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Machine Learning Applicability for Classification of PAD/VCD Chemotherapy Response Using 53 Multiple Myeloma RNA Sequencing Profiles
Multiple myeloma (MM) affects ~500,000 people and results in ~100,000 deaths annually, being currently considered treatable but incurable. There are several MM chemotherapy treatment regimens, among which eleven include bortezomib, a proteasome-targeted drug. MM patients respond differently to borte...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8083158/ https://www.ncbi.nlm.nih.gov/pubmed/33937058 http://dx.doi.org/10.3389/fonc.2021.652063 |
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author | Borisov, Nicolas Sergeeva, Anna Suntsova, Maria Raevskiy, Mikhail Gaifullin, Nurshat Mendeleeva, Larisa Gudkov, Alexander Nareiko, Maria Garazha, Andrew Tkachev, Victor Li, Xinmin Sorokin, Maxim Surin, Vadim Buzdin, Anton |
author_facet | Borisov, Nicolas Sergeeva, Anna Suntsova, Maria Raevskiy, Mikhail Gaifullin, Nurshat Mendeleeva, Larisa Gudkov, Alexander Nareiko, Maria Garazha, Andrew Tkachev, Victor Li, Xinmin Sorokin, Maxim Surin, Vadim Buzdin, Anton |
author_sort | Borisov, Nicolas |
collection | PubMed |
description | Multiple myeloma (MM) affects ~500,000 people and results in ~100,000 deaths annually, being currently considered treatable but incurable. There are several MM chemotherapy treatment regimens, among which eleven include bortezomib, a proteasome-targeted drug. MM patients respond differently to bortezomib, and new prognostic biomarkers are needed to personalize treatments. However, there is a shortage of clinically annotated MM molecular data that could be used to establish novel molecular diagnostics. We report new RNA sequencing profiles for 53 MM patients annotated with responses on two similar chemotherapy regimens: bortezomib, doxorubicin, dexamethasone (PAD), and bortezomib, cyclophosphamide, dexamethasone (VCD), or with responses to their combinations. Fourteen patients received both PAD and VCD; six received only PAD, and 33 received only VCD. We compared profiles for the good and poor responders and found five genes commonly regulated here and in the previous datasets for other bortezomib regimens (all upregulated in the good responders): FGFR3, MAF, IGHA2, IGHV1-69, and GRB14. Four of these genes are linked with known immunoglobulin locus rearrangements. We then used five machine learning (ML) methods to build a classifier distinguishing good and poor responders for two cohorts: PAD + VCD (53 patients), and separately VCD (47 patients). We showed that the application of FloWPS dynamic data trimming was beneficial for all ML methods tested in both cohorts, and also in the previous MM bortezomib datasets. However, the ML models build for the different datasets did not allow cross-transferring, which can be due to different treatment regimens, experimental profiling methods, and MM heterogeneity. |
format | Online Article Text |
id | pubmed-8083158 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-80831582021-04-30 Machine Learning Applicability for Classification of PAD/VCD Chemotherapy Response Using 53 Multiple Myeloma RNA Sequencing Profiles Borisov, Nicolas Sergeeva, Anna Suntsova, Maria Raevskiy, Mikhail Gaifullin, Nurshat Mendeleeva, Larisa Gudkov, Alexander Nareiko, Maria Garazha, Andrew Tkachev, Victor Li, Xinmin Sorokin, Maxim Surin, Vadim Buzdin, Anton Front Oncol Oncology Multiple myeloma (MM) affects ~500,000 people and results in ~100,000 deaths annually, being currently considered treatable but incurable. There are several MM chemotherapy treatment regimens, among which eleven include bortezomib, a proteasome-targeted drug. MM patients respond differently to bortezomib, and new prognostic biomarkers are needed to personalize treatments. However, there is a shortage of clinically annotated MM molecular data that could be used to establish novel molecular diagnostics. We report new RNA sequencing profiles for 53 MM patients annotated with responses on two similar chemotherapy regimens: bortezomib, doxorubicin, dexamethasone (PAD), and bortezomib, cyclophosphamide, dexamethasone (VCD), or with responses to their combinations. Fourteen patients received both PAD and VCD; six received only PAD, and 33 received only VCD. We compared profiles for the good and poor responders and found five genes commonly regulated here and in the previous datasets for other bortezomib regimens (all upregulated in the good responders): FGFR3, MAF, IGHA2, IGHV1-69, and GRB14. Four of these genes are linked with known immunoglobulin locus rearrangements. We then used five machine learning (ML) methods to build a classifier distinguishing good and poor responders for two cohorts: PAD + VCD (53 patients), and separately VCD (47 patients). We showed that the application of FloWPS dynamic data trimming was beneficial for all ML methods tested in both cohorts, and also in the previous MM bortezomib datasets. However, the ML models build for the different datasets did not allow cross-transferring, which can be due to different treatment regimens, experimental profiling methods, and MM heterogeneity. Frontiers Media S.A. 2021-04-15 /pmc/articles/PMC8083158/ /pubmed/33937058 http://dx.doi.org/10.3389/fonc.2021.652063 Text en Copyright © 2021 Borisov, Sergeeva, Suntsova, Raevskiy, Gaifullin, Mendeleeva, Gudkov, Nareiko, Garazha, Tkachev, Li, Sorokin, Surin and Buzdin https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Borisov, Nicolas Sergeeva, Anna Suntsova, Maria Raevskiy, Mikhail Gaifullin, Nurshat Mendeleeva, Larisa Gudkov, Alexander Nareiko, Maria Garazha, Andrew Tkachev, Victor Li, Xinmin Sorokin, Maxim Surin, Vadim Buzdin, Anton Machine Learning Applicability for Classification of PAD/VCD Chemotherapy Response Using 53 Multiple Myeloma RNA Sequencing Profiles |
title | Machine Learning Applicability for Classification of PAD/VCD Chemotherapy Response Using 53 Multiple Myeloma RNA Sequencing Profiles |
title_full | Machine Learning Applicability for Classification of PAD/VCD Chemotherapy Response Using 53 Multiple Myeloma RNA Sequencing Profiles |
title_fullStr | Machine Learning Applicability for Classification of PAD/VCD Chemotherapy Response Using 53 Multiple Myeloma RNA Sequencing Profiles |
title_full_unstemmed | Machine Learning Applicability for Classification of PAD/VCD Chemotherapy Response Using 53 Multiple Myeloma RNA Sequencing Profiles |
title_short | Machine Learning Applicability for Classification of PAD/VCD Chemotherapy Response Using 53 Multiple Myeloma RNA Sequencing Profiles |
title_sort | machine learning applicability for classification of pad/vcd chemotherapy response using 53 multiple myeloma rna sequencing profiles |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8083158/ https://www.ncbi.nlm.nih.gov/pubmed/33937058 http://dx.doi.org/10.3389/fonc.2021.652063 |
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