Dataset Information

Immunohistochemical analysis-based proteomic subclassification of newly diagnosed glioblastomas.

ABSTRACT: Recent gene expression and copy number profilings of glioblastoma multiforme (GBM) by The Cancer Genome Atlas (TCGA) Research Network suggest the existence of distinct subtypes of this tumor. However, these approaches might not be easily applicable in routine clinical practice. In the current study, we aimed to establish a proteomics-based subclassification of GBM by integrating their genomic and epigenomic profiles. We subclassified 79 newly diagnosed GBM based on expression patterns determined by comprehensive immunohistochemical observation in combination with their DNA copy number and DNA methylation patterns. The clinical relevance of our classification was independently validated in TCGA datasets. Consensus clustering identified the four distinct GBM subtypes: Oligodendrocyte Precursor (OPC) type, Differentiated Oligodendrocyte (DOC) type, Astrocytic Mesenchymal (AsMes) type and Mixed type. The OPC type was characterized by highly positive scores of Olig2, PDGFRA, p16, p53 and synaptophysin. In contrast, the AsMes type was strongly associated with strong expressions of nestin, CD44 and podoplanin, with a high glial fibrillary acidic protein score. The median overall survival of OPC-type patients was significantly longer than that of the AsMes-type patients (19.9 vs 12.8 months). This finding was in agreement with the Oncomine analysis of TCGA datasets, which revealed that PDGFRA and Olig2 were favorable prognostic factors and podoplanin and CD44 were associated with a poor clinical outcome. This is the first study to establish a subclassification of GBM on the basis of immunohistochemical analysis. Our study will shed light on personalized therapies that might be feasible in daily neuropathological practice.

SUBMITTER: Motomura K

PROVIDER: S-EPMC7659188 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature

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Immunohistochemical analysis-based proteomic subclassification of newly diagnosed glioblastomas.

Motomura Kazuya K Natsume Atsushi A Watanabe Reiko R Ito Ichiro I Kato Yukinari Y Momota Hiroyuki H Nishikawa Ryo R Mishima Kazuhiko K Nakasu Yoko Y Abe Tatsuya T Namba Hiroki H Nakazato Yoichi Y Tashiro Hiroshi H Takeuchi Ichiro I Mori Tsutomu T Wakabayashi Toshihiko T

Cancer science 20120807 10

Recent gene expression and copy number profilings of glioblastoma multiforme (GBM) by The Cancer Genome Atlas (TCGA) Research Network suggest the existence of distinct subtypes of this tumor. However, these approaches might not be easily applicable in routine clinical practice. In the current study, we aimed to establish a proteomics-based subclassification of GBM by integrating their genomic and epigenomic profiles. We subclassified 79 newly diagnosed GBM based on expression patterns determined ...[more]

PMID: 22747609

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Project description:Glioblastomas are the most aggressive primary brain tumors, characterized by their rapid proliferation and diffuse infiltration of the brain tissue. Survival patterns in patients with glioblastoma have been associated with a number of clinicopathologic factors including age and neurologic status, yet a significant quantitative link to in vivo growth kinetics of each glioma has remained elusive. Exploiting a recently developed tool for quantifying glioma net proliferation and invasion rates in individual patients using routinely available magnetic resonance images (MRI), we propose to link these patient-specific kinetic rates of biological aggressiveness to prognostic significance. Using our biologically based mathematical model for glioma growth and invasion, examination of serial pretreatment MRIs of 32 glioblastoma patients allowed quantification of these rates for each patient's tumor. Survival analyses revealed that even when controlling for standard clinical parameters (e.g., age and Karnofsky performance status), these model-defined parameters quantifying biological aggressiveness (net proliferation and invasion rates) were significantly associated with prognosis. One hypothesis generated was that the ratio of the actual survival time after whatever therapies were used to the duration of survival predicted (by the model) without any therapy would provide a therapeutic response index (TRI) of the overall effectiveness of the therapies. The TRI may provide important information, not otherwise available, about the effectiveness of the treatments in individual patients. To our knowledge, this is the first report indicating that dynamic insight from routinely obtained pretreatment imaging may be quantitatively useful in characterizing the survival of individual patients with glioblastoma. Such a hybrid tool bridging mathematical modeling and clinical imaging may allow for stratifying patients for clinical studies relative to their pretreatment biological aggressiveness.

Dataset Information

Immunohistochemical analysis-based proteomic subclassification of newly diagnosed glioblastomas.

Publications

Immunohistochemical analysis-based proteomic subclassification of newly diagnosed glioblastomas.

Similar Datasets

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