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Acute myeloid leukemia

Clinical presentation and differential splicing of SRSF2, U2AF1 and SF3B1 mutations in patients with acute myeloid leukemia

Abstract

Previous studies demonstrated that splicing factor mutations are recurrent events in hematopoietic malignancies with both clinical and functional implications. However, their aberrant splicing patterns in acute myeloid leukemia remain largely unexplored. In this study, we characterized mutations in SRSF2, U2AF1, and SF3B1, the most commonly mutated splicing factors. In our clinical analysis of 2678 patients, splicing factor mutations showed inferior relapse-free and overall survival, however, these mutations did not represent independent prognostic markers. RNA-sequencing of 246 and independent validation in 177 patients revealed an isoform expression profile which is highly characteristic for each individual mutation, with several isoforms showing a strong dysregulation. By establishing a custom differential splice junction usage pipeline, we accurately detected aberrant splicing in splicing factor mutated samples. A large proportion of differentially used junctions were novel, including several junctions in leukemia-associated genes. In SRSF2(P95H) mutants, we further explored the possibility of a cascading effect through the dysregulation of the splicing pathway. Furthermore, we observed a validated impact on overall survival for two junctions overused in SRSF2(P95H) mutants. We conclude that splicing factor mutations do not represent independent prognostic markers. However, they do have genome-wide consequences on gene splicing leading to dysregulated isoform expression of several genes.

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Fig. 1: Frequency and location of SF mutations.
Fig. 2: Correlations between SF mutations and recurrent abnormalities.
Fig. 3: Multiple Cox regression models for overall survival.
Fig. 4: Differential isoform expression analysis in the AMLCG cohort.
Fig. 5: Differential splice junction usage.
Fig. 6: Splicing dysregulation in SRSF2 mutants.
Fig. 7: Impact of differential splice junction usage on overall survival.

Data availability

Read counts and sample characteristics are available in the GEO database (GSE146173). Law restrictions prohibit us from publicly sharing raw sequencing data, which however can be made available upon reasonable request and permission of the local ethics committee.

References

  1. 1.

    Papaemmanuil E, Gerstung M, Malcovati L, Tauro S, Gundem G, Van Loo P, et al. Clinical and biological implications of driver mutations in myelodysplastic syndromes. Blood 2013;122:3616–27.

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Metzeler KH, Herold T, Rothenberg-Thurley M, Amler S, Sauerland MC, Görlich D, et al. Spectrum and prognostic relevance of driver gene mutations in acute myeloid leukemia. Blood 2016;128:686–98.

    CAS  PubMed  Google Scholar 

  3. 3.

    Makishima H, Visconte V, Sakaguchi H, Jankowska AM, Kar SA, Jerez A, et al. Mutations in the spliceosome machinery, a novel and ubiquitous pathway in leukemogenesis. Blood 2012;119:3203–10.

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Larsson CA, Cote G, Quintás-Cardama A. The changing mutational landscape of acute myeloid leukemia and myelodysplastic syndrome. Mol Cancer Res. 2013;11:815–27.

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Yoshida K, Sanada M, Shiraishi Y, Nowak D, Nagata Y, Yamamoto R, et al. Frequent pathway mutations of splicing machinery in myelodysplasia. Nature 2011;478:64–9.

    CAS  PubMed  Google Scholar 

  6. 6.

    Thol F, Kade S, Schlarmann C, Löffeld P, Morgan M, Krauter J, et al. Frequency and prognostic impact of mutations in SRSF2, U2AF1, and ZRSR2 in patients with myelodysplastic syndromes. Blood 2012;119:3578–84.

    CAS  PubMed  Google Scholar 

  7. 7.

    Dolatshad H, Pellagatti A, Fernandez-Mercado M, Yip BH, Malcovati L, Attwood M, et al. Disruption of SF3B1 results in deregulated expression and splicing of key genes and pathways in myelodysplastic syndrome hematopoietic stem and progenitor cells. Leukemia 2015;29:1092–103.

    CAS  PubMed  Google Scholar 

  8. 8.

    Wu S, Kuo Y, Hou H, Li L, Tseng M, Huang C, et al. The clinical implication of SRSF2 mutation in patients with myelodysplastic syndrome and its stability during disease evolution. Blood 2014;120:3106–12.

    Google Scholar 

  9. 9.

    Graubert TA, Shen D, Ding L, Okeyo-owuor T, Cara L, Shao J, et al. Recurrent mutations in the U2AF1 splicing factor in myelodysplastic syndromes. Nat Genet 2012;44:53–7.

    CAS  Google Scholar 

  10. 10.

    Hou H-A, Liu C-Y, Kuo Y-Y, Chou W-C, Tsai C-H, Lin C-C, et al. Splicing factor mutations predict poor prognosis in patients with de novo acute myeloid leukemia. Oncotarget 2016;7:9084–101.

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Cho Y-U, Jang S, Seo E-J, Park C-J, Chi H-S, Kim D-Y, et al. Preferential occurrence of spliceosome mutations in acute myeloid leukemia with a preceding myelodysplastic syndrome and/or myelodysplasia morphology. Leuk Lymphoma 2014;8194:1–25.

    Google Scholar 

  12. 12.

    Moon H, Cho S, Loh TJ, Jang HN, Liu Y, Choi N, et al. SRSF2 directly inhibits intron splicing to suppresses cassette exon inclusion. BMB Rep. 2017;50:423–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Kim E, Ilagan JO, Liang Y, Daubner GM, Lee SCW, Ramakrishnan A, et al. SRSF2 mutations contribute to myelodysplasia by mutant-specific effects on exon recognition. Cancer Cell 2015;27:617–30.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Alsafadi S, Houy A, Battistella A, Popova T, Wassef M, Henry E, et al. Cancer-associated SF3B1 mutations affect alternative splicing by promoting alternative branchpoint usage. Nat Commun 2016;7:10615.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Okeyo-Owuor T, White BS, Chatrikhi R, Mohan DR, Kim S, Griffith M, et al. U2AF1 mutations alter sequence specificity of pre-mRNA binding and splicing. Leukemia 2015;29:909–17.

    CAS  PubMed  Google Scholar 

  16. 16.

    Przychodzen B, Jerez A, Guinta K, Sekeres MA, Padgett R, Maciejewski JP, et al. Patterns of missplicing due to somatic U2AF1 mutations in myeloid neoplasms. Blood 2013;122:999–1006.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Shirai CL, Ley JN, White BS, Kim S, Tibbitts J, Shao J, et al. Mutant U2AF1 expression alters hematopoiesis and pre-mRNA splicing in vivo. Cancer Cell 2015;27:631–43.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Yang J, Yao D, Ma J, Yang L, Guo H, Wen X, et al. The prognostic implication of SRSF2 mutations in Chinese patients with acute myeloid leukemia. Tumor Biol 2016;37:10107–14.

    CAS  Google Scholar 

  19. 19.

    Papaemmanuil E, Gerstung M, Bullinger L, Gaidzik VI, Paschka P, Roberts ND, et al. Genomic classification and prognosis in acute myeloid leukemia. N. Engl J Med. 2016;374:2209–21.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Tyner JW, Tognon CE, Bottomly D, Wilmot B, Kurtz SE, Savage SL, et al. Functional genomic landscape of acute myeloid leukaemia. Nature 2018;562:526–31.

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 2013;29:15–21.

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods 2017;14:417–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47–e47.

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 2010;11:R25.

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Robinson MD, McCarthy DJ, Smyth GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2009;26:139–40.

    PubMed  PubMed Central  Google Scholar 

  26. 26.

    Liu R, Holik AZ, Su S, Jansz N, Chen K, Leong HSan, et al. Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses. Nucleic Acids Res. 2015;43:e97.

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    Law CW, Chen Y, Shi W, Smyth GK. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol 2014;15:R29.

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    Anders S, Reyes A, Huber W. Detecting differential usage of exons from RNA-seq data. Genome Res 2012;22:2008–17.

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Tang AD, Soulette CM, van Baren MJ, Hart K, Hrabeta-Robinson E, Wu CJ. et al. Full-length transcript characterization of SF3B1 mutation in chronic lymphocytic leukemia reveals downregulation of retained introns. Nat Commun. 2020;11:1–12.

    Google Scholar 

  30. 30.

    R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. 2017.

  31. 31.

    Herold T, Metzeler KH, Vosberg S, Hartmann L, Ollig C, Olzel FS. et al. Isolated trisomy 13 defines a homogeneous AML subgroup with high frequency of mutations in spliceosome genes and poor prognosis. Blood J Am Soc Hematol 2014;124:1304–11.

  32. 32.

    Chen L, Chen J-Y, Huang Y-J, Gu Y, Qiu J, Qian H, et al. The augmented R-loop is a unifying mechanism for myelodysplastic syndromes induced by high-risk splicing factor mutations. Mol Cell 2018;69:412–25.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Yoshimi A, Lin K-T, Wiseman DH, Rahman MA, Pastore A, Wang B, et al. Coordinated alterations in RNA splicing and epigenetic regulation drive leukaemogenesis. Nature 2019;574:273–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Roe J-S, Vakoc CR. The essential transcriptional function of BRD4 in acute myeloid leukemia. Cold Spring Harb Symp Quant Biol. 2016;81:61–6.

    PubMed  Google Scholar 

  35. 35.

    Endo A, Tomizawa D, Aoki Y, Morio T, Mizutani S, Takagi M. EWSR1/ELF5 induces acute myeloid leukemia by inhibiting p53/p21 pathway. Cancer Sci 2016;107:1745–54.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Perner F, Jayavelu AK, Schnoeder TM, Mashamba N, Mohr J, Hartmann M, et al. The cold-shock protein Ybx1 is required for development and maintenance of acute myeloid leukemia (AML) in vitro and in vivo. Blood. 2017;130:792.

  37. 37.

    McNerney ME, Brown CD, Wang X, Bartom ET, Karmakar S, Bandlamudi C, et al. CUX1 is a haploinsufficient tumor suppressor gene on chromosome 7 frequently inactivated in acute myeloid leukemia. Blood 2013;121:975–83.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    McGarvey T, Rosonina E, McCracken S, Li Q, Arnaout R, Mientjes E, et al. The acute myeloid leukemia-associated protein, DEK, forms a splicing-dependent interaction with exon-product complexes. J Cell Biol. 2000;150:309–20.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Fujita S, Honma D, Adachi N, Araki K, Takamatsu E, Katsumoto T, et al. Dual inhibition of EZH1/2 breaks the quiescence of leukemia stem cells in acute myeloid leukemia. Leukemia 2018;32:855–64.

    CAS  PubMed  Google Scholar 

  40. 40.

    Pallarès V, Hoyos M, Chillón MC, Barragán E, Prieto Conde MI, Llop M, et al. Focal adhesion genes refine the intermediate-risk cytogenetic classification of acute myeloid leukemia. Cancers. 2018;10:E436.

    PubMed  Google Scholar 

  41. 41.

    Shiozawa Y, Malcovati L, Gallì A, Sato-Otsubo A, Kataoka K, Sato Y, et al. Aberrant splicing and defective mRNA production induced by somatic spliceosome mutations in myelodysplasia. Nat Commun 2018;9:3649.

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Pellagatti A, Armstrong RN, Steeples V, Sharma E, Repapi E, Singh S, et al. Impact of spliceosome mutations on RNA splicing in myelodysplasia: dysregulated genes/pathways and clinical associations. Blood 2018;132:1225–40.

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Skrdlant L, Stark JM, Lin R-J. Myelodysplasia-associated mutations in serine/arginine-rich splicing factor SRSF2 lead to alternative splicing of CDC25C. BMC Mol Biol. 2016;17:18.

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Lemieux B, Blanchette M, Monette A, Mouland AJ, Wellinger RJ, Chabot B. A function for the hnRNP A1/A2 proteins in transcription elongation. PLoS ONE. 2015;10:e0126654.

  45. 45.

    Abelson S, Collord G, Ng SWK, Weissbrod O, Mendelson Cohen N, Niemeyer E, et al. Prediction of acute myeloid leukaemia risk in healthy individuals. Nature 2018;559:400–4.

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Malcovati L, Papaemmanuil E, Bowen DT, Boultwood J, Della Porta MG, Pascutto C, et al. Clinical significance of SF3B1 mutations in myelodysplastic syndromes and myelodysplastic/myeloproliferative neoplasms. Blood 2011;118:6239–46.

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Papaemmanuil E, Cazzola M, Boultwood J, Malcovati L, Vyas P, Bowen D, et al. Somatic SF3B1 mutation in myelodysplasia with ring sideroblasts. N. Engl J Med. 2011;365:1384–95.

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Wu L, Song L, Xu L, Chang C, Xu F, Wu D, et al. Genetic landscape of recurrent ASXL1, U2AF1, SF3B1, SRSF2, and EZH2 mutations in 304 Chinese patients with myelodysplastic syndromes. Tumor Biol 2016;37:4633–40.

    CAS  Google Scholar 

  49. 49.

    Zhang S-J, Rampal R, Manshouri T, Patel J, Mensah N, Kayserian A, et al. Genetic analysis of patients with leukemic transformation of myeloproliferative neoplasms shows recurrent SRSF2 mutations that are associated with adverse outcome. Blood 2012;119:4480–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Döhner H, Estey E, Grimwade D, Amadori S, Appelbaum FR, Büchner T, et al. Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood. 2017;129:424–47.

  51. 51.

    Anande G, Deshpande NP, Mareschal S, Batcha AMN, Hampton HR, Herold T, et al. RNA splicing alterations induce a cellular stress response associated with poor prognosis in AML. bioRxiv. 2020;2020.01.10.895714.

  52. 52.

    Liang Y, Tebaldi T, Rejeski K, Joshi P, Stefani G, Taylor A, et al. SRSF2 mutations drive oncogenesis by activating a global program of aberrant alternative splicing in hematopoietic cells. Leukemia 2018;32:2659–71.

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Lee SCW, Abdel-Wahab O. Therapeutic targeting of splicing in cancer [Internet]. Nat Med NIH Public Access. 2016;22:976–86.

    CAS  Google Scholar 

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Acknowledgements

The authors thank all participants and recruiting centers of the AMLCG, BEAT and AMLSG trials.

Funding

This work is supported by a grant of the Wilhelm-Sander-Stiftung (no. 2013.086.2) and the Physician Scientists Grant (G-509200–004) from the Helmholtz Zentrum München to T.H. and the German Cancer Consortium (Deutsches Konsortium für Translationale Krebsforschung, Heidelberg, Germany). K.H.M., K.S., and T.H. are supported by a grant from Deutsche Forschungsgemeinschaft (DFG SFB 1243, TP A06 and TP A07). S.K.B. is supported by Leukaemia & Blood Cancer New Zealand and the family of Marijanna Kumerich. A.M.N.B. is supported by the BMBF grant 01ZZ1804B (DIFUTURE).

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S.A.B., A.M.N.B., and T.H. conceived and designed the analysis. S.A.B., A.M.N.B., V.J., M.R.-T., H.J., A.G., S.C., N.K., K.S., K.H.M., and T.H. provided and analyzed data. A.M.N.B., V.J., and U.M. provided bioinformatics support. J.P.-M., S.K., and H.B. managed the HiSeq 1500 instrument and the RNA-sequencing of the AMLCG samples. M.R.-T., H.J., B.K., S.S., N.K., S.K.B., K.H.M., and K.S. characterized patient samples; M.C.S., D.G., W.B., B.W., J.B., and W.H. coordinated the AMLCG clinical trials. S.A.B. and T.H. wrote the manuscript. All authors approved the final manuscript.

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Correspondence to Stefanos A. Bamopoulos or Tobias Herold.

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H.J. is a current employee of Roche Pharma AG, Grenzach-Wyhlen, Germany. The authors declare that they have no conflict of interest.

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Bamopoulos, S.A., Batcha, A.M.N., Jurinovic, V. et al. Clinical presentation and differential splicing of SRSF2, U2AF1 and SF3B1 mutations in patients with acute myeloid leukemia. Leukemia 34, 2621–2634 (2020). https://doi.org/10.1038/s41375-020-0839-4

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