Integrative Subtype Discovery in Glioblastoma Using iCluster

Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.
PLoS ONE (Impact Factor: 3.23). 04/2012; 7(4):e35236. DOI: 10.1371/journal.pone.0035236
Source: PubMed


Large-scale cancer genome projects, such as the Cancer Genome Atlas (TCGA) project, are comprehensive molecular characterization efforts to accelerate our understanding of cancer biology and the discovery of new therapeutic targets. The accumulating wealth of multidimensional data provides a new paradigm for important research problems including cancer subtype discovery. The current standard approach relies on separate clustering analyses followed by manual integration. Results can be highly data type dependent, restricting the ability to discover new insights from multidimensional data. In this study, we present an integrative subtype analysis of the TCGA glioblastoma (GBM) data set. Our analysis revealed new insights through integrated subtype characterization. We found three distinct integrated tumor subtypes. Subtype 1 lacks the classical GBM events of chr 7 gain and chr 10 loss. This subclass is enriched for the G-CIMP phenotype and shows hypermethylation of genes involved in brain development and neuronal differentiation. The tumors in this subclass display a Proneural expression profile. Subtype 2 is characterized by a near complete association with EGFR amplification, overrepresentation of promoter methylation of homeobox and G-protein signaling genes, and a Classical expression profile. Subtype 3 is characterized by NF1 and PTEN alterations and exhibits a Mesenchymal-like expression profile. The data analysis workflow we propose provides a unified and computationally scalable framework to harness the full potential of large-scale integrated cancer genomic data for integrative subtype discovery.

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Available from: Nikolaus Schultz
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    • "Therefore, new diagnostic and therapeutic strategies for tumor recurrence might be required to improve clinical outcomes of patients. Previously, numerous genomic profiling studies have addressed the marked heterogeneity of glioblastomas6789. Particularly, The Cancer Genome Atlas (TCGA) project recognized four distinct molecular subtypes of proneural, neural, classical, and mesenchymal, which are different in response to aggressive therapies[10,11]. "
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    ABSTRACT: Previously, transcriptomic profiling studies have shown distinct molecular subtypes of glioblastomas. It has also been suggested that the recurrence of glioblastomas could be achieved by transcriptomic reprograming of tumors, however, their characteristics are not yet fully understood. Here, to gain the mechanistic insights on the molecular phenotypes of recurrent glioblastomas, gene expression profiling was performed on the 43 cases of glioblastomas including 15 paired primary and recurrent cases. Unsupervised clustering analyses revealed two subtypes of G1 and G2, which were characterized by proliferation and neuron-like gene expression traits, respectively. While the primary tumors were classified as G1 subtype, the recurrent glioblastomas showed two distinct expression types. Compared to paired primary tumors, the recurrent tumors in G1 subtype did not show expression alteration. By contrast, the recurrent tumors in G2 subtype showed expression changes from proliferation type to neuron-like one. We also observed the expression of stemness-related genes in G1 recurrent tumors and the altered expression of DNA-repair genes (i.e., AURK, HOX, MGMT, and MSH6) in the G2 recurrent tumors, which might be responsible for the acquisition of drug resistance mechanism during tumor recurrence in a subtype-specific manner. We suggest that recurrent glioblastomas may choose two different strategies for transcriptomic reprograming to escape the chemotherapeutic treatment during tumor recurrence. Our results might be helpful to determine personalized therapeutic strategy against heterogeneous glioma recurrence.
    Full-text · Article · Oct 2015 · PLoS ONE
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    • "Secondary GBM are rare (∼5-10% of total GBM), progress from lower grade tumors, occur more frequently in younger patients with better prognosis and have a different molecular profile. Studies using gene expression, DNA copy number, miRNA, and DNA methylation show these molecular characteristics can divide GBM into subclasses, some with different clinical characteristics [3], [4], [5], [6], [7], [8], [9]. Three subtypes emerged in early studies of WHO grade IV GBM (studies that combine histological subtypes or grades of glioma and use molecular classification to distinguish them are excluded from this discussion). "
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    ABSTRACT: Activity of GFR/PI3K/AKT pathway inhibitors in glioblastoma clinical trials has not been robust. We hypothesized variations in the pathway between tumors contribute to poor response. We clustered GBM based on AKT pathway genes and discovered new subtypes then characterized their clinical and molecular features. There are at least 5 GBM AKT subtypes having distinct DNA copy number alterations, enrichment in oncogenes and tumor suppressor genes and patterns of expression for PI3K/AKT/mTOR signaling components. Gene Ontology terms indicate a different cell of origin or dominant phenotype for each subgroup. Evidence suggests one subtype is very sensitive to BCNU or CCNU (median survival 5.8 vs. 1.5 years; BCNU/CCNU vs other treatments; respectively). AKT subtyping advances previous approaches by revealing additional subgroups with unique clinical and molecular features. Evidence indicates it is a predictive marker for response to BCNU or CCNU and PI3K/AKT/mTOR pathway inhibitors. We anticipate Akt subtyping may help stratify patients for clinical trials and augment discovery of class-specific therapeutic targets.
    Full-text · Article · Jul 2014 · PLoS ONE
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    • "Recent work on genome wide profiling with help of the cancer genome atlas (TCGA) [18] database, using various parameters like copy number analysis, miRNA and mRNA analysis, mutational and methylation analysis, have all led to generation of GBM tumor subtype specific network profiles [19] [20] [21]. These sub-types are classical , mesenchymal, neural, and pro-neural. "
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    ABSTRACT: MiRNA-34a is considered as a potential prognostic marker for glioma, as studies suggest that its expression negatively correlates with patient survival in grade III and IV glial tumors. Here, we show that expression of miR-34a was decreased in a graded manner in glioma and glioma stem cell-lines as compared to normal brain tissues. Ectopic expression of miR-34a in glioma stem cell-lines HNGC-2 and NSG-K16 decreased the proliferative and migratory potential of these cells, induced cell cycle arrest and caused apoptosis. Notably, the miR-34a glioma cells formed significantly smaller xenografts in immuno-deficient mice as compared with control glioma stem cell-lines. Here, using a bioinformatics approach and various biological assays, we identify Rictor, as a novel target for miR-34a in glioma stem cells. Rictor, a defining component of mTORC2 complex, is involved in cell survival signalling. mTORC2 lays downstream of Akt, and thus is a direct activator of Akt. Our earlier studies have elaborated on role of Rictor in glioma invasion (Das Get al.; Mol Carcinog. 2011 Jun;50(6):412-23). Here, we demonstrate that miR34a over-expression in glioma stem cells profoundly decreased levels of p-AKT (Ser473), increased GSK-3β levels and targeted for degradation β-catenin, an important mediator of Wnt signaling pathway. This led to diminished levels of the Wnt effectors cyclin D1 and c-myc. Collectively, we show that the tumor suppressive function of miR-34a in glioblastoma is mediated via Rictor, which through its effects on AKT/mTOR pathway and Wnt signaling causes pronounced effects on glioma malignancy.
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