Prostate cancer is a common but complex disease with a number of unresolved issues surrounding its natural history. These include concerns with screening, detection, and treatment, and reflect the substantial heterogeneity in prostate tumors: some will remain latent and have little impact on morbidity, whereas others progress rapidly in a potentially lethal manner. Genetic factors likely underlie some of these differences, and we propose a comprehensive evaluation of the genetic basis of prostate cancer risk and progression in a large, well-characterized study population. In particular, we will investigate rar functional variants across the exome and common SNPs across the genome. Our sample encompasses 8,078 prostate cancer cases and 8,078 age and ethnicity matched controls nested within the Kaiser / USCF Research Program on Genes, Environment and Health cohort. This population has existing genome-wide SNP measures, and uniformly collected clinical information on prostate cancer screening, diagnoses, and progression. We plan to type the new exome array on the cases and controls to assess the functional variants in protein coding regions across the human genome. With these data we will address our hypothesis that these genetic factors can be used to predict-and underlie an increasing proportion of the variation in-prostate cancer risk and progression. This project provides an efficient and innovative opportunity to obtain a comprehensive understanding of how these factors impact the natural history of prostate cancer. Our findings should supply important insights into the underlying mechanism of disease, with the ultimate goal of helping to improve screening and treatment for prostate cancer.
Prostate cancer is one of the most common and clearly genetic cancers, but finding the mechanisms underlying the natural history of this disease has proven difficult. Our efforts toward deciphering the genetic basis of prostate cancer will help improve screening, treatment, and our understanding of this disease, all important goals of the overall National Cancer Institute's Mission. These advances will improve the overall health of men, providing much needed information about individual and population-level risks of prostate cancer development and progression.
|Cario, Clinton L; Witte, John S (2018) Orchid: a novel management, annotation and machine learning framework for analyzing cancer mutations. Bioinformatics 34:936-942|
|Majumdar, Arunabha; Haldar, Tanushree; Bhattacharya, Sourabh et al. (2018) An efficient Bayesian meta-analysis approach for studying cross-phenotype genetic associations. PLoS Genet 14:e1007139|
|Wu, Yi-Hsuan; Graff, Rebecca E; Passarelli, Michael N et al. (2018) Identification of Pleiotropic Cancer Susceptibility Variants from Genome-Wide Association Studies Reveals Functional Characteristics. Cancer Epidemiol Biomarkers Prev 27:75-85|
|Hoffman, Joshua D; Graff, Rebecca E; Emami, Nima C et al. (2017) Cis-eQTL-based trans-ethnic meta-analysis reveals novel genes associated with breast cancer risk. PLoS Genet 13:e1006690|
|Emami, Nima C; Leong, Lancelote; Wan, Eunice et al. (2017) Tissue Sources for Accurate Measurement of Germline DNA Genotypes in Prostate Cancer Patients Treated With Radical Prostatectomy. Prostate 77:425-434|
|Graff, Rebecca E; Möller, Sören; Passarelli, Michael N et al. (2017) Familial Risk and Heritability of Colorectal Cancer in the Nordic Twin Study of Cancer. Clin Gastroenterol Hepatol 15:1256-1264|
|Conti, David V; Wang, Kan; Sheng, Xin et al. (2017) Two Novel Susceptibility Loci for Prostate Cancer in Men of African Ancestry. J Natl Cancer Inst 109:|
|Gauderman, W James; Mukherjee, Bhramar; Aschard, Hugues et al. (2017) Update on the State of the Science for Analytical Methods for Gene-Environment Interactions. Am J Epidemiol 186:762-770|
|Hoffmann, Thomas J; Passarelli, Michael N; Graff, Rebecca E et al. (2017) Genome-wide association study of prostate-specific antigen levels identifies novel loci independent of prostate cancer. Nat Commun 8:14248|
|Ng, Maggie C Y; Graff, Mariaelisa; Lu, Yingchang et al. (2017) Discovery and fine-mapping of adiposity loci using high density imputation of genome-wide association studies in individuals of African ancestry: African Ancestry Anthropometry Genetics Consortium. PLoS Genet 13:e1006719|
Showing the most recent 10 out of 94 publications