinform 2008, 9(4):326-332.
• [14]Li L: Dimension reduction for high-dimensional data. Methods Mol Biol 2010, 620:417-434.
• [15]Sill M, Kaiser S, Benner A, Kopp-Schneider A: Robust biclustering by sparse singular value decomposition incorporating stability selection. Bioinformatics 2011, 27(15):2089-2097.
• [16]Maneck M, Schrader A, Kube D, Spang R: Genomic data integration using guided clustering. Bioinformatics 2011, 27(16):2231-2238.
• [17]Fröhlich H, Tresch A, Beissbarth T: Nested effects models for learning signaling networks from perturbation data. Biom J 2009, 51(2):304-323.
• [18]Fertig EJ, Ding J, Favorov AV, Parmigiani G, Ochs MF: CoGAPS: an R/C++ package to identify patterns and biological process activity in transcriptomic data. Bioinformatics 2010, 26(21):2792-2793.
• [19]R Development Core Team: R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing; 2011.
• [20]Fertig EJ, Ren Q, Cheng H, Hatakeyama H, Dicker A, Rodeck U, Considine M, Ochs MF, Chung CH: Gene expression signatures modulated by epidermal growth factor 1 receptor activation and their relationship to cetuximab resistance in head and neck squamous cell carcinoma. BMC Genomics 2012, 13:160.