Consistency of predictive signature genes and classifiers generated using different microarray platforms

Microarray-based classifiers and associated signature genes generated from various platforms are abundantly reported in the literature; however, the utility of the classifiers and signature genes in cross-platform prediction applications remains largely uncertain. As part of the MicroArray Quality C...

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Bibliographic Details
Published inThe pharmacogenomics journal Vol. 10; no. 4; pp. 247 - 257
Main Authors Fan, X, Lobenhofer, E K, Chen, M, Shi, W, Huang, J, Luo, J, Zhang, J, Walker, S J, Chu, T-M, Li, L, Wolfinger, R, Bao, W, Paules, R S, Bushel, P R, Li, J, Shi, T, Nikolskaya, T, Nikolsky, Y, Hong, H, Deng, Y, Cheng, Y, Fang, H, Shi, L, Tong, W
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 01.08.2010
Nature Publishing Group
Subjects
631/208/191/2018
631/208/212/1728
692/53/2423
692/699/1503/1607/2749
Algorithms
Animals
Biomedical and Life Sciences
Biomedicine
Databases, Genetic
DNA microarrays
DNA Probes
Gene Expression
Gene Expression Profiling
Genes
Human Genetics
Humans
Methods
Oligonucleotide Array Sequence Analysis - methods
Oncology
Original
original-article
Pharmacogenetics
Pharmacogenetics - methods
Pharmacotherapy
Phenotype
Predictive Value of Tests
Psychopharmacology
Quality Control
Technology application
Toxicogenetics - methods
United States
classifier
microarray
cross-platform
gene signature
MAQC
hepatotoxicity
Online AccessGet full text
ISSN1470-269X
1473-1150
1473-1150
DOI10.1038/tpj.2010.34

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Abstract Microarray-based classifiers and associated signature genes generated from various platforms are abundantly reported in the literature; however, the utility of the classifiers and signature genes in cross-platform prediction applications remains largely uncertain. As part of the MicroArray Quality Control Phase II (MAQC-II) project, we show in this study 80–90% cross-platform prediction consistency using a large toxicogenomics data set by illustrating that: (1) the signature genes of a classifier generated from one platform can be directly applied to another platform to develop a predictive classifier; (2) a classifier developed using data generated from one platform can accurately predict samples that were profiled using a different platform. The results suggest the potential utility of using published signature genes in cross-platform applications and the possible adoption of the published classifiers for a variety of applications. The study reveals an opportunity for possible translation of biomarkers identified using microarrays to clinically validated non-array gene expression assays.
AbstractList Microarray-based classifiers and associated signature genes generated from various platforms are abundantly reported in the literature; however, the utility of the classifiers and signature genes in cross-platform prediction applications remains largely uncertain. As part of the MicroArray Quality Control Phase II (MAQC-II) project, we show in this study 80–90% cross-platform prediction consistency using a large toxicogenomics data set by illustrating that: (1) the signature genes of a classifier generated from one platform can be directly applied to another platform to develop a predictive classifier; (2) a classifier developed using data generated from one platform can accurately predict samples that were profiled using a different platform. The results suggest the potential utility of using published signature genes in cross-platform applications and the possible adoption of the published classifiers for a variety of applications. The study reveals an opportunity for possible translation of biomarkers identified using microarrays to clinically validated non-array gene expression assays.
Microarray-based classifiers and associated signature genes generated from various platforms are abundantly reported in the literature; however, the utility of the classifiers and signature genes in cross-platform prediction applications remains largely uncertain. As part of the MicroArray Quality Control Phase II (MAQC-II) project, we show in this study 80-90% cross-platform prediction consistency using a large toxicogenomics data set by illustrating that: (1) the signature genes of a classifier generated from one platform can be directly applied to another platform to develop a predictive classifier; (2) a classifier developed using data generated from one platform can accurately predict samples that were profiled using a different platform. The results suggest the potential utility of using published signature genes in cross-platform applications and the possible adoption of the published classifiers for a variety of applications. The study reveals an opportunity for possible translation of biomarkers identified using microarrays to clinically validated non-array gene expression assays.Microarray-based classifiers and associated signature genes generated from various platforms are abundantly reported in the literature; however, the utility of the classifiers and signature genes in cross-platform prediction applications remains largely uncertain. As part of the MicroArray Quality Control Phase II (MAQC-II) project, we show in this study 80-90% cross-platform prediction consistency using a large toxicogenomics data set by illustrating that: (1) the signature genes of a classifier generated from one platform can be directly applied to another platform to develop a predictive classifier; (2) a classifier developed using data generated from one platform can accurately predict samples that were profiled using a different platform. The results suggest the potential utility of using published signature genes in cross-platform applications and the possible adoption of the published classifiers for a variety of applications. The study reveals an opportunity for possible translation of biomarkers identified using microarrays to clinically validated non-array gene expression assays.
Microarray-based classifiers and associated signature genes generated from various platforms are abundantly reported in the literature;however, the utility of the classifiers and signature genes in cross-platform prediction applications remains largely uncertain. As part of the MicroArray Quality Control Phase II (MAQC-II) project, we show in this study 80-90% cross-platform prediction consistency using a large toxicogenomics data set by illustrating that: (1) the signature genes of a classifier generated from one platform can be directly applied to another platform to develop a predictive classifier;(2) a classifier developed using data generated from one platform can accurately predict samples that were profiled using a different platform. The results suggest the potential utility of using published signature genes in cross-platform applications and the possible adoption of the published classifiers for a variety of applications. The study reveals an opportunity for possible translation of biomarkers identified using microarrays to clinically validated non-array gene expression assays. The Pharmacogenomics Journal (2010) 10, 247-257;doi: 10.1038/tpj.2010.34 Keywords: microarray; cross-platform; gene signature; classifier; MAQC; hepatotoxicity
Audience Academic
Author Shi, T
Cheng, Y
Shi, W
Luo, J
Li, L
Paules, R S
Bushel, P R
Li, J
Nikolskaya, T
Shi, L
Chu, T-M
Fan, X
Chen, M
Walker, S J
Nikolsky, Y
Deng, Y
Fang, H
Huang, J
Wolfinger, R
Bao, W
Lobenhofer, E K
Hong, H
Tong, W
Zhang, J
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Keywords classifier
microarray
cross-platform
gene signature
MAQC
hepatotoxicity
Language English
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Current address: University of North Carolina—Chapel Hill, Chapel Hill, NC 27599, USA.
These authors contributed equally to this work.
Current address: Amgen, Thousand Oaks, CA 91320, USA.
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PublicationDate_xml – month: 08
  year: 2010
  text: 2010-08-01
  day: 01
PublicationDecade 2010
PublicationPlace London
PublicationPlace_xml – name: London
– name: United States
– name: New York
PublicationTitle The pharmacogenomics journal
PublicationTitleAbbrev Pharmacogenomics J
PublicationTitleAlternate Pharmacogenomics J
PublicationYear 2010
Publisher Nature Publishing Group UK
Nature Publishing Group
Publisher_xml – name: Nature Publishing Group UK
– name: Nature Publishing Group
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SSID ssj0016178
Score 2.163745
Snippet Microarray-based classifiers and associated signature genes generated from various platforms are abundantly reported in the literature; however, the utility of...
Microarray-based classifiers and associated signature genes generated from various platforms are abundantly reported in the literature;however, the utility of...
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StartPage 247
SubjectTerms 631/208/191/2018
631/208/212/1728
692/53/2423
692/699/1503/1607/2749
Algorithms
Animals
Biomedical and Life Sciences
Biomedicine
Databases, Genetic
DNA microarrays
DNA Probes
Gene Expression
Gene Expression Profiling
Genes
Human Genetics
Humans
Methods
Oligonucleotide Array Sequence Analysis - methods
Oncology
Original
original-article
Pharmacogenetics
Pharmacogenetics - methods
Pharmacotherapy
Phenotype
Predictive Value of Tests
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Title Consistency of predictive signature genes and classifiers generated using different microarray platforms
URI https://link.springer.com/article/10.1038/tpj.2010.34
https://www.ncbi.nlm.nih.gov/pubmed/20676064
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https://pubmed.ncbi.nlm.nih.gov/PMC2920073
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