Abstract：
BACKGROUND: Response to neoadjuvant chemotherapy (NC) in individuals with invasive breast cancer (IBC) must be monitored, and biomarkers are needed. NC can activate an anti-tumour immune response in its microenvironment, known as Tumor-infiltrating Lymphocytes (TIL). TIL components believed to have great potential as predictors are CD4+, CD8+, and FOXP3+ TIL. This study aims to explore TIL components that can potentially be predictive biomarkers of NC pathological responses.
METHODS: A sample size of 40 were analyzed based on the relationship between CD4+, CD8+, and FOXP3+ TIL expression with the Miller-Payne (MP) grading system. Age, tumour grade, PR, ER, Ki-67, and HER2 were also evaluated. CD4+, CD8+, and FOXP3+ TIL expressions were analayzed by IHC staining, while other data were collected from archives. Data was analyzed using univariate and multivariate analysis.
RESULTS: Univariate analysis showed a significant relationship between CD4+ TIL and MP (p<0.001), CD8+ and MP (p=0.004), and FOXP3 with MP (p<0.001). The simultaneous integration of the three biomarkers in one model was not good enough to be a predictive model. Therefore, an exploratory analysis was conducted by testing several alternative models that combined two of the three existing biomarkers. It turned out that CD4+ TIL in model 2 (CD4+CD8+) and FOXP3+ TIL in model 4 (CD8+FOXP3+) showed significant coefficient values. Moreover, all of the threshold coefficients in model 4 are significant.
CONCLUSIONS: This study shows that CD4+, CD8+, and FOXP3+ TIL have promising potential as predictive biomarkers. In particular, FOXP3+ is dominant in predictive models of pathological response in patients with IBC.
METHODS: A sample size of 40 were analyzed based on the relationship between CD4+, CD8+, and FOXP3+ TIL expression with the Miller-Payne (MP) grading system. Age, tumour grade, PR, ER, Ki-67, and HER2 were also evaluated. CD4+, CD8+, and FOXP3+ TIL expressions were analayzed by IHC staining, while other data were collected from archives. Data was analyzed using univariate and multivariate analysis.
RESULTS: Univariate analysis showed a significant relationship between CD4+ TIL and MP (p<0.001), CD8+ and MP (p=0.004), and FOXP3 with MP (p<0.001). The simultaneous integration of the three biomarkers in one model was not good enough to be a predictive model. Therefore, an exploratory analysis was conducted by testing several alternative models that combined two of the three existing biomarkers. It turned out that CD4+ TIL in model 2 (CD4+CD8+) and FOXP3+ TIL in model 4 (CD8+FOXP3+) showed significant coefficient values. Moreover, all of the threshold coefficients in model 4 are significant.
CONCLUSIONS: This study shows that CD4+, CD8+, and FOXP3+ TIL have promising potential as predictive biomarkers. In particular, FOXP3+ is dominant in predictive models of pathological response in patients with IBC.
摘要：
背景：必须监测浸润性乳腺癌（IBC）患者对新辅助化疗（NC）的反应，和生物标志物是必要的。NC可以在其微环境中激活抗肿瘤免疫反应，称为肿瘤浸润淋巴细胞（TIL）。TIL成分被认为具有很大的潜力作为预测因子是CD4+，CD8+,和FOXP3+TIL。这项研究旨在探索TIL成分，这些成分可能是NC病理反应的预测生物标志物。
方法：根据CD4+、CD8+,和FOXP3+TIL表达与Miller-Payne(MP)分级系统。年龄,肿瘤分级,PR,ER,还评估了Ki-67和HER2。CD4+,CD8+,和FOXP3+TIL表达通过IHC染色进行分析，而其他数据是从档案馆收集的。使用单变量和多变量分析对数据进行分析。
结果：单因素分析显示CD4+TIL和MP之间存在显著关系（p<0.001），CD8+和MP（p=0.004），和FOXP3与MP（p<0.001）。三种生物标志物在一个模型中的同时整合不足以成为预测模型。因此,本研究通过测试结合现有3种生物标志物中2种的几种替代模型进行了探索性分析.结果表明，模型2中的CD4TIL（CD4CD8）和模型4中的FOXP3TIL（CD8FOXP3）显示出明显的系数值。此外，模型4中的所有阈值系数都很重要。
结论：这项研究表明，CD4+，CD8+,和FOXP3+TIL具有作为预测生物标志物的潜力。特别是,FOXP3+在IBC患者病理反应的预测模型中占主导地位。
方法：根据CD4+、CD8+,和FOXP3+TIL表达与Miller-Payne(MP)分级系统。年龄,肿瘤分级,PR,ER,还评估了Ki-67和HER2。CD4+,CD8+,和FOXP3+TIL表达通过IHC染色进行分析，而其他数据是从档案馆收集的。使用单变量和多变量分析对数据进行分析。
结果：单因素分析显示CD4+TIL和MP之间存在显著关系（p<0.001），CD8+和MP（p=0.004），和FOXP3与MP（p<0.001）。三种生物标志物在一个模型中的同时整合不足以成为预测模型。因此,本研究通过测试结合现有3种生物标志物中2种的几种替代模型进行了探索性分析.结果表明，模型2中的CD4TIL（CD4CD8）和模型4中的FOXP3TIL（CD8FOXP3）显示出明显的系数值。此外，模型4中的所有阈值系数都很重要。
结论：这项研究表明，CD4+，CD8+,和FOXP3+TIL具有作为预测生物标志物的潜力。特别是,FOXP3+在IBC患者病理反应的预测模型中占主导地位。
