Abstract
The large heterogeneity in response to immune checkpoint inhibitors is driving the exploration of predictive biomarkers to identify patients who will respond to such treatment. We extended our previously suggested modeling framework of atezolizumab pharmacokinetics, IL18, and tumor size (TS) dynamics, to also include overall survival (OS). Baseline and model-derived variables were explored as predictors of OS in 88 patients with non-small cell lung cancer treated with atezolizumab. To investigate the impact of follow-up length on the inclusion of predictors of OS, four different censoring strategies were applied. The time-course of TS change was the most significant predictor in all scenarios, whereas IL18 was not significant. Identified predictors of OS were similar regardless of censoring strategy, although OS was underpredicted when patients were censored 5 months after last dose. The study demonstrated that the tumor-time course-OS relationship could be identified based on early phase I data.