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

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.

Comparing cancer vs normal gene expression profiles identifies new disease entities and common transcriptional programs in AML patients

“…33,[36][37][38][39] In addition, NF-kB, the key transcriptional regulator of inflammatory response, was demonstrated to be constitutively activated in various types of cancers, including AML, and to play an important role in malignant transformation in mouse models. 39,[40][41][42][43] Finally, the ability of various solid cancer cells to adapt to hypoxia and switch metabolism from oxidative phosphorylation toward glycolysis has emerged as a novel hallmark of cancer that defines more aggressive cancer phenotypes. 44 Consistent with these reports, a previous study 45 identified a cancer signature of malignant transformation that is not only shared by various types of cancers but also overlaps significantly with gene expression signatures of chronic inflammatory conditions (colitis ulcerosa, rheumatoid arthritis, systemic lupus erythematosus, Crohn's disease) and metabolic diseases (diabetes, obesity, hypercholesterolemia, atherosclerosis, cardiomyopathy).…”

Gene expression profiling in MDS and AML: potential and future avenues

“…69 Finally, a recent study reported an alternative, less biased strategy for the identification of novel drugs and drug targets in AML (Figure 4). In this study, Marstrand et al 70 identified signatures of deregulated genes in a subtype of AML, namely APL, by comparison of gene expression profiles from APL cells and their normal counterparts, that is promyelocytes, purified form healthy subjects. Subsequently, APL signatures were applied to a series of computational analyses including an in silico screen of the Connectivity MAP drug signature database.…”

“…71 These analyses led to the finding that APL cells exhibit stem cell properties in terms of gene expression and transcriptional regulation, and importantly identified novel candidate drugs and targets for therapeutic interventions. 70 Significantly, the identified candidate drugs including a histone deacetylase inhibitor (trichostatin A), an inhibitor of phosphoinositide 3-kinase (PI3K) (LY294002), and an inhibitor of phospholipase A2 (PLA2) (quinacrine), were all shown to inhibit proliferation and induce apoptosis of a human APL cell line (NB4).…”

HemaExplorer: a Web server for easy and fast visualization of gene expression in normal and malignant hematopoiesis

“…[1][2][3][4][5][6][7][8][9] To correct for batch effect the datasets were sorted by platform and laboratory, before RMA normalization and subsequent processing with ComBat. 10 As several platforms are represented in the dataset, only the overlapping probe sets were retained for further processing.…”