BMC women's health · 2026
Predictive value of serial dynamic lipid monitoring for pathologic complete response to neoadjuvant chemotherapy in luminal breast cancer: a retrospective study integrating metabolic and clinical indicators.
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Zeng Yi, Sun Yu
Аннотация
INTRODUCTION: Neoadjuvant chemotherapy (NAC) achieves a low pathological complete response (pCR) rate in Luminal breast cancer. Static lipid parameters have limited predictive value. This study evaluated the predictive role of dynamic cholesterol trajectory monitoring for pCR.
METHODS: This retrospective study enrolled 201 patients with Luminal breast cancer who received NAC. Exclusion criteria comprised statin users and patients who discontinued treatment. Blood lipids were dynamically monitored at 8 time points following each chemotherapy cycle, and the linear slope (mg/dL/week) was calculated. pCR defined as ypT0/Tis ypN0, served as the primary endpoint. Multivariate logistic regression was employed to construct a predictive model that integrated lipid slope with key clinicopathological features. Internal validation was carried out using the bootstrap method with resampling. Model calibration and clinical usefulness were assessed using calibration curves and decision curve analysis (DCA). Furthermore, using gene set enrichment analysis and the Kaplan Meier plotter database, the association between hyperlipidemia related gene sets and breast cancer patient prognosis was validated in an external cohort.
RESULT: A rising TC slope was inversely associated with pCR (non-pCR: 0.04 vs pCR: -0.01, p < 0.001). The standardized TC slope independently predicted pCR (HR = 0.40, 95% CI: 0.25-0.64, p < 0.001). A nomogram integrating TC slope, T stage, and HER2 status achieved an AUC of 0.816, outperforming clinical-only (AUC = 0.728) and lipid-only (AUC = 0.773) models (p < 0.05). High expression of hyperlipidemia-related genes correlated with poorer prognosis (p = 0.003). The calibration curve indicated good consistency between predicted and observed probabilities, and DCA demonstrated the model's clinical net benefit at threshold probabilities between 0.4 and 0.8.
CONCLUSION: Dynamic TC trajectory is a novel metabolic biomarker predicting NAC response in Luminal breast cancer. Integration of dynamic lipid monitoring with clinical features may guide personalized treatment and early intervention.
METHODS: This retrospective study enrolled 201 patients with Luminal breast cancer who received NAC. Exclusion criteria comprised statin users and patients who discontinued treatment. Blood lipids were dynamically monitored at 8 time points following each chemotherapy cycle, and the linear slope (mg/dL/week) was calculated. pCR defined as ypT0/Tis ypN0, served as the primary endpoint. Multivariate logistic regression was employed to construct a predictive model that integrated lipid slope with key clinicopathological features. Internal validation was carried out using the bootstrap method with resampling. Model calibration and clinical usefulness were assessed using calibration curves and decision curve analysis (DCA). Furthermore, using gene set enrichment analysis and the Kaplan Meier plotter database, the association between hyperlipidemia related gene sets and breast cancer patient prognosis was validated in an external cohort.
RESULT: A rising TC slope was inversely associated with pCR (non-pCR: 0.04 vs pCR: -0.01, p < 0.001). The standardized TC slope independently predicted pCR (HR = 0.40, 95% CI: 0.25-0.64, p < 0.001). A nomogram integrating TC slope, T stage, and HER2 status achieved an AUC of 0.816, outperforming clinical-only (AUC = 0.728) and lipid-only (AUC = 0.773) models (p < 0.05). High expression of hyperlipidemia-related genes correlated with poorer prognosis (p = 0.003). The calibration curve indicated good consistency between predicted and observed probabilities, and DCA demonstrated the model's clinical net benefit at threshold probabilities between 0.4 and 0.8.
CONCLUSION: Dynamic TC trajectory is a novel metabolic biomarker predicting NAC response in Luminal breast cancer. Integration of dynamic lipid monitoring with clinical features may guide personalized treatment and early intervention.