Comprehensive Analysis of Transcriptome Variation Uncovers Known and Novel Driver Events in T-Cell Acute Lymphoblastic Leukemia

English version České info

RNA-seq is a promising technology to re-sequence protein coding genes for the identification of single nucleotide variants (SNV), while simultaneously obtaining information on structural variations and gene expression perturbations. We asked whether RNA-seq is suitable for the detection of driver mutations in T-cell acute lymphoblastic leukemia (T-ALL). These leukemias are caused by a combination of gene fusions, over-expression of transcription factors and cooperative point mutations in oncogenes and tumor suppressor genes. We analyzed 31 T-ALL patient samples and 18 T-ALL cell lines by high-coverage paired-end RNA-seq. First, we optimized the detection of SNVs in RNA-seq data by comparing the results with exome re-sequencing data. We identified known driver genes with recurrent protein altering variations, as well as several new candidates including H3F3A, PTK2B, and STAT5B. Next, we determined accurate gene expression levels from the RNA-seq data through normalizations and batch effect removal, and used these to classify patients into T-ALL subtypes. Finally, we detected gene fusions, of which several can explain the over-expression of key driver genes such as TLX1, PLAG1, LMO1, or NKX2-1; and others result in novel fusion transcripts encoding activated kinases (SSBP2-FER and TPM3-JAK2) or involving MLLT10. In conclusion, we present novel analysis pipelines for variant calling, variant filtering, and expression normalization on RNA-seq data, and successfully applied these for the detection of translocations, point mutations, INDELs, exon-skipping events, and expression perturbations in T-ALL.

Vyšlo v časopise: Comprehensive Analysis of Transcriptome Variation Uncovers Known and Novel Driver Events in T-Cell Acute Lymphoblastic Leukemia. PLoS Genet 9(12): e32767. doi:10.1371/journal.pgen.1003997
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1003997

Souhrn

Zdroje

1. PietersR, CarrollWL (2008) Biology and treatment of acute lymphoblastic leukemia. Pediatr Clin North Am 55: 1–20–ix doi:10.1016/j.pcl.2007.11.002

2. van VlierbergheP, FerrandoA (2012) The molecular basis of T cell acute lymphoblastic leukemia. J Clin Invest 122: 3398–3406 doi:10.1172/JCI61269

3. GrauxC, CoolsJ, MichauxL, VandenbergheP, HagemeijerA (2006) Cytogenetics and molecular genetics of T-cell acute lymphoblastic leukemia: from thymocyte to lymphoblast. Leukemia 20: 1496–1510 doi:10.1038/sj.leu.2404302

4. Le NoirS, Ben AbdelaliR, LelorchM, BergeronJ, SungaleeS, et al. (2012) Extensive molecular mapping of TCRα/δ- and TCRβ-involved chromosomal translocations reveals distinct mechanisms of oncogene activation in T-ALL. Blood 120: 3298–3309 doi:10.1182/blood-2012-04-425488

5. Van VlierbergheP, HommingaI, ZuurbierL, Gladdines-BuijsJ, van WeringER, et al. (2008) Cooperative genetic defects in TLX3 rearranged pediatric T-ALL. Leukemia 22: 762–770 doi:10.1038/sj.leu.2405082

6. FerrandoAA, NeubergDS, StauntonJ, LohML, HuardC, et al. (2002) Gene expression signatures define novel oncogenic pathways in T cell acute lymphoblastic leukemia. Cancer Cell 1: 75–87.

7. SulongS, MoormanAV, IrvingJAE, StreffordJC, KonnZJ, et al. (2009) A comprehensive analysis of the CDKN2A gene in childhood acute lymphoblastic leukemia reveals genomic deletion, copy number neutral loss of heterozygosity, and association with specific cytogenetic subgroups. Blood 113: 100–107 doi:10.1182/blood-2008-07-166801

8. LahortigaI, de KeersmaeckerK, van VlierbergheP, GrauxC, CauwelierB, et al. (2007) Duplication of the MYB oncogene in T cell acute lymphoblastic leukemia. Nat Genet 39: 593–595 doi:10.1038/ng2025

9. GrauxC, CoolsJ, MelotteC, QuentmeierH, FerrandoA, et al. (2004) Fusion of NUP214 to ABL1 on amplified episomes in T-cell acute lymphoblastic leukemia. Nat Genet 36: 1084–1089 doi:10.1038/ng1425

10. WengAP, FerrandoAA, LeeW, MorrisJP, SilvermanLB, et al. (2004) Activating mutations of NOTCH1 in human T cell acute lymphoblastic leukemia. Science 306: 269–271 doi:10.1126/science.1102160

11. ShochatC, TalN, BandapalliOR, PalmiC, GanmoreI, et al. (2011) Gain-of-function mutations in interleukin-7 receptor-α (IL7R) in childhood acute lymphoblastic leukemias. Journal of Experimental Medicine 208: 901–908 doi:10.1084/jem.20110580

12. ZenattiPP, RibeiroD, LiW, ZuurbierL, SilvaMC, et al. (2011) Oncogenic IL7R gain-of-function mutations in childhood T-cell acute lymphoblastic leukemia. Nat Genet 43: 932–939 doi:10.1038/ng.924

13. ZhangJ, DingL, HolmfeldtL, WuG, HeatleySL, et al. (2012) The genetic basis of early T-cell precursor acute lymphoblastic leukaemia. Nature 481: 157–163 doi:10.1038/nature10725

14. Kalender AtakZ, de KeersmaeckerK, GianfeliciV, GeerdensE, VandepoelR, et al. (2012) High accuracy mutation detection in leukemia on a selected panel of cancer genes. PLoS ONE 7: e38463 doi:10.1371/journal.pone.0038463

15. BainsT, HeinrichMC, LoriauxMM, BeadlingC, NelsonD, et al. (2012) Newly described activating JAK3 mutations in T-cell acute lymphoblastic leukemia. Leukemia 26: 2144–2146 doi:10.1038/leu.2012.74

16. ElliottNE, ClevelandSM, GrannV, JanikJ, WaldmannTA, et al. (2011) FERM domain mutations induce gain of function in JAK3 in adult T-cell leukemia/lymphoma. Blood 118: 3911–3921 doi:10.1182/blood-2010-12-319467

17. de KeersmaeckerK, AtakZK, LiN, VicenteC, PatchettS, et al. (2013) Exome sequencing identifies mutation in CNOT3 and ribosomal genes RPL5 and RPL10 in T-cell acute lymphoblastic leukemia. Nat Genet 45: 186–190 doi:10.1038/ng.2508

18. FlexE, PetrangeliV, StellaL, ChiarettiS, HornakovaT, et al. (2008) Somatically acquired JAK1 mutations in adult acute lymphoblastic leukemia. Journal of Experimental Medicine 205: 751–758 doi:10.1158/1078-0432.CCR-05-2832

19. PorcuM, KleppeM, GianfeliciV, GeerdensE, de KeersmaeckerK, et al. (2012) Mutation of the receptor tyrosine phosphatase PTPRC (CD45) in T-cell acute lymphoblastic leukemia. Blood 119: 4476–4479 doi:10.1182/blood-2011-09-379958

20. MeyersonM, GabrielS, GetzG (2010) Advances in understanding cancer genomes through second-generation sequencing. Nat Rev Genet 11: 685–696 doi:10.1038/nrg2841

21. van VlierbergheP, PalomeroT, KhiabanianH, van der MeulenJ, CastilloM, et al. (2010) PHF6 mutations in T-cell acute lymphoblastic leukemia. Nat Genet 42: 338–342 doi:10.1038/ng.542

22. LiH, DurbinR (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25: 1754–1760 doi:10.1093/bioinformatics/btp324

23. DePristoMA, BanksE, PoplinR, GarimellaKV, MaguireJR, et al. (2011) A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet 43: 491–8 doi:10.1038/ng.806

24. LarsonDE, HarrisCC, ChenK, KoboldtDC, AbbottTE, et al. (2011) SomaticSniper: Identification of Somatic Point Mutations in Whole Genome Sequencing Data. Bioinformatics 28: 311–7 doi:10.1093/bioinformatics/btr665

25. McLarenW, PritchardB, RiosD, ChenY, FlicekP, et al. (2010) Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics 26: 2069–2070 doi:10.1093/bioinformatics/btq330

26. KimD, PerteaG, TrapnellC, PimentelH, KelleyR, et al. (2013) TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol 14: R36 doi:10.1186/gb-2013-14-4-r36

27. LiH, HandsakerB, WysokerA, FennellT, RuanJ, et al. (2009) The Sequence Alignment/Map format and SAMtools. Bioinformatics 25: 2078–2079 doi:10.1093/bioinformatics/btp352

28. BassB, HundleyH, LiJB, PengZ, PickrellJ, et al. (2012) The difficult calls in RNA editing. Interviewed by H Craig Mak. Nature Biotechnology 30: 1207–1209 doi:10.1038/nbt.2452

29. GarberM, GrabherrMG, GuttmanM, TrapnellC (2011) Computational methods for transcriptome annotation and quantification using RNA-seq. Nature Methods 8: 469–477 doi:10.1038/nmeth.1613

30. ShahSP, RothA, GoyaR, OloumiA, HaG, et al. (2012) The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature 486: 395–399 doi:10.1038/nature10933

31. MorinRD, Mendez-LagoM, MungallAJ, GoyaR, MungallKL, et al. (2011) Frequent mutation of histone-modifying genes in non-Hodgkin lymphoma. Nature 476: 298–303 doi:10.1038/nature10351

32. KentWJ (2002) BLAT—The BLAST-Like Alignment Tool. Genome Res 12: 656–664 doi:10.1101/gr.229202

33. SherryST, WardMH, KholodovM, BakerJ, PhanL, et al. (2001) dbSNP: the NCBI database of genetic variation. Nucleic Acids Res 29: 308–311.

34. Genomes Project Consortium (2012) AbecasisGR, AutonA, BrooksLD, DePristoMA, et al. (2012) An integrated map of genetic variation from 1,092 human genomes. Nature 491: 56–65 doi:10.1038/nature11632

35. DrmanacR, SparksAB, CallowMJ, HalpernAL, BurnsNL, et al. (2010) Human genome sequencing using unchained base reads on self-assembling DNA nanoarrays. Science 327: 78–81 doi:10.1126/science.1181498

36. ForbesSA, BhamraG, BamfordS, DawsonE, KokC, et al. (2008) The Catalogue of Somatic Mutations in Cancer (COSMIC). Curr Protoc Hum Genet Chapter 10: Unit10.11 doi:10.1002/0471142905.hg1011s57

37. VogelsteinB, PapadopoulosN, VelculescuVE, ZhouS, DiazLA, et al. (2013) Cancer genome landscapes. Science 339: 1546–1558 doi:10.1126/science.1235122

38. SturmD, WittH, HovestadtV, Khuong-QuangD-A, JonesDTW, et al. (2012) Hotspot mutations in H3F3A and IDH1 define distinct epigenetic and biological subgroups of glioblastoma. Cancer Cell 22: 425–437 doi:10.1016/j.ccr.2012.08.024

39. AertsS, LambrechtsD, MaityS, Van LooP, CoessensB, et al. (2006) Gene prioritization through genomic data fusion. Nature Biotechnology 24: 537–544 doi:10.1038/nbt1203

40. BashRO, HallS, TimmonsCF, CristWM, AmylonM, et al. (1995) Does activation of the TAL1 gene occur in a majority of patients with T-cell acute lymphoblastic leukemia? A pediatric oncology group study. Blood 86: 666–676.

41. SoulierJ, ClappierE, CayuelaJ-M, RegnaultA, García-PeydróM, et al. (2005) HOXA genes are included in genetic and biologic networks defining human acute T-cell leukemia (T-ALL). Blood 106: 274–286 doi:10.1182/blood-2004-10-3900

42. MiyazakiT, TakaokaA, NogueiraL, DikicI, FujiiH, et al. (1998) Pyk2 is a downstream mediator of the IL-2 receptor-coupled Jak signaling pathway. Genes Dev 12: 770–775.

43. GardinaPJ, ClarkTA, ShimadaB, StaplesMK, YangQ, et al. (2006) Alternative splicing and differential gene expression in colon cancer detected by a whole genome exon array. BMC Genomics 7: 325 doi:10.1186/1471-2164-7-325

44. ThorsenK, SørensenKD, Brems-EskildsenAS, ModinC, GaustadnesM, et al. (2008) Alternative splicing in colon, bladder, and prostate cancer identified by exon array analysis. Mol Cell Proteomics 7: 1214–1224 doi:10.1074/mcp.M700590-MCP200

45. GutteryDS, ShawJA, LloydK, PringleJH, WalkerRA (2010) Expression of tenascin-C and its isoforms in the breast. Cancer Metastasis Rev 29: 595–606 doi:10.1007/s10555-010-9249-9

46. FutrealPA, CoinL, MarshallM, DownT, HubbardT, et al. (2004) A census of human cancer genes. Nat Rev Cancer 4: 177–183 doi:10.1038/nrc1299

47. CauwelierB, DastugueN, CoolsJ, PoppeB, HerensC, et al. (2006) Molecular cytogenetic study of 126 unselected T-ALL cases reveals high incidence of TCRbeta locus rearrangements and putative new T-cell oncogenes. Leukemia 20: 1238–1244 doi:10.1038/sj.leu.2404243

48. OramSH, ThomsJ, SiveJI, Calero-NietoFJ, KinstonSJ, et al. (2013) Bivalent promoter marks and a latent enhancer may prime the leukaemia oncogene LMO1 for ectopic expression in T-cell leukaemia. Leukemia 27: 1348–57 doi:10.1038/leu.2013.2

49. McPhersonA, HormozdiariF, ZayedA, GiulianyR, HaG, et al. (2011) deFuse: an algorithm for gene fusion discovery in tumor RNA-Seq data. PLoS Comput Biol 7: e1001138 doi:10.1371/journal.pcbi.1001138

50. NacuS, YuanW, KanZ, BhattD, RiversCS, et al. (2011) Deep RNA sequencing analysis of readthrough gene fusions in human prostate adenocarcinoma and reference samples. BMC Med Genomics 4: 11 doi:10.1186/1755-8794-4-11

51. ZhouJ, LiaoJ, ZhengX, ShenH (2012) Chimeric RNAs as potential biomarkers for tumor diagnosis. BMB Rep 45: 133–140.

52. HuangDW, ShermanBT, LempickiRA (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37: 1–13 doi:10.1093/nar/gkn923

53. HuangDW, ShermanBT, LempickiRA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4: 44–57 doi:10.1038/nprot.2008.211

54. BrandimarteL, PieriniV, Di GiacomoD, BorgaC, NozzaF, et al. (2013) New MLLT10 gene recombinations in pediatric T-acute lymphoblastic leukemia. Blood 121: 5064–7 doi:10.1182/blood-2013-02-487256

55. WarmuthM, KimS, GuX-J, XiaG, AdriánF (2007) Ba/F3 cells and their use in kinase drug discovery. Curr Opin Oncol 19: 55–60 doi:10.1097/CCO.0b013e328011a25f

56. O'NeilJ, TchindaJ, GutierrezA, MoreauL, MaserRS, et al. (2007) Alu elements mediate MYB gene tandem duplication in human T-ALL. Journal of Experimental Medicine 204: 3059–3066 doi:10.1084/jem.20071637

57. VainchenkerW, ConstantinescuSN (2013) JAK/STAT signaling in hematological malignancies. Oncogene 32: 2601–2613 doi:10.1038/onc.2012.347

58. SeoJ-S, JuYS, LeeW-C, ShinJ-Y, LeeJK, et al. (2012) The transcriptional landscape and mutational profile of lung adenocarcinoma. Genome Res 22: 2109–2119 doi:10.1101/gr.145144.112

59. BergerMF, LevinJZ, VijayendranK, SivachenkoA, AdiconisX, et al. (2010) Integrative analysis of the melanoma transcriptome. Genome 20: 413–27.

60. GoyaR, SunMGF, MorinRD, LeungG, HaG, et al. (2010) SNVMix: predicting single nucleotide variants from next-generation sequencing of tumors. Bioinformatics 26: 730–736 doi:10.1093/bioinformatics/btq040

61. QuinnEM, CormicanP, KennyEM, HillM, AnneyR, et al. (2013) Development of strategies for SNP detection in RNA-seq data: application to lymphoblastoid cell lines and evaluation using 1000 Genomes data. PLoS ONE 8: e58815 doi:10.1371/journal.pone.0058815

62. LiuJ, LeeW, JiangZ, ChenZ, JhunjhunwalaS, et al. (2012) Genome and transcriptome sequencing of lung cancers reveal diverse mutational and splicing events. Genome Res 22: 2315–2327 doi:10.1101/gr.140988.112

63. BettegowdaC, AgrawalN, JiaoY, SausenM, WoodLD (2011) Mutations in CIC and FUBP1 contribute to human oligodendroglioma. Science 333: 1453–1455.

64. SchwartzentruberJ, KorshunovA, LiuX-Y, JonesDTW, PfaffE, et al. (2012) Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma. Nature 482: 226–231 doi:10.1038/nature10833

65. WuG, BroniscerA, McEachronTA, LuC, PaughBS, et al. (2012) Somatic histone H3 alterations in pediatric diffuse intrinsic pontine gliomas and non-brainstem glioblastomas. Nat Genet 44: 251–253 doi:10.1038/ng.1102

66. SunCK, ManK, NgKT, HoJW, LimZX, et al. (2008) Proline-rich tyrosine kinase 2 (Pyk2) promotes proliferation and invasiveness of hepatocellular carcinoma cells through c-Src/ERK activation. Carcinogenesis 29: 2096–2105 doi:10.1093/carcin/bgn203

67. SunCK, NgKT, LimZX, ChengQ, LoC-M, et al. (2011) Proline-rich tyrosine kinase 2 (Pyk2) promotes cell motility of hepatocellular carcinoma through induction of epithelial to mesenchymal transition. PLoS ONE 6: e18878 doi:10.1371/journal.pone.0018878

68. RajalaHLM, EldforsS, KuusanmäkiH, van AdrichemAJ, OlsonT, et al. (2013) Discovery of somatic STAT5b mutations in large granular lymphocytic leukemia. Blood 121: 4541–50 doi:10.1182/blood-2012-12-474577

69. NollJE, JefferyJ, Al-EjehF, KumarR, KhannaKK, et al. (2012) Mutant p53 drives multinucleation and invasion through a process that is suppressed by ANKRD11. Oncogene 31: 2836–2848 doi:10.1038/onc.2011.456

70. MamtaniM, KulkarniH (2012) Association of HADHA expression with the risk of breast cancer: targeted subset analysis and meta-analysis of microarray data. BMC Res Notes 5: 25 doi:10.1186/1756-0500-5-25

71. KuehHY, RothenbergEV (2012) Regulatory gene network circuits underlying T cell development from multipotent progenitors. Wiley Interdiscip Rev Syst Biol Med 4: 79–102 doi:10.1002/wsbm.162

72. UribesalgoI, BenitahSA, Di CroceL (2012) From oncogene to tumor suppressor: the dual role of Myc in leukemia. Cell Cycle 11: 1757–64.

73. RobinsonMD, McCarthyDJ, SmythGK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26: 139–140 doi:10.1093/bioinformatics/btp616

74. CarroMS, LimWK, AlvarezMJ, BolloRJ, ZhaoX, et al. (2010) The transcriptional network for mesenchymal transformation of brain tumours. Nature 463: 318–325 doi:10.1038/nature08712

75. MaherCA, Kumar-SinhaC, CaoX, Kalyana-SundaramS, HanB, et al. (2009) Transcriptome sequencing to detect gene fusions in cancer. Nature 458: 97–101 doi:doi:10.1038/nature07638

76. HalvardsonJ, ZaghloolA, FeukL (2013) Exome RNA sequencing reveals rare and novel alternative transcripts. Nucleic Acids Res 41: e6 doi:10.1093/nar/gks816

77. BeneMC, CastoldiG, KnappW, LudwigWD, MatutesE, et al. (1995) Proposals for the immunological classification of acute leukemias. European Group for the Immunological Characterization of Leukemias (EGIL) Vol. 9: 1783–1786.

78. SteidlC, ShahSP, WoolcockBW, RuiL, KawaharaM, et al. (2011) MHC class II transactivator CIITA is a recurrent gene fusion partner in lymphoid cancers. Nature 471: 377–381 doi:10.1038/nature09754

79. RissoD, SchwartzK, SherlockG, DudoitS (2011) GC-content normalization for RNA-Seq data. BMC Bioinformatics 12: 480 doi:10.1186/1471-2105-12-480

80. GillisJ, PavlidisP (2013) Assessing identity, redundancy and confounds in Gene Ontology annotations over time. Bioinformatics 29: 476–482 doi:10.1093/bioinformatics/bts727

81. TrapnellC, WilliamsBA, PerteaG, MortazaviA, KwanG, et al. (2010) Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature Biotechnology 28: 511–515 doi:10.1038/nbt.1621

82. RobertsA, PimentelH, TrapnellC, PachterL (2011) Identification of novel transcripts in annotated genomes using RNA-Seq. Bioinformatics 27: 2325–2329 doi:10.1093/bioinformatics/btr355

83. SubramanianA, TamayoP, MoothaVK, MukherjeeS, EbertBL, et al. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 102: 15545–15550 doi:10.1073/pnas.0506580102

84. RobinsonJT, ThorvaldsdóttirH, WincklerW, GuttmanM, LanderES, et al. (2011) Integrative genomics viewer. Nature Biotechnology 29: 24–26 doi:10.1038/nbt.1754

85. ArtimoP, JonnalageddaM, ArnoldK, BaratinD, CsardiG, et al. (2012) ExPASy: SIB bioinformatics resource portal. Nucleic Acids Res 40: W597–W603 doi:10.1093/nar/gks400

86. SchultzJ, MilpetzF, BorkP, PontingCP (1998) SMART, a simple modular architecture research tool: identification of signaling domains. Proc Natl Acad Sci USA 95: 5857–5864.

87. LetunicI, DoerksT, BorkP (2012) SMART 7: recent updates to the protein domain annotation resource. Nucleic Acids Res 40: D302–D305 doi:10.1093/nar/gkr931

88. de KeersmaeckerK, GrauxC, OderoMD, MentensN, SomersR, et al. (2005) Fusion of EML1 to ABL1 in T-cell acute lymphoblastic leukemia with cryptic t(9;14)(q34;q32). Blood 105: 4849–4852 doi:10.1182/blood-2004-12-4897