Hannah Carter Laboratory at UC San Diego

Carter Lab Members | Current Projects | Publications | Postdoc Positions

 

Hannah Carter Lab

Dr. Hannah Carter’s research focuses on computationally modeling how DNA mutations in tumor genomes impact intracellular biological processes and cellular behaviors, and how these cellular level changes cause cancer.

Large-scale efforts to exhaustively catalog mutations in thousands of tumors using next generation DNA sequencing technologies have produced large lists of candidate cancer causing genes and mutations. Even in cases where mutations are known to cause cancer, the precise mechanisms by which distinct DNA alterations to these genes cause cancer is poorly understood. Furthermore, each tumor develops on the specific genetic background of a patient, such that the same mutation could have subtly different effects in different individuals.

To address these challenges, the Carter Lab uses tools from fields including machine learning, information theory, bioinformatics and biostatistics to model how cancer mutations rewire networks of biological molecules and change cellular behaviors. These models can provide insights into cancer biology that will help clinicians develop individualized cancer therapies, identify new therapeutic opportunities and suggest new biomarkers for early detection of tumors.

Laboratory Members

Postdoctoral fellow Billur Engin
Billur Engin
Postdoctoral Fellow

Seeking Postdoctoral Fellows

Dr. Carter is currently looking for post-docs with strong quantitative backgrounds in fields including bioinformatics, biostatistics, machine learning and algorithms, experience in programing languages and a passion for tackling challenging problems at the interface of computation and biology.

Current Projects

1) Modeling of the Consequences of Somatic Variants for Cellular Behaviors

Application of next-generation sequencing technologies has uncovered large numbers of somatic mutations in tumor genomes. However, only a small fraction of these alterations are expected to contribute to tumor initiation and progression; the rest are random events that provide no fitness advantage to tumor cells. Cancer-driving mutations often have diagnostic and prognostic value and in some cases may be viable therapeutic targets. We have previously developed methods to identify the subset of drivers among mutations detected in tumor genomes based on their predicted functional consequences at the protein level. We are now extending these models to capture how somatic mutations affect the network of protein-protein interactions that mediate higher order biological processes within the cell. These models will be used to investigate the biological mechanisms by which mutations cause normal cells to become malignant and suggest novel targets for therapeutic intervention.

2) Identification of Genetic Variation Underlying Cancer Predisposition

Decades of research have established clear evidence that genetic factors contribute to cancer predisposition, however the genetic factors uncovered to date through familial and genome-wide association studies only account for a small fraction of the expected genetic risk. Knowledge of the genetic risk factors for cancer is essential for early identification of individuals at risk for cancer that may benefit from regular screening or lifestyle changes. Identifying cancer risk variants has proven challenging for a variety of reasons. Unrelated individuals can differ from each other at over a million sites genome-wide resulting in a large number of candidate genetic variants to assess for disease association. In addition, the number of individuals with increased risk that go on to develop cancer may be small or may be influenced by epigenetic or environmental factors, further masking disease associations. We are developing new computational strategies to identify novel genetic determinants of cancer risk using tools from biostatistics, systems biology, machine learning and information theory.

3) Integration of Different ‘Omics Data Types to Study Tumor Cell Behaviors

As novel high-throughput molecular measurement technologies enable detailed characterization of the molecular composition of cells, new opportunities and challenges are emerging for using these data to model biological systems in health and disease. We are interested in constructing more accurate models of biological processes in normal and cancer cells by integrating information about gene regulation, gene expression and protein activity derived from multiple ‘omics measurements.

4) Personalized Medicine Based on Molecular Profiling of Tumors

Targeted and autologous immune therapies are emerging as effective strategies for treating cancer. Not all patients with targetable mutations respond to therapy, however and those that do respond rapidly develop resistance over time. We are building models to predict which patients are likely to respond to a given therapy and the mechanisms by which they are most likely to develop therapeutic resistance.

Publications

Selected publications and link to full publication list on PubMed.

Google Citations Page:
http://scholar.google.com/citations?user=nlWQnXMAAAAJ

  1. Carter H, Hofree M, and Ideker T. Genotype to phenotype via network analysis. Current opinion in genetics & development 23.6: 611-621 (2013).
  2. Hofree M, Shen JP, Carter H, Gross A, Ideker T Network-based stratification of tumor genomes Nat Methods. Sep 15. doi: 10.1038/nmeth.2651. (2013)
  3. Gonzalez-Perez A, Mustonen F, Reva B, Ritchie G, Creixell P, Karchin R, Vazquez M, Fink JL, Kassahn KS, Pearson JV, Bader G, Boutros P, Muthuswamy L, Ouellette BF, Reiman J, Linding R, Shibata T, Valencia A, Butler A, Dronov S, Flicek P, Shannon NB, Carter H, Ding L, Sander C, Stuart J, Stein LD, Lopez-Bigas N Identifying functional genetic variants in cancer genomes Nat Methods.10(8):723-9 (2013)
  4. Gartner JJ, Parker SCJ, Prickett TD, Dutton-Regester K, Lin JC, Simhadri VL, Jha S, Katagiri N, Gotea V, Teer JK, Wei X, Bhanot UK, NISC Comparative Sequencing Program, Willard MD, Chen G, Elnitski L, Davies MA, Gershenwald JE, Carter H, Karchin R, Barber TD, Robinson W, Robinson S, Rosenberg SA, Komar AA, Kimchi-Sarfaty C, Hayward NK, Margulies EH, Samuels Y Whole-genome sequencing identifies a recurrent functional synonymous mutation in melanoma PNAS. 110(33):13481-6 (2013)
  5. Carter H, Douville C, Yeo G, Stenson PD, Cooper DN, Karchin R Identifying Mendelian disease genes with the Variant Effect Scoring Tool BMC Genomics, 14 (Suppl 3):S3 (2013)
  6. Chen YC, Carter H, Parla J, Kramer M, Goes FS, Pirooznia M, Zandi PP, McCombie WR, Potash JB, Karchin R A hybrid likelihood model for sequence-based disease association studies PLoS Genetics Jan;9(1):e1003224 (2013)
  7. Liang H, Cheung LWT, Li J, Ju Z, Yu S, Stemke-Hale K, Dogruluk T, Lu Y, Liu X, Gu C, Scherer SE, Carter H, Westin SN, Verhaak R, Zhang F, Karchin R, Liu CG, Lu KH, Broaddus RR, Scott KL, Hennessy BT, Mills GB Whole-exome Sequencing Combined with Functional Genomics Reveals Novel Candidate Driver Cancer Genes in Endometrial Cancer Genome Research Nov;22(11):2120-9 (2012)
  8. X. Jiao, L. Wood, M. Lindman, P. Buckhaults, K. Polyak, S. Sukumar, H. Carter, D. Kim, R. Karchin, S. Jones, B. Vogelstein, V. Velculescu, K. Kinzler, T. Sjoblom Somatic mutations in the Notch, NF-KB, PIK3CA, and Hedgehog pathways in human breast cancers Genes, Chromosomes and Cancer May 2012;51:480-489 (2012)
  9. Wu J, Jiao Y, Dal Molin M, Maitra A, deWilde RF, Wood LD, Eshleman JR, Goggins MG, Wolfgang CL, Canto ML, Schulick RD, Edil BH, CHoti, MA, Adsay V, Kimstra DS, Offerhaus GJA, Klein AP, Kopelovic L, Carter H, Karchin R, Allen PJ, Schmidt CM, Matio Y, Diaz L, Kinzler KW, Papadopolus N, Hruban RH, Vogelstein B Whole exome sequencing of neoplastic cysts of the pancreas reveals recurrent mutations in components of ubiquitin-dependent pathways Proc Natl Acad Sci USA Dec 27;108(52):21188-93 (2011)
  10. Cancer Genome Atlas Research Network, Integrated genomic analyses of ovarian carcinoma Nature 474, 609, Jun 30 (2011).
  11. X. Zhang, M. Reis, R. Khoriaty, Y. Li, P. Ouillette, J. Samayoa, H. Carter, R. Karchin, M. Li, L. A. Diaz, Jr., V. E. Velculescu, N. Papadopoulos, K. W. Kinzler, B. Vogelstein, S. N. Malek, Sequence analysis of 515 kinase genes in chronic lymphocytic leukemia, Leukemia, Dec;25(12):1908-10 (2011)
  12. D. W. Parsons, M. Li, X. Zhang, S. Jones, R. J. Leary, J. C. Lin, S. M. Boca, H. Carter, J. Samayoa, C. Bettegowda, G. L. Gallia, G. I. Jallo, Z. A. Binder, Y. Nikolsky, J. Hartigan, D. R. Smith, D. S. Gerhard, D. W. Fults, S. VandenBerg, M. S. Berger, S. K. Marie, S. M. Shinjo, C. Clara, P. C. Phillips, J. E. Minturn, J. A. Biegel, A. R. Judkins, A. C. Resnick, P. B. Storm, T. Curran, Y. He, B. A. Rasheed, H. S. Friedman, S. T. Keir, R. McLendon, P. A. Northcott, M. D. Taylor, P. C. Burger, G. J. Riggins, R. Karchin, G. Parmigiani, D. D. Bigner, H. Yan, N. Papadopoulos, B. Vogelstein, K. W. Kinzler, V. E. Velculescu, The genetic landscape of the childhood cancer medulloblastoma, Science 331, 435, Jan 28 (2011)
  13. I. Bozic, T. Antal, H. Ohtsuki, H. Carter, D. Kim, S. Chen, R. Karchin, K. W. Kinzler, B. Vogelstein, M. A. Nowak, Accumulation of driver and passenger mutations during tumor progression, Proc Natl Acad Sci U S A 107, 18545, Oct 26 (2010)
  14. H. Carter, J. Samayoa, R. H. Hruban, R. Karchin, Prioritization of driver mutations in pancreatic cancer using cancer-specific high-throughput annotation of somatic mutations (CHASM), Cancer Biol Ther 10, 582, Sep (2010)
  15. H. Carter, S. Chen, L. Isik, S. Tyekucheva, V. E. Velculescu, K. W. Kinzler, B. Vogelstein, R. Karchin, Cancer-specific high-throughput annotation of somatic mutations: computational prediction of driver missense mutations, Cancer Res 69, 6660, Aug 15 (2009)
  16. S. Jones, X. Zhang, D. W. Parsons, J. C. Lin, R. J. Leary, P. Angenendt, P. Mankoo, H. Carter, H. Kamiyama, A. Jimeno, S. M. Hong, B. Fu, M. T. Lin, E. S. Calhoun, M. Kamiyama, K. Walter, T. Nikolskaya, Y. Nikolsky, J. Hartigan, D. R. Smith, M. Hidalgo, S. D. Leach, A. P. Klein, E. M. Jaffee, M. Goggins, A. Maitra, C. Iacobuzio-Donahue, J. R. Eshleman, S. E. Kern, R. H. Hruban, R. Karchin, N. Papadopoulos, G. Parmigiani, B. Vogelstein, V. E. Velculescu, K. W. Kinzler, Core signaling pathways in human pancreatic cancers revealed by global genomic analyses Science 321, 1801, Sep 26 (2008)
  17. D. W. Parsons, S. Jones, X. Zhang, J. C. Lin, R. J. Leary, P. Angenendt, P. Mankoo, H. Carter, I. M. Siu, G. L. Gallia, A. Olivi, R. McLendon, B. A. Rasheed, S. Keir, T. Nikolskaya, Y. Nikolsky, D. A. Busam, H. Tekleab, L. A. Diaz, Jr., J. Hartigan, D. R. Smith, R. L. Strausberg, S. K. Marie, S. M. Shinjo, H. Yan, G. J. Riggins, D. D. Bigner, R. Karchin, N. Papadopoulos, G. Parmigiani, B. Vogelstein, V. E. Velculescu, K. W. Kinzler, An integrated genomic analysis of human glioblastoma multiforme Science 321, 1807, Sep 26 (2008)


Hannah Carter, PhD
Assistant Professor of Medicine
Division of Medical Genetics

Location

Skaggs Pharmaceutical Sciences Building
UCSD La Jolla Campus

Postdoctoral Positions Available

We are now recruiting post-docs with strong computational and quantitative skills to work on modeling somatic alterations in protein coding and regulatory regions of tumor genomes. If you are interested in applying, please e-mail hkcarter at ucsd dot edu.

Honors and Awards

In 2013, Dr. Carter received a prestigious NIH Early Independence Award for her project, Network Approaches to Identify Cancer Drivers from High-Dimensional Tumor Data - Read UC San Diego Clinical and Translational Research Institute News Story