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A gene expression signature distinguishes innate response and resistance to proteasome inhibitors in multiple myeloma

Extensive interindividual variation in response to chemotherapy is a major stumbling block in achieving desirable efficacy in the treatment of cancers, including multiple myeloma (MM). In this study, our goal was to develop a gene expression signature that predicts response specific to proteasome in...

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Autores principales: Mitra, A K, Harding, T, Mukherjee, U K, Jang, J S, Li, Y, HongZheng, R, Jen, J, Sonneveld, P, Kumar, S, Kuehl, W M, Rajkumar, V, Van Ness, B
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5520403/
https://www.ncbi.nlm.nih.gov/pubmed/28665416
http://dx.doi.org/10.1038/bcj.2017.56
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author Mitra, A K
Harding, T
Mukherjee, U K
Jang, J S
Li, Y
HongZheng, R
Jen, J
Sonneveld, P
Kumar, S
Kuehl, W M
Rajkumar, V
Van Ness, B
author_facet Mitra, A K
Harding, T
Mukherjee, U K
Jang, J S
Li, Y
HongZheng, R
Jen, J
Sonneveld, P
Kumar, S
Kuehl, W M
Rajkumar, V
Van Ness, B
author_sort Mitra, A K
collection PubMed
description Extensive interindividual variation in response to chemotherapy is a major stumbling block in achieving desirable efficacy in the treatment of cancers, including multiple myeloma (MM). In this study, our goal was to develop a gene expression signature that predicts response specific to proteasome inhibitor (PI) treatment in MM. Using a well-characterized panel of human myeloma cell lines (HMCLs) representing the biological and genetic heterogeneity of MM, we created an in vitro chemosensitivity profile in response to treatment with the four PIs bortezomib, carfilzomib, ixazomib and oprozomib as single agents. Gene expression profiling was performed using next-generation high-throughput RNA-sequencing. Applying machine learning-based computational approaches including the supervised ensemble learning methods Random forest and Random survival forest, we identified a 42-gene expression signature that could not only distinguish good and poor PI response in the HMCL panel, but could also be successfully applied to four different clinical data sets on MM patients undergoing PI-based chemotherapy to distinguish between extraordinary (good and poor) outcomes. Our results demonstrate the use of in vitro modeling and machine learning-based approaches to establish predictive biomarkers of response and resistance to drugs that may serve to better direct myeloma patient treatment options.
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spelling pubmed-55204032017-07-26 A gene expression signature distinguishes innate response and resistance to proteasome inhibitors in multiple myeloma Mitra, A K Harding, T Mukherjee, U K Jang, J S Li, Y HongZheng, R Jen, J Sonneveld, P Kumar, S Kuehl, W M Rajkumar, V Van Ness, B Blood Cancer J Original Article Extensive interindividual variation in response to chemotherapy is a major stumbling block in achieving desirable efficacy in the treatment of cancers, including multiple myeloma (MM). In this study, our goal was to develop a gene expression signature that predicts response specific to proteasome inhibitor (PI) treatment in MM. Using a well-characterized panel of human myeloma cell lines (HMCLs) representing the biological and genetic heterogeneity of MM, we created an in vitro chemosensitivity profile in response to treatment with the four PIs bortezomib, carfilzomib, ixazomib and oprozomib as single agents. Gene expression profiling was performed using next-generation high-throughput RNA-sequencing. Applying machine learning-based computational approaches including the supervised ensemble learning methods Random forest and Random survival forest, we identified a 42-gene expression signature that could not only distinguish good and poor PI response in the HMCL panel, but could also be successfully applied to four different clinical data sets on MM patients undergoing PI-based chemotherapy to distinguish between extraordinary (good and poor) outcomes. Our results demonstrate the use of in vitro modeling and machine learning-based approaches to establish predictive biomarkers of response and resistance to drugs that may serve to better direct myeloma patient treatment options. Nature Publishing Group 2017-06 2017-06-30 /pmc/articles/PMC5520403/ /pubmed/28665416 http://dx.doi.org/10.1038/bcj.2017.56 Text en Copyright © 2017 The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Original Article
Mitra, A K
Harding, T
Mukherjee, U K
Jang, J S
Li, Y
HongZheng, R
Jen, J
Sonneveld, P
Kumar, S
Kuehl, W M
Rajkumar, V
Van Ness, B
A gene expression signature distinguishes innate response and resistance to proteasome inhibitors in multiple myeloma
title A gene expression signature distinguishes innate response and resistance to proteasome inhibitors in multiple myeloma
title_full A gene expression signature distinguishes innate response and resistance to proteasome inhibitors in multiple myeloma
title_fullStr A gene expression signature distinguishes innate response and resistance to proteasome inhibitors in multiple myeloma
title_full_unstemmed A gene expression signature distinguishes innate response and resistance to proteasome inhibitors in multiple myeloma
title_short A gene expression signature distinguishes innate response and resistance to proteasome inhibitors in multiple myeloma
title_sort gene expression signature distinguishes innate response and resistance to proteasome inhibitors in multiple myeloma
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5520403/
https://www.ncbi.nlm.nih.gov/pubmed/28665416
http://dx.doi.org/10.1038/bcj.2017.56
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