Dataset Information

Genome-wide analysis of transcriptional reprogramming in mouse models of acute myeloid leukaemia.

ABSTRACT: Acute leukaemias are commonly caused by mutations that corrupt the transcriptional circuitry of haematopoietic stem/progenitor cells. However, the mechanisms underlying large-scale transcriptional reprogramming remain largely unknown. Here we investigated transcriptional reprogramming at genome-scale in mouse retroviral transplant models of acute myeloid leukaemia (AML) using both gene-expression profiling and ChIP-sequencing. We identified several thousand candidate regulatory regions with altered levels of histone acetylation that were characterised by differential distribution of consensus motifs for key haematopoietic transcription factors including Gata2, Gfi1 and Sfpi1/Pu.1. In particular, downregulation of Gata2 expression was mirrored by abundant GATA motifs in regions of reduced histone acetylation suggesting an important role in leukaemogenic transcriptional reprogramming. Forced re-expression of Gata2 was not compatible with sustained growth of leukaemic cells thus suggesting a previously unrecognised role for Gata2 in downregulation during the development of AML. Additionally, large scale human AML datasets revealed significantly higher expression of GATA2 in CD34+ cells from healthy controls compared with AML blast cells. The integrated genome-scale analysis applied in this study represents a valuable and widely applicable approach to study the transcriptional control of both normal and aberrant haematopoiesis and to identify critical factors responsible for transcriptional reprogramming in human cancer.

SUBMITTER: Bonadies N

PROVIDER: S-EPMC3030562 | biostudies-literature | 2011 Jan

REPOSITORIES: biostudies-literature

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Genome-wide analysis of transcriptional reprogramming in mouse models of acute myeloid leukaemia.

Bonadies Nicolas N Foster Samuel D SD Chan Wai-In WI Kvinlaug Brynn T BT Spensberger Dominik D Dawson Mark A MA Spooncer Elaine E Whetton Anthony D AD Bannister Andrew J AJ Huntly Brian J BJ Göttgens Berthold B

PloS one 20110128 1

Acute leukaemias are commonly caused by mutations that corrupt the transcriptional circuitry of haematopoietic stem/progenitor cells. However, the mechanisms underlying large-scale transcriptional reprogramming remain largely unknown. Here we investigated transcriptional reprogramming at genome-scale in mouse retroviral transplant models of acute myeloid leukaemia (AML) using both gene-expression profiling and ChIP-sequencing. We identified several thousand candidate regulatory regions with alte ...[more]

PMID: 21297973

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Project description:Background Although anti-apoptotic proteins of the B-cell lymphoma-2 (BCL2) family have been utilized as therapeutic targets in acute myeloid leukaemia (AML), their complicated regulatory networks make individualized therapy difficult. This study aimed to discover the transcriptional signatures of BCL2 family genes that reflect regulatory dynamics, which can guide individualized therapeutic strategies. Methods From three AML RNA-seq cohorts (BeatAML, LeuceGene, and TCGA; n = 451, 437, and 179, respectively), we constructed the BCL2 family signatures (BFSigs) by applying an innovative gene-set selection method reflecting biological knowledge followed by non-negative matrix factorization (NMF). To demonstrate the significance of the BFSigs, we conducted modelling to predict response to BCL2 family inhibitors, clustering, and functional enrichment analysis. Cross-platform validity of BFSigs was also confirmed using NanoString technology in a separate cohort of 47 patients. Results We established BFSigs labeled as the BCL2, MCL1/BCL2, and BFL1/MCL1 signatures that identify key anti-apoptotic proteins. Unsupervised clustering based on BFSig information consistently classified AML patients into three robust subtypes across different AML cohorts, implying the existence of biological entities revealed by the BFSig approach. Interestingly, each subtype has distinct enrichment patterns of major cancer pathways, including MAPK and mTORC1, which propose subtype-specific combination treatment with apoptosis modulating drugs. The BFSig-based classifier also predicted response to venetoclax with remarkable performance (area under the ROC curve, AUROC = 0.874), which was well-validated in an independent cohort (AUROC = 0.950). Lastly, we successfully confirmed the validity of BFSigs using NanoString technology. Conclusions This study proposes BFSigs as a biomarker for the effective selection of apoptosis targeting treatments and cancer pathways to co-target in AML. Supplementary Information The online version contains supplementary material available at 10.1186/s13073-022-01115-w.

Dataset Information

Genome-wide analysis of transcriptional reprogramming in mouse models of acute myeloid leukaemia.

Publications

Genome-wide analysis of transcriptional reprogramming in mouse models of acute myeloid leukaemia.

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