Department of Breast Cancer Center, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, No. 127, Dongming Road, Zhengzhou, China.
Sci Rep. 2021 Jan 14;11(1):1350. doi: 10.1038/s41598-020-80037-2.
Neutrophils and lymphocytes are key regulators of breast cancer (BC) development and progression. Neutrophil to lymphocyte ratio (NLR) values have been found to offer clear prognostic utility when evaluating BC patients. In this study, we sought to determine whether BC patient baseline NLR values are correlated with pathological complete response (pCR) following neoadjuvant chemotherapy (NCT) treatment. In total, 346 BC patients underwent NCT at our hospital from January 1, 2014 to October 31, 2019, and data pertaining to these patients were retrospectively analyzed. Correlations between clinicopathological characteristics and pCR rates were assessed via multivariate logistic regression analyses. A predictive scoring model was used to gauge the likelihood of pCR based upon regression coefficient (β) values for each significant variable identified through these analyses. NLR cut-off values suitable for identifying patients likely to achieve pCR following NCT treatment were calculated using receiver operating characteristic (ROC) curves. All patients in the present study were females with a median age of 48 years old (range 22-77). An optimal NLR cut-off value of 1.695 was identified and was associated with respective sensitivity and specificity values of 63.6% and 45.5%. We found that higher NLR values were significantly associated with younger age, premenopausal status, and non-pCR status. Logistic regression analyses indicated that NLR, tumor size, hormone receptor (HR) status, and Ki-67 expression were all independent predictors of pCR. The area under the curve (AUC) for the resultant predictive scoring model was 0.705, and this model was assessed via K-fold cross-validation (k = 10) and bootstrapping validation, yielding respective AUC values of 0.68 and 0.694. Moreover, the incorporation of NLR into this predictive model incrementally improved its overall prognostic value relative to that of a model not incorporating NLR (AUC = 0.674). BC patients with a lower baseline NLR are more likely to exhibit pCR following NCT treatment, indicating that NLR may be a valuable biomarker for BC patient prognostic evaluation and treatment planning. Overall, our results demonstrate that this NLR-based predictive model can efficiently predict NCT efficacy in early BC patients with a high degree of accuracy.
中性粒细胞与淋巴细胞是乳腺癌（BC）发生和发展的关键调节因子。研究发现，中性粒细胞与淋巴细胞比值（NLR）值可在评估 BC 患者时提供明确的预后价值。在本研究中，我们旨在确定 BC 患者的基线 NLR 值是否与新辅助化疗（NCT）治疗后的病理完全缓解（pCR）相关。共有 346 名 BC 患者于 2014 年 1 月 1 日至 2019 年 10 月 31 日在我院接受 NCT，回顾性分析了这些患者的数据。通过多变量逻辑回归分析评估了临床病理特征与 pCR 率之间的相关性。使用回归系数（β）值，基于通过这些分析确定的每个显著变量，建立预测评分模型，以评估 pCR 的可能性。使用接收器操作特征（ROC）曲线计算适合识别接受 NCT 治疗后可能实现 pCR 的患者的 NLR 截断值。本研究所有患者均为女性，中位年龄为 48 岁（范围 22-77 岁）。确定了最佳 NLR 截断值为 1.695，其相应的敏感性和特异性值分别为 63.6%和 45.5%。我们发现，较高的 NLR 值与年龄较小、绝经前状态和非 pCR 状态显著相关。逻辑回归分析表明，NLR、肿瘤大小、激素受体（HR）状态和 Ki-67 表达均是 pCR 的独立预测因子。由此产生的预测评分模型的曲线下面积（AUC）为 0.705，该模型通过 K 折交叉验证（k=10）和自举验证进行评估，分别得到 AUC 值为 0.68 和 0.694。此外，与不包含 NLR 的模型相比，将 NLR 纳入该预测模型可渐进式地提高其整体预后价值（AUC=0.674）。NCT 治疗后，基线 NLR 值较低的 BC 患者更可能出现 pCR，表明 NLR 可能是评估 BC 患者预后和治疗计划的有价值的生物标志物。总体而言，我们的研究结果表明，该基于 NLR 的预测模型可高效、准确地预测早期 BC 患者的 NCT 疗效。
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