Novel joint lipid score based on longitudinal metabolic remodeling patterns predicts chemotherapy response in luminal breast cancer

Luminal breast cancer typically exhibits low sensitivity to neoadjuvant chemotherapy(NAC). Identifying reliable biomarkers for predicting treatment response is crucial for optimizing individualized therapy. This study aims to clarify the dynamic changes in peripheral blood lipid parameters during chemotherapy and their predictive value for achieving pathological complete response(pCR). Data from 202 Luminal breast cancer patients who underwent standardized chemotherapy regimens were retrospectively collected. Serial measurements of total cholesterol(TC), triglycerides(TG), high-density lipoprotein(HDL), and low-density lipoprotein(LDL) levels at nine time points were obtained. Group-based trajectory modeling was employed to identify distinct metabolic trajectory patterns. Furthermore, a composite lipid score was developed by integrating these four parameters, and its predictive performance for pathological response was validated. Patients who achieved pCR exhibited higher baseline levels of TC and LDL. In contrast, those achieved Non-pCR displayed a progressive increase in lipid parameters during the entire chemotherapy period. The composite lipid score showed superior predictive performance. With an area under the curve of 0.957 (95% CI: 0.917-0.998), this score served as a robust predictor independent of conventional clinicopathological factors. Dynamic changes in lipid metabolism during NAC are strongly correlated with pCR. The composite lipid score facilitates early and accurate evaluation of treatment response in Luminal breast cancer, offering a simple yet effective metabolic tool to aid in clinical decision-making optimization.

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Publication Details

Journal
BMC Cancer
Published
2026-10-13
DOI
https://doi.org/10.1186/s12885-026-17145-1
Primary Topic
Breast Cancer Treatment Studies
Type
article
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article

Novel joint lipid score based on longitudinal metabolic remodeling patterns predicts chemotherapy response in luminal breast cancer

Yu Sun, Yi Zeng
BMC Cancer
Breast Cancer Treatment Studies
article

Novel joint lipid score based on longitudinal metabolic remodeling patterns predicts chemotherapy response in luminal breast cancer

Yu Sun, Yi Zeng
article en

Abstract

Luminal breast cancer typically exhibits low sensitivity to neoadjuvant chemotherapy(NAC). Identifying reliable biomarkers for predicting treatment response is crucial for optimizing individualized therapy. This study aims to clarify the dynamic changes in peripheral blood lipid parameters during chemotherapy and their predictive value for achieving pathological complete response(pCR). Data from 202 Luminal breast cancer patients who underwent standardized chemotherapy regimens were retrospectively collected. Serial measurements of total cholesterol(TC), triglycerides(TG), high-density lipoprotein(HDL), and low-density lipoprotein(LDL) levels at nine time points were obtained. Group-based trajectory modeling was employed to identify distinct metabolic trajectory patterns. Furthermore, a composite lipid score was developed by integrating these four parameters, and its predictive performance for pathological response was validated. Patients who achieved pCR exhibited higher baseline levels of TC and LDL. In contrast, those achieved Non-pCR displayed a progressive increase in lipid parameters during the entire chemotherapy period. The composite lipid score showed superior predictive performance. With an area under the curve of 0.957 (95% CI: 0.917-0.998), this score served as a robust predictor independent of conventional clinicopathological factors. Dynamic changes in lipid metabolism during NAC are strongly correlated with pCR. The composite lipid score facilitates early and accurate evaluation of treatment response in Luminal breast cancer, offering a simple yet effective metabolic tool to aid in clinical decision-making optimization.

BMC Cancer
Guangzhou Medical University (CN)
Openalex Percentile: Top 19%
Breast Cancer Treatment Studies
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