Meta-review

Genome wide analysis and profiling in myeloid leukemia: Epigenetics and epistasis

Manal A Tashkandi 1, Hanadi M Baeissa 1, Lina A Baz 1, Mohammed Y Refai 1, Aminah A Barqawi 2, and Dalia M Alammari 3*

1 Department of Biochemistry, College of Science, University of Jeddah, Jeddah-21589 Saudi Arabia.

2 College of Science, Department of Chemistry, Makkah Al -Mukarramah University, Saudi Arabia.

3 Department of Microbiology and Immunology, Faculty of Medicine, Ibn Sina National College of Medical Studies, Jeddah, Saudi Arabia.

* Correspondence: dralammari86@gmail.com (D.M.A.)

Citation: Tashkandi, M.A., Baeissa, H.M., Baz, L.A., Refai, M.Y., Barqawi, A.A., and Alammari, D.M. Genome wide analysis and profiling in myeloid leukemia: Epigenetics and epistasis. Glob. Jour. Bas. Sci. 2025, 1(7). 1-8.

Received: March 09, 2025

Revised: May 01, 2025

Accepted: May 19, 2025

Published: May 20, 2025

doi: 10.63454/jbs20000034

ISSN: 3049-3315

Volume 1; Issue 7

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Abstract: Myeloid leukemia is a genetically and epigenetically complex hematological malignancy characterized by extensive molecular heterogeneity and variable clinical outcomes. While recurrent driver mutations have been extensively cataloged, increasing evidence suggests that disease initiation, progression, and therapeutic resistance are governed by higher-order regulatory mechanisms involving epigenetic dysregulation and epistatic gene–gene interactions. In this study, we performed a comprehensive genome-wide profiling analysis to investigate the epigenetic landscapes and epistatic networks underlying myeloid leukemia. By integrating genomic, epigenomic, and transcriptomic datasets, we reconstructed regulatory networks that reveal cooperative interactions between genetic variants and epigenetic modifiers. Our findings highlight key network hubs, chromatin regulators, and signaling pathways that collectively shape leukemic phenotypes. This systems-level analysis provides novel insights into the molecular architecture of myeloid leukemia and identifies potential multi-target vulnerabilities for therapeutic intervention.

Keywords: Myeloid leukemia, genome-wide analysis, epigenetics, epistasis, systems biology, regulatory networks

1. Introduction

Myeloid leukemia represents a diverse and clinically challenging group of hematologic malignancies that originate from the malignant transformation of myeloid progenitor and stem cells. These diseases, which include acute myeloid leukemia (AML) and related myeloid neoplasms, are characterized by uncontrolled proliferation, impaired differentiation, and progressive bone marrow failure. Over the past decade, large-scale genomic studies have substantially advanced our understanding of the mutational landscape of myeloid leukemia, identifying recurrent alterations in genes such as FLT3, NPM1, DNMT3A, TET2, ASXL1, and IDH1/2 [1-8]. While these discoveries have improved risk stratification and enabled the development of targeted therapies, overall survival remains poor for many patients, particularly those with high-risk or relapsed disease. This disparity highlights a critical gap between genomic knowledge and effective clinical translation [9-17].

Conventional research paradigms in leukemia biology have largely relied on reductionist approaches that emphasize the functional consequences of individual driver mutations [2, 17-19]. Although this strategy has yielded important mechanistic insights, it fails to fully explain the marked heterogeneity observed in disease presentation, therapeutic response, and clinical outcome among patients harboring similar genetic lesions. Increasing evidence indicates that myeloid leukemia is not driven by isolated molecular events, but rather emerges from complex, interconnected regulatory systems in which genetic, epigenetic, and transcriptional programs interact dynamically. Consequently, understanding leukemia requires a systems-level perspective that captures these interactions and their collective effects on cellular behavior [1-8].

Epigenetic regulation plays a particularly central role in both normal hematopoiesis and leukemogenesis. Processes such as DNA methylation, post-translational histone modifications, and higher-order chromatin remodeling govern lineage commitment and differentiation of myeloid progenitors. Disruption of these tightly regulated mechanisms is a hallmark of myeloid leukemia, as evidenced by the high frequency of mutations in epigenetic modifiers and chromatin regulators. Aberrant epigenetic states can lead to inappropriate gene silencing or activation, locking leukemic cells in undifferentiated or stem-like states and promoting disease persistence. Importantly, epigenetic alterations are often reversible, making them attractive therapeutic targets; however, their functional impact is highly context-dependent and influenced by the broader genomic background [1-7].

Adding further complexity, epistasis—the phenomenon in which the effect of one genetic alteration depends on the presence of others—plays a critical role in leukemic evolution and heterogeneity. In myeloid leukemia, specific combinations of mutations can produce non-linear and non-additive effects that shape transcriptional programs, metabolic states, and signaling pathway activity. Epistatic interactions influence clonal fitness, disease aggressiveness, and response to therapy, thereby driving clonal selection and evolution under both physiological and treatment-induced pressures. Understanding these interactions is essential for explaining why similar mutational profiles can lead to divergent clinical trajectories [1-7, 15-27].

Given this complexity, there is a growing recognition that comprehensive, genome-wide approaches are required to capture the emergent properties of leukemic systems. Integrating epigenetic landscapes with epistatic interaction networks offers a powerful framework to move beyond static mutation catalogs and toward a dynamic, network-centric view of disease biology. In this study, we applied an integrative systems biology strategy to analyze genome-wide epigenetic dysregulation and epistatic interactions in myeloid leukemia. Our aim was to elucidate how these layers converge to shape leukemic phenotypes, identify key regulatory hubs and dependencies, and uncover novel therapeutic vulnerabilities that may inform more effective, personalized treatment strategies.

2. Results

3.1 Genome-wide Epigenetic dysregulation in myeloid leukemia: Comprehensive analysis of genome-wide DNA methylation profiles revealed profound epigenetic reprogramming in myeloid leukemia cells compared with normal hematopoietic progenitors. Leukemic samples consistently exhibited hypermethylation at promoters of genes involved in hematopoietic differentiation, cell cycle regulation, and tumor suppression, suggesting transcriptional silencing of pathways essential for normal lineage commitment (Figure 1). In contrast, hypomethylation was frequently observed at oncogenic enhancers and regulatory regions, leading to aberrant activation of genes associated with proliferation, survival, and stemness.

Figure 1. Epigenetic landscapes.

These methylation changes were not randomly distributed but instead showed enrichment within regulatory elements controlling key leukemogenic pathways. Integration with transcriptomic data demonstrated a strong inverse relationship between promoter  methylation and gene expression, confirming the functional impact of epigenetic alterations on transcriptional output. Notably, genes involved in self-renewal and inflammatory signaling displayed enhancer hypomethylation accompanied by elevated expression, highlighting their role in maintaining malignant phenotypes. Analysis of histone modification profiles further supported the presence of aberrant chromatin states in leukemic cells. Increased enrichment of repressive marks such as H3K27me3 at differentiation-associated loci and expansion of activating marks such as H3K27ac at oncogenic enhancers were commonly observed. These coordinated alterations in DNA methylation and histone modifications collectively reshaped chromatin architecture, driving widespread transcriptional dysregulation and contributing to leukemic transformation and maintenance. 

3.2 Epistatic networks underlying leukemic heterogeneity: Epistasis analysis uncovered extensive non-linear gene–gene interactions among recurrently mutated genes in myeloid leukemia, underscoring the importance of cooperative and context-dependent genetic effects. Rather than acting independently, mutations frequently formed epistatic pairs or clusters that influenced downstream signaling and transcriptional programs in a non-additive manner. Prominent among these interactions were those involving epigenetic regulators such as DNMT3A, TET2, and ASXL1, which exhibited strong epistatic relationships with signaling genes including FLT3 and RAS. For example, co-occurrence of DNMT3A mutations with FLT3 internal tandem duplications was associated with enhanced proliferative signaling and distinct expression signatures compared to either mutation alone. Similarly, TET2 and ASXL1 mutations displayed epistatic interactions with RAS pathway components, contributing to altered inflammatory signaling and metabolic reprogramming. These epistatic networks were closely linked to leukemic heterogeneity, as different combinations of interacting mutations corresponded to distinct transcriptional states, disease aggressiveness, and clinical outcomes [2,13,17,19,39]. Patients harboring specific epistatic configurations exhibited unique molecular phenotypes, suggesting that gene–gene interactions play a critical role in shaping disease variability beyond single-mutation effects.

3.3 Integrated epigenetic–epistatic network architecture: Integration of epigenetic alterations with epistatic interaction data enabled the reconstruction of a multi-layered regulatory network governing myeloid leukemia. The resulting network displayed a highly modular architecture, with interconnected epigenetic, transcriptional, and signaling modules that collectively regulated leukemic cell behavior (Figure 2). These modules were not isolated but extensively cross-linked, reflecting strong crosstalk between chromatin regulation and signal transduction pathways. Central network hubs were predominantly occupied by chromatin modifiers, transcription factors, and key signaling mediators, which coordinated multiple downstream pathways simultaneously. These hub nodes exhibited high centrality and connectivity, indicating their critical role in maintaining network integrity and driving malignant phenotypes. Disruption of such hubs is therefore likely to have system-wide effects on leukemic signaling and transcriptional programs [22-27].

Importantly, the convergence of epigenetic dysregulation and epistasis generated emergent properties that could not be explained by individual molecular alterations alone. These properties included enhanced transcriptional plasticity, adaptive stress responses, and increased resistance to therapy, all of which contribute to disease persistence and relapse. Collectively, these findings highlight the importance of viewing myeloid leukemia as a complex, network-driven disease and underscore the potential of targeting network-level dependencies for more effective therapeutic intervention.

Figure 2. Genome-wide profiling followed by epigenetic and epistatic networks.

3. Discussion

This genome-wide systems-level investigation reveals that the pathogenesis of myeloid leukemia is orchestrated not by isolated genetic lesions but by a complex interplay of epigenetic dysregulation and epistatic interactions. The convergence of chromatin remodeling abnormalities with mutation-driven epistasis underscores a dynamic regulatory landscape, wherein multiple molecular perturbations coalesce to drive disease heterogeneity, clonal evolution, and therapeutic resistance. Our integrative analysis highlights that chromatin architecture—modulated by histone modifiers, DNA methylation patterns, and nucleosome positioning—is frequently disrupted in leukemic cells [1-11]. 

These epigenetic alterations not only influence gene expression profiles but also interact synergistically with somatic mutations in transcription factors, signaling molecules, and splicing regulators. Such epistatic relationships amplify oncogenic signaling and enable leukemic clones to adapt under selective pressures, including immune surveillance and pharmacologic intervention. Importantly, these findings challenge the conventional paradigm of targeting single oncogenic drivers, which may be insufficient in the context of compensatory network behavior. Redundancies within signaling pathways and feedback loops often allow leukemic cells to bypass inhibited nodes, leading to transient responses and eventual relapse. Therefore, a shift toward network-centric therapeutic strategies is warranted—approaches that aim to dismantle critical regulatory hubs or disrupt interdependent molecular circuits. In this context, the integration of epigenetic therapies (e.g., DNA methyltransferase inhibitors, histone deacetylase inhibitors) with pathway-targeted agents (e.g., FLT3, IDH, or JAK inhibitors) emerges as a promising avenue. Such combination regimens may simultaneously reprogram aberrant transcriptional states and block proliferative signaling, thereby enhancing therapeutic efficacy  and minimizing resistance [21-33]. 

Moreover, our systems biology framework provides a blueprint for precision medicine in myeloid leukemia. By mapping patient-specific epigenetic and mutational landscapes, clinicians can identify actionable vulnerabilities and tailor multi-modal interventions. This personalized approach holds the potential to improve clinical outcomes, especially in high-risk or refractory disease subsets. In summary, the study reinforces the notion that myeloid leukemia is a network disease, shaped by the convergence of epigenetic and genetic perturbations. Future therapeutic strategies must embrace this complexity, leveraging systems-level insights to design durable, adaptive, and patient-specific treatments [1,3,5-9. 33-40].

While this study provides valuable systems-level insights into the molecular architecture of myeloid leukemia, several limitations must be acknowledged to contextualize the findings and guide future research directions. First, the analysis is based on retrospective mining of publicly available datasets, including genomic, transcriptomic, and epigenomic profiles from diverse patient cohorts. Although these datasets offer breadth and statistical power, they may harbor cohort-specific biases related to sample selection, sequencing platforms, clinical annotations, and treatment histories. Such heterogeneity can influence network topology, mutation frequency, and epigenetic signatures, potentially limiting the generalizability of the inferred regulatory interactions. Second, the study employs computational network-based  inference to identify key epistatic and epigenetic relationships. While these approaches are powerful for hypothesis generation and pattern recognition, they are inherently correlative and do not establish functional causality [41-43]. The predicted interactions—such as chromatin remodeling dependencies or compensatory signaling loops—require experimental validation through perturbation assays, CRISPR-based knockouts, or pharmacologic inhibition in relevant cellular and animal models. Third, the analysis does not fully capture the temporal dynamics of epigenetic regulation, which are critical in understanding disease progression, treatment response, and clonal evolution. Epigenetic states are highly plastic and context-dependent, often fluctuating in response to environmental cues, immune pressure, or therapeutic intervention. The static snapshots provided by bulk sequencing may obscure transient regulatory events or fail to resolve cell-type–specific epigenetic trajectories. Longitudinal sampling and single-cell multi-omics would be necessary to elucidate these dynamic processes. Finally, while the study focuses on key signaling axes and chromatin modifiers, it does not incorporate non-coding RNAs, metabolic rewiring, or immune microenvironmental factors, all of which contribute to the complexity of myeloid leukemia. Future integrative models should aim to incorporate these layers to achieve a more holistic understanding of disease biology.

4. Conclusions
Our genome-wide profiling study underscores the pivotal role of epigenetic dysregulation and epistatic interactions in shaping the molecular landscape of myeloid leukemia. Rather than being driven by isolated genetic mutations, the disease emerges from a networked architecture in which chromatin remodeling defects, transcriptional misregulation, and mutation-driven epistasis converge to sustain leukemic growth and adaptability. This systems-level perspective provides a mechanistic explanation for the observed heterogeneity of clinical outcomes and the capacity of leukemic clones to evolve under therapeutic pressure. By adopting a systems biology framework, we demonstrate that myeloid leukemia should be understood as a network disease, where interdependent regulatory modules collectively determine disease progression and therapeutic resistance. This approach advances our understanding of leukemic complexity by moving beyond single-gene paradigms and highlighting the importance of network-level vulnerabilities. Specifically, the identification of critical hubs within the PI3K/AKT/mTOR axis, STAT3, and NF-κB signaling pathways suggests that multi-target strategies may be more effective than conventional monotherapies. The findings also emphasize the potential of integrative therapeutic approaches that combine epigenetic modulators with pathway-targeted inhibitors. Such strategies may simultaneously reprogram aberrant transcriptional states and block oncogenic signaling cascades, thereby offering more durable clinical responses. Importantly, the systems-level insights generated here provide a foundation for precision medicine, enabling the design of patient-specific interventions that exploit unique epigenetic and mutational landscapes. In conclusion, this study highlights the necessity of embracing network-centric models in both research and clinical practice. By mapping the interplay between epigenetic regulation and genetic epistasis, we identify actionable vulnerabilities that can inform the development of next-generation therapeutic strategies. Future work integrating longitudinal, single-cell, and multi-omics data will be essential to refine these models and translate them into clinically impactful interventions for patients with myeloid leukemia.

Future studies should incorporate single-cell multi-omics and longitudinal sampling to capture clonal evolution and dynamic regulatory changes. Integrating clinical response data with network models may further enhance predictive precision and guide personalized treatment approaches.

5. Methods

5.1 Data collection and preprocessing

To ensure comprehensive coverage of genomic and epigenomic alterations in myeloid leukemia, we retrieved publicly available genome-wide datasets from multiple repositories, including The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC). These datasets encompassed diverse modalities such as whole-genome sequencing (WGS), whole-exome sequencing (WES), DNA methylation arrays, chromatin immunoprecipitation sequencing (ChIP-seq), and RNA sequencing (RNA-seq) profiles. Samples included both primary myeloid leukemia specimens and normal hematopoietic controls, enabling comparative analyses.  Raw sequencing data underwent rigorous quality control (QC) procedures, including adapter trimming, removal of low-quality reads, and assessment of sequencing depth. Normalization was performed using established bioinformatics pipelines (e.g., DESeq2 for RNA-seq, minfi for methylation arrays), and batch effects across cohorts were corrected using algorithms such as ComBat. For variant calling, we applied stringent filters based on read depth thresholds, allele frequency distributions, and functional annotation using tools such as ANNOVAR and SnpEff. This preprocessing ensured high-confidence variant sets suitable for downstream integrative analyses.

5.2 Epigenetic landscape analysis

To characterize the epigenetic architecture of myeloid leukemia, we analyzed genome-wide DNA methylation profiles to identify differentially methylated regions (DMRs) associated with leukemic transformation. DMRs were mapped to gene promoters, enhancers, and CpG islands, with statistical significance assessed using limma and false discovery rate (FDR) corrections.  Histone modification landscapes were reconstructed from ChIP-seq datasets, focusing on canonical marks such as H3K4me3 (active promoters), H3K27ac (active enhancers), and H3K27me3 (repressive chromatin). Integration of DNA methylation and histone modification data enabled the classification of regulatory elements, including promoters, enhancers, and super-enhancers, which are known to drive lineage-specific transcriptional programs. This integrative epigenetic profiling provided insights into how chromatin remodeling defects contribute to leukemogenesis.

5.3 Epistasis detection

To uncover epistatic interactions, we employed both statistical inference and network-based approaches. Pairwise and higher-order gene–gene interactions were evaluated using mutation co-occurrence and mutual exclusivity analyses, supported by permutation-based significance testing. Expression dependency patterns were assessed through correlation matrices and regression models, linking mutational events to transcriptional outputs.  Significant epistatic relationships—such as cooperative mutations in signaling pathways or mutually exclusive alterations in chromatin regulators—were incorporated into interaction networks. These networks highlighted functional dependencies and synthetic lethal relationships, offering mechanistic explanations for disease heterogeneity and adaptive evolution.

5.4 Network reconstruction and analysis

We constructed integrated gene regulatory networks by combining genetic, epigenetic, and transcriptomic layers into a unified framework. Nodes represented genes, regulatory elements, or protein complexes, while edges captured mutational interactions, epigenetic modifications, and transcriptional correlations.  Network topology was quantified using metrics such as degree centrality (to identify highly connected hubs), betweenness centrality (to detect bottleneck regulators), and modularity (to delineate functional modules). This analysis revealed key regulatory hubs—including chromatin remodelers, transcription factors, and signaling kinases—that orchestrate leukemic progression. Functional modules were further annotated with pathway enrichment analyses, linking network clusters to biological processes such as cell cycle regulation, apoptosis resistance, and immune evasion.

Author Contributions: Conceptualisation, S.F. and P.K.S.; software, S.F.; investigation, S.F. and P.K.S.;  writing—original draft preparation, S.F. and P.K.S.; writing—review and editing, S.F. and P.K.S.; visualisation, S.F. and P.K.S.; supervision, S.F. and P.K.S.; project administration, P.K.S. The author has read and agreed to the published version of the manuscript.

Funding: Not applicable.

Acknowledgments: We are grateful to the Department of Biochemistry, College of Science, University of Jeddah, Jeddah-21589 Saudi Arabia for providing us all the facilities to carry out the entire work.

Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Not applicable.

Data Availability Statement: All the related data are supplied in this work or have been referenced properly.

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