J Cancer 2019; 10(21):5082-5089. doi:10.7150/jca.30478 This issue Cite
1. Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
2. Department of Radiation Oncology, Tianjin Medical University, Tianjin, China
3. Department of Radiation Oncology, Shandong Cancer Hospital and Institute Affiliated to Shandong University, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China
4. Department of Dean's Office, Shandong Cancer Hospital and Institute Affiliated to Shandong University, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China
5. Department of Radiation Oncology, School of Medicine, Shandong University, Jinan, China
Background: Bevacizumab combined with chemotherapy is still one of the standard options for treatment of advanced non-small cell lung cancer (NSCLC) patients without driver mutations. Serum inflammatory factors, representing the systemic immune status, are shown to have complicated relationships with tumor angiogenesis, and proved to be associated with survival of advanced NSCLC patients. However, the information from the baseline factors is relatively limited, which cannot reflect the dynamic changes of systemic immune status during bevacizumab treatment. We, thus, attempted to evaluate longitudinal kinetics of systemic inflammatory factors during treatment of bevacizumab and to explore their predictive role in treatment response and patient outcomes in advanced NSCLC.
Method: Systemic inflammatory factors (neutrophil/lymphocyte (NLR), platelet/lymphocyte (PLR), neutrophil×platelet/lymphocyte (SII) and lymphocyte/monocyte (LMR)) and clinical variables were collected and analyzed from 161 advanced NSCLC patients treated with bevacizumab. Mixed effect regression models were first performed for longitudinal analysis of the changes of serum inflammatory factors during bevacizumab treatment. Then, univariate and multivariate Cox models were used for overall survival (OS) and progression free survival (PFS) analyses to determine the independent prognostic factors.
Results: In the first 6 cycles of bevacizumab treatment, patients with complete response/partial response (CR/PR) had a -0.11, -0.066, -0.15, and 0.073 change every 2 cycles in transformed NLR (95%CI: -0.19--0.03, p=0.008), PLR (95%CI: -0.12--0.013, p=0.015), SII (95%CI: -0.23--0.05, p<0.001) and LMR (95%CI: 0.049-0.14, p=0.036), respectively, compared to patients with progressive disease (PD). With respect to analysis of the longitudinal changes before progression, patients experienced a significant increase in transformed NLR (Coef=0.09, 95%CI: 0.019-0.17, p=0.014), PLR (Coef=0.05, 95%CI: 0.002-0.10, p=0.04), and SII (Coef=0.091, 95%CI: 0.015-0.17, p=0.019), but a decrease in transformed LMR (Coef=-0.08, 95%CI: -0.14-0.018, p=0.012). On multivariate Cox model analyses, decrease of LMR (HR=0.62, 95% CI: 0.4-0.96, p=0.033) was shown to be the independent risk factor for PFS; and low level of baseline LMR (HR=0.4, 95% CI: 0.17-0.94, p=0.036), increase of NLR (HR=2.36, 95%CI: 1.25-4.44, p=0.008), and decrease of LMR (HR=0.42, 95%CI: 0.18-0.97, p=0.041) were the independent risk factors for death.
Conclusion: The activation of systemic immune status evaluated by the kinetic changes of serum inflammatory factors was associated with good response to bevacizumab; however, the suppressive status may indicate the resistance to bevacizumab. Dynamic changes of systemic inflammatory factors also had prognostic value in predicting outcomes of advanced NSCLC patients treated with bevacizumab.
Keywords: systemic immune status, mixed effect model, longitudinal changes, prognostic factors, survival analysis