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All Publications

  • Multiomic analysis of malignant pleural mesothelioma identifies molecular axes and specialized tumor profiles driving intertumor heterogeneity. Nature genetics Mangiante, L., Alcala, N., Sexton-Oates, A., Di Genova, A., Gonzalez-Perez, A., Khandekar, A., Bergstrom, E. N., Kim, J., Liu, X., Blazquez-Encinas, R., Giacobi, C., Le Stang, N., Boyault, S., Cuenin, C., Tabone-Eglinger, S., Damiola, F., Voegele, C., Ardin, M., Michallet, M. C., Soudade, L., Delhomme, T. M., Poret, A., Brevet, M., Copin, M. C., Giusiano-Courcambeck, S., Damotte, D., Girard, C., Hofman, V., Hofman, P., Mouroux, J., Cohen, C., Lacomme, S., Mazieres, J., de Montpreville, V. T., Perrin, C., Planchard, G., Rousseau, N., Rouquette, I., Sagan, C., Scherpereel, A., Thivolet, F., Vignaud, J. M., Jean, D., Ilg, A. G., Olaso, R., Meyer, V., Boland-Auge, A., Deleuze, J. F., Altmuller, J., Nuernberg, P., Ibáñez-Costa, A., Castaño, J. P., Lantuejoul, S., Ghantous, A., Maussion, C., Courtiol, P., Hernandez-Vargas, H., Caux, C., Girard, N., Lopez-Bigas, N., Alexandrov, L. B., Galateau-Salle, F., Foll, M., Fernandez-Cuesta, L. 2023


    Malignant pleural mesothelioma (MPM) is an aggressive cancer with rising incidence and challenging clinical management. Through a large series of whole-genome sequencing data, integrated with transcriptomic and epigenomic data using multiomics factor analysis, we demonstrate that the current World Health Organization classification only accounts for up to 10% of interpatient molecular differences. Instead, the MESOMICS project paves the way for a morphomolecular classification of MPM based on four dimensions: ploidy, tumor cell morphology, adaptive immune response and CpG island methylator profile. We show that these four dimensions are complementary, capture major interpatient molecular differences and are delimited by extreme phenotypes that-in the case of the interdependent tumor cell morphology and adapted immune response-reflect tumor specialization. These findings unearth the interplay between MPM functional biology and its genomic history, and provide insights into the variations observed in the clinical behavior of patients with MPM.

    View details for DOI 10.1038/s41588-023-01321-1

    View details for PubMedID 36928603

    View details for PubMedCentralID 8192079

  • A molecular phenotypic map of malignant pleural mesothelioma. GigaScience Di Genova, A., Mangiante, L., Sexton-Oates, A., Voegele, C., Fernandez-Cuesta, L., Alcala, N., Foll, M. 2022; 12


    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 ( ).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.

    View details for DOI 10.1093/gigascience/giac128

    View details for PubMedID 36705549