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Epithelial ovarian carcinoma diagnosis by desorption electrospray ionization mass spectrometry imaging
Ovarian cancer is highly prevalent among European women, and is the leading cause of gynaecological cancer death. Current histopathological diagnoses of tumour severity are based on interpretation of, for example, immunohistochemical staining. Desorption electrospray mass spectrometry imaging (DESI-...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5156945/ https://www.ncbi.nlm.nih.gov/pubmed/27976698 http://dx.doi.org/10.1038/srep39219 |
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author | Dória, Maria Luisa McKenzie, James S. Mroz, Anna Phelps, David L. Speller, Abigail Rosini, Francesca Strittmatter, Nicole Golf, Ottmar Veselkov, Kirill Brown, Robert Ghaem-Maghami, Sadaf Takats, Zoltan |
author_facet | Dória, Maria Luisa McKenzie, James S. Mroz, Anna Phelps, David L. Speller, Abigail Rosini, Francesca Strittmatter, Nicole Golf, Ottmar Veselkov, Kirill Brown, Robert Ghaem-Maghami, Sadaf Takats, Zoltan |
author_sort | Dória, Maria Luisa |
collection | PubMed |
description | Ovarian cancer is highly prevalent among European women, and is the leading cause of gynaecological cancer death. Current histopathological diagnoses of tumour severity are based on interpretation of, for example, immunohistochemical staining. Desorption electrospray mass spectrometry imaging (DESI-MSI) generates spatially resolved metabolic profiles of tissues and supports an objective investigation of tumour biology. In this study, various ovarian tissue types were analysed by DESI-MSI and co-registered with their corresponding haematoxylin and eosin (H&E) stained images. The mass spectral data reveal tissue type-dependent lipid profiles which are consistent across the n = 110 samples (n = 107 patients) used in this study. Multivariate statistical methods were used to classify samples and identify molecular features discriminating between tissue types. Three main groups of samples (epithelial ovarian carcinoma, borderline ovarian tumours, normal ovarian stroma) were compared as were the carcinoma histotypes (serous, endometrioid, clear cell). Classification rates >84% were achieved for all analyses, and variables differing statistically between groups were determined and putatively identified. The changes noted in various lipid types help to provide a context in terms of tumour biochemistry. The classification of unseen samples demonstrates the capability of DESI-MSI to characterise ovarian samples and to overcome existing limitations in classical histopathology. |
format | Online Article Text |
id | pubmed-5156945 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-51569452016-12-20 Epithelial ovarian carcinoma diagnosis by desorption electrospray ionization mass spectrometry imaging Dória, Maria Luisa McKenzie, James S. Mroz, Anna Phelps, David L. Speller, Abigail Rosini, Francesca Strittmatter, Nicole Golf, Ottmar Veselkov, Kirill Brown, Robert Ghaem-Maghami, Sadaf Takats, Zoltan Sci Rep Article Ovarian cancer is highly prevalent among European women, and is the leading cause of gynaecological cancer death. Current histopathological diagnoses of tumour severity are based on interpretation of, for example, immunohistochemical staining. Desorption electrospray mass spectrometry imaging (DESI-MSI) generates spatially resolved metabolic profiles of tissues and supports an objective investigation of tumour biology. In this study, various ovarian tissue types were analysed by DESI-MSI and co-registered with their corresponding haematoxylin and eosin (H&E) stained images. The mass spectral data reveal tissue type-dependent lipid profiles which are consistent across the n = 110 samples (n = 107 patients) used in this study. Multivariate statistical methods were used to classify samples and identify molecular features discriminating between tissue types. Three main groups of samples (epithelial ovarian carcinoma, borderline ovarian tumours, normal ovarian stroma) were compared as were the carcinoma histotypes (serous, endometrioid, clear cell). Classification rates >84% were achieved for all analyses, and variables differing statistically between groups were determined and putatively identified. The changes noted in various lipid types help to provide a context in terms of tumour biochemistry. The classification of unseen samples demonstrates the capability of DESI-MSI to characterise ovarian samples and to overcome existing limitations in classical histopathology. Nature Publishing Group 2016-12-15 /pmc/articles/PMC5156945/ /pubmed/27976698 http://dx.doi.org/10.1038/srep39219 Text en Copyright © 2016, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Dória, Maria Luisa McKenzie, James S. Mroz, Anna Phelps, David L. Speller, Abigail Rosini, Francesca Strittmatter, Nicole Golf, Ottmar Veselkov, Kirill Brown, Robert Ghaem-Maghami, Sadaf Takats, Zoltan Epithelial ovarian carcinoma diagnosis by desorption electrospray ionization mass spectrometry imaging |
title | Epithelial ovarian carcinoma diagnosis by desorption electrospray ionization mass spectrometry imaging |
title_full | Epithelial ovarian carcinoma diagnosis by desorption electrospray ionization mass spectrometry imaging |
title_fullStr | Epithelial ovarian carcinoma diagnosis by desorption electrospray ionization mass spectrometry imaging |
title_full_unstemmed | Epithelial ovarian carcinoma diagnosis by desorption electrospray ionization mass spectrometry imaging |
title_short | Epithelial ovarian carcinoma diagnosis by desorption electrospray ionization mass spectrometry imaging |
title_sort | epithelial ovarian carcinoma diagnosis by desorption electrospray ionization mass spectrometry imaging |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5156945/ https://www.ncbi.nlm.nih.gov/pubmed/27976698 http://dx.doi.org/10.1038/srep39219 |
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