Modeling and simulations relating overall survival to tumor growth inhibition in renal cell carcinoma patients

• Published in: Cancer chemotherapy and pharmacology Vol. 76; no. 3; pp. 567 - 573
• Main Authors: Claret, Laurent, Mercier, Francois, Houk, Brett E., Milligan, Peter A., Bruno, Rene
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2015; Springer Nature B.V
• Subjects: Cancer Research; Carcinoma, Renal Cell - drug therapy; Carcinoma, Renal Cell - mortality; Carcinoma, Renal Cell - pathology; Cell Growth Processes - drug effects; Clinical Trials, Phase II as Topic; Clinical Trials, Phase III as Topic; Computer Simulation; Humans; Kidney Neoplasms - drug therapy; Kidney Neoplasms - mortality; Kidney Neoplasms - pathology; Medicine; Medicine & Public Health; Models, Statistical; Oncology; Original Article; Pharmacology/Toxicology; Randomized Controlled Trials as Topic; Regression Analysis; Survival Analysis; Overall survival; Renal cell carcinoma; Tumor growth inhibition; Predictive model
• ISSN: 0344-5704; 1432-0843
• DOI: 10.1007/s00280-015-2820-x

Purpose To assess the link between tumor growth inhibition (TGI) and overall survival (OS) based on historical renal cell carcinoma (RCC) data. To illustrate how simulations can help to identify TGI thresholds based on target OS benefit [i.e., hazard ratio (HR) compared with standard of care] to support new drug development in RCC. Methods Tumor size (TS) data were modeled from 2552 patients with first-line or refractory RCC who received temsirolimus, interferon, sunitinib, sorafenib or axitinib in 10 Phase II or Phase III studies. Three model-based TGI metrics estimates [early tumor shrinkage (ETS) at week 8, 10 or 12, time to tumor growth (TTG) and growth rate] as well as baseline prognostic factors were tested in multivariate lognormal models of OS. Model performance was evaluated by posterior predictive check of the OS distributions and hazard ratio across treatments. Results TTG was the best TGI metric to predict OS. However, week 8 ETS had a satisfactory performance and was employed in order to maximize clinical utilization. The week 8 ETS to OS model was then used to simulate clinically relevant ETS thresholds for future Phase II studies with investigational treatments. Conclusions The published OS model and resultant simulations can be leveraged to support Phase II design and predict expected OS and HR (based on early observed TGI data obtained in Phase II or Phase III studies), thereby informing important mRCC development decisions, e.g., Go/No Go and dose regimen selection.