Validated five-factor prognostic model for survival of patients (pts) with metastatic urothelial carcinoma (mUC) receiving different post-platinum PD-L1 inhibitors.

476 Background: A prognostic model for overall survival (OS) of mUC was previously reported in the setting of post-platinum atezolizumab (Pond GR, GU ASCO 2018). This model was limited by employing only atezolizumab treated pts, small size of the validation dataset and unclear applicability to other PD-1/L1 inhibitors. Hence, we constructed a robust prognostic model utilizing the combined atezolizumab cohort as the discovery dataset and used a validation dataset comprised of post-platinum avelumab-treated pts. Methods: The discovery dataset consisted of pt level data from 2 phase I/II trials (IMvigor210 and PCD4989g) evaluating atezolizumab (n = 405). Pts enrolled on a phase I/II trial that received post-platinum avelumab (n = 242) comprised the validation dataset (EMR 100070-001). Cox regression analyses evaluated the association of candidate prognostic factors with OS. Factors were dichotomized and laboratory values were normalized by logarithmic transformation. Stepwise selection was employed to propose an optimal model using the discovery dataset. Discrimination (via c-statistic) and calibration were assessed in the avelumab dataset following the validation procedure by Royston and Altman (2013). Results: The 5 factors included in the optimal prognostic model in the discovery dataset were ECOG-PS (1 vs. 0; HR 1.80; 95% CI [1.36-2.36]), presence/absence of liver metastasis (HR 1.55; 95% CI [1.20-2.00]), number of platelets (HR 2.22; 95% CI [1.54-3.18]), neutrophil-lymphocyte ratio (NLR; HR 1.94; 95% CI [1.57-2.40]) and lactate dehydrogenase (LDH; HR 1.60; 95% CI [1.28-1.99]). The c-statistic for prediction of survival was 0.692 and 0.671 in the discovery and validation datasets, respectively. Acceptable or good calibration of expected 1-year survival was observed. Conclusions: A 5-factor externally validated prognostic model for OS is proposed employing a large dataset of 647 pts overall in the setting of post-platinum PD-L1 inhibitors for mUC. This model may assist in prognostic stratification and interpreting nonrandomized trials of post-platinum PD1/L1 inhibitors.