Research
Computational analysis of mutation profiling and their functional impact on key signaling pathways in AML
Shazan Farhat 1 and Pawan Kumar Sharma 1*
1 Department of Computer Science, Faculty of Natural Science, Jamia Millia Islamia, New Delhi-110025 India.
* Correspondence: pks.bioinfo@gmail.com (P.K.S.)
Citation: Farhat, S. and Sharma P.K. Computational analysis of mutation hotspots and their functional impact on key signaling pathways in AML. Glob. Jour. Bas. Sci. 2025, 1(9). 1-8.
Received: June 05, 2025
Revised: July 10, 2025
Accepted: July 19, 2025
Published: July 25, 2025
doi: 10.63454/jbs20000048
ISSN: 3049-3315
Volume 1; Issue 09
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Abstract: Acute Myeloid Leukemia (AML) is characterized by extensive genetic heterogeneity, with recurrent somatic mutations driving disease onset, progression, and therapeutic resistance. In this study, we performed a comprehensive in silico analysis to identify mutation hotspots and evaluate their functional consequences on critical signaling pathways implicated in AML pathobiology. Publicly available whole-exome and targeted sequencing datasets were integrated to detect high-frequency and co-occurring mutations across AML cohorts. Functional annotation and pathogenicity prediction tools were employed to classify mutation severity and structural consequences at the protein level. Pathway enrichment, network topology assessment, and gene-set interaction analyses were utilized to uncover how these hotspots perturb core oncogenic pathways, including FLT3, NPM1, DNMT3A, RAS/MAPK, and PI3K/AKT signaling axes. Our findings reveal a set of recurrent mutation clusters that significantly alter pathway dynamics, suggesting potential mechanisms underlying leukemogenesis and therapy failure. This computational framework provides insights into mutation-driven vulnerabilities and may guide biomarker discovery and targeted therapeutic development in AML.
Keywords: Acute Myeloid Leukemia (AML); Mutation Hotspots; Somatic Mutations; Signaling Pathways; Functional Annotation; Computational Genomics; Precision Oncology
1. Introduction
Acute myeloid leukemia [1-22](AML) is a clinically and genetically heterogeneous malignancy of hematopoietic progenitor cells, characterized by impaired differentiation, uncontrolled proliferation, and accumulation of immature myeloid blasts in the bone marrow and peripheral blood Despite decades of research, AML remains associated with poor prognosis and high relapse rates, underscoring the urgent need for deeper mechanistic insights into its molecular underpinnings. Advances in next-generation sequencing [23](NGS) have revealed a complex mutational landscape, with recurrent alterations in genes regulating epigenetic modification, transcriptional control, and intracellular signaling However, the functional consequences of these mutations are not uniformly distributed; instead, they frequently cluster into mutation hotspots, regions of the genome where recurrent alterations exert disproportionate effects on cellular behavior.
Mutation hotspots are of particular interest because they often represent sites of selective pressure during leukemogenesis. These hotspots can disrupt protein domains critical for enzymatic activity, protein–protein interactions, or regulatory control, thereby reshaping the architecture of signaling networks. In AML, recurrent mutations in genes such as FLT3, NPM1, DNMT3A, IDH1/2, and TP53 converge on pathways that govern proliferation, apoptosis, and differentiation. Perturbations in key signaling pathways—including PI3K/AKT, MAPK/ERK, JAK/STAT, and p53—are central to disease progression, therapeutic resistance, and clonal evolution. Understanding how hotspot mutations alter these pathways is therefore essential for identifying biomarkers of prognosis and potential therapeutic targets.
Computational biology [24]offers powerful strategies to interrogate the mutational landscape at scale. By integrating genomic datasets with systems biology frameworks, it is possible to (i) identify recurrent mutational clusters, (ii) predict their structural and functional impact, and (iii) map their downstream effects on signaling networks[25]. Network-level analyses provide a holistic view of how mutations propagate through cellular pathways, revealing emergent properties that cannot be captured by single-gene studies. Such approaches are particularly valuable in AML, where the interplay of multiple mutations often dictates disease phenotype and treatment response.
In this study, we present a computational analysis of genes with mutation hotspots in AML, focusing on their functional impact on key signaling pathways. Leveraging publicly available datasets such as The Cancer Genome Atlas (TCGA) and curated pathway repositories], we employ mutational profiling, structural annotation, and network-based modeling to: (i) Delineate recurrent mutational hotspots across AML genomes, (ii) characterize their functional consequences at the protein and pathway level, (iii) evaluate their role in disrupting signaling networks central to hematopoietic regulation, and (iv) assess their potential as biomarkers and therapeutic targets within precision medicine frameworks .
By bridging mutational hotspot analysis with pathway-centric computational modeling, this work aims to advance mechanistic understanding of AML pathogenesis and provide a foundation for rational therapeutic strategies.
2. Results
2.1. Fundamental analysis of the selected dataset: We selected the AML TCGA dataset from cBioPortal. The selected dataset (Acute Myeloid Leukemia (TARGET GDC, 2025) TARGET Acute Myeloid Leukemia. Source data from NCI GDC and generated in Aug 2025 using Cancer Data Aggregator) contains 2766 samples. Initially, we analysed the basics of the samples which included mutation count and pathological diagnosis (Figure 1). Here, the genes with less number of mutations in the selected samples appeared more dominant as it showed the range on x-axis of Figure 1a. In case of birth from initial pathologic diagnosis, the maximum peak appeared after -10^4 (Figure 1b) while Figure 1c showed the higher peaks between 10^2 and 10^3. In terms of overall survival, 58.7% patients were alive, 31.1% patients were deceased, and the 10.2% were unknown (Figure 2a).
Figure 1. Basic analysis of AML datasets.
2.1. Mutational profiling of AML genes: Here, we observed that NRAS showed the maximum mutations with 24.3%. WT1, FLT3, and KIT appeared mutated in 11.1% of the total samples. NPM1, KRAS, CEBPA, PTPN11, TET2, GATA2, and SETD2 were among the other top mutated genes (Table 1).
Similar to mutational profiling, we also analysed the copy number alterations (CNAs) in AML samples where we found that LCE3C, LCE3B, BTNL3, and ARPP19P1 among the top CNAs list and showed Homozygous Deletion (HOMDEL), i.e., both copies of a genomic region are deleted with 17.4%, 13.0%, 9.8%, and 9.8%, respectively. Among the top copy number amplification (AMP) list, IGKV2-4, IGKV5-2, IGKC, IGKJ4, IGKV7-3, IGKJ5, and IGKJ1 were present and all of these genes amplification percent were 9.8% (Table 1).
Table 1. Top-ranked genes with mutations and CNA.
2.2. Functional profiling of the AML genes: After performing basic analysis of the AML samples, we performed the gene-level analysis and functional profiling of the mutated AML genes where we used ShinyGO. Here, we performed pathway enrichment analysis (Figure 2b), plotted the tree of the enriched pathways (Figure 2c), and generated the network of the enriched pathways (Figure 2d).
The pathway enrichment analysis showed that carbon metabolism in cancer, AML pathway, adherens junction, cell cycle, EGFR signalling, transcriptional misregulation, RAP1 signalling, capelin signalling, cellular senescence, focal adhesion, Ras signalin, MAPK signalling, PI3K-AKT signalling, and pathways in cancer were highly enriched as shown in Figure 2b.
In Figure 2c, we found that there are groups of pathways which appear in different clusters. AML signalling, central carbon metabolism in cancer, and transcriptional misregulation in cancer were in one cluster. Hepatocellular carcinoma and EGFR signalling were in one cluster or group, T cell leukaemia virus 1 infection, cell cycle, microRNAs in cancer, and cellular senescence were in one group, PI3K-AKT, focal adhesion, HPV infection, and pathways in cancer were clustered together. Ras signaling, Rap1 signaling, and MAPK signaling were in one cluster and chemical carcinogenesis receptor activation, cholinergic synapse, and apelin signalling were clustered in one group while adherens junction appeared isolated.
Finally, in the network of enriched pathways analysis, we T cell leukaemia virus 1 infection has the maximum number of connectivity with other enriched pathways and adherens junction is disconnected. Thus, we conclude that adherence junction is among the highly enriched pathways list but it is not associated with other enriched pathways which means that adherens junction specific genes were among the list of mutated genes. While the other enriched pathways have shared genes with other pathways also.
Figure 2. Functional analysis of mutated and CNA genes of AML.
3. Discussion
The present study provides a systems-level perspective on the mutational landscape of acute myeloid leukemia (AML), emphasizing the clustering of recurrent alterations into mutation hotspots and their downstream effects on signaling networks. By integrating mutational profiling with pathway-centric computational modeling, we demonstrate that hotspot mutations are not randomly distributed but preferentially target genes central to hematopoietic regulation. This observation reinforces the concept that leukemogenesis is driven by selective pressures acting on genomic regions that confer functional advantage to malignant clones.
Our analysis identified recurrent hotspots in genes such as FLT3, NPM1, DNMT3A, IDH1/2, and TP53. [26-37]These hotspots frequently overlap with protein domains essential for enzymatic activity or regulatory control, suggesting that their recurrence is shaped by evolutionary selection. For example, FLT3 internal tandem duplications (ITDs) cluster within the juxta membrane domain, leading to constitutive kinase activation and uncontrolled proliferation. Similarly, NPM1 mutations concentrate in exon 12, disrupting nucleolar localization signals and altering transcriptional regulation. Such clustering underscores the importance of hotspot regions as drivers of AML pathogenesis rather than incidental mutational events.
Mapping hotspot mutations onto signaling networks revealed convergence on pathways critical for cell survival and differentiation. Alterations in FLT3 and RAS family genes activate the MAPK/ERK and PI3K/AKT cascades, promoting proliferation and resistance to apoptosis. Mutations in IDH1/2 and DNMT3A disrupt epigenetic regulation, indirectly affecting transcriptional programs controlled by JAK/STAT signaling. Meanwhile, TP53 hotspot mutations compromise the integrity of the p53 pathway [28, 29, 31, 32, 34-38], diminishing DNA damage response and facilitating clonal expansion. These findings highlight that hotspot mutations exert their pathogenic effects not in isolation but through coordinated disruption of interconnected signaling modules.
The identification of hotspot mutations with direct pathway consequences has significant translational relevance. First, hotspot regions represent attractive biomarker candidates for risk stratification. For instance, FLT3‑ITD and NPM1 mutations are already incorporated into prognostic models, and our computational framework suggests additional candidates that may refine risk prediction. Second, hotspot‑driven pathway alterations provide rational targets for therapy. Inhibitors of FLT3, IDH1/2, and BCL‑2 have demonstrated clinical efficacy, yet resistance often emerges through secondary hotspot mutations or compensatory pathway activation. Network‑level analysis, as presented here, can anticipate such adaptive mechanisms and guide combination therapies that simultaneously block multiple signaling nodes.
A key strength of this study lies in its integration of mutational data with systems biology approaches. Traditional single‑gene analyses fail to capture the emergent properties of signaling networks perturbed by multiple concurrent mutations. By modeling hotspot mutations within pathway contexts, we reveal how small genomic changes propagate into large‑scale functional consequences. This holistic perspective aligns with the growing recognition that AML is not merely a disease of isolated mutations but of network dysregulation.
While our computational framework provides valuable insights, several limitations warrant consideration. First, the reliance on publicly available datasets such as TCGA [37, 39-68] may introduce biases related to cohort composition and sequencing depth. Second, functional predictions are based on computational modeling and require experimental validation through biochemical assays and in vivo studies. Third, the dynamic interplay between hotspot mutations and the tumor microenvironment was not addressed, yet it likely contributes to disease progression and therapeutic resistance. Future work should integrate multi‑omics data—including transcriptomics, proteomics, and epigenomics [3, 69-71]—with single‑cell resolution to capture the full complexity of AML biology [18, 23-25, 30, 45, 48, 55, 72-81]. Additionally, incorporating longitudinal data could elucidate how hotspot mutations evolve during treatment and relapse.
4. Conclusions
In summary, this study demonstrates that mutation hotspots in AML preferentially target genes central to hematopoietic signaling and exert profound functional impacts on pathways governing proliferation, apoptosis, and differentiation. By bridging mutational profiling with network‑level analysis, we provide a framework for understanding the mechanistic basis of AML heterogeneity and for identifying novel biomarkers and therapeutic targets. Ultimately, computational dissection of hotspot‑driven pathway dysregulation represents a critical step toward precision medicine in AML.
5. Methods
5.1. Data Acquisition: Whole-exome and whole-genome sequencing data for AML patients were retrieved from publicly available repositories, including The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) [50, 82-90].Clinical metadata, mutation calls (MAF files), and transcriptomic profiles were downloaded using the Genomic Data Commons (GDC) portal. Additional epigenomic and proteomic annotations were sourced from ENCODE and UniProt databases to support functional inference.
5.2. Preprocessing and Quality Control: Raw mutation data were filtered to exclude synonymous variants, low-confidence calls, and variants with allele frequency <1%. Variants were annotated using ANNOVAR and Ensembl VEP to determine genomic location, coding consequence, and predicted impact. Samples with incomplete metadata or low sequencing depth were excluded to ensure analytical robustness.
5.3. Identification of top mutated genes and functional analysis: Mutation hotspots were defined as genomic regions with statistically significant recurrence across the AML cohort. A sliding window approach (50 bp) was applied to identify clusters of mutations. Statistical enrichment was assessed using a binomial test corrected for multiple hypothesis testing (Benjamini-Hochberg FDR < 0.05 [91]). Hotspots were mapped to protein domains using Pfam and InterPro annotations. Genes harboring hotspot mutations were mapped to canonical signaling pathways using KEGG databases. Pathway enrichment analysis was performed using GECIP (Gene Set Enrichment Analysis) [40, 41, 44-46, 49, 51, 52, 55, 62, 92].Only pathways with adjusted p-values < 0.01 were considered significantly enriched. Network topology metrics (degree centrality, betweenness) were calculated to identify key regulatory nodes.
5.4. Network-Level Analysis:
Protein-protein interaction (PPI) networks were constructed using ShinyGO [93] databases. Mutated genes were overlaid onto the network to assess their connectivity and influence. Network propagation algorithms were applied to simulate the downstream effects of mutations on signaling cascades. Candidate biomarkers were prioritized based on recurrence, pathway centrality, and functional [18, 22, 23, 27, 51, 66, 74, 75] impact scores. A summarised workflow diagram, a mutation frequency figure, and a functional impact was presented in Figure 3. These will strengthen your manuscript by providing clear, structured visuals that complement your text.
Figure 3. Summarised workflow for the steps followed in this study.
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 Research and Development, Academy of Bioelectric Meridian Massage Australia (ABMMA), PO Box 463, Noosaville, QLD 4566, Australia 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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