Personal and clinical characteristics associated with immunotherapy effectiveness in stage IV non-small cell lung cancer.

ABSTRACT

Background: Immunotherapy response rates in metastatic non-small cell lung cancer (NSCLC) are low and survival varies significantly. Factors like age, sex, race, and histology may modulate immunotherapy response. Existing analyses are limited to clinical trials, with limited generalizability, and meta-analyses where adjustment for potential confounders cannot be performed. Here, we conduct a cohort study with patient-level analysis to explore how personal and clinical characteristics moderate chemoimmunotherapy effectiveness in metastatic NSCLC. Methods: Stage IV NSCLC patients diagnosed in 2015 were drawn from Surveillance Epidemiology, and End Results-Medicare linked data. Receipt of chemoimmunotherapy and overall survival (OS) were the primary predictor and outcome of interest respectively. Multivariable Cox-proportional hazards regression and propensity-score matching were performed to evaluate the effectiveness of immunotherapy addition to chemotherapy. Results: From a total of 1,471 patients, 349 (24%) received chemoimmunotherapy and 1,122 (76%) received chemotherapy alone. Survival was significantly better among those treated with chemoimmunotherapy compared to those receiving chemotherapy alone [adjusted hazard ratio (HRadj) =0.72, 95% confidence interval (CI) 0.63-0.83]. Males saw significantly better OS from chemoimmunotherapy (HRadj =0.62, 95% CI 0.51-0.75) than females (HRadj =0.81, 95% CI 0.65-1.01, Pinteraction=0.0557). After propensity-score matching, the effect of chemoimmunotherapy was borderline significant according to sex (Pinteraction =0.0414), but not age or histology. Conclusions: Males may benefit more from chemoimmunotherapy, but there is limited evidence suggesting age, histology, race, and comorbidities contribute to differences in effectiveness. Future research should elucidate who responds best to chemoimmunotherapy, and further analyses of characteristics like race can inform how to tailor different treatment regimens to distinct patient subpopulations.