OPTIMA Trial Management Group (Including: Bartlett, John M. S., Bayani, Jane, Kornaga, Elizabeth N., Danaher, Patrick, Crozier, Cheryl, Piper, Tammy, Yao, Cindy Q., Dunn, Janet A., Boutros, Paul C. and Stein, Robert C.). (2020) Computational approaches to support comparative analysis of multiparametric tests : modelling versus training. PLOS ONE, 15 (9). e0238593. doi:10.1371/journal.pone.0238593

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Abstract

Multiparametric assays for risk stratification are widely used in the management of breast cancer, with applications being developed for a number of other cancer settings. Recent data from multiple sources suggests that different tests may provide different risk estimates at the individual patient level. There is an increasing need for robust methods to support cost effective comparisons of test performance in multiple settings. The derivation of similar risk classifications using genes comprising the following multi-parametric tests Oncotype DX® (Genomic Health.), Prosigna™ (NanoString Technologies, Inc.), MammaPrint® (Agendia Inc.) was performed using different computational approaches. Results were compared to the actual test results. Two widely used approaches were applied, firstly computational “modelling” of test results using published algorithms and secondly a “training” approach which used reference results from the commercially supplied tests. We demonstrate the potential for errors to arise when using a “modelling” approach without reference to real world test results. Simultaneously we show that a “training” approach can provide a highly cost-effective solution to the development of real-world comparisons between different multigene signatures. Comparisons between existing multiparametric tests is challenging, and evidence on discordance between tests in risk stratification presents further dilemmas. We present an approach, modelled in breast cancer, which can provide health care providers and researchers with the potential to perform robust and meaningful comparisons between multigene tests in a cost-effective manner. We demonstrate that whilst viable estimates of gene signatures can be derived from modelling approaches, in our study using a training approach allowed a close approximation to true signature results.

Item Type:	Journal Article
Subjects:	R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Divisions:	Faculty of Medicine > Warwick Medical School
SWORD Depositor:	Library Publications Router
Library of Congress Subject Headings (LCSH):	Breast -- Cancer -- Diagnosis, Cancer -- Diagnosis, Breast -- Cancer -- Treatment, Cancer -- Treatment
Journal or Publication Title:	PLOS ONE
Publisher:	Public Library of Science
ISSN:	1932-6203
Official Date:	3 September 2020
Dates:	Date Event 3 September 2020 Published 19 August 2020 Accepted
Date of first compliant deposit:	16 September 2020
Volume:	15
Number:	9
Article Number:	e0238593
DOI:	10.1371/journal.pone.0238593
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID UNSPECIFIED UCLH Biomedical Research Centre http://dx.doi.org/10.13039/501100012317 UNSPECIFIED CIHR Skin Research Training Centre http://dx.doi.org/10.13039/501100007202 New Investigator Award Terry Fox Research Institute http://dx.doi.org/10.13039/501100004376 UNSPECIFIED Government of Ontario http://dx.doi.org/10.13039/100013873
Contributors:	Contribution Name Contributor ID UNSPECIFIED Campbell , Amy 34342 UNSPECIFIED Higgins, Helen B. 28434 UNSPECIFIED Marshall, Andrea 17139 UNSPECIFIED Stallard, Nigel 13837

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