1. Introduction
Acute myeloid leukemia (AML) is a heterogenous hematologic malignancy characterized by the unregulated clonal expansion of abnormal myeloid progenitor cells in bone marrow. Immature leukemic cells accumulate in the bone marrow and blood, eventually infiltrating other organs, which disrupt production of normal blood cells. As the disease progresses, weakness, infection, bleeding and a myriad of other complications occur in the absence of treatment. AML can be categorized as de novo or secondary AML, with the latter subtype attributed to progression of other diseases or being a result of treatment with radiation or cytotoxic agents (known as therapy-related AML). Even with recent advancements in medical technology, treatment of patients diagnosed with AML remains challenging, with high rates of relapse and refractory disease contributing to dismal outcomes. Optimizing chemotherapy regimens to reduce adverse drug reactions and improve tolerability while attaining maximum therapeutic effect remains an unmet need.
Although it is the most common category of acute leukemia in adults, AML is considered a rare disease, having an approximate incidence rate in the United States of 4.1 per 100,000 men and women per year [ 1 ]. Diagnosis of AML is most frequent in persons aged 65–74, with the median age at diagnosis being approximately 68 years old [ 1 ]. Death rates are highest among persons aged 65 years and older, with an age-adjusted overall death rate in the United States (during the period 2016–2020) of 2.7 per 100,000 men and women [ 1 ]. Despite newer targeted therapies and advances in supportive care regimens, the 5-year relative overall survival rate remains a modest 30.5% [ 1 ]. Aside from chromosomal alterations, epidemiological studies have associated various environmental factors, such as exposure to ionizing radiation, chemical or biological compounds such as benzenes or other solvents, as well as tobacco use, with AML [ 2 , 3 , 4 ].
The numerous chromosomal and genetic abnormalities make AML challenging to treat. Current treatment approaches utilize an integrated approach whereby phenotypic and genetic data are used to stratify patients into risk groups at diagnosis and during treatment. Although risk stratification has improved prognostication, a more thorough understanding of AML will likely contribute to more personalized approaches to treatment, with the overarching goal being improving survival outcomes and maintaining a durable remission. Consistent with the polygenic nature of complex diseases, hundreds of genes with recurring mutations that detrimentally affect hematopoiesis have been identified; however, it has been shown that a small number of recurrent mutations in driver genes are attributed to those portions of the genome that act as coding regions [ 5 , 6 , 7 ]. With the advent of innovative sequencing methods, focus is shifting toward the patient’s genomic profile, with this focus being particularly salient for the more than 50% of patients who present a normal karyotype at diagnosis [ 6 ].
2. Evolution of Genetic Studies in AML
Initially, early studies focused on heritability within families that contained clusters of AML, which identified a few highly penetrant germline mutations that confer higher risk [ 8 , 9 , 10 ]. More recently, there has been increasing recognition of the fact that germline mutations may be associated with increased risk of development of AML and/or myelodysplastic syndromes (MDS), and identifying these subsets of patients is important in improving outcomes [ 11 , 12 ]. As a result, in 2016, the World Health Organization (WHO) updated their classifications for patients with acute leukemias, MDS, or myeloproliferative neoplasms with an underlying germline mutation to include “myeloid neoplasms with germline predisposition”, which was subsequently incorporated into the National Comprehensive Cancer Network’s (NCCN) Clinical Practice Guidelines [ 13 , 14 ]. Use of direct high-depth sequencing techniques to assist in differentiating de novo AML from other myeloid diseases, as well as inherited forms or those relating to clonal hematopoiesis, is essential [ 15 ]. Molecular recognition of germline predisposition includes the following genetic susceptibility factors: RUNX family transcription factor 1 (RUNX1), CCAAT enhancer-binding protein α (CEBPA) and GATA binding protein 2 (GATA2); all of these proteins are regulatory transcription factors, impact hematopoiesis and have been associated with a younger age of AML onset [ 16 , 17 ].
The lack of definitive findings from family-based linkage studies and other epidemiological reports underscore the polygenic nature of AML. Thus, GWAS-based approaches can facilitate the discovery of novel genetic variants in genes of biological relevance to AML. The premise of GWAS is based on the “common disease—common variation” hypothesis and uses a case–control study design [ 18 ]. The effects of single nucleotide polymorphisms (SNPs) on disease risk, clinical outcome, or other disease-related traits of interest can be explored using a GWAS-based approach. Generally speaking, GWAS utilize whole-genome genotyping using microarray-based technologies either with or without unobserved genotype imputation. Numerous GWAS have identified coding and non-coding variants across numerous complex diseases, the results of which have been used to inform disease risk or elucidate underlying biological mechanisms that can assist in identifying patients associated with a particular clinical outcome. As of July 2022, the National Human Genome Research Institute (NHGRI) and the European Bioinformatics Institute (EBI) GWAS Catalog contains approximately 400,000 SNP–trait associations derived from >45,000 GWAS studies in approximately 6000 published articles [ 19 ]. Furthermore, large cohorts with information about a wide range of diseases and traits, such as those contained in United Kingdom (UK) Biobank, have led to an increase in GWAS studies [ 20 ]. In this review, we summarize existing GWAS studies in AML and present the challenges and limitations associated with conducting research into this complex and rare disease.
3. Literature Search Strategy
An extensive literature search of the electronic databases PubMed and EMBASE was conducted to identify peer-reviewed scientific studies (published on or before 6 January 2023) that reported results from GWAS on AML disease risk and/or clinical outcomes using the search terms “acute myeloid leukemia”, “AML”, “genetic studies”, “genome wide association studies”, “GWAS” and “gene polymorphisms”. The searches were limited to studies of humans and written in the English language. In addition to studies reporting GWAS, we included one study that conducted a haplotype-based association analysis in AML, as well as another study that tested selected SNPs from a published GWAS.
4. Results
Using computer database retrieval, 333 articles were identified from PUBMED and EMBASE, of which 186 duplicate studies were removed. After following the exclusion criteria, 22 records were retrieved for further examination and an additional 15 articles were removed because results were not reported solely in relation to AML-diagnosed patients. Therefore, seven studies met the criteria for inclusion in this review ( Table 1 ).
Summary of GWAS Studies
Study 1 (Knight et al., 2009) [ 21 ]: Given that therapy-related acute myeloid leukemia (t-AML) and myelodysplastic syndrome (t-MDS), which are collectively referred to as t-AML, are increasingly common in patients previously treated with cytotoxic drugs, this study reasoned that larger effect sizes would be more readily identifiable in t-AML than in de novo AML or MDS. The study design included discovery ( n = 80, 62 with t-AML and 18 with t-MDS) and validation cohorts ( n = 70, 33 with t-AML and 37 with t-MDS) [ 21 ]. The control dataset included 150 healthy individuals with no history of cancer or cytotoxic therapy. Genotype information was generated via Affymetrix microarray for the discovery and control cohorts and pyrosequencing for the validation cohort. Of note, all genomic DNA from cases were obtained at the time of diagnosis and prior to administration of cytotoxic chemotherapy drugs for the treatment of t-AML. Though none of the SNPs passed the genome-wide significance level, 15 SNPs demonstrated differential abundance in cases vs. controls of p < 0.001. Of these cases, for five SNPs, minor allele was associated with significant risk of t-AML and for 10 SNPs, major allele was associated with significant risk of t-AML. Of note, three SNPs (rs719293, rs2375990, and rs2133508) were completely absent in cases but occurred with a frequency 6–8% in controls. These 15 SNPs were genotyped in independent validation cohorts (70 cases and 95 controls), and after exclusion of five SNPs due to linkage disequilibrium (LD) or minor allele frequency (<0.1 in cases and control), 10 SNPs were further analyzed. Though none of SNPs were validated at the pre-defined significance thresholds, two SNPs (rs1394384 and rs1381392) showed a trend towards significance. Comparing SNPs within both discovery and validation cohorts, six SNPs (rs953509, rs1394384, rs556831, rs1381392, rs374284 and rs1335546) were significant at p < 0.05 in both cohorts.
Given prior exposure to alkylating agents has been associated with acquired lesions in either or both chromosome 5 and 7, the authors conducted a subset analysis in patients from both cohorts who had these genetic aberrations. In this homogenous subset, three SNPs, namely rs1394384, rs1381392 and rs1199098, were validated as potential markers associated with t-AML. Of these SNPs, rs1394384 was found in the intron of the Acid Sensing Ion Channel Subunit 2 ( ASIC2 ) gene, which encodes an amiloride-sensitive cation channel that belongs to the DEG/ENaC superfamily and was protective of risk to t-AML. SNP rs1199098 was located in ~2 KB upstream of the inositol–polyphosphate multikinase ( IPMK ) gene, and the authors concluded the relevance of IPMK due to its interactions with AKT kinase and Wnt/beta-catenin signaling [ 28 ]. IPMK has also been implicated in transcriptional regulation and inositide metabolism in the nucleus and is, thus, a regulator of nuclear signaling pathways [ 29 , 30 ]. Moreover, it has also been shown to impact autophagy induction by interacting with AMPK/ULK1 signaling [ 31 , 32 ]. The third SNP, i.e., rs1381392, was present in a gene dessert region and, thus, its functional relevance is unclear; however, this region has been shown to have recurrent deletions in lung cancers [ 33 ]. The authors did not perform an in-depth functional analysis and concluded that further research is needed to determine the contribution of these variants to t-AML risk in larger cohorts.
Study 2 (Lv et al., 2017) [ 23 ]: A genome-wide haplotype association analysis (GWHAS) was performed in this study based on previous research that demonstrated that multi-marker haplotype analysis may have greater capacity to detect correlations with disease. The subject study was restricted to patients with core-binding factor AML [ 23 ] derived from data downloaded from the Gene Expression Omnibus ( GSE32462 ). Historically, CBF-AML, which represents t(8:21) translocation and inv 16, accounted for ~15% of AML and was associated with a favorable prognosis [ 34 ]. Genomic DNA obtained at remission from pediatric and adult CBF-AML patients ( n = 175, GSE32462 ) was genotyped using the Affymetrix SNP6.0 Array. The control dataset consisted of non-AML patients derived from the International HapMap Project ( n = 218) [ 35 ]. Approximately 700,000 autosomal SNPs common to both AML cases and healthy controls were used for haplotype analysis. Out of 118,057 haplotype blocks identified, 519,865 haplotypes were tested, and 1754 showed significant correlation with AML at p < 1 × 10 −5 and mapped to 591 genes. Using a novel candidate gene approach to prioritize haplotypes [ 36 ], four high-risk AML genes with significant association were ranked: RUNX1 , JAK1 , PDGFRA and FGFR2 . The Runt-related transcription factor 1 ( RUNX1 ) gene is well-characterized in CBF-AML and other hematologic malignancies, and it is known to impact hematopoiesis [ 37 ]. The Janus kinase 1 ( JAK1 ) gene is a member of the JAK/STAT signaling pathway and involved in the regulation of various cellular processes, including cell growth and differentiation, as well as immune responses. It has been implicated in the pathogenesis of several types of leukemia, including acute lymphoblastic leukemia (ALL) and AML [ 38 ]. The platelet-derived growth factor receptor alpha ( PDGFRA ) gene is more commonly associated with other types of AML, such as the subset of AML with eosinophilia and abnormalities of chromosome 5 or 7 [ 39 ]. Although the fibroblast growth factor receptor 2 ( FGFR2 ) gene is not typically altered in CBF-AML, mutations in FGFR2 have been associated with many other malignancies [ 40 ]. Moreover, translocation at the FGFR2 locus have been previously reported in multiple myeloma and myeloproliferative disorders [ 41 ].
The presence of RUNX1 , which is the primary susceptibility gene associated with CBF-AML in this study, is not surprising, given that the genotype data are enriched in patients with CBF-AML. However, its association with the other prioritized genes is intriguing. Due to the molecular heterogeneity in AML, genetic abnormalities can vary between individual cases of leukemia, and there may be rare cases where JAK1 , PDGFRA and FGFR2 abnormalities are present in CBF-AML. Nonetheless, the presence of mutations or aberrations in these genes is not a typical feature of CBF-AML. Of note, no other studies used GWHAS in AML to investigate the way in which genetic variation impacts risk of AML.
Study 3 (Bargal et al., 2018) [ 24 ]: Resistance to cytarabine and high relapse rates remain a major barrier to successful treatment in AML patients. As a result, Bargal et al. conducted a GWAS to investigate SNPs associated with the chemosensitivity of AML patients’ diagnostic leukemic cells to cytarabine [ 24 ]. The study included pediatric AML patients who were participants in the multi-center St. Jude AML02 study [ NCT00136084 ], and specimens of their leukemic cells were tested for cytarabine chemosensitivity. Patients with high-risk group features were more frequent among subgroups whose cells were resistant to cytarabine. Due to the small sample size, the study population was limited to 50 genetically Caucasian patients; thus, none of the variants reached genome-wide significance. Using an a priori p < 1 × 10 −4 suggestive significance threshold, the primary SNP, i.e., rs721947, which was located on chromosome 12, was most strongly associated with cytarabine resistance. It is located downstream of NEDD1 Gamma-Tubulin Ring Complex Targeting Factor ( NEDD1 ) gene and situated within a long intergenic non-coding RNA region. Another SNP, i.e., rs1376041, is located in an area close to the exon/intron junction of the Adhesion G Protein-Coupled Receptor G1 ( ADGRG1 ) gene (also referred to as GPR56 ). The minor allele A for rs1376041 was associated with resistance to cytarabine. ADGRG1 has been shown to participate in cancer-cell proliferation and associated with inferior results in AML [ 42 , 43 ]. Another SNP below suggestive significance, i.e., rs75400242, is located upstream of the Insulin-Like Growth Factor 1 Receptor ( IGF1R ) gene, which is expressed in human leukemia cells and was previously shown to be associated with AML [ 44 ]. IGF1R is one of the phosphorylated receptor tyrosine kinases present in AML cases, and it has been linked to mutant RAS-positive AML, having the potential therapeutic benefits of dual suppression of IGF1R and RAS signaling [ 44 ]. To enhance the validity of the results, Bargal et al. conducted an expression quantitative trait loci (eQTL) analysis to examine the correlation between SNPs identified via GWAS analysis and the expression of respective genes in the same samples, in addition to associations between gene expression and cytarabine sensitivity. The authors sought to evaluate primary candidate signals using a three-point criteria: (i) SNP vs. cytarabine chemosensitivity of primary leukemic cells; (ii) SNP vs. gene-expression levels; and (iii) gene-expression vs. chemosensitivity. Two genes, i.e., ADGRG1 and IGF1R , satisfied all three criterion and were chosen for in vitro mechanistic studies. Small inhibitory RNA (si-RNA)-mediated knockdown of ADGRG1 and IGF1R in AML cell lines confirmed the observations that primary AML cells with knock down resulted in increased apoptosis and, thus, sensitivity to cytarabine. This study utilized the most comprehensive collection of in vitro cytarabine chemosensitivity data from pediatric AML patients. However, the small sample size warrants additional research to confirm these results, as well as in-depth functional studies to establish the underlying mechanism that causes cytarabine resistance.
Study 4 (Walker et al., 2019) [ 25 ]: To evaluate the risk of AML, Walker et al. conducted a GWAS using three patient cohorts that consisted of 1533 adults of European descent diagnosed with de novo AML and 3969 non-AML controls [ 25 ]. Following imputation, approximately 16 million autosomal variants were tested in 1183 AML cases and 2369 controls of European ancestry. The discovery phase included two independent case–control cohorts of AML patients from the United States. The results of the association tests determined via meta-analysis of the combined discovery cohort sample sets identified 11 polymorphisms at five loci that were associated with AML at a suggestive significance threshold of p < 10 × 10 −6 (none of the variants achieved genome-wide significance). To validate the markers with the lowest p -values, an independent cohort of German cases and controls was used. Chromosomal loci 19q13, 19p13 and 13q22 were selected for validation due to the presence of SNPs with lowest overall p -values and representation of two or more variants below suggestive significance levels. The two SNPs located on chromosome 19 were validated using a third independent cohort, and SNP rs57706619 and the 19q13 SNP rs75797233 showed significant association with AML. Next, a meta-analysis combining all the cohorts confirmed the association between rs75797233 ( p = 4.15 × 10 −8 ; Odds ratio: 2.28) and AML risk.
SNP rs75797233 is located 13KB from BRD4 Interacting Chromatin Remodeling Complex Associated Protein ( BICRA ) gene. BIRCA is a candidate tumor suppressor gene for glioma and has been implicated in transcriptional regulation in colorectal cancer [ 45 , 46 ]. The Genotype-Tissue Expression (GTEx) project database showed that the rs75797233-A allele is associated with higher BICRA expression in whole blood. Furthermore, chromatin immunoprecipitation (ChIP) sequencing data confirmed the presence of a GATA2 transcription factor binding site in this region, as well as that the presence of the risk allele repressed a critical residue at the GATA2 binding site. Using quantitative reverse transcription polymerase chain reaction (PCR) and ChIP quantitative PCR, it was shown that cell lines heterozygous for the rs75797233 risk allele had significantly reduced BICRA expression and reduced GATA2 binding to the locus. These results suggest that rs75797233 may be associated with AML risk through regulating BICRA expression through GATA2; however, in-depth mechanistic studies are needed to establish the role of BICRA expression in the development of AML. Although this past study was one of the largest AML disease risk GWAS conducted, the genome-wide association in BICRA was not reported in other independent cohorts of AML patients [ 26 , 27 ]. This issue may be due to the low minor allele frequency of rs75797233 (0.02); thus, other GWAS studies may not be powered to detect the lower frequency of the effect allele in independent cohorts. The second SNP on chromosome 19 included rs57706619, which is an intronic SNP located in the BetaGal Beta-1,3-N-Acetylglucosaminyltransferase 3 ( B3GNT3 ) gene. The third SNP, i.e., rs2039647, was only significant in one of the three cohorts and mapped to chromosome 13q22. This haplotype block included the long non-coding RNA LINC00402 and KLF Transcription Factor 12 ( KLF12 ) gene, which encodes a protein that is a member of the Krüppel-like zinc finger protein family and has been broadly associated with various other cancers types.
Study 5 (Lin et al., 2021) [ 26 ]: This manuscript reported results from three independent GWAS that examined 3041AML cases and 6760 controls of European ancestry using genotype data [ 26 ]. Publicly available genotyping data used in this study have been deposited in the NCBI Gene Expression Omnibus under the following accession numbers: GSE20672 , GSE32462 , GSE34542 , GSE46745 and GSE46951 . Imputation was used to estimate missing genotypes for over 7 million variants. The association test statistics were pooled for 6,694,056 SNPs that were common to all three GWAS, followed by a meta-analysis that was conducted to identify variants associated with AML. Findings from the meta-analysis were replicated in a fourth cohort of patients, with similar effect sizes of associations across all four studies. Given the heterogeneity of AML, subgroup analysis was performed, with emphasis placed on the largest group, which consisted of patients with cytogenetically normal AML (CN-AML, n = 822). Within the discovery cohort, genome-wide significant ( p < 5 × 10 −8 ) susceptibility loci were observed in CN-AML and included SNP rs3997854 located in Major Histocompatibility Complex, Class II, DQ Alpha 2 ( HLA-DQA2 ) gene (Chr 6p21.32) and rs75391980 (Chr 4q22.3). Of note, a few other SNPs reached significance at a suggestive threshold of ( p < 10 −6 ), including rs4674579, rs2621279, rs13183143, rs11481 and rs6077414.
Validation of a fourth independent cohort ( n = 977 cases, of which 465 were CN-AML, and n = 3728 controls) and meta-analysis confirmed the presence of rs4930561 in Lysine Methyltransferase 5B ( KMT5B ) gene on Chr 11q13.2 in all-AMLregardless of subtypes and rs3916765 in HLA-DQ2 (Chr 6p21.32) in CN-AML. Further meta-analysis of SNPs common to all four cohorts identified rs10789158 in Cache Domain Containing 1 ( CACHD1 ) at Chr 1p31.1 risk locus in a consistent direction for all-AML and rs17773014 in the Aldo-Keto Reductase Family 1 Member B ( AKR1B1 ) gene at Chr 7q33 for CN-AML. These results remained consistent when analysis was performed to correct any underlying effects of population ancestry by re-examining cases and controls of British and German origin present in the four GWAS cohorts. An attempt to test these variants in a cohort of 767 AML patients from the UK, Germany and Hungary did not find any association between the variants and relapse-free or overall survival in all-AML or CN-AML patients ( n = 369). SNP rs4930561 was reported to be significantly associated with risk of AML, regardless of subtype. The variant is located in the KMT5B gene on chromosome 11q13.2 and a member of the histone–lysine methyltransferases (KMTs) family, which are essential in many tightly regulated cellular processes. Dysregulation of KMTs has been associated with many cancers [ 47 , 48 ]. Using the eQTLGen consortium [ 49 ], the authors showed this SNP to be associated with expression of 12 of the 47 genes mapped in 500KB of the SNP on Chr 11q13.2; however, KMT5B expression did not show significant association with the lead SNP.
The second genome-wide significant susceptibility locus for CN-AML was identified at Chr 6q21.32 in HLA-DQA2 gene. This region has been linked to an increased risk of numerous human cancers, including various hematological malignancies [ 50 , 51 , 52 ]. Evaluation of HLA alleles in 5225 individuals of European ancestry showed the HLA DQB1*03:02 and HLA-DQA1*03:01 alleles to be significantly less common in AML cases than in controls, thus implying that abnormal immune systems are associated with risk of CN-AML. Of note, the previously reported AML risk variant in the BICRA gene (rs75797233) was not found to have a significant association with AML in this study. One of the reasons for this finding might be its rarity and the fact that it was only imputed to a high enough quality in three GWAS studies. These data bolster the existing evidence of genetic and biological diversity in AML and highlight the need for collaborative studies to increase statistical accuracy and aid in the discovery of genetic risk loci that are specific to AML sub-types.
Study 6 (Wang et al., 2021) [ 27 ]: This large-scale GWAS in patients diagnosed with AML and myelodysplastic syndrome (MDS) in an unrelated donor bone marrow transplant population was conducted with the rationale that inherited genetic variants may overlap and contribute to the development of both AML and MDS [ 27 ]. Patients from DISCOVeRY-BMT cohorts (2309 cases and 2814 controls) from the Center for International Blood and Marrow Transplant Research (CIBMTR) [ 53 , 54 ] were evaluated, and SNP rs12203592 located at Chr 6p25.3 within the interferon regulatory factor 4 ( IRF4 ) gene was associated with an increased risk of de novo AML, de novo MDS and therapy-related MDS, but not therapy-related AML. IRF4 is a transcription factor involved in T and B lymphocyte differentiation. SNP rs12203592 is implicated in IRF4 regulation via interactions with the chromatin loop [ 55 ]. Though it was the largest genome-wide investigation of AML and MDS susceptibility loci, some associated limitations include variations in exposure to therapy for therapy-related MDS and AML, the heterogeneity of cytogenetic risk groups, and the predominantly non-Hispanic European Americans ethnic backgrounds of the patients studied.
Study 7 (Cao et al., 2016) [ 22 ]: Though not a standalone GWAS, a study by Cao et al. tested 16 SNPs that were previously identified as susceptibility loci in two prior GWAS conducted in AML patients of European ancestry [ 22 , 56 , 57 ]. The goal was to test if these markers could be replicated in AML patients of Han Chinese ancestry. A case–control study with 545 newly diagnosed AML cases and 1034 controls was conducted, and regression models were adjusted for age and gender. Three SNPs, i.e., rs2191566, in zinc finger protein 230 ( ZNF230) ; rs9290663, in potassium calcium-activated channel subfamily M regulatory beta subunit 2 ( KCNMB2 ); and rs11155133, in non-coding RNA gene (LOC102723724), were associated with a higher risk of developing AML, whereas rs10873876 in ST6 N-acetylgalactosaminide alpha-2,6-sialyltransferase 3 ( ST6GALNAC3 ) was associated with s lower risk of developing AML.
Among these primary SNPs, rs2191566 in ZNF360 was associated with a higher risk of M2-AML in cases with AML1/ETO fusion but not in cases without fusion, suggesting a subtype-specific impact of the SNP. ST6GALNAC SNP showed an inconsistent association relative to previous report; this lack of association was speculated to be due to the different study populations: ALL vs. AML compared to European vs. Chinese patients. Further investigation of the SNP in LOC102723724 using in silico databases predicted that A to G base-change might affect the binding of C/EBP; however, current evidence of this underlying mechanism warrants further research. While not an independent GWAS in AML, the objective of this replication study was to replicate findings from two previous GWAS studies on acute leukemias. However, ALL vs. AML differences, sample size limitations, and potential gene–environment interactions limited efforts at validation of the previous findings.
Figure 1 shows a summary of GWAS SNPs reported in the studies listed above, with annotation of the gene or nearest gene region in applicable SNPs.
5. Future Directions
While GWAS studies have been successful in other complex diseases, there has been a relative lack of such studies in relation to AML. This issue is partly due to challenges and limitations associated with conducting GWAS studies in AML. The rarity of the disease, the heterogeneity of AML subtypes, and the large and well-characterized cohorts required to identify statistically significant associations are some of these challenges. Additionally, AML is a complex disease in which both genetic and environmental factors contribute to disease risk, making it difficult to distinguish the relative contribution of each type of factor.
This article highlights findings from a handful of GWAS studies conducted in relation to AML. Overall, there are several limitations common to the majority of the studies included in this review. Other than one small study performed in pediatric AML patients, all studies were conducted in predominantly adult patient cohorts. Age is associated with distinct genetic and molecular AML characteristics [ 58 ]; as such, differences in age between and within the cohorts likely contributes to the variability in clinical factors, such as overall survival, treatment tolerability and response, and other clinically relevant patient characteristics. It is important to note that AML cases in these studies were derived from patients of mostly European ancestry; thus, results may not be generalizable to other populations, underscoring the need for more diverse representative genetic datasets.
Due to the rarity of AML cases, insufficient sample sizes, which reduce statistical power, make it challenging to detect significant associations. Thus, combining diverse AML cohorts or including MDS patients in genetic studies can introduce several challenges and limitations. AML and MDS are heterogeneous diseases with multiple subtypes and varying genetic profiles. Combining patients with different clinical features can introduce confounding factors that may impact the interpretation of genetic associations. Variability in treatment response, survival outcomes, and disease progression can mask or distort the true genetic associations within each disease entity. Furthermore, grouping diverse subtypes together can limit researchers’ ability to identify subtype-specific genetic alterations and their functional consequences. While combining AML with MDS patients certainly increases cohort size, it further expands the heterogeneity and limits the power to detect risk alleles; thus, it would be recommended to conduct separate genetic studies for AML and MDS whenever possible. It is important to carefully consider and address the inherent heterogeneity and potential confounding factors when interpreting and generalizing the results.
Another common feature of these studies has been the focus on effects of SNPs. These factors highlight the need to modify current approaches by performing GWAS studies in addition to establishing cohorts of larger sample size and integrating cases with other omics data, as well as established somatic cytogenetic and genetic lesions. Further integration of top hits to create polygenic risk scores may increase researchers’ ability to estimate the cumulative contribution of AML susceptibility loci to disease risk.
In recent years, there has been growing awareness of the importance of data sharing in scientific research, and several initiatives have been launched to promote and facilitate data sharing in genetics and genomics research. For example, the Global Alliance for Genomics and Health (GA4GH) is an international organization that aims to promote data sharing in genomics research through the development of standardized tools, policies and frameworks [ 59 ]. These initiatives can be instrumental to performing genetic association studies for rare diseases, such as AML. However, despite these initiatives, there are challenges associated with access to data, which may include concerns about patients’ privacy and requirements for computational and cloud computing resources. To address the issue of data sharing in AML research, the Beat AML Master Trial created a comprehensive dataset of genomic, clinical and treatment data for adult AML patients [ 60 ]. Additionally, the National Cancer Institute launched the Genomic Data Commons ( https://gdc.cancer.gov/ , accessed on 11 June 2023), which is a platform that allows researchers to access and analyze genomic and clinical data from numerous cancer studies. In summary, data sharing is essential for maximizing the scientific value of research data, and efforts to promote and facilitate data sharing in AML research should be continued and expanded.
6. Conclusions
Despite the success of GWAS studies in other complex diseases, there is paucity of such studies in AML, which highlights the unmet need for comprehensive and collaborative initiatives to enhance identification of risk alleles and development of polygenic scores that are associated with AML disease or treatment outcomes, especially in specific cytogenetic subgroups. One major limitation is that the majority of studies have been performed in patients with predominantly European ancestry, thus warranting the creation of future studies that consider patients from other racial/ethnic groups. Overall, genome-wide studies in AML offer opportunities to advance our understanding of the disease, improve patient outcomes through facilitating personalized medicine approaches.
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Author Contributions
R.J.M. and J.K.L. contributed to study design and the generation of data. All authors provided critical reviews of the manuscript. All authors contributed to the article. All authors have read and agreed to the published version of the manuscript.
Conflicts of Interest
The authors declare no conflict of interest.
References
1. National Cancer Institute SEER Cancer Stat Facts: Acute Myeloid Leukemia 2022 Available online: https://seer.cancer.gov/statfacts/html/amyl.html (accessed on 6 January 2023)
2. Levine E.G. Bloomfield C.D. Leukemias and myelodysplastic syndromes secondary to drug, radiation, and environmental exposure Semin. Oncol. 1992 19 47 84 1736370
3. McHale C.M. Zhang L. Smith M.T. Current understanding of the mechanism of benzene-induced leukemia in humans: Implications for risk assessment Carcinogenesis 2012 33 240 252 10.1093/carcin/bgr297 22166497 PMC3271273
4. Colamesta V. D’Aguanno S. Breccia M. Bruffa S. Cartoni C. La Torre G. Do the smoking intensity and duration, the years since quitting, the methodological quality and the year of publication of the studies affect the results of the meta-analysis on cigarette smoking and Acute Myeloid Leukemia (AML) in adults? Crit. Rev. Oncol. Hematol. 2016 99 376 388 10.1016/j.critrevonc.2016.01.003 26830008
5. Christen F. Hoyer K. Yoshida K. Hou H.A. Waldhueter N. Heuser M. Hills R.K. Chan W. Hablesreiter R. Blau O. Genomic landscape and clonal evolution of acute myeloid leukemia with t(8;21): An international study on 331 patients Blood 2019 133 1140 1151 10.1182/blood-2018-05-852822 30610028
6. Weinstein J.N. Collisson E.A. Mills G.B. Shaw K.R. Ozenberger B.A. Ellrott K. Shmulevich I. Sander C. Stuart J.M. Network C.G.A.R. The Cancer Genome Atlas Pan-Cancer analysis project Nat. Genet. 2013 45 1113 1120 10.1038/ng.2764 24071849 PMC3919969
7. Charrot S. Armes H. Rio-Machin A. Fitzgibbon J. AML through the prism of molecular genetics Br. J. Haematol. 2020 188 49 62 10.1111/bjh.16356 31863468
8. Hahn C.N. Chong C.E. Carmichael C.L. Wilkins E.J. Brautigan P.J. Li X.C. Babic M. Lin M. Carmagnac A. Lee Y.K. Heritable GATA2 mutations associated with familial myelodysplastic syndrome and acute myeloid leukemia Nat. Genet. 2011 43 1012 1017 21892162 10.1038/ng.913 PMC3184204
9. Smith M.L. Cavenagh J.D. Lister T.A. Fitzgibbon J. Mutation of CEBPA in familial acute myeloid leukemia N. Engl. J. Med. 2004 351 2403 2407 10.1056/NEJMoa041331 15575056
10. Pathak A. Seipel K. Pemov A. Dewan R. Brown C. Ravichandran S. Luke B.T. Malasky M. Suman S. Yeager M. Whole exome sequencing reveals a C-terminal germline variant in CEBPA-associated acute myeloid leukemia: 45-year follow up of a large family Haematologica 2016 101 846 852 10.3324/haematol.2015.130799 26721895 PMC5004464
11. Rio-Machin A. Vulliamy T. Hug N. Walne A. Tawana K. Cardoso S. Ellison A. Pontikos N. Wang J. Tummala H. The complex genetic landscape of familial MDS and AML reveals pathogenic germline variants Nat. Commun. 2020 11 1044 10.1038/s41467-020-14829-5 32098966 PMC7042299
12. Simon L. Spinella J.F. Yao C.Y. Lavallée V.P. Boivin I. Boucher G. Audemard E. Bordeleau M.E. Lemieux S. Hébert J. High frequency of germline RUNX1 mutations in patients with RUNX1-mutated AML Blood 2020 135 1882 1886 10.1182/blood.2019003357 32315381
13. NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines ® ) for Acute Myeloid Leukemia V 1.2023. © National Comprehensive Cancer Network, Inc. 2023. To View the Most Recent and Complete Version of the Guideline Available online: NCCN.org (accessed on 8 March 2023)
14. Arber D.A. Orazi A. Hasserjian R. Thiele J. Borowitz M.J. Le Beau M.M. Bloomfield C.D. Cazzola M. Vardiman J.W. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia Blood 2016 127 2391 2405 10.1182/blood-2016-03-643544 27069254
15. Babushok D.V. Bessler M. Olson T.S. Genetic predisposition to myelodysplastic syndrome and acute myeloid leukemia in children and young adults Leuk Lymphoma 2016 57 520 536 10.3109/10428194.2015.1115041 26693794 PMC4798888
16. Kim B. Yun W. Lee S.T. Choi J.R. Yoo K.H. Koo H.H. Jung C.W. Kim S.H. Prevalence and clinical implications of germline predisposition gene mutations in patients with acute myeloid leukemia Sci. Rep. 2020 10 14297 10.1038/s41598-020-71386-z 32868804 PMC7459095
17. Tawana K. Wang J. Renneville A. Bödör C. Hills R. Loveday C. Savic A. Van Delft F.W. Treleaven J. Georgiades P. Disease evolution and outcomes in familial AML with germline CEBPA mutations Blood 2015 126 1214 1223 10.1182/blood-2015-05-647172 26162409
18. Reich D.E. Lander E.S. On the allelic spectrum of human disease Trends Genet. 2001 17 502 510 10.1016/S0168-9525(01)02410-6 11525833
19. Sollis E. Mosaku A. Abid A. Buniello A. Cerezo M. Gil L. Groza T. Güneş O. Hall P. Hayhurst J. The NHGRI-EBI GWAS Catalog: Knowledgebase and deposition resource Nucleic. Acids. Res. 2022 51 D977 D985 10.1093/nar/gkac1010 PMC9825413 36350656
20. Sudlow C. Gallacher J. Allen N. Beral V. Burton P. Danesh J. Downey P. Elliott P. Green J. Landray M. UK biobank: An open access resource for identifying the causes of a wide range of complex diseases of middle and old age PLoS Med. 2015 12 e1001779 10.1371/journal.pmed.1001779 25826379 PMC4380465
21. Knight J.A. Skol A.D. Shinde A. Hastings D. Walgren R.A. Shao J. Tennant T.R. Banerjee M. Allan J.M. Le Beau M.M. Genome-wide association study to identify novel loci associated with therapy-related myeloid leukemia susceptibility Blood 2009 113 5575 5582 10.1182/blood-2008-10-183244 19299336 PMC2689055
22. Cao S. Yang G. Zhang J. Shen Y. Ma H. Qian X. Hu Z. Replication analysis confirms the association of several variants with acute myeloid leukemia in Chinese population J. Cancer Res. Clin. Oncol. 2016 142 149 155 10.1007/s00432-015-2010-6 26177813 PMC11819256
23. Lv H. Zhang M. Shang Z. Li J. Zhang S. Lian D. Zhang R. Genome-wide haplotype association study identify the FGFR2 gene as a risk gene for acute myeloid leukemia Oncotarget 2017 8 7891 7899 10.18632/oncotarget.13631 27903959 PMC5352368
24. Bargal S.A. Rafiee R. Crews K.R. Wu H. Cao X. Rubnitz J.E. Ribeiro R.C. Downing J.R. Pounds S.B. Lamba J.K. Genome-wide association analysis identifies SNPs predictive of Oncotarget 2018 9 34859 34875 10.18632/oncotarget.26163 30405880 PMC6201857
25. Walker C.J. Oakes C.C. Genutis L.K. Giacopelli B. Liyanarachchi S. Nicolet D. Eisfeld A.K. Scholz M. Brock P. Kohlschmidt J. Genome-wide association study identifies an acute myeloid leukemia susceptibility locus near BICRA Leukemia 2019 33 771 775 10.1038/s41375-018-0281-z 30291333 PMC6405293
26. Lin W.Y. Fordham S.E. Hungate E. Sunter N.J. Elstob C. Xu Y. Park C. Quante A. Strauch K. Gieger C. Genome-wide association study identifies susceptibility loci for acute myeloid leukemia Nat. Commun. 2021 12 6233 10.1038/s41467-021-26551-x 34716350 PMC8556284
27. Wang J. Clay-Gilmour A.I. Karaesmen E. Rizvi A. Zhu Q. Yan L. Preus L. Liu S. Wang Y. Griffiths E. Genome-Wide Association Analyses Identify Variants in Front. Genet. 2021 12 554948 10.3389/fgene.2021.554948 34220922 PMC8248805
28. Ramazzotti G. Billi A.M. Manzoli L. Mazzetti C. Ruggeri A. Erneux C. Kim S. Suh P.G. Cocco L. Faenza I. IPMK and β-catenin mediate PLC-β1-dependent signaling in myogenic differentiation Oncotarget 2016 7 84118 84127 10.18632/oncotarget.11527 27563828 PMC5356648
29. Malabanan M.M. Blind R.D. Inositol polyphosphate multikinase (IPMK) in transcriptional regulation and nuclear inositide metabolism Biochem. Soc. Trans. 2016 44 279 285 10.1042/BST20150225 26862216 PMC5380233
30. Kim E. Beon J. Lee S. Park J. Kim S. IPMK: A versatile regulator of nuclear signaling events Adv. Biol. Regul. 2016 61 25 32 10.1016/j.jbior.2015.11.005 26682649
31. Pan Z. Li S.J. Guo H. Li Z.H. Fei X. Chang S.M. Yang Q.C. Cheng D.D. Ebastine exerts antitumor activity and induces autophagy by activating AMPK/ULK1 signaling in an IPMK-dependent manner in osteosarcoma Int. J. Biol. Sci. 2023 19 537 551 10.7150/ijbs.69541 36632464 PMC9830506
32. Guha P. Tyagi R. Chowdhury S. Reilly L. Fu C. Xu R. Resnick A.C. Snyder S.H. IPMK Mediates Activation of ULK Signaling and Transcriptional Regulation of Autophagy Linked to Liver Inflammation and Regeneration Cell Rep. 2019 26 2692 2703.e7 10.1016/j.celrep.2019.02.013 30840891 PMC6494083
33. Tai A.L. Mak W. Ng P.K. Chua D.T. Ng M.Y. Fu L. Chu K.K. Fang Y. Qiang Song Y. Chen M. High-throughput loss-of-heterozygosity study of chromosome 3p in lung cancer using single-nucleotide polymorphism markers Cancer Res. 2006 66 4133 4138 10.1158/0008-5472.CAN-05-2775 16618734
34. Borthakur G. Kantarjian H. Core binding factor acute myelogenous leukemia-2021 treatment algorithm Blood Cancer J. 2021 11 114 10.1038/s41408-021-00503-6 34135311 PMC8209225
35. Consortium I.H. The International HapMap Project Nature 2003 426 789 796 10.1038/nature02168 14685227
36. Aerts S. Lambrechts D. Maity S. Van Loo P. Coessens B. De Smet F. Tranchevent L.C. De Moor B. Marynen P. Hassan B. Gene prioritization through genomic data fusion Nat. Biotechnol. 2006 24 537 544 10.1038/nbt1203 16680138
37. Sood R. Kamikubo Y. Liu P. Role of RUNX1 in hematological malignancies Blood 2017 129 2070 2082 10.1182/blood-2016-10-687830 28179279 PMC5391618
38. Fasouli E.S. Katsantoni E. JAK-STAT in Early Hematopoiesis and Leukemia Front. Cell Dev. Biol. 2021 9 669363 10.3389/fcell.2021.669363 34055801 PMC8160090
39. Bain B.J. Myeloid and lymphoid neoplasms with eosinophilia and abnormalities of PDGFRA, PDGFRB or FGFR1 Haematologica 2010 95 696 698 10.3324/haematol.2009.021675 20442440 PMC2864371
40. Ardizzone A. Bova V. Casili G. Repici A. Lanza M. Giuffrida R. Colarossi C. Mare M. Cuzzocrea S. Esposito E. Role of Basic Fibroblast Growth Factor in Cancer: Biological Activity, Targeted Therapies, and Prognostic Value Cells 2023 12 1002 10.3390/cells12071002 37048074 PMC10093572
41. Yu S. Ye J. Wang Y. Lu T. Liu Y. Liu N. Zhang J. Lu F. Ma D. Gale R.P. DNA damage to bone marrow stromal cells by antileukemia drugs induces chemoresistance in acute myeloid leukemia via paracrine FGF10-FGFR2 signaling J. Biol. Chem. 2023 299 102787 10.1016/j.jbc.2022.102787 36509141 PMC9860495
42. Ji B. Feng Y. Sun Y. Ji D. Qian W. Zhang Z. Wang Q. Zhang Y. Zhang C. GPR56 promotes proliferation of colorectal cancer cells and enhances metastasis via epithelial-mesenchymal transition through PI3K/AKT signaling activation Oncol. Rep. 2018 40 1885 1896 10.3892/or.2018.6582 30066935 PMC6111632
43. Daga S. Rosenberger A. Quehenberger F. Krisper N. Prietl B. Reinisch A. Zebisch A. Sill H. Wölfler A. High GPR56 surface expression correlates with a leukemic stem cell gene signature in CD34-positive AML Cancer Med. 2019 8 1771 1778 10.1002/cam4.2053 30848055 PMC6488118
44. Weisberg E. Nonami A. Chen Z. Nelson E. Chen Y. Liu F. Cho H. Zhang J. Sattler M. Mitsiades C. Upregulation of IGF1R by mutant RAS in leukemia and potentiation of RAS signaling inhibitors by small-molecule inhibition of IGF1R Clin. Cancer Res. 2014 20 5483 5495 10.1158/1078-0432.CCR-14-0902 25186968 PMC4216757
45. Alpsoy A. Dykhuizen E.C. Glioma tumor suppressor candidate region gene 1 (GLTSCR1) and its paralog GLTSCR1-like form SWI/SNF chromatin remodeling subcomplexes J. Biol. Chem. 2018 293 3892 3903 10.1074/jbc.RA117.001065 29374058 PMC5858003
46. Han F. Yang B. Zhou M. Huang Q. Mai M. Huang Z. Lai M. Xu E. Zhang H. GLTSCR1 coordinates alternative splicing and transcription elongation of ZO1 to regulate colorectal cancer progression J. Mol. Cell. Biol. 2022 14 mjac009 10.1093/jmcb/mjac009 35218185 PMC9188103
47. Vinci M. Burford A. Molinari V. Kessler K. Popov S. Clarke M. Taylor K.R. Pemberton H.N. Lord C.J. Gutteridge A. Functional diversity and cooperativity between subclonal populations of pediatric glioblastoma and diffuse intrinsic pontine glioma cells Nat. Med. 2018 24 1204 1215 10.1038/s41591-018-0086-7 29967352 PMC6086334
48. Hamamoto R. Nakamura Y. Dysregulation of protein methyltransferases in human cancer: An emerging target class for anticancer therapy Cancer Sci 2016 107 377 384 10.1111/cas.12884 26751963 PMC4832871
49. Võsa U. Claringbould A. Westra H.J. Bonder M.J. Deelen P. Zeng B. Kirsten H. Saha A. Kreuzhuber R. Yazar S. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression Nat. Genet. 2021 53 1300 1310 10.1038/s41588-021-00913-z 34475573 PMC8432599
50. Jiménez P. Cantón J. Collado A. Cabrera T. Serrano A. Real L.M. García A. Ruiz-Cabello F. Garrido F. Chromosome loss is the most frequent mechanism contributing to HLA haplotype loss in human tumors Int. J. Cancer 1999 83 91 97 10.1002/(SICI)1097-0215(19990924)83:1<91::AID-IJC17>3.0.CO;2-4 10449614
51. McGranahan N. Rosenthal R. Hiley C.T. Rowan A.J. Watkins T.B.K. Wilson G.A. Birkbak N.J. Veeriah S. Van Loo P. Herrero J. Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution Cell 2017 171 1259 1271.e11 10.1016/j.cell.2017.10.001 29107330 PMC5720478
52. Pagliuca S. Gurnari C. Rubio M.T. Visconte V. Lenz T.L. Individual HLA heterogeneity and its implications for cellular immune evasion in cancer and beyond Front. Immunol. 2022 13 944872 10.3389/fimmu.2022.944872 36131910 PMC9483928
53. Hahn T. Sucheston-Campbell L.E. Preus L. Zhu X. Hansen J.A. Martin P.J. Yan L. Liu S. Spellman S. Tritchler D. Establishment of Definitions and Review Process for Consistent Adjudication of Cause-specific Mortality after Allogeneic Unrelated-donor Hematopoietic Cell Transplantation Biol. Blood Marrow. Transpl. 2015 21 1679 1686 10.1016/j.bbmt.2015.05.019 26028504 PMC4537799
54. Karaesmen E. Rizvi A.A. Preus L.M. McCarthy P.L. Pasquini M.C. Onel K. Zhu X. Spellman S. Haiman C.A. Stram D.O. Replication and validation of genetic polymorphisms associated with survival after allogeneic blood or marrow transplant Blood 2017 130 1585 1596 10.1182/blood-2017-05-784637 28811306 PMC5620418
55. Visser M. Palstra R.J. Kayser M. Allele-specific transcriptional regulation of IRF4 in melanocytes is mediated by chromatin looping of the intronic rs12203592 enhancer to the IRF4 promoter Hum. Mol. Genet. 2015 24 2649 2661 10.1093/hmg/ddv029 25631878
56. Treviño L.R. Yang W. French D. Hunger S.P. Carroll W.L. Devidas M. Willman C. Neale G. Downing J. Raimondi S.C. Germline genomic variants associated with childhood acute lymphoblastic leukemia Nat. Genet. 2009 41 1001 1005 10.1038/ng.432 19684603 PMC2762391
57. Papaemmanuil E. Hosking F.J. Vijayakrishnan J. Price A. Olver B. Sheridan E. Kinsey S.E. Lightfoot T. Roman E. Irving J.A. Loci on 7p12.2, 10q21.2 and 14q11.2 are associated with risk of childhood acute lymphoblastic leukemia Nat. Genet. 2009 41 1006 1010 10.1038/ng.430 19684604 PMC4915548
58. Bolouri H. Farrar J.E. Triche T. Ries R.E. Lim E.L. Alonzo T.A. Ma Y. Moore R. Mungall A.J. Marra M.A. The molecular landscape of pediatric acute myeloid leukemia reveals recurrent structural alterations and age-specific mutational interactions Nat. Med. 2018 24 103 112 10.1038/nm.4439 29227476 PMC5907936
59. Rehm H.L. Page A.J.H. Smith L. Adams J.B. Alterovitz G. Babb L.J. Barkley M.P. Baudis M. Beauvais M.J.S. Beck T. GA4GH: International policies and standards for data sharing across genomic research and healthcare Cell Genom. 2021 1 100029 10.1016/j.xgen.2021.100029 35072136 PMC8774288
60. Burd A. Levine R.L. Ruppert A.S. Mims A.S. Borate U. Stein E.M. Patel P. Baer M.R. Stock W. Deininger M. Precision medicine treatment in acute myeloid leukemia using prospective genomic profiling: Feasibility and preliminary efficacy of the Beat AML Master Trial Nat. Med. 2020 26 1852 1858 10.1038/s41591-020-1089-8 33106665 PMC8530434