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Selectively hampered activation of lymph node-resident dendritic cells precedes profound T cell suppression and metastatic spread in the breast cancer sentinel lymph node.

ABSTRACT: BACKGROUND:Immune regulated pathways influence both breast cancer (BrC) development and response to (neo)adjuvant chemotherapy. The sentinel lymph node (SLN), as the first metastatic site, is also the first site where BrC-induced suppression of immune effector subsets occurs. Since intricate knowledge of the phenotypic and functional status of these immune effector subsets is lacking, we set out to map the immune landscape of BrC SLN. METHODS:Viable LN cells from BrC SLN (n = 58) were used for detailed flowcytometry-assisted mapping of the immune landscape of BrC SLN in a comparative analysis with healthy (i.e. prophylactic mastectomy-derived) axillary lymph nodes (HLN, n = 17). Findings were related to clinicopathological characteristics. RESULTS:Our data show that BrC-induced immune suppression in tumor-involved SLN, as evidenced by increased Treg and MDSC rates as well as by a generalized state of T cell anergy, coincides with hampered activation of LN-resident (LNR) dendritic cell (DC) subsets rather than of migratory DC subsets. Importantly, suppression of these LN-resident DC subsets preceded profoundly disabled T cell effector functions in tumor-involved SLN. Furthermore, we provide evidence that the suppressed state of LNR-cDC is not only related to nodal involvement but is also related to high-risk breast cancer subtypes that lack expression of hormone receptors and may be a negative predictor of disease-free survival. CONCLUSION:These data thus provide new insights in the mechanisms underlying loco-regional immune suppression induced by BrC and how these relate to clinical outcome. They identify the LNR-cDC subset as a pivotal regulatory node in cellular immune suppressive pathways and therefore as a promising therapeutic target to combat immune suppression and secure the induction of effective antitumor immunity, e.g. in combination with neo-adjuvant chemotherapy. .

SUBMITTER: van Pul KM

PROVIDER: S-EPMC6530094 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundCurrent practice is to perform a completion axillary lymph node dissection (ALND) for breast cancer patients with tumor-involved sentinel lymph nodes (SLNs), although fewer than half will have non-sentinel node (NSLN) metastasis. Our goal was to develop new models to quantify the risk of NSLN metastasis in SLN-positive patients and to compare predictive capabilities to another widely used model.MethodsWe constructed three models to predict NSLN status: recursive partitioning with receiver operating characteristic curves (RP-ROC), boosted Classification and Regression Trees (CART), and multivariate logistic regression (MLR) informed by CART. Data were compiled from a multicenter Northern California and Oregon database of 784 patients who prospectively underwent SLN biopsy and completion ALND. We compared the predictive abilities of our best model and the Memorial Sloan-Kettering Breast Cancer Nomogram (Nomogram) in our dataset and an independent dataset from Northwestern University.Results285 patients had positive SLNs, of which 213 had known angiolymphatic invasion status and 171 had complete pathologic data including hormone receptor status. 264 (93%) patients had limited SLN disease (micrometastasis, 70%, or isolated tumor cells, 23%). 101 (35%) of all SLN-positive patients had tumor-involved NSLNs. Three variables (tumor size, angiolymphatic invasion, and SLN metastasis size) predicted risk in all our models. RP-ROC and boosted CART stratified patients into four risk levels. MLR informed by CART was most accurate. Using two composite predictors calculated from three variables, MLR informed by CART was more accurate than the Nomogram computed using eight predictors. In our dataset, area under ROC curve (AUC) was 0.83/0.85 for MLR (n = 213/n = 171) and 0.77 for Nomogram (n = 171). When applied to an independent dataset (n = 77), AUC was 0.74 for our model and 0.62 for Nomogram. The composite predictors in our model were the product of angiolymphatic invasion and size of SLN metastasis, and the product of tumor size and square of SLN metastasis size.ConclusionWe present a new model developed from a community-based SLN database that uses only three rather than eight variables to achieve higher accuracy than the Nomogram for predicting NSLN status in two different datasets.

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Selectively hampered activation of lymph node-resident dendritic cells precedes profound T cell suppression and metastatic spread in the breast cancer sentinel lymph node.

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