Investigating Environmental Risk Factors for Breast Cancer Using Adductomics

Project: Research project

Project Details


PROJECT SUMMARY/ABSTRACT Despite years of research, few modifiable risk factors for breast cancer have been identified and the incidence of breast cancer remains high with over 260,000 new cases estimated to arise this year. Established risk factors, such as a woman’s reproductive history, body size, smoking, and alcohol intake, are estimated to explain only about half of breast cancer cases. In addition, the underlying biological mechanisms linking environmental risk factors and breast cancer are complex and are not well understood. As a result, new approaches are required that can help identify environmental causes of breast cancer. In this proposal, we apply a novel biomarker approach using protein adduct profiles (i.e., adductomics) to investigate associations between environmental exposures and breast cancer risk in the Sister Study. Because the Sister Study is a nationwide prospective cohort that was specifically designed to investigate environmental risk factors for breast cancer, it is an ideal cohort to investigate the hypotheses outlined in this proposal. We will focus specifically on Cys34 adducts of human serum albumin (HSA-Cys34), which is a nuecleophilic “hotspot” on HSA that scavenges reactive electrophiles from circulating blood arising from both exogenous and endogenous sources. We will first use an untargeted adductomics approach in our discovery experiments using high-resolution mass spectrometry to identify unique adduct features related to breast cancer status. We will then employ a complementary targeted adductomics approach with enhanced analytical sensitivity and higher sample throughput to measure adduct profiles in stored plasma samples collected at baseline from 1,800 incident breast cancer cases and a random sample of 1,800 Sister Study participants. Adducts will be compared across groups to identify discordant adducts, and we will use time-to-diagnosis in our regression models to help differentiate between causal and responsive b
Effective start/end date3/11/2112/31/25


  • National Institute of Environmental Health Sciences (5R01ES031809-04)


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