Prediction of overall survival in patients across solid tumors following atezolizumab treatments: A tumor growth inhibition–overall survival modeling framework

Abstract

The objectives of the study were to utilize 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 bi-exponential models and post-treatment 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 simulating 1000 times OS distributions and hazard ratio (HR) of the atezolizumab-containing arms vs. respective controls.Tumor growth rate estimate (KG) 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 indicate good model performance across the 10 studies, with observed HRs within the 95% prediction intervals for all study arms vs. control.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.

Extension of the Alternative Intravenous Dosing Regimens of Atezolizumab into Combination Settings through Modeling and Simulation

“…The exposure metric derivation method was described previously (see Cross‐Study PK Comparisons). Multivariate TGI‐PFS and TGI‐OS models were developed to mitigate confounding effects of prognostic factors on efficacy outcomes, as described previously 8,13,14 . Individual tumor growth rate constants (KGs) were estimated by post hoc empirical Bayesian estimation from a biexponential (i.e., tumor shrinkage rate and tumor growth rate) TGI model.…”

“…Individual estimates of tumor growth rate (i.e., logKG) were tested against exposure metrics with linear regression. The TGI model parameters were previously published 14 . Briefly, the impact of individual baseline prognostic factors and TGI metrics on PFS or OS were explored using Kaplan–Meier and Cox regression analyses.…”

“…Multivariate TGI-PFS and TGI-OS models were developed to mitigate confounding effects of prognostic factors on efficacy outcomes, as described previously. 8,13,14 Individual tumor growth rate constants (KGs) were estimated by post hoc empirical Bayesian estimation from a biexponential (i.e., tumor shrinkage rate and tumor growth rate) TGI model. Individual estimates of tumor growth rate (i.e., logKG) were tested against exposure metrics with linear regression.…”

“…The TGI model parameters were previously published. 14 Briefly, the impact of individual baseline prognostic factors and TGI metrics on PFS or OS were explored using Kaplan-Meier and Cox regression analyses. Parametric multivariate regression survival models were built using a backward elimination of the full models with a significance level of P < .01.…”

Update to improve reproducibility and interpretability: A response to “Machine Learning for Tumor Growth Inhibition”

“…As alluded to in our publication, a biexponential TGI model 4 was used to fit tumor size data using a nonlinear mixed effect modeling approach. 5 , 6 An example model file as implemented in NONMEM is available as supporting information in a separate publication on applying the TGI‐OS framework to several tumor types 7 and could be helpful for deriving TGI metrics from longitudinal tumor size data in general.…”