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Identifying prostate cancer and its clinical risk in asymptomatic men using machine learning of high dimensional peripheral blood flow cytometric natural killer cell subset phenotyping data

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posted on 25.06.2020, 10:26 by Simon Hood, Georgina CosmaGeorgina Cosma, Gemma Foulds A., Catherine Johnson, Stephen Reeder, Stéphanie McArdle E., Masood Khan, Graham Pockley A.
We demonstrate that prostate cancer can be identified by flow cytometric profiling of blood immune cell subsets. Herein, we profiled natural killer (NK) cell subsets in the blood of 72 asymptomatic men with Prostate Specific Antigen (PSA) levels < 20ng ml, of whom 31 had benign disease (no cancer) and 41 had prostate cancer. Statistical and computational methods identified a panel of eight phenotypic features(CD56 dimCD16high, CD56+DNAM-1-, CD56+LAIR-1+, CD56+LAIR-1-, CD56BRIGHTCD8+, CD56+NKp30+, CD56+NKp30-, CD56+NKp46+) which, when incorporated into an Ensemble machine learning prediction model, distinguished between the presence of benign prostate disease and prostate cancer. The machine learning model was then adapted to predict the D’Amico Risk Classification using data from 54 patients with prostate cancer and was shown to accurately differentiate between the presence of low/intermediate risk disease and high risk disease without the need for additional clinical data. This simple blood test has the potential to transform prostate cancer diagnostics

Funding

John and Lucille van Geest Foundation

The Leverhulme Trust (Research Project Grant RPG-2016-252)

History

School

  • Science

Department

  • Computer Science

Published in

eLife

Volume

9

Pages

e50936

Publisher

eLife Sciences Publications Ltd

Version

VoR (Version of Record)

Rights holder

© The authors

Publisher statement

This is an Open Access Article. It is published by Elife under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

Acceptance date

25/06/2020

Publication date

2020-07-28

Copyright date

2020

ISSN

2050-084X

Depositor

Dr Georgina Cosma. Deposit date: 25 June 2020

Article number

e50936

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