Introduction: As an aggressive hematologic malignancy, acute myeloid leukemia (AML) is characterized by impaired maturation and uncontrolled proliferation of myeloid precursor cells in the peripheral blood and bone marrow. Given its significant epigenetic and genetic heterogeneity, it causes major challenges during the treatment and research procedures. Thanks to the validity of large genomic databases and progress made in the next-generation sequencing (NGS), our knowledge of the detection of single-nucleotide polymorphisms (SNPs), mutations, and molecular signatures related to acute myeloid leukemia has been enhanced. In the same settings, bioinformatics plays a key role through the integration of transcriptomic, pharmacogenomic, and genomic data to provide us with an in-depth perception of AML pathogenesis and contribute to the evolution of precision medicine strategies.
Methods: In the present research, the single-nucleotide polymorphisms retrieved from the NCBI database underwent a bioinformatics-driven analysis. The MEGA Gene software was used to evaluate a total of 1,201 SNPs found in the genes associated with cancer. These SNPs were prioritized in accordance with anticipated functional consequences (missense, stop-gained mutations), involvement in DNA repair, their frequency distribution, cell cycle regulation, and metabolic pathways. In addition, the pharmacogenomic implications of the same variants were explored in the context of standard targeted therapies and chemotherapy approved so far for AML treatment.
Results: Based on the analysis, the significant enrichment of SNPs was detected in three major tumor suppressor genes: TP53 (6.6%; 79 SNPs), BRCA2 (7.7%; 93 SNPs), and BRCA1 (9.4%; 113 SNPs), which function as critical genes for the genomic stability, cell cycle regulation, and DNA repair. Moreover, the detected recurrent variants in MET (6.1%; 73 SNPs), FHIT (5.1%; 62 SNPs), and APC (5.2%; 63 SNPs) also implicate their role in leukemogenesis. It was anticipated that functionally disruptive variants, in particular, missense and stop-gained mutations in BRCA2, could result in significant impairment of the protein function. In addition, germline predisposition genes, e.g., CHEK2, ATM, PTEN, and CDKN2A, harbored multiple single-nucleotide polymorphisms, which reflect the higher genomic instability in AML. Furthermore, variants in MTRR, CUBN, and MTHFR underlined the effects of nutritional and metabolic pathways on AML susceptibility.
Based on the pharmacogenomic assessment, the continued centrality of cytarabine with anthracyclines (idarubicin, daunorubicin) was affirmed as the frontline chemotherapy; however, the toxicity and efficacy were affected by the genetic backgrounds of patients. TP53 mutations showed correlation with a higher risk of hepatotoxicity, cardiotoxicity, and resistance. Even though targeted therapies—such as IDH1/2 inhibitors (enasidenib, ivosidenib), FLT3 inhibitors (gilteritinib, midostaurin), the anti-CD33 antibody-drug conjugate gemtuzumab ozogamicin, and the BCL-2 inhibitor venetoclax— have provided expanded treatment opportunities, they have demonstrated genotype-dependent adverse events, e.g., cytopenia with venetoclax and differentiation syndrome with IDH mutations.
Conclusion: Based on our systematic bioinformatics analysis, the profound clinical implications of single-nucleotide polymorphisms were identified in critical genes, in particular, TP53, BRCA2, and BRCA1, for AML treatment resistance, prognosis, and pathogenesis. By integrating SNP profiling into pharmacogenomics, a framework is offered to predict therapeutic response, guide individualized treatment strategies, and minimize drug toxicity. Such findings affirm the transformative role played by bioinformatics in associating genomic variation with decision-making at the clinical level and the advancement of precision medicine in AML.
Keywords: Acute Myeloid Leukemia, Bioinformatics Analysis, Genomics, SNPs, Drug Response