A Multi-Omics Approach to Examine Symptoms and Medication Adherence in Women with Breast
Cancer
Breast cancer is most prevalent in postmenopausal women, and 3.4 million U.S. women are survivors. Most
women with breast cancer are postmenopausal at the time of diagnosis, and at least 70% of tumors are
hormone receptor positive (HR+). Aromatase inhibitors effectively prevent BC recurrence, and current standard
includes adjuvant aromatase inhibitor (AI) therapy in a once daily standard dose regimen for a minimum of five
years. However, AI adherence is a significant problem. Up to one third of women do not fill their initial AI
prescription, adherence to AIs averaged 48% in the first year, and adherence decreases in subsequent 2-5
years. AI-attributed symptoms are the leading reason for not adhering to AI regimens and a major barrier to AI
adherence. Moreover, AI-related symptom type and severity are highly variable among women. The source of
symptom variability is unknown. The etiology of symptoms experienced during AI therapy may have biological
underpinnings, yet little is known about factors in AI (anastrozole, letrozole, exemestane) absorption,
distribution, metabolism, and elimination (ADME) pathways and the resulting symptoms. Symptoms and
adherence, especially their relationship to each other, have not been well-characterized temporally. Further,
potential biologic mechanisms related to AI absorption, distribution, metabolism, and elimination (ADME) for
AIs have not been fully characterized.
The dissertation project (F99) will examine temporal patterns of AI symptoms and adherence and their
relationship over the first 18 months of AI therapy. It will also explore the role of genotypic (ADME) and
phenotypic factors in symptoms experienced and AI adherence. The postdoctoral project (K00 phase) will
incorporate two additional molecular methods—microbiomics and exosomics. They will be described and their
potential role in AI symptoms experienced and adherence will be explored.
The purpose of the proposed F99/K00 training and research is to utilize a biobehavioral, multi-omics approach
to gain a deeper understanding of the complex web of AI symptoms and adherence, including the temporal
variability among women and the interplay between symptoms and adherence. It will provide insight into
potential biological mechanisms by describing molecular and phenotypic characteristics associated with
symptoms and adherence. Ultimately, this research will inform future symptom management and adherence
interventions and their timing.