Highlights d Single-cell RNA sequencing of human lymph nodes unveils six types of LECs d LECs lining the floor and ceiling of the SCS, MS, and valve are the main types d LECs of the SCS floor and MS highly express neutrophil chemoattractants d Human MS LECs support neutrophil adhesion in the LN medulla via CD209

BackgroundBreast cancer consists of a variety of tumours, which differ by their morphological features, molecular characteristics and outcome. Well-known prognostic factors, e.g. tumour grade and size, Ki-67, hormone receptor status, HER2 expression, lymph node status and patient age have been traditionally related to prognosis. Although the conventional prognostic markers are reliable in general, better markers to predict the outcome of an individual tumour are needed.Matrix metalloproteinase-1 (MMP-1) expression has been reported to inversely correlate with survival in advanced cancers. In breast cancer MMP-1 is often upregulated, especially in basal-type breast tumours. The purpose of this retrospective study was to analyse MMP-1 expression in breast cancer cells and in cancer associated stromal cells and to correlate the results with traditional prognostic factors including p53 and bcl-2, as well as to patient survival in breast cancer subtypes.MethodsImmunohistochemical analysis of MMP-1, ER, PR, Ki-67, HER2, bcl-2, p53 and CK5/6 expression was performed on 125 breast cancers. Statistical analyses were carried out using Kruskal-Wallis and Mann-Whitney -tests. In pairwise comparison Bonferroni-adjustment was applied. Correlations were calculated using Spearman rank-order correlation coefficients. Kaplan-Meier survival analyses were carried out to compare breast cancer-specific survival curves. Factors significantly associated with disease-specific survival in univariate models were included in multivariate stepwise.ResultsPositive correlations were found between tumour grade and MMP-1 expression in tumour cells and in stromal cells. P53 positivity significantly correlated with MMP-1 expression in tumour cells, whereas HER2 expression correlated with MMP-1 both in tumour cells and stromal cells. MMP-1 expression in stromal cells showed a significant association with luminal A and luminal B, HER2 overexpressing and triple-negative breast cancer subtypes.ConclusionsThe most important finding of this study was the independent prognostic value of MMP-1 as well as Ki-67 and bcl-2 expression in tumour cells. Our study showed also that both tumoural and stromal MMP-1 expression is associated with breast tumour progression and poor prognosis. A significant difference of MMP-1 expression by cancer associated stromal cells in luminal A, luminal B and triple-negative breast cancer classes was also demonstrated.Please see related commentary article http://www.biomedcentral.com/1741-7015/9/95

BackgroundThe aim of this study was to use texture analysis (TA) of breast magnetic resonance (MR) images to assist in differentiating estrogen receptor (ER) positive breast cancer molecular subtypes.MethodsTwenty-seven patients with histopathologically proven invasive ductal breast cancer were selected in preliminary study. Tumors were classified into molecular subtypes: luminal A (ER-positive and/or progesterone receptor (PR)-positive, human epidermal growth factor receptor type 2 (HER2) -negative, proliferation marker Ki-67 < 20 and low grade (I)) and luminal B (ER-positive and/or PR-positive, HER2-positive or HER2-negative with high Ki-67 ≥ 20 and higher grade (II or III)). Co-occurrence matrix -based texture features were extracted from each tumor on T1-weighted non fat saturated pre- and postcontrast MR images using TA software MaZda. Texture parameters and tumour volumes were correlated with tumour prognostic factors.ResultsTextural differences were observed mainly in precontrast images. The two most discriminative texture parameters to differentiate luminal A and luminal B subtypes were sum entropy and sum variance (p = 0.003). The AUCs were 0.828 for sum entropy (p = 0.004), and 0.833 for sum variance (p = 0.003), and 0.878 for the model combining texture features sum entropy, sum variance (p = 0.001). In the LOOCV, the AUC for model combining features sum entropy and sum variance was 0.876. Sum entropy and sum variance showed positive correlation with higher Ki-67 index. Luminal B types were larger in volume and moderate correlation between larger tumour volume and higher Ki-67 index was also observed (r = 0.499, p = 0.008).ConclusionsTexture features which measure randomness, heterogeneity or smoothness and homogeneity may either directly or indirectly reflect underlying growth patterns of breast tumours. TA and volumetric analysis may provide a way to evaluate the biologic aggressiveness of breast tumours and provide aid in decisions regarding therapeutic efficacy.