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Predicting treatment benefit in multiple myeloma through simulation of alternative treatment effects

Many cancer treatments are associated with serious side effects, while they often only benefit a subset of the patients. Therefore, there is an urgent clinical need for tools that can aid in selecting the right treatment at diagnosis. Here we introduce simulated treatment learning (STL), which enabl...

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Autores principales: Ubels, Joske, Sonneveld, Pieter, van Beers, Erik H., Broijl, Annemiek, van Vliet, Martin H., de Ridder, Jeroen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6063966/
https://www.ncbi.nlm.nih.gov/pubmed/30054467
http://dx.doi.org/10.1038/s41467-018-05348-5
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author Ubels, Joske
Sonneveld, Pieter
van Beers, Erik H.
Broijl, Annemiek
van Vliet, Martin H.
de Ridder, Jeroen
author_facet Ubels, Joske
Sonneveld, Pieter
van Beers, Erik H.
Broijl, Annemiek
van Vliet, Martin H.
de Ridder, Jeroen
author_sort Ubels, Joske
collection PubMed
description Many cancer treatments are associated with serious side effects, while they often only benefit a subset of the patients. Therefore, there is an urgent clinical need for tools that can aid in selecting the right treatment at diagnosis. Here we introduce simulated treatment learning (STL), which enables prediction of a patient’s treatment benefit. STL uses the idea that patients who received different treatments, but have similar genetic tumor profiles, can be used to model their response to the alternative treatment. We apply STL to two multiple myeloma gene expression datasets, containing different treatments (bortezomib and lenalidomide). We find that STL can predict treatment benefit for both; a twofold progression free survival (PFS) benefit is observed for bortezomib for 19.8% and a threefold PFS benefit for lenalidomide for 31.1% of the patients. This demonstrates that STL can derive clinically actionable gene expression signatures that enable a more personalized approach to treatment.
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spelling pubmed-60639662018-07-30 Predicting treatment benefit in multiple myeloma through simulation of alternative treatment effects Ubels, Joske Sonneveld, Pieter van Beers, Erik H. Broijl, Annemiek van Vliet, Martin H. de Ridder, Jeroen Nat Commun Article Many cancer treatments are associated with serious side effects, while they often only benefit a subset of the patients. Therefore, there is an urgent clinical need for tools that can aid in selecting the right treatment at diagnosis. Here we introduce simulated treatment learning (STL), which enables prediction of a patient’s treatment benefit. STL uses the idea that patients who received different treatments, but have similar genetic tumor profiles, can be used to model their response to the alternative treatment. We apply STL to two multiple myeloma gene expression datasets, containing different treatments (bortezomib and lenalidomide). We find that STL can predict treatment benefit for both; a twofold progression free survival (PFS) benefit is observed for bortezomib for 19.8% and a threefold PFS benefit for lenalidomide for 31.1% of the patients. This demonstrates that STL can derive clinically actionable gene expression signatures that enable a more personalized approach to treatment. Nature Publishing Group UK 2018-07-27 /pmc/articles/PMC6063966/ /pubmed/30054467 http://dx.doi.org/10.1038/s41467-018-05348-5 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Ubels, Joske
Sonneveld, Pieter
van Beers, Erik H.
Broijl, Annemiek
van Vliet, Martin H.
de Ridder, Jeroen
Predicting treatment benefit in multiple myeloma through simulation of alternative treatment effects
title Predicting treatment benefit in multiple myeloma through simulation of alternative treatment effects
title_full Predicting treatment benefit in multiple myeloma through simulation of alternative treatment effects
title_fullStr Predicting treatment benefit in multiple myeloma through simulation of alternative treatment effects
title_full_unstemmed Predicting treatment benefit in multiple myeloma through simulation of alternative treatment effects
title_short Predicting treatment benefit in multiple myeloma through simulation of alternative treatment effects
title_sort predicting treatment benefit in multiple myeloma through simulation of alternative treatment effects
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6063966/
https://www.ncbi.nlm.nih.gov/pubmed/30054467
http://dx.doi.org/10.1038/s41467-018-05348-5
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