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Asian Pacific Journal of Cancer Prevention : APJCP logoLink to Asian Pacific Journal of Cancer Prevention : APJCP
. 2022 Mar;23(3):977–984. doi: 10.31557/APJCP.2022.23.3.977

High Frequency of ASXL1 and IDH Mutations in Young Acute Myeloid Leukemia Egyptian Patients

Yasser H El Nahass 1,*, Heba A Nader 2, Salwa Sabet 3, Hend A Nooh 1, Heba Bassiony 3, Mahmoud Kamel 1, Mohamed A Samra 1, Hossam K Mahmoud 1, Wafaa H El Metnawy 4, Fatma A El Refaey 1
PMCID: PMC9360955  PMID: 35345371

Abstract

Background:

Prognostication of AML patients depends on association of genetic and epigenetic abnormalities. We aimed to evaluate the frequency and prognostic significance of Additional Sex comb’s Like1 (ASXL1), Isocitrate Dehydrogenase (IDH) and Casitas B- lineage Lymphoma (CBL) mutations in AML assessing their association with different cytogenetic risk category.

Methods:

We used High Resolution Melting (HRM) technology that detects small differences in PCR amplified sequences by direct melting using EvaGreen saturating dye to analyze epigenetic mutations in 70 denovo AML patients.

Results:

Median age of AML patients was 39.5 years (18-75). ASXL1, IDH and CBL mutations were detected in 14 (20%), 10 (14%) and 5 (7%) patients, respectively. Mean age of ASXL1 and IDH mutants vs. wild type was 35.9±14.6 years and 42.9±14.4 years (p=0.114) and 46.7±15.2 years vs. 40.6±14.5 years (p=0.290), respectively. AML cytogenetic risk groups included low (25/70, 36%), intermediate (33/70, 47%) and high-risk (12/70, 17%). Nine/14 (64%) ASXL1 and 8/10 (80%) IDH mutants were classified as intermediate risk and 9 ASXL1 positive (64%) were adolescent and young adults (AYA). Overall survival (OS) of mutant ASXL1 vs. wild type was 1.1 years (95% CI 0.83-1.4) vs. 1.9 years (95% CI 0.71-7.51), respectively (p=0.056). OS of mutant IDH vs. wild type was 1.25 years (95% CI 0.85-1.6) vs. 1.8 years (95% CI 1.2-6.7), respectively (p=0.020). In intermediate risk cytogenetic group, ASXL1 and IDH mutants had shorter OS than wild type; 1.1 years (95% CI 0.97-1.2) vs. 2.1 years (95% CI 0.14-10.8) (p=0.002) and 1.8 years (95% CI 0.69-3.15) vs. 2.3 years (95% CI 1.1-5.5) (p=0.05), respectively.

Conclusion:

ASXL1 and IDH mutations occur at a high incidence among young Egyptian AML patients with intermediate risk cytogenetics and confer a poorer outcome. Integration of mutations into risk profiling may predict outcome and impact therapeutic approach of young AML patient with uncertain prognosis.

Key Words: Acute myeloid leukemia, ASXL1, IDH, survival, intermediate risk cytogenetics

Introduction

Acute Myeloid Leukemia (AML) is a disease of elderly driven by many genetic and epigenetic aberrations that variably impact outcome. Recently, understanding AML pathogenesis has been clarified by newer molecular techniques (Döhner et al., 2015; Watts et al., 2018). Age is a strong prognostic factor in AML and unfavorable genetic profiles increase proportionately with advanced age (Creutzig et al., 2018). However, apart from age, genetic mutations can also affect outcome (Kuwatsuka et al., 2018).

In Egypt, the median age of incidence for AML is around 40 years (Ibrahim et al., 2014; El Gammal et al., 2019; Khaled et al., 2019); an age relatively younger than western reports where the median age of diagnosis is 68 years (Schnittger et al., 2013; Howlader et al., 2021). About 55% of newly diagnosed AML patients carry chromosomal abnormalities in addition to many genetic mutations as fms like tyrosine kinase 3(FLT3-ITD), Nucleophosmin (NPM1), CCAAT/enhancer binding protein alpha (CEBPA) and Runt-related transcription factor 1(RUNX1) mutations (Patriarca et al., 2015; Saultz et al., 2016). The European Leukemia Net (ELN) guidelines stratify AML into three prognostic risk categories (Favorable, Intermediate and Adverse) by combining the presence of karyotypic aberrations with genetic mutations (Döhner et al., 2017). As more mutations are discovered, molecular pathways in AML continue to be unraveled (Rocquain et al., 2010; De Kouchkovsky et al., 2016). However, molecular profiles across different age groups were different with increasing mutational burden with age (Creutzig et al., 2018). Genes regulating epigenetic modifications and chromatin structure as Additional Sex comb’s Like1 (ASXL1), Isocitrate Dehydrogenase (IDH) and Casitas B- lineage Lymphoma (CBL) have emerged as critical for AML pathogenesis and their alterations represent important prognostic markers in AML (Gallipoli et al., 2015). The incidence of alterations in these genes was mainly reported in elderly AML (Creutzig et al., 2018). Therefore, we sought to study their incidence in our young AML patients including a group of adolescent and young adults (AYA) in an attempt to identify disease outcome according to genetic mutations in a different age category.

ASXL1 gene plays a central role as an epigenetic regulator that participates in modulation of the transcription of genes involved in differentiation or proliferation as it affects histone methylation (Rahmani et al., 2019). Mutations of ASXL1 exon 12 have been demonstrated as relatively new molecular aberrations (Asada et al., 2019) reported in about 6% to 30% among AML patients (Schnittger et al., 2013; Paschka et al., 2015; Kakosaiou et al., 2018) and show a worse prognosis and inferior overall survival (OS) (Sasaki et al., 2020).

ASXL1 mutations are considered as early founder mutations in AML pathogenesis beside their inclusion among the high-risk genetic category in the last ELN recommendations (Kakosaiou et al., 2018). Association between ASXL1 mutations and different karyotype abnormalities are quite diverse (Paschka et al., 2015).

Mutations of IDH genes in AML were reported around 20% and were found more frequently in elderly individuals (Medeiros et al., 2017). IDH mutations are commonly found within the intermediate-risk cytogenetic group especially with normal karyotype (Inoue et al., 2016; Papaemmanuil et al., 2016; ElNahass et al., 2020). AML with IDH1 mutations is characterized by abnormal histone and DNA methylation which may result in a blocked cell differentiation (Inoue et al., 2016).

Novel mutations in CBL gene have also been found in AML such as several gain-of-function mutations (Nadeau et al., 2017). CBL gene (localized on human chromosome 11q23) is a negative regulator of activated FLT3 receptor tyrosine kinase. CBL gene mutations have been found in approximately 5% of de novo and secondary AML (Liyasova et al., 2015).

The incidence of epigenetic mutations in younger AML patients has not been estimated. Genomic analysis for newly diagnosed young AML patients plays a crucial role in their categorization especially for the intermediate risk category that represents half of newly diagnosed AML. Approximately 45% of AML patients show a normal karyotype at diagnosis and are classified as intermediate risk (Bolli et al., 2015; Lagunas-Rangel et al., 2017). The optimal therapeutic strategies for this subgroup are still largely debatable and a considerable heterogeneity is found in this population regarding outcome and survival. Identifying a high risk subgroup among young intermediate risk AML patients according to epigenetic markers is an additional integrated molecular risk stratification based on detection of acquired mutations (Mahmoud et al., 2016). Molecular profiling provides further classification, and prognostication which may help in more specific selection for therapeutic interventions (Wertheim et al., 2015; Papaemmanuil et al., 2016; Sun et al., 2018; Waitkus et al., 2018).

This study was undertaken to assess the incidence and frequency of ASXL1, IDH and CBL mutations in de novo young AML patients to evaluate their association with cytogenetic risk category and assess their impact on disease outcome.

Materials and Methods

Patients and samples

Seventy de novo AML patients with median age 39.5 years (18-75), along with fifty healthy controls were enrolled in this study. Patients presented to the Medical Oncology and Hematology Unit at the National Cancer Institute (NCI), Cairo University between 2016 and 2019. AML diagnosis was based on morphology, cytochemistry, immunophenotyping, cytogenetics and routine molecular detection of NPM1, CEBPA, C-KIT and FLT3-ITD by Polymerase Chain Reaction (PCR). Diagnosis was established according to the World Health Organization (WHO) classification of tumors of hematopoietic and lymphoid tissues (Arber et al., 2016). Cytomorphology was based on May-Grunwald-Giemsa stain. Karyotypes by short term cultures were analyzed by G-banding according to International System for Human Cytogenomic Nomenclature (ISHCN) (McGowan-Jordan., 2016). Immunophenotyping was performed by Coulter EPICS XL-MCL (Coulter Corporation, Hialeah) (Kern et al., 2004). Patients were risk classified according to the combination between karyotypic aberrations and genetic mutations according to the ELN classification (Döhner et al., 2017).

Patients with Acute promyelocytic leukemia and history of AML treatment or therapy related were excluded. Median follow up was 22 months (0.03-35.69). Informed consents were provided by all patients and healthy controls. Study was approved by NCI Institutional Review Board according to Helsinki Declaration, (IRB No: IRB00004025).

Methods

ASXL1, IDH1/2 and CBL mutations screening by High-Resolution Melting (HRM) Analysis

DNA was extracted from bone marrow (BM) aspirates or peripheral blood using Puregene® Blood Core Kit A (Qiagen, Germany). Primers were designed, using “primer 3 plus”, to amplify exon 12 of ASXL1, exon 4 of IDH1 and IDH2, and exons 8 and 9 of CBL genes covering most common reported mutations in ASXL1; c.1934dupG “rs750318549”, c.1900_1922del “rs766433101” and c.1934delG, IDH1; R132C “rs121913499” and RI32H “rs121913500”, IDH2; R140Q “rs121913502”, R172K “rs121913503” and R172W “rs1057519906”, and CBL; p.(Glu366Lys) “rs397517076”.

Twenty nanogram of DNA was amplified in a final volume of 10 uL containing 1X High Resolution Melting (HRM) PCR Master Mix (Type-it® HRM™ PCR KIT (Qiagen, Germany) including EvaGreen dsDNA (double-stranded deoxyribonucleic acid) saturating fluorescent binding dye, 0.2mM of each primer and 2.5mM MgCl2.

Positive and non- template controls were included in each experiment. All samples were performed in duplicates. Cyclic parameters consisted of: initial denaturation at 95ºC for 10 min; 45 cycles of 95ºC for 10 sec, a 13-second annealing at the indicated temperature in Table S1, and 72ºC for 20 sec. Final melting program was denaturation at 95ºC for 1min, renaturation at 45ºC for 1 min and melting from 60ºC to 95ºC with a ramp of 0.2ºC/sec and 25 fluorescence acquisitions/ºC (Ibáñez et al., 2012). The complete details of designed primer sequence and annealing temperatures are available in Table S1. Wild-type and mutated samples were defined as negative and positive controls in the software. All HRM results were analyzed as fluorescence versus temperature graphs by Eco Illumina software (San Diego, CA) with normalized, temperature-shifted melting curves displayed as difference plot.

Statistical Methods

Statistical analysis was done using IBM SPSS® Statistics version 22 (IBM® Corp., Armonk, NY, USA). Numerical data were expressed as mean and standard deviation (SD) or median and range as appropriate. Qualitative data were expressed as frequency and percentage. Pearson’s Chi-square or Fisher’s exact tests were used to examine the relation between qualitative variables. Numeric data were tested for normality using Kolmogorov-Smirnov test and Shapiro-Wilk test. For not normally distributed quantitative data, comparison between two groups was done using Mann-Whitney test (non-parametric t-test). Survival analysis was done using Kaplan-Meier method and comparison between two survival curves was done using log-rank test. All tests were two-tailed. A p-value < 0.05 was considered significant.

Results

Median age of 70 newly diagnosed AML patients was 39.5 years (18-75); mean age 41.5±14.6 years. Patients’ characteristics and frequency of genetic mutations are provided in Table 1. No mutations in ASXL1, IDH1/2 or CBL genes were detected in the 50 healthy controls recruited in this study.

Table 1.

Clinical and Genetic Characteristics of 70 AML Patients

Patients number (percent)
Gender
Male 32 (45.7%)
Female 38 (54.3%)
FAB subtype
AML M0 6 (9%)
AML M1 12 (17%)
AML M2 25 (35%)
AML M4 20 (29%)
AML M5 7 (10%)
Cytogenetic risk
Low 25 (36%)
Intermediate 33 (47%)
High 12 (17%)
Genetic Mutations
ASXL1 14 (20%)
IDH 10 (14%)
CBL 5 (7%)
FLT3-ITD 12 (17%)
NPM1 20 (29%)
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Median peripheral blood (PBL) blasts of mutant ASXL1 was 74 % (7-85) vs. 58 % (4 - 98) for wild type ASXL1. Ten/12 (83%) mutant ASXL1 patients had PBL blasts ≥ 50% vs. 2/12 (17%) wild type ASXL1 (p =0.051). Seven/10 (70%) mutant IDH had BM blasts ≥ 50% vs. 3/10 (30%) wild type patients (p ≥0.001). Four/5 (80%) mutant CBL patients had BM blasts ≥ 50% vs. 1/5 (20%) wild type patients. From 10 patients with IDH mutations, IDH1 was positive in 2 (20%) while IDH2 mutations in 8 (80%) patients. NPM1 and FLT3-ITD co-occurred in only 4 patients. Table 2 demonstrates the correlation between genetic mutations and laboratory parameters.

Table 2.

Correlation between ASXL1, IDH and CBL Mutations and Laboratory Parameters

Hb(g/dl) TLC (×109/L) Platelets (×109/L) PB blasts (%) BM blasts (%)
<8 gm/dl ≥8gm/dl <11x 109/L ≥11x109/L <100x109/L ≥100x109/L ≤50% >50% ≤50% >50%
ASXL1 Wild (n=56) 28 (50%) 28(50%) 12 (21%) 33 (59%) 37 (66%) 19 (34%) 32(57%) 24(43%) 24 (43%) 42(75%)
ASXL1 Mutant (n=14) 10 (71%) 4 (29%) 5 (36%) 9 (64%) 10 (71%) 4 (28%) 4 (28%) 10(71%) 2(14%) 12 (86%)
IDH Wild (n=60) 31 (51%) 29 (49%) 26 (43%) 34 (57%) 40 (67%) 20 (33%) 33(55%) 27(45%) 14(23%) 46(77%)
IDH Mutant (n=10) 7(70%) 3 (30%) 2 (20%) 8 (80%) 8 (80%) 2 (20%) 3(30%) 7(70%) 2(20%) 8(80%)
CBL Wild (n=65) 33 (51%) 32(49%) 28 (43%) 37 (57%) 43(66%) 22 (34%) 32 (49%) 33 (51%) 14(22%) 51(78%)
CBL Mutant (n=5) 4 (80%) 1 (20%) 1(20%) 4(80%) 4(80%) 1 (20%) 4 (80%) 1 (20%) 2(40%) 3(60%)
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There was a slight male predominance in ASXL1 and IDH mutations occurrence as 8/14 (57%) and 7/10 (70%) were males; respectively in addition 3/5 (60%) mutants CBL were males. Interestingly, the mean age of ASXL1 mutant was lower than wild type counterpart and ASXL1 mutations were found at a higher frequency in AYA. The mean age of different mutational category is provided in Table 3. The incidence of genetic mutations in AYA vs. older AML patients is demonstrated in Table 4.

Table 3.

Mean and Median Age of AML Patients According to ASXL1, IDH and CBL Mutations

Mean age ±SD (years) Median age (range)
Mutant ASXL1 (n:14) 35.9±14.6 35.5 (18-60)
Wild ASXL1 (n: 56 ) 42.9±14.4 42.5 (19-75)
Mutant IDH (n: 10) 46.7±15.2 46.5 (26-75)
Wild IDH (n: 60 ) 40.6±14.5 37 (18-70)
Mutant CBL (n:5) 36.8±10 37 (22-49)
Wild CBL (n: 65 ) 41.8±14.9 40 (18-75)
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Table 4.

Incidence of Epigenetic Mutations in 70 AML Patients According to Two Ages Cut off 39 and 55 Years

Age ASXL1 mutant IDH mutant CBL mutant All
mutations
<39 years (n=34) 9 (26%) 3 (9%) 3 (9%) 15 (44%)
≥39 years (n=36) 5 (14%) 7 (19%) 2 (6%) 14 (39%)
<55 years (n=54) 12 (22%) 7 (13%) 5 (9%) 24 (44%)
≥55 years (n=16) 2 (13%) 3 (19%) 0 (0%) 5 (31%)
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Association between ASXL1 mutations and karyotype and other molecular abnormalities

Twelve /14 (85.7%) ASXL1 mutant patients were CN-AML. ASXL1 and FLT3-ITD were mutually exclusive; 13/14 (93%) mutant ASXL1 patients were FLT3-ITD negative. ASXL1 mutation showed an inverse association with NPM1; 8/14 (79%) ASXL1 mutant were NPM1 negative. ASXL1 and CBL mutations co-occurred in one patient.

Mutual exclusivity between genetic markers

All 14 mutant ASXL1 patients were negative for IDH mutations (p=0.088). All mutant CBL patients were negative for core binding factor (CBF) translocations and IDH mutations (p<0.001). CBL mutations were significantly associated with FLT3-ITD (3/5, 60%) (p=0.050). The association between ASXL1, IDH and CBL mutations and other molecular and cytogenetic findings is presented in Table 5.

Table 5.

Association between ASXL1, IDH and CBL Mutations and Other Molecular and Cytogenetic Findings

CN-AML FLT3 +ve NPM1 +ve CBF translocations
ASXL1 mutant (n=14) 12/14 (86%) 1/14 (7%) 3/14 (21%) 2/14 (14%)
IDH mutant (n=10) 9/10 (90%) 2/10 (20%) 0/10 (0%) 0/10 (0%)
CBL mutant (n=5) 4/5 (80%) 3/5 (60%) 2/5 (40%) 0/5 (0%)
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Association between ASXL1, IDH and CBL mutations and AML cytogenetic risk

We observed an association between ASXL1 and IDH mutations and cytogenetic risk group (Table 6). Genetic mutations occurred more frequently within the intermediate risk category. No association between CBL mutations and cytogenetic risk groups could be established due to low number of positive CBL mutations.

Table 6.

Frequency of ASXL1 and IDH Mutations among Different AML Cytogenetic Risk Group

Low risk Intermediate risk High risk P
Mutant ASXL1 (n=14) 4 (29%) 9 (64%) 1 (7%) 0.05
Wild ASXL1 (n=56) 21 (37%) 24 (43%) 11 (20%)
Mutant IDH (n=10) 0 (0%) 8 (80%) 2 (20%) 0.015
Wild IDH (n=60) 25 (42%) 25(42%) 10 (16%)
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Response to therapy and survival in ASXL1 and IDH mutant patients

At end of induction chemotherapy, 42/70 (60%) patients achieved hematological complete remission (CR). A significant relation was detected between mutant IDH patients and CR rates as only 3/10 mutant IDH (30%) achieved CR at day 28 vs. 7/10 patients (70%) who failed to achieve CR (p=0.036). Median follow up period was 1.85 years (0.3 -5) after exclusion of early deaths. The median OS was 1.97 years. Median OS was significantly superior in wild type ASXL1 and IDH vs. mutant patients (Table 7).

Table 7.

Association between day 28 CR and Survival and ASXL1 and IDH Mutations

CR Day 28 P Survival P
CR (n%) No CR (n%) No of events Median survival (years)
53 1.97
ASXL1 Mutant (n=14) 7 (50%) 7 (50%) 0.393 13 1.12 0.056
ASXL1 Wild (n=56) 35(63%) 21 (37%) 30 1.9
IDH Mutant (n=10) 3 (30%) 7 (70%) 0.036 10 1.25 0.02
IDH Wild (n=60) 39(65%) 21(35%) 33 1.8
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OS of ASXL1 mutant was 1.1 years (95% CI 0.83-1.4) vs. 1.9 years (95% CI 0.71-7.51) for wild type patients (p=0.056) (Figure 1a). A significant association was established between IDH mutation and survival as median OS of mutant IDH was 1.25 years (95% CI 0.85-1.6) vs. 1.8 years (95% CI 1.2-6.7) for wild type patients (p=0.020) (Figure 1b).

Figure 1.

Figure 1.

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Survival Data and Outcome of AML Patients Using Kaplan-Meier Plot Showing Inferior OS in (a) ASXL1 Mutant and (b) IDH Mutant

Response to therapy and survival in ASXL1 and IDH mutant patients in the intermediate risk cytogenetic category

In the intermediate risk cytogenetic group, OS of ASXL1 mutant patients was significantly inferior to wild type ASXL1; 1.1 years (95% CI 0.97-1.21) vs. 2.1 years (95% CI 0.14-10.83) respectively (p=0.002) (Figure 2a). Median OS of IDH mutants was 1.8 years (0.7-3.1) vs. 2.3 years (1.1-5.5) for wild type patients (p=0.05) (Figure 2b).

Figure 2.

Figure 2

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Survival Data and Outcome of AML Patients with Intermediate Risk Cytogenetics Showing Inferior OS in (a) ASXL1 Mutant and (b) IDH Mutant

Discussion

Results of National population based Cancer Registry and National Cancer Institute in Egypt showed a lower median age of AML patients compared to Western population (Ibrahim et al., 2014; El Gammal et al., 2019). In one Egyptian report including 468 myeloid leukemia, median age was 43 years (Khaled et al., 2019). The median age of AML patients in western countries is around 65 years (Grimwade et al., 2001; Shallis et al., 2019). The prognostic role of many genetic mutations and their association with AML pathophysiology has been largely examined in elderly patients (Papaemmanuil et al., 2016). However, the prevalence of these genetic mutations has not been examined in young AML cohorts. Specific gene mutations as ASXL1 and IDH can further classify patients and affect prognosis (Medinger et al., 2016). This is particularly relevant for intermediate risk AML (Wang et al., 2017). Results regarding frequency of genetic mutations and association with karyotype abnormalities are quite diverse (Schnittger et al., 2013). ASXL1 mutations were reported from 6-30% of AML and were found more common in older patients compared to younger population (Schnittger et al., 2013; Tsai et al., 2016). In the current study, ASXL1 was the most frequently mutated gene after NPM1 with a slightly higher incidence in males and its frequency was comparably higher in our younger patients especially AYA which may denote a different age-related molecular pattern in AML. Regarding laboratory parameters, a significantly higher number of patients in ASXL1 mutant arm had Hb<8 gm/dl (64%), PBL blasts >50% (86%) and BM blasts >50% (86%) than in the wild type ASXL1 arm. Most ASXL1 mutant patients were classified as intermediate risk cytogenetics (9/14, 64%); an incidence higher than previously reported regarding age and cytogenetic risk classification (Schnittger et al., 2013). Variable reports about the incidence and associations with other molecular markers and with biologic characteristics are still reported, mainly because of selected cohorts or different ethnical backgrounds (Schnittger et al., 2013). Among older patients, ASXL1 mutations were associated with CBFA, wild-type NPM1, negative FLT3-ITD, mutated CEBPA, and inferior CR and OS (Kuwatsuka et al., 2018). In our study, epigenetic mutations were detected in 44% of patient <39 years vs 39% in patients >39 years. Among 29 mutations detected, 5 (31%) mutations (2 ASXL1/3 IDH) were encountered in age group >55 years vs. 24 (44%) mutations (12 ASXL1 / 7 IDH/ 5 CBL) in age group <55 years. In the current work, ASXL1 mutation was associated with distinct clinical features like male sex, younger age (mean 35.9±14.6), intermediate risk cytogenetics (64% of mutant patients) in addition to adverse prognosis and lower OS. Median survival of ASXL1 mutant was inferior to wild type patients. Furthermore, AML patients with intermediate risk cytogenetics showed inferior OS for ASXL1 mutants vs. wild type (p=0.002). ASXL1 mutation was mutually exclusive with FLT3-ITD and NPM1 mutations and found as independent adverse prognostic factor for OS. We tested ASXL1 exon 12, which actually comprises >50% of the whole coding region of ASXL1 using HRM technique which is an alternative and more rapid and cost-effective method of detection in countries of limited resources. All mutations found in ASXL1 were heterozygous as previously reported (Patel et al., 2012).

Fourteen% of patients were positive for IDH mutations with a mean age of 46.7±15.2 years. IDH1 and 2 mutations were also mutually exclusive as previously reported (Kuwatsuka et al., 2018; ElNahass et al., 2020). This incidence is in agreement with others (Rocquain et al., 2010; Montalban-Bravo et al., 2018) however, mean age of mutation occurrence is still younger than western population. IDH1 and IDH2 mutations have been reported in 15-20% and 25- 30% of patients with intermediate risk-AML, respectively (Saultz et al., 2016). Most IDH mutations (80%) in our study resided in the intermediate risk cytogenetic category and were higher than reported; where only 20% mutant IDH patients were high-risk AML. We have correlated IDH mutations with patient characteristics, different laboratory parameters and AML prognostic factors. In the IDH mutant arm, a higher number of patients had <8gm/dl (70%), TLC> 11x109/L (80%) and PBL blasts >50% (80%) vs. wild type arm. IDH mutations showed mutual exclusivity with NPM1, FLT3-ITD, ASXL1 and CBL mutations. All mutant IDH patients were negative for CBF translocations. Median survival of IDH mutant was inferior to wild type (p=0.020). OS of IDH mutant patients in the intermediate risk AML group was inferior to wild type (p=0.05). IDH mutations were associated with lower DFS and OS in CN-AML cases with NPM1 mutations and wild-type FLT3. DFS and OS of IDH mutant patients in the intermediate risk AML group were significantly inferior to wild type. CBL mutations were significantly associated with FLT3-ITD; however due to low number of mutated patients, conclusions regarding outcome could not be drawn. Regarding the poor outcome of our intermediate risk AML patients with ASXL1 and IDH mutations, the evidence for early allogeneic stem cell transplantation in CR1 in our young patients may be stronger based on these high molecular risk markers.

In conclusion, this study demonstrates an overall high incidence of ASXL1 and IDH mutations in young AML patients and a high incidence of ASXL1 mutations in AYA. ASXL1 and IDH mutations are frequently occurring in CN-AML and are mutually exclusive with NPM1 and FLT3-ITD and can sub-stratify patients within AML intermediate risk cytogenetic category. Therefore, molecular detection of ASXL1 and IDH mutations could potentially be used in addition to cytogenetics to redefine risk stratification and prognostication of young AML patients with uncertain outcome.

Acknowledgments

Acknowledegments

We would like to thank all staff in Genome oncocenter and NCI for providing data.

Ethics approval and consent to participate

Informed consents were provided by all patients. Study was approved by NCI Institutional Review Board according to Helsinki Declaration, (IRB No: IRB00004025).

Funding Statement

The authors received no financial support for the research, authorship, and/or publication of this article.This manuscript is part of an approved student MSc thesis

Conflict of interest

The authors declare that they have no competing interests.

References

  1. Arber DA, Orazi A, Hasserjian RP, et al. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood. 2016;127:2391–405. doi: 10.1182/blood-2016-03-643544. [DOI] [PubMed] [Google Scholar]
  2. Asada S, Fujino T, Goyama S, KITamura T. The role of ASXL1 in hematopoiesis and myeloid malignancies. Cell Mol Life Sci. 2019;76:2511–23. doi: 10.1007/s00018-019-03084-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Bolli N, Manes N, McKerrell T, et al. Characterization of gene mutations and copy number changes in acute myeloid leukemia using a rapid target enrichment protocol. Haematologica. 2015;100:214–22. doi: 10.3324/haematol.2014.113381. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Creutzig U, Kutny MA, Barr R, Schlenk RF, Ribeiro RC. Acute myelogenous leukemia in adolescents and young adults. Pediatr Blood Cancer. 2018;65:e27089. doi: 10.1002/pbc.27089. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. De Kouchkovsky I, Abdul-Hay M. Acute myeloid leukemia: a comprehensive review and 2016 update. Blood Cancer J. 2016;6:441. doi: 10.1038/bcj.2016.50. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Döhner H, Weisdorf DJ, Bloomfield CD. Acute myeloid leukemia. N Engl J Med. 2015;373:1136–52. doi: 10.1056/NEJMra1406184. [DOI] [PubMed] [Google Scholar]
  7. Döhner H, Estey E, Grimwade D, et al. Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood. 2017;129:424–47. doi: 10.1182/blood-2016-08-733196. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. El Gammal MM, Owaidat HM, Rashed RA, Abdel Fatah R, Samra MA. Prognostic and therapeutic value of day 14 bone marrow aspiration in adult acute myeloid leukemia patients. Clin Lymphoma Myeloma Leuk. 2019;19:406–13. doi: 10.1016/j.clml.2019.03.024. [DOI] [PubMed] [Google Scholar]
  9. ElNahass YH, Badawy RH, ElRefaey FA, et al. IDH mutations in AML patients; A higher Association with Intermediate Risk Cytogenetics. Asian Pac J Cancer Prev. 2020;21:721–5. doi: 10.31557/APJCP.2020.21.3.721. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Gallipoli P, Giotopoulos G, Huntly BJP. Epigenetic regulators as promising therapeutic targets in acute myeloid leukemia. Ther Adv Hematol. 2015;6:103–9. doi: 10.1177/2040620715577614. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Grimwade D, Walker H, Harrison G, et al. The predictive value of hierarchical cytogenetic classification in older adults with acute myeloid leukemia (AML): analysis of 1065 patients entered into the United Kingdom Medical Research Council AML11 trial. Blood. 2001;98:1312–20. doi: 10.1182/blood.v98.5.1312. [DOI] [PubMed] [Google Scholar]
  12. Ibáñez M, Such E, Cerverna J, et al. Rapid screening of ASXL1, IDH1, IDH2, and c-CBL mutations in de novo acute myeloid leukemia by high-resolution melting. J Mol Diagn. 2012;14:594–601. doi: 10.1016/j.jmoldx.2012.06.006. [DOI] [PubMed] [Google Scholar]
  13. Ibrahim AS, Khaled HM, Mikhail NN, Baraka H, Kamel H. Cancer incidence in Egypt: results of the national population-based cancer registry program. J Cancer Epidemiol. 2014;2014:437971. doi: 10.1155/2014/437971. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Inoue S, Lemonnier F, Mak TW. Roles of IDH1/2 and TET2 mutations in myeloid disorders. Int J Hematol. 2016;103:627–33. doi: 10.1007/s12185-016-1973-7. [DOI] [PubMed] [Google Scholar]
  15. Inoue S, Li WY, Tseng A, et al. Mutant IDH1 downregulates ATM and alters DNA repair and sensitivity to DNA damage independent of TET2. Cancer Cell. 2016;30:337–48. doi: 10.1016/j.ccell.2016.05.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Kakosaiou K, Panitsas F, Daraki A, et al. ASXL1 mutations in AML are associated with specific clinical and cytogenetic characteristics. Leuk Lymph. 2018;59:2439–46. doi: 10.1080/10428194.2018.1433298. [DOI] [PubMed] [Google Scholar]
  17. Kern W, Voskova D, Schoch C, et al. Determination of relapse risk based on assessment of minimal residual disease during complete remission by multiparameter flow cytometry in unselected patients with acute myeloid leukemia. Blood. 2004;104:3078–85. doi: 10.1182/blood-2004-03-1036. [DOI] [PubMed] [Google Scholar]
  18. Khaled SA, Nabih O, Abdel Aziz NM, Mahran DG. Myeloid leukemias: A Glance at Middle Eastern Centers. J Blood Med. 2019;10:425. doi: 10.2147/JBM.S221317. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Kuwatsuka Y, Tomizawa D, Kihara R, et al. Prognostic value of genetic mutations in adolescent and young adults with acute myeloid leukemia. Int J Hematol. 2018;107:201–10. doi: 10.1007/s12185-017-2340-z. [DOI] [PubMed] [Google Scholar]
  20. Lagunas-Rangel FA, Chávez-Valencia V, Gómez-Guijosa MA, Cortes-Penagos C. Acute myeloid leukemia genetic alterations and their clinical prognosis. Int J Hematol Oncol Stem Cell Res. 2017;11:328. [PMC free article] [PubMed] [Google Scholar]
  21. Liyasova MS, Ma K, Lipkowitz S. Molecular pathways: CBL proteins in tumorigenesis and antitumor immunity—opportunities for cancer treatment. Clin Cancer Res. 2015;21:1789–94. doi: 10.1158/1078-0432.CCR-13-2490. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Mahmoud HK, ElHaddad AM, Fahmy OA, et al. Comparable outcome of allogeneic versus autologous hematopoietic peripheral blood stem cell transplantation in acute myeloid leukemia patients with normal karyotype and FLT3-ITD Negative. Leukemia. 2016;4:3. [Google Scholar]
  23. Medeiros BC, Fathi AT, DiNadro CD, et al. Isocitrate dehydrogenase mutations in myeloid malignancies. Leukemia. 2017;31:272–81. doi: 10.1038/leu.2016.275. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Medinger M. Lengerke C, Passweg J. Novel prognostic and therapeutic mutations in acute myeloid leukemia. Cancer Genom Proteom. 2016;13:317–29. [PMC free article] [PubMed] [Google Scholar]
  25. Micol JB, Abdel-Wahab O. The role of additional sex combs-like proteins in cancer. Cold Spring Harb Perspect Med. 2016;6:a026526. doi: 10.1101/cshperspect.a026526. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Montalban-Bravo G, Di Nardo CD. The role of IDH mutations in acute myeloid leukemia. Future Oncol. 2018;14:979–93. doi: 10.2217/fon-2017-0523. [DOI] [PubMed] [Google Scholar]
  27. Nadeau SA, An W, MohaPatra BC, et al. Structural determinants of the gain-of-function phenotype of human leukemia-associated mutant CBL oncogene. J Biol Chem. 2017;292:3666–82. doi: 10.1074/jbc.M116.772723. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Papaemmanuil E, Gerstung M, Bullinger L, et al. Genomic classification and prognosis in acute myeloid leukemia. N Engl J Med. 2016;374:2209–21. doi: 10.1056/NEJMoa1516192. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Patel JP, Gönen M, Figueroa ME, et al. Prognostic relevance of integrated genetic profiling in acute myeloid leukemia. N Engl J Med. 2012;366:1079–89. doi: 10.1056/NEJMoa1112304. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Patriarca A, Salutari P, Di Zacomo S. The impact of molecular genetic in acute myeloid Leukemia’s. J Blood Disord Transfus. 2015;6:1. [Google Scholar]
  31. Paschka P, Schlenk RF, Gaidzic VL, et al. ASXL1 mutations in younger adult patients with acute myeloid leukemia: a study by the German-Austrian Acute Myeloid Leukemia Study Group. Haematologica. 2015;100:324–30. doi: 10.3324/haematol.2014.114157. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Rahmani NE, Ramachandra N, Bahgat TD, et al. ASXL1 mutations are associated with widespread and distinct DNA methylation alterations. Blood. 2019;134:2989. [Google Scholar]
  33. Rocquain J, Carbuccia N, Trouplin V, et al. Combined mutations of ASXL1, CBL, FLT3, IDH1, IDH2, JAK2, KRAS, NPM1, NRAS, RUNX1, TET2 and WT1 genes in myelodysplastic syndromes and acute myeloid leukemias. BMC Cancer. 2010;10:1–7. doi: 10.1186/1471-2407-10-401. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Sasaki K, Kanagal Shamanna R, Montalban-Bravo G, et al. Impact of the variant allele frequency of ASXL1, DNMT3A, JAK2, TET2, TP53, and NPM1 on the outcomes of patients with newly diagnosed acute myeloid leukemia. Cancer. 2020;126:765–74. doi: 10.1002/cncr.32566. [DOI] [PubMed] [Google Scholar]
  35. Saultz JN, Garzon R. Acute myeloid leukemia: a concise review. J Clin Med. 2016;5:33. doi: 10.3390/jcm5030033. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Schnittger S, Eder C, Jeromin S, et al. ASXL1 exon 12 mutations are frequent in AML with intermediate risk karyotype and are independently associated with an adverse outcome. Leukemia. 2013;27:82. doi: 10.1038/leu.2012.262. [DOI] [PubMed] [Google Scholar]
  37. Shallis RM, Wang R, Davidoff A, Ma X, Zeidan AM. Epidemiology of acute myeloid leukemia: Recent progress and enduring challenges. Blood Rev. 2019;36:70–87. doi: 10.1016/j.blre.2019.04.005. [DOI] [PubMed] [Google Scholar]
  38. Sun Y, Chen BR, Deshpande A. Epigenetic regulators in the development, maintenance, and therapeutic targeting of acute myeloid leukemia. Front Oncol. 2018;8 doi: 10.3389/fonc.2018.00041. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Tsai CH, Hou HA, Tang JL, et al. Genetic alterations and their clinical implications in older patients with acute myeloid leukemia. Leukemia. 2016;30:1485–92. doi: 10.1038/leu.2016.65. [DOI] [PubMed] [Google Scholar]
  40. Wang M, Yang C, Zhang L, Schaar DG. Molecular mutations and their cooccurrences in cytogenetically normal acute myeloid leukemia. Stem Cells Int. 2017;5:1–11. doi: 10.1155/2017/6962379. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Watts J, Nimer S. Recent advances in the understanding and treatment of acute myeloid leukemia. F1000. 2018 doi: 10.12688/f1000research.14116.1. Faculty Rev-1196. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Waitkus MS, Diplas BH, Yan H. Biological role and therapeutic potential of IDH mutations in cancer. Cancer Cell. 2018;34:186–95. doi: 10.1016/j.ccell.2018.04.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Wertheim GB. Molecular characterization and testing in acute myeloid leukemia. J Hematop. 2015;8:177–89. [Google Scholar]

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