Multiomic analysis of malignant pleural mesothelioma identifies molecular axes and specialized tumor profiles driving intertumor heterogeneity.
Journal
Nature genetics
ISSN: 1546-1718
Titre abrégé: Nat Genet
Pays: United States
ID NLM: 9216904
Informations de publication
Date de publication:
04 2023
04 2023
Historique:
received:
25
12
2021
accepted:
26
01
2023
medline:
17
4
2023
pubmed:
18
3
2023
entrez:
17
3
2023
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 36928603
doi: 10.1038/s41588-023-01321-1
pii: 10.1038/s41588-023-01321-1
pmc: PMC10101853
doi:
Substances chimiques
Biomarkers, Tumor
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
607-618Informations de copyright
© 2023. The Author(s).
Références
Carbone, M. et al. Mesothelioma: scientific clues for prevention, diagnosis, and therapy. CA Cancer J. Clin. 69, 402–429 (2019).
pubmed: 31283845
doi: 10.3322/caac.21572
pmcid: 8192079
WHO Classification of Tumours, Thoracic Tumours (5th edn) (International Agency for Research on Cancer, 2020).
Bueno, R. et al. Comprehensive genomic analysis of malignant pleural mesothelioma identifies recurrent mutations, gene fusions and splicing alterations. Nat. Genet. 48, 407–416 (2016).
pubmed: 26928227
doi: 10.1038/ng.3520
Hmeljak, J. et al. Integrative molecular characterization of malignant pleural mesothelioma. Cancer Discov. 8, 1548–1565 (2018).
pubmed: 30322867
doi: 10.1158/2159-8290.CD-18-0804
pmcid: 6310008
De Reyniès, A. et al. Molecular classification of malignant pleural mesothelioma: identification of a poor prognosis subgroup linked to the epithelial-to-mesenchymal transition. Clin. Cancer Res. 20, 1323–1334 (2014).
pubmed: 24443521
doi: 10.1158/1078-0432.CCR-13-2429
Alcala, N. et al. Redefining malignant pleural mesothelioma types as a continuum uncovers immune–vascular interactions. EBioMedicine 48, 191–202 (2019).
pubmed: 31648983
doi: 10.1016/j.ebiom.2019.09.003
pmcid: 6838392
Blum, Y. et al. Dissecting heterogeneity in malignant pleural mesothelioma through histo-molecular gradients for clinical applications. Nat. Commun. 10, 1333 (2019).
pubmed: 30902996
doi: 10.1038/s41467-019-09307-6
pmcid: 6430832
Nicholson, A. G. et al. EURACAN/IASLC proposals for updating the histologic classification of pleural mesothelioma: towards a more multidisciplinary approach. J. Thorac. Oncol. 15, 29–49 (2020).
pubmed: 31546041
doi: 10.1016/j.jtho.2019.08.2506
Fernandez-Cuesta, L., Mangiante, L., Alcala, N. & Foll, M. Challenges in lung and thoracic pathology: molecular advances in the classification of pleural mesotheliomas. Virchows Arch. 478, 73–80 (2021).
pubmed: 33411030
doi: 10.1007/s00428-020-02980-9
Cortés-Ciriano, I. et al. Comprehensive analysis of chromothripsis in 2,658 human cancers using whole-genome sequencing. Nat. Genet. 52, 331–341 (2020).
pubmed: 32025003
doi: 10.1038/s41588-019-0576-7
pmcid: 7058534
ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature 578, 82–93 (2020).
doi: 10.1038/s41586-020-1969-6
Quinton, R. J. et al. Whole-genome doubling confers unique genetic vulnerabilities on tumour cells. Nature 590, 492–497 (2021).
pubmed: 33505027
doi: 10.1038/s41586-020-03133-3
pmcid: 7889737
Creaney, J. et al. Comprehensive genomic and tumour immune profiling reveals potential therapeutic targets in malignant pleural mesothelioma. Genome Med. 14, 58 (2022).
pubmed: 35637530
doi: 10.1186/s13073-022-01060-8
pmcid: 9150319
Argelaguet, R. et al. MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data. Genome Biol. 21, 111 (2020).
pubmed: 32393329
doi: 10.1186/s13059-020-02015-1
pmcid: 7212577
Courtiol, P. et al. Deep learning-based classification of mesothelioma improves prediction of patient outcome. Nat. Med. 25, 1519–1525 (2019).
pubmed: 31591589
doi: 10.1038/s41591-019-0583-3
Baylin, S. B. & Jones, P. A. Epigenetic determinants of cancer. Cold Spring Harb. Perspect. Biol. 8, a019505 (2016).
pubmed: 27194046
doi: 10.1101/cshperspect.a019505
pmcid: 5008069
Sondka, Z. et al. The COSMIC cancer gene census: describing genetic dysfunction across all human cancers. Nat. Rev. Cancer 18, 696–705 (2018).
pubmed: 30293088
doi: 10.1038/s41568-018-0060-1
pmcid: 6450507
Iorio, F. et al. A landscape of pharmacogenomic interactions in cancer. Cell 166, 740–754 (2016).
pubmed: 27397505
doi: 10.1016/j.cell.2016.06.017
pmcid: 4967469
Hausser, J. & Alon, U. Tumour heterogeneity and the evolutionary trade-offs of cancer. Nat. Rev. Cancer 20, 247–257 (2020).
pubmed: 32094544
doi: 10.1038/s41568-020-0241-6
Hausser, J. et al. Tumor diversity and the trade-off between universal cancer tasks. Nat. Commun. 10, 5423 (2019).
pubmed: 31780652
doi: 10.1038/s41467-019-13195-1
pmcid: 6882839
Turini, S., Bergandi, L., Gazzano, E., Prato, M. & Aldieri, E. Epithelial to mesenchymal transition in human mesothelial cells exposed to asbestos fibers: role of TGF-β as mediator of malignant mesothelioma development or metastasis via EMT event. Int. J. Mol. Sci. 20, 150 (2019).
pubmed: 30609805
doi: 10.3390/ijms20010150
pmcid: 6337211
Shipony, Z. et al. Dynamic and static maintenance of epigenetic memory in pluripotent and somatic cells. Nature 513, 115–119 (2014).
pubmed: 25043040
doi: 10.1038/nature13458
Chapel, D. B. et al. MTAP immunohistochemistry is an accurate and reproducible surrogate for CDKN2A fluorescence in situ hybridization in diagnosis of malignant pleural mesothelioma. Mod. Pathol. 33, 245–254 (2020).
pubmed: 31231127
doi: 10.1038/s41379-019-0310-0
Alexandrov, L. B. et al. The repertoire of mutational signatures in human cancer. Nature 578, 94–101 (2020).
pubmed: 32025018
doi: 10.1038/s41586-020-1943-3
pmcid: 7054213
Steele, C. D. et al. Signatures of copy number alterations in human cancer. Nature 606, 984–991 (2022).
pubmed: 35705804
doi: 10.1038/s41586-022-04738-6
pmcid: 9242861
Bergstrom, E. N. et al. Mapping clustered mutations in cancer reveals APOBEC3 mutagenesis of ecDNA. Nature 602, 510–517 (2022).
pubmed: 35140399
doi: 10.1038/s41586-022-04398-6
pmcid: 8850194
Ladan, M. M., van Gent, D. C. & Jager, A. Homologous recombination deficiency testing for BRCA-like tumors: the road to clinical validation. Cancers 13, 1004 (2021).
pubmed: 33670893
doi: 10.3390/cancers13051004
pmcid: 7957671
Toh, M. & Ngeow, J. Homologous recombination deficiency: cancer predispositions and treatment implications. Oncologist 26, e1526–e1537 (2021).
pubmed: 34021944
doi: 10.1002/onco.13829
pmcid: 8417864
Ghafoor, A. et al. Phase 2 study of olaparib in malignant mesothelioma and correlation of efficacy with germline or somatic mutations in BAP1 gene. JTO Clin. Res Rep. 2, 100231 (2021).
pubmed: 34661178
pmcid: 8502774
Martínez-Jiménez, F. et al. A compendium of mutational cancer driver genes. Nat. Rev. Cancer 20, 555–572 (2020).
pubmed: 32778778
doi: 10.1038/s41568-020-0290-x
De Rienzo, A. et al. Gender-specific molecular and clinical features underlie malignant pleural mesothelioma. Cancer Res. 76, 319–328 (2016).
pubmed: 26554828
doi: 10.1158/0008-5472.CAN-15-0751
Kato, S. et al. Genomic landscape of malignant mesotheliomas. Mol. Cancer Ther. 15, 2498–2507 (2016).
pubmed: 27507853
doi: 10.1158/1535-7163.MCT-16-0229
Shukuya, T. et al. Identification of actionable mutations in malignant pleural mesothelioma. Lung Cancer 86, 35–40 (2014).
pubmed: 25174276
doi: 10.1016/j.lungcan.2014.08.004
Mansfield, A. S. et al. Neoantigenic potential of complex chromosomal rearrangements in mesothelioma. J. Thorac. Oncol. 14, 276–287 (2019).
pubmed: 30316012
doi: 10.1016/j.jtho.2018.10.001
McLoughlin, K. C., Kaufman, A. S. & Schrump, D. S. Targeting the epigenome in malignant pleural mesothelioma. Transl. Lung Cancer Res. 6, 350–365 (2017).
pubmed: 28713680
doi: 10.21037/tlcr.2017.06.06
pmcid: 5504118
Pastorino, S. et al. A subset of mesotheliomas with improved survival occurring in carriers of BAP1 and other germline mutations. J. Clin. Oncol. 36, 3485–3494 (2018).
doi: 10.1200/JCO.2018.79.0352
pmcid: 7162737
Hylebos, M. et al. Molecular analysis of an asbestos-exposed Belgian family with a high prevalence of mesothelioma. Fam. Cancer 17, 569–576 (2018).
pubmed: 29961174
doi: 10.1007/s10689-018-0095-1
Bielski, C. M. et al. Genome doubling shapes the evolution and prognosis of advanced cancers. Nat. Genet. 50, 1189–1195 (2018).
pubmed: 30013179
doi: 10.1038/s41588-018-0165-1
pmcid: 6072608
Turcan, S. et al. IDH1 mutation is sufficient to establish the glioma hypermethylator phenotype. Nature 483, 479–483 (2012).
pubmed: 22343889
doi: 10.1038/nature10866
pmcid: 3351699
Margueron, R. & Reinberg, D. The Polycomb complex PRC2 and its mark in life. Nature 469, 343–349 (2011).
pubmed: 21248841
doi: 10.1038/nature09784
pmcid: 3760771
Zauderer, M. G. et al. A randomized phase II trial of adjuvant galinpepimut-S, WT-1 analogue peptide vaccine, after multimodality therapy for patients with malignant pleural mesothelioma. Clin. Cancer Res. 23, 7483–7489 (2017).
pubmed: 28972039
doi: 10.1158/1078-0432.CCR-17-2169
pmcid: 5732877
Phipps, A. I. et al. Association between molecular subtypes of colorectal cancer and patient survival. Gastroenterology 148, 77–87.e2 (2015).
pubmed: 25280443
doi: 10.1053/j.gastro.2014.09.038
Malta, T. M. et al. Glioma CpG island methylator phenotype (G-CIMP): biological and clinical implications. Neuro. Oncol. 20, 608–620 (2018).
pubmed: 29036500
doi: 10.1093/neuonc/nox183
Sreejit, G. et al. The ESAT-6 protein of Mycobacterium tuberculosis interacts with beta-2-microglobulin (β2M) affecting antigen presentation function of macrophage. PLoS Pathog. 10, e1004446 (2014).
pubmed: 25356553
doi: 10.1371/journal.ppat.1004446
pmcid: 4214792
Zanetti, M. Chromosomal chaos silences immune surveillance. Science 355, 249–250 (2017).
pubmed: 28104855
doi: 10.1126/science.aam5331
Gerstung, M. et al.The evolutionary history of 2,658 cancers. Nature 578, 122–128 (2020).
pubmed: 32025013
doi: 10.1038/s41586-019-1907-7
pmcid: 7054212
Fujiwara, T. et al. Cytokinesis failure generating tetraploids promotes tumorigenesis in p53-null cells. Nature 437, 1043–1047 (2005).
pubmed: 16222300
doi: 10.1038/nature04217
López, S. et al. Interplay between whole-genome doubling and the accumulation of deleterious alterations in cancer evolution. Nat. Genet. 52, 283–293 (2020).
pubmed: 32139907
doi: 10.1038/s41588-020-0584-7
pmcid: 7116784
Advani, S. M. et al. Clinical, pathological, and molecular characteristics of CpG island methylator phenotype in colorectal cancer: a systematic review and meta-analysis. Transl. Oncol. 11, 1188–1201 (2018).
pubmed: 30071442
doi: 10.1016/j.tranon.2018.07.008
pmcid: 6080640
Noushmehr, H. et al. Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. Cancer Cell 17, 510–522 (2010).
pubmed: 20399149
doi: 10.1016/j.ccr.2010.03.017
pmcid: 2872684
Hughes, L. A. E. et al. The CpG island methylator phenotype: what’s in a name? Cancer Res. 73, 5858–5868 (2013).
pubmed: 23801749
doi: 10.1158/0008-5472.CAN-12-4306
Moarii, M., Reyal, F. & Vert, J.-P. Integrative DNA methylation and gene expression analysis to assess the universality of the CpG island methylator phenotype. Hum. Genomics 9, 26 (2015).
pubmed: 26463173
doi: 10.1186/s40246-015-0048-9
pmcid: 4603341
Maley, C. C. et al. Classifying the evolutionary and ecological features of neoplasms. Nat. Rev. Cancer 17, 605–619 (2017).
pubmed: 28912577
doi: 10.1038/nrc.2017.69
pmcid: 5811185
Vendramin, R., Litchfield, K. & Swanton, C. Cancer evolution: Darwin and beyond. EMBO J. 40, e108389 (2021).
pubmed: 34459009
doi: 10.15252/embj.2021108389
pmcid: 8441388
Gould, S. J. & Eldredge, N. Punctuated equilibria: an alternative to phyletic gradualism. In Schopf, T.J.M. Models in Paleobiology 82–115 (Freeman Cooper, 1972).
Zolondick, A. A. et al. Asbestos-induced chronic inflammation in malignant pleural mesothelioma and related therapeutic approaches—a narrative review. Precis. Cancer Med. 4, 27–27 (2021).
pubmed: 35098108
doi: 10.21037/pcm-21-12
pmcid: 8797751
Southwood, T. R. E., May, R. M., Hassell, M. P. & Conway, G. R. Ecological strategies and population parameters. Am. Nat. 108, 791–804 (1974).
doi: 10.1086/282955
Napolitano, A. et al. Minimal asbestos exposure in germline BAP1 heterozygous mice is associated with deregulated inflammatory response and increased risk of mesothelioma. Oncogene 35, 1996–2002 (2016).
pubmed: 26119930
doi: 10.1038/onc.2015.243
Adashek, J. J., Goloubev, A., Kato, S. & Kurzrock, R. Missing the target in cancer therapy. Nat. Cancer 2, 369–371 (2021).
pubmed: 34368781
doi: 10.1038/s43018-021-00204-w
pmcid: 8336921
Gay, C. M. et al. Patterns of transcription factor programs and immune pathway activation define four major subtypes of SCLC with distinct therapeutic vulnerabilities. Cancer Cell 39, 346–360.e7 (2021).
pubmed: 33482121
doi: 10.1016/j.ccell.2020.12.014
pmcid: 8143037
Dora, D. et al. Neuroendocrine subtypes of small cell lung cancer differ in terms of immune microenvironment and checkpoint molecule distribution. Mol. Oncol. 14, 1947–1965 (2020).
pubmed: 32506804
doi: 10.1002/1878-0261.12741
pmcid: 7463307
Owonikoko, T. K. et al. YAP1 expression in SCLC defines a distinct subtype with T-cell-inflamed phenotype. J. Thorac. Oncol. 16, 464–476 (2021).
pubmed: 33248321
doi: 10.1016/j.jtho.2020.11.006
Galateau-Salle, F., Churg, A., Roggli, V., Travis, W. D. & World Health Organization Committee for Tumors of the Pleura. The 2015 World Health Organization Classification of Tumors of the Pleura: advances since the 2004 classification. J. Thorac. Oncol. 11, 142–154 (2016).
pubmed: 26811225
doi: 10.1016/j.jtho.2015.11.005
WHO Classification of Tumours of the Lung, Pleura, Thymus and Heart (4th edn) (International Agency for Research on Cancer, 2015).
Wasserstein, R. L. & Lazar, N. A. The ASA statement on P-values: context, process, and purpose. Am Stat. 70, 129–133 (2016).
doi: 10.1080/00031305.2016.1154108
Alcala, N. et al. Integrative and comparative genomic analyses identify clinically relevant pulmonary carcinoid groups and unveil the supra-carcinoids. Nat. Commun. 10, 3407 (2019).
pubmed: 31431620
doi: 10.1038/s41467-019-11276-9
pmcid: 6702229
Di Tommaso, P. et al. Nextflow enables reproducible computational workflows. Nat. Biotechnol. 35, 316–319 (2017).
pubmed: 28398311
doi: 10.1038/nbt.3820
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
pubmed: 19451168
doi: 10.1093/bioinformatics/btp324
pmcid: 2705234
Faust, G. G. & Hall, I. M. SAMBLASTER: fast duplicate marking and structural variant read extraction. Bioinformatics 30, 2503–2505 (2014).
pubmed: 24812344
doi: 10.1093/bioinformatics/btu314
pmcid: 4147885
Tarasov, A., Vilella, A. J., Cuppen, E., Nijman, I. J. & Prins, P. Sambamba: fast processing of NGS alignment formats. Bioinformatics 31, 2032–2034 (2015).
pubmed: 25697820
doi: 10.1093/bioinformatics/btv098
pmcid: 4765878
Van der Auwera, G. A. & O’Connor, B. D. Genomics in the Cloud: Using Docker, GATK, and WDL in Terra (O’Reilly Media, 2020).
Benjamin, D. et al. Calling somatic SNVs and indels with Mutect2. Preprint at bioRxiv https://doi.org/10.1101/861054 (2019).
Kim, S. et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat. Methods 15, 591–594 (2018).
pubmed: 30013048
doi: 10.1038/s41592-018-0051-x
Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).
pubmed: 20601685
doi: 10.1093/nar/gkq603
pmcid: 2938201
Cameron, D. L. et al. GRIDSS, PURPLE, LINX: Unscrambling the tumor genome via integrated analysis of structural variation and copy number. Preprint at bioRxiv https://doi.org/10.1101/781013 (2019).
Wala, J. A. et al. SvABA: genome-wide detection of structural variants and indels by local assembly. Genome Res. 28, 581–591 (2018).
pubmed: 29535149
doi: 10.1101/gr.221028.117
pmcid: 5880247
Chen, X. et al. Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics 32, 1220–1222 (2016).
pubmed: 26647377
doi: 10.1093/bioinformatics/btv710
Rausch, T. et al. DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics 28, i333–i339 (2012).
pubmed: 22962449
doi: 10.1093/bioinformatics/bts378
pmcid: 3436805
Jeffares, D. C. et al. Transient structural variations have strong effects on quantitative traits and reproductive isolation in fission yeast. Nat. Commun. 8, 14061 (2017).
pubmed: 28117401
doi: 10.1038/ncomms14061
pmcid: 5286201
Mose, L. E., Perou, C. M. & Parker, J. S. Improved indel detection in DNA and RNA via realignment with ABRA2. Bioinformatics 35, 2966–2973 (2019).
pubmed: 30649250
doi: 10.1093/bioinformatics/btz033
pmcid: 6735753
Du, P. et al. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics 11, 587 (2010).
pubmed: 21118553
doi: 10.1186/1471-2105-11-587
pmcid: 3012676
Genova, A. D. et al. A molecular phenotypic map of malignant pleural mesothelioma. Gigascience 12, giac128 (2022).
pubmed: 36705549
doi: 10.1093/gigascience/giac128