Immune Profile Analysis of Tumor-Draining Lymph Nodes in Breast Cancer

乳腺癌肿瘤引流淋巴结的免疫特征分析

• 批准号: 8061630
• 负责人: Peter Poon-Hang Lee
• 金额: $ 11.61万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-07-01 至 2012-07-10
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8061630
• 关键词: Axillary lymph node group; CD4 Positive T Lymphocytes; Cancer Patient; Cells; Characteristics; Clinical; Dendritic Cells; Diagnostic Neoplasm Staging; Disease-Free Survival; Goals; Helper-Inducer T-Lymphocyte; Hematoxylin and Eosin Staining Method; Histology; Immune; Investigation; Lead; Malignant Neoplasms; Methods; Modeling; Nature; Neoplasm Metastasis; Nodal; Outcome; Pathologic; Patients; Population; Prognostic Factor; Recurrence; Relapse; Research Personnel; Sentinel; Sentinel Lymph Node; Staging; Staining method; Stains; Stratification; Survival Rate; T-Lymphocyte; Testing; Tumor Cell Invasion; Tumor stage; Work; base; clinical practice; follow-up; high risk; insight; lymph nodes; malignant breast neoplasm; novel strategies; novel therapeutics; prognostic; prognostic indicator; tool; tumor

DESCRIPTION (provided by applicant): Lymph node metastasis is well established as one of the strongest prognostic indicators of clinical outcome for patients with breast cancer. Current clinical practice involves only histological examination of nodes for the presence or absence of tumor, ignoring the immunological nature of lymph nodes in cancer. We hypothesize that immune profile analysis of tumor-draining lymph nodes (TDLN) may be a more sensitive and earlier method of detecting metastasis, and may provide additional information on clinical outcome. In preliminary studies, we analyzed the lymph node immune profiles in 77 breast cancer patients with tumor-involved sentinel lymph nodes (SLN) and 5-year clinical follow-up. We found significant perturbations in the immune profiles of all tumor-involved sentinel (SLN) and non-sentinel axillary lymph nodes (NSALN), with decreases in CD4 helper T cell and CD1a dendritic cell populations identifying nodal metastasis with an average accuracy of 95% and sensitivity of 96% from a single nodal section - a 20% greater accuracy compared to multilevel hematoxylin and eosin staining. Intriguingly, we observed immune profile changes even in some tumor-free NSALNs, suggesting that such changes may precede metastasis. Immune profile changes within NSALNs were highly predictive of disease-free survival and independent of tumor invasion status of such nodes. Stratification of patients with T2 tumors by NSALN CD4 showed a 5-year DPS rate of 88% for patients with a high CD4 population, versus 0% for patients with a low CD4 population (p=0.007) - this is superior to other clinical or pathologic factors. The goal of this proposal is to expand on these findings to develop a new clinical prognostic tool for breast cancer management based on immune analysis of TDLNs. The central hypothesis that we will test is that immune profile analysis of SLN and NSALN adds substantial prognostic power to tumor invasion status of such nodes in predicting clinical outcome in early-stage breast cancer patients. We propose to confirm the prognostic clinical value (5-yr DPS) of NSALN immune analysis (T and dendritic cells) in SLN+ patients with a larger, multi-center population, and to investigate clinical correlation with other immune cell populations (Aim 1), to assess the prognostic clinical value of immune analysis of tumor-free SLN (Aim 2), and to combine tumor invasion status and immune profile of SLN and NSALN together as a comprehensive predictor of clinical outcome (Aim 3). If successful, this work will establish immune profile analysis of SLN and NSALN as a useful adjunct to tumor invasion status as a prognostic factor to predict breast cancer patients likely to relapse. In addition, we will identify a more complete picture of immune cell populations impacted by breast cancer within SLN and NSALN that could lead to mechanistic insights and novel therapeutic strategies. Lastly, this work may support a novel approach to TDLN analysis in breast cancer - to remove an optimal, minimum number of SLN and NSALN for tumor and immune profile analysis as a comprehensive predictor of clinical outcome.

描述(由申请人提供)：淋巴转移被公认为乳腺癌患者临床预后的最强预后指标之一。目前的临床实践只涉及对肿瘤存在或不存在的淋巴结进行组织学检查，而忽略了癌症中淋巴结的免疫学性质。我们推测，肿瘤引流淋巴结(TDLN)的免疫图谱分析可能是一种更敏感、更早发现转移的方法，并可能提供更多关于临床结果的信息。在初步研究中，我们分析了77例有肿瘤相关前哨淋巴结(SLN)的乳腺癌患者的淋巴免疫状况，并进行了5年的临床随访。我们发现所有肿瘤相关前哨(SLN)和非前哨腋窝淋巴结(NSALN)的免疫谱显著扰动，CD4辅助T细胞和CD1a树突状细胞数量减少，从单个结节切片识别淋巴结转移的平均准确率和敏感度分别为95%和96%-与多水平苏木精-伊红染色相比，准确率高20%。有趣的是，我们甚至在一些无肿瘤的NSALN中观察到了免疫图谱的变化，这表明这种变化可能发生在转移之前。NSALN内免疫特征的变化对无病生存有很高的预测作用，并且与这些结节的肿瘤侵袭状态无关。根据NSALNCD4对T2肿瘤患者的分层显示，CD4值高的患者的5年DPS率为88%，而CD4值低的患者的5年DPS率为0%(p=0.007)--这优于其他临床或病理因素。这项建议的目的是扩展这些发现，以开发一种基于TDLN免疫分析的乳腺癌治疗的新的临床预后工具。我们将检验的中心假设是，SLN和NSALN的免疫图谱分析在预测早期乳腺癌患者的临床结果时，增加了这些节点的肿瘤侵袭状态的实质性预后能力。我们建议确认NSALN免疫分析(T和树突状细胞)在SLN+患者中的预后临床价值(5年DPS)，并调查与其他免疫细胞群体的临床相关性(AIM 1)，评估无肿瘤SLN免疫分析的预后临床价值(AIM 2)，并结合肿瘤侵袭状态和SLN和NSALN的免疫状况作为临床结果的综合预测因子(AIM 3)。如果成功，这项工作将建立SLN和NSALN的免疫图谱分析，作为肿瘤侵袭状态的有用辅助指标，作为预测乳腺癌患者可能复发的预后因素。此外，我们将确定SLN和NSALN中受乳腺癌影响的免疫细胞群的更完整的图景，这可能导致机械性见解和新的治疗策略。最后，这项工作可能支持乳腺癌TDLN分析的一种新方法--去除用于肿瘤和免疫图谱分析的最优、最小数量的SLN和NSALN，作为临床结果的综合预测因子。