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Purpose: There is growing evidence that interaction between stromal and tumor cells is pivotal in breast cancer progression and response to therapy. Based on earlier research suggesting that during breast cancer progression, striking changes occur in CD10+ stromal cells, we aimed to better characterize this cell population and its clinical relevance.Experimental Design: We developed a CD10+ stroma gene expression signature (using HG U133 Plus 2.0) on the basis of the comparison of CD10 cells isolated from tumoral (n = 28) and normal (n = 3) breast tissue. We further characterized the CD10+ cells by coculture experiments of representative breast cancer cell lines with the different CD10+ stromal cell types (fibroblasts, myoepithelial, and mesenchymal stem cells). We then evaluated its clinical relevance in terms of in situ to invasive progression, invasive breast cancer prognosis, and prediction of efficacy of chemotherapy using publicly available data sets.Results: This 12-gene CD10+ stroma signature includes, among others, genes involved in matrix remodeling (MMP11, MMP13, and COL10A1) and genes related to osteoblast differentiation (periostin). The coculture experiments showed that all 3 CD10+ cell types contribute to the CD10+ stroma signature, although mesenchymal stem cells have the highest CD10+ stroma signature score. Of interest, this signature showed an important role in differentiating in situ from invasive breast cancer, in prognosis of the HER2+ subpopulation of breast cancer only, and potentially in nonresponse to chemotherapy for those patients.Conclusions: Our results highlight the importance of CD10+ cells in breast cancer prognosis and efficacy of chemotherapy, particularly within the HER2+ breast cancer disease. Clin Cancer Res; 18(4); 1004–14. ©2012 AACR.

International audience; Purpose - To describe the burden, and characteristics, of influenza-like illness (ILI) associated with non-influenza respiratory viruses (NIRV). Methods - We performed a prospective, multicenter, observational study of adults admitted with ILI during three influenza seasons (2012-2015). Patients were screened for picornavirus, respiratory syncytial virus (RSV), coronavirus, human metapneumovirus, adenovirus, bocavirus, parainfluenza virus, and influenza, by PCR on nasopharyngeal samples. We excluded patients coinfected with NIRV and influenza. Results - Among 1421 patients enrolled, influenza virus was detected in 535 (38%), and NIRV in 215 (15%), mostly picornavirus (n = 61), RSV (n = 53), coronavirus 229E (n = 48), and human metapneumovirus (n = 40). In-hospital mortality was 5% (NIRV), 4% (influenza), and 5% (no respiratory virus). As compared to influenza, NIRV were associated with age (median, 73 years vs. 68, P = 0.026), chronic respiratory diseases (53% vs. 45%, P = 0.034), cancer (14% vs. 9%, P = 0.029), and immunosuppressive drugs (21% vs. 14%, P = 0.028), and inversely associated with diabetes (18% vs. 25%, P = 0.038). On multivariable analysis, only chronic respiratory diseases (OR 1.5 [1.1-2.0], P = 0.008), and diabetes (OR 0.5 [0.4-0.8], P = 0.01) were associated with NIRV detection. Conclusions - NIRV are common in adults admitted with ILI during influenza seasons. Outcomes are similar in patients with NIRV, influenza, or no respiratory virus.

One of the key mechanisms linking cell signaling and control of gene expression is reversible phosphorylation of transcription factors. FOXC2 is a forkhead transcription factor that is mutated in the human vascular disease lymphedema-distichiasis and plays an essential role in lymphatic vascular development. However, the mechanisms regulating FOXC2 transcriptional activity are not well understood. We report here that FOXC2 is phosphorylated on eight evolutionarily conserved proline-directed serine/threonine residues. Loss of phosphorylation at these sites triggers substantial changes in the FOXC2 transcriptional program. Through genome-wide location analysis in lymphatic endothelial cells, we demonstrate that the changes are due to selective inhibition of FOXC2 recruitment to chromatin. The extent of the inhibition varied between individual binding sites, suggesting a novel rheostat-like mechanism by which expression of specific genes can be differentially regulated by FOXC2 phosphorylation. Furthermore, unlike the wild-type protein, the phosphorylation-deficient mutant of FOXC2 failed to induce vascular remodeling in vivo. Collectively, our results point to the pivotal role of phosphorylation in the regulation of FOXC2-mediated transcription in lymphatic endothelial cells and underscore the importance of FOXC2 phosphorylation in vascular development.

In this paper, we present an efficient approach for unsupervised segmenta- tion of natural and textural images based on the extraction o fi mage features and a fast active contour segmentation model. We address the problem of textures where neither the gray-level information nor the boundary information is adequate for object extraction. This is often the case of natural images compose do f both homogeneous and textured regions. Because these images cannot be in generaldirectly processed by the gray-level information, we propose a new texture descripto rw hich intrinsically def ines the geometry of textures using semi-local image informatio na nd tools from differen- tial geometry. Then, we use the popular Kullback-Leibler distance to design an active contour model which distinguishes the background and textures of interest. The exis- tence of a minimizing solution to the proposed segmentationmodel is proven. Finally, at exture segmentation algorithm based on the Split-Bregma nm ethod is introduced to extract meaningful objects in a fast way. Promising synthetic and real-world results for gray-scale and color images are presented.

PURPOSE: There is growing evidence that interaction between stromal and tumor cells is pivotal in breast cancer progression and response to therapy. Based on earlier research suggesting that during breast cancer progression, striking changes occur in CD10(+) stromal cells, we aimed to better characterize this cell population and its clinical relevance. EXPERIMENTAL DESIGN: We developed a CD10(+) stroma gene expression signature (using HG U133 Plus 2.0) on the basis of the comparison of CD10 cells isolated from tumoral (n = 28) and normal (n = 3) breast tissue. We further characterized the CD10(+) cells by coculture experiments of representative breast cancer cell lines with the different CD10(+) stromal cell types (fibroblasts, myoepithelial, and mesenchymal stem cells). We then evaluated its clinical relevance in terms of in situ to invasive progression, invasive breast cancer prognosis, and prediction of efficacy of chemotherapy using publicly available data sets. RESULTS: This 12-gene CD10(+) stroma signature includes, among others, genes involved in matrix remodeling (MMP11, MMP13, and COL10A1) and genes related to osteoblast differentiation (periostin). The coculture experiments showed that all 3 CD10(+) cell types contribute to the CD10(+) stroma signature, although mesenchymal stem cells have the highest CD10(+) stroma signature score. Of interest, this signature showed an important role in differentiating in situ from invasive breast cancer, in prognosis of the HER2(+) subpopulation of breast cancer only, and potentially in nonresponse to chemotherapy for those patients. CONCLUSIONS: Our results highlight the importance of CD10(+) cells in breast cancer prognosis and efficacy of chemotherapy, particularly within the HER2(+) breast cancer disease. Clin Cancer Res; 18(4); 1004-14. ©2012 AACR., JOURNAL ARTICLE, SCOPUS: ar.j, info:eu-repo/semantics/published

We propose a semi-supervised image segmentation method that relies on a non-local continuous version of the min-cut algorithm and labels or seeds provided by a user. The segmentation process is performed via energy minimization. The proposed energy is composed of three terms. The first term defines labels or seed points assigned to objects that the user wants to identify and the background. The second term carries out the diffusion of object and background labels and stops the diffusion when the interface between the object and the background is reached. The diffusion process is performed on a graph defined from image intensity patches. The graph of intensity patches is known to better deal with textures because this graph uses semi-local and non-local image information. The last term is the standard TV term that regularizes the geometry of the interface. We introduce an iterative scheme that provides a unique minimizer. Promising results are presented on synthetic textures a nd real-world images.

Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the segmentation. However, such priors tend to limit these methods to a specific target or imaging settings, and they are not always applicable to pathological cases. This work introduces a semi-supervised segmentation framework for ultrasound imaging that alleviates the limitation of fully automatic segmentation, that is, it is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm. We validate our segmentation framework on clinical ultrasound imaging (prostate, fetus, and tumors of the liver and eye). We obtain high similarity agreement with the ground truth provided by medical expert delineations in all applications (94% DICE values in average) and the proposed algorithm performs favorably with the literature.

Segmenting ultrasound images is a challenging problem where standard unsupervised segmentation methods such as the well-known Chan-Vese method fail. We propose in this paper an efficient segmentation method for this class of images. Our proposed algorithm is based on a semi-supervised approach (user labels) and the use of image patches as data features. We also consider the Pearson distance between patches, which has been shown to be robust w.r.t speckle noise present in ultrasound images. Our results on phantom and clinical data show a very high similarity agreement with the ground truth provided by a medical expert.