Activated EGFR typifies numerous epithelial malignancies, including cancers of the breast, lung, colon, head-and-neck, pancreas etc. CUDC-907 cost Therapies that inhibit EGFR became a paradigm for targeted treatment of human cancers and use inhibitors of EGFR kinase, Gefitinib and Erlotinib, or antibodies Lapatinib, Cetuximab, Panitumumab, Zalutumumab, Nimotuzumab and Matuzumab. They can induce tumor regression avoiding some adverse effects of chemotherapy. Drawbacks of EGFR inhibitor therapies are cardiac and renal side-effects, skin toxicity, and intrinsic or acquired resistance to therapy; these limit the duration or dosage of treatment. Whereas all these agents target the same protein, EGFR, different inhibitors use different mechanisms and have different effects. For example, Gefitinib and Erlotinib compete with ATP and inhibit receptor autophosphorylation, retaining effectiveness against constitutively active kinase mutants. Antibodies bind the extracellular domain of receptor, occluding ligand binding, preventing receptor dimerization and activating host immune responses. Many studies used transcriptional profiling to define cellular responses of targeting EGFR. However, the use of different agents, microarray platforms and experimental protocols makes it difficult to characterize the commonalities and the particulars of EGFR inhibition. Our objective here is to use metaanalysis for a comprehensive investigation of transcriptional data. We metaanalysed 20 published transcriptional studies, comprising 346 microarrays, using free, readily available computer programs, e.g., RankProd. We determined the ontological categories overrepresented in the regulated genes and identified potential protein kinases and transcription factors involved. The results describe large lists of over 2537 suppressed genes and 2251 induced by EGFR inhibitors, with high statistical significance. They identify crucial differences in the genes regulated by antibodies and by kinase inhibitors and specifically the consequences of Gefitinib vs. Erlotinib treatments. We also demonstrate the great advantage 
of metaanalysis over single studies. The work can serve as a paradigm for integration and metaanalysis of transcriptional data in public repositories. We recently published detailed metaanalysis protocols for finding relevant studies in public repositories, downloading data files, microarray quality control, cross-referencing and merging different platforms, dealing with empty cells and negative numbers, selecting data subsets and differentially expressed genes using RankProd, gene lists annotation using the Lists2Networks algorithm, clustering and comparing lists and promoter analysis. The workflow diagram describing meta-profiling and cluster analysis is shown in Fig. 1 Briefly, for the data deposited in studies using the Affymetrix arrays we downloaded CEL files and then processed these using RMAExpress software. To combine data from different platforms we used a non-parametric approach, RankProd, which identifies differentially expressed genes, both upor down- regulated, based on the SU5416 estimated percentage of false predictions. Lists of official symbols of regulated genes was submitted to the Lists2Networks analysis program.