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Purpose: Epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) is the standard therapy for advanced lung adenocarcinomas with common EGFR mutations. However, the efficacy of EGFR-TKIs in patients with uncommon EGFR mutations (other than exon 19 deletions or exon 21 L858R mutation) remains undetermined.Methods: Seven hundred and fifty-five non-small cell lung cancer (NSCLC) patients with EGFR mutation analyses for TKI therapy were identified between October 2010 and December 2015 in East of China. And 66 patients bearing uncommon EGFR mutations were included to collect data from TKI response and prognosis. We categorised EGFR uncommon mutations as: sensitizing rare mutations (group 1: G719X, L861Q, S768I); Ex20 ins (group 2), or complex mutations (G719X + L861Q, G719X + S768I, 19 del + T790M, 19 del + L858R, L858R + S768I, and L858R + T790M; group 3).Results: Of 66 patients given EGFR-TKI treatment, rare sensitive mutations, Ex20 ins, and complex mutations were identified in 37 (56.1%), 9 (13.6%), and 20 (33.3%) cases, respectively. TKI efficacy in patients harboring uncommon EGFR mutations exhibited a tumor response rate of 28.8% and a median progression-free survival (PFS) of 4.8 months. Additionally, patients with complex EGFR mutations had significantly longer PFS when compared with the remaining sensitizing rare mutations or Ex20 ins cases (8.6 vs. 4.1 vs. 3.1 months; p = 0.041). Importantly, complex EGFR mutations were independent predictors of increased overall survival (Hazard Ratios = 0.31; 95% confidence intervals: 0.11-0.90; p = 0.031). Among them, patients harboring Del-19 combined with L858R mutations showed a tendency to have higher response rate (RR) and improved PFS than those with other complex mutation patterns (RR: 66.7 vs. 14.3%, p = 0.021; PFS: 10.1 vs. 8.6 months, p = 0.232).Conclusions: Personalized treatment should be evolving in different types of uncommon EGFR mutations. Clinical benefit from EGFR-TKIs was higher in NSCLC patients with complex EGFR mutations than those with other uncommon EGFR mutation types. [ABSTRACT FROM AUTHOR]

Pathway analyses are playing an increasingly important role in understanding biological mechanism, cellular function and disease states. Current pathway-identification methods generally focus on only the changes of gene expression levels; however, the biological relationships among genes are also the fundamental components of pathways, and the dysregulated relationships may also alter the pathway activities. We propose a powerful computational method, Edge Set Enrichment Analysis (ESEA), for the identification of dysregulated pathways. This provides a novel way of pathway analysis by investigating the changes of biological relationships of pathways in the context of gene expression data. Simulation studies illustrate the power and performance of ESEA under various simulated conditions. Using real datasets from p53 mutation, Type 2 diabetes and lung cancer, we validate effectiveness of ESEA in identifying dysregulated pathways. We further compare our results with five other pathway enrichment analysis methods. With these analyses, we show that ESEA is able to help uncover dysregulated biological pathways underlying complex traits and human diseases via specific use of the dysregulated biological relationships. We develop a freely available R-based tool of ESEA. Currently, ESEA can support pathway analysis of the seven public databases (KEGG; Reactome; Biocarta; NCI; SPIKE; HumanCyc; Panther). [ABSTRACT FROM AUTHOR]