# Aromatase Inhibitor Therapy Is Associated with Distinct Plasma Lipidomic Profiles in Postmenopausal Breast Cancer Patients

## TL;DR

Aromatase inhibitor therapy in postmenopausal breast cancer patients is linked to specific changes in plasma lipid profiles, suggesting metabolic effects from estrogen deprivation.

## Contribution

This study identifies specific lipidomic changes associated with long-term aromatase inhibitor therapy in breast cancer patients.

## Key findings

- Phosphatidylcholine and sphingomyelin species were more abundant before AI therapy.
- Ceramides and phosphatidylinositol species increased during AI therapy.
- A Naive Bayes model achieved 0.79 AUC in classifying patients based on lipid-class levels.

## Abstract

Aromatase inhibitors (AIs) are the standard adjuvant endocrine therapy for postmenopausal women with hormone receptor-positive breast cancer; however, their effects on lipid metabolism remain incompletely characterized. In this study, we investigated AI-associated alterations in the plasma lipidome using mass spectrometry-based lipidomics. Plasma samples were collected from 30 patients prior to AI initiation and 29 patients receiving non-steroidal AI therapy for at least 24 months. Ultra-high-performance liquid chromatography–tandem mass spectrometry identified and relatively quantified 649 lipid species across 23 lipid classes and subclasses. Lipidomic analysis revealed significant differences in specific lipid species. Several phosphatidylcholine, sphingomyelin, and lysophosphatidylethanolamine species were significantly more abundant in patient plasma prior to AI therapy, whereas higher levels of selected ceramides, hexosylceramides, phosphatidylinositol (PI 16:0_16:0), and a polyunsaturated diacylglycerol species were observed in patients receiving AI therapy. Multivariate analyses revealed patient group separation, and a Naive Bayes classification model based on lipid-class levels achieved an area under the curve of 0.79. Additionally, lipid network and hierarchical clustering analyses identified systematic lipid-class trends. Protein–protein interaction network analysis based on lipidomic profiles highlighted enzymes associated with sphingolipid metabolism pathways. These findings demonstrate that long-term AI therapy is associated with specific alterations in the plasma lipidome, consistent with estrogen-deprivation-related metabolic differences. Targeted lipidomic profiling may provide mechanistic insights into therapy-associated metabolic effects and support future efforts to optimize long-term management of breast cancer survivors.