Using the neutrophil-to-lymphocyte ratio to predict the outcome of individuals with nonsquamous non-small cell lung cancer receiving pembrolizumab plus platinum and pemetrexed

Background: Factors predicting the response to pembrolizumab plus platinum and pemetrexed combination therapy (Pemb-Plt-PEM) in nonsquamous non- small cell lung cancer (non-sq NSCLC) are unclear. We investigated the Glasgow Prognostic (GP) score, neutrophil-to-lymphocyte ratio (NLR), and body mass index (BMI) as predictors of response to initial treatment with combination therapy in individuals with advanced non-sq NSCLC. Methods: We retrospectively reviewed 236 patients who received initial treatment with combination therapy for non-sq NSCLC at 13 institutions between December 2018 and December 2020. The usefulness of the GP score, NLR, and BMI as prognostic indicators was assessed. Cox proportional hazard models and the Kaplan-Meier method were used to compare progression-free survival (PFS) and overall survival (OS). Results: The response rate was 51.2% (95% CI: 44.9-57.5%). The median PFS and OS after beginning Pemb-Plt-PEM were 8.8 (95% CI: 7.0-11.9) months and 23.6 (95% CI: 18.7-28.6) months, respectively. The NLR independently predicted the efficacy of Pemb-Plt-PEM-the PFS and OS were more prolonged in individuals with NLR <5 than in those with NLR =5 (PFS: 12.8 vs. 5.3 months, p = 0.0002; OS: 29.4 vs. 12.0 months, p < 0.0001). BMI predicted the treatment response-individuals with BMI =22.0 kg/m(2) had longer OS than did those with BMI < 22.0 kg/m(2) (OS: 28.4 vs. 18.4 months, p = 0.0086). Conclusions: The NLR significantly predicted PFS and OS, whereas BMI predicted OS, in individuals who initially received Pemb-Plt-PEM for non-sq NSCLC. These factors might be prognosis predictors in non-sq NSCLC.

Automated summary

We investigated the predictive factors for the response to Pemb-Plt-PEM combination therapy in non-sq NSCLC. Our results showed that NLR and BMI can serve as prognostic indicators, with NLR predicting PFS and OS, and BMI predicting OS.