Fourth-Line Therapy in Metastatic Renal Cell Carcinoma (mRCC): Results from the International mRCC Database Consortium (IMDC)

Background: Fourth-line therapy (4LT) in the treatment of metastatic renal cell carcinoma (mRCC) varies significantly due to the lack of data and recommendations to guide treatment decisions. Objective: To evaluate the use and efficacy of 4LT in mRCC patients. Methods: The International mRCC Database Consortium (IMDC) dataset was used to identify patients with mRCC treated with 4LT. This is a multicenter, retrospective cohort study. Overall survival (OS) and progression-free survival (PFS) were calculated using Kaplan-Meier curves. Patients were evaluated for overall response. The six prognostic variables included in the IMDC prognostic model were used to stratify patients into favorable-, intermediate- and poor-risk groups. Exploratory analyses were performed examining the elderly (>70 years old) and non-clear cell RCC subgroups. Proportional hazards regression modelling was performed adjusting these covariates by IMDC criteria measured at initiation of 4th line therapy. Results: 7498 patients were treated with first line targeted therapy and out of these 594 (7.9%) received 4LT. Everolimus was the most frequently used 4LT (16.8%). Sorafenib, axitinib, pazopanib, sunitinib and clinical trial drugs were also used in >10% of patients. The OS of patients on any 4LT was 12.8 months, with a PFS of 4.4 months. The overall response rate (ORR) was 13.7%. Favorable-risk patients using IMDC criteria (5%) displayed an OS of 23.1 months, intermediate-risk patients (66%) had an OS of 13.8 months and poor-risk patients (29%) had an OS of 7.8 (p < 0.0001) months. Age >70 years and non-clear cell histology did not impact OS. Our study is limited by its retrospective design. Conclusions: 4LT use appears to have activity in mRCC patients. The IMDC continues to be of prognostic value in the fourth-line setting for OS. This study helps to set a benchmark for response rate and survival for which clinical trials can plan sample size calculations and aim to improve upon.