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

描述（由申请人提供）：淋巴结转移是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淋巴结的组织学检查是否存在肿瘤，忽视了肿瘤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敏感和更早发现转移的方法，并可能为临床结果提供额外的信息。在初步研究中，我们分析了77例乳腺癌肿瘤累及前哨淋巴结（SLN）患者的淋巴结免疫特征并进行了5年的临床随访。我们发现所有肿瘤累及的前哨淋巴结（SLN）和非前哨腋窝淋巴结（NSALN）的免疫谱都有明显的紊乱，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率为95%，单个淋巴结切片的灵敏度为96%，与多水平苏木精和伊红染色相比，准确率提高了20%。有趣的是，我们甚至在一些无肿瘤的nsaln中观察到免疫谱的变化，这表明这种变化可能先于转移。nsaln内的免疫谱变化可高度预测无病生存，且与此类淋巴结的肿瘤侵袭状态无关。通过NSALN CD4对T2肿瘤患者进行分层显示，高CD4人群患者的5年DPS率为88%，而低CD4人群患者的5年DPS率为0% (p=0.007) -这优于其他临床或病理因素。本提案的目标是在这些发现的基础上，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。我们将验证的中心假设是，SLN和NSALN的免疫谱分析在预测早期乳腺癌患者的临床结局时，为这些淋巴结的肿瘤侵袭状态增加了实质性的预后能力。我们建议在更大的多中心人群中确认NSALN免疫分析（T细胞和树突状细胞）在SLN+患者中的预后临床价值（5年DPS），并研究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），并将肿瘤侵袭状态和SLN和NSALN的免疫谱结合起来作为临床预后的综合预测因子（Aim 3）。如果成功，这项工作将建立SLN和NSALN的免疫谱分析作为肿瘤侵袭状态的有用辅助因素，作为预测乳腺癌患者复发可能性的预后因素。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，这可能会导致机制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分析乳腺癌的TDLN -去除肿瘤和免疫谱分析的最佳，最小数量的SLN和NSALN，作为临床结果的综合预测因子。

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.