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Nature cancer · 2026

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

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

Аннотация

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.

Предметные рубрики MeSH

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

Реквизиты и контакты