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Identification of key clinical phenotypes of breast cancer using a reduced panel of protein biomarkers

BACKGROUND: Breast cancer is a heterogeneous disease characterised by complex molecular alterations underlying the varied behaviour and response to therapy. However, translation of cancer genetic profiling for use in routine clinical practice remains elusive or prohibitively expensive. As an alterna...

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Autores principales: Green, A R, Powe, D G, Rakha, E A, Soria, D, Lemetre, C, Nolan, C C, Barros, F F T, Macmillan, R D, Garibaldi, J M, Ball, G R, Ellis, I O
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3790179/
https://www.ncbi.nlm.nih.gov/pubmed/24008658
http://dx.doi.org/10.1038/bjc.2013.528
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author Green, A R
Powe, D G
Rakha, E A
Soria, D
Lemetre, C
Nolan, C C
Barros, F F T
Macmillan, R D
Garibaldi, J M
Ball, G R
Ellis, I O
author_facet Green, A R
Powe, D G
Rakha, E A
Soria, D
Lemetre, C
Nolan, C C
Barros, F F T
Macmillan, R D
Garibaldi, J M
Ball, G R
Ellis, I O
author_sort Green, A R
collection PubMed
description BACKGROUND: Breast cancer is a heterogeneous disease characterised by complex molecular alterations underlying the varied behaviour and response to therapy. However, translation of cancer genetic profiling for use in routine clinical practice remains elusive or prohibitively expensive. As an alternative, immunohistochemical analysis applied to routinely processed tissue samples could be used to identify distinct biological classes of breast cancer. METHODS: In this study, 1073 archival breast tumours previously assessed for 25 key breast cancer biomarkers using immunohistochemistry and classified using clustering algorithms were further refined using naïve Bayes classification performance. Criteria for class membership were defined using the expression of a reduced panel of 10 proteins able to identify key molecular classes. We examined the association between these breast cancer classes with clinicopathological factors and patient outcome. RESULTS: We confirm patient classification similar to established genotypic biological classes of breast cancer in addition to novel sub-divisions of luminal and basal tumours. Correlations between classes and clinicopathological parameters were in line with expectations and showed highly significant association with patient outcome. Furthermore, our novel biological class stratification provides additional prognostic information to the Nottingham Prognostic Index. CONCLUSION: This study confirms that distinct molecular phenotypes of breast cancer can be identified using robust and routinely available techniques and both the luminal and basal breast cancer phenotypes are heterogeneous and contain distinct subgroups.
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spelling pubmed-37901792014-10-01 Identification of key clinical phenotypes of breast cancer using a reduced panel of protein biomarkers Green, A R Powe, D G Rakha, E A Soria, D Lemetre, C Nolan, C C Barros, F F T Macmillan, R D Garibaldi, J M Ball, G R Ellis, I O Br J Cancer Molecular Diagnostics BACKGROUND: Breast cancer is a heterogeneous disease characterised by complex molecular alterations underlying the varied behaviour and response to therapy. However, translation of cancer genetic profiling for use in routine clinical practice remains elusive or prohibitively expensive. As an alternative, immunohistochemical analysis applied to routinely processed tissue samples could be used to identify distinct biological classes of breast cancer. METHODS: In this study, 1073 archival breast tumours previously assessed for 25 key breast cancer biomarkers using immunohistochemistry and classified using clustering algorithms were further refined using naïve Bayes classification performance. Criteria for class membership were defined using the expression of a reduced panel of 10 proteins able to identify key molecular classes. We examined the association between these breast cancer classes with clinicopathological factors and patient outcome. RESULTS: We confirm patient classification similar to established genotypic biological classes of breast cancer in addition to novel sub-divisions of luminal and basal tumours. Correlations between classes and clinicopathological parameters were in line with expectations and showed highly significant association with patient outcome. Furthermore, our novel biological class stratification provides additional prognostic information to the Nottingham Prognostic Index. CONCLUSION: This study confirms that distinct molecular phenotypes of breast cancer can be identified using robust and routinely available techniques and both the luminal and basal breast cancer phenotypes are heterogeneous and contain distinct subgroups. Nature Publishing Group 2013-10-01 2013-09-05 /pmc/articles/PMC3790179/ /pubmed/24008658 http://dx.doi.org/10.1038/bjc.2013.528 Text en Copyright © 2013 Cancer Research UK http://creativecommons.org/licenses/by-nc-sa/3.0/ From twelve months after its original publication, this work is licensed under the Creative Commons Attribution-NonCommercial-Share Alike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/
spellingShingle Molecular Diagnostics
Green, A R
Powe, D G
Rakha, E A
Soria, D
Lemetre, C
Nolan, C C
Barros, F F T
Macmillan, R D
Garibaldi, J M
Ball, G R
Ellis, I O
Identification of key clinical phenotypes of breast cancer using a reduced panel of protein biomarkers
title Identification of key clinical phenotypes of breast cancer using a reduced panel of protein biomarkers
title_full Identification of key clinical phenotypes of breast cancer using a reduced panel of protein biomarkers
title_fullStr Identification of key clinical phenotypes of breast cancer using a reduced panel of protein biomarkers
title_full_unstemmed Identification of key clinical phenotypes of breast cancer using a reduced panel of protein biomarkers
title_short Identification of key clinical phenotypes of breast cancer using a reduced panel of protein biomarkers
title_sort identification of key clinical phenotypes of breast cancer using a reduced panel of protein biomarkers
topic Molecular Diagnostics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3790179/
https://www.ncbi.nlm.nih.gov/pubmed/24008658
http://dx.doi.org/10.1038/bjc.2013.528
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