Investigate interactive roles of environmental, behavioral and genetic factors on racial disparities in breast cancer outcomes

Lsu Health Sciences Center, New Orleans LA

Investigators

Linked publications, trials & patents

Abstract

Project Summary: Health disparities exist widely in the breast cancer outcomes (incidence, survival, and quality-of-life). When compared with white women, African American women have higher recurrence and death rates from breast cancer although they have a lower incidence rate. It is well established that both community and individual level risk factors play important roles in breast cancer outcomes. In our previous studies, we have developed a mediation analysis method to identify new biomarkers that contribute to health disparities in triple-negative breast cancer (TNBC). We also developed a new multilevel mediation analysis method that allows for longitudinal assessments of both residential environments and individual risk factors to be jointly utilized in determining the mechanistic link between race and health outcomes. By including measures of individual and environmental factors, our methods are capable of explaining existing disparities in health outcomes.In this project, we aim to utilize the comprehensive database collected by the All of Us project to investigate the observed racial disparities in breast cancer outcomes. Using the novel multilevel mediation analysis method, we will identify modifiable factors that can explain the racial disparities observed in breast cancer outcomes. The risk factors to be considered include measures at both the environmental (e.g. pollution measurements) and individual levels (e.g., smoking behavior and gene expressions). The information will help caregivers design precision medicine and policymakers evaluate interventions that aim at improving treatment and reducing health disparities. We have implemented our method as an R package (mlma) to provide the research community with open access to software for performing multilevel mediation analysis. We plan to further develop an API for an interactive web implementation of the software so that all researchers can use the method without the programming requirement.