Oncotarget

Research Papers:

Parenclitic networks for predicting ovarian cancer

Harry J. Whitwell _, Oleg Blyuss, Usha Menon, John F. Timms and Alexey Zaikin

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Oncotarget. 2018; 9:22717-22726. https://doi.org/10.18632/oncotarget.25216

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Abstract

Harry J. Whitwell1, Oleg Blyuss2, Usha Menon3, John F. Timms3 and Alexey Zaikin3,4

1Chemical Engineering, Imperial College London, London, United Kingdom

2Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, United Kingdom

3Institute for Women’s Health, University College London, London, United Kingdom

4Department of Mathematics, University College London, London, United Kingdom

Correspondence to:

Harry J. Whitwell, email: h.whitwell@imperial.ac.uk

John F. Timms, email: john.timms@ucl.ac.uk

Alexey Zaikin, email: alexey.zaikin@ucl.ac.uk

Keywords: parenclitic; network; ovarian cancer; biomarker; serum

Received: March 13, 2018     Accepted: April 07, 2018     Published: April 27, 2018

ABSTRACT

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.


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