Classification And Regression Tree Analysis Of Non-Small-Cell Lung Cancer Patients With EGFR Mutation Treated With Gefitinib

Background:Many randomized studies have shown that epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) are advantageous over standard chemotherapy in non-small-cell lung cancer (NSCLC) patients with EGFR mutation not only in front-line treatment, but further management. But which subgroup of advanced NSCLC with EGFR mutation could benefit more from EGFR-TKIs needs to be continued explorered. We explore predictive factors of these population treated by gefitinib by classification and regression tree (CART) analysis.Methods:95cases of advanced NSCLC patients with EGFR active mutation treated with gefitinib between February,2010and October,2013at Cancer Institute (Hospital), Chinese Academy of Medical Sciences. Multivariate analysis of progression-free survival (PFS) was performed using recursive partitioning referred to as classification and regression tree (CART) analysis, which uses recursive partitioning to assess the effect of specific variables on PFS, thereby ultimately generating groups of patients with similar clinical features on PFS.Results:The median PFS in NSCLC patients with EGFR active mutation treated with gefitinib was13.3months (95%CI9.4-17.2). CART was performed with an initial split on body mass index (BMI), the followed is Exon19mutation, and three terminal subgroups were formed. The median PFS of the three subsets ranged from8.2months to15.2months. The subgroup which BMI less than or equal to20.768Kg/m2had the longest PFS (15.2months). The PFS of the EGFR exon19mutation group was better than other sties mutation (10.3m vs.8.2m).Conclusions:BMI and exon19mutation are predictors of PFS in NSCLC patients with EGFR mutation treated with gefitinib. Not only EGFR active mutation, but also patient’s own factor could be the predictive factors of EGFR-TKIs.