GWAS in the SIGNAL/PHARE clinical cohort restricts the association between the FGFR2 locus and estrogen receptor status to HER2-negative breast cancer patients

International audienceGenetic polymorphisms are associated with breast cancer risk. Clinical and epidemiological observations suggest that clinical characteristics of breast cancer, such as estrogen receptor or HER2 status, are also influenced by hereditary factors. To identify genetic variants associated with pathological characteristics of breast cancer patients, a Genome Wide Association Study was performed in a cohort of 9365 women from the French nationwide SIGNAL/PHARE studies (NCT00381901/RECF1098). Strong association between the FGFR2 locus and ER status of breast cancer patients was observed (ER-positive n=6211, ER-negative n=2516; rs3135718 OR=1.34 p=5.46x10-12). This association was limited to patients with HER2-negative tumors (ER-positive n=4267, ER-negative n=1185; rs3135724 OR=1.85 p=1.16x10-11). The FGFR2 locus is known to be associated with breast cancer risk. This study provides sound evidence for an association between variants in the FGFR2 locus and ER status among breast cancer patients, particularly among patients with HER2-negative disease. This refinement of the association between FGFR2 variants and ER-status to HER2-negative disease provides novel insight to potential biological and clinical influence of genetic polymorphisms on breast tumors

Impact of social media in security and crisis management: a review

International audienceSocial media and more generally online social networking technologies have emerged as powerful tools to exchange information among a large variety of players, including the public, authorities, companies and journalists. In this paper, we review the present and potential uses of social media and how to value information they contain to manage security and safety matters. We present some examples of their uses during emergencies and crises, how relevant information can be extracted to support fighting against cybercrime and how security forces and emergency managers may benefit from integrating social media into their organisations. Finally we propose an overview of technical limitations and possible misuses of social media

Contribución a la Paleolimnología del Pleistoceno inferior de la Cuenca de Baza (sector Orce -Venta Micena).

Peer Reviewe

A phase i dose escalation study to determine the optimal biological dose of irosustat, an oral steroid sulfatase inhibitor, in postmenopausal women with estrogen receptor-positive breast cancer

Steroid sulfatase (STS) inhibition may have a therapeutic role in suppression of endocrine-responsive breast cancer. This study aimed to determine the optimal biological dose and recommended dose (RD) of the STS inhibitor irosustat. A three-part, open-label, multicenter, dose escalation study of irosustat in estrogen receptor-positive breast cancer patients involved administration of a single dose of irosustat with a 7-day observation period; followed by a daily oral dose of irosustat for 28 days; and an extension phase, in which the daily oral dose of irosustat was continued at the discretion of the investigator and as long as the patient was benefitting from the treatment. Five doses of irosustat were tested (1, 5, 20, 40, and 80 mg) in 50 patients. After 28 days of daily administration of irosustat, all the evaluated patients in the 5, 20, 40, and 80 mg cohorts achieved ≥95 % STS inhibition in peripheral blood mononuclear cells and corresponding endocrine suppression. The maximum tolerated dose was not reached, and the 40 mg dose was established as the RD. The median time to disease progression in the 40 mg cohort was 11.2 weeks. Disease stabilization was achieved in 10 % of patients potentially indicative of drug activity. Dry skin was the most frequent adverse event. The RD of irosustat is 40 mg. Disease stabilization occurred in 10 % of this heavily pretreated patient population. A larger study is required to define an accurate response rate to irosustat as a single agent and whether co-administration with an aromatase inhibitor is needed. © 2013 Springer Science+Business Media New York.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Hormonoresistance in advanced breast cancer: a new revolution in endocrine therapy

Endocrine therapy is the mainstay of treatment of estrogen-receptor-positive (ER+) breast cancer with an overall survival benefit. However, some adaptive mechanisms in the tumor emerge leading to the development of a resistance to this therapy. A better characterization of this process is needed to overcome this resistance and to develop new tailored therapies. Mechanisms of resistance to hormone therapy result in activation of transduction signal pathways, including the cell cycle regulation with cyclin D/CDK4/6/Rb pathway. The strategy of combined hormone therapy with targeted agents has shown an improvement of progression-free survival (PFS) in several phase II or III trials, including three different classes of drugs: mTOR inhibitors, PI3K and CDK4/6 inhibitors. A recent phase III trial has shown that fulvestrant combined with a CDK 4/6 inhibitor doubles PFS in aromatase inhibitor-pretreated postmenopausal ER+ breast cancer. Other combinations are ongoing to disrupt the interaction between PI3K/AKT/mTOR and cyclin D/CDK4/6/Rb pathways. Despite these successful strategies, reliable and reproducible biomarkers are needed. Tumor genomics are dynamic over time, and blood-based biomarkers such as circulating tumor DNA represent a major hope to elucidate the adaptive mechanisms of endocrine resistance. The optimal combinations and biomarkers to guide this strategy need to be determined

A practical guide for conducting calibration and decision-making optimisation with complex ecological models

pdf available at https://www.preprints.org/manuscript/201912.0249/v1Calibrating ecological models or making decisions with them is an optimisation problem with challenging methodological issues. Depending on the optimisation formulation, there may be a large variety of optimisation configurations (e.g. multiple objectives, constraints, stochastic criteria) and finding a single acceptable solution may be difficult. The challenges are exacerbated by the high computational cost and the non linear or elusive mathematical properties that increased with the complexity of numerical models. From the feedbacks of practitioners, the need for a guideline for conducting optimisation of complex models has emerged. In this context, we propose a practical guide for the complex model optimisation process, covering both calibration and decision-making. The guide sets out the workflow with recommendations for each step based on existing tools and methods usually scattered throughout the literature. This guide is accompanied with an ODDO template (Overview, Design, Details of Optimisation) to standardise the published description of model-based optimisation and suggests research directions

Suivez le guide! Optimiser un modèle complexe suppose une bonne démarche et de bons outils

Face aux enjeux de compréhension des écosystèmes marins et de gestion des usagesmarins, les modèles complexes se révèlent des outils pertinents pour tester lesmodifications induites par le changement global, anticiper des évolutions des socioécosystèmesmarins et aider à la sélection de stratégies de gestion. Construire unmodèle numérique et faire des simulations est une chose, mesurer la confiance dessorties du modèle en est une autre. Une étape indispensable dans l’usage des modèlesnumériques est la confrontation des sorties du modèle aux observations du systèmemodélisé pour caler le modèle. La sélection de stratégies de gestion et la calibrationsont deux finalités de l’optimisation.Les problèmes d’optimisation en modélisation halieutique sont le plus souventcomplexes avec des caractéristiques mathématiques diverses. La fonction à optimiserpeut être déterministe ou stochastique, avec ou sans contraintes, à une ou plusieursdimensions. Le nombre de paramètres à optimiser peut varier de l’unité à plusieurscentaines et le coût informatique peut induire de fortes restrictions sur le nombre desimulations réalisables avec le modèle, d’une centaine à quelques milliers pour lesmoins coûteux.Aucun guide pratique n’est disponible dans la littérature pour mettre en oeuvre uneoptimisation rigoureuse avec un modèle complexe. Nous proposons ici une démarched’optimisation articulée en 3 étapes (prétraitement, choix de l’algorithme et posttraitement),basée des outils et méthodes existants et dont la réalisation peut être nonlinéaire. Ce guide inspiré d’une analyse des expériences d’un groupe de modélisateursouvre des pistes de recherche pour pallier aux difficultés, aux autocensures etfrustrations des modélisateurs