Title: Soluble immune checkpoint-related proteins as predictors of tumor recurrence, survival and T cell phenotypes in clear cell renal cell carcinoma patients

Abstract

Immune checkpoint inhibitors have achieved unprecedented success in cancer immunotherapy. With the exception of a few candidate biomarkers, the prognostic role of soluble immune checkpoint-related proteins in clear cell renal cell cancer (ccRCC) patients is largely uninvestigated. We profiled the circulating levels of 14 immune checkpoint-related proteins panel (BTLA, GITR, HVEM, IDO, LAG-3, PD-1, PD-L1, PD-L2, Tim-3, CD28, CD80, CD137, CD27 and CTLA-4) and their associations with the risk of recurrence and death in 182 ccRCC patients using a multiplex Luminex assay. Gene expression in tumors from a subset of participating patients (n = 47) and another 533 primary ccRCC from TCGA were analyzed to elucidate potential mechanisms. Our primary endpoint is overall survival; secondary endpoint is recurrence-free survival. Multivariate Cox proportional hazard model, unconditional logistic regression model, and Kaplan-Meier analysis were applied in the study.sTIM3 and sLAG3 were significantly associated with advanced (stage III) disease (P < 0.05). sPD-L2 was the strongest predictor of recurrence (HR 2.51, 95%CI 1.46-4.34, P = 9.33E-04), whereas high sBTLA and sTIM3 was associated with decreased survival (HR 6.02, 95%CI 2.0-18.1, P = 1.39E-03 and HR 3.12, 95%CI 1.44-6.75, P = 3.94E-03, respectively). Risk scores based on sTIM3 and sBTLA indicated that the soluble immune checkpoint-related proteins jointly predicted recurrence and death risks of ccRCC (P = 0.01 and 4.44E-04, respectively). Moreover, sLAG3 and sCD28 were found negatively correlated with cytolytic activity of T cells in tumors (rho = -0.31 and - 0.33, respectively).Our study provides evidence that soluble immune checkpoint-related proteins may associate with advanced disease, recurrence and survival in ccRCC patients, which highlights the prognostic values of soluble immune checkpoint-related proteins. Future independent validation in prospective studies is warranted.

Biography

Xifeng Wu, M.D., Ph.D., is Dean and Professor of School of Public Health; Vice President of The Second Affiliated Hospital of School of Medicine; Director of National Institute for Data Science in Health and Medicine; Director of Center for Big Data Research in Medical Insurance & Health Policy at Zhejiang University; China National Top Expert; and Zhejiang Province Top Expert. Dr. Wu’s research focus has been on human genetics and health big data.