Assistant Professor, Department of Pathology
Dr. Jun Wang is currently an assistant professor at Department of Pathology and the Laura and Isaac Perlmutter Cancer Center of NYU Langone health who has been working on cancer immunology and immunotherapy for over fifteen years. He finished his postdoctoral research in Dr. Lieping Chen’s laboratory at Johns Hopkins Medicine and the Yale Department of Immunobiology. His research interest lies in discovering and understanding novel receptor-ligand pathways with immune-modulatory functions and how to best utilize them as potential targets for cancer immunotherapy. During these years, Dr. Wang developed several genome-scale proprietary screening platforms leading to valuable industrial partnerships, some of which formed the basis of NextCure, Inc. He has discovered several first-in-class targets, such as FGL1/LAG-3 and Siglec-15, which represent novel tumor immune evasion mechanisms and new approaches for cancer immunotherapy. The first-in-human cancer clinical trials targeting the Siglec-15 pathway are currently ongoing at Yale and NYU, in addition to other sites. Moreover, Dr. Wang also examined the mechanisms of anti-tumor efficacy versus liver toxicity of agonistic anti-CD137 (4-1BB) therapy and provided new insights for alleviating liver pathology without disruption of anti-tumor immunity.Moving forward, Dr. Wang is planning to use innovative technologies to identify key mechanisms within the tumor microenvironment in the control of anti-tumor immunity and to design useful next-generation cancer immunotherapies with rational biomarkers.
PhD from Chinese Academy of Sciences
Yale University, Cancer Immunology and Immunotherapy
Cell research. 2021 Oct; 31(10):1047-1060
Journal of thrombosis & haemostasis : JTH. 2021 Sep 19;
Cancer discovery. 2021 Jul; 11(7):1700-1715
Immunity. 2021 Jun 08; 54(6):1304-1319.e9
Tropical medicine & international health. 2021 03; 26(3):290-300
Clinical cancer research. 2021 Feb 01; 27(3):680-688
Proceedings of the National Academy of Sciences of the United States of America (PNAS). 2021 01 26; 118(4):
Nature machine intelligence. 2021 Jan 01; ?-?