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Frontiers of Medicine

ISSN 2095-0217

ISSN 2095-0225(Online)

CN 11-5983/R

Postal Subscription Code 80-967

2018 Impact Factor: 1.847

Front. Med.    2019, Vol. 13 Issue (2) : 229-237    https://doi.org/10.1007/s11684-018-0616-1
RESEARCH ARTICLE
Mutation profiling of 16 candidate genes in de novo acute myeloid leukemia patients
Yang Zhang, Fang Wang, Xue Chen, Wenjing Liu, Jiancheng Fang, Mingyu Wang, Wen Teng, Panxiang Cao, Hongxing Liu()
Pathology & Laboratory Medicine Division, Hebei Yanda Lu Daopei Hospital, Langfang 065201, China
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Abstract

This retrospective analysis aimed to investigate the mutation profile of 16 common mutated genes in de novo acute myeloid leukemia (AML) patients. A total of 259 patients who were diagnosed of de novo AML were enrolled in this study. Mutation profiling of 16 candidate genes were performed in bone marrow samples by using Sanger sequencing. We identified at least 1 mutation in 199 of the 259 samples (76.8%), and 2 or more mutations in 31.7% of samples. FLT3-ITD was the most common mutated gene (16.2%, 42/259), followed by CEBPA (15.1%, 39/259), NRAS (14.7%, 38/259), and NPM1 (13.5%, 35/259). Concurrence was observed in 97.1% of the NPM1 mutated cases and in 29.6% of the double mutated CEBPA cases. Distinct patterns of co-occurrence were observed for different hotspot mutations within the IDH2 gene: R140 mutations were associated with NPM1 and/or FLT3-ITD mutations, whereas R172 mutations co-occurred with DNMT3A mutations only. Concurrence was also observed in 86.6% of epigenetic regulation genes, most of which co-occurred with NPM1 mutations. The results showed certain rules in the mutation profiling and concurrence of AML patients, which was related to the function classification of genes. Defining the mutation spectrum and mutation pattern of AML will contribute to the comprehensive assessment of patients and identification of new therapeutic targets.

Keywords leukemia      myeloid      acute      gene      mutation     
Corresponding Author(s): Hongxing Liu   
Just Accepted Date: 04 April 2018   Online First Date: 29 May 2018    Issue Date: 28 March 2019
 Cite this article:   
Yang Zhang,Fang Wang,Xue Chen, et al. Mutation profiling of 16 candidate genes in de novo acute myeloid leukemia patients[J]. Front. Med., 2019, 13(2): 229-237.
 URL:  
https://academic.hep.com.cn/fmd/EN/10.1007/s11684-018-0616-1
https://academic.hep.com.cn/fmd/EN/Y2019/V13/I2/229
Fig.1  Mutation spectrum of 16 common mutated genes in AML. Rows in the graph represent individual gene mutations, and the columns represent individual patients in the study. Vertical blue lines indicate the presence of one mutation in a patient. Vertical yellow lines and red lines indicate the presence of double and triple mutations in a patient, respectively.
Fig.2  Gene function classification and co-occurrence of 16 common mutated genes in AML. (A) Co-occurrence of genes within three different function classifications. (B) Co-occurrence of signal transduction genes. (C) Co-occurrence of transcription factor genes. (D) Co-occurrence of epigenetic regulation genes.
FLT3-ITD M FLT3-ITD W P value NRAS M NRAS W P value KIT M KIT W P value
n = 42 n = 157 n = 38 n = 161 n = 28 n = 171
FLT3-ITD 4(10.5%) 38(23.6%) * 1(3.6%) 41(24.0%) 0.014
CEBPA 5(11.9%) 34(21.7%) * 3(7.9%) 36(22.4%) 0.043 1(3.6%) 38(22.2%) 0.021
NRAS 4(9.5%) 34(21.7%) * 2(7.1%) 36(21.1%) *
NPM1 14(33.3%) 21(13.4) 0.003 2(5.3%) 33(20.5%) 0.027 1(3.6%) 34(19.9%) *
KIT 1(2.4%) 27(17.2%) 0.014 2(5.3%) 26(16.1%) *
PTPN11 1(2.4%) 17(10.8%) * 1(2.6%) 17(10.6%) * 0 18(10.5%) *
RUNX1 5(11.9%) 13(8.3%) * 3(7.9%) 15(9.3%) * 0 18(10.5%) *
DNMT3A 2(4.8%) 15(9.6%) * 2(5.3%) 15(9.3%) * 0 17(9.9%) *
IDH2 5(11.9%) 12(7.6%) * 2(5.3%) 15(9.3%) * 0 17(9.9%) *
TET2 2(4.8%) 14(8.9%) * 2(5.3%) 14(8.7%) * 0 16(9.4%) *
ASXL1 0 10(6.4%) * 2(5.3%) 8(5.0%) * 0 10(5.8%) *
FLT3-TKD 2(4.8%) 6(3.8%) * 1(2.6%) 7(4.3%) * 1(3.6%) 7(4.1%) *
KRAS 0 8(5.1%) * 1(2.6%) 7(4.3%) * 1(3.6%) 7(4.1%) *
ETV6 1(2.4%) 6(3.8%) * 0 7(4.3%) * 0 7(4.1%) *
IDH1 2(4.8%) 4(2.5%) * 0 6(3.7%) * 0 6(3.5%) *
PHF6 2(4.8%) 2(1.3%) * 0 4(2.5%) * 0 4(2.3%) *
TP53 0 4(2.5%) * 0 4(2.5%) * 0 4(2.3%) *
Tab.1  Co-occurrence of FLT3, NRAS, and KIT gene mutations
CEBPA M CEBPA W P value NPM1 M NPM1 W P value RUNX1 M RUNX1 W P value
n = 39 n = 160 n = 35 n = 164 n = 18 n = 181
FLT3-ITD 5(12.8%) 37(23.1%) * 14(40.0%) 28(17.1%) 0.003 5(2.8%) 42(23.2%) *
CEBPA 2(5.7%) 37(22.6%) 0.023 0 39(21.5%) *
NRAS 3(7.7%) 35(21.9%) 0.043 2(5.7%) 36(22.0%) 0.027 3(16.7%) 38(21.0%) *
NPM1 2(5.1%) 33(20.6%) 0.023 1(5.6%) 34(18.8%) *
KIT 1(2.6%) 27(16.9%) 0.021 1(2.9%) 27(16.5%) * 0 28(15.5%) *
PTPN11 1(2.6%) 17(10.6%) * 5(14.3%) 13(7.9%) * 1(5.6%) 18(9.9%) *
RUNX1 0 18(11.3%) * 1(2.9%) 17(10.4%) *
DNMT3A 1(2.6%) 16(10.0%) * 10(28.6%) 7(4.3%) 0.000 0 17(9.4%) *
IDH2 1(2.6%) 16(10.0%) * 8(22.9%) 9(5.5%) 0.003 0 17(9.4%) *
TET2 1(2.6%) 15(9.4%) * 6(17.1%) 10(6.1%) * 1(5.6%) 15(8.3%) *
ASXL1 0 10(6.3%) * 0 10(6.1%) * 3(16.7%) 7(3.9%) *
FLT3-TKD 0 8(5.0%) * 3(8.6%) 5(3.0%) * 0 8(4.4%) *
KRAS 0 8(5.0%) * 0 8(4.9%) * 1(5.6%) 8(4.4%) *
ETV6 1(2.6%) 6(3.8%) * 1(2.9%) 6(3.7%) * 0 7(3.9%) *
IDH1 1(2.6%) 5(3.1%) * 3(8.6%) 3(1.8%) * 0 6(3.3%) *
PHF6 0 4(2.5%) * 1(2.9%) 3(1.8%) * 1(5.6%) 3(1.7%) *
TP53 0 4(2.5%) * 0 4(2.4%) * 0 4(2.2%) *
Tab.2  Co-occurrence of CEBPA, NPM1, and RUNX1 gene mutations
DNMT3A M DNMT3A W P value IDH2 M IDH2 W P value TET2 M TET2 W P value
n = 17 n = 182 n = 17 n = 182 n = 16 n = 183
FLT3-ITD 2(11.8%) 40(22.0%) * 2(11.8%) 40(22.0%) * 2(12.5%) 40(21.9%) *
CEBPA 1(5.9%) 38(20.9%) * 1(5.9%) 38(20.9%) * 1(6.3%) 38(20.8%) *
NRAS 2(11.8%) 36(19.8%) * 2(11.8%) 36(19.8%) * 2(12.5%) 36(19.7%) *
NPM1 10(58.8%) 25(13.7%) 0.000 8(47.1%) 27(14.8%) 0.003 6(37.5%) 29(15.8%) *
KIT 0 28(15.4%) * 0 28(15.4%) * 0 28(15.3%) *
PTPN11 1(5.9%) 17(9.3%) * 1(5.9%) 17(9.3%) * 0 18(9.8%) *
RUNX1 0 18(9.9%) * 0 18(9.9%) * 1(6.3%) 17(9.3%) *
DNMT3A 8(47.1%) 9(4.9%) 0.000 3(18.8%) 14(7.7%) *
IDH2 8(47.1%) 9(4.9%) 0.000 2(12.5%) 15(8.2%) *
TET2 3(17.6%) 13(7.1%) * 2(11.8%) 14(7.7%) *
ASXL1 0 10(5.5%) * 0 10(5.5%) * 1(6.3%) 9(4.9%) *
FLT3-TKD 2(11.8%) 6(3.3%) * 0 8(4.4%) * 1(6.3%) 7(3.8%) *
KRAS 0 8(4.4%) * 0 8(4.4%) * 0 8(4.4%) *
ETV6 0 7(3.8%) * 0 7(3.8%) * 0 7(3.8%) *
IDH1 0 6(3.3%) * 0 6(3.3%) * 1(6.3%) 5(2.7%) *
PHF6 1(5.9%) 3(1.6%) * 0 4(2.2%) * 0 4(2.2%) *
TP53 0 4(2.2%) * 0 4(2.2%) * 1(6.3%) 3(1.6%) *
Tab.3  Co-occurrence of DNMT3A, IDH2, and TET2 gene mutations
1 HDöhner, DJ Weisdorf, CDBloomfield. Acute myeloid leukemia. N Engl J Med 2015; 373(12): 1136–1152
https://doi.org/10.1056/NEJMra1406184 pmid: 26376137
2 KHMetzeler, T Herold, MRothenberg-Thurley, SAmler, MCSauerland, DGörlich, SSchneider, NPKonstandin, ADufour, KBräundl, BKsienzyk, EZellmeier, LHartmann, PAGreif, MFiegl, MSubklewe, SKBohlander, UKrug, A Faldum, WEBerdel, BWörmann, TBüchner, WHiddemann, JBraess, KSpiekermann; AMLCG Study Group. Spectrum and prognostic relevance of driver gene mutations in acute myeloid leukemia. Blood 2016; 128(5): 686–698
https://doi.org/10.1182/blood-2016-01-693879 pmid: 27288520
3 DGrimwade, A Ivey, BJPHuntly. Molecular landscape of acute myeloid leukemia in younger adults and its clinical relevance. Blood 2016; 127(1): 29–41
https://doi.org/10.1182/blood-2015-07-604496 pmid: 26660431
4 DAArber, A Orazi, RHasserjian, JThiele, MJBorowitz, MMLe Beau, CDBloomfield, MCazzola, JWVardiman. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood 2016; 127(20): 2391–2405
https://doi.org/10.1182/blood-2016-03-643544 pmid: 27069254
5 SHSwerdlow, E Campo, SAPileri, NLHarris, HStein, RSiebert, RAdvani, MGhielmini, GASalles, ADZelenetz, ESJaffe. The 2016 revision of the World Health Organization classification of lymphoid neoplasms. Blood 2016; 127(20): 2375–2390
https://doi.org/10.1182/blood-2016-01-643569 pmid: 26980727
6 HXLiu, F Wang, WTeng, YHLin, JF Yang, XZhang, QYin, X Chen, PZhu, CRTong. Mutaome profiling and retrospective mutaome profiling using archived bone marrow smear in AML. Blood 2013; 122(21): 4983
7 JPan, Y Zhang, YLZhao, JFYang, JPZhang, HXLiu, T Wu, CRTong. Impact of clinical factors on outcome of leukemia patients with TLS-ERG fusion gene. Leuk Lymphoma 2017; 58(7): 1655–1663
https://doi.org/10.1080/10428194.2016.1260124 pmid: 27874290
8 CSaygin, HE Carraway. Emerging therapies for acute myeloid leukemia. J Hematol Oncol 2017; 10(1): 93
https://doi.org/10.1186/s13045-017-0463-6 pmid: 28420416
9 YLi, Q Xu, NLv, LWang, H Zhao, XWang, JGuo, C Chen, YLi, LYu. Clinical implications of genome-wide DNA methylation studies in acute myeloid leukemia. J Hematol Oncol 2017; 10(1): 41
https://doi.org/10.1186/s13045-017-0409-z pmid: 28153026
10 EPapaemmanuil, M Gerstung, LBullinger, VIGaidzik, PPaschka, NDRoberts, NEPotter, MHeuser, FThol, N Bolli, GGundem, PVan Loo, IMartincorena, PGanly, LMudie, SMcLaren, SO’Meara, KRaine, DRJones, JWTeague, APButler, MFGreaves, AGanser, KDöhner, RFSchlenk, HDöhner, PJCampbell. Genomic classification and prognosis in acute myeloid leukemia. N Engl J Med 2016; 374(23): 2209–2221
https://doi.org/10.1056/NEJMoa1516192 pmid: 27276561
11 LIShlush, S Zandi, AMitchell, WCChen, JMBrandwein, VGupta, JAKennedy, ADSchimmer, ACSchuh, KWYee, JL McLeod, MDoedens, JJFMedeiros, RMarke, HJKim, K Lee, JDMcPherson, TJHudson; HALT Pan-Leukemia Gene Panel Consortium, Brown AM, Yousif F, Trinh QM, Stein LD, Minden MD, Wang JC, Dick JE. Identification of pre-leukaemic haematopoietic stem cells in acute leukaemia. Nature 2014; 506(7488): 328–333
https://doi.org/10.1038/nature13038 pmid: 24522528
12 GGenovese, AK Kähler, REHandsaker, JLindberg, SARose, SFBakhoum, KChambert, EMick, BM Neale, MFromer, SMPurcell, OSvantesson, MLandén, MHöglund, SLehmann, SBGabriel, JLMoran, ESLander, PFSullivan, PSklar, HGrönberg, CMHultman, SAMcCarroll. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N Engl J Med 2014; 371(26): 2477–2487
https://doi.org/10.1056/NEJMoa1409405 pmid: 25426838
13 SJaiswal, P Fontanillas, JFlannick, AManning, PVGrauman, BGMar, RC Lindsley, CHMermel, NBurtt, AChavez, JMHiggins, VMoltchanov, FCKuo, MJ Kluk, BHenderson, LKinnunen, HAKoistinen, CLadenvall, GGetz, A Correa, BFBanahan, SGabriel, SKathiresan, HMStringham, MIMcCarthy, MBoehnke, JTuomilehto, CHaiman, LGroop, GAtzmon, JGWilson, DNeuberg, DAltshuler, BLEbert. Age-related clonal hematopoiesis associated with adverse outcomes. N Engl J Med 2014; 371(26): 2488–2498
https://doi.org/10.1056/NEJMoa1408617 pmid: 25426837
14 Cancer Genome Atlas Research Network, TJLey, C Miller, LDing, BJRaphael, AJMungall, ARobertson, KHoadley, TJ JrTriche, PWLaird, JDBaty, LLFulton, RFulton, SEHeath, JKalicki-Veizer, CKandoth, JMKlco, DCKoboldt, KLKanchi, SKulkarni, TLLamprecht, DELarson, LLin, C Lu, MDMcLellan, JFMcMichael, JPayton, HSchmidt, DHSpencer, MHTomasson, JWWallis, LDWartman, MAWatson, JWelch, MCWendl, AAlly, M Balasundaram, IBirol, YButterfield, RChiu, A Chu, EChuah, HJChun, RCorbett, NDhalla, RGuin, A He, CHirst, MHirst, RAHolt, SJones, AKarsan, DLee, HI Li, MAMarra, MMayo, RA Moore, KMungall, JParker, EPleasance, PPlettner, JSchein, DStoll, LSwanson, ATam, N Thiessen, RVarhol, NWye, Y Zhao, SGabriel, GGetz, C Sougnez, LZou, MDLeiserson, FVandin, HTWu, F Applebaum, SBBaylin, RAkbani, BMBroom, KChen, TC Motter, KNguyen, JNWeinstein, NZhang, MLFerguson, CAdams, ABlack, JBowen, JGastier-Foster, TGrossman, TLichtenberg, LWise, T Davidsen, JADemchok, KRShaw, MSheth, HJSofia, LYang, JR Downing, GEley. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med 2013; 368(22): 2059–2074
https://doi.org/10.1056/NEJMoa1301689 pmid: 23634996
15 JWVardiman, J Thiele, DAArber, RDBrunning, MJBorowitz, APorwit, NLHarris, MMLe Beau, EHellström-Lindberg, ATefferi, CDBloomfield. The 2008 revision of the World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia: rationale and important changes. Blood 2009; 114(5): 937–951
https://doi.org/10.1182/blood-2009-03-209262 pmid: 19357394
16 HDöhner, EH Estey, SAmadori, FRAppelbaum, TBüchner, AKBurnett, HDombret, PFenaux, DGrimwade, RALarson, FLo-Coco, TNaoe, D Niederwieser, GJOssenkoppele, MASanz, JSierra, MSTallman, BLöwenberg, CDBloomfield; European LeukemiaNet. Diagnosis and management of acute myeloid leukemia in adults: recommendations from an international expert panel, on behalf of the European LeukemiaNet. Blood 2010; 115(3): 453–474
https://doi.org/10.1182/blood-2009-07-235358 pmid: 19880497
17 MWang, C Yang, LZhang, DGSchaar. Molecular mutations and their cooccurrences in cytogenetically normal acute myeloid leukemia. Stem Cells Int 2017; 2017: 1–11
18 AFasan, C Haferlach, TAlpermann, SJeromin, VGrossmann, CEder, S Weissmann, FDicker, AKohlmann, SSchindela, WKern, T Haferlach, SSchnittger. The role of different genetic subtypes of CEBPA mutated AML. Leukemia 2014; 28(4): 794–803
https://doi.org/10.1038/leu.2013.273 pmid: 24056881
19 VGrossmann, C Haferlach, NNadarajah, AFasan, SWeissmann, ARoller, CEder, E Stopp, WKern, THaferlach, AKohlmann, SSchnittger. CEBPA double-mutated acute myeloid leukaemia harbours concomitant molecular mutations in 76.8% of cases with TET2 and GATA2 alterations impacting prognosis. Br J Haematol 2013; 161(5): 649–658
https://doi.org/10.1111/bjh.12297 pmid: 23521373
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