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Original Article: Research

Distinct transcriptomic and exomic abnormalities within myelodysplastic syndrome marrow cells

, ORCID Icon, , , , , , , , , ORCID Icon, & show all
Pages 2952-2962 | Received 14 Dec 2017, Accepted 10 Mar 2018, Published online: 04 Apr 2018

References

  • Greenberg PL. The multifaceted nature of myelodysplastic syndromes: clinical, molecular, and biological prognostic features. J Natl Compr Canc Netw. 2013;11:877–884.
  • Greenberg PL, Stone RM, Bejar R, et al. Myelodysplastic syndromes, version 2.2015. J Natl Compr Canc Netw. 2015;13:261–272.
  • Greenberg P, Cox C, LeBeau MM, et al. International scoring system for evaluating prognosis in myelodysplastic syndromes. Blood. 1997;89:2079–2088.
  • Greenberg PL, Tuechler H, Schanz J, et al. Revised international prognostic scoring system for myelodysplastic syndromes. Blood. 2012;120:2454–2465.
  • Della Porta MG, Tuechler H, Malcovati L, et al. Validation of WHO classification-based Prognostic Scoring System (WPSS) for myelodysplastic syndromes and comparison with the revised International Prognostic Scoring System (IPSS-R). A study of the International Working Group for Prognosis in Myelodysplasia (IWG-PM). Leukemia. 2015;29:1502–1513.
  • Ebert BL, Golub TR. Genomic approaches to hematologic malignancies. Blood. 2004;104:923–932.
  • Bullinger L, Dohner K, Bair E, et al. Use of gene-expression profiling to identify prognostic subclasses in adult acute myeloid leukemia. N Engl J Med. 2004;350:1605–1616.
  • Marcucci G, Maharry K, Radmacher MD, et al. Prognostic significance of, and gene and microRNA expression signatures associated with, CEBPA mutations in cytogenetically normal acute myeloid leukemia with high-risk molecular features: a Cancer and Leukemia Group B Study. J Clin Oncol. 2008;26:5078–5087.
  • Miyazato A, Ueno S, Ohmine K, et al. Identification of myelodysplastic syndrome-specific genes by DNA microarray analysis with purified hematopoietic stem cell fraction. Blood. 2001;98:422–427.
  • Hofmann WK, de Vos S, Komor M, et al. Characterization of gene expression of CD34+ cells from normal and myelodysplastic bone marrow. Blood. 2002;100:3553–3560.
  • Ueda M, Ota J, Yamashita Y, et al. DNA microarray analysis of stage progression mechanism in myelodysplastic syndrome. Br J Haematol. 2003;123:288–296.
  • Pellagatti A, Cazzola M, Giagounidis AA, et al. Gene expression profiles of CD34+ cells in myelodysplastic syndromes: involvement of interferon-stimulated genes and correlation to FAB subtype and karyotype. Blood. 2006;108:337–345.
  • Pellagatti A, Cazzola M, Giagounidis A, et al. Deregulated gene expression pathways in myelodysplastic syndrome hematopoietic stem cells. Leukemia. 2010;24:756–764.
  • Sridhar K, Ross DT, Tibshirani R, et al. Relationship of differential gene expression profiles in CD34+ myelodysplastic syndrome marrow cells to disease subtype and progression. Blood. 2009;114:4847–4858.
  • Pellagatti A, Benner A, Mills KI, et al. Identification of gene expression-based prognostic markers in the hematopoietic stem cells of patients with myelodysplastic syndromes. J Clin Oncol. 2013;31:3557–3564.
  • Mortazavi A, Williams BA, McCue K, et al. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5:621–628.
  • Pepke S, Wold B, Mortazavi A. Computation for ChIP-seq and RNA-seq studies. Nat Methods. 2009;6:S22–S32.
  • Soon WW, Hariharan M, Snyder MP. High-throughput sequencing for biology and medicine. Mol Syst Biol. 2013;9:640.
  • Joshi P, Halene S, Abdel-Wahab O. How do messenger RNA splicing alterations drive myelodysplasia? Blood. 2017;129:2465–2470.
  • Grech G, Pollacco J, Portelli M, et al. Expression of different functional isoforms in haematopoiesis. Int J Hematol. 2014;99:4–11.
  • Makishima H, Visconte V, Sakaguchi H, et al. Mutations in the spliceosome machinery, a novel and ubiquitous pathway in leukemogenesis. Blood. 2012;119:3203–3210.
  • Crews LA, Balaian L, Delos Santos NP, et al. RNA splicing modulation selectively impairs leukemia stem cell maintenance in secondary human AML. Cell Stem Cell. 2016;19:599–612.
  • Bejar R, Stevenson K, Abdel-Wahab O, et al. Clinical effect of point mutations in myelodysplastic syndromes. N Engl J Med. 2011;364:2496–2506.
  • Yoshida K, Sanada M, Shiraishi Y, et al. Frequent pathway mutations of splicing machinery in myelodysplasia. Nature. 2011;478:64–69.
  • Papaemmanuil E, Gerstung M, Malcovati L, et al. Clinical and biological implications of driver mutations in myelodysplastic syndromes. Blood. 2013;122:3616–3627. quiz3699.
  • Haferlach T, Nagata Y, Grossmann V, et al. Landscape of genetic lesions in 944 patients with myelodysplastic syndromes. Leukemia. 2014;28:241–247.
  • Im H, Rao V, Sridhar KJ, et al. Transcriptomic evaluation of CD34+ marrow cells from myelodysplastic syndrome (MDS) patients. Blood. 2014;124:1894.
  • R Core Team. R: a language and environment for statistical computing. Austria, Vienna: R Foundation for Statistical Computing; 2015.
  • Anders S, Pyl PT, Huber W. HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31:166–169.
  • Nikolayeva O, Robinson MD. edgeR for differential RNA-seq and ChIP-seq analysis: an application to stem cell biology. Methods Mol Biol. 2014;1150:45–79.
  • Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–140.
  • Ritchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47.
  • Leek JT, Storey JD. Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 2007;3:1724–1735.
  • Tibshirani R, Hastie T, Narasimhan B, et al. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci USA. 2002;99:6567–6572.
  • Kwok B, Hall JM, Witte JS, et al. MDS-associated somatic mutations and clonal hematopoiesis are common in idiopathic cytopenias of undetermined significance. Blood. 2015;126:2355–2361.
  • Irizarry RA, Love MI. Rafalib: convenience functions for routine data exploration. R package version 1.0.0. 2015.
  • Dobin A, Davis CA, Schlesinger F, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21.
  • Trapnell C, Roberts A, Goff L, et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 2012;7:562–578.
  • Niu L, Huang W, Umbach DM, et al. IUTA: a tool for effectively detecting differential isoform usage from RNA-Seq data. BMC Genomics. 2014;15:862.
  • Kramer A, Green J, Pollard J Jr, et al. Causal analysis approaches in ingenuity pathway analysis. Bioinformatics. 2014;30:523–530.
  • Reis PP, Waldron L, Goswami RS, et al. mRNA transcript quantification in archival samples using multiplexed, color-coded probes. BMC Biotechnol. 2011;11:46.
  • Vukmirovic M, Herazo-Maya JD, Blackmon J, et al. Identification and validation of differentially expressed transcripts by RNA-sequencing of formalin-fixed, paraffin-embedded (FFPE) lung tissue from patients with idiopathic pulmonary fibrosis. BMC Pulm Med. 2017;17:15.
  • Boultwood J, Pellagatti A, Cattan H, et al. Gene expression profiling of CD34+ cells in patients with the 5q-syndrome. Br J Haematol. 2007;139:578–589.
  • Majeti R, Becker MW, Tian Q, et al. Dysregulated gene expression networks in human acute myelogenous leukemia stem cells. Proc Natl Acad Sci USA. 2009;106:3396–3401.
  • Ng SW, Mitchell A, Kennedy JA, et al. A 17-gene stemness score for rapid determination of risk in acute leukaemia. Nature. 2016;540:433–437.
  • Mills KI, Kohlmann A, Williams PM, et al. Microarray-based classifiers and prognosis models identify subgroups with distinct clinical outcomes and high risk of AML transformation of myelodysplastic syndrome. Blood. 2009;114:1063–1072.
  • Notta F, Doulatov S, Laurenti E, et al. Isolation of single human hematopoietic stem cells capable of long-term multilineage engraftment. Science. 2011;333:218–221.
  • Woll PS, Kjallquist U, Chowdhury O, et al. Myelodysplastic syndromes are propagated by rare and distinct human cancer stem cells in vivo. Cancer Cell. 2014;25:794–808.
  • Jan M, Snyder TM, Corces-Zimmerman MR, et al. Clonal evolution of preleukemic hematopoietic stem cells precedes human acute myeloid leukemia. Sci Transl Med. 2012;4:149ra118.
  • Walter MJ, Shen D, Ding L, et al. Clonal architecture of secondary acute myeloid leukemia. N Engl J Med. 2012;366:1090–1098.
  • Gerstung M, Pellagatti A, Malcovati L, et al. Combining gene mutation with gene expression data improves outcome prediction in myelodysplastic syndromes. Nat Commun. 2015;6:5901.
  • Shiozawa Y, Malcovati L, Galli A, et al. Gene expression and risk of leukemic transformation in myelodysplasia. Blood. 2017;130:2642–2653.
  • Kramarzova K, Stuchly J, Willasch A, et al. Real-time PCR quantification of major Wilms’ tumor gene 1 (WT1) isoforms in acute myeloid leukemia, their characteristic expression patterns and possible functional consequences. Leukemia. 2012;26:2086–2095.
  • Yip BH, Dolatshad H, Roy S, et al. Impact of splicing factor mutations on pre-mRNA splicing in the myelodysplastic syndromes. Curr Pharm Des. 2016;22:2333–2344.
  • Wang X, Hou J, Quedenau C, et al. Pervasive isoform-specific translational regulation via alternative transcription start sites in mammals. Mol Syst Biol. 2016;12:875.
  • Lee Y, Rio DC. Mechanisms and regulation of alternative pre-mRNA splicing. Annu Rev Biochem. 2015;84:291–323.
  • Sakurai H, Harada Y, Ogata Y, et al. Overexpression of RUNX1 short isoform has an important role in the development of myelodysplastic/myeloproliferative neoplasms. Blood Adv. 2017;1:1382–1386.
  • Hopfer O, Komor M, Koehler IS, et al. Aberrant promotor methylation in MDS hematopoietic cells during in vitro lineage specific differentiation is differently associated with DNMT isoforms. Leuk Res. 2009;33:434–442.
  • Dolatshad H, Pellagatti A, Fernandez-Mercado 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;29:1798.
  • Qiu J, Zhou B, Thol F, et al. Distinct splicing signatures affect converged pathways in myelodysplastic syndrome patients carrying mutations in different splicing regulators. RNA. 2016;22:1535–1549.

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