Prognostic and Predictive Role of Inflammatory Blood Markers in Early and Locally Advanced Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy: An Egyptian Single-Center Experience

Abstract

Background: Inflammatory blood biomarkers (IBMs), including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), lymphocyte-to-monocyte ratio (LMR), systemic inflammation response index (SIRI), pan-immune-inflammation value (PIV), and systemic inflammation index (SII), have been proposed as prognostic and predictive markers in cancer. This study evaluated their predictive value for pathological complete response (pCR), disease-free survival (DFS), overall survival (OS), and neoadjuvant chemotherapy (NACT)-related toxicities in early and locally advanced breast cancer (BC).

Methods: A retrospective analysis was conducted on 284 BC patients receiving NACT. Associations between IBMs, treatment response, survival outcomes, and chemotherapy-related toxicities were analyzed.

Results: -IBMs were significantly associated with chemotherapy-related toxicities. Neutrophils, lymphocytes, monocytes, NLR, SII, SIRI, and PIV (all p < 0.001) strongly predicted febrile neutropenia, along with doublet anti-HER2 therapy (p = 0.032). Predictors of neutropenia included neutrophil, monocyte, NLR, MLR, LMR, SII, SIRI, PIV (p < 0.05), HER2-positive status, and doublet anti-HER2 therapy. -Subgroup analyses showed IBM predictive performance varied by subtype. NLR predicted DFS in HER2+ patients (AUC = 0.839, p = 0.010); neutrophil count was linked to peripheral neuropathy in HR+/HER2− patients (p = 0.042). PLR and LMR showed excellent discrimination for febrile neutropenia in TNBC (AUCs > 0.92). In TNBC, MLR, SIRI, and PIV showed moderate-to-high discrimination for OS (AUCs 0.71–0.74). Neutrophil (p = 0.0058) and lymphocyte (p = 0.0248) levels were associated with pCR in HER2+ patients. HR+ subtypes showed limited IBM predictive value.

Conclusion: IBMs demonstrated strong predictive value for chemotherapy-related toxicities and showed subtype-specific relevance for survival and treatment response. These findings support the integration of molecular stratification to enhance the predictive utility of IBMs in breast cancer, highlighting their clinical potential in anticipating and managing treatment-related adverse events and guiding personalized supportive care strategies.