Imperial College London
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DrHarryWhitwell

Faculty of Medicine, Department of Metabolism, Digestion and Reproduction

Lecturer in Proteomics and Integrative Data Analysis Proteom
 
 
 
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h.whitwell Website CV

 
 
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Location

 

312Burlington DanesHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Whitwell:2018:10.18632/oncotarget.25216,
author = {Whitwell, HJ and Blyuss, O and Menon, U and Timms, JF and Zaikin, A},
doi = {10.18632/oncotarget.25216},
journal = {Oncotarget},
pages = {22717--22726},
title = {Parenclitic networks for predicting ovarian cancer},
url = {http://dx.doi.org/10.18632/oncotarget.25216},
volume = {9},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Prediction and diagnosis of complex disease may not always be possible with a small number of biomarkers. Modern ‘omics’ technologies make it possible to cheaply and quantitatively assay hundreds of molecules generating large amounts of data from individual samples. In this study, we describe a parenclitic network-based approach to disease classification using a synthetic data set modelled on data from the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) and serological assay data from a nested set of samples from the same study. This approach allows us to integrate quantitative proteomic and categorical metadata into a single network, and then use network topologies to construct logistic regression models for disease classification. In this study of ovarian cancer, comprising of 30 controls and cases with samples taken <14 months to diagnosis (n = 30) and/or >34 months to diagnosis (n = 29), we were able to classify cases with a sensitivity of 80.3% within 14 months of diagnosis and 18.9% in samples exceeding 34 months to diagnosis at a specificity of 98%. Furthermore, we use the networks to make observations about proteins within the cohort and identify GZMH and FGFBP1 as changing in cases (in relation to controls) at time points most distal to diagnosis. We conclude that network-based approaches may offer a solution to the problem of complex disease classification that can be used in personalised medicine and to describe the underlying biology of cancer progression at a system level.
AU  - Whitwell,HJ
AU  - Blyuss,O
AU  - Menon,U
AU  - Timms,JF
AU  - Zaikin,A
DO  - 10.18632/oncotarget.25216
EP  - 22726
PY  - 2018///
SN  - 1949-2553
SP  - 22717
TI  - Parenclitic networks for predicting ovarian cancer
T2  - Oncotarget
UR  - http://dx.doi.org/10.18632/oncotarget.25216
UR  - http://hdl.handle.net/10044/1/58966
VL  - 9
ER  -
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