Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than Histology
Gliomas are the most common primary brain tumors with heterogeneous morphology and variable prognosis. Treatment decisions in patients rely mainly on histologic classification and clinical parameters. However, differences between histologic subclasses and grades are subtle, and classifying gliomas i...
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| Published in | Cancer research (Chicago, Ill.) Vol. 69; no. 23; pp. 9065 - 9072 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Philadelphia, PA
American Association for Cancer Research
01.12.2009
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Gliomas are the most common primary brain tumors with heterogeneous morphology and variable prognosis. Treatment decisions in patients rely mainly on histologic classification and clinical parameters. However, differences between histologic subclasses and grades are subtle, and classifying gliomas is subject to a large interobserver variability. To improve current classification standards, we have performed gene expression profiling on a large cohort of glioma samples of all histologic subtypes and grades. We identified seven distinct molecular subgroups that correlate with survival. These include two favorable prognostic subgroups (median survival, >4.7 years), two with intermediate prognosis (median survival, 1–4 years), two with poor prognosis (median survival, <1 year), and one control group. The intrinsic molecular subtypes of glioma are different from histologic subgroups and correlate better to patient survival. The prognostic value of molecular subgroups was validated on five independent sample cohorts (The Cancer Genome Atlas, Repository for Molecular Brain Neoplasia Data, GSE12907, GSE4271, and Li and colleagues). The power of intrinsic subtyping is shown by its ability to identify a subset of prognostically favorable tumors within an external data set that contains only histologically confirmed glioblastomas (GBM). Specific genetic changes (epidermal growth factor receptor amplification, IDH1 mutation, and 1p/19q loss of heterozygosity) segregate in distinct molecular subgroups. We identified a subgroup with molecular features associated with secondary GBM, suggesting that different genetic changes drive gene expression profiles. Finally, we assessed response to treatment in molecular subgroups. Our data provide compelling evidence that expression profiling is a more accurate and objective method to classify gliomas than histologic classification. Molecular classification therefore may aid diagnosis and can guide clinical decision making. [Cancer Res 2009;69(23):9065–72] |
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| AbstractList | Gliomas are the most common primary brain tumors with heterogeneous morphology and variable prognosis. Treatment decisions in patients rely mainly on histologic classification and clinical parameters. However, differences between histologic subclasses and grades are subtle, and classifying gliomas is subject to a large interobserver variability. To improve current classification standards, we have performed gene expression profiling on a large cohort of glioma samples of all histologic subtypes and grades. We identified seven distinct molecular subgroups that correlate with survival. These include two favorable prognostic subgroups (median survival, >4.7 years), two with intermediate prognosis (median survival, 1-4 years), two with poor prognosis (median survival, <1 year), and one control group. The intrinsic molecular subtypes of glioma are different from histologic subgroups and correlate better to patient survival. The prognostic value of molecular subgroups was validated on five independent sample cohorts (The Cancer Genome Atlas, Repository for Molecular Brain Neoplasia Data, GSE12907, GSE4271, and Li and colleagues). The power of intrinsic subtyping is shown by its ability to identify a subset of prognostically favorable tumors within an external data set that contains only histologically confirmed glioblastomas (GBM). Specific genetic changes (epidermal growth factor receptor amplification, IDH1 mutation, and 1p/19q loss of heterozygosity) segregate in distinct molecular subgroups. We identified a subgroup with molecular features associated with secondary GBM, suggesting that different genetic changes drive gene expression profiles. Finally, we assessed response to treatment in molecular subgroups. Our data provide compelling evidence that expression profiling is a more accurate and objective method to classify gliomas than histologic classification. Molecular classification therefore may aid diagnosis and can guide clinical decision making.Gliomas are the most common primary brain tumors with heterogeneous morphology and variable prognosis. Treatment decisions in patients rely mainly on histologic classification and clinical parameters. However, differences between histologic subclasses and grades are subtle, and classifying gliomas is subject to a large interobserver variability. To improve current classification standards, we have performed gene expression profiling on a large cohort of glioma samples of all histologic subtypes and grades. We identified seven distinct molecular subgroups that correlate with survival. These include two favorable prognostic subgroups (median survival, >4.7 years), two with intermediate prognosis (median survival, 1-4 years), two with poor prognosis (median survival, <1 year), and one control group. The intrinsic molecular subtypes of glioma are different from histologic subgroups and correlate better to patient survival. The prognostic value of molecular subgroups was validated on five independent sample cohorts (The Cancer Genome Atlas, Repository for Molecular Brain Neoplasia Data, GSE12907, GSE4271, and Li and colleagues). The power of intrinsic subtyping is shown by its ability to identify a subset of prognostically favorable tumors within an external data set that contains only histologically confirmed glioblastomas (GBM). Specific genetic changes (epidermal growth factor receptor amplification, IDH1 mutation, and 1p/19q loss of heterozygosity) segregate in distinct molecular subgroups. We identified a subgroup with molecular features associated with secondary GBM, suggesting that different genetic changes drive gene expression profiles. Finally, we assessed response to treatment in molecular subgroups. Our data provide compelling evidence that expression profiling is a more accurate and objective method to classify gliomas than histologic classification. Molecular classification therefore may aid diagnosis and can guide clinical decision making. Gliomas are the most common primary brain tumors with heterogeneous morphology and variable prognosis. Treatment decisions in patients rely mainly on histologic classification and clinical parameters. However, differences between histologic subclasses and grades are subtle, and classifying gliomas is subject to a large interobserver variability. To improve current classification standards, we have performed gene expression profiling on a large cohort of glioma samples of all histologic subtypes and grades. We identified seven distinct molecular subgroups that correlate with survival. These include two favorable prognostic subgroups (median survival, >4.7 years), two with intermediate prognosis (median survival, 1–4 years), two with poor prognosis (median survival, <1 year), and one control group. The intrinsic molecular subtypes of glioma are different from histologic subgroups and correlate better to patient survival. The prognostic value of molecular subgroups was validated on five independent sample cohorts (The Cancer Genome Atlas, Repository for Molecular Brain Neoplasia Data, GSE12907, GSE4271, and Li and colleagues). The power of intrinsic subtyping is shown by its ability to identify a subset of prognostically favorable tumors within an external data set that contains only histologically confirmed glioblastomas (GBM). Specific genetic changes (epidermal growth factor receptor amplification, IDH1 mutation, and 1p/19q loss of heterozygosity) segregate in distinct molecular subgroups. We identified a subgroup with molecular features associated with secondary GBM, suggesting that different genetic changes drive gene expression profiles. Finally, we assessed response to treatment in molecular subgroups. Our data provide compelling evidence that expression profiling is a more accurate and objective method to classify gliomas than histologic classification. Molecular classification therefore may aid diagnosis and can guide clinical decision making. [Cancer Res 2009;69(23):9065–72] Gliomas are the most common primary brain tumors with heterogeneous morphology and variable prognosis. Treatment decisions in patients rely mainly on histologic classification and clinical parameters. However, differences between histologic subclasses and grades are subtle, and classifying gliomas is subject to a large interobserver variability. To improve current classification standards, we have performed gene expression profiling on a large cohort of glioma samples of all histologic subtypes and grades. We identified seven distinct molecular subgroups that correlate with survival. These include two favorable prognostic subgroups (median survival, >4.7 years), two with intermediate prognosis (median survival, 1-4 years), two with poor prognosis (median survival, <1 year), and one control group. The intrinsic molecular subtypes of glioma are different from histologic subgroups and correlate better to patient survival. The prognostic value of molecular subgroups was validated on five independent sample cohorts (The Cancer Genome Atlas, Repository for Molecular Brain Neoplasia Data, GSE12907, GSE4271, and Li and colleagues). The power of intrinsic subtyping is shown by its ability to identify a subset of prognostically favorable tumors within an external data set that contains only histologically confirmed glioblastomas (GBM). Specific genetic changes (epidermal growth factor receptor amplification, IDH1 mutation, and 1p/19q loss of heterozygosity) segregate in distinct molecular subgroups. We identified a subgroup with molecular features associated with secondary GBM, suggesting that different genetic changes drive gene expression profiles. Finally, we assessed response to treatment in molecular subgroups. Our data provide compelling evidence that expression profiling is a more accurate and objective method to classify gliomas than histologic classification. Molecular classification therefore may aid diagnosis and can guide clinical decision making. |
| Author | Gevaert, Olivier de Rooi, Johan J. Duijm, J. Elza Kouwenhoven, Mathilde C.M. De Moor, Bart Bralten, Linda B.C. van der Spek, Peter J. Daemen, Anneleen Bleeker, Fonnet E. Stubbs, Andrew P. Sillevis Smitt, Peter A.E. Eilers, Paul H.C. van den Bent, Martin J. French, Pim J. Kloosterhof, Nanne K. Gravendeel, Lonneke A.M. Kros, Johan M. |
| Author_xml | – sequence: 1 givenname: Lonneke A.M. surname: Gravendeel fullname: Gravendeel, Lonneke A.M. – sequence: 2 givenname: Mathilde C.M. surname: Kouwenhoven fullname: Kouwenhoven, Mathilde C.M. – sequence: 3 givenname: Olivier surname: Gevaert fullname: Gevaert, Olivier – sequence: 4 givenname: Johan J. surname: de Rooi fullname: de Rooi, Johan J. – sequence: 5 givenname: Andrew P. surname: Stubbs fullname: Stubbs, Andrew P. – sequence: 6 givenname: J. Elza surname: Duijm fullname: Duijm, J. Elza – sequence: 7 givenname: Anneleen surname: Daemen fullname: Daemen, Anneleen – sequence: 8 givenname: Fonnet E. surname: Bleeker fullname: Bleeker, Fonnet E. – sequence: 9 givenname: Linda B.C. surname: Bralten fullname: Bralten, Linda B.C. – sequence: 10 givenname: Nanne K. surname: Kloosterhof fullname: Kloosterhof, Nanne K. – sequence: 11 givenname: Bart surname: De Moor fullname: De Moor, Bart – sequence: 12 givenname: Paul H.C. surname: Eilers fullname: Eilers, Paul H.C. – sequence: 13 givenname: Peter J. surname: van der Spek fullname: van der Spek, Peter J. – sequence: 14 givenname: Johan M. surname: Kros fullname: Kros, Johan M. – sequence: 15 givenname: Peter A.E. surname: Sillevis Smitt fullname: Sillevis Smitt, Peter A.E. – sequence: 16 givenname: Martin J. surname: van den Bent fullname: van den Bent, Martin J. – sequence: 17 givenname: Pim J. surname: French fullname: French, Pim J. |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=22375306$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/19920198$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Adolescent Adult Aged Aged, 80 and over Antineoplastic agents Biological and medical sciences Brain Neoplasms - genetics Brain Neoplasms - pathology Child Cluster Analysis Female Gene Expression Profiling Glioma - genetics Glioma - pathology Humans Male Medical sciences Middle Aged Neurology Pharmacology. Drug treatments Prognosis Reproducibility of Results Survival Rate Tumors Tumors of the nervous system. Phacomatoses Young Adult |
| Title | Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than Histology |
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