Éditeur(s) : HAL CCSD BioMed Central
Résumé : International audience
Cell proliferation is a hallmark of cancer and depends on complex signaling networks that are chiefly supported by protein kinase activities. Therapeutic strategies have been used to target specific kinases but new methods are required to identify combined targets and improve treatment. Here, we propose a small interfering RNA genetic screen and an integrative approach to identify kinase networks involved in the proliferation of cancer cells. The functional siRNA screen of 714 kinases in HeLa cells identified 91 kinases implicated in the regulation of cell growth, most of them never being reported in previous whole-genome siRNA screens. Based on gene ontology annotations, we have further discriminated between two classes of kinases that, when suppressed, result in alterations of the mitotic index and provoke cell-cycle arrest. Extinguished kinases that lead to a low mitotic index mostly include kinases implicated in cytosolic signaling. In contrast, extinguished kinases that result in a high mitotic index mostly include kinases implicated in cell division. By mapping hit kinases in the PhosphPOINT phosphoprotein database, we generated scale-free networks consisting of 449 and 661 protein-protein interactions for kinases from low MI and high MI groups, respectively. Further analyses of the kinase interactomes revealed specific modules such as FER- and CRKL-containing modules that connect three members of the epidermal growth factor receptor (EGFR) family, suggesting a tight control of the mitogenic EGF-dependent pathway. Based on experimental studies, we confirm the involvement of these two kinases in the regulation of tumor cell growth. Based on a combined approach of large kinome-wide siRNA screens and ontology annotations, our study identifies for the first time two kinase groups differentially implicated in the control of cell proliferation. We further demonstrate that integrative analysis of the kinase interactome provides key information which can be used to facilitate or optimize target design for new therapeutic strategies. The complete list of protein-protein interactions from the two functional kinase groups will provide a useful database for future investigations.
ISSN: 1471-2164

hal-01118475
https://hal-univ-rennes1.archives-ouvertes.fr/hal-01118475
DOI : 10.1186/1471-2164-15-1169
PUBMED : 25540073

Éditeur(s) : HAL CCSD Scientific Research Publishing
Résumé : International audience
In order to study social inequalities, indices can be used to summarize the multiple dimensions of the socioeconomic status. As a part of the Equit'Area Project, a public health program focused on social and environmental health inequalities; a statistical procedure to create (neighborhood) socioeconomic indices was developed. This procedure uses successive principal components analyses to select variables and create the index. In order to simplify the application of the procedure for non-specialists, the R package SesIndexCreatoR was created. It allows the creation of the index with all the possible options of the procedure, the classification of the resulting index in categories using several classical methods, the visualization of the results, and the generation of automatic reports.
ISSN: 2161-718X

hal-01161775
https://hal.archives-ouvertes.fr/hal-01161775
https://hal.archives-ouvertes.fr/hal-01161775/document
https://hal.archives-ouvertes.fr/hal-01161775/file/OJS_2015060814030742.pdf
DOI : 10.4236/ojs.2015.54031

Éditeur(s) : HAL CCSD BioMed Central
Résumé : International audience
Background: The evaluation of geographical healthcare accessibility in residential areas provides crucial information to public policy. Traditional methods - such as Physician Population Ratios (PPR) or shortest travel time - offer only a one-dimensional view of accessibility. This paper developed an improved indicator: the Index of Spatial Accessibility (ISA) to measure geographical healthcare accessibility at the smallest available infra-urban level, that is, the Ilot Regroupe pour des Indicateurs Statistiques. Methods: This study was carried out in the department of Nord, France. Healthcare professionals are geolocalized using postal addresses available on the French state health insurance website. ISA is derived from an Enhanced Two-Step Floating Catchment Area (E2FCA). We have constructed a catchment for each healthcare provider, by taking into account residential building centroids, car travel time as calculated by Google Maps and the edge effect. Principal Component Analyses (PCA) were used to build a composite ISA to describe the global accessibility of different kinds of health professionals. Results: We applied our method to studying geographical healthcare accessibility for pregnant women, by selecting three types of healthcare provider: general practitioners, gynecologists and midwives. A total of 3587 healthcare providers are potentially able to provide care for inhabitants of the department of Nord. On average there are 92 general practitioners, 22 midwives and 21 gynecologists per 100,000 residents. The composite ISA for the three types of healthcare provider is 39 per 100,000 residents. A comparative analysis between ISA and physician-population ratios indicates that ISA represents a more even distribution whereas the physician-population ratios show an 'all-or-nothing' approach. Conclusion: ISA is a multidimensional and improved measure, which combines the volume of services relative to population size with the proximity of services relative to the population's location, available at the smallest feasible geographical scale. It could guide policy makers towards highlighting critical areas in need of more healthcare providers, and these areas should be earmarked for further knowledge-based policy making.
ISSN: 1475-9276

hal-01359567
https://hal-univ-rennes1.archives-ouvertes.fr/hal-01359567
DOI : 10.1186/s12939-016-0411-z
PUBMED : 27485740

Éditeur(s) : HAL CCSD BioMed Central
Résumé : International audience
Background - Pregnant women are a vulnerable population. Although regular follow-ups are recommended during pregnancy, not all pregnant women seek care. This pilot study wanted to assess whether the integration of data from administrative health information systems and socio-economic features allows identifying disparities in prenatal care trajectories.Methods - Prenatal care trajectories were extracted from the permanent sample of the French health insurance information system linked to the hospital discharge information system. The records of 2518 women who gave birth without complications in France in 2009 were analyzed. State sequence data analysis was performed to identify homogeneous groups of prenatal care trajectories. Socio-economic data were used to characterize their living environment.Results -We identified three groups of homogeneous prenatal care trajectories: (i) women with relatively high prenatal care consumption (~11 %), (ii) women with no prenatal care (~21 %), and (iii) women with an intermediate level of prenatal care (~66 %). Analysis of the socio-economic data demonstrated the association between disparities in prenatal care trajectories and the women’s living environment. Women with relatively high care consumption generally lived in socio-economically privileged areas (better education levels, employment status and housing conditions) compared with women with few or no prenatal care.Conclusions -Although ecological, our approach demonstrates that data from health administrative information systems could be used to describe prenatal care. However, more individual variables and an improvement of the data quality are needed to efficiently monitor the content and timing of prenatal care. Moreover, state sequence analysis, which was used in this context for the first time, proves to be an interesting approach to explore care trajectories. Finally, the integration of heterogeneous sources of data, including contextual information, might help identifying areas that require health promotion actions toward vulnerable populations, such as pregnant women.
ISSN: 1472-6963

hal-01169792
https://hal-univ-rennes1.archives-ouvertes.fr/hal-01169792
https://hal-univ-rennes1.archives-ouvertes.fr/hal-01169792/document
https://hal-univ-rennes1.archives-ouvertes.fr/hal-01169792/file/Mining%20care%20trajectories%20using%20health%20administrative.pdf
DOI : 10.1186/s12913-015-0857-5

Éditeur(s) : HAL CCSD
Résumé : National audience
Afin de prendre en compte les inégalités sociales de santé, il est fréquent d'utiliser des indices socio-économique qui synthétisent différents aspects du statut socio-économique (SES) à l'échelle de l'individu, du voisinage ou de la région. De nombreux indices existent déjà mais nombre d'entre eux utilisent un faible nombre de variables, des méthodes d'agrégation simples et/ou sélectionnent les variables uniquement depuis la littérature. Nous avons développé une nouvelle procédure de création d'indices SES visant à sélectionner à partir d'un grand nombre de variables celles qui composeront l'indice et à les agréger à l'aide de techniques d'analyses de données. Cette procédure est plus complexe à mettre en œuvre pour des non statisticiens que d'autres approches existantes, c'est pourquoi nous l'avons implémentée dans un package R nommé SesIndexCreatoR. Ce package vise à donner des outils aussi simples d'emploi que possible pour effectuer la procédure de création et exploiter ses résultats, tout en conservant les différentes possibilités et la flexibilité qu'elle offre.
Deuxièmes rencontres R
Lyon, France

Droits : info:eu-repo/semantics/OpenAccess
hal-01433087
https://hal.archives-ouvertes.fr/hal-01433087
https://hal.archives-ouvertes.fr/hal-01433087/document
https://hal.archives-ouvertes.fr/hal-01433087/file/SesIndexCreatoR_20130328.pdf

Éditeur(s) : HAL CCSD BioMed Central
Résumé : International audience
INTRODUCTION: In order to study social health inequalities, contextual (or ecologic) data may constitute an appropriate alternative to individual socioeconomic characteristics. Indices can be used to summarize the multiple dimensions of the neighborhood socioeconomic status. This work proposes a statistical procedure to create a neighborhood socioeconomic index. METHODS: The study setting is composed of three French urban areas. Socioeconomic data at the census block scale come from the 1999 census. Successive principal components analyses are used to select variables and create the index. Both metropolitan area-specific and global indices are tested and compared. Socioeconomic categories are drawn with hierarchical clustering as a reference to determine "optimal" thresholds able to create categories along a one-dimensional index. RESULTS: Among the twenty variables finally selected in the index, 15 are common to the three metropolitan areas. The index explains at least 57% of the variance of these variables in each metropolitan area, with a contribution of more than 80% of the 15 common variables. CONCLUSIONS: The proposed procedure is statistically justified and robust. It can be applied to multiple geographical areas or socioeconomic variables and provides meaningful information to public health bodies. We highlight the importance of the classification method. We propose an R package in order to use this procedure.
ISSN: 1475-9276

inserm-00809310
http://www.hal.inserm.fr/inserm-00809310
http://www.hal.inserm.fr/inserm-00809310/document
http://www.hal.inserm.fr/inserm-00809310/file/1475-9276-12-21.pdf
DOI : 10.1186/1475-9276-12-21

Éditeur(s) : HAL CCSD BioMed Central
Résumé : International audience
Introduction In order to study social health inequalities, contextual (or ecologic) data may constitute an appropriate alternative to individual socioeconomic characteristics. Indices can be used to summarize the multiple dimensions of the neighborhood socioeconomic status. This work proposes a statistical procedure to create a neighborhood socioeconomic index. Methods The study setting is composed of three French urban areas. Socioeconomic data at the census block scale come from the 1999 census. Successive principal components analyses are used to select variables and create the index. Both metropolitan area-specific and global indices are tested and compared. Socioeconomic categories are drawn with hierarchical clustering as a reference to determine "optimal" thresholds able to create categories along a one-dimensional index. Results Among the twenty variables finally selected in the index, 15 are common to the three metropolitan areas. The index explains at least 57% of the variance of these variables in each metropolitan area, with a contribution of more than 80% of the 15 common variables. Conclusions The proposed procedure is statistically justified and robust. It can be applied to multiple geographical areas or socioeconomic variables and provides meaningful information to public health bodies. We highlight the importance of the classification method. We propose an R package in order to use this procedure.
International Journal for Equity in Health

hal-00854028
https://hal.archives-ouvertes.fr/hal-00854028
https://hal.archives-ouvertes.fr/hal-00854028/document
DOI : 10.1186/1475-9276-12-21