A conceptual framework for the identification of candidate drugs and drug targets in acute promyelocytic leukemia

T. T. Marstrand, R. Borup, A. Willer, N. Borregaard, A. Sandelin, B. T. Porse, Kim Theilgaard-Moench

Research output: Contribution to journalArticlepeer-review

Abstract

Chromosomal translocations of transcription factors generating fusion proteins with aberrant transcriptional activity are common in acute leukemia. In acute promyelocytic leukemia (APL), the promyelocytic leukemia-retinoic-acid receptor alpha (PML-RARA) fusion protein, which emerges as a consequence of the t(15;17) translocation, acts as a transcriptional repressor that blocks neutrophil differentiation at the promyelocyte (PM) stage. In this study, we used publicly available microarray data sets and identified signatures of genes dysregulated in APL by comparison of gene expression profiles of APL cells and normal PMs representing the same stage of differentiation. We next subjected our identified APL signatures of dysregulated genes to a series of computational analyses leading to (i) the finding that APL cells show stem cell properties with respect to gene expression and transcriptional regulation, and (ii) the identification of candidate drugs and drug targets for therapeutic interventions. Significantly, our study provides a conceptual framework that can be applied to any subtype of AML and cancer in general to uncover novel information from published microarray data sets at low cost. In a broader perspective, our study provides strong evidence that genomic strategies might be used in a clinical setting to prospectively identify candidate drugs that subsequently are validated in vitro to define the most effective drug combination for individual cancer patients on a rational basis. Leukemia (2010) 24, 1265-1275; doi:10.1038/leu.2010.95; published online 27 May 2010
Original languageEnglish
Pages (from-to)1265-1275
JournalLeukemia
Volume24
Issue number7
DOIs
Publication statusPublished - 2010

Subject classification (UKÄ)

  • Cancer and Oncology

Free keywords

  • MAP(CMAP)
  • gene set enrichment analysis (GSEA)
  • connectivity
  • gene expression profiling
  • drug discovery
  • AML
  • stemness

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