Energy homeostasis genes and survival after breast cancer diagnosis: the Breast Cancer Health Disparities Study
- 250 Downloads
The leptin-signaling pathway and other genes involved in energy homeostasis (EH) have been examined in relation to breast cancer risk as well as to obesity. We test the hypothesis that genetic variation in EH genes influences survival after diagnosis with breast cancer and that body mass index (BMI) will modify that risk.
We evaluated associations between 10 EH genes and survival among 1,186 non-Hispanic white and 1,155 Hispanic/Native American women diagnosed with breast cancer. Percent Native American (NA) ancestry was determined from 104 ancestry-informative markers. Adaptive rank truncation product (ARTP) was used to determine gene and pathway significance.
The overall EH pathway was marginally significant for all-cause mortality among women with low NA ancestry (P ARTP = 0.057). Within the pathway, ghrelin (GHRL) and leptin receptor (LEPR) were significantly associated with all-cause mortality (P ARTP = 0.035 and 0.007, respectively). The EH pathway was significantly associated with breast cancer-specific mortality among women with low NA ancestry (P ARTP = 0.038). Three genes cholecystokinin (CCK), GHRL, and LEPR were significantly associated with breast cancer-specific mortality among women with low NA ancestry (P ARTP = 0.046, 0.015, and 0.046, respectively), while neuropeptide Y (NPY) was significantly associated with breast cancer-specific mortality among women with higher NA ancestry (P ARTP = 0.038). BMI did not modify these associations.
Our data support our hypothesis that certain EH genes influence survival after diagnosis with breast cancer; associations appear to be most important among women with low NA ancestry.
KeywordsBreast cancer Energy homeostasis Leptin receptor Ghrelin Neuropeptide Y Cholecystokinin
We would also like to acknowledge the contributions of the following individuals to the study: Sandra Edwards and Jennifer Herrick for data harmonization and management; Erica Wolff and Michael Hoffman for laboratory support; Jocelyn Koo for data management for the San Francisco Bay Area Breast Cancer Study; Dr. Tim Byer, Dr. Kathy Baumgartner, and Dr. Anna Giuliano for their contribution to the 4-Corners Breast Cancer Study; and Dr. Josh Galanter for assistance in selection of AIMs markers.
The Breast Cancer Health Disparities Study was funded by Grant CA14002 from the National Cancer Institute to Dr. Slattery. The San Francisco Bay Area Breast Cancer Study was supported by Grants CA63446 and CA77305 from the National Cancer Institute, Grant DAMD17-96-1-6071 from the US Department of Defense, and Grant 7 PB-0068 from the California Breast Cancer Research Program. The collection of cancer incidence data used in this study was supported by the California Department of Public Health as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885; the National Cancer Institute’s Surveillance, Epidemiology and End Results Program under contract HHSN261201000036C awarded to the Cancer Prevention Institute of California; and the Centers for Disease Control and Prevention’s National Program of Cancer Registries, under agreement #1U58 DP000807-01 awarded to the Public Health Institute. The 4-Corners Breast Cancer Study was funded by Grants CA078682, CA078762, CA078552, and CA078802 from the National Cancer Institute. The research also was supported by the Utah Cancer Registry, which is funded by contract N01-PC-67000 from the National Cancer Institute, with additional support from the State of Utah Department of Health, the New Mexico Tumor Registry, and the Arizona and Colorado Cancer Registries, funded by the Centers for Disease Control and Prevention National Program of Cancer Registries and additional state support. The contents of this manuscript are solely the responsibility of the authors and do not necessarily represent the official view of the National Cancer Institute or endorsement by the State of California Department of Public Health, the National Cancer Institute, and the Centers for Disease Control and Prevention or their Contractors and Subcontractors.
Compliance with ethical standards
Conflict of interest
The authors have no conflict of interest to report.
- 3.Dossus L, McKay JD, Canzian F, Wilkening S, Rinaldi S, Biessy C et al (2008) Polymorphisms of genes coding for ghrelin and its receptor in relation to anthropometry, circulating levels of IGF-I and IGFBP-3, and breast cancer risk: a case–control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC). Carcinogenesis 29(7):1360–1366CrossRefPubMedGoogle Scholar
- 15.Slattery ML, Lundgreen A, Hines L, Wolff RK, Torres-Mejia G, Baumgartner KN, John EM (2015) Energy homeostasis genes and breast cancer risk: the influence of ancestry, body size, and menopausal status, the Breast Cancer Health Disparities Study. Cancer Epidemiol. doi: 10.1016/j.canep.2015.08.012
- 16.Slattery ML, Lundgreen A, Stern MC, Hines L, Wolff RK, Giuliano AR et al (2014) The influence of genetic ancestry and ethnicity on breast cancer survival associated with genetic variation in the TGF-beta-signaling pathway: the Breast Cancer Health Disparities Study. Cancer Causes Control 25(3):293–307CrossRefPubMedPubMedCentralGoogle Scholar
- 19.John EM, Sangaramoorthy M, Hines LM, Stern MC, Baumgartner KB, Giuliano AR et al (2015) Body size throughout adult life influences postmenopausal breast cancer risk among Hispanic women: the Breast Cancer Health Disparities Study. Cancer Epidemiol Biomarkers Prev 24(1):128–137CrossRefPubMedPubMedCentralGoogle Scholar
- 20.John EM, Sangaramoorthy M, Hines LM, Stern MC, Baumgartner KB, Giuliano AR et al (2015) Overall and abdominal adiposity and premenopausal breast cancer risk among Hispanic women: the Breast Cancer Health Disparities Study. Cancer Epidemiol Biomarkers Prev. 24(1):138–147CrossRefPubMedPubMedCentralGoogle Scholar
- 27.Kai Yu OL, Wheeler W (2011) ARTP gene and pathway p-values computed using the adaptive rank truncated product. 2.0.0 pp. R packageGoogle Scholar
- 32.Herlevic VC, Mowad R, Miller JK, Darensburg NA, Li BD, Kim RH (2015) Breast cancer outcomes in a population with high prevalence of obesity. J Surg Res. doi: 10.1016/j.jss.2015.03.088
- 51.Slattery ML, Lundgreen A, Torres-Mejia G, Wolff RK, Hines L, Baumgartner K et al (2014) Diet and lifestyle factors modify immune/inflammation response genes to alter breast cancer risk and prognosis: the Breast Cancer Health Disparities Study. Mutat Res 770:19–28CrossRefPubMedPubMedCentralGoogle Scholar