To investigate frequency and prognostic impact of Wilms tumor 1 (WT1) mutations (mut), we analyzed 3157 unselected acute myeloid leukemia patients for WT1mut in exons 7 and 9. In total, 188 WT1 mutations were detected (exon 7: n=150, exon 9: n=38); 141 were frameshift, 24 missense, 14 non-sense, 7 splice site and 2 indel mutations. In 175/3157 (5.5%) patients, a WT1mut was found. Higher frequencies were detected in patients with biallelic CEBPAmut (13.6%; P=0.001), followed by t(15;17)/PML-RARA (11.0%, P=0.004), and FLT3-ITD (8.5%, P<0.001). WT1mut were rare in DNMT3Amut (4.4%, P=0.014), ASXL1mut (1.7%, P<0.001), IDH2R140 (1.7%, P=0.001) and IDH1R132 (0.9%, P<0.001), and not detected in complex karyotypes (P=0.047). They were more frequent in females than in males (6.6 vs 4.7%; P=0.014) and in patients <60 years (P<0.001). Analysis of paired samples of 35 patients revealed a relatively unstable character of WT1mut (65.7% retained, 34.3% lost WT1mut at relapse). In the total cohort and subgroups with high WT1mut incidences (biallelic CEBPAmut, PML-RARA), WT1mut had no impact on prognosis. In normal karyotype AML, WT1mut patients had shorter event-free survival (EFS) (10.8 vs 17.9 m, P=0.008). In multivariate analysis, WT1mut had an independent adverse impact on EFS (P=0.002, hazard ratio (HR): 1.64) besides FLT3-ITD status (P<0.001, HR: 1.71) and age (P<0.001, HR: 1.28).
The outcome of patients with acute myeloid leukemia (AML) is largely heterogeneous and depends, among others, on the cytogenetic and molecular genetic profiles. Cytogenetically normal AML (CN-AML) is the largest cytogenetic subgroup of AML, representing approximately 45% of adult patients with AML who are younger than 60 years.1, 2, 3 In the last two decades, CN-AML has been recognized to be highly heterogeneous on the molecular level, with mutations being discovered, for example in FLT3, NPM1, CEBPA and many other genes.4 These genetic markers mediate a significant impact on treatment outcomes and survival and may serve as a basis for a molecularly guided risk stratification in CN-AML.4,5
The discovery of additional genetic markers is likely to improve molecular risk stratification especially in CN-AML and will thus allow a more accurate prediction of response to current treatment strategies. A big step forward in evaluation of mutations has recently been made by next-generation sequencing approaches.6 However, to further work out the impact of certain rare constellations and combinations of mutations, larger cohorts are needed.
Wilms tumor 1 (WT1) gene was originally identified for its involvement in the pathogenesis of Wilms tumor7 in pediatric patients and congenital syndromes but also has been shown to be highly expressed in several myeloid malignancies, including AML.7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 The WT1 gene, located on chromosome 11p13,20 encodes a zinc-finger transcriptional regulator for genes involved in cellular growth and metabolism.21 Although initially considered only to be a tumor suppressor gene,22 WT1 also has been demonstrated to influence cell survival, proliferation and differentiation and may therefore act as an oncogene.21,23, 24, 25 However, this functional duality, to act as a tumor suppressor as well as an oncogene, is not well understood yet.21
Dysregulation of the WT1 gene by mutations and/or overexpression is relatively frequent in AML and might play a role in leukemic cell proliferation and impaired cell differentiation.21 Overexpression of the WT1 gene has been detected in 75–100% of patients with AML. However, the results of studies that evaluated the prognostic impact of WT1 expression levels at diagnosis have been inconsistent.17,18,26, 27, 28 On the other hand, patients with AML may also be affected by WT1 mutations. Mutations in the WT1 gene belong to the first genetic aberrations described in AML, but the impact of WT1mut still is discussed controversially. The prognostic impact of WT1 mutations in CN-AML was recently reported for cohorts comprising exclusively29, 30, 31 or almost exclusively32,33 younger (<60 years) adults. WT1 mutations, clustering in exons 7 and 9, occurred in 9–13% of patients29, 30, 31, 32, 33 and were associated with lower complete remission rates,33 higher relapse rates,29,30,33 shorter disease-free survival29,33 and overall survival (OS),29,30,33 or had no prognostic impact31 compared with WT1 wild-type (WT1wt) patients.
We report here the frequency of WT1 mutations and their association with clinical outcome in a large cohort of 3157 AML patients, characterized comprehensively for clinical and molecular prognosticators. To investigate characteristics of WT1-mutated (WT1mut) AML across age groups, we compared our findings in patients below vs above 60 years of age.
Patients and methods
A total of 3157 unselected AML patients were analyzed (de novo AML: n=2699, s-AML: n=234, t-AML: n=224). In all, 1708 patients were males and 1449 females. Median age was 67.1 years (range: 17.8–100.4 years) with 1108 patients <60 years and 2049 ⩾60 years (Table 1). Samples were sent from different hematological centers to the MLL Munich Leukemia Laboratory in the period 08/2005 to 04/2013 for diagnostic purposes. All patients gave their written informed consent for genetic analysis and for the use of the laboratory results for scientific studies. The study was approved by the Internal Review Board of the MLL Munich Leukemia Laboratory and adhered to the tenets of the Declaration of Helsinki.
Analysis of WT1 mutations
In 2708 cases bone marrow and in 449 cases peripheral blood was used for the molecular analysis. Isolation of mononuclear cells, DNA extraction and mRNA extraction as well as random primed cDNA synthesis were done following previous descriptions.34 The mutational hot spot regions of WT1 (exons 7 and 9) were analyzed in all patients by direct Sanger sequencing using genomic DNA, as previously described with a sensitivity of ~10%. In detail, exon 7 was amplified using primers Ex7F-IndexTermGACCTACGTGAATGTTCACATG and Ex7R-IndexTermACAACACCTGGATCAGACCT and exon 9 with primers Ex9F-IndexTermTGCAGACATTGCAGGCATGGCAGG and Ex9R-IndexTermGCACTATTCCTTCTCTCAACTGAG. The PCRs were carried out in a 50-μl reaction volume containing 25 μl Taq PCR master mix (Qiagen, Hilden, Germany) and 0.5 μM of both forward and reverse primers. Amplification was performed by touchdown PCR with 14 cycles at 67 °C and 19 cycles at 60 °C for 30 s each followed by elongation at 72 °C for 45 s. Amplicons were analyzed by direct Sanger sequencing in all cases using BigDye Term v1.1 cycle sequencing chemistry (Life Technologies, Darmstadt, Germany). For sequence evaluation, the Mutation Surveyor Software (Softgenetics, State College, PA, USA) was used.
Analysis for other gene mutations
Other gene mutations were assessed in subsets of patients either by direct Sanger Sequencing or by screening assay as described previously in the indicated references: ASXL1 (n=1951), CEBPA (n=2670), CBL (n=1427), DNMT3A35 (n=1293), FLT3-ITD34 (n=3149), FLT3-TKD36 (n=3004), IDH1R13237 (n=2431), IDH2R140 (n=2380), IDH2R172 (n=2412), JAK238 (n=658), KITD81639 (n=426), KRAS (n=1409), NRAS40 (n=1790), NPM141,42 (n=3003), MLL-PTD43 (n=2961), RUNX1 (n=2390), SF3B1 (n=456), TET244 (n=1016) and TP5345 (n=1215).
Cytomorphology, cytogenetics and immunophenotyping
Cytomorphologic assessment was done by May-Grünwald-Giemsa stains, myeloperoxidase reaction, and non-specific esterase using alpha-naphtyl-acetate following FAB and WHO classifications.46, 47, 48 Chromosome banding analysis was performed in all patients following standard methods.49,50 Karyotypes were described according to the International System for Human Cytogenetic Nomenclature.51 Complex karyotypes were defined as ⩾3 aberrations. Immunophenotyping was performed in 1539 cases (48.7%) as previously described.52,53
Definition of clinical end points and statistical analysis
Overall survival (OS) and event-free survival (EFS) were calculated according to Kaplan–Meier method. OS was the time from diagnosis to death or last follow-up. OS of patients receiving allogeneic hematopoietic stem cell transplantation was censored on the day of allogeneic stem cell transplantation. EFS was the time from diagnosis to treatment failure, relapse, death or last follow-up in complete remission. Survival outcomes were compared using log-rank test. Relapse was defined according to International Working Group Criteria.54 Median follow-up was calculated taking the respective last observations in surviving patients into account and censoring non-surviving patients at the time of death. Dichotomous variables were compared between different groups using the χ2-test and continuous variables by Student's T-test. Differences were considered as significant at P⩽0.05. All reported P-values are two-sided. SPSS (version 19.0.0) software (IBM Corporation, Armonk, NY, USA) was used for statistical analysis.
Frequency and characterization of WT1 mutations
In the overall cohort, WT1 mutations were found in 175/3157 patients, leading to a frequency of 5.5%. A total of 133 patients had WT1 mutations affecting exon 7 (76.0%) whereas 33 patients had WT1 mutations in exon 9 (18.9%). Four patients had WT1 mutations in exon 7 and exon 9 (2.3%) in combination. In five patients the exact sequence of the WT1 mutation could not be resolved, due to the low mutation load and the limited sensitivity of Sanger Sequencing (~10%). The total number of WT1 mutations detected was 188 (exon 7: n=150, exon 9: n=38). In patients with WT1 mutations affecting exon 7, six patients had more than two WT1 mutations (4.0%). Due to the methodology, it was not possible to resolve whether these mutations were at one or different alleles. Most mutations led to a shortened protein (frameshift: n=141/188, 75.0%; non-sense: n=14, 7.4%), whereas 24 mutations (12.8%) were missense (mostly in exon 9: n=19). In addition, we found seven splice site mutations (3.7%) and two indel mutations (1.1%) (Figure 1).
WT1 mutations within distinct cytogenetic subgroups
When comparing patients with normal to those with aberrant karyotype, no significant difference in WT1 mutation frequencies could be observed (107/1943, 5.5% vs 68/1214, 5.6%, P=n.s). Additionally, in the group of core binding factor leukemias, no significant difference in WT1 mutation frequency could be observed in comparison with the total cohort (12/213, 5.6% vs 163/2944, 5.5%, P=n.s.). Further, WT1 mutations were not detected in patients with complex karyotypes (0/53). However, WT1 mutations were significantly more frequent in patients with t(15;17)/PML-RARA (18/164; 11.0% vs 157/2993, 5.2%; P=0.004) as compared with patients without the respective genetic alteration (Figure 2a; Table 1).
WT1 mutations within distinct molecular subgroups
In the total cohort, WT1 mutations were significantly more frequent in patients with biallelic CEBPA mutations as compared to patients with monoallelic CEBPAmut or CEBPAwt (15/110, 13.6% vs 132/2560, 5.2%; P=0.001). Monoallelic CEBPAmut and wild-type forms were counted together since there was no difference in WT1mut frequency when analyzed separately. Also, WT1mut were significantly more frequent in patients with FLT3-ITD (58/682; 8.5% vs 117/2467; 4.7%; P<0.001) as compared to patients without FLT3-ITD. WT1 mutations showed lower frequency in patients with DNMT3A mutations (18/412; 4.4% vs 68/881; 7.7%; P=0.014), ASXL1mut (6/355; 1.7% vs 98/1596; 6.1%; P<0.001), IDH2R140 (5/286; 1.7% vs 128/2094; 6.1%; P=0.001), IDH1R132 (2/222; 0.9% vs 136/2209; 6.2%; P<0.001) and TET2mut (8/272; 2.9% vs 63/744; 8.5%; P=0.001) as compared to patients without the respective mutation. WT1 mutations were not detected in patients with IDH2R172 mutations (0/68; 0.0% vs 133/2344; 5.7%; P=0.020) (Figure 2b; Table 1).
WT1 mutation frequency according to age and sex
WT1 mutations were more frequent in females than in males (95/1449; 6.6% vs 80/1708; 4.7%; P=0.014) and in patients <60 years than in patients ⩾60 years (102/1108; 9.2% vs 73/2049, 3.6%; P<0.001). Median age of patients with WT1 mutations was lower with 55.4 years as compared with 67.5 years in WT1 wild type (P<0.001). The analysis of different decades of age revealed most striking differences in the frequencies of WT1 mutations between patients younger than 50 years (up to 12.4%) in comparison to patients with 50 years or more (50–59 years: 6.2%; 60–69 years: 3.3%; 70–79 years: 4.0%; ⩾80 years: 2.8%). We also compared patients <60 years and ⩾60 years in the prognostically favorable subgroups with CEBPA biallelic mutation or t(15;17)/PML-RARA. In the subgroup of CEBPA biallelic mutated patients 16.1% were <60 years and 11.1% ⩾60 years of age (P=0.317). In the subgroup of patients with t(15;17)/PML-RARA, 13.9% were <60 years and 5.4% were ⩾60 years of age (P=0.077).
Paired diagnostic and relapse samples
Stability of WT1 mutations was analyzed in 35 paired diagnostic and relapse samples. The median interval between diagnosis and relapse was 11.1 months (range: 2.6–60.6 months). WT1 mutation was retained at relapse in 23/35 cases (65.7%) and was lost in 12 cases (34.3%). There was no significant difference in time intervals from initial diagnosis to first relapse when comparing patients who retained or lost WT1 mutations (11.6 vs 9.4 months, P= n.s.) At second relapse, five cases were analyzed (median interval from first to second relapse: 8.5 months, range: 6.0–18.0 months). Three of these cases retained and two lost the WT1 mutation. This pattern revealed a relatively unstable character of WT1 mutation. However, almost all patients (34/35) had another mutation or genetic aberration in addition to WT1mut at diagnosis that remained stable at relapse and thus seems to be the primary aberration (Figure 3).
Prognostic impact of WT1 mutations in the total cohort
Analysis of prognostic impact was restricted to intensively treated patients (n=1936, WT1 mutations: n=132, 6.8%). Overall, there was no significant impact of WT1 mutations on prognosis (median EFS: 12.1 vs 18.1 months). In patients ⩾60 years, WT1 mutations conferred a trend to shorter EFS (9.3 vs 12.3 months, P=0.052).
Prognostic impact of WT1 mutations in patients with normal karyotype
When restricting the analysis to CN-AML (WT1 mutated: n=85, WT1 wild type: n=1093) patients with WT1 mutation had shorter EFS (10.8 vs 17.9 months, P=0.008) (Figure 4a). This was true for patients <60 years (12.2 vs 29.0 months, P=0.007) as well as for patients ⩾60 years (9.3 vs 13.9 months, P=0.016).
Univariate Cox analyses identified the following parameters as significantly impacting on EFS: age (P<0.001, hazard ratio (HR): 1.24 per 10 years of increase), ASXL1 mutations (P=0.020, HR: 1.36), FLT3-ITD (P<0.001, HR: 1.55), NPM1mut/FLT3-ITD negative (P<0.001, HR: 0.68), RUNX1mut (P=0.019, HR: 1.28), and WT1 mutations (P=0.009, HR: 1.44). In multivariate analysis, WT1 mutations had an independent adverse impact on EFS (P=0.002, HR: 1.64) in CN-AML, besides FLT3-ITD status (P<0.001, HR: 1.71) and age (P<0.001, HR: 1.28) (Table 2). However, there was again no significant impact of WT1 mutations on OS in CN-AML.
Prognostic impact of WT1 mutations in patients with favorable risk (CEBPA biallelic mutated and t(15;17)/PML-RARA)
As WT1 mutations were most frequent in patients of the two prognostically very favorable subgroups with biallelic CEBPA mutations or t(15;17)/PML-RARA, we analyzed these groups separately for prognosis. In the subgroup of biallelic CEBPA mutated patients median EFS was 40.2 compared with 45.9 months in all others and thus was not significantly different. In the subgroup of t(15;17)/PML-RARA-positive patients, also with FLT3-ITD, difference in EFS was also not significant (median was not reached; 2-year survival rate: 93.8 vs 87.9 vs 87.8%). Five patients exhibited both mutations (WT1mut and FLT3-ITD) and were included in the group of WT1mut patients (Figure 4b). All in all, in both AML subgroups, a significant prognostically more favorable impact of WT1 mutations in addition to CEBPAmut or t(15;17)/PML-RARA could not be shown. This may be due to the per se excellent favorable prognosis which renders the identification of other prognostically relevant genetic markers difficult. Moreover, the limited size of the subgroup of patients with CEBPA biallelic mutated or t(15;17)/PML-RARA and WT1 mutations in our study (n=14 and n=16, respectively) has to be considered. Overall, when taking also FLT3-ITD into account, in t(15;17)/PML-RARA-positive AML there was trend for longer EFS in the WT1mut/FLT3-ITD-negative compared with WT1wt/FLT3-ITD-positive (P=0.11) patients (Figure 4b).
So far, the role of WT1 mutations remains controversial for patients with AML with regard to the prognostic impact and their correlations to biologic parameters and genetic subgroups. This is in part due to the fact that most previous studies investigated selected cohorts of patients focusing on CN-AML.29,31,32 In this study, we investigated a large cohort of 3157 unselected patients with AML thus being able to delineate the frequency and characteristics of WT1 mutations more comprehensively. First, WT1 mutations were found at a frequency of 5.5% in overall AML. They were more frequent in patients <60 years than in patients ⩾60 years (9.2 vs 3.6%, P<0.001). Interestingly, WT1 mutations were also more frequent in prognostically favorable genetic subgroups like biallelic mutated CEBPA or PML-RARA-positive AML, where patients are usually younger (<60 years) at diagnosis. However, the frequent occurrence of WT1 mutations in younger AML patients cannot be explained sufficiently by this phenomenon, since the subgroups of these AML entities were very small as compared with the total cohort. With regard to patients with CN-AML, the corresponding frequencies in our cohort were 10.0% in patients <60 years and 3.4% in patients ⩾60 years. Similarly, in patients with CN-AML, Becker et al.55 reported on a higher frequency of WT1 mutations in those <60 years as compared with those ⩾60 years (12 vs 7%; P=0.07). In pediatric AML, WT1 mutations were reported by Ho et al.56 to occur in 35.3%, by Hollink et al.57 in 12%. Thus, our study provides further confirmation to the observation that the frequency of WT1 mutations in AML is decreasing by higher age.
Second, WT1 mutations showed significant positive correlations with other mutations such as biallelic CEBPA mutations and PML-RARA, which suggests cooperation in leukemogenesis. A significant coincidence of WT1 mutations with CEBPA mutations had also been described by Gaidzik et al.31 in CN-AML. To our knowledge, the positive correlation of WT1 mutations and PML-RARA as detected in our study had not been described before. In contrast, WT1 mutations never occurred in combination with complex karyotypes and IDH1/IDH2 mutations, which suggest mutual exclusiveness of WT1 mutations with the respective genetic alterations.
Furthermore, comparison of 35 samples at diagnosis and at relapse demonstrated a rather high level of instability of WT1 mutations as the mutations were lost in around a third of the cases at first relapse of the AML. Thus, WT1 mutations seem to contribute to leukemogenesis as secondary events rather than to initiate the AML. Considering the above described positive correlation of WT1 mutations with PML-RARA, it seems that the WT1 mutations represent another accompanying molecular marker that similarly to the FLT3-ITD or FLT3-TKD mutations may cooperate with PML-RARA.58 However, in this AML subgroup, a significant prognostically more favorable impact of WT1 mutations in addition to PML-RARA could not be shown. This may be due to the per se excellent favorable prognosis of APL which renders the identification of other prognostically relevant genetic markers in this genetic subgroup difficult. Moreover, the limited size of the subgroup of patients with APL and WT1 mutations in our study (n=16) has to be considered. Overall, when taking also FLT3-ITD into account in PML-RARA positive AML outcome seems to be heterogeneous depending on additional mutations (Figure 4b).
In the total cohort, WT1 mutations mediated a trend to shorter EFS in patients ⩾60 years and conferred an adverse impact on the EFS in the normal karyotype subgroup irrespective of age group. In multivariate analysis taking age, ASXL1mut, FLT3-ITD, NPM1mut/FLT3-ITD, RUNX1mut and WT1mut into account, WT1mut was shown to be an independent prognostic marker besides age and FLT3-ITD. This confirms most of the previous studies on WT1 mutations. Paschka et al.29 reported on an independent prognostically adverse effect of WT1 mutations in adult patients younger than 60 years with CN-AML. WT1 mutations conferred a significantly worse disease-free survival and OS and maintained prognostic significance also in multivariate analysis when mutations such as CEBPAmut and NPM1mut/FLT3-ITD were taken into account. Becker et al.55 had described in elderly CN-AML patients a shorter OS for WT1 mutated as compared with WT1 wild-type patients (P=0.08). Furthermore, WT1 mutations were reported to mediate an adverse effect on the response to induction therapy in a study performed in patients with CN-AML that mostly were younger than 60 years.33 In the study from Hou et al.59 in patients with non-M3 AML, WT1 mutation was identified to be an independent poor prognostic parameter in the total cohort and also in patients with CN-AML. Only one study did not observe a prognostic impact of WT1 mutations in their cohort of CN-AML patients. However, they demonstrated a significantly lower complete remission rate and an inferior relapse-free survival and OS in patients with WT1mut/FLT3-ITD-positive status as compared with WT1mut/FLT3-ITD-negative status.31 In pediatric AML, Hollink et al.57 described an independent significantly adverse effect of WT1 mutations.
In conclusion, compared with the total cohort WT1 mutations are more frequent in females and younger patients, in t(15;17)/PML-RARA, biallelic CEBPAmut and FLT3-ITD mutated AML. On the other hand, WT1 mutations were nearly mutually exclusive of ASXL1mut, IDH1 and IDH2 mutations and complex karyotypes. The distribution pattern in different genetic subtypes and the instability during follow-up as shown by paired sample analyses clearly emphasize a secondary character of WT1 mutations. Our study provides further confirmation to the adverse prognostic impact of WT1 mutations in CN-AML. Overall, this pattern suggests an interpretation as a non-subtype-defining mutation but as additional genetic event that may increase prognostic accuracy in patients with CN-AML.
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SS, WK, CH and TH are part owners of the MLL Munich Leukemia Laboratory GmbH. MTK, TA, UB, CE, FD, MU, SK and NN are employed by the MLL Munich Leukemia Laboratory GmbH.
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Krauth, MT., Alpermann, T., Bacher, U. et al. WT1 mutations are secondary events in AML, show varying frequencies and impact on prognosis between genetic subgroups. Leukemia 29, 660–667 (2015). https://doi.org/10.1038/leu.2014.243
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