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aberrant splicing was of particular interest.34,42 Patients that have the less aggressive t(11;14) and have high novel splic- ing had no change in their OS. In the t(4;14) with high novel splicing (top 20%), the OS is significantly poorer, suggesting that there is an ultra-high risk group of t(4;14) with increased alternative splicing which showed some association with high-risk features such as LOH and dou- ble-hit. However, in the case of OS, it was the increased splicing which was the most significant. Although increased splicing is not exclusively associated with aggressive disease, these results provide evidence that changes to a cell's splicing as either a mechanism or result of other adverse features may suggest a more aggressive phenotype that may need to be treated differently.11
We showed that alternative splicing might be a signifi- cant mechanism that has pathogenic relevance to MM. Hotspot mutations in the driver gene, SF3B1, result in a sig- nificant impact on the MM transcriptome. Although SF3B1 mutations represent a small percentage of driver mutations, it does present a promising target for splice modulating drugs. In addition to the hotspot SF3B1 mutations, we also show a general increase in novel splicing in MM, and at its extreme, this is strongly associated with decreased PFS and OS. There are a number of spliceosome modulators under investigation including spliceostatins A-G, E7107 and H3B-8800, that target SF3B1.43,44 Recent work has shown that these compounds are more effective in cells that have higher expression of SF3B1 and are, there- fore, more dependent on SF3B1.45 Our observation of increased SF3B1 expression in the high splice group, which constitutes up to 20% of patients, may increase the number
of patients that may benefit from spliceosome modulators, rather than just the patients who have mutated SF3B1.
The results of the study make it clear that only looking at gene, or even transcript expression, can obscure impor- tant changes in the transcriptome. Alternative splicing may be an important pathogenic disease mechanism in MM that affects a wide range of important pathways. Additional studies of the MM transcriptome may provide important insights into the disease pathogenesis.
Disclosures
Celgene Corporation: Employment, Equity Ownership: MO, EF, AT.
Contributions
MB performed research; MB, CA and CW performed data analysis; MB took the lead in writing the manuscript; CA, CW, EB, MO, EF, AT, GM, BW provided critical feedback and helped shape the research, analysis and manuscript; BW super- vised the study.
Acknowledgments
Funding support for the CoMMpass dataset was provided by the Myeloma Genome Project. The CoMMpass dataset was generated by the Multiple Myeloma Research Foundation in col- laboration with the Multiple Myeloma Research Consortium.
Funding
Funding for data processing and storage was provided by Celgene Corporation. Other authors declare no competing inter- ests.
References
1. Walker BA, Mavrommatis K, Wardell CP, et al. Identification of novel mutational drivers reveals oncogene dependencies in multiple myeloma. Blood. 2018;132(6):587-597.
2. Prideaux SM, O’Brien EC, Chevassut TJ. The genetic architecture of multiple myelo- ma. Adv Hematol. 2014;2014:864058.
3.Lee SC-W, Dvinge H, Kim E, et al. Modulation of splicing catalysis for thera- peutic targeting of leukemia with mutations in genes encoding spliceosomal proteins. Nat Med. 2016;22(6):672-678.
4. Urbanski LM, Leclair N, Anczuków O. Alternative-splicing defects in cancer: splic- ing regulators and their downstream targets, guiding the way to novel cancer therapeu- tics. Wiley Interdiscip Rev RNA. 2018; 9(4):e1476.
5. Li YI, van de Geijn B, Raj A, et al. RNA splic- ing is a primary link between genetic varia- tion and disease. Science. 2016; 352(6285): 600-604.
6. Climente-González H, Porta-Pardo E, Godzik A, Eyras E. The functional impact of alternative splicing in cancer. Cell Rep. 2017; 20(9):2215-2226.
7. Sebestyén E, Singh B, Miñana B, et al. Large- scale analysis of genome and transcriptome alterations in multiple tumors unveils novel cancer-relevant splicing networks. Genome Res. 2016;26(6):732-744.
8. Lee Y, Rio DC. Mechanisms and regulation of alternative pre-mRNA splicing. Annu Rev Biochem. 2015;84(1):291-323.
9. Corvelo A, Hallegger M, Smith CWJ, Eyras
E. Genome-wide association between branch point properties and alternative splic- ing. PLoS Comput Biol. 2010; 6(11):e1001016.
10. Lin P-C, Xu R-M. Structure and assembly of the SF3a splicing factor complex of U2 snRNP. EMBO J. 2012;31(6):1579-1590.
11. Wang L, Brooks AN, Fan J, et al. Transcriptomic characterization of SF3B1 mutation reveals its pleiotropic effects in chronic lymphocytic leukemia. Cancer Cell. 2016;30(5):750-763.
12. DeBoever C, Ghia EM, Shepard PJ, et al. Transcriptome sequencing reveals potential mechanism of cryptic 3’ splice site selection in SF3B1-mutated cancers. PLOS Comput Biol. 2015;11(3):e1004105.
13. Dolatshad H, Pellagatti A, Fernandez- Mercado M, et al. Disruption of SF3B1 results in deregulated expression and splic- ing of key genes and pathways in myelodys- plastic syndrome hematopoietic stem and progenitor cells. Leukemia. 2015;29(5): 1092-1103.
14. Inoue D, Abdel-Wahab O. Modeling SF3B1 mutations in cancer: advances, challenges, and opportunities. Cancer Cell. 2016;30(3): 371-373.
15.Furney SJ, Pedersen M, Gentien D, et al. SF3B1 mutations are associated with alter- native splicing in uveal melanoma. Cancer Discov. 2013;3(10):1122-1129.
16. Maguire SL, Leonidou A, Wai P, et al. SF3B1 mutations constitute a novel therapeutic tar- get in breast cancer. J Pathol. 2015; 235(4):571-580.
17. Kesarwani AK, Ramirez O, Gupta AK, et al. Cancer-associated SF3B1 mutants recognize otherwise inaccessible cryptic 3’ splice sites
within RNA secondary structures.
Oncogene. 2017;36(8):1123-1133. 18.Alsafadi S, Houy A, Battistella A, et al. Cancer-associated SF3B1 mutations affect alternative splicing by promoting alternative branchpoint usage. Nat Commun. 2016;
7:10615.
19. Walker BA, Mavrommatis K, Wardell CP, et
al. A high-risk, double-hit, group of newly diagnosed myeloma identified by genomic analysis. Leukemia. 2019;33(1):159-170.
20.Dobin A, Davis CA, Schlesinger F, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15-21.
21. Patro R, Duggal G, Kingsford C. Salmon: accurate, versatile and ultrafast quantifica- tion from RNA-seq data using lightweight- alignment. bioRxiv. 2015;021592.
22. Hartley SW, Mullikin JC. QoRTs: a compre- hensive toolset for quality control and data processing of RNA-Seq experiments. BMC Bioinformatics. 2015;16(1):224.
23. Hartley SW, Mullikin JC. Detection and visualization of differential splicing in RNA- Seq data with JunctionSeq. Nucleic Acids Res. 2016;44(15):e127.
24. Reyes A, Anders S, Huber W. Analyzing RNA-seq data for differential exon usage with the DEXSeq package. Gastroenterology. 2011;138(3):958-968.
25. Alamancos GP, Pagès A, Trincado JL, Bellora N, Eyras E. SUPPA: a super-fast pipeline for alternative splicing analysis from RNA-Seq. bioRxiv. 2014;008763.
26. Zhao J, Song X, Wang K. lncScore: align- ment-free identification of long noncoding RNA from assembled novel transcripts. Sci Rep. 2016;6(1):34838.
27. Riese DJ, Cullum RL. Epiregulin: roles in
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