Integrated proteogenomic and metabolomic profiling of acute myeloid leukemias to identify molecular subtypes and associated therapy targets

Acute myeloid leukemia (AML) is a genetically and phenotypically heterogeneous hematological malignancy. Here, to better define this clinically taxing and translationally challenging malignancy, we applied a multiomics approach, consisting of 13 modalities to analyze 173 treatment-naive individuals with AML. By integrating these 'omes', we identified distinct AML subtypes, genotype-phenotype associations, biomarkers and pathobiological mechanisms. Across the spectrum of primitive and committed AML, we found extensive metabolomic and lipidomic reprogramming driven by divergent MYC and mTOR activity. We linked metabolic changes to striking hyperacetylation of mitochondrial proteins in CEBPA-mutant AML. Protein-centric subtyping revealed a distinct NPM1-mutant subset characterized by outlier expression of FOXC1 and HOXB8/9. To nominate therapeutic targets across subtypes, we developed a multiomic machine-learning approach and validated MTA1 as a contributor to panobinostat resistance. Altogether our findings underscore the complex nature of AML and provide a clinically and translationally informed unified view that reveals coalescent phenotypes across multiomic layers.

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Publication Details

Journal
Nature Cancer
Published
2026-06-12
DOI
https://doi.org/10.1038/s43018-026-01175-6
Citations
1
Primary Topic
Histone Deacetylase Inhibitors Research
Type
article
Field-Weighted Citation Impact
7.14

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article

Integrated proteogenomic and metabolomic profiling of acute myeloid leukemias to identify molecular subtypes and associated therapy targets

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1 citations
Nature Cancer
Histone Deacetylase Inhibitors Research
7.14
article

Integrated proteogenomic and metabolomic profiling of acute myeloid leukemias to identify molecular subtypes and associated therapy targets

Simona Colantonio, Arul M. Chinnaiyan, Richard D. Smith, Leanne E. Henry, Josie Eder, Kelly G. Stratton, Gilbert S. Omenn, Fengchao Yu, Paul Piehowski, Cristina E. Tognon, Hanbyul Cho, Camilo Posso, Eunkyung An, Rosalie Chu, Charles A. Goldthwaite, Karl Weitz, John M. Elizarraras, Ronald Moore, Vanessa Paurus, Chaevien Clendinen, Brenda Fevrier-Sullivan, Haitham Abdelhakim, Mehdi Mesri, Raghav Jain, James C. Pino, Ginny Xiaohe Li, Mahnoor Gondal, Marcin Cieślik, Athena Schepmoes, Anand Basu, KD Rodland, Zhiao Shi, Jeffrey Tyner, Vladislav Petyuk, Yamei Deng, Yongchao Dou, Michael A. Gillette, Daniel Chan, Tyler J. Sagendorf, Ana I. Robles, Marina Gritsenko, Chia‐Feng Tsai, Bahar Tercan, Mathangi Thiagarajan, Priscila M. Lalli, Melissa Borucki, Omar M. Ibrahim, Daniel A. Polasky, Alexey I. Nesvizhskii, Javier E. Flores, Victoria Fulidou, Sharon Gaheen, Hui Zhang, Tao Liu, Bing Zhang, Chandan Kumar‐Sinha, Xi Zhang, Li Ding, Chenwei Wang, Lu Yin, Yi Hsiao, Veera Baladandayuthapani, Xiaojun Jing, Sara JC Gosline, Matthew Monroe, Пенчо Георгиев, Jennifer Dunlap, Patricia Castro, Shuang Cai, Vladislav Golubkov, Jonathan T. Lei, Rajiv Dhir, Saravana M. Dhanasekaran, Galen Hostetter, Gad Getz, Yuanwei Xu, Lidia Gil, Chelsea Newton, Jin Chen, Andrew K. Godwin, Jennifer E. Kyle, John Freymann, Nathan J. Edwards, Steven Carr, William Bocik, Sunil K. Joshi, John Evangelista, Reese Crispen, Anupriya Agarwal, Garana Belinda, Diwaker Davar, Marcin Domagalski, Shelby Abts, Rui Zhao, Rafael Fonseca, Shih-Chun A. Chu, Elie Traer, Stancioaica Maria Camelia
article en
1 citations

Abstract

Acute myeloid leukemia (AML) is a genetically and phenotypically heterogeneous hematological malignancy. Here, to better define this clinically taxing and translationally challenging malignancy, we applied a multiomics approach, consisting of 13 modalities to analyze 173 treatment-naive individuals with AML. By integrating these 'omes', we identified distinct AML subtypes, genotype-phenotype associations, biomarkers and pathobiological mechanisms. Across the spectrum of primitive and committed AML, we found extensive metabolomic and lipidomic reprogramming driven by divergent MYC and mTOR activity. We linked metabolic changes to striking hyperacetylation of mitochondrial proteins in CEBPA-mutant AML. Protein-centric subtyping revealed a distinct NPM1-mutant subset characterized by outlier expression of FOXC1 and HOXB8/9. To nominate therapeutic targets across subtypes, we developed a multiomic machine-learning approach and validated MTA1 as a contributor to panobinostat resistance. Altogether our findings underscore the complex nature of AML and provide a clinically and translationally informed unified view that reveals coalescent phenotypes across multiomic layers.

Nature Cancer
Broad Institute (US), James S. McDonnell Foundation (US), Leidos (United States) (US), Van Andel Institute (US), Howard Hughes Medical Institute (US), Pacific Northwest National Laboratory (US), Johns Hopkins University (US), Oregon Health & Science University (US), Baylor College of Medicine (US), Georgetown University (US), Washington University in St. Louis (US), Bulgarian Academy of Sciences (BG), University of Michigan (US), Johns Hopkins Medicine (US), Georgetown University Medical Center (US), Environmental Molecular Sciences Laboratory (US), Specim (Finland) (FI), Institute for Systems Biology (US), International Institute for Molecular Oncology (PL), The University of Kansas Cancer Center (US), Michigan Medicine (US), Michigan Center for Translational Pathology (US), Mayo Clinic in Arizona (US), Frederick National Laboratory for Cancer Research (US), National Cancer Institute (MY), University of Pittsburgh Medical Center (US), Stanford Medicine (US), National Cancer Institute (US), Texas Children's Hospital (US), Children's Cancer Center (US), Baylor Genetics (US), ICF International (United States) (US), Stanford University (US), Icahn School of Medicine at Mount Sinai (US)
National Cancer Institute
Good health and well-being
Openalex Percentile: Top 3%
Histone Deacetylase Inhibitors Research
7.14
DOIOpen Access PDFOpenAlex
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