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

Authors

Shih-Chun A Chu, Yi Hsiao, Chenwei Wang, Jennifer E Kyle, Raghav Jain, Yamei Deng, Marina A Gritsenko, Leanne E Henry, Jonathan T Lei, Yongchao Dou, Bahar Tercan, Zhiao Shi, Mahnoor N Gondal, Chia-Feng Tsai, John M Elizarraras, Rosalie K Chu, Fengchao Yu, Sunil K Joshi, Xiaojun Jing, Daniel A Polasky, Karl K Weitz, Ginny Xiaohe Li, Vanessa L Paurus, Chaevien S Clendinen, Athena A Schepmoes, Priscila M Lalli, Josie G Eder, Javier E Flores, Kelly G Stratton, James C Pino, Camilo Posso, Vladislav A Petyuk, Tyler J Sagendorf, Yuanwei Xu, Omar M Ibrahim, Ronald J Moore, Rui Zhao, Jin Chen, Matthew E Monroe, Mathangi Thiagarajan, Galen Hostetter, Chelsea Newton, Eunkyung An, Ana I Robles, Xu Zhang, Nathan J Edwards, Yin Lu, Hui Zhang, Haitham Abdelhakim, Paul D Piehowski, Mehdi Mesri, Richard D Smith, Chandan Kumar-Sinha, Cristina E Tognon, Jennifer Dunlap, Elie Traer, Li Ding, Jeffrey W Tyner, Arul M Chinnaiyan, Gilbert S Omenn, Karin D Rodland, Saravana M Dhanasekaran, Sara J C Gosline, Alexey I Nesvizhiskii, Bing Zhang, Tao Liu, Marcin P Cieslik, Clinical Proteomic Tumor Analysis Consortium

Published in

Nature cancer. Jun 12, 2026. Epub Jun 12, 2026.

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.

PMID:
42286338
Bibliographic data and abstract were imported from PubMed on 13 Jun 2026.

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