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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Autores principales:	Lucas Venezian Povoa, Carlos Henrique Costa Ribeiro, Israel Tojal da Silva
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7c77c80bec084898ae71b113c9779f59
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spelling	oai:doaj.org-article:7c77c80bec084898ae71b113c9779f592021-12-02T20:04:48ZMachine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response.1932-620310.1371/journal.pone.0254596https://doaj.org/article/7c77c80bec084898ae71b113c9779f592021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254596https://doaj.org/toc/1932-6203Providing 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.Lucas Venezian PovoaCarlos Henrique Costa RibeiroIsrael Tojal da SilvaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254596 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lucas Venezian Povoa Carlos Henrique Costa Ribeiro Israel Tojal da Silva Machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response.
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.
format	article
author	Lucas Venezian Povoa Carlos Henrique Costa Ribeiro Israel Tojal da Silva
author_facet	Lucas Venezian Povoa Carlos Henrique Costa Ribeiro Israel Tojal da Silva
author_sort	Lucas Venezian Povoa
title	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_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_sort	machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/7c77c80bec084898ae71b113c9779f59
work_keys_str_mv	AT lucasvenezianpovoa machinelearningpredictstreatmentsensitivityinmultiplemyelomabasedonmolecularandclinicalinformationcoupledwithdrugresponse AT carloshenriquecostaribeiro machinelearningpredictstreatmentsensitivityinmultiplemyelomabasedonmolecularandclinicalinformationcoupledwithdrugresponse AT israeltojaldasilva machinelearningpredictstreatmentsensitivityinmultiplemyelomabasedonmolecularandclinicalinformationcoupledwithdrugresponse
_version_	1718375551189647360

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

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