Daniela S. Gerhard, Ph.D.
Daniela S. Gerhard is a human genetics and molecular cancer biologist. She is a co-author of ~100 publications. She was on faculty of Washington University School of Medicine (Department of Genetics) and a postdoctoral fellow at the Massachusetts Institute of Technology. Since 2004 she is the Director of the Office of Cancer Genomics, CCG, OD, NCI. Her current research interests include the identification of cancer-relevant somatic mutations in pediatric cancers and other rare cancer types; the influence of germline risk factors on cancer initiation and progression; and translating the molecular results rapidly into improved patient outcomes. She is an enthusiastic proponent of data sharing and access with sensitivity to patients’ right to privacy and confidentiality.
Dr. Gerhard is the program co-leader of the Therapeutically Applicable Research to Generate Effective Treatment initiative whose goals are to obtain high-quality, detailed molecular characterization of the genomes, transcriptomes and epigenomes of large numbers of tumors from pediatric cases (target.cancer.gov). She is the program director of Cancer Genomic Characterization Initiation [LINK BROKEN], whose goals are to characterize tumors from Burkitt Lymphoma and HIV+ patients. TARGET and CGCI are just two examples of large scale characterization projects that generate “tsunami” of data that need to be effectively analyzed before their impact on patient outcome can be realized. Dr. Gerhard is the program director of the Cancer Target Discovery and Development (CTD2) Network [LINK BROKEN] whose goals are to accelerate the “translation” of the molecular genotype to the identification of therapeutic targets, perturbagens (small molecules, sh/siRNAs, CRISPRs) and markers (diagnostic, prognostic, stratifying, etc.). Finally, to improve the predictability of results of “high-content” in vitro screening such as carried out in CTD2, Dr. Gerhard is the program director of a pilot program to develop the next generation of cancer models that better represent the biology and heterogeneity of cancer subtypes