Clinical factors affecting progression-free survival with crizotinib in ALK-positive non-small cell lung cancer
- Affiliations
-
- 1Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea. bhumsuk@snu.ac.kr
- 2Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea.
- 3Department of Pathology, Seoul National University Hospital, Seoul, Korea.
Abstract
- BACKGROUND/AIMS
Although crizotinib is standard chemotherapy for advanced anaplastic lymphoma kinase (ALK)-positive non-small cell lung cancer (NSCLC), clinical factors affecting progression-free survival (PFS) have not been reported. The purpose of this study was to identify clinical factors affecting PFS of crizotinib and develop a prognostic model for advanced ALK-positive NSCLC.
METHODS
Clinicopathologic features of patients enrolled in PROFILE 1001, 1005, 1007, and 1014 (training cohort) were reviewed. We conducted multivariate Cox analysis for PFS and overall survival (OS) in the training cohort (n = 159) and generated a proportional hazards model based on significant clinicopathologic factors, and then validated the model in an independent validation cohort (n = 40).
RESULTS
In the training cohort, the objective response rate was 81.5%. Median PFS and OS from the start of crizotinib were 12.4 and 31.3 months, respectively. Multivariate Cox analysis showed poor performance status, number of metastatic organs (≥ 3), and no response to crizotinib independently associated shorter PFS. Based on a score derived from these three factors, median PFS and OS of patients with one or two factors were significantly shorter compared to those without these factors (median PFS, 22.4 months vs. 10.5 months vs. 6.5 months; median OS, not reached vs. 29.1 months vs. 11.8 months, respectively; p < 0.001 for each group). This model also had validated in an independent validation cohort.
CONCLUSIONS
Performance status, number of metastatic organs, and response to crizotinib affected PFS of crizotinib in ALK-positive NSCLC. Based on these factors, we developed a simple and useful prediction model for PFS.