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HannahCarter-WebRes_bw

Hannah Carter

Appointment

CIFAR Azrieli Global Scholar 2017-2019

Genetic Networks

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Google Scholar

About

Hannah Carter uses computation to study the role of genetic variation in cancer, for applications in precision medicine.

Her research is divided between efforts to understand how inherited variation shapes individual cancer risk, and how tumour-associated DNA mutations impact intracellular biological processes and cellular behaviours. Carter has demonstrated that acquired mutations in tumour genomes frequently affect specific protein-protein interactions. She has also uncovered new ways in which the inherited genome influences the probability that a particular cancer gene will acquire a mutation during tumour development. One of Carter’s current projects uses statistical modelling to investigate the extent to which the inherited immune system contributes to differences in cancer predisposition and outcome. A second project uses graphical modelling techniques to study the combined consequences of cancer mutations.

Awards

  • Johns Hopkins University Alumni Outstanding Recent Graduate Award, 2015
  • NIH Director’s Early Independence Award, 2013
  • Siebel Scholar, 2012
  • NDSEG Research Fellowship, 2008
  • Samuel T. Fife Outstanding Graduate Award, 2005

Relevant Publications

  • Marty, R. et al. “MHC-I genotype restricts the oncogenic mutational landscape.” Cell 171, no. 6 (November 2017): 1272–1283.

  • Carter, H. et al. “Interaction Landscape of Inherited Polymorphisms with Somatic Events in Cancer.” Cancer Discov. 7, no. 4 (April 2017): 410–23.

  • Engin, B., J. Kreisberg, and H. Carter. “Structure-based Analysis Reveals Cancer Missense Mutations Target Protein Interaction Interfaces.” PLoS One 4, no. 11 (April 2016): e0152929.

  • Engin, B., M. Hofree, and H. Carter. “Identifying Mutation-Specific Cancer Pathways Using a Structurally Resolved Protein Interaction Network.” Pac Symp Biocomput. 20 (2015): 84–95.

  • Carter, H. et al. “Cancer-specific high-throughput annotation of somatic mutations: computational prediction of driver missense mutations.” Cancer Res 69, no. 6660 (August 2009).

Institution

University of California San Diego

Department

Department of Medicine, Division of Medical Genetics

Education

  • PhD (Biomedical Engineering), Johns Hopkins University
  • MEng (Electrical and Computer Engineering), University of Louisville
  • BS (Electrical and Computer Engineering), University of Louisville

Country

United States

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