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: 7251050
• 负责人: Peter Poon-Hang Lee
• 金额: $ 35.89万
• 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 2012-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7251050
• 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; Negative Lymph Node; Neoplasm Metastasis; Nodal; Numbers; Outcome; Pathologic; Patients; Personal Satisfaction; Population; Prognostic Factor; Rate; Recurrence; Relapse; Research Personnel; Risk; Sentinel; Sentinel Lymph Node; Staging; Staining method; Stains; Stratification; Survival Rate; Testing; Tumor Cell Invasion; Tumor stage; Work; base; follow-up; insight; lymph nodes; malignant breast neoplasm; novel strategies; novel therapeutics; prognostic; 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）：淋巴结转移是乳腺癌患者临床结局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检测转移的更敏感和更早的方法，并可能提供有关临床结果的额外信息。在初步研究中，我们分析了77例乳腺癌患者的淋巴结免疫状况，并进行了5年的临床随访。我们发现，所有肿瘤相关前哨淋巴结（SLN）和非前哨腋窝淋巴结（NSALN）的免疫特征显著紊乱，CD 4辅助T细胞和CD 1a树突状细胞群减少，从单个淋巴结切片中识别淋巴结转移的平均准确度为95%，灵敏度为96%-与多层次苏木精和伊红染色相比，准确度高20%。有趣的是，我们甚至在一些无肿瘤的NSALN中观察到免疫特征的变化，这表明这种变化可能先于转移。NSALN内的免疫谱变化高度预测无病生存期，并且独立于此类淋巴结的肿瘤侵袭状态。根据NSALN CD 4对T2肿瘤患者进行分层，显示高CD 4人群患者的5年DPS率为88%，而低CD 4人群患者的5年DPS率为0%（p=0.007）-这上级于其他临床或病理因素。该提案的目标是扩展这些发现，以开发一种基于TDLN免疫分析的乳腺癌管理新的临床预后工具。我们将检验的中心假设是，SLN和NSALN的免疫谱分析在预测早期乳腺癌患者的临床结果中增加了对这些淋巴结的肿瘤侵袭状态的实质性预后能力。我们建议确认预后的临床价值NSALN免疫分析（5年DPS）（T和树突状细胞），并研究与其他免疫细胞群的临床相关性（目的1），评估无肿瘤SLN免疫分析的预后临床价值（目的2），并将联合收割机肿瘤侵袭状态和SLN和NSALN的免疫特征结合在一起作为临床结果的综合预测因子（目的3）。如果成功的话，这项工作将建立SLN和NSALN作为一个有用的辅助肿瘤浸润状态作为一个预后因素，预测乳腺癌患者可能复发的免疫分析。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片，这可能导致机制的见解和新的治疗策略。最后，这项工作可能支持一种新的方法来TDLN分析乳腺癌-删除最佳的，最小数量的SLN和NSALN的肿瘤和免疫概况分析作为一个全面的预测临床结果。