Hematological indices reflecting the activity of the systemic inflammatory response (SIR) have been shown to be useful prognostic factors in breast cancer (BC).
OBJECTIVE
To comparatively assess the significance of hematological SIR parameters in patients with triple-negative breast cancer (TNBC) as predictors of neoadjuvant polychemotherapy (NAPCh) efficacy.
MATERIAL AND METHODS
A retrospective study included the analysis of data from examination and treatment of 82 patients with early and locally advanced TNBC. Indices reflecting the relative counts of neutrophils, platelets, monocytes, and lymphocytes (NLR, PLR, MLR, SII, SIRI, and PIV) were calculated based on complete blood count data obtained before the start of treatment. Statistical data analysis was conducted using non-parametric analysis methods, ROC analysis, and logistic regression.
RESULTS
Elevated levels of integrated SIRI and PIV in patients with TNBC before treatment were significantly associated with involvement of regional lymph nodes (N0 vs. N+; p=0.009). NLR, PLR, MLR, and SII did not correlate with the disease stage. There was no correlation between all studied hematological SIR parameters and tumor differentiation grade or Ki-67 index. The likelihood of achieving a complete pathomorphological response (pCR) after NAPCh in patients with TNBC was higher at IA—IIA disease stage (p=0.011), tumor size T1-2 (p=0.031), absence of metastases in lymph nodes (p=0.053), Ki-67 index ³75% (p<0.0001) and SII£1010 (p=0.034). In multivariate analysis, it was established that SII was an independent predictor associated with pCR (OR=0.20 [0.06—0.76]; p=0.018). The inclusion of SII in the algorithm for assessing the probability of pCR in patients with TNBC together with disease stage and Ki-67 index increased the efficiency of the predictive model: the area under the curve (AUC) was 0.792, sensitivity of pCR detection was 76.2%, and specificity was 83.6%.
CONCLUSION
The advantages of an integrated assessment of SIR parameters suggest the need for continued search for comprehensive algorithms that combine clinical, morphological, and laboratory indicators.
