Abstract
Background/Objectives: Acute myeloid leukemia (AML) is the most common acute leukemia in adults, with over 50% of individuals succumbing to the disease annually. This study aimed to assess the correlation between human leukocyte antigen (HLA) genes and acute myeloid leukemia (AML) in an adult Moroccan cohort. We included 60 persons with acute myeloid leukemia (AML) who were eligible for hematopoietic stem cell transplantation and compared them to a control group of 90 healthy adults. Methods: Patients and controls were subjected to HLA class I and II typing utilizing either sequence-specific primers (SSP) or sequence-specific oligonucleotides (SSO) in polymerase chain reaction-based methodologies. Results: The AML categories were predominantly represented by AML2, AML3, and AML4, comprising 36.66%, 30%, and 16.66%, respectively. We identified a notable correlation between HLA-A*11 (p = 0.003) and HLA-B*27 (p = 0.005) with acute myeloid leukemia (AML), and for HLA class II allele groups, we detected an elevated frequency of HLA-DQB1*05 (p = 0.002) in adult AML patients. We identified a notable correlation between AML 2 and the allele groups examined, namely with HLA class I: HLA-A*11 (p = 0.0003) and HLA-B*27 (p = 0.00006). Conclusion: Our study suggests a potential association between specific HLA alleles and the development of AML specifically AML type 2 in adults. Further larger studies are needed to confirm these findings.
1. Introduction
Acute myeloid leukemia (AML) is the most common acute leukemia in adults and is responsible for the highest number of leukemia-related deaths, with about 50% of afflicted patients succumbing annually [1]. In the United States alone, AML is responsible for over 10,000 deaths annually [2]. It represents a diverse group of hematologic malignancies originating from progenitor cells of erythroid, megakaryocytic, myeloid, and monocytic lineages [3]. AML is marked by the buildup of undifferentiated blasts which severely inhibit the immune system and disrupt normal hematopoiesis, frequently resulting in bleeding and anemia. Approximately 97.3% of AML patients possess at least one driver gene mutation, with the disorder being primarily attributed to acquired somatic mutations [4]. While its precise etiology is often unidentified, AML accounts for about 80% of all leukemia cases. It may arise after exposure to specific risk factors, including prior cytotoxic chemotherapy, benzene, ionizing radiation, genotoxic chemicals, chromosomal abnormalities, or antecedent hematological disorders (e.g., bone marrow failure); however, its precise etiology remains unidentified. Acute myeloid leukemia (AML) originates in a multipotential hematopoietic stem cell that experiences malignant transformation, thereafter undergoing a sequence of genetic alterations prior to clinical presentation [5]. The median age at diagnosis in Western countries ranges from 65 to 72 years [6]. AML primarily impacts older adults, with more than two-thirds of cases occurring in those over 50 years of age [7]. As the second most common form of leukemia, it accounts for approximately 1.9% of cancer-related fatalities. Acute Myeloid Leukemia (AML) has a median diagnostic age of 68 years, primarily impacting middle-aged and older adults, with more than two-thirds of cases occurring in those over 50 years of age [7].
The genetic predisposition to AML remains unidentified, with the exception of highly penetrant syndromic or familial conditions [8]. Furthermore, recent investigations indicate that a significant number of AML patients lack mutations in the known driver genes currently associated with its pathophysiology [9].
Considered as the most polymorphic human genome, human leukocyte antigens (HLA) encompass a large number of class I (A, B, and C) and class II (DQ, DR, and DP) alleles, with a high heterozygosity rate (over 85%) observed in human populations, reflecting the significant diversity within these genes [1]. HLA molecules, particularly HLA-DRB1, are associated with over 100 diseases, including autoimmunity, infections, and cancer. In recent decades, numerous studies have highlighted the relationship between some HLA alleles and AML in certain pediatric populations, identifying specific alleles that may either predispose to AML or offer protection [2]. The associations between HLA alleles and AML have been shown by studies conducted in various countries, including Lebanon [3], the USA [4], Mexico [5], China [6], Turkey [7], India [8], and Iran [9]. However, the link between HLA, and susceptibility to AML remains contradictory, or at least insufficiently explored in different contexts. The objective of this study is to determine the frequency of HLA-A, -B, -DRB1, and DQB1 allele groups in Moroccan adult patients with AML and evaluate their potential association.
2. Materials and Methods
2.1. Study Design
We conducted a retrospective, cross-sectional, controlled study involving 150 participants. The patient cohort included 60 individuals diagnosed with AML, stratified as follows: AML-2 (n = 22), AML-3 (n = 18), AML-4 (n = 10), AML-1 (n = 2), AML-5 (n = 2), and unclassified AML (n = 4; no patients were diagnosed with AML-0 or AML-7); according to the most widely used classification system was the France, America, and Britain (FAB) system [10]. The control group consisted of 90 healthy adults. All participants were recruited from the southern regions of Morocco, including Marrakech-Safi, Souss-Massa, Beni-Mellal-Khenifra, Guelmim-Oued Noun, and Draa-Tafilalt between 2014 and 2025.
The AML diagnosis was confirmed based on complete blood count, peripheral smear analysis, and bone marrow examination. Inclusion criteria were meticulously established through a careful review of participants’ medical records in close collaboration with the hematology department. Patients and controls were selected from all cases referred to the HLA laboratory of the University Hospital, either as candidates for hematopoietic stem cell transplantation (HSCT) or as potential healthy donors. We excluded adults with lymphoid leukemia, other non-AML hematological malignancies, and pediatric patients.
2.2. Ethical Considerations
The patient and control samples used in this investigation were acquired during the standard operations of the HLA laboratory. Sociodemographic and clinical data were retrieved anonymously from the database under the oversight of the laboratory manager. In this instance, ethical approval and informed consent were not required.
2.3. Sample Collection and Processing
Peripheral venous blood was collected using two 5 mL tubes containing Ethylenediaminetetraacetic Acid (EDTA) as an anticoagulant according to a rigorously defined protocol designed for HLA typing. Upon collection, the samples were packaged in specific transportation bags and immediately transferred to the laboratory to maintain sample integrity during transit, which is critical to prevent degradation and to ensure the preservation of the cellular components necessary for accurate HLA typing.
2.4. DNA Extraction
Genomic DNA extraction from peripheral blood mononuclear cells (PBMCs) was executed using the QIAmp DNA Mini kit (Qiagen, Hilden, Germany) following a multi-step protocol. Briefly, this process began with cell lysis, where PBMCs were treated with a lysis buffer containing chaotropic salts to disrupt cell membranes and release genomic DNA. The lysate was then applied to a silica-based column, where the genomic DNA was selectively bound to the silica membrane, allowing for impurities to be washed away. A series of ethanol-based wash buffers were used to cleanse the DNA, ensuring the removal of salts, metabolites, and other contaminants. Finally, the purified DNA was eluted from the membrane using an elution buffer, carefully releasing the DNA without causing damage.
The resulting DNA was quantitatively and qualitatively assessed using a Nano Drop TM 2000/2000c Spectrophotometer (Thermo Scientific™, Waltham, MA, USA), which measured DNA concentration and purity, crucial for downstream molecular analyses.
2.5. HLA Typing
HLA class I (A and B loci) and class II (DRB1 and DQB1 loci) typing was performed utilizing two separate polymerase chain reaction (PCR) platforms, employing Sequence-specific Oligonucleotide (SSO) PCR, supplied by Immucor™ (LIFECODES® HLA-SSO Typing, Waltham, MA, USA) and Onelambda (Thermo Fisher Scientific, LabType™, Waltham, MA, USA). This method involves PCR amplification, succeeded by the hybridization of the amplified products to beads coated with oligonucleotide probes unique to known HLA sequences. The hybridization patterns were further evaluated using Luminex technology to detect the fluorescent signals of the microspheres. The color coding of each microsphere corresponds to a specific HLA probe, while the fluorescence intensity reflects the hybridization strength, facilitating the identification of the HLA A, B, DRB1, and DQB1 loci and specificities present in the sample.
The analysis of the supplied PCR typing data was conducted using Fusion 4.4® (One Lambda) or MATCH IT!® (Immucor) software, version 4.1.0 (Norcross, GA, USA) which incorporates fluorescence intensity data from Luminex xMAP technology. This software employs a database of established allele sequences to correlate with the observed probe fluorescence patterns, delivering an extensive profiling of HLA class I and class II loci and specificities for patients and controls.
2.6. Statistical Analysis
Data analysis was performed with the IBM SPSS Statistics (V26)—Code: 0G53BG (IBM, Armonk, NY, USA) program, and the χ2 test was used to establish the statistical significance of differences in HLA allele group frequencies between patients and controls. We also used Jamovi v.1.2 (The jamovi project, Sidney, Australia) to make the graphs of each significant allelic groups. Results were considered statistically significant when p-value ≤ 0.05.
3. Results
3.1. Patient Characteristics
As indicated in Table 1, the mean age of all patients was 32 ± 13 years (range: 19–45 years) compared to 35.5 ± 16.5 years in the control group (range: 19–52 years). This study revealed a male predominance of 58.33%, resulting in a male-to-female sex ratio of 1.4, compared to 0.8 in the control group. In regard to blood types, we found that type O positive was the most common in our sample and in the control sample, with a percentage of 40%; in addition, examining the association between HLA allelic groups and blood types showed no significant relation. Patients’ characteristics are shown in Table 1.
Table 1.
Patient demographic characteristics.
3.2. Frequency Determination of All AML Populations in HLA-A, B, DRB1, and DQB1 Allelic Groups
HLA-A and HLA-B typing was conducted for a total of 60 adult AML patients and 90 control individuals, while HLA-DRB1 and -DQB1 typing was performed for 44 patients and 73 controls. In total, 18 HLA-A alleles, 29 HLA-B alleles, 13 HLA-DRB1 alleles, and 5 HLA-DQB1 alleles were analyzed. The mean age of the AML patients was 32 years (ranging from 19 to 45 years).
In comparison to the control group, the following HLA class I alleles showed increased frequency in adult AML patients: A*11 (p = 0.003), B*27 (p = 0.005). For class II, an association with DQB1*05 (p = 0.02) was found (Table 1, Table 2, Table 3 and Table 4).
Table 2.
Likening of the frequency the HLA-A alleles in Moroccan adults with AML and adult healthy patients.
Table 3.
Likening of the frequency of HLA-B alleles in Moroccan adults with AML and adult healthy patients.
Table 4.
Likening of the frequency of HLA-DRB1 alleles in Moroccan adults with AML and adult healthy patients.
No significant differences were observed between males and females regarding the frequencies of various HLA alleles (p > 0.05).
3.2.1. HLA-A Allele Groups
The distribution of allelic groups within the study population: Among the 18 identified HLA-A allelic groups, HLA-A*02 was the most common in adult patients with AML, representing 17.50% of cases compared to 21.70% in the control group (Table 2).
3.2.2. HLA-B Allele Groups
Among the 30 HLA-B genes analyzed, HLA-B*44 was the most prevalent, occurring in 10.00% of patients and 10.00% of controls.
3.2.3. HLA-DRB1 Allele Groups
Among class II HLA molecules, DRB1*03 was the predominant allele in patients, accounting for 16.70%, while the control group exhibited 12 allele groupings, with HLA-DRB1*13 being the most prevalent at 15.00%.
3.2.4. HLA-DQB1 Allele Groups
Our study identified five allele categories for HLA-DQB1, with HLA-DQB1*02 exhibiting the highest frequency in the AML group at 29.20% compared to 28.90% in the control group as it showed in Table 5.
Table 5.
Likening of the frequency of HLA-DQB1 alleles in Moroccan adults with AML and adult healthy patients.
Figure 1 illustrates all the allelic groups associated with all type AML.
Figure 1.
Comparative frequency of HLA-A*11, B*27, and DQB1*05 between adult patients with AML and controls.
3.3. Frequency Determination of AML in 2HLA-A, B, DRB1, and DQB1 Allelic Groups
HLA-A and HLA-B typing was conducted on a total of 22 adult with AML 2 patients and 90 control individuals, while HLA-DRB1 and DQB1 typing was performed on 36 patients and 73 controls. In total, 18 alleles of HLA-A, 29 of HLA-B, and 13 of DRB1, and also 5 alleles of HLA-DQB1, were analyzed. The mean age of the AML patients was 32 years (ranging from 19 to 45 years).
In comparison to the control group, the following HLA class I alleles showed increased frequency in adult AML patients: A*11 (p = 0.0003), B*27 (p = 0.00006). With regard to the association between HLA class II and AML 2, this study shows no significant relationship (Table 6, Table 7, Table 8 and Table 9).
Table 6.
Comparison of the frequency of HLA-A alleles in Moroccan adults with AML 2 and adult healthy patients.
Table 7.
Comparison of the frequency of HLA-B alleles in Moroccan adults with AML 2 and adult healthy patients.
Table 8.
Comparison of the frequency of HLA-DRB1 alleles in Moroccan adults with AML 2 and adult healthy patients.
Table 9.
Comparison of the frequency of HLA-DQB1 alleles in Moroccan adults with AML 2 and adult healthy patients.
3.3.1. HLA-A Allele Groups in AML 2 Patients
The distribution of allelic groups within the study population: Among the 18 identified HLA-A allelic groups, HLA-A*03 was the most common in adult patients with AML 2, representing 20.5% of cases.
3.3.2. HLA-B Allele Groups AML 2 Patients
Among the 30 HLA-B genes analyzed, HLA-B*27 was the most prevalent, occurring in 13.6% of patients with AML 2.
3.3.3. HLA-DRB1 Allele Groups AML 2 Patients
Among class II HLA molecules, DRB1*15 was the predominant allele in patients with AML 2, accounting for 30.6%, while the control group exhibited 12 allele groupings, with HLA-DRB1*13 being the most prevalent at 15.00%.
3.3.4. HLA-DQB1 Allele Groups AML 2 Patients
Our study identified five allele categories for HLA-DQB1, with HLA-DQB1*02 exhibiting the highest frequency in the AML 2 group at 36.1% compared to 28.90% in the control group, but no significant association was found between HLA-DQB1 and AML 2.
The following Figure 2 represents the allele groups associated with AML 2.
Figure 2.
Comparative frequency of HLA-A*11 and B*27 between adult patients with AML 2 and controls.
3.4. Frequency Determination of Other Classes of AML in HLA-A, B, DRB1, and DQB1 Allelic Groups
Given the very limited number of patients with AML types other than AML 2 studied above, we examined the association of HLA-A, B, DRB1, and DQB1 allele groups with patients with AML 3 and 4, with percentages of 30% and 16.66%, respectively, which meet the statistical test standards used in our studies. Consequently, the studies did not reveal any significant association.
4. Discussion
The association between hematological malignancies and the HLA system was initially investigated in a 1967 study, which revealed an elevated prevalence of the HLA-A2 allele in individuals with acute leukemia. The HLA system has since been associated with other illnesses, including leukemia [11].
Our study reveals a heightened prevalence of particular HLA alleles in Moroccan people with AML, in contrast to the control group, within the framework of genetic risk factors for AML. We identified a significant relationship between HLA-A*11 (p = 0.003) and HLA-B*27 (p = 0.005), as well as HLA-DQB1*05 in adult patients with AML, corroborating data from Algeria [11] and India [12] regarding HLA-B*27. Additional findings have been documented in many populations, including the USA, where HLA-A*68 and HLA-B*52 were associated with AML [13]. Moreover, the HLA-A*30, HLA-A*68, and HLA-B*40 alleles have been identified in Lebanese, Chinese, and Mexican populations, respectively [14,15,16].
A prior Moroccan investigation on the general populace revealed a notable prevalence of specific alleles, including HLA-B*44 (12.7%; p = 0.02) and HLA-DRB1*13 (11.8%; p = 0.04), but HLA-DRB1*01 shown a diminished frequency (4.5%; p = 0.05). The data indicate that HLA-B44, HLA-DRB101, and HLA-DRB113 may contribute to either susceptibility to or protection against leukemia [17]. Our research additionally revealed that HLA-A11 is more prevalent among adult patients with AML, corroborating findings from Iranian and Chinese investigations [18,19]. Moreover, research conducted in India and Algeria indicated a correlation between HLA-B27 and AML, aligning with our results [20].
Ultimately, it has been noted that DRB* 15 and HLA-B49 are positively correlated with AML in Western countries. Conversely, it was shown that DRB1*01, A*23, A*68, B*13, B*38, and B*40 had a negative correlation with HLA-A*11. HLA-A*01 showed an inverse correlation with AML in the Asian demographic, while A*23, B*13, B*27, and B*37 demonstrated a positive correlation [21].
Our investigation indicated that the HLA-A*11 (p = 0.003) and B*27 (p = 0.005) allele groups were considerably more prevalent in AML 2 patients compared to controls; however, no significant correlation was observed in HLA class II (DQB1 and DRB1). Nonetheless, no research has been conducted to ascertain the relationship between HLA alleles and various types of AML.
On the other hand, we found that HLA-A*02 (p = 0.01) were protective alleles for AML 2 in our study, which is consistent with findings in the USA [22]. A recent study in Turkey reported that HLA-B*55 may serve as a protective factor against both acute lymphoblastic leukemia (ALL) and AML. Other protective alleles, such as HLA-A*26 (p = 0.025), HLA-A*33 (p = 0.02), and HLA-DRB1*03 (p = 0.035), have been reported to occur at significantly lower frequencies in AML patients [23]. However, it is important to note that, due to sample size limitations, there is no consensus on these associations. Some studies, including those in India and Romania, have reported contradictory findings compared to our results [23].
According to The Cancer Genome Atlas (TCGA), the most prevalent form of acute leukemia among adult Americans is acute myeloid leukemia (AML), with an average age of 67 [15], while the mean age of our population patients was 32 ± 13 years (range: 19–45 years); however, AML might occur to anyone at any age. Between the ages of 30 and 34 (1.3 instances per 100,000 people) and 65–69 (12.1 cases per 100,000 people), the probability of having AML increases almost nine times. The incidence peaks between the ages of 80 and 84 (28.5 instances per 100,000 persons), and the risk is still rising [24].
As a limitation of our study the HLA typing of the adult population with AML and the control group was carried out as part of hematopoietic stem cell transplantation from 2014 to 2025 in siblings of patients with AML using low-resolution typing due to the high cost of high-resolution typing and limited resources in Moroccan hospitals. This is a significant limitation, as sub-alleles can have distinct immunological properties and disease associations, but there are many recent studies that are performed using low-resolution HLA typing, such as in Turkey, for example [25]. However, high-resolution HLA typing will be very useful for this type of study.
5. Conclusions
Our study indicates a substantial correlation between particular HLA allele groupings and the onset of AML in Moroccan adults. The identification of both risk and protective alleles may enhance our understanding of the genetic factors leading to AML susceptibility. These findings correspond with research from other populations, reinforcing the usefulness of HLA alleles as indicators of AML risk. Nonetheless, additional extensive investigations are required to corroborate these correlations and investigate their functional underpinnings. Comprehending these genetic determinants may ultimately result in more efficacious individualized treatments for AML patients.
Author Contributions
Conceptualization, B.A.; Data curation, I.T., K.L., F.E.L., A.Z. and E.M.M.; formal analysis, K.L., O.A. and R.H.; investigation, K.L., N.L. and F.E.L.; methodology, K.L., A.Z. and E.M.M.; project administration, B.A.; resources, B.A., O.A. and I.T.; supervision, B.A. and E.M.M.; validation, B.A.; writing—original draft, K.L.; writing—review and editing, N.L., I.B., R.H. and B.A. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
The Research Ethics Committee (REC) of the Faculty of Medicine and Pharmacy at Cadi Ayyad University, Marrakech, Morocco. Approval Code: 34/2022. Approval date: 27 February 2023. Informed Consent Statement: The samples analyzed in this study were obtained as a part of the routine activities of the HLA laboratory of Mohammed VI University Hospital in Marrakech.
Informed Consent Statement
Sociodemographic data were extracted from the computer database anonymously under the supervision of the laboratory manager. In this case, approval of ethics and informed consent were not necessary.
Data Availability Statement
The data used in this study are not publicly available due to privacy restrictions. Access to the data is restricted in accordance with the ethical guidelines and regulations governing the protection of participant confidentiality and privacy. However, researchers interested in replicating or verifying the findings presented in this study may request access to the data through the appropriate institutional review board. Requests for data access will be considered on a case-by case basis, subject to approval by the relevant authorities and compliance with applicable privacy regulations. For inquiries regarding data access, please contact Pr. Brahim ADMOU/Clinical research Center/Mohammed VI University Hospital Center at br.admou@uca.ac.ma.
Conflicts of Interest
The authors whose names are listed immediately above certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.
Abbreviations
The following abbreviations are used in this manuscript:
| AML | Acute myeloid leukemia |
| AL | Acute leukemia |
| SSOs | Sequence-specific oligonucleotides |
| SSPs | Sequence-specific primers |
| HLA | Human leukocyte antigen |
| MHC | Major histocompatibility complex |
| TCGA | The Cancer Genome Atlas |
| FAB | The France, America and Britain |
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