Computational approaches to support comparative analysis of multiparametric tests: Modelling versus Training

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 l...

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Published inPloS one Vol. 15; no. 9; p. e0238593
Main Authors Bartlett, John M. S., Bayani, Jane, Kornaga, Elizabeth N., Danaher, Patrick, Crozier, Cheryl, Piper, Tammy, Yao, Cindy Q., Dunn, Janet A., Boutros, Paul C., Stein, Robert C.
Format Journal Article
LanguageEnglish
Published San Francisco Public Library of Science 03.09.2020
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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.
AbstractList 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.
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.
Author Stein, Robert C.
Dunn, Janet A.
Yao, Cindy Q.
Boutros, Paul C.
Piper, Tammy
Bayani, Jane
Crozier, Cheryl
Bartlett, John M. S.
Kornaga, Elizabeth N.
Danaher, Patrick
AuthorAffiliation 8 Department of Pharmacology & Toxicology, University of Toronto, Toronto, Ontario, Canada
1 Diagnostic Development, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
3 Edinburgh Cancer Research Centre, Edinburgh, United Kingdom
5 Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
9 UCL (University College London) and National Institute for Health Research University College London Hospitals Biomedical Research Centre, London, United Kingdom
4 Tom Baker Cancer Centre, Calgary, Alberta, Canada
6 Warwick Medical School, University of Warwick, Coventry, United Kingdom
7 Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
2 Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
Gangnam Severance Hospital, Yonsei University College of Medicine, REPUBLIC OF KOREA
AuthorAffiliation_xml – name: 2 Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
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CitedBy_id crossref_primary_10_1038_s41523_021_00301_0
crossref_primary_10_1038_s41523_021_00297_7
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2020 Bartlett et al 2020 Bartlett et al
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Membership of the OPTIMA Trial Management Group is provided in the Acknowledgments.
Current address: Department of Human Genetics & Jonsson Comprehensive Cancer Center, University of California, Los Angeles, California, United States of America
Competing Interests: JMSB reports consultancies from Insight Genetics, BioNTech AG, Biotheranostics, Pfizer, RNA Diagnostics and oncoXchange, honoraria from NanoString Technologies, Oncology Education and Biotheranostics, travel and accommodation expenses from Biotheranostics and NanoString Technologies, research funding from Thermo Fisher Scientific, Genoptix, Agendia, NanoString Technoloiges, Stratifyer GmbH and Biotheranostics, a disclosure “A Molecular Classifier for Personalized Risk Stratification for Patients with Prostate Cancer” (Aug 2019), and applied for patents, including: “Methods and Devices for Predicting Anthracycline Treatment Efficacy”, US utility – 15/325,472; EPO – 15822898.1; Canada – not yet assigned (Jan 2017); “Systems, Devices and Methods for Constructing and Using a Biomarker”, US utility – 15/328,108; EPO –15824751.0; Canada – not yet assigned (Jan 2017); “Histone gene module predicts anthracycline benefit”, PCT/CA2016/000247 (Oct 2016); “Immune Gene Signature Predicts Anthracycline Benefit”, PCT/CA2016/000305 (Dec 2016). JMSB, JB, CQY and PCB are co-inventors on the applied for patent: “95-Gene Signature of Residual Risk Following Endocrine Treatment”, PCT/CA2016/000304 (Dec 2016). PD is an employee and shareholder of NanoString Technologies. This does not alter the authors’ adherence to all the PLoS ONE policies on sharing data and materials. The remaining authors do not declare any other competing interests.
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Snippet Multiparametric assays for risk stratification are widely used in the management of breast cancer, with applications being developed for a number of other...
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SubjectTerms Algorithms
Breast cancer
Chemotherapy
Comparative analysis
Computer and Information Sciences
Computer applications
Discordance
Gene expression
Medical research
Medicine and Health Sciences
Methods
Patients
Physical Sciences
Research and Analysis Methods
Risk
Robustness
Signatures
Training
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Title Computational approaches to support comparative analysis of multiparametric tests: Modelling versus Training
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http://dx.doi.org/10.1371/journal.pone.0238593
Volume 15
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