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Extent, impact, and mitigation of batch effects in tumor biomarker studies using tissue microarrays

Tissue microarrays (TMAs) have been used in thousands of cancer biomarker studies. To what extent batch effects, measurement error in biomarker levels between slides, affects TMA-based studies has not been assessed systematically. We evaluated 20 protein biomarkers on 14 TMAs with prospectively coll...

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Autores principales: Stopsack, Konrad H, Tyekucheva, Svitlana, Wang, Molin, Gerke, Travis A, Vaselkiv, J Bailey, Penney, Kathryn L, Kantoff, Philip W, Finn, Stephen P, Fiorentino, Michelangelo, Loda, Massimo, Lotan, Tamara L, Parmigiani, Giovanni, Mucci, Lorelei A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8849344/
https://www.ncbi.nlm.nih.gov/pubmed/34939926
http://dx.doi.org/10.7554/eLife.71265
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author Stopsack, Konrad H
Tyekucheva, Svitlana
Wang, Molin
Gerke, Travis A
Vaselkiv, J Bailey
Penney, Kathryn L
Kantoff, Philip W
Finn, Stephen P
Fiorentino, Michelangelo
Loda, Massimo
Lotan, Tamara L
Parmigiani, Giovanni
Mucci, Lorelei A
author_facet Stopsack, Konrad H
Tyekucheva, Svitlana
Wang, Molin
Gerke, Travis A
Vaselkiv, J Bailey
Penney, Kathryn L
Kantoff, Philip W
Finn, Stephen P
Fiorentino, Michelangelo
Loda, Massimo
Lotan, Tamara L
Parmigiani, Giovanni
Mucci, Lorelei A
author_sort Stopsack, Konrad H
collection PubMed
description Tissue microarrays (TMAs) have been used in thousands of cancer biomarker studies. To what extent batch effects, measurement error in biomarker levels between slides, affects TMA-based studies has not been assessed systematically. We evaluated 20 protein biomarkers on 14 TMAs with prospectively collected tumor tissue from 1448 primary prostate cancers. In half of the biomarkers, more than 10% of biomarker variance was attributable to between-TMA differences (range, 1–48%). We implemented different methods to mitigate batch effects (R package batchtma), tested in plasmode simulation. Biomarker levels were more similar between mitigation approaches compared to uncorrected values. For some biomarkers, associations with clinical features changed substantially after addressing batch effects. Batch effects and resulting bias are not an error of an individual study but an inherent feature of TMA-based protein biomarker studies. They always need to be considered during study design and addressed analytically in studies using more than one TMA.
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spelling pubmed-88493442022-02-17 Extent, impact, and mitigation of batch effects in tumor biomarker studies using tissue microarrays Stopsack, Konrad H Tyekucheva, Svitlana Wang, Molin Gerke, Travis A Vaselkiv, J Bailey Penney, Kathryn L Kantoff, Philip W Finn, Stephen P Fiorentino, Michelangelo Loda, Massimo Lotan, Tamara L Parmigiani, Giovanni Mucci, Lorelei A eLife Cancer Biology Tissue microarrays (TMAs) have been used in thousands of cancer biomarker studies. To what extent batch effects, measurement error in biomarker levels between slides, affects TMA-based studies has not been assessed systematically. We evaluated 20 protein biomarkers on 14 TMAs with prospectively collected tumor tissue from 1448 primary prostate cancers. In half of the biomarkers, more than 10% of biomarker variance was attributable to between-TMA differences (range, 1–48%). We implemented different methods to mitigate batch effects (R package batchtma), tested in plasmode simulation. Biomarker levels were more similar between mitigation approaches compared to uncorrected values. For some biomarkers, associations with clinical features changed substantially after addressing batch effects. Batch effects and resulting bias are not an error of an individual study but an inherent feature of TMA-based protein biomarker studies. They always need to be considered during study design and addressed analytically in studies using more than one TMA. eLife Sciences Publications, Ltd 2021-12-23 /pmc/articles/PMC8849344/ /pubmed/34939926 http://dx.doi.org/10.7554/eLife.71265 Text en © 2021, Stopsack et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Cancer Biology
Stopsack, Konrad H
Tyekucheva, Svitlana
Wang, Molin
Gerke, Travis A
Vaselkiv, J Bailey
Penney, Kathryn L
Kantoff, Philip W
Finn, Stephen P
Fiorentino, Michelangelo
Loda, Massimo
Lotan, Tamara L
Parmigiani, Giovanni
Mucci, Lorelei A
Extent, impact, and mitigation of batch effects in tumor biomarker studies using tissue microarrays
title Extent, impact, and mitigation of batch effects in tumor biomarker studies using tissue microarrays
title_full Extent, impact, and mitigation of batch effects in tumor biomarker studies using tissue microarrays
title_fullStr Extent, impact, and mitigation of batch effects in tumor biomarker studies using tissue microarrays
title_full_unstemmed Extent, impact, and mitigation of batch effects in tumor biomarker studies using tissue microarrays
title_short Extent, impact, and mitigation of batch effects in tumor biomarker studies using tissue microarrays
title_sort extent, impact, and mitigation of batch effects in tumor biomarker studies using tissue microarrays
topic Cancer Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8849344/
https://www.ncbi.nlm.nih.gov/pubmed/34939926
http://dx.doi.org/10.7554/eLife.71265
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