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Metabolite profiling in retinoblastoma identifies novel clinicopathological subgroups

BACKGROUND: Tumour classification, based on histopathology or molecular pathology, is of value to predict tumour behaviour and to select appropriate treatment. In retinoblastoma, pathology information is not available at diagnosis and only exists for enucleated tumours. Alternative methods of tumour...

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Autores principales: Kohe, Sarah, Brundler, Marie-Anne, Jenkinson, Helen, Parulekar, Manoj, Wilson, Martin, Peet, Andrew C, McConville, Carmel M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4647873/
https://www.ncbi.nlm.nih.gov/pubmed/26348444
http://dx.doi.org/10.1038/bjc.2015.318
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author Kohe, Sarah
Brundler, Marie-Anne
Jenkinson, Helen
Parulekar, Manoj
Wilson, Martin
Peet, Andrew C
McConville, Carmel M
author_facet Kohe, Sarah
Brundler, Marie-Anne
Jenkinson, Helen
Parulekar, Manoj
Wilson, Martin
Peet, Andrew C
McConville, Carmel M
author_sort Kohe, Sarah
collection PubMed
description BACKGROUND: Tumour classification, based on histopathology or molecular pathology, is of value to predict tumour behaviour and to select appropriate treatment. In retinoblastoma, pathology information is not available at diagnosis and only exists for enucleated tumours. Alternative methods of tumour classification, using noninvasive techniques such as magnetic resonance spectroscopy, are urgently required to guide treatment decisions at the time of diagnosis. METHODS: High-resolution magic-angle spinning magnetic resonance spectroscopy (HR-MAS MRS) was undertaken on enucleated retinoblastomas. Principal component analysis and cluster analysis of the HR-MAS MRS data was used to identify tumour subgroups. Individual metabolite concentrations were determined and were correlated with histopathological risk factors for each group. RESULTS: Multivariate analysis identified three metabolic subgroups of retinoblastoma, with the most discriminatory metabolites being taurine, hypotaurine, total-choline and creatine. Metabolite concentrations correlated with specific histopathological features: taurine was correlated with differentiation, total-choline and phosphocholine with retrolaminar optic nerve invasion, and total lipids with necrosis. CONCLUSIONS: We have demonstrated that a metabolite-based classification of retinoblastoma can be obtained using ex vivo magnetic resonance spectroscopy, and that the subgroups identified correlate with histopathological features. This result justifies future studies to validate the clinical relevance of these subgroups and highlights the potential of in vivo MRS as a noninvasive diagnostic tool for retinoblastoma patient stratification.
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spelling pubmed-46478732015-12-01 Metabolite profiling in retinoblastoma identifies novel clinicopathological subgroups Kohe, Sarah Brundler, Marie-Anne Jenkinson, Helen Parulekar, Manoj Wilson, Martin Peet, Andrew C McConville, Carmel M Br J Cancer Molecular Diagnostics BACKGROUND: Tumour classification, based on histopathology or molecular pathology, is of value to predict tumour behaviour and to select appropriate treatment. In retinoblastoma, pathology information is not available at diagnosis and only exists for enucleated tumours. Alternative methods of tumour classification, using noninvasive techniques such as magnetic resonance spectroscopy, are urgently required to guide treatment decisions at the time of diagnosis. METHODS: High-resolution magic-angle spinning magnetic resonance spectroscopy (HR-MAS MRS) was undertaken on enucleated retinoblastomas. Principal component analysis and cluster analysis of the HR-MAS MRS data was used to identify tumour subgroups. Individual metabolite concentrations were determined and were correlated with histopathological risk factors for each group. RESULTS: Multivariate analysis identified three metabolic subgroups of retinoblastoma, with the most discriminatory metabolites being taurine, hypotaurine, total-choline and creatine. Metabolite concentrations correlated with specific histopathological features: taurine was correlated with differentiation, total-choline and phosphocholine with retrolaminar optic nerve invasion, and total lipids with necrosis. CONCLUSIONS: We have demonstrated that a metabolite-based classification of retinoblastoma can be obtained using ex vivo magnetic resonance spectroscopy, and that the subgroups identified correlate with histopathological features. This result justifies future studies to validate the clinical relevance of these subgroups and highlights the potential of in vivo MRS as a noninvasive diagnostic tool for retinoblastoma patient stratification. Nature Publishing Group 2015-10-20 2015-09-08 /pmc/articles/PMC4647873/ /pubmed/26348444 http://dx.doi.org/10.1038/bjc.2015.318 Text en Copyright © 2015 Cancer Research UK http://creativecommons.org/licenses/by/4.0/ This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Molecular Diagnostics
Kohe, Sarah
Brundler, Marie-Anne
Jenkinson, Helen
Parulekar, Manoj
Wilson, Martin
Peet, Andrew C
McConville, Carmel M
Metabolite profiling in retinoblastoma identifies novel clinicopathological subgroups
title Metabolite profiling in retinoblastoma identifies novel clinicopathological subgroups
title_full Metabolite profiling in retinoblastoma identifies novel clinicopathological subgroups
title_fullStr Metabolite profiling in retinoblastoma identifies novel clinicopathological subgroups
title_full_unstemmed Metabolite profiling in retinoblastoma identifies novel clinicopathological subgroups
title_short Metabolite profiling in retinoblastoma identifies novel clinicopathological subgroups
title_sort metabolite profiling in retinoblastoma identifies novel clinicopathological subgroups
topic Molecular Diagnostics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4647873/
https://www.ncbi.nlm.nih.gov/pubmed/26348444
http://dx.doi.org/10.1038/bjc.2015.318
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