The objective of this study is to build NSCLC gene mutation profile in China and find related correlation between gene mutation panel and clinical outcome.
Approximately 600 surgical tissue samples will be collected during operation in Tianjin Cancer hospital from 2009-2012, including lung squamous cell carcinoma and adenocarcinoma. The target area of 295 genes, including lung cancer drive genes, important signal pathway genes, drug resistance genes will be detected by New generation Sequencing (NGS) deep (average 1000X). This genes was selected from Mutations can guide treatment or as prognosis factors in NCCN/FDA/CFDA guideline, related mutations in phase II/III studies and NCCN/FDA/CFDA approved in other type tumors and related mutations in phase I or pre-clinical studies and can not guide treatment or as prognosis factors.
All mutations detected in 600 samples are summarized for statistics, calculating the mutation proportion in overall population. Clustering analysis is performed according to the main drive genes related biological pathways, correlation between gene mutation data and categorical clinical variables is performed by Fisher's Exact Test. The Log-rank test will be used to explore the relationship between the clinical outcomes (DFS and OS, respectively) and gene mutations (present or absent) or each of the clinical features (gender, age, smoking status, TNM staging, histology, tumor location, recurrence, number of lymph node metastasis, tumor size, postoperative adjuvant treatment, DFS and OS). Then a Cox Proportional Hazards model will be constructed to evaluate the effect of multiple variables (genomic and clinical features) on DFS, and OS, respectively. Benjamini-Hochberg false discovery rate (FDR) method is used to adjust the p-value and calculate the statistically differences. All p values were two-sided, and P<0.05 was assumed to be significant.