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However, biological factors driving the therapeutic response of such hypomethylating agent (HMA)\u2010based therapies remain unknown. Herein, the transcriptome and\/or genome\u2010wide 5\u2010hydroxymethylcytosine (5hmC) is characterized for 41 patients with high\u2010risk AML from a phase 1 clinical trial treated with AZA epigenetic priming followed by high\u2010dose cytarabine and mitoxantrone (AZA\u2010HiDAC\u2010Mito). Digital cytometry reveals that responders have elevated Granulocyte\u2010macrophage\u2010progenitor\u2010like (GMP\u2010like) malignant cells displaying an active cell cycle program. Moreover, the enrichment of natural killer (NK) cells predicts a favorable outcome in patients receiving AZA\u2010HiDAC\u2010Mito therapy or other AZA\u2010based therapies. Comparing 5hmC profiles before and after five\u2010day treatment of AZA shows that AZA exposure induces dose\u2010dependent 5hmC changes, in which the magnitude correlates with overall survival (<jats:italic>p<\/jats:italic> = 0.015). An extreme gradient boosting (XGBoost) machine learning model is developed to predict the treatment response based on 5hmC levels of 11 genes, achieving an area under the curve (AUC) of 0.860. These results suggest that cellular composition markedly impacts the treatment response, and showcase the prospect of 5hmC signatures in predicting the outcomes of HMA\u2010based therapies in AML.<\/jats:p>","DOI":"10.1002\/advs.202300445","type":"journal-article","created":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T14:38:57Z","timestamp":1686148737000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Cellular Composition and 5hmC Signature Predict the Treatment Response of AML Patients to Azacitidine Combined with Chemotherapy"],"prefix":"10.1002","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0327-9874","authenticated-orcid":false,"given":"Guanghao","family":"Liang","sequence":"first","affiliation":[{"name":"Key Laboratory of Genomic and Precision Medicine Beijing Institute of Genomics Chinese Academy of Sciences and China National Center for Bioinformation  Beijing 100101 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