Prognostic Significance of Peripheral Blood Parameters as Predictor of Neoadjuvant Chemotherapy Response in Breast Cancer.

The standard treatment for breast cancer typically includes surgery, often followed by systemic therapy and individualized treatment regimens. However, there is growing interest in identifying pre-therapeutic biomarkers that can predict tumor response to neoadjuvant chemotherapy (NACT). This study systematically evaluated various analytical parameters, including age, TNM stage, histological type, molecular subtype, and several biomarker ratios, such as the platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), systemic immune-inflammatory index (SII), and prognostic nutritional index (PNI). We aimed to assess the predictive value of these parameters regarding the tumor's response rate to NACT. The analysis revealed a statistically significant association between the pathological complete response-pCR (absence of any detectable cancer cells in the tissue following neoadjuvant chemotherapy (NACT))-rate and NLR in the subgroup with values between 1 and 3 (p = 0.001). The optimal cut-off for PLR was determined to be 120.45, with 80.55% of patients achieving pCR showing PLR values below this threshold (p = 0.000). Similarly, the LMR cut-off was found to be 12.34, with 77.77% of patients with pCR having LMR values below this threshold (p = 0.002). Additionally, lower pre-therapeutic values of NLR (p < 0.001), PLR (p = 0.002), SII (p = 0.001), and LMR (p = 0.001) were significantly correlated with pCR compared to the non-pCR subgroup (p < 0.005). These findings highlight the predictive potential of these biomarkers for achieving pCR following NACT. Our study supports the hypothesis that pre-therapeutic values of NLR, PLR, SII, and LMR can serve as predictive biomarkers for pCR in breast cancer patients undergoing NACT. However, the PNI did not demonstrate predictive potential in relation to pCR. These biomarkers may provide valuable insights into patient prognosis and guide personalized treatment strategies.