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Microarray-Based Class Discovery for Molecular Classification of Breast Cancer: Analysis of Interobserver Agreement
BACKGROUND: Breast cancers can be classified by hierarchical clustering using an “intrinsic” gene list into one of at least five molecular subtypes: basal-like, HER2, luminal A, luminal B, and normal breast-like. Five different intrinsic gene lists composed of varying numbers of genes have been used...
Autores principales: | , , , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
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Oxford University Press
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3079850/ https://www.ncbi.nlm.nih.gov/pubmed/21421860 http://dx.doi.org/10.1093/jnci/djr071 |
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author | Mackay, Alan Weigelt, Britta Grigoriadis, Anita Kreike, Bas Natrajan, Rachael A’Hern, Roger Tan, David S.P. Dowsett, Mitch Ashworth, Alan Reis-Filho, Jorge S. |
author_facet | Mackay, Alan Weigelt, Britta Grigoriadis, Anita Kreike, Bas Natrajan, Rachael A’Hern, Roger Tan, David S.P. Dowsett, Mitch Ashworth, Alan Reis-Filho, Jorge S. |
author_sort | Mackay, Alan |
collection | PubMed |
description | BACKGROUND: Breast cancers can be classified by hierarchical clustering using an “intrinsic” gene list into one of at least five molecular subtypes: basal-like, HER2, luminal A, luminal B, and normal breast-like. Five different intrinsic gene lists composed of varying numbers of genes have been used for molecular subtype identification and classification of breast cancers. The aim of this study was to determine the objectivity and interobserver reproducibility of the assignment of molecular subtype classes by hierarchical cluster analysis. METHODS: Three publicly available breast cancer datasets (n = 779) were subjected to two-way average-linkage hierarchical cluster analysis using five distinct intrinsic gene lists. We used free-marginal Kappa statistics to analyze interobserver agreement among five breast cancer researchers for the whole classification and for each molecular subtype separately according to each intrinsic gene list for each breast cancer dataset. RESULTS: None of the classification systems tested produced almost perfect agreement (Kappa ≥ 0.81) among observers. However, substantial interobserver agreement (70.8% to 76.1% of the samples and free-marginal Kappa scores from 0.635 to 0.701) was consistently observed in all datasets for four molecular subtypes (luminal, basal-like, HER2, and normal breast-like). When luminal cancers were subdivided (luminal A, B, and C), none of the classification systems produced substantial agreement (Kappa ≥ 0.61) in all the datasets analyzed. Analysis of each subtype separately revealed that only two (basal-like and HER2) could be reproducibly identified by independent observers (Kappa ≥ 0.81). CONCLUSIONS: Assignment of molecular subtype classes of breast cancer based on the analysis of dendrograms obtained with hierarchical cluster analysis is subjective and shows modest interobserver reproducibility. For the development of a molecular taxonomy, objective definitions for each molecular subtype and standardized methods for their identification are required. |
format | Text |
id | pubmed-3079850 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-30798502011-04-21 Microarray-Based Class Discovery for Molecular Classification of Breast Cancer: Analysis of Interobserver Agreement Mackay, Alan Weigelt, Britta Grigoriadis, Anita Kreike, Bas Natrajan, Rachael A’Hern, Roger Tan, David S.P. Dowsett, Mitch Ashworth, Alan Reis-Filho, Jorge S. J Natl Cancer Inst Articles BACKGROUND: Breast cancers can be classified by hierarchical clustering using an “intrinsic” gene list into one of at least five molecular subtypes: basal-like, HER2, luminal A, luminal B, and normal breast-like. Five different intrinsic gene lists composed of varying numbers of genes have been used for molecular subtype identification and classification of breast cancers. The aim of this study was to determine the objectivity and interobserver reproducibility of the assignment of molecular subtype classes by hierarchical cluster analysis. METHODS: Three publicly available breast cancer datasets (n = 779) were subjected to two-way average-linkage hierarchical cluster analysis using five distinct intrinsic gene lists. We used free-marginal Kappa statistics to analyze interobserver agreement among five breast cancer researchers for the whole classification and for each molecular subtype separately according to each intrinsic gene list for each breast cancer dataset. RESULTS: None of the classification systems tested produced almost perfect agreement (Kappa ≥ 0.81) among observers. However, substantial interobserver agreement (70.8% to 76.1% of the samples and free-marginal Kappa scores from 0.635 to 0.701) was consistently observed in all datasets for four molecular subtypes (luminal, basal-like, HER2, and normal breast-like). When luminal cancers were subdivided (luminal A, B, and C), none of the classification systems produced substantial agreement (Kappa ≥ 0.61) in all the datasets analyzed. Analysis of each subtype separately revealed that only two (basal-like and HER2) could be reproducibly identified by independent observers (Kappa ≥ 0.81). CONCLUSIONS: Assignment of molecular subtype classes of breast cancer based on the analysis of dendrograms obtained with hierarchical cluster analysis is subjective and shows modest interobserver reproducibility. For the development of a molecular taxonomy, objective definitions for each molecular subtype and standardized methods for their identification are required. Oxford University Press 2011-04-20 2011-03-18 /pmc/articles/PMC3079850/ /pubmed/21421860 http://dx.doi.org/10.1093/jnci/djr071 Text en © The Author 2011. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Articles Mackay, Alan Weigelt, Britta Grigoriadis, Anita Kreike, Bas Natrajan, Rachael A’Hern, Roger Tan, David S.P. Dowsett, Mitch Ashworth, Alan Reis-Filho, Jorge S. Microarray-Based Class Discovery for Molecular Classification of Breast Cancer: Analysis of Interobserver Agreement |
title | Microarray-Based Class Discovery for Molecular Classification of Breast Cancer: Analysis of Interobserver Agreement |
title_full | Microarray-Based Class Discovery for Molecular Classification of Breast Cancer: Analysis of Interobserver Agreement |
title_fullStr | Microarray-Based Class Discovery for Molecular Classification of Breast Cancer: Analysis of Interobserver Agreement |
title_full_unstemmed | Microarray-Based Class Discovery for Molecular Classification of Breast Cancer: Analysis of Interobserver Agreement |
title_short | Microarray-Based Class Discovery for Molecular Classification of Breast Cancer: Analysis of Interobserver Agreement |
title_sort | microarray-based class discovery for molecular classification of breast cancer: analysis of interobserver agreement |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3079850/ https://www.ncbi.nlm.nih.gov/pubmed/21421860 http://dx.doi.org/10.1093/jnci/djr071 |
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