William Y. Kim

William Y. Kim, MD, is a UNC Lineberger Comprehensive Cancer Center member and Distinguished Professor of Medicine and Genetics in the Department of Medicine and Division of Hematology and Oncology at UNC-Chapel Hill. Kim is involved with Cancer Genetics, Clinical Research, Urologic Oncology Program and the Cancer Genetics Program. Kim Lab studies the genetics of bladder and kidney cancers.

M.D.

Distinguished Professor of Medicine, Professor of Genetics
UNC-Chapel Hill
Cancer Genetics
Clinical Research
Urologic Oncology Program
Cancer Genetics Program

Area of interest

The Kim Lab is focused on understanding the genetic and epigenetic events involved in the initiation and progression of renal cell carcinoma (RCC) and bladder cancer. Through NextGeneration sequencing of primary human tumors and the use of genetically engineered mouse models (GEMMs), as well as in vitro systems, our goal is to identify the critical genomic and epigenetic changes that are drivers of RCC and bladder cancer. Our ultimate goal is to elucidate the functional consequences of these genomic events in order to pinpoint novel, high impact therapeutic targets for therapy.

Awards and Honors

  • 2015 Rush S. Dickson Distinguished Professor
  • 2014 Bladder Cancer Advocacy Network (BCAN) Innovation Award
  • 2014 ASCI, elected member
  • 2012 AACR, Kure It, Grant for Kidney Cancer Research
  • 2010 University Cancer Research Fund, Innovation Award
  • 2010 DOD Prostate Cancer, Physician Research Training Award
  • 2009 Damon Runyon, Clinical Investigator Award
  • 2008 Joan’s Legacy Lung Cancer Foundation Award
  • 2008 DOD Prostate Cancer New Investigator Award
  • 2003 Harvard Medical School Scholars in Medicine Award
  • 2002 ASCO Young Investigator Award
  • 1992 White Prize, for study in Economics, Wesleyan University
  • 1991 Howard Hughes Research Fellow, Wesleyan University

Reach NC profile

Find publications on PubMed

UNC Lineberger's William Kim, MD, and colleagues report that cognitive computing can scour large volumes of scientific data to identify potentially relevant cancer clinical trials or therapeutic options.