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Determining clinical course of diffuse large B-cell lymphoma using targeted transcriptome and machine learning algorithms
Multiple studies have demonstrated that diffuse large B-cell lymphoma (DLBCL) can be divided into subgroups based on their biology; however, these biological subgroups overlap clinically. Using machine learning, we developed an approach to stratify patients with DLBCL into four subgroups based on su...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8807629/ https://www.ncbi.nlm.nih.gov/pubmed/35105854 http://dx.doi.org/10.1038/s41408-022-00617-5 |
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author | Albitar, Maher Zhang, Hong Goy, Andre Xu-Monette, Zijun Y. Bhagat, Govind Visco, Carlo Tzankov, Alexandar Fang, Xiaosheng Zhu, Feng Dybkaer, Karen Chiu, April Tam, Wayne Zu, Youli Hsi, Eric D. Hagemeister, Fredrick B. Huh, Jooryung Ponzoni, Maurilio Ferreri, Andrés J. M. Møller, Michael B. Parsons, Benjamin M. van Krieken, J. Han Piris, Miguel A. Winter, Jane N. Li, Yong Xu, Bing Young, Ken H. |
author_facet | Albitar, Maher Zhang, Hong Goy, Andre Xu-Monette, Zijun Y. Bhagat, Govind Visco, Carlo Tzankov, Alexandar Fang, Xiaosheng Zhu, Feng Dybkaer, Karen Chiu, April Tam, Wayne Zu, Youli Hsi, Eric D. Hagemeister, Fredrick B. Huh, Jooryung Ponzoni, Maurilio Ferreri, Andrés J. M. Møller, Michael B. Parsons, Benjamin M. van Krieken, J. Han Piris, Miguel A. Winter, Jane N. Li, Yong Xu, Bing Young, Ken H. |
author_sort | Albitar, Maher |
collection | PubMed |
description | Multiple studies have demonstrated that diffuse large B-cell lymphoma (DLBCL) can be divided into subgroups based on their biology; however, these biological subgroups overlap clinically. Using machine learning, we developed an approach to stratify patients with DLBCL into four subgroups based on survival characteristics. This approach uses data from the targeted transcriptome to predict these survival subgroups. Using the expression levels of 180 genes, our model reliably predicted the four survival subgroups and was validated using independent groups of patients. Multivariate analysis showed that this patient stratification strategy encompasses various biological characteristics of DLBCL, and only TP53 mutations remained an independent prognostic biomarker. This novel approach for stratifying patients with DLBCL, based on the clinical outcome of rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone therapy, can be used to identify patients who may not respond well to these types of therapy, but would otherwise benefit from alternative therapy and clinical trials. |
format | Online Article Text |
id | pubmed-8807629 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-88076292022-02-07 Determining clinical course of diffuse large B-cell lymphoma using targeted transcriptome and machine learning algorithms Albitar, Maher Zhang, Hong Goy, Andre Xu-Monette, Zijun Y. Bhagat, Govind Visco, Carlo Tzankov, Alexandar Fang, Xiaosheng Zhu, Feng Dybkaer, Karen Chiu, April Tam, Wayne Zu, Youli Hsi, Eric D. Hagemeister, Fredrick B. Huh, Jooryung Ponzoni, Maurilio Ferreri, Andrés J. M. Møller, Michael B. Parsons, Benjamin M. van Krieken, J. Han Piris, Miguel A. Winter, Jane N. Li, Yong Xu, Bing Young, Ken H. Blood Cancer J Article Multiple studies have demonstrated that diffuse large B-cell lymphoma (DLBCL) can be divided into subgroups based on their biology; however, these biological subgroups overlap clinically. Using machine learning, we developed an approach to stratify patients with DLBCL into four subgroups based on survival characteristics. This approach uses data from the targeted transcriptome to predict these survival subgroups. Using the expression levels of 180 genes, our model reliably predicted the four survival subgroups and was validated using independent groups of patients. Multivariate analysis showed that this patient stratification strategy encompasses various biological characteristics of DLBCL, and only TP53 mutations remained an independent prognostic biomarker. This novel approach for stratifying patients with DLBCL, based on the clinical outcome of rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone therapy, can be used to identify patients who may not respond well to these types of therapy, but would otherwise benefit from alternative therapy and clinical trials. Nature Publishing Group UK 2022-02-01 /pmc/articles/PMC8807629/ /pubmed/35105854 http://dx.doi.org/10.1038/s41408-022-00617-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Albitar, Maher Zhang, Hong Goy, Andre Xu-Monette, Zijun Y. Bhagat, Govind Visco, Carlo Tzankov, Alexandar Fang, Xiaosheng Zhu, Feng Dybkaer, Karen Chiu, April Tam, Wayne Zu, Youli Hsi, Eric D. Hagemeister, Fredrick B. Huh, Jooryung Ponzoni, Maurilio Ferreri, Andrés J. M. Møller, Michael B. Parsons, Benjamin M. van Krieken, J. Han Piris, Miguel A. Winter, Jane N. Li, Yong Xu, Bing Young, Ken H. Determining clinical course of diffuse large B-cell lymphoma using targeted transcriptome and machine learning algorithms |
title | Determining clinical course of diffuse large B-cell lymphoma using targeted transcriptome and machine learning algorithms |
title_full | Determining clinical course of diffuse large B-cell lymphoma using targeted transcriptome and machine learning algorithms |
title_fullStr | Determining clinical course of diffuse large B-cell lymphoma using targeted transcriptome and machine learning algorithms |
title_full_unstemmed | Determining clinical course of diffuse large B-cell lymphoma using targeted transcriptome and machine learning algorithms |
title_short | Determining clinical course of diffuse large B-cell lymphoma using targeted transcriptome and machine learning algorithms |
title_sort | determining clinical course of diffuse large b-cell lymphoma using targeted transcriptome and machine learning algorithms |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8807629/ https://www.ncbi.nlm.nih.gov/pubmed/35105854 http://dx.doi.org/10.1038/s41408-022-00617-5 |
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