Search

Summary For many years, evolutionary and molecular biologists have been working with phylogenetic supertrees, which are oriented acyclic graph structures. In the standard approaches, supertrees are obtained by concatenating a set of phylogenetic trees defined on different but overlapping sets of taxa (i.e. species). More recent approaches propose alternative solutions for supertree inference. The testing of new metrics for comparing supertrees and adapting clustering algorithms to overlapping phylogenetic trees with different numbers of leaves requires large amounts of data. In this context, designing a new approach and developing a computer program to generate phylogenetic tree clusters with different numbers of overlapping leaves are key elements to advance research on phylogenetic supertrees and evolution. The main objective of the project is to propose a new approach to simulate clusters of phylogenetic trees defined on different, but mutually overlapping, sets of taxa, with biological events. The proposed generator can be used to generate a certain number of clusters of phylogenetic trees in Newick format with a variable number of leaves and with a defined level of overlap between trees in clusters. Availability and implementation A Python script version 3.7, called GPTree Cluster, which implements the discussed approach, is freely available at: https://github.com/tahiri-lab/GPTree/tree/GPTreeCluster

The evolutionary histories of genes are susceptible of differing greatly from each other which could be explained by evolutionary variations in horizontal gene transfers or biological recombinations. A phylogenetic tree would therefore represent the evolutionary history of each gene, which may present different patterns from the species tree that defines the main evolutionary patterns. In addition, phylogenetic trees of closely related species should be merged, thus minimizing the topological conflicts they present and obtaining consensus trees (in the case of homogeneous data) or supertrees (in the case of heterogeneous data). The traditional approaches are consensus tree inference (if the set of trees contains the same set of species) or supertrees (if the set of trees contains different, but overlapping sets of species). Consensus trees and supertrees are constructed to produce unique trees. However, these methods lose precision with respect to different evolutionary variability. Other approaches have been implemented to preserve this variability using the [Formula: see text]-means algorithm or the [Formula: see text]-medoids algorithm. Using a new method, we determine all possible consensus trees and supertrees that best represent the most significant evolutionary models in a set of phylogenetic trees, thereby increasing the precision of the results and decreasing the time required. Results: This paper presents in detail a new method for predicting the number of clusters in a Robinson and Foulds (RF) distance matrix using a convolutional neural network (CNN). We developed a new CNN approach (called CNNTrees) for multiple tree classification. This new strategy returns a number of clusters of the input phylogenetic trees for different-size sets of trees, which makes the new approach more stable and more robust. The paper provides an in-depth analysis of the relevant, but very difficult, problem of constructing alternative supertrees using phylogenies with different but overlapping sets of taxa. This new model will play an important role in the inference of Trees of Life (ToL). Availability and implementation: CNNTrees is available through a web server at https://tahirinadia.github.io/ . The source code, data and information about installation procedures are also available at https://github.com/TahiriNadia/CNNTrees . Supplementary information: Supplementary data are available on GitHub platform. The evolutionary history of species is not unique, but is specific to sets of genes. Indeed, each gene has its own evolutionary history that differs considerably from one gene to another. For example, some individual genes or operons may be affected by specific horizontal gene transfer and recombination events. Thus, the evolutionary history of each gene must be represented by its own phylogenetic tree, which may exhibit different evolutionary patterns than the species tree that accounts for the major vertical descent patterns. The result of traditional consensus tree or supertree inference methods is a single consensus tree or supertree. In this paper, we present in detail a new method for predicting the number of clusters in a Robinson and Foulds (RF) distance matrix using a convolutional neural network (CNN). We developed a new CNN approach (CNNTrees) to construct multiple tree classification. This new strategy returns a number of clusters in the order of the input trees, which allows this new approach to be more stable and also more robust.

This prospective study reports the design and results obtained after the EMPODaT project implementation. This project was funded by the Tempus programme of the European Commission with the objective to implement a common postgraduate programme on organ donation and transplantation (ODT) in six selected universities from Middle East/North Africa (MENA) countries (Egypt, Lebanon and Morocco). The consortium, coordinated by the University of Barcelona, included universities from Spain, Germany, Sweden and France. The first phase of the project was to perform an analysis of the current situation in the beneficiary countries, including existing training programmes on ODT, Internet connection, digital facilities and competences, training needs, and ODT activity and accreditation requirements. A total of 90 healthcare postgraduate students participated in the 1-year training programme (30 ECTS academic credits). The methodology was based on e-learning modules and face-to-face courses in English and French. Training activities were evaluated through pre- and post-tests, self-assessment activities and evaluation charts. Quality was assessed through questionnaires and semi-structured interviews. The project results on a reproducible and innovative international postgraduate programme, improvement of knowledge, satisfaction of the participants and confirms the need on professionalizing the activity as the cornerstone to ensure organ transplantation self-sufficiency in MENA countries.

Each gene has its own evolutionary history which can substantially differ from the evolutionary histories of other genes. For example, some individual genes or operons can be affected by specific horizontal gene transfer and recombination events. Thus, the evolutionary history of each gene should be represented by its own phylogenetic tree which may display different evolutionary patterns from the species tree that accounts for the main patterns of vertical descent. The output of traditional consensus tree or supertree inference methods is a unique consensus tree or supertree. Here, we describe a new efficient method for inferring multiple alternative consensus trees and supertrees to best represent the most important evolutionary patterns of a given set of phylogenetic trees (i.e. additive trees or X-trees). We show how a specific version of the popular k-means clustering algorithm, based on some interesting properties of the Robinson and Foulds topological distance, can be used to partition a given set of trees into one (when the data are homogeneous) or multiple (when the data are heterogeneous) cluster(s) of trees. We adapt the popular Caliński-Harabasz, Silhouette, Ball and Hall, and Gap cluster validity indices to tree clustering with k-means. A special attention is paid to the relevant but very challenging problem of inferring alternative supertrees, built from phylogenies constructed for different, but mutually overlapping, sets of taxa. The use of the Euclidean approximation in the objective function of the method makes it faster than the existing tree clustering techniques, and thus perfectly suitable for the analysis of large genomic datasets. In this study, we apply it to discover alternative supertrees characterizing the main patterns of evolution of SARS-CoV-2 and the related betacoronaviruses.

Each gene has its own evolutionary history which can substantially differ from the evolutionary histories of other genes. For example, some individual genes or operons can be affected by specific horizontal gene transfer and recombination events. Thus, the evolutionary history of each gene should be represented by its own phylogenetic tree which may display different evolutionary patterns from the species tree that accounts for the main patterns of vertical descent. The output of traditional consensus tree or supertree inference methods is a unique consensus tree or supertree. We describe a new efficient method for inferring multiple alternative consensus trees and supertrees to best represent the most important evolutionary patterns of a given set of gene phylogenies. We show how an adapted version of the popular k-means clustering algorithm, based on some interesting properties of the Robinson and Foulds distance, can be used to partition a given set of trees into one (for homogeneous data) or multiple (for heterogeneous data) cluster(s) of trees. Moreover, we adapt the popular Cali\'nski-Harabasz, Silhouette, Ball and Hall, and Gap cluster validity indices to tree clustering with k-means. A special attention is given to the relevant but very challenging problem of inferring alternative supertrees. The use of the Euclidean property of the objective function of the method makes it faster than the existing tree clustering techniques, and thus perfectly suitable for analyzing large evolutionary datasets. We apply the new method to discover alternative supertrees characterizing the main patterns of evolution of SARS-CoV-2 and genetically related betacoronaviruses., Comment: Bioinformatics (in press)

Background: Depression is a group of syndromes characterized by notable and persistent mood disorders, and is one of the most prevalent psychiatric disorders, while the existing treatments have an altered risk-benefit balance. The therapeutic properties of Nigella have been confirmed, suggesting the reliance on phytotherapy. The objective of the present paper is to investigate the antidepressive-like effect of Nigella sativa on rats exposed to the Unpredictable Chronic Mild Stress procedure. Methods: Wistar rats were used to investigate the antidepressive-like effect. The stress procedure used in this study combined many stressful conditions. After 6 weeks of treatment, behavioral test (forced swim test) was conducted, and histological changes of the hippocampus were examined. Results: Treatment by nigella and fluoxetine significantly reduced the struggling time. Conclusion: Histopathological analysis showed that control treatments result in more loosely arranged cells, significant apoptotic neurons characterized by an irregular appearance, and pyknotic hyperchromatic. A reduction of the thickness of the pyramid layer was also observed in the groups treated with nigella and fluoxetine, suggesting that nigella could be used as a treatment or an adjuvant preventing depressive-like disorders.

Motivation A phylogenetic tree reconciliation is a mapping of one phylogenetic tree onto another which represents the co-evolution of two sets of taxa (e.g. parasite–host co-evolution, gene–species co-evolution). The reconciliation framework was extended to allow modeling the co-evolution of three sets of taxa such as transcript–gene–species co-evolutions. Several web-based tools have been developed for the display and manipulation of phylogenetic trees and co-phylogenetic trees involving two trees, but there currently exists no tool for visualizing the joint reconciliation between three phylogenetic trees. Results Here, we present DoubleRecViz, a web-based tool for visualizing double reconciliations between phylogenetic trees at three levels: transcript, gene and species. DoubleRecViz extends the RecPhyloXML model—developed for gene–species tree reconciliation—to represent joint transcript–gene and gene–species tree reconciliations. It is implemented using the Dash library, which is a toolbox that provides dynamic visualization functionalities for web data visualization in Python. Availability and implementation DoubleRecViz is available through a web server at https://doublerecviz.cobius.usherbrooke.ca. The source code and information about installation procedures are also available at https://github.com/UdeS-CoBIUS/DoubleRecViz. Supplementary information Supplementary data are available at Bioinformatics online.

One of the major types of cardiovascular diseases is Coronary Artery Disease (CAD). This study tackles the problem of CAD detection using a new accurate hybrid machine learning model. The proposed ensemble model combines several classical machine learning techniques. Our base algorithm is used with four different kernel functions (linear, polynomial, radial basis and sigmoid). The new model was applied to analyze the well-known Cleveland CAD dataset from the UCI repository. To improve the performance of the model, we first selected the most important features of this dataset using a genetic search algorithm. Second, we applied a multi-level filtering technique to balance the data using the ClassBalancer and Resample methods. Our model provided the average CAD prediction accuracy of 98.34% for the Cleveland data (the average was taken over the four kernel functions).

Introduction Le cancer colorectal demeure un véritable fléau et un problème majeur de santé publique. L'objectif principal de cette étude est d'identifier l'impact des facteurs socio-économiques et du niveau d'instruction sur la survenue du cancer colorectal et stade de diagnostic chez la population marocaine. Méthodes Nous avons réalisé une étude cas témoin de janvier 2015 à janvier 2017 au Centre Mohammed VI pour le traitement des cancers. Deux cent vingt-cinq patients et 225 témoins éligibles et consentants sont interviewés de façon prospective en utilisant un questionnaire structuré et pré-testé. Résultats L'âge moyen de nos patients était de 55,49 ± 14,06 ans. Cinquante-trois pourcent ont été diagnostiqués à un stade précoce du cancer du colorectal et 47% à un stade avancé. Par ailleurs, l'analyse détaillée de la population étudiée selon le statut socio-économique (SSE), montre que chez le groupe SSE faible, on note une proportion de 25,33% de patients contre 17,33% de témoins, tandis que dans le groupe SSE moyen, on retrouve des taux équivalents (45,33%) chez les patients et les témoins. Quant au groupe SSE élevé, le taux des patients ne représente que 16,89% contre 37,34% des témoins (P = 0,0001). Le niveau intellectuel et le niveau SSE étaient fortement corrélés au stade du diagnostic avec une différence significative. Ainsi 36,44% des analphabètes ont été diagnostiqués à un stade avancé contre 5,33 des patients ayant un niveau d'étude secondaire ou universitaire avec p = 0,02. De même 20,45% des patients de la catégorie SSE la plus basse étaient diagnostiqués à un stade tardif contre 5,33% des patients avec un SSE le plus élevé avec p=0,03. Conclusion Nos résultats soulignent que le risque du cancer colorectal est fortement dépendant du niveau intellectuel et du statut socio-économique des patients. Une enquête plus approfondie est nécessaire pour clarifier les raisons de cette disparité.

Background Gene trees carry important information about specific evolutionary patterns which characterize the evolution of the corresponding gene families. However, a reliable species consensus tree cannot be inferred from a multiple sequence alignment of a single gene family or from the concatenation of alignments corresponding to gene families having different evolutionary histories. These evolutionary histories can be quite different due to horizontal transfer events or to ancient gene duplications which cause the emergence of paralogs within a genome. Many methods have been proposed to infer a single consensus tree from a collection of gene trees. Still, the application of these tree merging methods can lead to the loss of specific evolutionary patterns which characterize some gene families or some groups of gene families. Thus, the problem of inferring multiple consensus trees from a given set of gene trees becomes relevant. Results We describe a new fast method for inferring multiple consensus trees from a given set of phylogenetic trees (i.e. additive trees or X-trees) defined on the same set of species (i.e. objects or taxa). The traditional consensus approach yields a single consensus tree. We use the popular k-medoids partitioning algorithm to divide a given set of trees into several clusters of trees. We propose novel versions of the well-known Silhouette and Caliński-Harabasz cluster validity indices that are adapted for tree clustering with k-medoids. The efficiency of the new method was assessed using both synthetic and real data, such as a well-known phylogenetic dataset consisting of 47 gene trees inferred for 14 archaeal organisms. Conclusions The method described here allows inference of multiple consensus trees from a given set of gene trees. It can be used to identify groups of gene trees having similar intragroup and different intergroup evolutionary histories. The main advantage of our method is that it is much faster than the existing tree clustering approaches, while providing similar or better clustering results in most cases. This makes it particularly well suited for the analysis of large genomic and phylogenetic datasets.

To measure productivity in scientific publications of teachers of the Faculty of Medicine and Pharmacy of Casablanca (FMPC).This is a descriptive bibliometric study of the publications of the FMPC, indexed in the Medline and Scopus databases, between 2008 and 2017. Articles of physicians affiliated to the FMPC or its university hospital center were included in this study.With 1041 articles, the average scientific productivity of the FMPC was 38 articles / 100 teachers-year. These articles were published in 244 journals of which 18% in Pan African Medical Journal and 67% in French. In 58% of the articles, the type was "case reports". Dermatology and Genetics were the most prolific medical disciplines with 122 and 76 articles respectively. In surgery, Oto-Rhino-Laryngology and Gynecology-Obstetrics ranked first, with respectively 75 and 60 articles. The impact factor of the journals that published the articles of the FMPC ranged from 0.05 to 26.56 and was less than two in 84% of the cases. National and international scientific collaboration was 2.6% and 6.4% respectively.The publication at the FMPC was largely unproductive in English-language journals with a high impact factor. Training in scientific medical writing would be a priority for faculty development and institutional visibility of the FMPC.

A grocery list is an integral part of the shopping experience of many consumers. First, we propose a new machine learning model written in Python 3 that predicts which grocery products the consumer will buy again or will try to buy for the first time, and in which store(s) the purchase will be made. Second, we introduce a smart shopping template to provide consumers with a personalized weekly shopping list based on their shopping history and known preferences. As the explanatory variables, we used available grocery shopping history, weekly product promotion information for a given region, as well as the product price statistics.

Purpose Aberrant glycosylation of the histo-blood group antigens (including the angina bullosa haemorrhagica [ABH]) is often observed during malignant transformation in most types of carcinomas. Data concerning their ethnic distributions are diverse which explains why their biological characteristics have to be studied in different populations. Our aim was to analyze the expression of the histo-blood group (specifically the ABH) antigens in breast carcinoma. Methods The expression of the histo-blood group (specifically the ABH) antigens was studied in 109 patients with breast carcinoma using immunohistochemistry. Statistical analysis was performed using χ2 and Fisher analyses. Results The loss of expression of histo-blood group (ABH) antigens in breast carcinoma was observed in 81.13% of patients with blood group O, 37.93% with blood group A, and 96.30% with blood group B. One key finding of this study was that the loss of expression of the ABH antigen was also observed in normal tissues adjacent to the tumor. The loss of expression was associated with higher tumor grade (p < 0.05). Expression of H antigen was observed in 50% of cases with loss of expression of B antigen and was associated with human epidermal growth factor receptor 2 (HER2) overexpression (p < 0.05). The loss of H antigen in patients with blood group O was associated with estrogen receptor expression (p < 0.001). Incompatible A antigen in tumor was expressed in 20.75% of patients with blood group O. Conclusion Loss of the ABH antigens correlated with the Scarff-Bloom-Richardson histologic grading. H antigen was associated with HER2 overexpression in breast cancer. However, further studies are needed to determine the role of incompatible A antigen in mammary carcinogenesis.

Introduction: le cancer colorectal constitue un probleme majeur de sante publique. L’objectif de notre etude est d’analyser le profil epidemiologique, nutritionnel, clinique et anatomo-pathologique des cancers colorectaux recrutes au CHU de Casablanca. Methodes: notre etude cas-temoins a porte sur les patients pris en charge pour un cancer colorectal durant l’annee 2015, compares a des temoins non suivi pour un cancer. Resultats: l’âge moyen des patients etait de 56,65 ans avec un ecart type de 14,64. Le type histologique le plus frequent chez nos patients etait represente par l’adenocarcinome Lieberkhunien avec une proportion de 82 %. L’analyse de l’indice de masse corporelle a permis de retrouver une obesite chez 50% des patients contre 20% des temoins et un diabete chez 19% des patients versus 8% des temoins (p < 0,019). Par ailleurs, l’etude du regime alimentaire des patients compare a celui des temoins semble montrer que la moyenne de la frequence de consommation hebdomadaire de viandes rouges est plus elevee chez les patients que chez les temoins (4,24 vs 3,26; p = 0,009) et inversement pour la consommation du poissons (0,97 contre 1,76; p = 0,0001). En revanche, la moyenne de consommation des legumes et des fruits est plus faible chez les patients que chez les temoins (5,00 vs 9,50; p = 0,0001). Concernant les habitudes toxiques de nos patients, 32% des patients etaient fumeurs vs 13 % des temoins. Conclusion: nos resultats montrent que la prise de conscience a propos du regime alimentaire et des changements dans nos habitudes de vie pourrait reduire l’incidence du cancer colorectal et par consequent la mortalite et la morbidite.

Raw data relative to Fig. 4. (DOCX 2993 kb)

The intelligent Minkowski and weighted Minkowski K-means are recently developed effective clustering algorithms capable of computing feature weights. Their cluster-specific weights follow the intuitive idea that a feature with a low dispersion in a specific cluster should have a greater weight in this cluster than a feature with a high dispersion. The final clustering provided by these techniques obviously depends on the selection of the Minkowski exponent. The median-based central consensus rule we introduce in this paper allows one to select an optimal value of the Minkowski exponent. Our rule takes into account the values of the Adjusted Rand Index (ARI) between clustering solutions obtained for different Minkowski exponents and selects the clustering that provides the highest average value of ARI. Our simulations, carried out with real and synthetic data, show that the proposed median-based consensus procedure usually outperforms clustering strategies based on the selection of the highest value of the Silhouette or Calinski–Harabasz cluster validity indices.

International audience; Glioblastomas, the most common form of primary brain tumors, are the fourth cause of death by cancer in adults. Increasing evidences suggest that glioblastoma resistance to existing radio- and chemotherapies rely on glioblastoma stem cells (GSCs). GSCs are endowed with a unique combination of stem-like properties alike to normal neural stem cells (NSCs), and of tumor initiating properties. The natural polyphenol resveratrol is known to exert opposite actions on neural cells according to their normal or cancerous status. Here, we used resveratrol to explore the molecular mechanisms differing between GSCs and NSCs. We observed a dual action of resveratrol on GSCs: resveratrol blocked GSC proliferation up to 150 mu M and induced their necrosis at higher doses. On the opposite, resveratrol had no effect on NSC behavior. To determine the mechanisms underlying resveratrol effects, we focused our attention on the family of NAD-dependent deacetylases sirtuins (SIRT). A member of this family, SIRT1, has been repetitively shown to constitute a preferential resveratrol target, at least in normal cells. Western blot analysis showed that SIRT1 and SIRT3 were expressed by both GSCs and NSCs whereas SIRT2 expression was restricted to GSCs. Pharmacological blockade of SIRT2 activity or down-regulation of SIRT2 expression with siRNAs counteracted the inhibitory effect of resveratrol on cell proliferation. On the contrary, inhibition of SIRT2 activity or expression did not counteract GSC necrosis observed in presence of high doses of resveratrol. Our results highlight SIRT2 as a novel target for altering GSC properties.

Introduction le cancer colorectal constitue un problème majeur de santé publique. L’objectif de notre étude est d’analyser le profil épidémiologique, nutritionnel, clinique et anatomo-pathologique des cancers colorectaux recrutés au CHU de Casablanca. Méthodes notre étude cas-témoins a porté sur les patients pris en charge pour un cancer colorectal durant l’année 2015, comparés à des témoins non suivi pour un cancer. Résultats l’âge moyen des patients était de 56,65 ans avec un écart type de 14,64. Le type histologique le plus fréquent chez nos patients était représenté par l’adénocarcinome Lieberkühnien avec une proportion de 82 %. L’analyse de l’indice de masse corporelle a permis de retrouver une obésité chez 50% des patients contre 20% des témoins et un diabète chez 19% des patients versus 8% des témoins (p < 0,019). Par ailleurs, l’étude du régime alimentaire des patients comparé à celui des témoins semble montrer que la moyenne de la fréquence de consommation hebdomadaire de viandes rouges est plus élevée chez les patients que chez les témoins (4,24 vs 3,26; p = 0,009) et inversement pour la consommation du poisson (0,97 contre 1,76; p = 0,0001). En revanche, la moyenne de consommation des légumes et des fruits est plus faible chez les patients que chez les témoins (5,00 vs 9,50; p = 0,0001). Concernant les habitudes toxiques de nos patients, 32% des patients étaient fumeurs vs 13 % des témoins. Conclusion nos résultats montrent que la prise de conscience à propos du régime alimentaire et des changements dans nos habitudes de vie pourrait réduire l’incidence du cancer colorectal et par conséquent la mortalité et la morbidité.

MYCN protooncogene status was assessed for the first time in Morocco in peripheral neuroblastic tumors, including neuroblastoma, ganglioneuroblastoma, and ganglioneuroma. Correlations with age at diagnosis, stage, mitosis-karyorrhexis index, differentiation, and Shimada histology were evaluated. Thirty-six formalin-fixed, paraffin-embedded peripheral neuroblastic tumor tissue specimens collected between 2007 and 2010 from the Pathology Department were assessed for MYCN amplification using fluorescence in situ hybridization. MYCN amplification was found in 27.8% of cases. An association of MYCN amplification with unfavorable Shimada grading, higher mitosis-karyorrhexis index, and undifferentiated morphologic phenotype was found. We found no correlation with older age, advanced stage, or the presence of metastasis. Our results suggested that the presence of MYCN amplification is a strong biological indicator of a poor outcome and aggressive disease in neuroblastoma and nodular ganglioneuroblastoma.

Several algorithms and software have been developed for inferring phylogenetic trees. However, there exist some biological phenomena such as hybridization, recombination, or horizontal gene transfer which cannot be represented by a tree topology. We need to use phylogenetic networks to adequately represent these important evolutionary mechanisms. In this article, we present a new efficient heuristic algorithm for inferring hybridization networks from evolutionary distance matrices between species. The famous Neighbor-Joining concept and the least-squares criterion are used for building networks. At each step of the algorithm, before joining two given nodes, we check if a hybridization event could be related to one of them or to both of them. The proposed algorithm finds the exact tree solution when the considered distance matrix is a tree metric (i.e. it is representable by a unique phylogenetic tree). It also provides very good hybrids recovery rates for large trees (with 32 and 64 leaves in our simulations) for both distance and sequence types of data. The results yielded by the new algorithm for real and simulated datasets are illustrated and discussed in detail.

Background Peripheral neuroblastic tumors (pNTs), including neuroblastoma (NB), ganglioneuroblastoma (GNB) and ganglioneuroma (GN), are extremely heterogeneous pediatric tumors responsible for 15 % of childhood cancer death. The aim of the study was to evaluate the expression of CD44s (‘s’: standard form) cell adhesion molecule by comparison with other specific prognostic markers. Methods An immunohistochemical profile of 32 formalin-fixed paraffin-embedded pNTs tissues, diagnosed between January 2007 and December 2010, was carried out. Results Our results have demonstrated the association of CD44s negative pNTs cells to lack of differentiation and tumour progression. A significant association between absence of CD44s expression and metastasis in human pNTs has been reported. We also found that expression of CD44s defines subgroups of patients without MYCN amplification as evidenced by its association with low INSS stages, absence of metastasis and favorable Shimada histology. Discussion These findings support the thesis of the role of CD44s glycoprotein in the invasive growth potential of neoplastic cells and suggest that its expression could be taken into consideration in the therapeutic approaches targeting metastases. Virtual Slides The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1034403150888863 Résumé Introduction les tumeurs neuroblastiques périphériques (TNPs), comprenant le neuroblastome (NB), le ganglioneuroblastome (GNB) et le ganglioneurome (GN), sont des tumeurs pédiatriques extrêmement hétérogènes responsables de 15% des décès par cancer chez les enfants. Le but de cette étude était d’évaluer l’expression de la molécule d’adhésion cellulaire CD44s (‘s’: pour standard) par rapport à d’autres facteurs pronostiques spécifiques. Méthodes Un profil immunohistochimique de 32 TNPs fixées au formol et incluses en paraffine, diagnostiquées entre Janvier 2007 et Décembre 2010, a été réalisé. Résultats Nos résultats ont mis en évidence l’association des TNPs n’exprimant pas le CD44s avec une perte de différenciation et une progression tumorale et nous avons rapporté une association significative entre l’absence d’expression du CD44s et la présence de métastases. Nous avons également constaté que l’expression du CD44s définit des sous-groupes de patients dans les tumeurs n’amplifiant pas le MYCN, comme en témoigne son association avec les stades INSS bas, l’absence de métastases et l’histologie favorable de Shimada. Discussion Ces résultats appuient l’hypothèse du rôle de la glycoprotéine CD44s dans le potentiel de croissance invasive des cellules néoplasiques et suggèrent que son expression pourrait être prise en considération dans des voies thérapeutiques ciblant les métastases.

Department of computer science, Université du Québec à Montréal, Case postale 8888, Succursale Centre-ville, H3C 3P8 Montréal, CA