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Molecular profiling of pre-and post-5-azacytidine myelodysplastic syndrome samples identifies predictors of response

ABSTRACT: Molecular profiling of pre-and post-5-azacytidine myelodysplastic syndrome samples identifies predictors of response

PROVIDER: PRJNA1135256 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR29816600_1.fastq.gz	Fastqsanger.gz
	SRR29816600_2.fastq.gz	Fastqsanger.gz
	SRR29816601_1.fastq.gz	Fastqsanger.gz
	SRR29816601_2.fastq.gz	Fastqsanger.gz
	SRR29816502_1.fastq.gz	Fastqsanger.gz

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Molecular profiling of pre-and post-5-azacytidine myelodysplastic syndrome samples identifies predictors of response

Project description:Treatment with the hypomethylating agent 5-azacytidine (AZA) increases survival in high-risk (HR) myelodysplastic syndrome (MDS) patients, but predicting patient response and overall survival remains challenging. To address these issues, we analyzed mutational and transcriptional profiles in CD34+ hematopoietic stem/progenitor cells (HSPCs) before and following AZA therapy in MDS patients. AZA treatment led to a greater reduction in the mutational burden in both blast and hematological responders than non-responders. Blast and hematological responders showed transcriptional evidence of pre-treatment enrichment for pathways such as oxidative phosphorylation, MYC targets, and mTORC1 signaling. While blast non-response was associated with TNFa signaling and leukemia stem cell signature, hematological non-response was associated with cell-cycle related pathways. AZA induced similar transcriptional responses in MDS patients regardless of response type. Comparison of blast responders and non-responders to normal controls, allowed us to generate a transcriptional classifier that could predict AZA response and survival. This classifier outperformed a previously developed gene signature in a second MDS patient cohort, but signatures of hematological responses were unable to predict survival. Overall, these studies characterize the molecular consequences of AZA treatment in MDS HSPCs and identify a potential tool for predicting AZA therapy responses and overall survival prior to initiation of therapy.

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