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）：淋巴结转移已被确定为乳腺癌患者临床结果的最强预后指标之一。目前的临床实践仅涉及淋巴结的组织学检查是否存在肿瘤，忽略了癌症淋巴结的免疫学性质。我们假设肿瘤引流淋巴结（TDLN）的免疫谱分析可能是一种更灵敏、更早期的转移检测方法，并且可以提供有关临床结果的更多信息。在初步研究中，我们分析了 77 名具有肿瘤相关前哨淋巴结 (SLN) 的乳腺癌患者的淋巴结免疫特征和 5 年临床随访。我们发现所有肿瘤相关前哨淋巴结 (SLN) 和非前哨腋窝淋巴结 (NSALN) 的免疫特征均存在显着扰动，CD4 辅助 T 细胞和 CD1a 树突状细胞群减少，识别淋巴结转移的平均准确度为 95%单个淋巴结切片的灵敏度为 96%，与多级苏木精和伊红染色相比，准确度提高 20%。有趣的是，我们甚至在一些无肿瘤的 NSALN 中也观察到免疫谱的变化，这表明这种变化可能先于转移。 NSALN 内的免疫特征变化高度预测无病生存，并且与此类节点的肿瘤侵袭状态无关。按 NSALN CD4 对 T2 肿瘤患者进行分层显示，高 CD4 群体的患者 5 年 DPS 率为 88%，而低 CD4 群体的患者为 0% (p=0.007) - 这优于其他临床或病理因素。该提案的目标是扩展这些发现，开发一种基于 TDLN 免疫分析的新的乳腺癌管理临床预后工具。我们将测试的中心假设是，SLN 和 NSALN 的免疫谱分析在预测早期乳腺癌患者的临床结果方面为此类淋巴结的肿瘤侵袭状态增加了实质性的预后能力。我们建议在较大的多中心人群中确认 NSALN 免疫分析（T 细胞和树突状细胞）的预后临床价值（5 年 DPS），并研究与其他免疫细胞群的临床相关性（目标 1） ，评估无肿瘤 SLN 免疫分析的预后临床价值（目标 2），并将 SLN 和 NSALN 的肿瘤侵袭状态和免疫特征结合在一起作为临床结果的综合预测因子（目标 3）。如果成功，这项工作将建立 SLN 和 NSALN 的免疫谱分析，作为肿瘤侵袭状态的有用辅助手段，作为预测乳腺癌患者可能复发的预后因素。此外，我们将更全面地了解 SLN 和 NSALN 内受乳腺癌影响的免疫细胞群，这可能会带来机制见解和新的治疗策略。最后，这项工作可能支持一种乳腺癌 TDLN 分析的新方法——去除最佳、最小数量的 SLN 和 NSALN，用于肿瘤和免疫谱分析，作为临床结果的综合预测因子。