869P - Modeling of Tumor Kinetics and Overall Survival to Identify Predictive Factors for Efficacy of Durvalumab in Patients with Urothelial Carcinoma (UC)

Abstract

Background: Durvalumab is a human mAb that binds to PD-L1 and blocks its interaction with PD-1 and CD80. The objectives of this analysis were to describe the longitudinal tumor size profiles, identify factors predicting tumor growth and regression, and associate tumor kinetics with overall survival (OS). Methods: Longitudinal tumor size and OS data obtained from UC patients (Study 1108; NCT# CD-ON-MEDI4736-1108) who received durvalumab were analyzed using a nonlinear mixed effect model that describe tumor growth, tumor killing, and delay in immune response leading to tumor killing. An OS model was developed by linking model-predicted tumor size over time to survival hazard in a constant hazard model. Potential predictive factors of tumor growth and regression, as well as survival were evaluated in a multivariate covariate analysis in the tumor kinetic and OS model, respectively. Results: Tumor kinetic and OS models adequately described the longitudinal tumor size and survival data from UC patients. The most influential factor associated with more rapid tumor growth was high baseline neutrophil-to-lymphocyte ratio (NLR), while lymph node disease was associated with decreased growth rate. Tumor (TC) or immune cell PD-L1 expression (IC), baseline tumor size and liver metastasis were identified as predictive factors for tumor killing. Simulations showed increased response rates with higher TC and/or IC (by 6/9%, and 18/24%, with 25% and 50% cutoff for TC/IC, respectively). After accounting for tumor response, the risk of death decreased with higher TC/IC and lower baseline hemoglobin and albumin levels, while liver metastasis, lymph node disease, and prior carboplatin treatment were associated with higher risk. Conclusions: Tumor kinetic modeling identified factors that predict tumor growth and shrinkage following durvalumab therapy in UC patients, and permits investigation of predictive biomarker strategies considering confounding factors. Joint modeling that associates predicted tumor kinetics with OS allows model-based extrapolation of missing data and evaluation of other factors influencing OS after accounting for change in tumor size over time. Clinical trial identification: NCT01693562 (September 14, 2012) Legal entity responsible for the study: MedImmune Funding: MedImmune Disclosure: Y. Zheng: Corporate sponsored research: MedImmune, Shareholder: Roche, MedImmune, Employee: MedImmune. X. Jin: Employee MedImmune and shareholder AstraZeneca. R. Narwal: Employee and shareholder MedImmune. C-Y. D Jin: Employee and stock holder of AstraZeneca. A. Gupta: Employment MedImmune, shareholder AstraZeneca + Bristol-Myers Squibb. Y. Ben, P. Mukhopadhyay: Employee and shareholder AstraZeneca. B. Higgs, L. Roskos: Employment MedImmune, shareholder AstraZeneca.