GSE131325
Identification of drugs in leukaemia differentiation therapy by network pharmacology
Acute leukaemias differ from their normal haematopoietic counterparts in their inability to differentiate. This phenomenon is thought to be the result of aberrant transcriptional reprogramming involving transcription factors (TFs). Here we leveraged on Mogrify, a network-based algorithm for predicting reprogramming factors, to identify TFs and their gene regulatory networks that drive ATRA-induced differentiation of the acute promyelocytic leukaemia (APL) cell line NB4. We further integrated the detected TF regulatory networks with the Connectivity Map (CMAP) repository and recovered small molecule compounds which induce similar transcriptional changes. Our method outperformed standard approaches, retrieving ATRA as the top hit. Of the other drug hits, dimaprit and mebendazole enhanced ATRA-mediated differentiation in both parental NB4 and ATRA-resistant NB4-MR2 cells. Thus, we provide a proof-of-principle of our network-based computational platform for drug discovery and repositioning in leukaemia differentiation therapy, which can be extended to other dysregulated disease states. Overall design: 33 NB4 APL samples consisting of No Treatment(basal=3), ATRA (treatment=15) and DMSO (control=15) conditions in 5 time points 4h, 12h, 24h, 72h and 120h in 3 biological replicates. Each biological replicate was further subjected to paired-end RNA-Seq in 2 technical replicates.