Structural variants shape the genomic landscape and clinical outcome of multiple myeloma.
Journal
Blood cancer journal
ISSN: 2044-5385
Titre abrégé: Blood Cancer J
Pays: United States
ID NLM: 101568469
Informations de publication
Date de publication:
30 05 2022
30 05 2022
Historique:
received:
08
12
2021
accepted:
22
04
2022
revised:
11
03
2022
entrez:
31
5
2022
pubmed:
1
6
2022
medline:
3
6
2022
Statut:
epublish
Résumé
Deciphering genomic architecture is key to identifying novel disease drivers and understanding the mechanisms underlying myeloma initiation and progression. In this work, using the CoMMpass dataset, we show that structural variants (SV) occur in a nonrandom fashion throughout the genome with an increased frequency in the t(4;14), RB1, or TP53 mutated cases and reduced frequency in t(11;14) cases. By mapping sites of chromosomal rearrangements to topologically associated domains and identifying significantly upregulated genes by RNAseq we identify both predicted and novel putative driver genes. These data highlight the heterogeneity of transcriptional dysregulation occurring as a consequence of both the canonical and novel structural variants. Further, it shows that the complex rearrangements chromoplexy, chromothripsis and templated insertions are common in MM with each variant having its own distinct frequency and impact on clinical outcome. Chromothripsis is associated with a significant independent negative impact on clinical outcome in newly diagnosed cases consistent with its use alongside other clinical and genetic risk factors to identify prognosis.
Identifiants
pubmed: 35637217
doi: 10.1038/s41408-022-00673-x
pii: 10.1038/s41408-022-00673-x
pmc: PMC9151656
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
85Subventions
Organisme : NIGMS NIH HHS
ID : P20 GM121176
Pays : United States
Informations de copyright
© 2022. The Author(s).
Références
Bioinformatics. 2009 Jul 15;25(14):1754-60
pubmed: 19451168
Front Immunol. 2019 May 21;10:1121
pubmed: 31231360
Nat Commun. 2017 Dec 5;8(1):1937
pubmed: 29203764
Nat Genet. 2020 Mar;52(3):331-341
pubmed: 32025003
Bioinformatics. 2014 Sep 1;30(17):2503-5
pubmed: 24812344
Haematologica. 2020 Apr;105(4):1055-1066
pubmed: 31221783
Leukemia. 2012 Dec;26(12):2521-9
pubmed: 22565645
PLoS Genet. 2017 Nov 22;13(11):e1007087
pubmed: 29166413
Bioinformatics. 2014 Dec 15;30(24):3515-23
pubmed: 25183486
Clin Cancer Res. 2021 Jun 1;27(11):3178-3189
pubmed: 33731366
Cancer Cell. 2013 May 13;23(5):567-9
pubmed: 23680143
J Clin Oncol. 2005 May 20;23(15):3412-20
pubmed: 15809451
Blood. 2018 Aug 9;132(6):587-597
pubmed: 29884741
Leukemia. 2019 Jan;33(1):159-170
pubmed: 29967379
Nat Commun. 2019 Oct 24;10(1):4843
pubmed: 31649247
Blood. 2011 Jan 13;117(2):553-62
pubmed: 20944071
Blood. 2010 Oct 14;116(15):e56-65
pubmed: 20616218
Bioinformatics. 2012 Feb 1;28(3):423-5
pubmed: 22155870
Science. 2016 Mar 25;351(6280):1454-1458
pubmed: 26940867
Cell. 2013 Apr 11;153(2):320-34
pubmed: 23582323
BMC Bioinformatics. 2015 Jul 19;16:224
pubmed: 26187896
Bioinformatics. 2013 Jan 1;29(1):15-21
pubmed: 23104886
J Clin Oncol. 2015 Nov 20;33(33):3911-20
pubmed: 26282654
Proc Natl Acad Sci U S A. 2019 Apr 23;116(17):8451-8456
pubmed: 30962382
Blood Cancer Discov. 2020 Nov;1(3):258-273
pubmed: 33392515
Nat Methods. 2017 Apr;14(4):417-419
pubmed: 28263959