Cédric Balsat

Matrix metalloproteinase-2 governs lymphatic vessel formation as an interstitial collagenase

Lymphatic dysfunctions are associated with several human diseases, including lymphedema and metastatic spread of cancer. Although it is well recognized that lymphatic capillaries attach directly to interstitial matrix mainly composed of fibrillar type I collagen, the interactions occurring between lymphatics and their surrounding matrix have been overlooked. In this study, we demonstrate how matrix metalloproteinase (MMP)-2 drives lymphatic morphogenesis through Mmp2-gene ablation in mice, mmp2 knockdown in zebrafish and in 3D-culture systems, and through MMP2 inhibition. In all models used in vivo (3 murine models and thoracic duct development in zebrafish) and in vitro (lymphatic ring and spheroid assays), MMP2 blockage or down-regulation leads to reduced lymphangiogenesis or altered vessel branching. Our data show that lymphatic endothelial cell (LEC) migration through collagen fibers is affected by physical matrix constraints (matrix composition, density, and cross-linking). Transmission electron microscopy and confocal reflection microscopy using DQ-collagen highlight the contribution of MMP2 to mesenchymal-like migration of LECs associated with collagen fiber remodeling. Our findings provide new mechanistic insight into how LECs negotiate an interstitial type I collagen barrier and reveal an unexpected MMP2-driven collagenolytic pathway for lymphatic vessel formation and morphogenesis.

Identification of the tumour transition states occurring during EMT

In cancer, the epithelial-to-mesenchymal transition (EMT) is associated with tumour stemness, metastasis and resistance to therapy. It has recently been proposed that, rather than being a binary process, EMT occurs through distinct intermediate states. However, there is no direct in vivo evidence for this idea. Here we screen a large panel of cell surface markers in skin and mammary primary tumours, and identify the existence of multiple tumour subpopulations associated with different EMT stages: from epithelial to completely mesenchymal states, passing through intermediate hybrid states. Although all EMT subpopulations presented similar tumour-propagating cell capacity, they displayed differences in cellular plasticity, invasiveness and metastatic potential. Their transcriptional and epigenetic landscapes identify the underlying gene regulatory networks, transcription factors and signalling pathways that control these different EMT transition states. Finally, these tumour subpopulations are localized in different niches that differentially regulate EMT transition states.Epithelial-to-mesenchymal transition in tumour cells occurs through distinct intermediate states, associated with different metastatic potential, cellular properties, gene expression, and chromatin landscape

Whole Slide Quantification of Stromal Lymphatic Vessel Distribution and Peritumoral Lymphatic Vessel Density in Early Invasive Cervical Cancer: A Method Description

Peritumoral Lymphatic Vessel Density (LVD) is considered to be a predictive marker for the presence of lymph node metastases in cervical cancer. However, when LVD quantification relies on conventional optical microscopy and the hot spot technique, interobserver variability is significant and yields inconsistent conclusions. In this work, we describe an original method that applies computed image analysis to whole slide scanned tissue sections following immunohistochemical lymphatic vessel staining. This procedure allows to determine an objective LVD quantification as well as the lymphatic vessel distribution and its heterogeneity within the stroma surrounding the invasive tumor bundles. The proposed technique can be useful to better characterize lymphatic vessel interactions with tumor cells and could potentially impact on prognosis and therapeutic decisions.

Improved computer-assisted analysis of the global lymphatic network in human cervical tissues

Lymphatic dissemination is a key event in cervical cancer progression and related tumor lymphatic markers are viewed as promising prognostic factor of nodal extension. However, validating such parameters requires an objective characterization of the lymphatic vasculature. Here, we performed a global analysis of the lymphatic network using a new computerized method applied on whole uterine cervical digital images. Sixty-eight cases of cervical neoplasia (12 CIN3, 10 FIGO stage 1A and 46 stage IB1) and 10 cases of normal cervical tissue were reacted with antibodies raised against D2-40, D2-40/p16 and D2-40/Ki67. Immunostained structures were automatically detected on whole slides. The lymphatic vessel density (D2-40), proliferating lymphatic vessel density (D2-40/ki67) and spatial lymphatic distribution in respect to the adjacent epithelium were assessed from normal cervix to early cervical cancer and correlated with lymphovascular space invasion and lymph node status. Prominent lymphatic vessel density and proliferating lymphatic vessel density are detected under the transformation zone of benign cervix and no further increase is noted during cancer progression. Notably, a shift of lymphatic vessel distribution toward the neoplastic edges is detected. In IB1 cervical cancer, although intra- and peritumoral lymphatic vessel density are neither correlated with lymphovascular space invasion nor with lymph node metastasis, a specific spatial distribution with more lymphatic vessels in the vicinity of tumor edges is predictive of lymphatic dissemination. Herein, we provide a new computerized method suitable for an innovative detailed analysis of the lymphatic network. We show that the transformation zone of the benign cervix acts as a baseline lymphangiogenic niche before the initiation of neoplastic process. During cancer progression, this specific microenvironment is maintained with lymphatic vessels even in closer vicinity to tumor cells.

Modeling pre-metastatic lymphvascular niche in the mouse ear sponge assay

Lymphangiogenesis, the formation of new lymphatic vessels, occurs in primary tumors and in draining lymph nodes leading to pre-metastatic niche formation. Reliable in vivo models are becoming instrumental for investigating alterations occurring in lymph nodes before tumor cell arrival. In this study, we demonstrate that B16F10 melanoma cell encapsulation in a biomaterial, and implantation in the mouse ear, prevents their rapid lymphatic spread observed when cells are directly injected in the ear. Vascular remodeling in lymph nodes was detected two weeks after sponge implantation, while their colonization by tumor cells occurred two weeks later. In this model, a huge lymphangiogenic response was induced in primary tumors and in pre-metastatic and metastatic lymph nodes. In control lymph nodes, lymphatic vessels were confined to the cortex. In contrast, an enlargement and expansion of lymphatic vessels towards paracortical and medullar areas occurred in pre-metastatic lymph nodes. We designed an original computerized-assisted quantification method to examine the lymphatic vessel structure and the spatial distribution. This new reliable and accurate model is suitable for in vivo studies of lymphangiogenesis, holds promise for unraveling the mechanisms underlying lymphatic metastases and pre-metastatic niche formation in lymph nodes, and will provide new tools for drug testing.

A specific immune and lymphatic profile characterizes the pre-metastatic state of the sentinel lymph node in patients with early cervical cancer

ABSTRACT The lymph node (LN) pre-metastatic niche is faintly characterized in lymphophilic human neoplasia, although LN metastasis is considered as the strongest prognostic marker of patient survival. Due to its specific dissemination through a complex bilateral pelvic lymphatic system, early cervical cancer is a relevant candidate for investigating the early nodal metastatic process. In the present study, we analyzed in-depth both the lymphatic vasculature and the immune climate of pre-metastatic sentinel LN (SLN), in 48 cases of FIGO stage IB1 cervical neoplasms. An original digital image analysis methodology was used to objectively determine whole slide densities and spatial distributions of immunostained structures. We observed a marked increase in lymphatic vessel density (LVD) and a specific capsular and subcapsular distribution in pre-metastatic SLN when compared with non-sentinel counterparts. Such features persisted in the presence of nodal metastatic colonization. The inflammatory profile attested by CD8+, Foxp3, CD20 and PD-1expression was also significantly increased in pre-metastatic SLN. Remarkably, the densities of CD20+ B cells and PD-1 expressing germinal centers were positively correlated with LVD. All together, these data strongly support the existence of a pre-metastatic dialog between the primary tumor and the first nodal relay. Both lymphatic and immune responses contribute to the elaboration of a specific pre-metastatic microenvironment in human SLN. Moreover, this work provides evidence that, in the context of early cervical cancer, a pre-metastatic lymphangiogenesis occurs within the SLN (pre-metastatic niche) and is associated with a specific humoral immune response.

Segmentation of glandular epithelium in colorectal tumours to automatically compartmentalise IHC biomarker quantification: A deep learning approach

HighlightsWe propose a method to automate glandular epithelium segmentation.Our approach combines Deep Learning and a new method of data augmentation.Our algorithm efficiently processes H&E as well as IHC images.Our algorithm achieves state‐of‐the‐art performances.Our method enables to automate the compartmentalisation of IHC biomarker analyses. Graphical abstract Figure. No caption available. ABSTRACT In this paper, we propose a method for automatically annotating slide images from colorectal tissue samples. Our objective is to segment glandular epithelium in histological images from tissue slides submitted to different staining techniques, including usual haematoxylin‐eosin (H&E) as well as immunohistochemistry (IHC). The proposed method makes use of Deep Learning and is based on a new convolutional network architecture. Our method achieves better performances than the state of the art on the H&E images of the GlaS challenge contest, whereas it uses only the haematoxylin colour channel extracted by colour deconvolution from the RGB images in order to extend its applicability to IHC. The network only needs to be fine‐tuned on a small number of additional examples to be accurate on a new IHC dataset. Our approach also includes a new method of data augmentation to achieve good generalisation when working with different experimental conditions and different IHC markers. We show that our methodology enables to automate the compartmentalisation of the IHC biomarker analysis, results concurring highly with manual annotations.