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A molecular phenotypic map of malignant pleural mesothelioma
BACKGROUND: Malignant pleural mesothelioma (MPM) is a rare understudied cancer associated with exposure to asbestos. So far, MPM patients have benefited marginally from the genomics medicine revolution due to the limited size or breadth of existing molecular studies. In the context of the MESOMICS p...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9881451/ https://www.ncbi.nlm.nih.gov/pubmed/36705549 http://dx.doi.org/10.1093/gigascience/giac128 |
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author | Di Genova, Alex Mangiante, Lise Sexton-Oates, Alexandra Voegele, Catherine Fernandez-Cuesta, Lynnette Alcala, Nicolas Foll, Matthieu |
author_facet | Di Genova, Alex Mangiante, Lise Sexton-Oates, Alexandra Voegele, Catherine Fernandez-Cuesta, Lynnette Alcala, Nicolas Foll, Matthieu |
author_sort | Di Genova, Alex |
collection | PubMed |
description | BACKGROUND: Malignant pleural mesothelioma (MPM) is a rare understudied cancer associated with exposure to asbestos. So far, MPM patients have benefited marginally from the genomics medicine revolution due to the limited size or breadth of existing molecular studies. In the context of the MESOMICS project, we have performed the most comprehensive molecular characterization of MPM to date, with the underlying dataset made of the largest whole-genome sequencing series yet reported, together with transcriptome sequencing and methylation arrays for 120 MPM patients. RESULTS: We first provide comprehensive quality controls for all samples, of both raw and processed data. Due to the difficulty in collecting specimens from such rare tumors, a part of the cohort does not include matched normal material. We provide a detailed analysis of data processing of these tumor-only samples, showing that all somatic alteration calls match very stringent criteria of precision and recall. Finally, integrating our data with previously published multiomic MPM datasets (n = 374 in total), we provide an extensive molecular phenotype map of MPM based on the multitask theory. The generated map can be interactively explored and interrogated on the UCSC TumorMap portal (https://tumormap.ucsc.edu/?p=RCG_MESOMICS/MPM_Archetypes ). CONCLUSIONS: This new high-quality MPM multiomics dataset, together with the state-of-art bioinformatics and interactive visualization tools we provide, will support the development of precision medicine in MPM that is particularly challenging to implement in rare cancers due to limited molecular studies. |
format | Online Article Text |
id | pubmed-9881451 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-98814512023-01-31 A molecular phenotypic map of malignant pleural mesothelioma Di Genova, Alex Mangiante, Lise Sexton-Oates, Alexandra Voegele, Catherine Fernandez-Cuesta, Lynnette Alcala, Nicolas Foll, Matthieu Gigascience Data Note BACKGROUND: Malignant pleural mesothelioma (MPM) is a rare understudied cancer associated with exposure to asbestos. So far, MPM patients have benefited marginally from the genomics medicine revolution due to the limited size or breadth of existing molecular studies. In the context of the MESOMICS project, we have performed the most comprehensive molecular characterization of MPM to date, with the underlying dataset made of the largest whole-genome sequencing series yet reported, together with transcriptome sequencing and methylation arrays for 120 MPM patients. RESULTS: We first provide comprehensive quality controls for all samples, of both raw and processed data. Due to the difficulty in collecting specimens from such rare tumors, a part of the cohort does not include matched normal material. We provide a detailed analysis of data processing of these tumor-only samples, showing that all somatic alteration calls match very stringent criteria of precision and recall. Finally, integrating our data with previously published multiomic MPM datasets (n = 374 in total), we provide an extensive molecular phenotype map of MPM based on the multitask theory. The generated map can be interactively explored and interrogated on the UCSC TumorMap portal (https://tumormap.ucsc.edu/?p=RCG_MESOMICS/MPM_Archetypes ). CONCLUSIONS: This new high-quality MPM multiomics dataset, together with the state-of-art bioinformatics and interactive visualization tools we provide, will support the development of precision medicine in MPM that is particularly challenging to implement in rare cancers due to limited molecular studies. Oxford University Press 2023-01-27 /pmc/articles/PMC9881451/ /pubmed/36705549 http://dx.doi.org/10.1093/gigascience/giac128 Text en © World Health Organization, [2023]. All rights reserved. The World Health Organization has granted the Publisher permission for the reproduction of this article. https://creativecommons.org/licenses/by/3.0/igo/This is an Open Access article distributed under the terms of the Creative Commons Attribution 3.0 IGO License (https://creativecommons.org/licenses/by/3.0/igo/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Data Note Di Genova, Alex Mangiante, Lise Sexton-Oates, Alexandra Voegele, Catherine Fernandez-Cuesta, Lynnette Alcala, Nicolas Foll, Matthieu A molecular phenotypic map of malignant pleural mesothelioma |
title | A molecular phenotypic map of malignant pleural mesothelioma |
title_full | A molecular phenotypic map of malignant pleural mesothelioma |
title_fullStr | A molecular phenotypic map of malignant pleural mesothelioma |
title_full_unstemmed | A molecular phenotypic map of malignant pleural mesothelioma |
title_short | A molecular phenotypic map of malignant pleural mesothelioma |
title_sort | molecular phenotypic map of malignant pleural mesothelioma |
topic | Data Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9881451/ https://www.ncbi.nlm.nih.gov/pubmed/36705549 http://dx.doi.org/10.1093/gigascience/giac128 |
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