Imperial College London
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ProfessorBobBrown

Faculty of Medicine, Department of Surgery & Cancer

Senior Research Investigator
 
 
 
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Contact

 

+44 (0)20 7594 1804b.brown Website

 
 
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Assistant

 

Ms Sophie Lions +44 (0)20 7594 2792

 
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Location

 

1 007Institute of Reproductive and Developmental BiologyHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Doria:2016:10.1038/srep39219,
author = {Doria, ML and McKenzie, JS and Mroz, A and Phelps, DL and Speller, A and Rosini, F and Strittmatter, N and Golf, O and Veselkov, K and Brown, R and Ghaem-Maghami, S and Takats, Z},
doi = {10.1038/srep39219},
journal = {Scientific Reports},
title = {Epithelial ovarian carcinoma diagnosis by desorption electrospray ionization mass spectrometry imaging},
url = {http://dx.doi.org/10.1038/srep39219},
volume = {6},
year = {2016}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Doria,ML
AU  - McKenzie,JS
AU  - Mroz,A
AU  - Phelps,DL
AU  - Speller,A
AU  - Rosini,F
AU  - Strittmatter,N
AU  - Golf,O
AU  - Veselkov,K
AU  - Brown,R
AU  - Ghaem-Maghami,S
AU  - Takats,Z
DO  - 10.1038/srep39219
PY  - 2016///
SN  - 2045-2322
TI  - Epithelial ovarian carcinoma diagnosis by desorption electrospray ionization mass spectrometry imaging
T2  - Scientific Reports
UR  - http://dx.doi.org/10.1038/srep39219
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000390279500001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/43714
VL  - 6
ER  -
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