Article

Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1.

The Eli and Edythe L. Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA.
Cancer cell (Impact Factor: 23.89). 01/2010; 17(1):98-110. DOI: 10.1016/j.ccr.2009.12.020
Source: PubMed

ABSTRACT The Cancer Genome Atlas Network recently cataloged recurrent genomic abnormalities in glioblastoma multiforme (GBM). We describe a robust gene expression-based molecular classification of GBM into Proneural, Neural, Classical, and Mesenchymal subtypes and integrate multidimensional genomic data to establish patterns of somatic mutations and DNA copy number. Aberrations and gene expression of EGFR, NF1, and PDGFRA/IDH1 each define the Classical, Mesenchymal, and Proneural subtypes, respectively. Gene signatures of normal brain cell types show a strong relationship between subtypes and different neural lineages. Additionally, response to aggressive therapy differs by subtype, with the greatest benefit in the Classical subtype and no benefit in the Proneural subtype. We provide a framework that unifies transcriptomic and genomic dimensions for GBM molecular stratification with important implications for future studies.

2 Followers
 · 
254 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Lateral ventricle contact (LVC) by glioblastomas has been proposed to reveal their origin and may have prognostic value; however, results from previous studies have been controversial. This study explored the association between LVC and tumor origin and prognosis in glioblastoma patients. Magnetic resonance imaging and clinical data from 115 glioma patients were retrospectively reviewed, and Kaplan-Meier analysis and Cox proportional hazards models were used to assess the occurrence of LVC as a function of survival in 43 glioblastoma patients. The mRNA expression profiles were compared by microarray analysis in LV-contacting and non-LV-contacting glioblastomas (LVCGs and NLVCGs, respectively). The sphere-forming and invasive capabilities of LVCG- and NLVCG-derived stem cells were compared in primary glioma stem cell cultures with tumorsphere formation and Matrigel assays, respectively. LVC was more frequently detected in high-grade gliomas which, along with LVCGs, were significantly larger than low-grade gliomas and NLVCGs. LVC parameters were not independent predictors of glioblastoma patient prognosis; the expression profiles (including stemness genes expression) were similar between LVCGs and NLVCGs, and no significant differences were observed in tumorsphere-forming capacity and invasiveness between stem cells derived from the two glioblastoma types. Our results suggest that the origin of glioblastomas cannot be simply estimated by radiographic LVC, and after standard therapy the prognostic value of LVC needs to be carefully interpreted.
    Journal of Neuro-Oncology 05/2015; DOI:10.1007/s11060-015-1818-x · 2.79 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Recent advances in genomic technology have led to a better understanding of key molecular alterations that underlie glioblastoma (GBM). The current WHO-based classification of GBM is mainly based on histologic features of the tumor, which frequently do not reflect the molecular differences that describe the diversity in the biology of these lesions. The current WHO definition of GBM relies on the presence of high-grade astrocytic neoplasm with the presence of either microvascular proliferation and/or tumor necrosis. High-throughput analyses have identified molecular subtypes and have led to progress in more accurate classification of GBM. These findings, in turn, would result in development of more effective patient stratification, targeted therapeutics, and prediction of patient outcome. While consensus has not been reached on the precise nature and means to sub-classify GBM, it is clear that IDH-mutant GBMs are clearly distinct from GBMs without IDH1/2 mutation with respect to molecular and clinical features, including prognosis. In addition, recent findings in pediatric GBMs regarding mutations in the histone H3F3A gene suggest that these tumors may represent a 3rd major category of GBM, separate from adult primary (IDH1/2 wt), and secondary (IDH1/2 mut) GBMs. In this review, we describe major clinically relevant genetic and epigenetic abnormalities in GBM-such as mutations in IDH1/2, EGFR, PDGFRA, and NF1 genes-altered methylation of MGMT gene promoter, and mutations in hTERT promoter. These markers may be incorporated into a more refined classification system and applied in more accurate clinical decision-making process. In addition, we focus on current understanding of the biologic heterogeneity and classification of GBM and highlight some of the molecular signatures and alterations that characterize GBMs as histologically defined. We raise the question whether IDH-wild type high grade astrocytomas without microvascular proliferation or necrosis might best be classified as GBM, even if they lack the histologic hallmarks as required in the current WHO classification. Alternatively, an astrocytic tumor that fits the current histologic definition of GBM, but which shows an IDH mutation may in fact be better classified as a distinct entity, given that IDH-mutant GBM are quite distinct from a biological and clinical perspective.
    Acta Neuropathologica 05/2015; 129(6). DOI:10.1007/s00401-015-1432-1 · 9.78 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Feature-based methods are widely used in the brain tumor recognition system. Robust of early cancer detection is one of the most powerful image processing tools. Specifically, statistical features, such as geometric mean, harmonic mean, mean excluding outliers, median, percentiles, skewness and kurtosis, have been extracted from brain tumor glioma to aid in discriminating two levels namely, Level I and Level II using fluid attenuated inversion recovery (FLAIR) sequence in the diagnosis of brain tumor. Statistical feature describes the major characteristics of each level from glioma which is an important step to evaluate heterogeneity of cancer area pixels. In this paper, we address the task of feature selection to identify the relevant subset of features in the statistical domain, while discarding those that are either redundant or confusing, thereby improving the performance of feature-based scheme to distinguish between Level I and Level II. We apply a Decision Structure algorithm to find the optimal combination of non-homogeneity based statistical features for the problem at hand. We employ a Naive Bayes classifier to evaluate the performance of the optimal statistical feature based scheme in terms of its glioma Level I and Level II discrimination capability and use real-data collected from 17 patients have a glioblastoma multiforme (GBM). Dataset provided from 3 Tesla MR imaging system by MD Anderson Cancer Center. For the specific data analyzed, it is shown that the identified dominant features yield higher classification accuracy, with lower number of false alarms and missed detections, compared to the full statistical based feature set. This work has been proposed and analyzed specific GBM types which Level I and Level II and the dominant features were considered as feature aid to prognostic indicators. These features were selected automatically to be better able to determine prognosis from classical imaging studies.
    SPIE Optical Engineering + Applications; 09/2014