Supporting data for "A molecular phenotypic map of Malignant Pleural Mesothelioma"

Dataset type: Genomic, Transcriptomic, Workflow, Epigenomic, Bioinformatics
Data released on December 19, 2022

Di Genova A; Mangiante L; Sexton-Oates A; Voegele C; Fernandez-Cuesta L; Alcala N; Foll M (2022): Supporting data for "A molecular phenotypic map of Malignant Pleural Mesothelioma" GigaScience Database.


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.
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 multi-omic MPM datasets (n=374 in total), we provide an extensive molecular phenotype map of MPM based on the multi-task theory. The generated map can be interactively explored and interrogated on the UCSC TumorMap portal.
This new high quality MPM multi-omics 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.

Additional details

Read the peer-reviewed publication(s):

(PubMed: 36705549)

Github links:

Additional information:

Accessions (data generated as part of this study):

EGA: EGAS00001004812

File NameSample IDData TypeFile FormatSizeRelease Date 
Data Access AgreementDOC332.38 KB2022-12-08
GitHub archivezip72.81 MB2022-12-08
ReadmeTEXT2.75 KB2022-12-19
Displaying 1-3 of 3 File(s).
Funding body Awardee Award ID Comments
Ligue Contre le Cancer L Fernandez-Cuesta & M Foll Appel d’offres 2016
Institut National Du Cancer L Fernandez-Cuesta & M Foll PRT-K 2016-039 Programme de Recherche Translationnelle en Cancérologie
Date Action
December 19, 2022 Dataset publish
April 4, 2023 Manuscript Link added : 10.1093/gigascience/giac128