Unsupervised Analysis of Transcriptomic Profiles Reveals Six Glioma Subtypes

Li, Aiguo; Walling, Jennifer; Ahn, Susie; Kotliarov, Yuri; Su, Qin; Quezado, Martha; Oberholtzer, J. Carl; Park, John; Zenklusen, Jean C.; Fine, Howard A.

doi:10.1158/0008-5472.can-08-2100

Cited by 194 publications

(186 citation statements)

References 24 publications

(35 reference statements)

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“…Here, we extend previous studies (4,6,11,12) by taking a conceptually different approach, exploring whether gene coexpression modules around key signaling pathways conserved between neural development and glioma genesis might enable molecular classification of gliomas. EGFR and PDGFRA are two receptor tyrosine kinases (RTKs) that govern cell fate specification, cell proliferation, migration in the neural stem cell (NSC) compartment and glial development (15,16).…”

Section: Significancementioning

confidence: 76%

“…NL and Mes gliomas are enriched in neuronal and mesenchymal markers, respectively (6). Other studies identified characteristic gene expression signatures among high-grade gliomas (7-10) or all major glioma subtypes (11,12), and found correlations between expression signatures and the prognoses of patients and the cellular and genomic abnormalities of gliomas. A DNA methylome-based approach has identified a glioma-CpG island methylator phenotype…”

mentioning

confidence: 99%

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A glioma classification scheme based on coexpression modules of EGFR and PDGFRA

Sun

Zhang

Chen

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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We hypothesized that key signaling pathways of glioma genesis might enable the molecular classification of gliomas. Gene coexpression modules around epidermal growth factor receptor (EGFR) (EM, 29 genes) or platelet derived growth factor receptor A (PDGFRA) (PM, 40 genes) in gliomas were identified. Based on EM and PM expression signatures, nonnegative matrix factorization reproducibly clustered 1,369 adult diffuse gliomas WHO grades II-IV from four independent databases generated in three continents, into the subtypes (EM, PM and EM low PM low gliomas) in a morphology-independent manner. Besides their distinct patterns of genomic alterations, EM gliomas were associated with higher age at diagnosis, poorer prognosis, and stronger expression of neural stem cell and astrogenesis genes. Both PM and EM low PM low gliomas were associated with younger age at diagnosis and better prognosis. PM gliomas were enriched in the expression of oligodendrogenesis genes, whereas EM low PM low gliomas were enriched in the signatures of mature neurons and oligodendrocytes. The EM/PM-based molecular classification scheme is applicable to adult low-grade and high-grade diffuse gliomas, and outperforms existing classification schemes in assigning diffuse gliomas to subtypes with distinct transcriptomic and genomic profiles. The majority of the EM/PM classifiers, including regulators of glial fate decisions, have not been extensively studied in glioma biology. Subsets of these classifiers were coexpressed in mouse glial precursor cells, and frequently amplified or lost in an EM/PM glioma subtypespecific manner, resulting in somatic copy number alteration-dependent gene expression that contributes to EM/PM signatures in glioma samples. EM/PM-based molecular classification provides a molecular diagnostic framework to expedite the search for new glioma therapeutic targets.

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Section: Significancementioning

confidence: 76%

mentioning

confidence: 99%

A glioma classification scheme based on coexpression modules of EGFR and PDGFRA

Sun

Zhang

Chen

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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“…The identification of codeletion of 1p/19q as a genetic marker that identifies a subgroup of high-grade oligodendrogliomas with less aggressive growth and better responsiveness to therapy has stimulated a search for additional molecular and genetic characteristics that help to define patient selection for specific treatment [2,7]. These studies have provided valuable information on the spatial and temporal genetic variations that characterize malignant gliomas.…”

Section: Tumor Classification: Reductionismmentioning

confidence: 99%

“…In 2009, the publication of an unsupervised analysis of transcriptomic profiles in 159 gliomas revealed further subclassification of these tumors into two major groups (oligodendroglioma-rich and glioblastoma-rich) with further subclassification into six prognostic groups [7]. These studies utilize large tissue banks from a variety of sources to try to further characterize malignant gliomas by their molecular and genetic profiles.…”

Section: Tumor Classification: Reductionismmentioning

confidence: 99%

The future of neuro-oncology

Piepmeier

2009

Acta Neurochir

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“…Recently, microarray technology has permitted development of multi-organ cancer classification including gliomas (4-6), identification of glioma subclasses (7)(8)(9)(10)(11)(12)(13)(14)(15), discovery of molecular markers (16)(17)(18)(19)(20)(21)(22)(23) and prediction of disease outcomes (24)(25)(26)(27). Unlike clinicopathological staging, molecular staging can predict long-term outcomes of any individual based on the gene expression profile of the tumor at diagnosis, helping clinicians make an optimal clinical decisions.…”

Section: Introductionmentioning

confidence: 99%

Identification and validation of a gene expression signature that predicts outcome in malignant glioma patients

Kawaguchi

Yajima

Komohara³

et al. 2011

Int J Oncol

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Abstract. Better understanding of the underlying biology of malignant gliomas is critical for the development of early detection strategies and new therapeutics. This study aimed to define genes associated with survival. We investigated whether genes selected using random survival forests model could be used to define subgroups of gliomas objectively. RNAs from 50 non-treated gliomas were analyzed using the GeneChip Human Genome U133 Plus 2.0 Expression array. We identified 82 genes whose expression was strongly and consistently related to patient survival. For practical purposes, a 15-gene set was also selected. Both the complete 82 gene signature and the 15 gene set subgroup indicated their significant predictivity in the 3 out of 4 independent external dataset. Our method was effective for objectively classifying gliomas, and provided a more accurate predictor of prognosis. We assessed the relationship between gene expressions and survival time by using the random survival forests model and this performance was a better classifier compared to significance analysis of microarrays.

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Unsupervised Analysis of Transcriptomic Profiles Reveals Six Glioma Subtypes

Abstract: Gliomas are the most common type of primary brain tumors in adults and a significant cause of cancer-related mortality.

Cited by 194 publications

References 24 publications

A glioma classification scheme based on coexpression modules of EGFR and PDGFRA

A glioma classification scheme based on coexpression modules of EGFR and PDGFRA

The future of neuro-oncology

Identification and validation of a gene expression signature that predicts outcome in malignant glioma patients

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