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Cellular Composition and 5hmC Signature Predict the Treatment Response of AML Patients to Azacitidine Combined with Chemotherapy

ABSTRACT: Cellular Composition and 5hmC Signature Predict the Treatment Response of AML Patients to Azacitidine Combined with Chemotherapy

PROVIDER: PRJNA639306 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR12008800_1.fastq.gz	Fastqsanger.gz
	SRR12008800_2.fastq.gz	Fastqsanger.gz
	SRR12008900_1.fastq.gz	Fastqsanger.gz
	SRR12008900_2.fastq.gz	Fastqsanger.gz
	SRR12009000_1.fastq.gz	Fastqsanger.gz

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Cellular Composition and 5hmC Signature Predict the Treatment Response of AML Patients to Azacitidine Combined with Chemotherapy

Project description:Azacitidine (AZA) is a DNA methyltransferase inhibitor and epigenetic modulator that can be an effective agent in combination with chemotherapy for patients with high-risk acute myeloid leukemia (AML). However, biological factors driving the therapeutic response of such hypomethylating agent (HMA)-based therapies remain unknown. Herein, the transcriptome and/or genome-wide 5-hydroxymethylcytosine (5hmC) are characterized for 41 patients with high-risk AML from a phase 1 clinical trial treated with AZA epigenetic priming followed by high-dose cytarabine and mitoxantrone (AZA-HiDAC-Mito). Digital cytometry reveals that responders have elevated Granulocyte-macrophage-progenitor-like (GMP-like) malignant cells displaying an active cell cycle program. Moreover, the enrichment of Natural killer (NK) cells predicts favorable outcome in patients receiving AZA-HiDAC-Mito therapy or other AZA-based therapies. Comparing 5hmC profiles before and after five-day treatment of AZA shows that AZA exposure induces dose-dependent 5hmC changes, the magnitude of which predicts overall survival (p=0.015). An XGBoost machine learning model is developed to predict the treatment response based on 5hmC levels of only 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 signature in predicting the outcomes of HMA-based therapies in AML.

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Cellular Composition and 5hmC Signature Predict the Treatment Response of AML Patients to Azacitidine Combined with Chemotherapy.

Project description:Azacitidine (AZA) is a DNA methyltransferase inhibitor and epigenetic modulator that can be an effective agent in combination with chemotherapy for patients with high-risk acute myeloid leukemia (AML). However, biological factors driving the therapeutic response of such hypomethylating agent (HMA)-based therapies remain unknown. Herein, the transcriptome and/or genome-wide 5-hydroxymethylcytosine (5hmC) is characterized for 41 patients with high-risk AML from a phase 1 clinical trial treated with AZA epigenetic priming followed by high-dose cytarabine and mitoxantrone (AZA-HiDAC-Mito). Digital cytometry reveals that responders have elevated Granulocyte-macrophage-progenitor-like (GMP-like) malignant cells displaying an active cell cycle program. Moreover, the enrichment of natural killer (NK) cells predicts a favorable outcome in patients receiving AZA-HiDAC-Mito therapy or other AZA-based therapies. Comparing 5hmC profiles before and after five-day treatment of AZA shows that AZA exposure induces dose-dependent 5hmC changes, in which the magnitude correlates with overall survival (p = 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-based therapies in AML.

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