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Machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response

Providing treatment sensitivity stratification at the time of cancer diagnosis allows better allocation of patients to alternative treatment options. Despite many clinical and biological risk markers having been associated with variable survival in cancer, assessing the interplay of these markers th...

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Detalles Bibliográficos
Autores principales: Venezian Povoa, Lucas, Ribeiro, Carlos Henrique Costa, da Silva, Israel Tojal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8318243/
https://www.ncbi.nlm.nih.gov/pubmed/34320000
http://dx.doi.org/10.1371/journal.pone.0254596
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author Venezian Povoa, Lucas
Ribeiro, Carlos Henrique Costa
da Silva, Israel Tojal
author_facet Venezian Povoa, Lucas
Ribeiro, Carlos Henrique Costa
da Silva, Israel Tojal
author_sort Venezian Povoa, Lucas
collection PubMed
description Providing treatment sensitivity stratification at the time of cancer diagnosis allows better allocation of patients to alternative treatment options. Despite many clinical and biological risk markers having been associated with variable survival in cancer, assessing the interplay of these markers through Machine Learning (ML) algorithms still remains to be fully explored. Here, we present a Multi Learning Training approach (MuLT) combining supervised, unsupervised and self-supervised learning algorithms, to examine the predictive value of heterogeneous treatment outcomes for Multiple Myeloma (MM). We show that gene expression values improve the treatment sensitivity prediction and recapitulates genetic abnormalities detected by Fluorescence in situ hybridization (FISH) testing. MuLT performance was assessed by cross-validation experiments, in which it predicted treatment sensitivity with 68.70% of AUC. Finally, simulations showed numerical evidences that in average 17.07% of patients could get better response to a different treatment at the first line.
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spelling pubmed-83182432021-07-31 Machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response Venezian Povoa, Lucas Ribeiro, Carlos Henrique Costa da Silva, Israel Tojal PLoS One Research Article Providing treatment sensitivity stratification at the time of cancer diagnosis allows better allocation of patients to alternative treatment options. Despite many clinical and biological risk markers having been associated with variable survival in cancer, assessing the interplay of these markers through Machine Learning (ML) algorithms still remains to be fully explored. Here, we present a Multi Learning Training approach (MuLT) combining supervised, unsupervised and self-supervised learning algorithms, to examine the predictive value of heterogeneous treatment outcomes for Multiple Myeloma (MM). We show that gene expression values improve the treatment sensitivity prediction and recapitulates genetic abnormalities detected by Fluorescence in situ hybridization (FISH) testing. MuLT performance was assessed by cross-validation experiments, in which it predicted treatment sensitivity with 68.70% of AUC. Finally, simulations showed numerical evidences that in average 17.07% of patients could get better response to a different treatment at the first line. Public Library of Science 2021-07-28 /pmc/articles/PMC8318243/ /pubmed/34320000 http://dx.doi.org/10.1371/journal.pone.0254596 Text en © 2021 Venezian Povoa et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Venezian Povoa, Lucas
Ribeiro, Carlos Henrique Costa
da Silva, Israel Tojal
Machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response
title Machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response
title_full Machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response
title_fullStr Machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response
title_full_unstemmed Machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response
title_short Machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response
title_sort machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8318243/
https://www.ncbi.nlm.nih.gov/pubmed/34320000
http://dx.doi.org/10.1371/journal.pone.0254596
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