Tumor growth inhibition‐overall survival modeling in non‐small cell lung cancer: A case study from GEMSTONE‐302

Abstract

Abstract Overall survival is vital for approving new anticancer drugs but is often impractical for early‐phase studies. The tumor growth inhibition‐overall survival (TGI‐OS) model could bridge the gap between early‐ and late‐stage development. This study aimed to identify an appropriate TGI‐OS model for patients with non‐small cell lung cancer from the GEMSTONE‐302 study of sugemalimab. We used three TGI models to delineate tumor trajectories and investigated three OS model for linking TGI metric to OS. All three TGI models accurately captured tumor profiles at the individual level. The published atezolizumab‐based TGI‐OS model predicted survival time satisfactorily through simulation‐based evaluation, whereas the other published model built from multi‐treatment underestimated OS. Our study‐specific TGI‐OS model identified time‐to‐growth as the most significant metric with the number of metastatic sites and neutrophil‐to‐lymphocyte ratio at baseline as covariates and exhibited robust OS predictability. Our findings demonstrated the effectiveness of the TGI‐OS models in predicting phase III outcomes, which underpins their value as a powerful tool for antitumor drug development.