Ensemble analyses improve signatures of tumour hypoxia and reveal inter-platform differences

The reproducibility of transcriptomic biomarkers across datasets remains poor, limiting clinical application. We and others have suggested that this is in-part caused by differential error-structure between datasets, and their incomplete removal by pre-processing algorithms. To test this hypothesis,...

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Published inBMC bioinformatics Vol. 15; no. 1; p. 170
Main Authors Fox, Natalie S, Starmans, Maud H W, Haider, Syed, Lambin, Philippe, Boutros, Paul C
Format Journal Article
LanguageEnglish
Published England BioMed Central Ltd 06.06.2014
BioMed Central
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Abstract The reproducibility of transcriptomic biomarkers across datasets remains poor, limiting clinical application. We and others have suggested that this is in-part caused by differential error-structure between datasets, and their incomplete removal by pre-processing algorithms. To test this hypothesis, we systematically assessed the effects of pre-processing on biomarker classification using 24 different pre-processing methods and 15 distinct signatures of tumour hypoxia in 10 datasets (2,143 patients). We confirm strong pre-processing effects for all datasets and signatures, and find that these differ between microarray versions. Importantly, exploiting different pre-processing techniques in an ensemble technique improved classification for a majority of signatures. Assessing biomarkers using an ensemble of pre-processing techniques shows clear value across multiple diseases, datasets and biomarkers. Importantly, ensemble classification improves biomarkers with initially good results but does not result in spuriously improved performance for poor biomarkers. While further research is required, this approach has the potential to become a standard for transcriptomic biomarkers.
AbstractList Background: The reproducibility of transcriptomic biomarkers across datasets remains poor, limiting clinical application. We and others have suggested that this is in-part caused by differential error-structure between datasets, and their incomplete removal by pre-processing algorithms. Methods: To test this hypothesis, we systematically assessed the effects of pre-processing on biomarker classification using 24 different pre-processing methods and 15 distinct signatures of tumour hypoxia in 10 datasets (2,143 patients). Results: We confirm strong pre-processing effects for all datasets and signatures, and find that these differ between microarray versions. Importantly, exploiting different pre-processing techniques in an ensemble technique improved classification for a majority of signatures. Conclusions: Assessing biomarkers using an ensemble of pre-processing techniques shows clear value across multiple diseases, datasets and biomarkers. Importantly, ensemble classification improves biomarkers with initially good results but does not result in spuriously improved performance for poor biomarkers. While further research is required, this approach has the potential to become a standard for transcriptomic biomarkers.
The reproducibility of transcriptomic biomarkers across datasets remains poor, limiting clinical application. We and others have suggested that this is in-part caused by differential error-structure between datasets, and their incomplete removal by pre-processing algorithms. To test this hypothesis, we systematically assessed the effects of pre-processing on biomarker classification using 24 different pre-processing methods and 15 distinct signatures of tumour hypoxia in 10 datasets (2,143 patients). We confirm strong pre-processing effects for all datasets and signatures, and find that these differ between microarray versions. Importantly, exploiting different pre-processing techniques in an ensemble technique improved classification for a majority of signatures. Assessing biomarkers using an ensemble of pre-processing techniques shows clear value across multiple diseases, datasets and biomarkers. Importantly, ensemble classification improves biomarkers with initially good results but does not result in spuriously improved performance for poor biomarkers. While further research is required, this approach has the potential to become a standard for transcriptomic biomarkers.
Doc number: 170 Abstract Background: The reproducibility of transcriptomic biomarkers across datasets remains poor, limiting clinical application. We and others have suggested that this is in-part caused by differential error-structure between datasets, and their incomplete removal by pre-processing algorithms. Methods: To test this hypothesis, we systematically assessed the effects of pre-processing on biomarker classification using 24 different pre-processing methods and 15 distinct signatures of tumour hypoxia in 10 datasets (2,143 patients). Results: We confirm strong pre-processing effects for all datasets and signatures, and find that these differ between microarray versions. Importantly, exploiting different pre-processing techniques in an ensemble technique improved classification for a majority of signatures. Conclusions: Assessing biomarkers using an ensemble of pre-processing techniques shows clear value across multiple diseases, datasets and biomarkers. Importantly, ensemble classification improves biomarkers with initially good results but does not result in spuriously improved performance for poor biomarkers. While further research is required, this approach has the potential to become a standard for transcriptomic biomarkers.
BACKGROUNDThe reproducibility of transcriptomic biomarkers across datasets remains poor, limiting clinical application. We and others have suggested that this is in-part caused by differential error-structure between datasets, and their incomplete removal by pre-processing algorithms. METHODSTo test this hypothesis, we systematically assessed the effects of pre-processing on biomarker classification using 24 different pre-processing methods and 15 distinct signatures of tumour hypoxia in 10 datasets (2,143 patients). RESULTSWe confirm strong pre-processing effects for all datasets and signatures, and find that these differ between microarray versions. Importantly, exploiting different pre-processing techniques in an ensemble technique improved classification for a majority of signatures. CONCLUSIONSAssessing biomarkers using an ensemble of pre-processing techniques shows clear value across multiple diseases, datasets and biomarkers. Importantly, ensemble classification improves biomarkers with initially good results but does not result in spuriously improved performance for poor biomarkers. While further research is required, this approach has the potential to become a standard for transcriptomic biomarkers.
The reproducibility of transcriptomic biomarkers across datasets remains poor, limiting clinical application. We and others have suggested that this is in-part caused by differential error-structure between datasets, and their incomplete removal by pre-processing algorithms. To test this hypothesis, we systematically assessed the effects of pre-processing on biomarker classification using 24 different pre-processing methods and 15 distinct signatures of tumour hypoxia in 10 datasets (2,143 patients). We confirm strong pre-processing effects for all datasets and signatures, and find that these differ between microarray versions. Importantly, exploiting different pre-processing techniques in an ensemble technique improved classification for a majority of signatures. Assessing biomarkers using an ensemble of pre-processing techniques shows clear value across multiple diseases, datasets and biomarkers. Importantly, ensemble classification improves biomarkers with initially good results but does not result in spuriously improved performance for poor biomarkers. While further research is required, this approach has the potential to become a standard for transcriptomic biomarkers.
ArticleNumber 170
Audience Academic
Author Starmans, Maud H W
Fox, Natalie S
Lambin, Philippe
Haider, Syed
Boutros, Paul C
AuthorAffiliation 5 Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
2 Department of Radiation Oncology (Maastro), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
4 Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
1 Informatics and Bio-computing Platform, Ontario Institute for Cancer Research, Toronto, Canada
3 Centre for Molecular Oncology, Barts Cancer Institute, London EC1M 6BQ, UK
AuthorAffiliation_xml – name: 1 Informatics and Bio-computing Platform, Ontario Institute for Cancer Research, Toronto, Canada
– name: 4 Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
– name: 3 Centre for Molecular Oncology, Barts Cancer Institute, London EC1M 6BQ, UK
– name: 5 Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
– name: 2 Department of Radiation Oncology (Maastro), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
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  givenname: Natalie S
  surname: Fox
  fullname: Fox, Natalie S
– sequence: 2
  givenname: Maud H W
  surname: Starmans
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  givenname: Syed
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  givenname: Paul C
  surname: Boutros
  fullname: Boutros, Paul C
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  organization: Informatics and Bio-computing Platform, Ontario Institute for Cancer Research, Toronto, Canada. Paul.Boutros@oicr.on.ca
BackLink https://www.ncbi.nlm.nih.gov/pubmed/24902696$$D View this record in MEDLINE/PubMed
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2014 Fox et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Copyright © 2014 Fox et al.; licensee BioMed Central Ltd. 2014 Fox et al.; licensee BioMed Central Ltd.
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– notice: 2014 Fox et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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Snippet The reproducibility of transcriptomic biomarkers across datasets remains poor, limiting clinical application. We and others have suggested that this is in-part...
Background The reproducibility of transcriptomic biomarkers across datasets remains poor, limiting clinical application. We and others have suggested that this...
Doc number: 170 Abstract Background: The reproducibility of transcriptomic biomarkers across datasets remains poor, limiting clinical application. We and...
BACKGROUNDThe reproducibility of transcriptomic biomarkers across datasets remains poor, limiting clinical application. We and others have suggested that this...
Background: The reproducibility of transcriptomic biomarkers across datasets remains poor, limiting clinical application. We and others have suggested that...
BACKGROUND: The reproducibility of transcriptomic biomarkers across datasets remains poor, limiting clinical application. We and others have suggested that...
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StartPage 170
SubjectTerms Algorithms
Biomarkers
Breast cancer
Cancer therapies
Cell Hypoxia
Classification
Colleges & universities
Gene expression
Gene Expression Regulation, Neoplastic
Genetic aspects
Genomes
Humans
Hypoxia
Medical prognosis
Medical research
Neoplasms - pathology
Oncology
Patients
Physiological aspects
Prognosis
Reproducibility of Results
Tissue Array Analysis
Tumors
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Title Ensemble analyses improve signatures of tumour hypoxia and reveal inter-platform differences
URI https://www.ncbi.nlm.nih.gov/pubmed/24902696
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http://dx.doi.org/10.1186/1471-2105-15-170
https://pubmed.ncbi.nlm.nih.gov/PMC4061774
Volume 15
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