ArticleNature cancer2026
Integrated proteogenomic and metabolomic profiling of acute myeloid leukemias to identify molecular subtypes and associated therapy targets.
Article in Nature cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Two Decades ofCancers · 2026Review
- Assessing current capabilities for incorporating lipidomics in multiomics data integration.Briefings in bioinformatics · 2026Review
Corrections and comments
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Authors and funding
68 authors.
Funding
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.
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Registered trials
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