Correlation Between Early Trends of a Prognostic Biomarker and Overall Survival in Non-Small-Cell Lung Cancer Clinical Trials.

Abstract

Overall survival (OS) is the primary end point in phase III oncology trials. Given low success rates, surrogate end points, such as progression-free survival or objective response rate, are used in early go/no-go decision making. Here, we investigate whether early trends of OS prognostic biomarkers, such as the ROPRO and DeepROPRO, can also be used for this purpose. Using real-world data, we emulated a series of 12 advanced non-small-cell lung cancer (aNSCLC) clinical trials, originally conducted by six different sponsors and evaluated four different mechanisms, in a total of 19,920 individuals. We evaluated early trends (until 6 months) of the OS biomarker alongside early OS within the joint model (JM) framework. Study-level estimates of early OS and ROPRO trends were correlated against the actual final OS hazard ratios (HRs). We observed a strong correlation between the JM estimates and final OS HR at 3 months (adjusted = 0.88) and at 6 months (adjusted = 0.85). In the leave-one-out analysis, there was a low overall prediction error of the OS HR at both 3 months (root-mean-square error [RMSE] = 0.11) and 6 months (RMSE = 0.12). In addition, at 3 months, the absolute prediction error of the OS HR was lower than 0.05 for three trials. We describe a pipeline to predict trial OS HRs using emulated aNSCLC studies and their early OS and OS biomarker trends. The method has the potential to accelerate and improve decision making in drug development.