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

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

FLOW CYTOMETRY ANALYSIS OF TUMOR-INFILTRATING CELLS IN A LARGE SERIES OF GLIOBLASTOMA PATIENTS: IMPACT OF LYMPHOCYTE INFILTRATION ON SURVIVAL

4th Quadrennial Meeting of the World-Federation-of-Neuro-Oncology (WFNO) held in conjunction with the 18th Annual Meeting of the Society-for-Neuro-Oncology (SNO), San Francisco, CA, NOV 21-24, 2013International audienceno abstrac

Integration of Genomic Alterations and Expression Profiling in Glioblastoma Multiforme.

International audiencePurpose: Glioblastomas (GBM) are highly malignant and heterogeneous gliomas with very poor prognosis. The biological and molecular characterization of these tumors is still challenging and impacts their therapeutic management. Previous genomic surveys have revealed the highly rearranged nature of GBM genome and transcriptome. However, the impacts of tumor DNA aberrations on gene expression remain unclear. Methods: We investigated copy number alterations (CNA) and gene expression to identify causal genetic events in GBM. High-resolution maps of somatic chromosomal alterations were obtained for 20 GBM. Gene expression profiling was carried out on the same tumor samples, and compared to those obtained on nonneoplastic brain samples. Concordance between CNA and gene expression was identified by two complementary approaches (correlated or targeted probes). The resulting GBM signature was validated with an independent microarray data set of 81 GBM and 23 normal brains. Results: Loci targeted for high-priority minimal common regions (MCR) of recurrent CNA were defined and combined with gene expression profiles performed on the same tumor samples. Genes with concordant changes in CNA and expression levels were defined as over/underexpressed genes located in amplified/deleted regions, or as MCR genes with expression correlated to the corresponding genomic state. After validation, we found that the expression of 318 genes was significantly affected by CNA. Associated enriched GO process annotations were related to cell cycle disorder, cellular adhesion and angiogenesis. The gene signature included well-known GBM genes such as EGFR, PDGFA, and p16INK4 but also novel candidate genes. Two tumor suppressor genes PCDH9 and STARD13, involved in tumor invasiveness and resistance to etoposide, were validated by qPCR in an independent set of 57 glioblastoma. Conclusion: This study shows the power of combining genomic alterations and gene expression to identify robust transcriptome signature and putative tumor biomarkers in GBM

Immune genes are associated with human glioblastoma pathology and patient survival

International audienceBackground: 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: 22.3 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. 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

First French Pilot Quality Assessment of the EndoPredict Test for Early Luminal Breast Carcinoma

International audienceBackground/aim - Genomic signatures are needed for the determination of prognosis in patients with early stage, estrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancers. EndoPredict test is a RNA-based multigene assay that assesses the risk of 10-year relapse in this context. Quality assessment is a mandatory requirement for a laboratory to address the analytical quality of these molecular analyses. The aim of the study was to demonstrate the robustness of this prognostic test, its usefulness for the patient's treatment strategy, at the national level. Materials and methods - This study presents a pilot quality assessment (QA) of the EndoPredict test using composite design, including the follow-up of internal control values (qREF) of the 12 genes of the assay for 151 independent tests and one formalin-fixed paraffin embedded (FFPE) breast cancer sample. The evaluation of the test was performed by comparing the results of six independent medical laboratories. Results - All measures were highly reproducible and quantification of the qREF showed a standard deviation of less than 0.50 and a coefficient of variation always of <2%. All laboratories found concordant results for the breast cancer samples. The mean EndoPredict (EP) score for the breast cancer sample was 4.97±0.24. The mean of EPclin score was 3.07±0.05. Conclusion - This first French independent reported QA assessed the robustness and reproducibility of the EndoPredict test. Such a simple composite design could represent an adapted QA for an expensive diagnostic test

CD90 Expression Controls Migration and Predicts Dasatinib Response in Glioblastoma

International audiencePurpose: CD90 (Thy-1) is a glycophosphatidylinositol-anchored glycoprotein considered as a surrogate marker for a variety of stem cells, including glioblastoma (GBM) stem cells (GSC). However, the molecular and cellular functions of CD90 remain unclear.Experimental Design: The function of CD90 in GBM was addressed using cellular models from immortalized and primary GBMlines, in vivo orthotopic mouse models, and GBM specimens' transcriptome associated with MRI features from GBM patients. CD90 expression was silenced in U251 and GBM primary cells and complemented in CD90-negative U87 cells.Results: We showed that CD90 is not only expressed on GSCs but also on more differentiated GBM cancer cells. In GBM patients, CD90 expression was associated with an adhesion/migration gene signature and with invasive tumor features. Modulation of CD90 expression in GBM cells dramatically affected their adhesion and migration properties. Moreover, orthotopic xenografts revealed that CD90 expression induced invasive phenotypes in vivo. Indeed, CD90 expression led to enhanced SRC and FAK signaling in our GBM cellular models and GBM patients' specimens. Pharmacologic inhibition of these signaling nodes blunted adhesion and migration in CD90-positive cells. Remarkably, dasatinib blunted CD90-dependent GBM cell invasion in vivo and killed CD90(high) primary GSC lines.Conclusions: Our data demonstrate that CD90 is an actor of GBM invasiveness through SRC-dependent mechanisms and could be used as a predictive factor for dasatinib response in CD90high GBM patients. (C) 2017 AACR

Prognostic value of <i>DGKI</i> methylation status.

<p>(A) <i>MGMT</i>-methylated GBM patients assigned to standard treatment (population 2). Kaplan-Meier estimation of OS in training and validation cohorts. (B) GBM patients assigned to standard treatment (population 1). Kaplan-Meier estimation of OS and PFS. M: methylated patients, UM: unmethylated patients, LR: low-risk patients, HR: high-risk patients, mo: month. The difference in survival between groups is reported (log-rank test <i>p</i>-value). The size and the median survival of each group are also specified.</p

Multivariate analyses of survival prognostic factors.

<p>NA = not available; N = not enough events to calculate upper 95% CI boundary; NS = not significant. For ordered categorical factors, the first value is the reference.</p><p>Multivariate analyses of survival prognostic factors.</p