Abstract 3158: Prediction of overall survival in urothelial cancer patients using tumor sizes and baseline risk factors: longitudinal modeling approach for durvalumab and durvalumab + tremelimumab

Abstract

Abstract Background: Durvalumab [D] is a human mAb that binds to PD-L1 and blocks its interaction with PD-1 and CD80. Tremelimumab [T] blocks the inhibitory effects of CTLA-4, and therefore enhances T-cell activation. The objectives of this analysis were to develop a model linking overall survival (OS) to baseline risk factors and changes in tumor size during treatment to identify key factors impacting response to D or D +T. Methods: The analysis dataset included UC patients from two clinical trials: Study 1108 (D 10 mg/kg Q2W; n=201) and Study 10 (D 20 mg/kg Q4W + T 1 mg/kg Q4W for 4 doses, followed by D 20 mg/kg Q4W alone; n=168). Longitudinal tumor size data were analyzed using a nonlinear mixed effect model with key parameters describing tumor growth, tumor killing, and delay in immune response. Subsequently, a parametric survival model was developed to link baseline risk factors and predicted percent change in tumor size at week 8 to OS. Results: Tumor kinetic model adequately described the longitudinal tumor size data from UC patients. Baseline tumor size (p<0.01) and PD-L1 status (p<0.01) were identified as significant covariates for tumor killing rate. The most influential factor associated with faster tumor growth was liver metastasis (p<0.01), while higher hemoglobin levels (p<0.01) were associated with decreased tumor growth rate. Based on parametric survival modeling, liver metastasis (~34% decrease in OS, p<0.0001), albumin (~ 1-fold increase in OS per 1g/dL increase, p<0.0001), and percent change in tumor size at week 8 (~52% increase in OS with 30% tumor shrinkage at week 8, p<0.0001) were found to be significant and clinically relevant predictors of OS. Conclusions: The parametric survival model coupled with tumor kinetic model adequately described clinical outcomes in UC patients treated with D or D+T and enabled identification of key factors potentially impacting response to immune therapy in UC. This approach can be a useful tool for guiding patient selection/enrichment strategies and optimizing trial designs for immuno-oncology (IO) therapies. Further validation and prospective evaluation of this model may be conducted in other IO trials. Citation Format: Mei Tang, Yu Jiang, Han Si, Yanan Zheng, Chen Gao, Guozhi Gao, Natasha Angra, Shaad Abdullah, Brandon Higgs, Lorin Roskos, Rajesh Narwal. Prediction of overall survival in urothelial cancer patients using tumor sizes and baseline risk factors: longitudinal modeling approach for durvalumab and durvalumab + tremelimumab [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3158.