Clinical Genetics Branch
Working towards understanding the causes of cancer to end the burden of cancer in families and populations.
Clinical Genetics Branch (CGB) investigators study individuals at and populations at high genetic risk of cancer in order to improve our understanding of cancer etiology and to advance clinical care. Our multidisciplinary approach combines clinical, genetic, genomic, epidemiologic, behavioral, statistical, and laboratory scientific research modalities. Read about some of our contributions to improving public health for these high-risk individuals.
The CGB research mission is to conduct clinical, genomic, and epidemiologic cancer research and translate that knowledge to improve prevention, screening, and management of cancer in families and populations at high risk. Learn about specific CGB research areas.
Clinical Epidemiology Unit
The Clinical Epidemiology Unit (CEU) within CGB conducts etiologic research with potential clinical and public health applications, and leads studies evaluating population-based early detection and cancer prevention strategies.
CGB fellows work with researchers engaged in conducting clinical, genetic, and epidemiologic studies focused on high risk families, individuals, and populations. They pursue astute clinical observations that might provide new clues to cancer etiology, and apply and develop epidemiologic methods to the study of high risk individuals. Meet the current CGB fellows and learn about research training opportunities in CGB.
Alter BP, Giri N, Savage SA, et al. Cancer in the National Cancer Institute inherited bone marrow failure syndrome cohort after fifteen years of follow-up. Haematologica. 2018
Gadalla SM, et al. Association of donor IFNL4 genotype and non-relapse mortality after unrelated donor myeloablative haematopoietic stem-cell transplantation for acute leukaemia: a retrospective cohort study. Lancet Haematol. 2020
Mirabello L, et al. Frequency of Pathogenic Germline Variants in Cancer-Susceptibility Genes in Patients With Osteosarcoma. JAMA Oncol. 2020
Wentzensen N, et al. Accuracy and Efficiency of Deep-Learning-Based Automation of Dual Stain Cytology in Cervical Cancer Screening. J Natl Cancer Inst. 2020
Cheung LC, et al. Life-Gained-Based Versus Risk-Based Selection of Smokers for Lung CancerScreening. Ann Intern Med. 2019