乳腺癌前哨淋巴结阳性患者腋窝非前哨淋巴结转移风险预测模型的建立和验证

目的 建立预测中国前哨淋巴结阳性的早期乳腺癌患者，其非前哨腋窝淋巴结是否转移的模型；验证美国纪念斯隆-凯特琳癌症中心(MSKCC)的预测模型在中国患者中预测非前哨淋巴结是否转移的临床应用价值。

方法 回顾性分析2011年1月至2022年2月之间，2561例乳腺癌患者经前哨淋巴结活检证实为转移，并行腋窝淋巴结清扫术的临床病理特征，将患者分为训练组和验证组。使用单因素分析，确定非前哨淋巴结转移的预测因素，根据多因素Logistic回归分析的结果构建非前哨腋窝淋巴结转移的预测模型。并在1760例前哨淋巴结阳性的患者中使用MSKCC 预测模型计算中国人群非前哨淋巴结转移的概率。通过计算受试者工作特征曲线下的面积来评估该模型和MSKCC 预测模型在中国早期乳腺癌患者中预测的准确性及临床应用价值。

结果 根据多因素Logistic回归分析显示，前哨淋巴结阳性数量、前哨淋巴结阴性数量、肿瘤分期、是否存在脉管瘤栓、是否存在神经侵犯以及是否存在淋巴结被膜外侵犯是腋窝非前哨淋巴结是否转移的独立预测因素，并根据此结果建立了预测模型。该预测模型在预测非前哨淋巴结是否转移方面显示出了较好的预测能力，训练组和验证组的AUC值分别为0.765和0.741。MSKCC预测模型在中国人群中预测非前哨淋巴结是否转移的AUC值为0.755。

结论 本研究建立并验证了一个Nomogram预测模型，以协助临床医生评估非前哨淋巴结转移的可能性，有助于下一步治疗方式的选择。对于术前已知雌激素受体状况的中国患者，MSKCC预测模型可用于预测腋窝非前哨淋巴结是否转移。

第二章T1N3期和T3N0期乳腺癌组织中M1型巨噬细胞的浸润及意义

目的  探讨T1N3期和T3N0期乳腺癌免疫微环境的差异及M1型巨噬细胞浸润与乳腺癌淋巴结转移的关系。

方法  从国际乳腺癌协会分子分类（METABRIC）数据库中提取9例T1N3期和11例T3N0期乳腺癌患者的RNA-Seq测序数据和临床数据，采用CIBERSORT反卷积算法计算乳腺癌组织中22种浸润性免疫细胞的占比，比较T1N3期和T3N0期乳腺癌组织中免疫细胞浸润的差异。收集2011至2022年间在中国医学科学院肿瘤医院行根治性手术切除的乳腺癌组织标本，T1N3期77例，T3N0期58例，采用免疫组化双染技术检测组织中M1型巨噬细胞的细胞密度，以验证基于METABRIC数据库资料的分析结果。

结果  对METABRIC数据库资料的分析显示，在T1N3期乳腺癌组织中占比最高的浸润性免疫细胞为M1巨噬细胞，占15.85%；在T3N0期乳腺癌组织中占比最高的浸润性免疫细胞为M2巨噬细胞，占13.07%。T1N3期和T3N0期样本中M1巨噬细胞的占比差异有统计学意义（Z=-2.545，P=0.010）。对乳腺癌组织标本的免疫组化双染检测结果显示，T1N3期和T3N0期乳腺癌组织中M1型巨噬细胞的细胞密度[M（IQR）]分别为62.0（72.0）和38.0（58.3）个/mm²，差异有统计学意义（Z=-3.062，P=0.002）。

结论  M1型巨噬细胞在T1N3期乳腺癌患者中密度升高，M1型巨噬细胞的浸润与乳腺癌的淋巴结转移有关。

Chapter 1 Development and validation of nomograms for predicting axillary non-SLN metastases in breast cancer patients: A retrospective analysis

Objective To develop a nomogram for predicting positive non sentinel lymph nodes (non-SLN) in positive-SLN breast cancer patients and validate the Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram for non-SLN metastasis in Chinese patients.

Methods The pathological features of 2561 breast cancer patients were retrospectively reviewed, and the patients were divided into the training and validation cohorts. Positive non-SLN predictors were identified using univariate and multivariate analyses and used to construct the nomogram. In patients with positive SLNs, the MSKCC nomogram was used to calculate the probability of non-SLN metastasis in 1760 breast cancer patients. The area under the receiver operating characteristic curve (AUC) was calculated to assess the accuracy of this model and the MSKCC nomogram.

Results According to multivariate logistic regression analysis, the number of positive and negative SLNs, tumor stage, lymphovascular invasion, perineural invasion, and extracapsular extension were independent predictive factors for non-SLN metastasis and were selected to establish the nomogram for predicting positive non-SLNs. This nomogram performed favorably in predicting positive non-SLNs, with AUCs of 0.765 and 0.741 for the training and validation cohorts, respectively. The MSKCC nomogram predicted non-SLN metastasis with an AUC of 0.755.

Conclusions A nomogram was developed and validated to assist clinicians in evaluating the likelihood of positive non-SLN. For Chinese patients with a known ER status before surgery, the MSKCC nomogram can be used to predict non-SLN metastases.

Chapter 2 Expression and significance of M1 Macrophage in breast cancer: An Analysis of METABRIC Database and Clinical Validation

Objective To Investigate the immune microenvironment difference between stage T1N3 and stage T3N0 breast cancer patients and to explore the relationship between M1 macrophage infiltration and lymph node metastasis in breast cancer.   

Methods Clinical information and RNA-sequencing (RNA-Seq) expression data of stage T1N3 (n=9) and stage T3N0 (n=11) breast cancer patients were extracted from Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases. Using CIBERSORT, the proportions of 22 types of immune cells were calculated, and then the differences in immune cell infiltration between stage T1N3 and T3N0 patients were compared. From 2011 to 2022, pathologic specimens were collected from breast cancer patients who underwent curative resection at the Cancer Hospital, Chinese Academy of Medical Sciences, including 77 at stage T1N3 and 58 at stage T3N0.The METABRIC database analysis results were verified by examining the density of M1 macrophages in tissues using dual-staining immunohistochemistry.

Results METABRIC data analysis showed M1 macrophage was the highest proportion in stage T1N3 breast cancer (15.85%); M2 macrophage was the highest proportion in stage T3N0 breast cancer (13.07%).M1 macrophage proportions were statistically different between patients with stage T1N3 and stage T3N0(Z=-2.545, P=0.010). The dual-staining immunohistochemistry analysis of breast cancer tissues showed M1 macrophage density (median) of 62.0 and 38.0 cells/mm2 for stage T1N3 and T3N0, respectively. The differences were statistically significant (P=0.002).

Conclusions The density of M1 macrophages was notably higher in stage T1N3 patients; M1 macrophage was associated with lymph node metastasis.