Pharmacogenomic analysis of acute promyelocytic leukemia cells highlights CYP26 cytochrome metabolism in differential all-trans retinoic acid sensitivity

Quere R, Baudet A, Cassinat B, Bertrand G, Marti J, Manchon L, Piquemal D, Chomienne C, Commes T
Source: Blood
Publication Date: (2007)
Issue: 109(10): 4450-60
Research Area:
Cancer Research/Cell Biology
Immunotherapy / Hematology
Cells used in publication:
Species: human
Tissue Origin: bone marrow
Nucleofectorâ„¢ I/II/2b
Disease relapse sometimes occurs after acute promyelocytic leukemia (APL) therapy with all-trans retinoic acid (ATRA). Among the diagnostic parameters predicting relapse, heterogeneity in the in vitro differentiation rate of blasts is an independent factor. To identify biologic networks involved in resistance, we conducted pharmacogenomic studies in APL blasts displaying distinct ATRA sensitivities. Although the expression profiles of genes invested in differentiation were similarly modulated in low- and high-sensitive blasts, low-sensitive cells showed higher levels of transcription of ATRA-target genes, transcriptional regulators, chromatin remodelers, and transcription factors. In opposition, only high-sensitive blasts expressed the CYP26A1 gene, encoding the p450 cytochrome which is known to be involved in retinoic acid catabolism. In NB4 cells, ATRA treatment activates a novel signaling pathway, whereby interleukin-8 stimulates the expression of the homeobox transcription factor HOXA10v2, an effective enhancer of CYP26A1 transcription. These data were corroborated in primary APL cells, as maturation levels correlated with CYP26A1 expression. Treatment with a retinoic acid metabolism blocking agent (RAMBA) results in high-nucleoplasmic concentrations of retinoid and growth of NB4-resistant subclones. Hence, for APL blasts associated with poor prognosis, the low CYP26A1 expression may explain high risk of resistance installation, by increased retinoid pressure. Pharmacogenomic profiles of genes involved in retinoid acid metabolism may help to optimize anticancer therapies, including retinoids.