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Astronomical algorithms for automated analysis of tissue protein expression in breast cancer

BACKGROUND: High-throughput evaluation of tissue biomarkers in oncology has been greatly accelerated by the widespread use of tissue microarrays (TMAs) and immunohistochemistry. Although TMAs have the potential to facilitate protein expression profiling on a scale to rival experiments of tumour tran...

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Autores principales: Ali, H R, Irwin, M, Morris, L, Dawson, S-J, Blows, F M, Provenzano, E, Mahler-Araujo, B, Pharoah, P D, Walton, N A, Brenton, J D, Caldas, C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3593538/
https://www.ncbi.nlm.nih.gov/pubmed/23329232
http://dx.doi.org/10.1038/bjc.2012.558
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author Ali, H R
Irwin, M
Morris, L
Dawson, S-J
Blows, F M
Provenzano, E
Mahler-Araujo, B
Pharoah, P D
Walton, N A
Brenton, J D
Caldas, C
author_facet Ali, H R
Irwin, M
Morris, L
Dawson, S-J
Blows, F M
Provenzano, E
Mahler-Araujo, B
Pharoah, P D
Walton, N A
Brenton, J D
Caldas, C
author_sort Ali, H R
collection PubMed
description BACKGROUND: High-throughput evaluation of tissue biomarkers in oncology has been greatly accelerated by the widespread use of tissue microarrays (TMAs) and immunohistochemistry. Although TMAs have the potential to facilitate protein expression profiling on a scale to rival experiments of tumour transcriptomes, the bottleneck and imprecision of manually scoring TMAs has impeded progress. METHODS: We report image analysis algorithms adapted from astronomy for the precise automated analysis of IHC in all subcellular compartments. The power of this technique is demonstrated using over 2000 breast tumours and comparing quantitative automated scores against manual assessment by pathologists. RESULTS: All continuous automated scores showed good correlation with their corresponding ordinal manual scores. For oestrogen receptor (ER), the correlation was 0.82, P<0.0001, for BCL2 0.72, P<0.0001 and for HER2 0.62, P<0.0001. Automated scores showed excellent concordance with manual scores for the unsupervised assignment of cases to ‘positive' or ‘negative' categories with agreement rates of up to 96%. CONCLUSION: The adaptation of astronomical algorithms coupled with their application to large annotated study cohorts, constitutes a powerful tool for the realisation of the enormous potential of digital pathology.
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spelling pubmed-35935382014-02-19 Astronomical algorithms for automated analysis of tissue protein expression in breast cancer Ali, H R Irwin, M Morris, L Dawson, S-J Blows, F M Provenzano, E Mahler-Araujo, B Pharoah, P D Walton, N A Brenton, J D Caldas, C Br J Cancer Molecular Diagnostics BACKGROUND: High-throughput evaluation of tissue biomarkers in oncology has been greatly accelerated by the widespread use of tissue microarrays (TMAs) and immunohistochemistry. Although TMAs have the potential to facilitate protein expression profiling on a scale to rival experiments of tumour transcriptomes, the bottleneck and imprecision of manually scoring TMAs has impeded progress. METHODS: We report image analysis algorithms adapted from astronomy for the precise automated analysis of IHC in all subcellular compartments. The power of this technique is demonstrated using over 2000 breast tumours and comparing quantitative automated scores against manual assessment by pathologists. RESULTS: All continuous automated scores showed good correlation with their corresponding ordinal manual scores. For oestrogen receptor (ER), the correlation was 0.82, P<0.0001, for BCL2 0.72, P<0.0001 and for HER2 0.62, P<0.0001. Automated scores showed excellent concordance with manual scores for the unsupervised assignment of cases to ‘positive' or ‘negative' categories with agreement rates of up to 96%. CONCLUSION: The adaptation of astronomical algorithms coupled with their application to large annotated study cohorts, constitutes a powerful tool for the realisation of the enormous potential of digital pathology. Nature Publishing Group 2013-02-19 2013-01-17 /pmc/articles/PMC3593538/ /pubmed/23329232 http://dx.doi.org/10.1038/bjc.2012.558 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
Ali, H R
Irwin, M
Morris, L
Dawson, S-J
Blows, F M
Provenzano, E
Mahler-Araujo, B
Pharoah, P D
Walton, N A
Brenton, J D
Caldas, C
Astronomical algorithms for automated analysis of tissue protein expression in breast cancer
title Astronomical algorithms for automated analysis of tissue protein expression in breast cancer
title_full Astronomical algorithms for automated analysis of tissue protein expression in breast cancer
title_fullStr Astronomical algorithms for automated analysis of tissue protein expression in breast cancer
title_full_unstemmed Astronomical algorithms for automated analysis of tissue protein expression in breast cancer
title_short Astronomical algorithms for automated analysis of tissue protein expression in breast cancer
title_sort astronomical algorithms for automated analysis of tissue protein expression in breast cancer
topic Molecular Diagnostics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3593538/
https://www.ncbi.nlm.nih.gov/pubmed/23329232
http://dx.doi.org/10.1038/bjc.2012.558
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