Risk factors for endometrial cancer, including weight and a high-fat diet, operate through estrogen-related mechanisms. Estrogen promotes growth of transformed cells and there is evidence that some metabolites may have carcinogenic properties. Some of the genes that are important in estrogen metabolism have been found to be polymorphic (CYP17, CYP1 Al, catechol 0-methyltransferase, CYP1 Bi, and CYP19), raising the question of whether genotypes are related to risk. The primary aim of this study is to compare endometrial cancer cases to controls to determine the associations of weight, diet, and genetic susceptibility, independently and jointly, with endometrial cancer. Most endometrial tumors are of endometrioid histology. The rarer serous and clear cell tumors are more aggressive and lethal. Aside from their higher prevalence in older women and in blacks, little is known about risk factors for these tumors, although they appear not to be related to estrogen. The secondary aim is to compare cases with these tumor types to controls to assess risk factors for these poorly-understood tumors. We will conduct a population-based case-control study in six counties in New Jersey. Cases will be women with newly-diagnosed endometrial cancer. Controls will be matched by 5-year age groups and selected by random digit dialing (for those under 65) or from HCFA files (for those aged 65 and over). There will be 600 cases with endometrioid tumors, 200 with serous or clear cell tumors, and 600 controls. Endometrial cancer, already a serious cause of morbidity in the US, is likely to become more common in future years as the population ages and obesity increases. Risk factors such as obesity and poor diets are potentially modifiable. This study will provide data on genetic susceptibility in endometrial cancer in conjunction with established risk factors, as well as the first epidemiologic data on the more lethal serous and clear cell tumors.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA083918-03
Application #
6603120
Study Section
Epidemiology and Disease Control Subcommittee 2 (EDC)
Program Officer
Hartmuller, Virginia W
Project Start
2001-07-02
Project End
2006-06-30
Budget Start
2003-07-01
Budget End
2004-06-30
Support Year
3
Fiscal Year
2003
Total Cost
$594,378
Indirect Cost
Name
Sloan-Kettering Institute for Cancer Research
Department
Type
DUNS #
064931884
City
New York
State
NY
Country
United States
Zip Code
10065
Earp, Madalene; Tyrer, Jonathan P; Winham, Stacey J et al. (2018) Variants in genes encoding small GTPases and association with epithelial ovarian cancer susceptibility. PLoS One 13:e0197561
Shivappa, Nitin; Hébert, James R; Paddock, Lisa E et al. (2018) Dietary inflammatory index and ovarian cancer risk in a New Jersey case-control study. Nutrition 46:78-82
Harris, Holly R; Babic, Ana; Webb, Penelope M et al. (2018) Polycystic Ovary Syndrome, Oligomenorrhea, and Risk of Ovarian Cancer Histotypes: Evidence from the Ovarian Cancer Association Consortium. Cancer Epidemiol Biomarkers Prev 27:174-182
Lu, Yingchang; Beeghly-Fadiel, Alicia; Wu, Lang et al. (2018) A Transcriptome-Wide Association Study Among 97,898 Women to Identify Candidate Susceptibility Genes for Epithelial Ovarian Cancer Risk. Cancer Res 78:5419-5430
Liu, Gang; Mukherjee, Bhramar; Lee, Seunggeun et al. (2018) Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence. Am J Epidemiol 187:366-377
Ong, Jue-Sheng; Hwang, Liang-Dar; Cuellar-Partida, Gabriel et al. (2018) Assessment of moderate coffee consumption and risk of epithelial ovarian cancer: a Mendelian randomization study. Int J Epidemiol 47:450-459
Jordan, Susan J; Na, Renhua; Johnatty, Sharon E et al. (2017) Breastfeeding and Endometrial Cancer Risk: An Analysis From the Epidemiology of Endometrial Cancer Consortium. Obstet Gynecol 129:1059-1067
Reid, Brett M; Permuth, Jennifer B; Chen, Y Ann et al. (2017) Integration of Population-Level Genotype Data with Functional Annotation Reveals Over-Representation of Long Noncoding RNAs at Ovarian Cancer Susceptibility Loci. Cancer Epidemiol Biomarkers Prev 26:116-125
Satagopan, Jaya M; Olson, Sara H; Elston, Robert C (2017) Statistical interactions and Bayes estimation of log odds in case-control studies. Stat Methods Med Res 26:1021-1038
Minlikeeva, Albina N; Freudenheim, Jo L; Cannioto, Rikki A et al. (2017) History of thyroid disease and survival of ovarian cancer patients: results from the Ovarian Cancer Association Consortium, a brief report. Br J Cancer 117:1063-1069

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