Gene Expression-Based Molecular Diagnostic System for Malignant Gliomas Is Superior to Histological Diagnosis
Purpose: Current morphology-based glioma classification methods do not adequately reflect the complex biology of gliomas, thus limiting their prognostic ability. In this study, we focused on anaplastic oligodendroglioma and glioblastoma, which typically follow distinct clinical courses. Our goal was...
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creator | SHIRAHATA, Mitsuaki IWAO-KOIZUMI, Kyoko SAITO, Sakae UENO, Noriko ODA, Masashi HASHIMOTO, Nobuo TAKAHASHI, Jun A KATO, Kikuya |
description | Purpose: Current morphology-based glioma classification methods do not adequately reflect the complex biology of gliomas, thus limiting
their prognostic ability. In this study, we focused on anaplastic oligodendroglioma and glioblastoma, which typically follow
distinct clinical courses. Our goal was to construct a clinically useful molecular diagnostic system based on gene expression
profiling.
Experimental Design: The expression of 3,456 genes in 32 patients, 12 and 20 of whom had prognostically distinct anaplastic oligodendroglioma
and glioblastoma, respectively, was measured by PCR array. Next to unsupervised methods, we did supervised analysis using
a weighted voting algorithm to construct a diagnostic system discriminating anaplastic oligodendroglioma from glioblastoma.
The diagnostic accuracy of this system was evaluated by leave-one-out cross-validation. The clinical utility was tested on
a microarray-based data set of 50 malignant gliomas from a previous study.
Results: Unsupervised analysis showed divergent global gene expression patterns between the two tumor classes. A supervised binary
classification model showed 100% (95% confidence interval, 89.4-100%) diagnostic accuracy by leave-one-out cross-validation
using 168 diagnostic genes. Applied to a gene expression data set from a previous study, our model correlated better with
outcome than histologic diagnosis, and also displayed 96.6% (28 of 29) consistency with the molecular classification scheme
used for these histologically controversial gliomas in the original article. Furthermore, we observed that histologically
diagnosed glioblastoma samples that shared anaplastic oligodendroglioma molecular characteristics tended to be associated
with longer survival.
Conclusions: Our molecular diagnostic system showed reproducible clinical utility and prognostic ability superior to traditional histopathologic
diagnosis for malignant glioma. |
doi_str_mv | 10.1158/1078-0432.CCR-06-2789 |
format | Article |
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their prognostic ability. In this study, we focused on anaplastic oligodendroglioma and glioblastoma, which typically follow
distinct clinical courses. Our goal was to construct a clinically useful molecular diagnostic system based on gene expression
profiling.
Experimental Design: The expression of 3,456 genes in 32 patients, 12 and 20 of whom had prognostically distinct anaplastic oligodendroglioma
and glioblastoma, respectively, was measured by PCR array. Next to unsupervised methods, we did supervised analysis using
a weighted voting algorithm to construct a diagnostic system discriminating anaplastic oligodendroglioma from glioblastoma.
The diagnostic accuracy of this system was evaluated by leave-one-out cross-validation. The clinical utility was tested on
a microarray-based data set of 50 malignant gliomas from a previous study.
Results: Unsupervised analysis showed divergent global gene expression patterns between the two tumor classes. A supervised binary
classification model showed 100% (95% confidence interval, 89.4-100%) diagnostic accuracy by leave-one-out cross-validation
using 168 diagnostic genes. Applied to a gene expression data set from a previous study, our model correlated better with
outcome than histologic diagnosis, and also displayed 96.6% (28 of 29) consistency with the molecular classification scheme
used for these histologically controversial gliomas in the original article. Furthermore, we observed that histologically
diagnosed glioblastoma samples that shared anaplastic oligodendroglioma molecular characteristics tended to be associated
with longer survival.
Conclusions: Our molecular diagnostic system showed reproducible clinical utility and prognostic ability superior to traditional histopathologic
diagnosis for malignant glioma.</description><identifier>ISSN: 1078-0432</identifier><identifier>EISSN: 1557-3265</identifier><identifier>DOI: 10.1158/1078-0432.CCR-06-2789</identifier><identifier>PMID: 18094416</identifier><language>eng</language><publisher>Philadelphia, PA: American Association for Cancer Research</publisher><subject>Algorithms ; Antineoplastic agents ; Biological and medical sciences ; Brain Neoplasms - classification ; Brain Neoplasms - diagnosis ; Brain Neoplasms - genetics ; Clinical Trials, Phase II as Topic ; Gene Expression ; gene expression profiling ; Gene Expression Profiling - methods ; Glioblastoma - classification ; Glioblastoma - diagnosis ; Glioblastoma - genetics ; Humans ; Kaplan-Meier Estimate ; Loss of Heterozygosity ; malignant glioma ; Medical sciences ; molecular diagnosis ; Neurology ; Oligodendroglioma - diagnosis ; Oligodendroglioma - genetics ; Oligodendroglioma - mortality ; Oligonucleotide Array Sequence Analysis ; Pharmacology. Drug treatments ; Polymerase Chain Reaction ; Prognosis ; Reproducibility of Results ; Sensitivity and Specificity ; Tumors of the nervous system. Phacomatoses</subject><ispartof>Clinical cancer research, 2007-12, Vol.13 (24), p.7341-7356</ispartof><rights>2008 INIST-CNRS</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c515t-15f1b9becc25d3565466e584cefd665477e4f34d8eabf13c76563c2e977634f3</citedby><cites>FETCH-LOGICAL-c515t-15f1b9becc25d3565466e584cefd665477e4f34d8eabf13c76563c2e977634f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,3356,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=20036984$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/18094416$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>SHIRAHATA, Mitsuaki</creatorcontrib><creatorcontrib>IWAO-KOIZUMI, Kyoko</creatorcontrib><creatorcontrib>SAITO, Sakae</creatorcontrib><creatorcontrib>UENO, Noriko</creatorcontrib><creatorcontrib>ODA, Masashi</creatorcontrib><creatorcontrib>HASHIMOTO, Nobuo</creatorcontrib><creatorcontrib>TAKAHASHI, Jun A</creatorcontrib><creatorcontrib>KATO, Kikuya</creatorcontrib><title>Gene Expression-Based Molecular Diagnostic System for Malignant Gliomas Is Superior to Histological Diagnosis</title><title>Clinical cancer research</title><addtitle>Clin Cancer Res</addtitle><description>Purpose: Current morphology-based glioma classification methods do not adequately reflect the complex biology of gliomas, thus limiting
their prognostic ability. In this study, we focused on anaplastic oligodendroglioma and glioblastoma, which typically follow
distinct clinical courses. Our goal was to construct a clinically useful molecular diagnostic system based on gene expression
profiling.
Experimental Design: The expression of 3,456 genes in 32 patients, 12 and 20 of whom had prognostically distinct anaplastic oligodendroglioma
and glioblastoma, respectively, was measured by PCR array. Next to unsupervised methods, we did supervised analysis using
a weighted voting algorithm to construct a diagnostic system discriminating anaplastic oligodendroglioma from glioblastoma.
The diagnostic accuracy of this system was evaluated by leave-one-out cross-validation. The clinical utility was tested on
a microarray-based data set of 50 malignant gliomas from a previous study.
Results: Unsupervised analysis showed divergent global gene expression patterns between the two tumor classes. A supervised binary
classification model showed 100% (95% confidence interval, 89.4-100%) diagnostic accuracy by leave-one-out cross-validation
using 168 diagnostic genes. Applied to a gene expression data set from a previous study, our model correlated better with
outcome than histologic diagnosis, and also displayed 96.6% (28 of 29) consistency with the molecular classification scheme
used for these histologically controversial gliomas in the original article. Furthermore, we observed that histologically
diagnosed glioblastoma samples that shared anaplastic oligodendroglioma molecular characteristics tended to be associated
with longer survival.
Conclusions: Our molecular diagnostic system showed reproducible clinical utility and prognostic ability superior to traditional histopathologic
diagnosis for malignant glioma.</description><subject>Algorithms</subject><subject>Antineoplastic agents</subject><subject>Biological and medical sciences</subject><subject>Brain Neoplasms - classification</subject><subject>Brain Neoplasms - diagnosis</subject><subject>Brain Neoplasms - genetics</subject><subject>Clinical Trials, Phase II as Topic</subject><subject>Gene Expression</subject><subject>gene expression profiling</subject><subject>Gene Expression Profiling - methods</subject><subject>Glioblastoma - classification</subject><subject>Glioblastoma - diagnosis</subject><subject>Glioblastoma - genetics</subject><subject>Humans</subject><subject>Kaplan-Meier Estimate</subject><subject>Loss of Heterozygosity</subject><subject>malignant glioma</subject><subject>Medical sciences</subject><subject>molecular diagnosis</subject><subject>Neurology</subject><subject>Oligodendroglioma - diagnosis</subject><subject>Oligodendroglioma - genetics</subject><subject>Oligodendroglioma - mortality</subject><subject>Oligonucleotide Array Sequence Analysis</subject><subject>Pharmacology. Drug treatments</subject><subject>Polymerase Chain Reaction</subject><subject>Prognosis</subject><subject>Reproducibility of Results</subject><subject>Sensitivity and Specificity</subject><subject>Tumors of the nervous system. Phacomatoses</subject><issn>1078-0432</issn><issn>1557-3265</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkU1v1DAQhi0EoqXwE0C-gNRDih1_xUe6lG2lVki0d8vrTHaNknjxJIL--zraLRw52dY8M2O9DyHvObvgXDWfOTNNxaSoL1arHxXTVW0a-4KccqVMJWqtXpb7M3NC3iD-ZIxLzuRrcsIbZqXk-pQMaxiBXv3ZZ0CMaawuPUJL71IPYe59pl-j344Jpxjo_SNOMNAuZXrn-7gd_TjRdR_T4JHeIL2f95BjqU6JXkecUp-2Mfj-eUbEt-RV53uEd8fzjDx8u3pYXVe339c3qy-3VVBcTRVXHd_YDYRQq1YoraTWoBoZoGt1eRkDshOybcBvOi6C0UqLUIM1RotSOSOfDmP3Of2aASc3RAzQ936ENKPTljXMlGD-B3JrG2ulLaA6gCEnxAyd2-c4-PzoOHOLD7dk7ZasXfHhmHaLj9L34bhg3gzQ_us6CijAxyPgsUTVZT-GiH-5mjGhbbP89PzA7eJ29ztmcKGQkIs28DnsHBeuls4IycUTU-uiVQ</recordid><startdate>20071215</startdate><enddate>20071215</enddate><creator>SHIRAHATA, Mitsuaki</creator><creator>IWAO-KOIZUMI, Kyoko</creator><creator>SAITO, Sakae</creator><creator>UENO, Noriko</creator><creator>ODA, Masashi</creator><creator>HASHIMOTO, Nobuo</creator><creator>TAKAHASHI, Jun A</creator><creator>KATO, Kikuya</creator><general>American Association for Cancer Research</general><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope></search><sort><creationdate>20071215</creationdate><title>Gene Expression-Based Molecular Diagnostic System for Malignant Gliomas Is Superior to Histological Diagnosis</title><author>SHIRAHATA, Mitsuaki ; IWAO-KOIZUMI, Kyoko ; SAITO, Sakae ; UENO, Noriko ; ODA, Masashi ; HASHIMOTO, Nobuo ; TAKAHASHI, Jun A ; KATO, Kikuya</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c515t-15f1b9becc25d3565466e584cefd665477e4f34d8eabf13c76563c2e977634f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Algorithms</topic><topic>Antineoplastic agents</topic><topic>Biological and medical sciences</topic><topic>Brain Neoplasms - classification</topic><topic>Brain Neoplasms - diagnosis</topic><topic>Brain Neoplasms - genetics</topic><topic>Clinical Trials, Phase II as Topic</topic><topic>Gene Expression</topic><topic>gene expression profiling</topic><topic>Gene Expression Profiling - methods</topic><topic>Glioblastoma - classification</topic><topic>Glioblastoma - diagnosis</topic><topic>Glioblastoma - genetics</topic><topic>Humans</topic><topic>Kaplan-Meier Estimate</topic><topic>Loss of Heterozygosity</topic><topic>malignant glioma</topic><topic>Medical sciences</topic><topic>molecular diagnosis</topic><topic>Neurology</topic><topic>Oligodendroglioma - diagnosis</topic><topic>Oligodendroglioma - genetics</topic><topic>Oligodendroglioma - mortality</topic><topic>Oligonucleotide Array Sequence Analysis</topic><topic>Pharmacology. Drug treatments</topic><topic>Polymerase Chain Reaction</topic><topic>Prognosis</topic><topic>Reproducibility of Results</topic><topic>Sensitivity and Specificity</topic><topic>Tumors of the nervous system. Phacomatoses</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>SHIRAHATA, Mitsuaki</creatorcontrib><creatorcontrib>IWAO-KOIZUMI, Kyoko</creatorcontrib><creatorcontrib>SAITO, Sakae</creatorcontrib><creatorcontrib>UENO, Noriko</creatorcontrib><creatorcontrib>ODA, Masashi</creatorcontrib><creatorcontrib>HASHIMOTO, Nobuo</creatorcontrib><creatorcontrib>TAKAHASHI, Jun A</creatorcontrib><creatorcontrib>KATO, Kikuya</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Clinical cancer research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>SHIRAHATA, Mitsuaki</au><au>IWAO-KOIZUMI, Kyoko</au><au>SAITO, Sakae</au><au>UENO, Noriko</au><au>ODA, Masashi</au><au>HASHIMOTO, Nobuo</au><au>TAKAHASHI, Jun A</au><au>KATO, Kikuya</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Gene Expression-Based Molecular Diagnostic System for Malignant Gliomas Is Superior to Histological Diagnosis</atitle><jtitle>Clinical cancer research</jtitle><addtitle>Clin Cancer Res</addtitle><date>2007-12-15</date><risdate>2007</risdate><volume>13</volume><issue>24</issue><spage>7341</spage><epage>7356</epage><pages>7341-7356</pages><issn>1078-0432</issn><eissn>1557-3265</eissn><abstract>Purpose: Current morphology-based glioma classification methods do not adequately reflect the complex biology of gliomas, thus limiting
their prognostic ability. In this study, we focused on anaplastic oligodendroglioma and glioblastoma, which typically follow
distinct clinical courses. Our goal was to construct a clinically useful molecular diagnostic system based on gene expression
profiling.
Experimental Design: The expression of 3,456 genes in 32 patients, 12 and 20 of whom had prognostically distinct anaplastic oligodendroglioma
and glioblastoma, respectively, was measured by PCR array. Next to unsupervised methods, we did supervised analysis using
a weighted voting algorithm to construct a diagnostic system discriminating anaplastic oligodendroglioma from glioblastoma.
The diagnostic accuracy of this system was evaluated by leave-one-out cross-validation. The clinical utility was tested on
a microarray-based data set of 50 malignant gliomas from a previous study.
Results: Unsupervised analysis showed divergent global gene expression patterns between the two tumor classes. A supervised binary
classification model showed 100% (95% confidence interval, 89.4-100%) diagnostic accuracy by leave-one-out cross-validation
using 168 diagnostic genes. Applied to a gene expression data set from a previous study, our model correlated better with
outcome than histologic diagnosis, and also displayed 96.6% (28 of 29) consistency with the molecular classification scheme
used for these histologically controversial gliomas in the original article. Furthermore, we observed that histologically
diagnosed glioblastoma samples that shared anaplastic oligodendroglioma molecular characteristics tended to be associated
with longer survival.
Conclusions: Our molecular diagnostic system showed reproducible clinical utility and prognostic ability superior to traditional histopathologic
diagnosis for malignant glioma.</abstract><cop>Philadelphia, PA</cop><pub>American Association for Cancer Research</pub><pmid>18094416</pmid><doi>10.1158/1078-0432.CCR-06-2789</doi><tpages>16</tpages><oa>free_for_read</oa></addata></record> |
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source | MEDLINE; American Association for Cancer Research; EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection |
subjects | Algorithms Antineoplastic agents Biological and medical sciences Brain Neoplasms - classification Brain Neoplasms - diagnosis Brain Neoplasms - genetics Clinical Trials, Phase II as Topic Gene Expression gene expression profiling Gene Expression Profiling - methods Glioblastoma - classification Glioblastoma - diagnosis Glioblastoma - genetics Humans Kaplan-Meier Estimate Loss of Heterozygosity malignant glioma Medical sciences molecular diagnosis Neurology Oligodendroglioma - diagnosis Oligodendroglioma - genetics Oligodendroglioma - mortality Oligonucleotide Array Sequence Analysis Pharmacology. Drug treatments Polymerase Chain Reaction Prognosis Reproducibility of Results Sensitivity and Specificity Tumors of the nervous system. Phacomatoses |
title | Gene Expression-Based Molecular Diagnostic System for Malignant Gliomas Is Superior to Histological Diagnosis |
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