Imperial College London
Portrait Picture

Dr James Kinross

Faculty of Medicine, Department of Surgery & Cancer

Reader in General Surgery
 
 
 
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Contact

 

+44 (0)20 3312 1947j.kinross

 
 
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Location

 

1029Queen Elizabeth the Queen Mother Wing (QEQM)St Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Inglese:2017:10.1039/c6sc03738k,
author = {Inglese, P and McKenzie, JS and Mroz, A and Kinross, J and Veselkov, K and Holmes, E and Takats, Z and Nicholson, JK and Glen, RC},
doi = {10.1039/c6sc03738k},
journal = {Chemical Science},
pages = {3500--3511},
title = {Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer},
url = {http://dx.doi.org/10.1039/c6sc03738k},
volume = {8},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Visual inspection of tumour tissues does not reveal the complex metabolic changes that differentiate cancer and its sub-types from healthy tissues. Mass spectrometry imaging, which quantifies the underlying chemistry, represents a powerful tool for the molecular exploration of tumour tissues. A 3-dimensional topological description of the chemical properties of the tumour permits the formulation of hypotheses about the biological composition and interactions and the possible causes of its heterogeneous structure. The large amount of information contained in such datasets requires powerful tools for its analysis, visualisation and interpretation. Linear methods for unsupervised dimensionality reduction, such as PCA, are inadequate to capture the complex non-linear relationships present in these data. For this reason, a deep unsupervised neural network based technique, parametric t-SNE, is adopted to map a 3D-DESI-MS dataset from a human colorectal adenocarcinoma biopsy onto a 2-dimensional manifold. This technique allows the identification of clusters not visible with linear methods. The unsupervised clustering of the tumour tissue results in the identification of sub-regions characterised by the abundance of identified metabolites, making possible the formulation of hypotheses to account for their significance and the underlying biological heterogeneity in the tumour.
AU  - Inglese,P
AU  - McKenzie,JS
AU  - Mroz,A
AU  - Kinross,J
AU  - Veselkov,K
AU  - Holmes,E
AU  - Takats,Z
AU  - Nicholson,JK
AU  - Glen,RC
DO  - 10.1039/c6sc03738k
EP  - 3511
PY  - 2017///
SN  - 2041-6539
SP  - 3500
TI  - Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer
T2  - Chemical Science
UR  - http://dx.doi.org/10.1039/c6sc03738k
UR  - http://hdl.handle.net/10044/1/45054
VL  - 8
ER  -
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