Project 1: Carolina Breast Cancer Study

Stage-specific mortality varies by rurality, age, race, and other factors, along with varying frequencies of late stage, ER-negative, high grade, and high genomic risk tumors (such as Basal-like and Luminal B tumors). Mortality differences are especially notable among the most treatable, ER+/HER2- tumors. Early onset, severity, and poor survival occur in context of higher risk of multiple conditions, including obesity and cardiovascular disease. As such, breast cancer can be understood as part of a broader health system, with a convergence of multiple overlapping chronic and infectious factors contributing to outcomes. Previous research has also linked ‘accelerated aging’, ‘premature age’, and ‘biological weathering’ to early onset of multiple diseases of aging, but not to breast cancer. Research is needed to understand how genetics, social environments, and other risk factors work together to influence breast cancer progression and recurrence. Using data from the study population of the Carolina Breast Cancer Study (CBCS) with oversampled groups that are susceptible to the worst outcomes, we have previously demonstrated important tumor biological variables. We have found that women with higher proliferation scores, higher risk of recurrence scores, more frequent p53 mutation status, and differential expression of DNA repair, stromal, and immune response signatures have worse outcomes. We have also found that treatment delay and individual-level social and clinical factors such as quality of life, education, income, and comorbidities modify these relationships. More recently, we have observed differences in CBCS participant exposure to community-level factors (such as community assets and deprivation). Studying each of these domains individually is important, but joint analysis is needed to identify the most effective interventions. Under a cells-to-society conceptual model of breast cancer outcomes, we hypothesize that poor breast cancer outcomes arise due to aggregation of factors across multiple levels (biological, individual, community, and structural). In context of this system of factors, we propose to evaluate how differences in individual (e.g., obesity, comorbidities) and community-level (disadvantage, advantage) exposures lead to biological (and especially immunologic and DNA repair) differences in tumors (Aim 1). We further hypothesize that multilevel determinants alter treatment response and breast cancer recurrence and survival (Aim 2). Across both aims, our project will use innovative data integration tools to identify novel intervention targets in precision medicine, extending previous studies of molecular factors and identifying new interventions to improve outcomes.