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Combining gene expression profiling and machine learning to diagnose B-cell non-Hodgkin lymphoma

Non-Hodgkin B-cell lymphomas (B-NHLs) are a highly heterogeneous group of mature B-cell malignancies. Their classification thus requires skillful evaluation by expert hematopathologists, but the risk of error remains higher in these tumors than in many other areas of pathology. To facilitate diagnos...

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Autores principales: Bobée, Victor, Drieux, Fanny, Marchand, Vinciane, Sater, Vincent, Veresezan, Liana, Picquenot, Jean-Michel, Viailly, Pierre-Julien, Lanic, Marie-Delphine, Viennot, Mathieu, Bohers, Elodie, Oberic, Lucie, Copie-Bergman, Christiane, Molina, Thierry Jo, Gaulard, Philippe, Haioun, Corinne, Salles, Gilles, Tilly, Hervé, Jardin, Fabrice, Ruminy, Philippe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7244768/
https://www.ncbi.nlm.nih.gov/pubmed/32444689
http://dx.doi.org/10.1038/s41408-020-0322-5
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author Bobée, Victor
Drieux, Fanny
Marchand, Vinciane
Sater, Vincent
Veresezan, Liana
Picquenot, Jean-Michel
Viailly, Pierre-Julien
Lanic, Marie-Delphine
Viennot, Mathieu
Bohers, Elodie
Oberic, Lucie
Copie-Bergman, Christiane
Molina, Thierry Jo
Gaulard, Philippe
Haioun, Corinne
Salles, Gilles
Tilly, Hervé
Jardin, Fabrice
Ruminy, Philippe
author_facet Bobée, Victor
Drieux, Fanny
Marchand, Vinciane
Sater, Vincent
Veresezan, Liana
Picquenot, Jean-Michel
Viailly, Pierre-Julien
Lanic, Marie-Delphine
Viennot, Mathieu
Bohers, Elodie
Oberic, Lucie
Copie-Bergman, Christiane
Molina, Thierry Jo
Gaulard, Philippe
Haioun, Corinne
Salles, Gilles
Tilly, Hervé
Jardin, Fabrice
Ruminy, Philippe
author_sort Bobée, Victor
collection PubMed
description Non-Hodgkin B-cell lymphomas (B-NHLs) are a highly heterogeneous group of mature B-cell malignancies. Their classification thus requires skillful evaluation by expert hematopathologists, but the risk of error remains higher in these tumors than in many other areas of pathology. To facilitate diagnosis, we have thus developed a gene expression assay able to discriminate the seven most frequent B-cell NHL categories. This assay relies on the combination of ligation-dependent RT-PCR and next-generation sequencing, and addresses the expression of more than 130 genetic markers. It was designed to retrieve the main gene expression signatures of B-NHL cells and their microenvironment. The classification is handled by a random forest algorithm which we trained and validated on a large cohort of more than 400 annotated cases of different histology. Its clinical relevance was verified through its capacity to prevent important misclassification in low grade lymphomas and to retrieve clinically important characteristics in high grade lymphomas including the cell-of-origin signatures and the MYC and BCL2 expression levels. This accurate pan-B-NHL predictor, which allows a systematic evaluation of numerous diagnostic and prognostic markers, could thus be proposed as a complement to conventional histology to guide the management of patients and facilitate their stratification into clinical trials.
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spelling pubmed-72447682020-06-04 Combining gene expression profiling and machine learning to diagnose B-cell non-Hodgkin lymphoma Bobée, Victor Drieux, Fanny Marchand, Vinciane Sater, Vincent Veresezan, Liana Picquenot, Jean-Michel Viailly, Pierre-Julien Lanic, Marie-Delphine Viennot, Mathieu Bohers, Elodie Oberic, Lucie Copie-Bergman, Christiane Molina, Thierry Jo Gaulard, Philippe Haioun, Corinne Salles, Gilles Tilly, Hervé Jardin, Fabrice Ruminy, Philippe Blood Cancer J Article Non-Hodgkin B-cell lymphomas (B-NHLs) are a highly heterogeneous group of mature B-cell malignancies. Their classification thus requires skillful evaluation by expert hematopathologists, but the risk of error remains higher in these tumors than in many other areas of pathology. To facilitate diagnosis, we have thus developed a gene expression assay able to discriminate the seven most frequent B-cell NHL categories. This assay relies on the combination of ligation-dependent RT-PCR and next-generation sequencing, and addresses the expression of more than 130 genetic markers. It was designed to retrieve the main gene expression signatures of B-NHL cells and their microenvironment. The classification is handled by a random forest algorithm which we trained and validated on a large cohort of more than 400 annotated cases of different histology. Its clinical relevance was verified through its capacity to prevent important misclassification in low grade lymphomas and to retrieve clinically important characteristics in high grade lymphomas including the cell-of-origin signatures and the MYC and BCL2 expression levels. This accurate pan-B-NHL predictor, which allows a systematic evaluation of numerous diagnostic and prognostic markers, could thus be proposed as a complement to conventional histology to guide the management of patients and facilitate their stratification into clinical trials. Nature Publishing Group UK 2020-05-22 /pmc/articles/PMC7244768/ /pubmed/32444689 http://dx.doi.org/10.1038/s41408-020-0322-5 Text en © The Author(s) 2020 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/.
spellingShingle Article
Bobée, Victor
Drieux, Fanny
Marchand, Vinciane
Sater, Vincent
Veresezan, Liana
Picquenot, Jean-Michel
Viailly, Pierre-Julien
Lanic, Marie-Delphine
Viennot, Mathieu
Bohers, Elodie
Oberic, Lucie
Copie-Bergman, Christiane
Molina, Thierry Jo
Gaulard, Philippe
Haioun, Corinne
Salles, Gilles
Tilly, Hervé
Jardin, Fabrice
Ruminy, Philippe
Combining gene expression profiling and machine learning to diagnose B-cell non-Hodgkin lymphoma
title Combining gene expression profiling and machine learning to diagnose B-cell non-Hodgkin lymphoma
title_full Combining gene expression profiling and machine learning to diagnose B-cell non-Hodgkin lymphoma
title_fullStr Combining gene expression profiling and machine learning to diagnose B-cell non-Hodgkin lymphoma
title_full_unstemmed Combining gene expression profiling and machine learning to diagnose B-cell non-Hodgkin lymphoma
title_short Combining gene expression profiling and machine learning to diagnose B-cell non-Hodgkin lymphoma
title_sort combining gene expression profiling and machine learning to diagnose b-cell non-hodgkin lymphoma
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7244768/
https://www.ncbi.nlm.nih.gov/pubmed/32444689
http://dx.doi.org/10.1038/s41408-020-0322-5
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