Mutation profiling of 16 candidate genes in de novo acute myeloid leukemia patients

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

Introduction

Acute myeloid leukemia (AML) is a group of heterogeneous diseases [¹]. Along with the progress in molecular biology regarding hematological malignancies and the use of next generation sequencing technology, more molecular genetic markers have been identified. Different mutations can respectively be of unilateral or multivariate clinical significance in clonality, diagnosis, prognosis assessment, and individual therapy [²,³]. Moreover, the newly published 2016 revision to the World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia and lymphoid neoplasms include several molecular genetic markers with well-defined diagnostic and prognostic values, which provide molecular basis for highly precise classification and treatment of diseases, including mutations of CALR, CSF3R, SF3B1, RUNX1, and PCM1-JAK2. In de novo AML, AML with mutated RUNX1 has been added as a provisional category that has a possibly worse prognosis. AML with biallelic mutated CEBPA is specifically defined and associated with improved prognosis, but not single CEBPA mutation [⁴,⁵]. Furthermore, the hematological malignancy gene mutation profiling is no longer confined to the detection of only a few molecular targets, and the current study and application of leukemia has entered the “mutaome” [⁶] profile era.

The initiation and progression of tumors result from the mutational synergism of many different functional genes. Current studies have discovered certain rules on the occurrence and mutation pattern of gene mutation. In this study, we analyzed 16 common mutated genes in de novo AML patients and investigated the rules of mutation spectrum, thus offering an approach to further explore the molecular biological mechanism of tumorigenesis and find new therapeutic targets.

Materials and methods

Subjects

From June 2014 to June 2016, a total of 259 patients were diagnosed with AML at Hebei Yanda Lu Daopei Hospital and included 153 males and 106 females aged from 2 to 68 years, with a median age of 23 years. We defined the children group as aged≤14 years, adult group as aged between 15 and 55 years, and the elderly group as aged≥55 years. The diagnosis of AML was performed according to the WHO 2008 classification criteria.

DNA extraction

Bone marrow specimens (n = 78) or bone marrow smear specimens (n = 181) of the de novo AML patients were collected. The number of white blood cells was measured by using a blood cell counter for each sample before extraction. Additional handling before counting the bone marrow smear samples involved pipetting with purified water repeatedly to dissolve the cells. Moreover, 1.0 × 10⁷/mL karyocytes were acquired to ensure enough genomic DNA output. Genomic DNA were extracted by using a column DNA extraction kit (Tiangen Biotech (Beijing) Co., Ltd., Item No. DP318-03) according to the manufacturer’s protocol.

Gene sequencing and mutation analysis

A total of 16 common mutated genes, namely, ASXL1, CEBPA, DNMT3A, ETV6, FLT3, IDH1, IDH2, KIT, KRAS, NRAS, NPM1, PTPN11, PHF6, RUNX1, TET2, and TP53 were detected of mutation hotspot regions that have already been reported in the literature (Supplementary Table S1). Sanger sequencing was performed by using an AB 3500XL sequencer, whose detection sensitivity is approximately 15%−20%; additionally, FLT3-ITD and NPM1 mutations were detected by fragment length analysis by using an AB 3500XL sequencer with a detection sensitivity of approximately 1%−3%. Gene mutation analysis was performed by using Variant Reporter V1.1 software (Life, America). The evaluation of leukemia-associated somatic mutations was followed by previously reported principles [⁷].

Statistical analysis

The c² test and Fisher exact test were performed with SPSS (version 19.0; SPSS Inc., Chicago, IL) to analyze the gene-gene co-occurrence correlation and the correlation of mutations and age. P<0.05 was considered statistically significant.

Results

Overall gene mutation frequency and hotspots

We identified at least 1 mutation in 199 of the 259 samples (76.8%), and 2 or more mutations in 31.7% of samples, among which, 53 (20.5%), 24 (9.3%), and 5 (2.0%) samples carried 2, 3, and 4 kinds of gene mutations, respectively. A total of 64 combinations of gene mutations were detected. The most frequently mutated two-gene combinations included NPM1+FLT3-ITD, DNMT3A+NPM1, DNMT3A+IDH2, IDH2+NPM1, and TET2+NPM1, and these were respectively detected in 9, 9, 6, 6, and 6 samples; moreover, the most frequently mutated three-gene combinations included DNMT3A+NPM1+FLT3 (ITD/TKD) and DNMT3A+TET2+NPM1, and these were respectively detected in 4 and 3 samples. FLT3-ITD (16.2%, 42/259) was the most frequently mutated gene, followed by CEBPA (15.1%, 39/259), NRAS (14.7%, 38/259), and NPM1 (13.5%, 35/249), which accounted for 59.5% of the total mutation frequency. The mutation frequencies of other genes are shown in Fig. 1.

A total of 26 kinds of insert-fragment lengths were found in the FLT3-ITD mutation, of which the minimum length was 12 base pair (bp), the maximum length was 204 bp, and the common insert lengths were 21 bp, 33 bp, and 57 bp, of which 3 cases carried 2 insert fragments and 2 cases carried 3 fragments. A total of 6 of 8 patients carried FLT3-TKD D835 mutations, and the other 2 cases carried D839G and I836_M837insG mutations. A total of 28 patients carried 10 kinds of KIT mutations, among which 14 patients carried D816 mutations and 11 patients carried N822K mutations. Moreover, 2 patients carried more than 2 kinds of mutations (2 and 3 mutations, respectively). Furthermore, 28 cases carried NRAS mutations, all of which were G12 (19/38), G13 (9/38), Q61 (10/38), and G60 (2/38) mutations, of which 2 cases carried two mutations. A total of 7 cases carried KRAS mutations, all of which were G12 (6/7) and G13 (2/7) mutations, of which 1 case carried 2 mutations. A total of 18 cases carried PTPN11 mutations, of which 15 cases were in exon 3, and 3 cases were in exon 13. A total of 39 cases carried CEBPA mutations, of which 12 cases were single-mutated CEBPA, 9 cases were at the N-terminal, and 3 cases were at the C-terminal; moreover, 27 cases were double-mutated CEBPA, and all were at the N-terminal and C-terminal. A total of 18 cases carried RUNX1 mutations scattered in the whole coding area, of which 8 cases were missense mutations and were mainly concentrated on exons 5 and 6, and 7 cases were frameshift mutations and mainly concentrated on exon 9, of which 2 cases carried 2 and 3 mutations at the same time. A total of 35 cases carried NPM1 mutations, of which 22 cases were A-type mutations, B- and D-type mutations were found in 1 case, and 4 cases were rare type; furthermore, the remaining 7 cases were not classified owing to being below the detection sensitivity of Sanger sequencing. A total of 10 cases carried ASXL1 mutations, of which 5 cases were nonsense mutations, 4 cases were frameshift mutations, and 1 case was missense mutation. A total of 17 cases carried DNMT3A mutations, of which 16 cases were R882 mutations, 1 case carried two kinds of mutations at the same time. A total of 6 cases carried IDH1 mutations, and all of which were R132 mutations. A total of 17 cases carried IDH2 mutations, of which 13 cases were R140 mutations and 4 cases were R172K mutations. Finally, TET2, ETV6, PHF6, and TP53 mutations were scattered without significant mutation hotspots.

Mutation spectra of genes in different function classifications

The results showed that 142 of the 259 samples (54.8%) carried signal transduction mutations, among which FLT3-ITD, NRAS, and KIT were frequently mutated with 16.2%, 14.7%, and 10.8%, respectively. A total of 103 of the 259 samples (39.8%) carried transcription factor mutations, among which CEBPA, NPM1, and RUNX1 were frequently mutated with 15.1%, 13.5%, and 7.0%, respectively. A total of 66 of the 259 samples (25.5%) carried epigenetic regulation mutations, among which DNMT3A, IDH2, and TET2 were frequently mutated with 6.6%, 6.2%, and 6.2%, respectively. A total of 4 of the 259 samples (1.5%) carried TP53 mutations. The gene mutations of different function classifications could co-occur with each other, and we observed 87.9% (58/66), 64.1% (66/103), and 50.7% (72/142) of the epigenetic regulation, transcription factor, and signal transduction mutations to be co-occurring with other gene mutations (Fig. 2A).

Gene-gene co-occurrence

Signal transduction genes (Fig. 2B, Table 1): We identified 29 of 42 cases (69.1%) with FLT3-ITD mutation co-occurred with other gene mutations, among which 14 mutated with NPM1. NPM1 was the most frequent concomitant mutation partner of FLT3-ITD (33.3% vs. 13.4%, P<0.05). Moreover, NRAS and KRAS mutations can occur alone or co-occur with other gene mutations, but rarely with CEBPA and NPM1. Among patients with mutated KIT, 21 of 28 cases (75.0%) occurred alone, 7 cases co-occurred with other gene mutations, but rarely with FLT3-ITD and CEBPA. Furthermore, PTPN11 mutations co-occurred in 10 of 18 cases (55.6%), among which 5 were NPM1 mutation (27.8% vs. 12.4%, P>0.05).

Transcription factor genes (Fig. 2C, Table 2): We identified 26 of 39 cases (66.7%) with CEBPA mutations occurred alone, and 33.3% (13/39) of CEBPA mutations co-occurred; among the 27 patients carrying double-mutated CEBPA, 19 cases carried double-mutated CEBPA alone, and 8 cases co-occurred with other mutations (3 FLT3-ITD, 3 NRAS, 1 KIT, and 1 ETV6). Among patients with mutated NPM1, 34 of 35 cases (97.1%) co-occurred with other gene mutations, including 14 with FLT3-ITD mutation (40.0% vs. 17.1%, P<0.05), 10 with DNMT3A mutation (28.6% vs. 4.3%, P<0.05), 8 with IDH2 mutations (22.9% vs. 5.5%, P<0.05), 6 with TET2 mutations, and 3 with IDH1 mutation. Moreover, FLT3-ITD, DNMT3A, and IDH2 are the most common concomitant mutant partners of NPM1. RUNX1 mutation co-occurred in 11 of 18 cases (61.1%), and its frequent mutated partner is ASXL1 mutation. Furthermore, ETV6 mutations can occur alone or with other gene mutations. Only 4 cases of PHF6 mutations were detected, and all of which co-occurred.

Epigenetic regulation genes (Fig. 2D, Table 3): ALL DNMT3A mutations co-occurred with other gene mutations, including 10 with NPM1 (58.8% vs. 13.7%, P<0.05), and 7 with IDH2 (4 R140Q mutations, 3 R172K mutations) (47.1% vs. 4.9%, P<0.05). NPM1 and IDH2 are the most common concomitant mutant partners of DNMT3A. IDH2 mutations co-occurred in 16 of 17 cases (94.1%), including 8 cases with NPM1 (47.1% vs. 14.8%, P<0.05), and 8 cases with DNMT3A (47.1% vs. 4.9%, P<0.05). Moreover, the NPM1 and DNMT3A mutations were the most common concomitant mutant partners of IDH2. IDH1 mutations co-occurred in 6 of 7 cases (85.7%), including 3 cases mutated with NPM1. TET2 mutations co-occurred in 12 of 16 cases (75.0%), and 6 cases were associated with NPM1 mutations. ASXL1 mutations co-occurred in 8 of 10 cases (80.0%), and 6 cases were associated with RUNX1 mutations.

Gene hotspot co-occurrence

Mutational hotspots were present in some genes, which could be single or multiple. Among the 16 genes detected, 2 hotspots were found in the IDH2 gene, namely R140 and R172. The two most common concomitant mutant partners of IDH2 were NPM1 and DNMT3A, among which R172 mutation only co-occurred with DNMT3A mutation (50.0% vs. 0%, P<0.05), whereas R140 mutation could co-occur with NPM1 or DNMT3A mutation. However, mutations that co-occurred with NPM1 were all R140 mutations. Through analyses of NRAS and KIT, no evidence indicated that different mutation hotspots were present with different concurrence rules.

Gene mutation and age

The total gene mutation frequency and mutation frequency of carrying more than one gene were higher in the adult and elderly groups than in the children group (82.5%, 85.0% vs. 66.7%, P<0.05; 49.2%, 58.8% vs. 25.0%, P<0.05). Moreover, the single gene mutation frequency was higher in the children group than those in other two groups (75.0% vs. 42.0%, 41.2%, P<0.05). Among different gene functional groups, the mutation frequency of kinase genes in the children group was higher than that in the adult group (78.1% vs. 55.9%, P<0.05), the mutation frequency of transcription factor genes in the adult group was higher than that in the children group (57.6% vs. 29.7%, P<0.05), and the mutation frequency of epigenetic regulation genes was higher in the adult and elderly groups than that in the children group (31.4%, 41.1% vs. 10.9%, P<0.05). Among different gene mutations, the frequency of KIT mutation was higher in the children group than in the adult group (25.0% vs. 7.6%, P<0.05), the frequency of CEBPA and DNMT3A mutations were higher in the adult group than in the children group (CEBPA 26.3% vs. 10.9%, P<0.05; DNMT3A 11.9% vs. 1.6%, P<0.05), and the frequency of NPM1 and IDH2 mutations were higher in the adult and elderly group than in the children group (NPM1 22.0%, 29.4% vs. 6.3%, P<0.05; IDH2 10.2%, 23.5% vs. 0%, P<0.05).

Discussion

We selected 16 genes that are commonly mutated in AML, including genes that clearly guide diagnosis and prognosis stratification, such as NPM1, CEBPA, FLT3-ITD, TP53, ASXL1, and RUNX1; genes with definite or potential guidance for therapy, such as FLT3, KIT, DNMT3A, TET2, and IDH1/2; and other common genes that can assist in clonal judgments, such as NRAS, KRAS, PTPN11, ETV6, and PHF6. By detecting 16 common mutated genes in AML, 76.8% of newly diagnosed AML patients were observed to carry at least one gene mutation. Targeted sequencing of 16 genes can assist nearly 80% of patients find molecular etiology and help with adjuvant diagnosis, prognosis stratification, and treatment in a cost-effective manner compared with whole genome sequencing (WGS) or whole exome sequencing (WES).

Despite our understanding of the pathobiology and genomic landscape of AML having improved substantially, the chemotherapy-directed management has remained largely unchanged in the past 40 years [⁸]. In recent years, various novel agents have demonstrated clinical activity, such as FLT3 inhibitors and epigenetic modifiers (IDH inhibitors, DNMTs inhibitors, HDAC inhibitors) [⁸,⁹]. In our research, 54.8% and 25.5% of the samples carried signal transduction genes and epigenetic regulation gene mutations, respectively, among which 52.3% of the patients who carried FLT3, KIT, DNMT3A, IDH1/2, and TET2 mutations could benefit from these novel agents.

Previous studies have discovered that the combination of gene mutations has certain rules, and these are closely related to gene function classification [¹⁰]. This study has been observed in similar patterns, suggesting that in the initiation and progress of AML, the synergistic effect of gene mutations with different kinds of functions is invariable. In addition, the different mutation hotspots within the same gene have different concomitance rules, and DNMT3A mutations could co-occur either with R140 or R172 mutations; whereas IDH2 R172 mutations only co-occurred with DNMT3A mutations. Moreover, NPM1 and/or FLT3-ITD mutations only co-occurred with R140 mutations, which suggests that the occurrence of NPM1 and FLT3-ITD mutation clones was later than those of DNMT3A and IDH2 mutations clone, and different mutation hotspots may function differently, and their synergistic mutation types may also be different. Papaemmanuil et al. found that different hotspots within the NRAS gene have different concurrence rules, and NPM1 mutations are often accompanied by NRAS G12/13 mutations while being mutually exclusive with the NRAS Q61 mutations [¹⁰]. However, in our study, only 2 of 38 patients with NRAS mutations co-occurred with NPM1 mutations, which were Q61R and G13D mutations. No evidence in this study indicated that different mutation hotspots within the NRAS gene have different concurrence rules.

We observed distinct gene mutation profiles in different age groups; moreover, the mutation frequency of kinase genes in children was higher, and the mutation frequency of transcription factor genes and epigenetic regulation genes were higher in the adult and elderly groups. The results were consistent with other large studies. Papaemmanuil et al. reported a large AML group that mainly included adults and elderly patients, in which the three largest subgroups were mutations of NPM1, CEBPA, and chromatin and RNA-splicing genes.

The initiation of tumors is the result of abnormal interactions of a number of genes. At present, some clues point to pre-leukemia clones being related to leukemia onset. Shlush LI et al. detected recurring DNMT3A and IDH2 mutations in hematopoietic stem cells (HSCs) of AML patients and persisted in remission samples. In the allo-graft model, HSCs carrying those mutations showed significant proliferative advantages than those without mutations [¹¹]. In other studies, clonal mutations in epigenetic regulation genes, such as DNMT3A, TET2, IDH1/2, and ASXL1, were detected in approximately 10% of healthy elderly people aged more than 65 years, and these patients exhibited a risk to progress to hematologic malignancies [¹²,¹³]. In our study, 86.6% of the epigenetic regulation genes co-occurred with other mutations and the most frequently concomitant mutation partner was NPM1, which is consistent with previous studies [¹⁰,¹⁴]. The results suggested that mutations in epigenetic regulation genes often occur at the early stage of the disease and is not sufficient to transform the disease into hematological malignancies independently. Mutations that occur relatively late in NPM1 or FLT3-ITD may eventually lead to tumorigenesis and clinical-observed phenotypes. Therefore, co-occurrence with different mutated genes will determine the phenotype of hematological malignancies, and this will assist in clinical diagnoses; for example, mutations in epigenetic regulation genes concomitant with JAK2 mutations could lead to MPN, whereas concomitant with SF3B1 mutations could lead to MDS with increased sideroblasts.

Additionally, different concomitant mutant partners have potential effects on the prognosis. NPM1 and CEBPA mutations are recurrent genetic abnormalities of AML according to the WHO 2008 and 2016 revision editions of the Classification of Hematologic Malignancies, which has distinct diagnostic classification and prognostic value. In normal cytogenetics AML patients, the NPM1 or CEBPA biallelic mutation alone is associated with favorable prognosis [¹⁵,¹⁶]. However, from clinical observations, some patients still show poor prognosis, thereby suggesting the possibility of other genetic-aberrant mutations that affect prognosis. In this study, 97.1% of the NPM1 mutations and 29.6% of the double-mutated CEBPA co-occurred with other genetic mutations. NPM1 usually co-mutates with FLT3-ITD, DNMT3A, and/or IDH2. Reports have shown a three-way interaction among NPM1, DNMT3A, and FLT3-ITD, and the deleterious prognostic effects of FLT3-ITD is significantly greater when both DNMT3A and NPM1 are mutated [¹⁰,¹⁷]. Double-mutated CEBPA that is concomitant with TET2 mutation has been associated with impaired overall survival (OS) [¹⁸,¹⁹]. However, the effect of other mutations on the prognosis of NPM1 or CEBPA biallelic mutations remains unclear. Therefore, further weight-coefficient and multivariate analyses are needed for processing in large-scale samples.

Different genes, different mutations in the same gene, and combinations of gene mutations may have different effects on the initiation, progression, and prognoses of diseases. Defining the mutation spectrum and mutation pattern of AML will contribute to the comprehensive assessment of patients and identification of new therapeutic targets.

References

[1] Döhner H, Weisdorf DJ, Bloomfield CD. Acute myeloid leukemia. N Engl J Med 2015; 373(12): 1136–1152

[2] Metzeler KH, Herold T, Rothenberg-Thurley M, Amler S, Sauerland MC, Görlich D, Schneider S, Konstandin NP, Dufour A, Bräundl K, Ksienzyk B, Zellmeier E, Hartmann L, Greif PA, Fiegl M, Subklewe M, Bohlander SK, Krug U, Faldum A, Berdel WE, Wörmann B, Büchner T, Hiddemann W, Braess J, Spiekermann K; AMLCG Study Group. Spectrum and prognostic relevance of driver gene mutations in acute myeloid leukemia. Blood 2016; 128(5): 686–698

[3] Grimwade D, Ivey A, Huntly BJP. Molecular landscape of acute myeloid leukemia in younger adults and its clinical relevance. Blood 2016; 127(1): 29–41

[4] Arber DA, Orazi A, Hasserjian R, Thiele J, Borowitz MJ, Le Beau MM, Bloomfield CD, Cazzola M, Vardiman JW. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood 2016; 127(20): 2391–2405

[5] Swerdlow SH, Campo E, Pileri SA, Harris NL, Stein H, Siebert R, Advani R, Ghielmini M, Salles GA, Zelenetz AD, Jaffe ES. The 2016 revision of the World Health Organization classification of lymphoid neoplasms. Blood 2016; 127(20): 2375–2390

[6] Liu HX, Wang F, Teng W, Lin YH, Yang JF, Zhang X, Yin Q, Chen X, Zhu P, Tong CR. Mutaome profiling and retrospective mutaome profiling using archived bone marrow smear in AML. Blood 2013; 122(21): 4983

[7] Pan J, Zhang Y, Zhao YL, Yang JF, Zhang JP, Liu HX, Wu T, Tong CR. Impact of clinical factors on outcome of leukemia patients with TLS-ERG fusion gene. Leuk Lymphoma 2017; 58(7): 1655–1663

[8] Saygin C, Carraway HE. Emerging therapies for acute myeloid leukemia. J Hematol Oncol 2017; 10(1): 93

[9] Li Y, Xu Q, Lv N, Wang L, Zhao H, Wang X, Guo J, Chen C, Li Y, Yu L. Clinical implications of genome-wide DNA methylation studies in acute myeloid leukemia. J Hematol Oncol 2017; 10(1): 41

[10] Papaemmanuil E, Gerstung M, Bullinger L, Gaidzik VI, Paschka P, Roberts ND, Potter NE, Heuser M, Thol F, Bolli N, Gundem G, Van Loo P, Martincorena I, Ganly P, Mudie L, McLaren S, O’Meara S, Raine K, Jones DR, Teague JW, Butler AP, Greaves MF, Ganser A, Döhner K, Schlenk RF, Döhner H, Campbell PJ. Genomic classification and prognosis in acute myeloid leukemia. N Engl J Med 2016; 374(23): 2209–2221

[11] Shlush LI, Zandi S, Mitchell A, Chen WC, Brandwein JM, Gupta V, Kennedy JA, Schimmer AD, Schuh AC, Yee KW, McLeod JL, Doedens M, Medeiros JJF, Marke R, Kim HJ, Lee K, McPherson JD, Hudson TJ; 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

[12] Genovese G, Kähler AK, Handsaker RE, Lindberg J, Rose SA, Bakhoum SF, Chambert K, Mick E, Neale BM, Fromer M, Purcell SM, Svantesson O, Landén M, Höglund M, Lehmann S, Gabriel SB, Moran JL, Lander ES, Sullivan PF, Sklar P, Grönberg H, Hultman CM, McCarroll SA. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N Engl J Med 2014; 371(26): 2477–2487

[13] Jaiswal S, Fontanillas P, Flannick J, Manning A, Grauman PV, Mar BG, Lindsley RC, Mermel CH, Burtt N, Chavez A, Higgins JM, Moltchanov V, Kuo FC, Kluk MJ, Henderson B, Kinnunen L, Koistinen HA, Ladenvall C, Getz G, Correa A, Banahan BF, Gabriel S, Kathiresan S, Stringham HM, McCarthy MI, Boehnke M, Tuomilehto J, Haiman C, Groop L, Atzmon G, Wilson JG, Neuberg D, Altshuler D, Ebert BL. Age-related clonal hematopoiesis associated with adverse outcomes. N Engl J Med 2014; 371(26): 2488–2498

[14] Cancer Genome Atlas Research Network, Ley TJ, Miller C, Ding L, Raphael BJ, Mungall AJ, Robertson A, Hoadley K, Triche TJ Jr, Laird PW, Baty JD, Fulton LL, Fulton R, Heath SE, Kalicki-Veizer J, Kandoth C, Klco JM, Koboldt DC, Kanchi KL, Kulkarni S, Lamprecht TL, Larson DE, Lin L, Lu C, McLellan MD, McMichael JF, Payton J, Schmidt H, Spencer DH, Tomasson MH, Wallis JW, Wartman LD, Watson MA, Welch J, Wendl MC, Ally A, Balasundaram M, Birol I, Butterfield Y, Chiu R, Chu A, Chuah E, Chun HJ, Corbett R, Dhalla N, Guin R, He A, Hirst C, Hirst M, Holt RA, Jones S, Karsan A, Lee D, Li HI, Marra MA, Mayo M, Moore RA, Mungall K, Parker J, Pleasance E, Plettner P, Schein J, Stoll D, Swanson L, Tam A, Thiessen N, Varhol R, Wye N, Zhao Y, Gabriel S, Getz G, Sougnez C, Zou L, Leiserson MD, Vandin F, Wu HT, Applebaum F, Baylin SB, Akbani R, Broom BM, Chen K, Motter TC, Nguyen K, Weinstein JN, Zhang N, Ferguson ML, Adams C, Black A, Bowen J, Gastier-Foster J, Grossman T, Lichtenberg T, Wise L, Davidsen T, Demchok JA, Shaw KR, Sheth M, Sofia HJ, Yang L, Downing JR, Eley G. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med 2013; 368(22): 2059–2074

[15] Vardiman JW, Thiele J, Arber DA, Brunning RD, Borowitz MJ, Porwit A, Harris NL, Le Beau MM, Hellström-Lindberg E, Tefferi A, Bloomfield CD. 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

[16] Döhner H, Estey EH, Amadori S, Appelbaum FR, Büchner T, Burnett AK, Dombret H, Fenaux P, Grimwade D, Larson RA, Lo-Coco F, Naoe T, Niederwieser D, Ossenkoppele GJ, Sanz MA, Sierra J, Tallman MS, Löwenberg B, Bloomfield CD; 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

[17] Wang M, Yang C, Zhang L, Schaar DG. Molecular mutations and their cooccurrences in cytogenetically normal acute myeloid leukemia. Stem Cells Int 2017; 2017: 1–11

[18] Fasan A, Haferlach C, Alpermann T, Jeromin S, Grossmann V, Eder C, Weissmann S, Dicker F, Kohlmann A, Schindela S, Kern W, Haferlach T, Schnittger S. The role of different genetic subtypes of CEBPA mutated AML. Leukemia 2014; 28(4): 794–803

[19] Grossmann V, Haferlach C, Nadarajah N, Fasan A, Weissmann S, Roller A, Eder C, Stopp E, Kern W, Haferlach T, Kohlmann A, Schnittger S. 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

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