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De Tayrac M.,University of Rennes 2 – Upper Brittany | De Tayrac M.,University Hospital Rennes | Aubry M.,University of Rennes 2 – Upper Brittany | Aubry M.,University Hospital Rennes | And 16 more authors.
Clinical Cancer Research | Year: 2011

Purpose: Gene expression studies provide molecular insights improving the classification of patients with high-grade gliomas. We have developed a risk estimation strategy based on a combined analysis of gene expression data to search for robust biomarkers associated with outcome in these tumors. Experimental Design: We performed a meta-analysis using 3 publicly available malignant gliomas microarray data sets (267 patients) to define the genes related to both glioma malignancy and patient outcome. These biomarkers were used to construct a risk-score equation based on a Cox proportional hazards model on a subset of 144 patients. External validations were performed on microarray data (59 patients) and on RT-qPCR data (194 patients). The risk-score model performances (discrimination and calibration) were evaluated and compared with that of clinical risk factors, MGMT promoter methylation status, and IDH1 mutational status. Results: This interstudy cross-validation approach allowed the identification of a 4-gene signature highly correlated to survival (CHAF1B, PDLIM4, EDNRB, and HJURP), from which an optimal survival model was built (P < 0.001 in training and validation sets). Multivariate analysis showed that the 4-gene risk score was strongly and independently associated with survival (hazard ratio = 0.46; 95% CI, 0.26-0.81; P = 0.007). Performance estimations indicated that this score added beyond standard clinical parameters and beyond both the MGMT methylation status and the IDH1 mutational status in terms of discrimination (C statistics, 0.827 versus 0.835; P < 0.001). Conclusion: The 4-gene signature provides an independent risk score strongly associated with outcome of patients with high-grade gliomas. ©2011 AACR.


Vauleon E.,Eugene Marquis Cancer Institute | Vauleon E.,French National Center for Scientific Research | Tony A.,French National Center for Scientific Research | Tony A.,Eugene Marquis Cancer Institute | And 12 more authors.
BMC Medical Genomics | Year: 2012

Background: Glioblastoma (GBM) is the most common and lethal primary brain tumor in adults. Several recent transcriptomic studies in GBM have identified different signatures involving immune genes associated with GBM pathology, overall survival (OS) or response to treatment. Methods. In order to clarify the immune signatures found in GBM, we performed a co-expression network analysis that grouped 791 immune-associated genes (IA genes) in large clusters using a combined dataset of 161 GBM specimens from published databases. We next studied IA genes associated with patient survival using 3 different statistical methods. We then developed a 6-IA gene risk predictor which stratified patients into two groups with statistically significantly different survivals. We validated this risk predictor on two other Affymetrix data series, on a local Agilent data series, and using RT-Q-PCR on a local series of GBM patients treated by standard chemo-radiation therapy. Results: The co-expression network analysis of the immune genes disclosed 6 powerful modules identifying innate immune system and natural killer cells, myeloid cells and cytokine signatures. Two of these modules were significantly enriched in genes associated with OS. We also found 108 IA genes linked to the immune system significantly associated with OS in GBM patients. The 6-IA gene risk predictor successfully distinguished two groups of GBM patients with significantly different survival (OS low risk: 22.3months versus high risk: 7.3months; p<0.001). Patients with significantly different OS could even be identified among those with known good prognosis (methylated MGMT promoter-bearing tumor) using Agilent (OS 25 versus 8.1months; p<0.01) and RT-PCR (OS 21.8 versus 13.9months; p<0.05) technologies. Interestingly, the 6-IA gene risk could also distinguish proneural GBM subtypes. Conclusions: This study demonstrates the immune signatures found in previous GBM genomic analyses and suggests the involvement of immune cells in GBM biology. The robust 6-IA gene risk predictor should be helpful in establishing prognosis in GBM patients, in particular in those with a proneural GBM subtype, and even in the well-known good prognosis group of patients with methylated MGMT promoter-bearing tumors. © 2012 Vauleon et al.; licensee BioMed Central Ltd.


Collet B.,Eugene Marquis Cancer Institute | Avril T.,Eugene Marquis Cancer Institute | Avril T.,French National Center for Scientific Research | Aubry M.,University of Rennes 1 | And 8 more authors.
Journal of Proteomics | Year: 2014

Primary cell lines derived as neurospheres, enriched in cancer stem cells, are currently the focus of interest in glioblastoma to test new drugs, because of their tumor initiating abilities and resistance to conventional therapies. However, not all glioblastoma samples are propagatable under neurosphere culture and not all neurosphere cell lines are tumorigenic. These cells therefore cannot recapitulate the heterogeneity of glioblastoma samples. We have conducted a proteomic analysis of primary glioblastoma cell lines derived either as adherent cells in the presence of serum (n. =. 11) or as neurospheres (n. =. 12). A total of 963 proteins were identified by nano-LC/Q-TOF MS: 342 proteins were found only in neurosphere lines and were mostly implicated in various metabolic and cellular processes, while 112 proteins were found only in adherent cells and mostly linked to cell adhesion. A protein signature of 10 proteins, 9 of them involved in a cell adhesion pathway, characterized adherent lines. Neurospheres were characterized by 73 proteins mostly linked to DNA metabolic processes associated to cell cycle and protein metabolism. In the Repository of Molecular Brain Neoplasia Data, expression of genes coding for several proteins related to adherent cells or neurospheres were of prognostic relevance for glioblastoma. Biological significance: Primary cell lines enriched in cancer stem cells (CSC) have become popular models for testing new drugs for glioblastoma. In this proteomic study on an important number of cell lines obtained either as adherent cells in the presence of serum (a classic way to derive cell lines) or as neurospheres (enriched in CSC), we show that each type of cell line displays different GBM-specific features, highlighting that these two culture types are complementary tools for drug screening. © 2014 Elsevier B.V.


Beuzit L.,Rennes University Hospital Center | Beuzit L.,University of Rennes 2 – Upper Brittany | Eliat P.-A.,French Institute of Health and Medical Research | Brun V.,Rennes University Hospital Center | And 7 more authors.
Journal of Magnetic Resonance Imaging | Year: 2016

Purpose To test the reproducibility and accuracy of pharmacokinetic parameter measurements on five analysis software packages (SPs) for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), using simulated and clinical data. Materials and Methods This retrospective study was Institutional Review Board-approved. Simulated tissues consisted of pixel clusters of calculated dynamic signal changes for combinations of Tofts model pharmacokinetic parameters (volume transfer constant [Ktrans], extravascular extracellular volume fraction [ve]), longitudinal relaxation time (T1). The clinical group comprised 27 patients treated for rectal cancer, with 36 3T DCE-MR scans performed between November 2012 and February 2014, including dual-flip-angle T1 mapping and a dynamic postcontrast T1-weighted, 3D spoiled gradient-echo sequence. The clinical and simulated images were postprocessed with five SPs to measure Ktrans, ve, and the initial area under the gadolinium curve (iAUGC). Modified Bland-Altman analysis was conducted, intraclass correlation coefficients (ICCs) and within-subject coefficients of variation were calculated. Results Thirty-one examinations from 23 patients were of sufficient technical quality and postprocessed. Measurement errors were observed on the simulated data for all the pharmacokinetic parameters and SPs, with a bias ranging from -0.19 min-1 to 0.09 min-1 for Ktrans, -0.15 to 0.01 for ve, and -0.65 to 1.66 mmol.L-1.min for iAUGC. The ICC between SPs revealed moderate agreement for the simulated data (Ktrans: 0.50; ve: 0.67; iAUGC: 0.77) and very poor agreement for the clinical data (Ktrans: 0.10; ve: 0.16; iAUGC: 0.21). Conclusion Significant errors were found in the calculated DCE-MRI pharmacokinetic parameters for the perfusion analysis SPs, resulting in poor inter-software reproducibility. © 2015 Wiley Periodicals, Inc.


PubMed | Eugene Marquis Cancer Institute
Type: | Journal: BMC medical genomics | Year: 2012

Glioblastoma (GBM) is the most common and lethal primary brain tumor in adults. Several recent transcriptomic studies in GBM have identified different signatures involving immune genes associated with GBM pathology, overall survival (OS) or response to treatment.In order to clarify the immune signatures found in GBM, we performed a co-expression network analysis that grouped 791 immune-associated genes (IA genes) in large clusters using a combined dataset of 161 GBM specimens from published databases. We next studied IA genes associated with patient survival using 3 different statistical methods. We then developed a 6-IA gene risk predictor which stratified patients into two groups with statistically significantly different survivals. We validated this risk predictor on two other Affymetrix data series, on a local Agilent data series, and using RT-Q-PCR on a local series of GBM patients treated by standard chemo-radiation therapy.The co-expression network analysis of the immune genes disclosed 6 powerful modules identifying innate immune system and natural killer cells, myeloid cells and cytokine signatures. Two of these modules were significantly enriched in genes associated with OS. We also found 108 IA genes linked to the immune system significantly associated with OS in GBM patients. The 6-IA gene risk predictor successfully distinguished two groups of GBM patients with significantly different survival (OS low risk: 22.3 months versus high risk: 7.3 months; p<0.001). Patients with significantly different OS could even be identified among those with known good prognosis (methylated MGMT promoter-bearing tumor) using Agilent (OS 25 versus 8.1 months; p<0.01) and RT-PCR (OS 21.8 versus 13.9 months; p<0.05) technologies. Interestingly, the 6-IA gene risk could also distinguish proneural GBM subtypes.This study demonstrates the immune signatures found in previous GBM genomic analyses and suggests the involvement of immune cells in GBM biology. The robust 6-IA gene risk predictor should be helpful in establishing prognosis in GBM patients, in particular in those with a proneural GBM subtype, and even in the well-known good prognosis group of patients with methylated MGMT promoter-bearing tumors.

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