Integrative Genomic and Immune Profiling to Identify and Characterize High-Risk Subgroups in Acute Myeloid Leukemia: Development of a 20-Gene Predictive Signature and Its Clinical Implications

Acute myeloid leukemia (AML) is a heterogeneous malignancy with diverse genetic mutations and oncogenic pathways influencing treatment response. Despite therapeutic advances, relapse and resistance remain persistent issues. This study integrates genomic and transcriptomic profiling to identify biomarkers of high-risk AML, informing personalized medicine strategies. Nonnegative matrix factorization was applied to RNA sequencing data from the BeatAML cohort (<i>N</i> = 462) for patient subtyping, with survival analysis using the Kaplan-Meier method. Immune profiling via xCell and Gene Set Variation Analysis assessed the tumor microenvironment, with findings validated in the Cancer Genome Atlas AML cohort (<i>N</i> = 173). Using a random forest machine learning model, we developed a 20-gene signature identifying a high-risk subgroup comprising approximately 20% of patients with AML. The high-risk AML subtype was enriched for recurrent <i>FLT3</i>, <i>NPM1</i>, and <i>DNMT3A</i> mutations, activation of PI3K/AKT/mTOR, and complement pathways. Immune profiling revealed an immunosuppressive microenvironment with increased M2 macrophages and mesenchymal stem cells. The 20-gene signature predicted high-risk AML with high accuracy (area under the curve = 0.995, F1 = 0.89). AML cell lines representing high- and low-risk phenotypes identified using the 20-gene signature were tested for drug sensitivity, including the standard-of-care cytarabine, and two targeted therapies, the PI3K inhibitor LY294002 and the MAPK inhibitor selumetinib, selected based on enriched pathways in high-risk AML. High-risk AML cell lines exhibited reduced cytarabine sensitivity but greater responsiveness to PI3K and MAPK/ERK inhibitors, consistent with pathway enrichment results. These findings support molecular stratification and predictive signatures as tools to guide therapy in high-risk AML. Further clinical validation is warranted.

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Journal
OMICS A Journal of Integrative Biology
Published
2025-08-29
DOI
https://doi.org/10.1177/15578100251370570
Citations
1
Primary Topic
Acute Myeloid Leukemia Research
Type
article
Field-Weighted Citation Impact
1.77
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article

Integrative Genomic and Immune Profiling to Identify and Characterize High-Risk Subgroups in Acute Myeloid Leukemia: Development of a 20-Gene Predictive Signature and Its Clinical Implications

Brandon W. Higgs, M. Guy Roukens, Kubra Karagoz, Lauren Brady et al.
1 citations
OMICS A Journal of Integrative Biology
Acute Myeloid Leukemia Research
1.77
article

Integrative Genomic and Immune Profiling to Identify and Characterize High-Risk Subgroups in Acute Myeloid Leukemia: Development of a 20-Gene Predictive Signature and Its Clinical Implications

Brandon W. Higgs, M. Guy Roukens, Kubra Karagoz, Lauren Brady, Alexander A. Celik, Han Si, Lizzy Comijn
article en
1 citations

Abstract

Acute myeloid leukemia (AML) is a heterogeneous malignancy with diverse genetic mutations and oncogenic pathways influencing treatment response. Despite therapeutic advances, relapse and resistance remain persistent issues. This study integrates genomic and transcriptomic profiling to identify biomarkers of high-risk AML, informing personalized medicine strategies. Nonnegative matrix factorization was applied to RNA sequencing data from the BeatAML cohort (<i>N</i> = 462) for patient subtyping, with survival analysis using the Kaplan-Meier method. Immune profiling via xCell and Gene Set Variation Analysis assessed the tumor microenvironment, with findings validated in the Cancer Genome Atlas AML cohort (<i>N</i> = 173). Using a random forest machine learning model, we developed a 20-gene signature identifying a high-risk subgroup comprising approximately 20% of patients with AML. The high-risk AML subtype was enriched for recurrent <i>FLT3</i>, <i>NPM1</i>, and <i>DNMT3A</i> mutations, activation of PI3K/AKT/mTOR, and complement pathways. Immune profiling revealed an immunosuppressive microenvironment with increased M2 macrophages and mesenchymal stem cells. The 20-gene signature predicted high-risk AML with high accuracy (area under the curve = 0.995, F1 = 0.89). AML cell lines representing high- and low-risk phenotypes identified using the 20-gene signature were tested for drug sensitivity, including the standard-of-care cytarabine, and two targeted therapies, the PI3K inhibitor LY294002 and the MAPK inhibitor selumetinib, selected based on enriched pathways in high-risk AML. High-risk AML cell lines exhibited reduced cytarabine sensitivity but greater responsiveness to PI3K and MAPK/ERK inhibitors, consistent with pathway enrichment results. These findings support molecular stratification and predictive signatures as tools to guide therapy in high-risk AML. Further clinical validation is warranted.

OMICS A Journal of Integrative BiologyVol. 29(9)
Stevens Institute of Technology (US), Genmab (United States) (US), Genmab (Netherlands) (NL)
Openalex Percentile: Top 13%
Acute Myeloid Leukemia Research
1.77
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