Molecular characterization of triple-negative myeloproliferative neoplasms by next-generation sequencing.
MPIG6B
Philadelphia chromosome-negative myeloproliferative neoplasms
Targeted sequencing
Triple-negative MPN
Journal
Annals of hematology
ISSN: 1432-0584
Titre abrégé: Ann Hematol
Pays: Germany
ID NLM: 9107334
Informations de publication
Date de publication:
Sep 2022
Sep 2022
Historique:
received:
14
03
2022
accepted:
27
06
2022
pubmed:
16
7
2022
medline:
17
8
2022
entrez:
15
7
2022
Statut:
ppublish
Résumé
The role of next-generation sequencing (NGS) in identifying mutations in the driver, epigenetic regulator, RNA splicing, and signaling pathway genes in myeloproliferative neoplasms (MPNs) has contributed substantially to our understanding of the disease pathogenesis as well as disease evolution. NGS aids in determining the clonal nature of the disease in a subset of these disorders where mutations in the driver genes are not detected. There is a paucity of real-world data on the utility of this test in the characterization of triple-negative myeloproliferative neoplasms (TN-MPN). In this study, 46 samples of TN-MPN (essential thrombocythemia (ET) = 17; primary myelofibrosis (PMF) = 23; & myeloproliferative neoplasm unclassified (MPN-u) = 6) were screened for markers of clonality using targeted NGS. Among these, 25 (54.3%) patients had mutations that would help determine the clonal nature of the disease. Eight of the 17 TN-ET (47%) and 13 of the 23 TN-PMF (56.5%) patients had noncanonical mutations in the driver genes and mutations in the genes involved in epigenetic regulation. Identification of mutations categorized as high molecular markers (HMR) in 2 patients helped classify them as PMF with high risk according to the MIPSS 70 scoring system. A novel mutation in the MPIG6B (C6orf25) gene associated with childhood myelofibrosis was detected in a 14-year-old girl. The presence of clonal hematopoiesis could be confirmed in four of the six MPN-u patients in this cohort. This study demonstrates the utility of NGS in improving the characterization of TN-MPN by establishing clonality and detecting noncanonical mutations in driver genes, thereby aiding in clinical decision-making.
Identifiants
pubmed: 35840818
doi: 10.1007/s00277-022-04920-w
pii: 10.1007/s00277-022-04920-w
doi:
Substances chimiques
Janus Kinase 2
EC 2.7.10.2
Types de publication
Case Reports
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1987-2000Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Références
Tefferi A, Lasho TL, Finke CM, Knudson RA, Ketterling R, Hanson CH et al (2014) CALR vs JAK2 vs MPL -mutated or triple-negative myelofibrosis: clinical, cytogenetic and molecular comparisons. Leukemia 28(7):1472–1477
doi: 10.1038/leu.2014.3
Langabeer SE (2016) Chasing down the triple-negative myeloproliferative neoplasms: implications for molecular diagnostics. JAK-STAT 5(2–4):e1248011
doi: 10.1080/21623996.2016.1248011
Milosevic Feenstra JD, Nivarthi H, Gisslinger H, Leroy E, Rumi E, Chachoua I et al (2016) Whole-exome sequencing identifies novel MPL and JAK2 mutations in triple-negative myeloproliferative neoplasms. Blood 127(3):325–332
doi: 10.1182/blood-2015-07-661835
Diaconnu C, Mambet C, Necula L, Gurban P, Matei L, Aldea-Pitica I et al (2017) Triple negative myeloproliferative neoplasms - sometimes driver mutations stay low-key in plain sight. Roman Biotechnol Lett 23(4). https://doi.org/10.26327/RBL2017.12
McClure RF, Ewalt MD, Crow J, Temple-Smolkin RL, Pullambhatla M, Sargent R et al (2018) Clinical significance of DNA variants in chronic myeloid neoplasms: a report of the association for molecular pathology. J Mol Diagn 20(6):717–737
doi: 10.1016/j.jmoldx.2018.07.002
Alduaij W, McNamara CJ, Schuh A et al (2018) Clinical utility of next-generation sequencing in the management of myeloproliferative neoplasms: a single-center experience. Hemasphere. 2(3):e44. https://doi.org/10.1097/HS9.0000000000000044. (Published 2018 May 4)
doi: 10.1097/HS9.0000000000000044.
pubmed: 31723772
pmcid: 6745993
Lee J, Godfrey AL, Nangalia J (2020) Genomic heterogeneity in myeloproliferative neoplasms and applications to clinical practice. Blood Rev 42:100708
doi: 10.1016/j.blre.2020.100708
Cabagnols X, Favale F, Pasquier F, Messaoudi K, Defour JP, Ianotto JC et al (2016) Presence of atypical thrombopoietin receptor (MPL) mutations in triple-negative essential thrombocythemia patients. Blood 127(3):333–342
doi: 10.1182/blood-2015-07-661983
Tefferi A, Lasho TL, Finke CM, Elala Y, Hanson CA, Ketterling RP et al (2016) Targeted deep sequencing in primary myelofibrosis. Blood Adv 1(2):105–111
doi: 10.1182/bloodadvances.2016000208
NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) Myeloproliferative Neoplasms [Internet]. NCCN. 2022 [cited 16 June 2022]. Available from: https://www.nccn.org/professionals/physician_gls/pdf/mpn.pdf
Barbui T, Tefferi A, Vannucchi AM, Passamonti F, Silver RT, Hoffman R et al (2018) Philadelphia chromosome-negative classical myeloproliferative neoplasms: revised management recommendations from European LeukemiaNet. Leukemia 32(5):1057–1069
doi: 10.1038/s41375-018-0077-1
M. Maddali, U.P. Kulkarni, N. Ravindra, et al., Mutation profile in BCR-ABL1-negative myeloproliferative neoplasms: a single-center experience from India. Hematol Oncol Stem Cell Ther. https://doi.org/10.1016/j.hemonc.2021.03.002
Freed D, Aldana R, Weber JA, Edwards JS. The Sentieon Genomics Tools - A fast and accurate solution to variant calling from next-generation sequence data [Internet]. Bioinformatics; 2017 Mar [cited 2021 Nov 24]. Available from: http://biorxiv.org/lookup/doi/ https://doi.org/10.1101/115717
Cunningham F, Achuthan P, Akanni W, Allen J, Amode MR, Armean IM et al (2019) Ensembl 2019. Nucleic Acids Res 47(D1):D745–D751
doi: 10.1093/nar/gky1113
Zerbino DR, Achuthan P, Akanni W, Amode MR, Barrell D, Bhai J et al (2018) Ensembl 2018. Nucleic Acids Res 46(D1):D754–D761
doi: 10.1093/nar/gkx1098
Plagnol V, Curtis J, Epstein M, Mok KY, Stebbings E, Grigoriadou S et al (2012) A robust model for read count data in exome sequencing experiments and implications for copy number variant calling. Bioinformatics 28(21):2747–2754
doi: 10.1093/bioinformatics/bts526
Landrum MJ, Lee JM, Benson M, Brown G, Chao C, Chitipiralla S et al (2016) ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res 44(D1):D862–D868
doi: 10.1093/nar/gkv1222
Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA (2005) Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 33(suppl_1):D514-7
pubmed: 15608251
Buniello A, MacArthur JAL, Cerezo M, Harris LW, Hayhurst J, Malangone C et al (2019) The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res 47(D1):D1005–D1012
doi: 10.1093/nar/gky1120
Welter D, MacArthur J, Morales J, Burdett T, Hall P, Junkins H et al (2014) The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res 42(D1):D1001–D1006
doi: 10.1093/nar/gkt1229
Stenson PD, Ball EV, Mort M, Phillips AD, Shiel JA, Thomas NST et al (2003) Human Gene Mutation Database (HGMD®): 2003 update. Hum Mutat 21(6):577–581
doi: 10.1002/humu.10212
Mottaz A, David FPA, Veuthey A-L, Yip YL (2010) Easy retrieval of single amino-acid polymorphisms and phenotype information using SwissVar. Bioinformatics 26(6):851–852
doi: 10.1093/bioinformatics/btq028
Auton A, Abecasis GR, Altshuler DM, Durbin RM, Abecasis GR, Bentley DR et al (2015) A global reference for human genetic variation. Nature 526(7571):68–74
doi: 10.1038/nature15393
Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T et al (2016) analysis of protein-coding genetic variation in 60,706 humans. Nature 536(7616):285–291
doi: 10.1038/nature19057
Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM et al (2001) dbSNP: the NCBI database of genetic variation. Nucleic Acids Res 29(1):308–311
doi: 10.1093/nar/29.1.308
gnomAD [Internet]. [cited 2022 Feb 24]. Available from: https://gnomad.broadinstitute.org/
Sim NL, Kumar P, Hu J, Henikoff S, Schneider G, Ng PC (2012) SIFT web server: predicting effects of amino acid substitutions on proteins. Nucleic Acids Res. 40(Web Server Issue):W452-7. https://doi.org/10.1093/nar/gks539
doi: 10.1093/nar/gks539
pubmed: 22689647
pmcid: 3394338
Adzhubei I, Jordan DM, Sunyaev SR (2013) Predicting functional effect of human missense mutations using PolyPhen-2. Curr Protoc Hum Genet Chapter 7:Unit7.20. https://doi.org/10.1002/0471142905.hg0720s76 .
Schwarz JM, Cooper DN, Schuelke M, Seelow D (2014) MutationTaster2: mutation prediction for the deep-sequencing age. Nat Methods 11(4):361–362
doi: 10.1038/nmeth.2890
Chun S, Fay JC (2009) Identification of deleterious mutations within three human genomes. Genome Res 19(9):1553–1561
doi: 10.1101/gr.092619.109
Cosmic. COSMIC - catalogue of somatic mutations in cancer [Internet]. [cited 2022 Feb 24]. Available from: https://cancer.sanger.ac.uk/cosmic
Integrative Genomics Viewer - PMC [Internet]. [cited 2022 Feb 24]. Available from: https://www.ncbi.nlm.nih.gov/labs/pmc/articles/PMC3346182/
Kopanos C, Tsiolkas V, Kouris A, Chapple CE, Albarca Aguilera M, Meyer R et al (2019) VarSome: the human genomic variant search engine. Bioinformatics 35(11):1978–1980
doi: 10.1093/bioinformatics/bty897
Passamonti F, Thiele J, Girodon F, Rumi E, Carobbio A, Gisslinger H et al (2012) A prognostic model to predict survival in 867 World Health Organization–defined essential thrombocythemia at diagnosis: a study by the International Working Group on Myelofibrosis Research and Treatment. Blood 120(6):1197–1201
doi: 10.1182/blood-2012-01-403279
Passamonti F, Cervantes F, Vannucchi AM, Morra E, Rumi E, Pereira A et al (2010) A dynamic prognostic model to predict survival in primary myelofibrosis: a study by the IWG-MRT (International Working Group for Myeloproliferative Neoplasms Research and Treatment). Blood 115(9):1703–1708
doi: 10.1182/blood-2009-09-245837
Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J et al (2015) Standards and guidelines for the interpretation of sequence variants: a Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med Off J Am Coll Med Genet 17(5):405–424
Michail O, McCallion P, McGimpsey J et al (2021) Mutational profiling in suspected triple-negative essential thrombocythaemia using targeted next-generation sequencing in a real-world cohort. J Clin Pathol 74:808–811
doi: 10.1136/jclinpath-2020-206570
Acha P, Xandri M, Fuster-Tormo F, Palomo L, Xicoy B, Cabezón M et al (2019) Diagnostic and prognostic contribution of targeted NGS in patients with triple-negative myeloproliferative neoplasms. Am J Hematol 94(10):E264–E267
doi: 10.1002/ajh.25580
Angona A, Fernández-Rodríguez C, Alvarez-Larrán A, Camacho L, Longarón R, Torres E et al (2016) Molecular characterisation of triple negative essential thrombocythaemia patients by platelet analysis and targeted sequencing. Blood Cancer J 6(8):e463–e463
doi: 10.1038/bcj.2016.75
Alvarez-Larrán A, López-Guerra M, Rozman M, Correa J-G, Hernández-Boluda JC, Tormo M et al (2019) Genomic characterization in triple-negative primary myelofibrosis and other myeloid neoplasms with bone marrow fibrosis. Ann Hematol 98(10):2319–2328
doi: 10.1007/s00277-019-03766-z
Luque Paz D, Riou J, Verger E, Cassinat B, Chauveau A, Ianotto J-C et al (2021) Genomic analysis of primary and secondary myelofibrosis redefines the prognostic impact of ASXL1 mutations: a FIM study. Blood Adv 5(5):1442–1451
doi: 10.1182/bloodadvances.2020003444
Batis H, Almugairi A, Almugren O, Aljabry M, Alqahtani F, Elbashir E et al (2021) Detrimental variants in MPIG6B in two children with myelofibrosis: does immune dysregulation contribute to myelofibrosis? Pediatr Blood Cancer 68(8):e29062
doi: 10.1002/pbc.29062