My long-term goal is to become an independent scientific leader in the genetic epidemiology of cancer. My background includes an MD, a PhD in epidemiology, and post- doctoral training in statistical genetics. My immediate career goals are to obtain the necessary training in computational genomics and bioinformatics that would allow me to expand my methodological focus to encompass the integrative analysis of multiple types of genomic data to enhance the detection of cancer-associated genes. The application of these methods to two distinct diseases will considerably broaden my training as a cancer genetic epidemiologist. To this end, I have chosen highly qualified and experienced mentors: Drs. Alice Whittemore (cancer genetic epidemiology), Robert Tibshirani (statistical analysis of genomic data), James Brooks (cancer genomics), and Patrick Brown (genomic analysis of cancer);and developed a training plan that includes coursework in computational genomics and bioinformatics. The overall goal of this proposal is to better understand the genetic bases of ovarian and prostate cancer.
The specific aims are to: (1) identify genetic risk factors for ovarian cancer by re-sequencing one of the most promising regions implicated by genome-wide association studies in order to help find the most likely causal variants;(2) identify genetic risk factors for prostate cancer by mining genome-wide association studies and expression data repositories;and (3) define genomic signatures of aggressive prostate tumors based upon a new method for the combined analysis of genome-wide expression level, copy number, and genotype data. The elucidation of the genetic underpinnings of ovarian and prostate cancer may ultimately lead to improvements in risk stratification and prognostication, and better management of these important diseases. The proposed methodology is also applicable to other cancers with available genomic data. This multidisciplinary research and training will form an invaluable experience that will become increasingly important in cancer research as '-omics'technology rapidly advances.

Public Health Relevance

Cancers of the ovary and prostate are significant public health concerns in the aging U.S. population. This project addresses genetic factors that are associated with the development or progression of these diseases. A better understanding of the underlying genetics will help improve efforts to prevent, diagnose, and reduce morbidity and mortality from these important cancers.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Academic/Teacher Award (ATA) (K07)
Project #
5K07CA143047-05
Application #
8672608
Study Section
Subcommittee B - Comprehensiveness (NCI)
Program Officer
Perkins, Susan N
Project Start
2010-05-24
Project End
2015-04-30
Budget Start
2014-05-01
Budget End
2015-04-30
Support Year
5
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Stanford University
Department
Miscellaneous
Type
Schools of Medicine
DUNS #
City
Stanford
State
CA
Country
United States
Zip Code
94304
(2016) Functional mechanisms underlying pleiotropic risk alleles at the 19p13.1 breast-ovarian cancer susceptibility locus. Nat Commun 7:12675
(2016) No clinical utility of KRAS variant rs61764370 for ovarian or breast cancer. Gynecol Oncol 141:386-401
Cuellar-Partida, Gabriel; Lu, Yi; Dixon, Suzanne C et al. (2016) Assessing the genetic architecture of epithelial ovarian cancer histological subtypes. Hum Genet 135:741-56
Usset, Joseph L; Raghavan, Rama; Tyrer, Jonathan P et al. (2016) Assessment of Multifactor Gene-Environment Interactions and Ovarian Cancer Risk: Candidate Genes, Obesity, and Hormone-Related Risk Factors. Cancer Epidemiol Biomarkers Prev 25:780-90
Præstegaard, Camilla; Kjaer, Susanne K; Nielsen, Thor S S et al. (2016) The association between socioeconomic status and tumour stage at diagnosis of ovarian cancer: A pooled analysis of 18 case-control studies. Cancer Epidemiol 41:71-9
Habel, Laurel A; Lipson, Jafi A; Achacoso, Ninah et al. (2016) Case-control study of mammographic density and breast cancer risk using processed digital mammograms. Breast Cancer Res 18:53
Ioannidis, Nilah M; Rothstein, Joseph H; Pejaver, Vikas et al. (2016) REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants. Am J Hum Genet 99:877-885
(2016) PALB2, CHEK2 and ATM rare variants and cancer risk: data from COGS. J Med Genet 53:800-811
Lee, Alice W; Templeman, Claire; Stram, Douglas A et al. (2016) Evidence of a genetic link between endometriosis and ovarian cancer. Fertil Steril 105:35-43.e1-10
Clyde, Merlise A; Palmieri Weber, Rachel; Iversen, Edwin S et al. (2016) Risk Prediction for Epithelial Ovarian Cancer in 11 United States-Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci. Am J Epidemiol 184:579-589

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