Research Papers:
Integration of genomic data analysis for demonstrating potential targets in the subgroup populations of squamous cell lung cancer patients
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Yongcui Wang1,2, Weiling Zhao1, Xiaobo Zhou1,3
1Center for Bioinformatics & Systems Biology, Department of Radiology, Wake Forest School of Medicine, Winston Salem, NC, USA
2Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, China
3School of Life Science and Biotechnology, Xi’an Jiaotong University, Xi’an, China
Correspondence to:
Xiaobo Zhou, email: [email protected]
Keywords: squamous lung cancer, integrated genomic analysis, mutation, methylation, patients’ survival
Received: October 27, 2015 Accepted: May 14, 2016 Published: June 15, 2016
ABSTRACT
Squamous cell carcinoma (SCC) is the second most frequent histologic subtype of non-small cell lung cancer (NSCLC), causing approximately 400,000 deaths per year worldwide. Although targeted therapies have improved outcomes in patients with adenocarcinoma, the most common subtype of NSCLC, the genomic alterations in SCC have not been comprehensively characterized and no therapeutic agents have been approved specifically for the patients with SCC. Therefore, development of novel therapeutic approaches is urgently needed. Here, we developed an integrative approach, called DLSA, to integrate genomic, epigenomic and transcriptomic data. DLSA stratified SCC patients into distinct survival subgroups and identified the potential molecular drivers in individual survival subtypes. Three subgroups of SCC patients with diverse molecular and clinical characteristics were unveiled through DLSA. Combined analysis of clinical and molecular data on those subgroups suggested that the molecular features in the stratified subgroups are not only consistent with the previous findings, but also provide a guide to targeted agents that worth to be evaluated in clinical trials for SCC patients with poor survival. In conclusion, DLSA offers the possibility for faster, safer, and cheaper the development of novel anti-cancer therapeutics in the early-stage clinical trails.