Simultaneous analysis of distinct Omics data sets with integration of biological knowledge: Multiple Factor Analysis approach

<p>Abstract</p> <p>Background</p> <p>Genomic analysis will greatly benefit from considering in a global way various sources of molecular data with the related biological knowledge. It is thus of great importance to provide useful integrative approaches dedicated to ease the interpretation of microarray data.</p> <p>Results</p> <p>Here, we introduce a data-mining approach, Multiple Factor Analysis (MFA), to combine multiple data sets and to add formalized knowledge. MFA is used to jointly analyse the structure emerging from genomic and transcriptomic data sets. The common structures are underlined and graphical outputs are provided such that biological meaning becomes easily retrievable. Gene Ontology terms are used to build gene modules that are superimposed on the experimentally interpreted plots. Functional interpretations are then supported by a step-by-step sequence of graphical representations.</p> <p>Conclusion</p> <p>When applied to genomic and transcriptomic data and associated Gene Ontology annotations, our method prioritize the biological processes linked to the experimental settings. Furthermore, it reduces the time and effort to analyze large amounts of 'Omics' data.</p

Combining evidence, biomedical literature and statistical dependence: new insights for functional annotation of gene sets

BACKGROUND: Large-scale genomic studies based on transcriptome technologies provide clusters of genes that need to be functionally annotated. The Gene Ontology (GO) implements a controlled vocabulary organised into three hierarchies: cellular components, molecular functions and biological processes. This terminology allows a coherent and consistent description of the knowledge about gene functions. The GO terms related to genes come primarily from semi-automatic annotations made by trained biologists (annotation based on evidence) or text-mining of the published scientific literature (literature profiling). RESULTS: We report an original functional annotation method based on a combination of evidence and literature that overcomes the weaknesses and the limitations of each approach. It relies on the Gene Ontology Annotation database (GOA Human) and the PubGene biomedical literature index. We support these annotations with statistically associated GO terms and retrieve associative relations across the three GO hierarchies to emphasise the major pathways involved by a gene cluster. Both annotation methods and associative relations were quantitatively evaluated with a reference set of 7397 genes and a multi-cluster study of 14 clusters. We also validated the biological appropriateness of our hybrid method with the annotation of a single gene (cdc2) and that of a down-regulated cluster of 37 genes identified by a transcriptome study of an in vitro enterocyte differentiation model (CaCo-2 cells). CONCLUSION: The combination of both approaches is more informative than either separate approach: literature mining can enrich an annotation based only on evidence. Text-mining of the literature can also find valuable associated MEDLINE references that confirm the relevance of the annotation. Eventually, GO terms networks can be built with associative relations in order to highlight cooperative and competitive pathways and their connected molecular functions

Mouse genetic background impacts both on iron and non-iron metals parameters and on their relationships

International audienceIron is reported to interact with other metals. In addition, it has been shown that genetic background may impact iron metabolism. Our objective was to characterize, in mice of three genetic backgrounds, the links between iron and several non-iron metals. Thirty normal mice (C57BL/6, Balb/c and DBA/2; n = 10 for each group), fed with the same diet, were studied. Quantification of iron, zinc, cobalt, copper, manganese, magnesium and rubidium was performed by ICP/MS in plasma, erythrocytes, liver and spleen. Transferrin saturation was determined. Hepatic hepcidin1 mRNA level was evaluated by quantitative RT-PCR. As previously reported, iron parameters were modulated by genetic background with significantly higher values for plasma iron parameters and liver iron concentration in DBA/2 and Balb/c strains. Hepatic hepcidin1 mRNA level was lower in DBA/2 mice. No iron parameter was correlated with hepcidin1 mRNA levels. Principal component analysis of the data obtained for non-iron metals indicated that metals parameters stratified the mice according to their genetic background. Plasma and tissue metals parameters that are dependent or independent of genetic background were identified. Moreover, relationships were found between plasma and tissue content of iron and some other metals parameters. Our data: (i) confirms the impact of the genetic background on iron parameters, (ii) shows that genetic background may also play a role in the metabolism of non-iron metals, (iii) identifies links between iron and other metals parameters which may have implications in the understanding and, potentially, the modulation of iron metabolis

DGKI Methylation Status Modulates the Prognostic Value of MGMT in Glioblastoma Patients Treated with Combined Radio-Chemotherapy with Temozolomide

International audienceBackgroundConsistently reported prognostic factors for glioblastoma (GBM) are age, extent of surgery, performance status, IDH1 mutational status, and MGMT promoter methylation status. We aimed to integrate biological and clinical prognostic factors into a nomogram intended to predict the survival time of an individual GBM patient treated with a standard regimen. In a previous study we showed that the methylation status of the DGKI promoter identified patients with MGMT-methylated tumors that responded poorly to the standard regimen. We further evaluated the potential prognostic value of DGKI methylation status.Methods399 patients with newly diagnosed GBM and treated with a standard regimen were retrospectively included in this study. Survival modelling was performed on two patient populations: intention-to-treat population of all included patients (population 1) and MGMT-methylated patients (population 2). Cox proportional hazard models were fitted to identify the main prognostic factors. A nomogram was developed for population 1. The prognostic value of DGKI promoter methylation status was evaluated on population 1 and population 2.ResultsThe nomogram-based stratification of the cohort identified two risk groups (high/low) with significantly different median survival. We validated the prognostic value of DGKI methylation status for MGMT-methylated patients. We also demonstrated that the DGKI methylation status identified 22% of poorly responding patients in the low-risk group defined by the nomogram.ConclusionsOur results improve the conventional MGMT stratification of GBM patients receiving standard treatment. These results could help the interpretation of published or ongoing clinical trial outcomes and refine patient recruitment in the future

Five distinct biological processes and 14 differentially expressed genes characterize TEL/AML1-positive leukemia

<p>Abstract</p> <p>Background</p> <p>The t(12;21)(p13;q22) translocation is found in 20 to 25% of cases of childhood B-lineage acute lymphoblastic leukemia (B-ALL). This rearrangement results in the fusion of <it>ETV6 </it>(<it>TEL</it>) and <it>RUNX1 </it>(<it>AML1</it>) genes and defines a relatively uniform category, although only some patients suffer very late relapse. <it>TEL/AML1</it>-positive patients are thus an interesting subgroup to study, and such studies should elucidate the biological processes underlying TEL/AML1 pathogenesis. We report an analysis of gene expression in 60 children with B-lineage ALL using Agilent whole genome oligo-chips (44K-G4112A) and/or real time RT-PCR.</p> <p>Results</p> <p>We compared the leukemia cell gene expression profiles of 16 <it>TEL/AML1</it>-positive ALL patients to those of 44 <it>TEL/AML1</it>-negative patients, whose blast cells did not contain any additional recurrent translocation. Microarray analyses of 26 samples allowed the identification of genes differentially expressed between the TEL/AML1-positive and negative ALL groups. Gene enrichment analysis defined five enriched GO categories: cell differentiation, cell proliferation, apoptosis, cell motility and response to wounding, associated with 14 genes -<it>RUNX1, TCFL5, TNFRSF7, CBFA2T3</it>, <it>CD9</it>, <it>SCARB1, TP53INP1, ACVR1C, PIK3C3, EGFL7</it>, <it>SEMA6A, CTGF, LSP1, TFPI </it>– highlighting the biology of the <it>TEL/AML1 </it>sub-group. These results were first confirmed by the analysis of an additional microarray data-set (7 patient samples) and second by real-time RT-PCR quantification and clustering using an independent set (27 patient samples). Over-expression of <it>RUNX1 (AML1) </it>was further investigated and in one third of the patients correlated with cytogenetic findings.</p> <p>Conclusion</p> <p>Gene expression analyses of leukemia cells from 60 children with <it>TEL/AML1</it>-positive and -negative B-lineage ALL led to the identification of five biological processes, associated with 14 validated genes characterizing and highlighting the biology of the <it>TEL/AML1</it>-positive ALL sub-group.</p

DNA methylation in glioblastoma: impact on gene expression and clinical outcome

International audienceBACKGROUND: Changes in promoter DNA methylation pattern of genes involved in key biological pathways have been reported in glioblastoma. Genome-wide assessments of DNA methylation levels are now required to decipher the epigenetic events involved in the aggressive phenotype of glioblastoma, and to guide new treatment strategies. RESULTS: We performed a whole-genome integrative analysis of methylation and gene expression profiles in 40 newly diagnosed glioblastoma patients. We also screened for associations between the level of methylation of CpG sites and overall survival in a cohort of 50 patients uniformly treated by surgery, radiotherapy and chemotherapy with concomitant and adjuvant temozolomide (STUPP protocol). The methylation analysis identified 616 CpG sites differentially methylated between glioblastoma and control brain, a quarter of which was differentially expressed in a concordant way. Thirteen of the genes with concordant CpG sites displayed an inverse correlation between promoter methylation and expression level in glioblastomas: B3GNT5, FABP7, ZNF217, BST2, OAS1, SLC13A5, GSTM5, ME1, UBXD3, TSPYL5, FAAH, C7orf13, and C3orf14. Survival analysis identified six CpG sites associated with overall survival. SOX10 promoter methylation status (two CpG sites) stratified patients similarly to MGMT status, but with a higher Area Under the Curve (0.78 vs. 0.71, p-value < 5e-04). The methylation status of the FNDC3B, TBX3, DGKI, and FSD1 promoters identified patients with MGMT-methylated tumors that did not respond to STUPP treatment (p-value < 1e-04). CONCLUSIONS: This study provides the first genome-wide integrative analysis of DNA methylation and gene expression profiles obtained from the same GBM cohort. We also present a methylome-based survival analysis for one of the largest uniformly treated GBM cohort ever studied, for more than 27,000 CpG sites. We have identified genes whose expression may be tightly regulated by epigenetic mechanisms and markers that may guide treatment decisions

Catégorisation des glioblastomes : typologie et profilage du génome. [Classification of glioblastoma and genome profiling].

International audienc

Étude prospective randomisée comparant sacropexie infracoccygienne (IVS postérieur) et sacro-spino-fixation selon Richter dans les prolapsus du dôme vaginal

MONTPELLIER-BU Médecine UPM (341722108) / SudocPARIS-BIUM (751062103) / SudocMONTPELLIER-BU Médecine (341722104) / SudocSudocFranceF

Analyse génomique et transcriptomique des glioblastomes multiformes

Les glioblastomes comptent parmi les tumeurs du système nerveux central les plus dévastatrices. Les avancées de la Génomique Fonctionnelle et notamment le développement des technologies "haut-débit" - comme celui des puces à ADN - permet aujourd'hui l'appréhension globale du génome et du transcriptome de ces tumeurs. Elles rendent possibles l'identification de molécules impliquées dans l'initiation, le développement et la progression tumorale ainsi que la découverte de biomarqueurs utiles à améliorer la prise en charge des patients. Les travaux de recherche que nous rapportons ici s'incrivent dans un tel contexte, l'objectif initial ayant été de participer à la caractérisation génomique et transcriptomique des glioblastomes. Plus précisément, nous avons identifié les altérations "pilotes" de l'ADN, - celles susceptibles d'exercer des effets pro-tumoraux en modifiant l'expression ou la fonction de certains gènes et particulièrement ceux capables de participer à la dérégulation des voies de signalisation essentielles à la tumorigénèse et à la survie des cellules. La réalisation de ces travaux a nécessité un développement méthodologique préalable afin de permettre l'appréhension simultanée des données issues des profilages pangénomiques du génome et du transcriptome. L'obtention de ces profilages et l'identification de ces altérations "pilotes" a ensuite permis d'établir une signature moléculaire caractéristique des glioblastomes. Nous avons également étendu nos travaux à la recherche de biomarqueurs pour le diagnostic et pronostic des patients atteints de gliomes malins. Cette thèse a fait l'objet d'une approche pluridisciplinaire, - approche nécessaire à la réalisation des études "haut-débit". Nous avons par conséquent tenu à constituer une introduction conséquente dont la lecture donnerait à chacun, - cliniciens, biologistes, statisticiens -, les fondamentaux nécessaires à la compréhension de notre travail.Glioblastomas are among the most devastating of human nervous system tumors. Advances in Functional Genomic and particularly in the development of high-throughput technologies - such as microarrays -, allow the analysis of both DNA alterations and gene expression changes on a genome-wide scale. They make possible the identification of molecules involved in tumor initiations, development and progression as well as the discovery of useful biomarkers to improve patient care. The present research takes place in such a context. Our initial objective was to provide genomic and transcriptomic characterization of glioblastomas. We had specificially to identify the DNA alterations that directly drives the disease process - i.e. alterations that may exert their tumor-promoting effect by modifying the expression or function of distinct genes, so as to deregulate growth factor signaling and survivor pathways. Completion of this project has firstly required a methodological development to allow the simultaneous analysis of large scale data sets coming from different "-omic" areas. Glioblastoma genome and transcriptome profiling were obtained and combined to provide a robust molecular signature characterizing these tumors. We have also expanded our work to search for biomarkers for diagnosis and prognosis of patient with malignant gliomas. This thesis has been driven by a multidisciplinary approach - such an approach being necessary to the analysis of high throughput studies. We therefore wished to provide a substantial introduction, which would help everyone - clinicians, biologists, and statisticians - to get the fundamentals required to understand our work.RENNES1-BU Santé (352382103) / SudocSudocFranceF

Biomarkers for the prognosis and high-grade glioma clinical outcome

The present invention relates to the identification and use of biomarkers with clinical relevance to high grade gliomas (HGGs). In particular, the invention provides the identity of four genes, CHAF1B, PDLIM4, EDNRB and HJURP, whose expression, at the transcriptome and proteome levels, is correlated with HGG grading and clinical survival outcome. Methods and kits are provided for using these biomarkers in the prognostication of HGGs, and in the selection and/or monitoring of treatment regimens