Open AccessPrediction of overall survival in patients across solid tumors following atezolizumab treatments: A tumor growth inhibition–overall survival modeling framework
Author(s) -
Chan Phyllis,
Marchand Mathilde,
Yoshida Kenta,
Vadhavkar Shweta,
Wang Nina,
Lin Alyse,
Wu Benjamin,
Ballinger Marcus,
Sternheim Nitzan,
Jin Jin Y.,
Bruno René
Publication year - 2021
Publication title -
cpt: pharmacometrics and systems pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.53
H-Index - 37
ISSN - 2163-8306
DOI - 10.1002/psp4.12686
Subject(s) - oncology , atezolizumab , proportional hazards model , medicine , hazard ratio , multivariate analysis , univariate , multivariate statistics , survival analysis , covariate , cancer , confidence interval , statistics , immunotherapy , mathematics , nivolumab
The objectives of the study were to use tumor size data from 10 phase II/III atezolizumab studies across five solid tumor types to estimate tumor growth inhibition (TGI) metrics and assess the impact of TGI metrics and baseline prognostic factors on overall survival (OS) for each tumor type. TGI metrics were estimated from biexponential models and posttreatment longitudinal data of 6699 patients. TGI‐OS full models were built using parametric survival regression by including all significant baseline covariates from the Cox univariate analysis followed by a backward elimination step. The model performance was evaluated for each trial by 1000 simulations of the OS distributions and hazard ratios (HR) of the atezolizumab‐containing arms versus the respective controls. The tumor growth rate estimate was the most significant predictor of OS across all tumor types. Several baseline prognostic factors, such as inflammatory status (C‐reactive protein, albumin, and/or neutrophil‐to‐lymphocyte ratio), tumor burden (sum of longest diameters, number of metastatic sites, and/or presence of liver metastases), Eastern Cooperative Oncology Group performance status, and lactate dehydrogenase were also highly significant across multiple studies in the final multivariate models. TGI‐OS models adequately described the OS distribution. The model‐predicted HRs indicated good model performance across the 10 studies, with observed HRs within the 95% prediction intervals for all study arms versus controls. Multivariate TGI‐OS models developed for different solid tumor types were able to predict treatment effect with various atezolizumab monotherapy or combination regimens and could be used to support design and analysis of future studies.