Abstract
// Yongcui Wang 1, 2 , Weiling Zhao 1 , Xiaobo Zhou 1, 3 1 Center for Bioinformatics & Systems Biology, Department of Radiology, Wake Forest School of Medicine, Winston Salem, NC, USA 2 Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, China 3 School of Life Science and Biotechnology, Xi’an Jiaotong University, Xi’an, China Correspondence to: Xiaobo Zhou, email: xizhou@wakehealth.edu 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.
Highlights
Lung cancer is the leading cause of cancer death, resulting approximate 159,000 deaths in the United States in 2014 [1]
We proposed an integrative framework, called DLSA (Deep Learning for Survival Analysis) to stratify squamous cell carcinoma (SCC) patients into distinct survival subgroups and identify the potential molecular drivers in individual survival subtypes based on the concepts of deep learning framework (DLF)
We developed a novel computational method to discover the subtypes of SCC through incorporating genomic, epigenomic and transcriptomic data, called DLSA
Summary
Lung cancer is the leading cause of cancer death, resulting approximate 159,000 deaths in the United States in 2014 [1]. Adenocarcinomas and squamous cell carcinoma (SCC) are the most two frequent histologic subtype of non-small cell lung cancer (NSCLC). Targeted therapeutics with EGFR tyrosine kinase and ALK inhibitors have been afforded benefits to the patients with lung adenocarcinomas, they are not effective for those with lung SCC. Development of novel therapeutic approaches for the treatment of lung SCC is urgently needed. Through investigating the molecular features and survival outcomes of intrinsic subtypes, several novel targets for histologic diagnosis and therapy have been identified for lung adenocarcinoma and other cancers [2,3,4,5,6,7]. A clinically important challenge for SCC is to discover the novel survival subtypes and their molecular drivers through an integrative framework
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