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HashClone: a new tool to quantify the minimal residual disease in B-cell lymphoma from deep sequencing data
BACKGROUND: Mantle Cell Lymphoma (MCL) is a B cell aggressive neoplasia accounting for about the 6% of all lymphomas. The most common molecular marker of clonality in MCL, as in other B lymphoproliferative disorders, is the ImmunoGlobulin Heavy chain (IGH) rearrangement, occurring in B-lymphocytes....
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5701356/ https://www.ncbi.nlm.nih.gov/pubmed/29169317 http://dx.doi.org/10.1186/s12859-017-1923-2 |
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author | Beccuti, Marco Genuardi, Elisa Romano, Greta Monitillo, Luigia Barbero, Daniela Boccadoro, Mario Ladetto, Marco Calogero, Raffaele Ferrero, Simone Cordero, Francesca |
author_facet | Beccuti, Marco Genuardi, Elisa Romano, Greta Monitillo, Luigia Barbero, Daniela Boccadoro, Mario Ladetto, Marco Calogero, Raffaele Ferrero, Simone Cordero, Francesca |
author_sort | Beccuti, Marco |
collection | PubMed |
description | BACKGROUND: Mantle Cell Lymphoma (MCL) is a B cell aggressive neoplasia accounting for about the 6% of all lymphomas. The most common molecular marker of clonality in MCL, as in other B lymphoproliferative disorders, is the ImmunoGlobulin Heavy chain (IGH) rearrangement, occurring in B-lymphocytes. The patient-specific IGH rearrangement is extensively used to monitor the Minimal Residual Disease (MRD) after treatment through the standardized Allele-Specific Oligonucleotides Quantitative Polymerase Chain Reaction based technique. Recently, several studies have suggested that the IGH monitoring through deep sequencing techniques can produce not only comparable results to Polymerase Chain Reaction-based methods, but also might overcome the classical technique in terms of feasibility and sensitivity. However, no standard bioinformatics tool is available at the moment for data analysis in this context. RESULTS: In this paper we present HashClone, an easy-to-use and reliable bioinformatics tool that provides B-cells clonality assessment and MRD monitoring over time analyzing data from Next-Generation Sequencing (NGS) technique. The HashClone strategy-based is composed of three steps: the first and second steps implement an alignment-free prediction method that identifies a set of putative clones belonging to the repertoire of the patient under study. In the third step the IGH variable region, diversity region, and joining region identification is obtained by the alignment of rearrangements with respect to the international ImMunoGenetics information system database. Moreover, a provided graphical user interface for HashClone execution and clonality visualization over time facilitate the tool use and the results interpretation. The HashClone performance was tested on the NGS data derived from MCL patients to assess the major B-cell clone in the diagnostic samples and to monitor the MRD in the real and artificial follow up samples. CONCLUSIONS: Our experiments show that in all the experimental settings, HashClone was able to correctly detect the major B-cell clones and to precisely follow them in several samples showing better accuracy than the state-of-art tool. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1923-2) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5701356 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-57013562017-12-01 HashClone: a new tool to quantify the minimal residual disease in B-cell lymphoma from deep sequencing data Beccuti, Marco Genuardi, Elisa Romano, Greta Monitillo, Luigia Barbero, Daniela Boccadoro, Mario Ladetto, Marco Calogero, Raffaele Ferrero, Simone Cordero, Francesca BMC Bioinformatics Research Article BACKGROUND: Mantle Cell Lymphoma (MCL) is a B cell aggressive neoplasia accounting for about the 6% of all lymphomas. The most common molecular marker of clonality in MCL, as in other B lymphoproliferative disorders, is the ImmunoGlobulin Heavy chain (IGH) rearrangement, occurring in B-lymphocytes. The patient-specific IGH rearrangement is extensively used to monitor the Minimal Residual Disease (MRD) after treatment through the standardized Allele-Specific Oligonucleotides Quantitative Polymerase Chain Reaction based technique. Recently, several studies have suggested that the IGH monitoring through deep sequencing techniques can produce not only comparable results to Polymerase Chain Reaction-based methods, but also might overcome the classical technique in terms of feasibility and sensitivity. However, no standard bioinformatics tool is available at the moment for data analysis in this context. RESULTS: In this paper we present HashClone, an easy-to-use and reliable bioinformatics tool that provides B-cells clonality assessment and MRD monitoring over time analyzing data from Next-Generation Sequencing (NGS) technique. The HashClone strategy-based is composed of three steps: the first and second steps implement an alignment-free prediction method that identifies a set of putative clones belonging to the repertoire of the patient under study. In the third step the IGH variable region, diversity region, and joining region identification is obtained by the alignment of rearrangements with respect to the international ImMunoGenetics information system database. Moreover, a provided graphical user interface for HashClone execution and clonality visualization over time facilitate the tool use and the results interpretation. The HashClone performance was tested on the NGS data derived from MCL patients to assess the major B-cell clone in the diagnostic samples and to monitor the MRD in the real and artificial follow up samples. CONCLUSIONS: Our experiments show that in all the experimental settings, HashClone was able to correctly detect the major B-cell clones and to precisely follow them in several samples showing better accuracy than the state-of-art tool. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1923-2) contains supplementary material, which is available to authorized users. BioMed Central 2017-11-23 /pmc/articles/PMC5701356/ /pubmed/29169317 http://dx.doi.org/10.1186/s12859-017-1923-2 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Beccuti, Marco Genuardi, Elisa Romano, Greta Monitillo, Luigia Barbero, Daniela Boccadoro, Mario Ladetto, Marco Calogero, Raffaele Ferrero, Simone Cordero, Francesca HashClone: a new tool to quantify the minimal residual disease in B-cell lymphoma from deep sequencing data |
title | HashClone: a new tool to quantify the minimal residual disease in B-cell lymphoma from deep sequencing data |
title_full | HashClone: a new tool to quantify the minimal residual disease in B-cell lymphoma from deep sequencing data |
title_fullStr | HashClone: a new tool to quantify the minimal residual disease in B-cell lymphoma from deep sequencing data |
title_full_unstemmed | HashClone: a new tool to quantify the minimal residual disease in B-cell lymphoma from deep sequencing data |
title_short | HashClone: a new tool to quantify the minimal residual disease in B-cell lymphoma from deep sequencing data |
title_sort | hashclone: a new tool to quantify the minimal residual disease in b-cell lymphoma from deep sequencing data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5701356/ https://www.ncbi.nlm.nih.gov/pubmed/29169317 http://dx.doi.org/10.1186/s12859-017-1923-2 |
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