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.