bioRxivCancer Biology
Mutual antagonism between glucocorticoid and canonical Wnt signaling pathways in B-cell acute lymphoblastic leukemia
B. P. Bergeron, K. R. Barnett, K. R. Bhattarai, R. J. Mobley, B. S. Hansen, A. Brown, K. Kodali, A. A. High, S. Jeha, C.-H. Pui, J. Peng, S. M. Pruett-Miller, D. Savic
Abstract
Glucocorticoids (GCs; i.e., steroids) are important chemotherapeutic agents in the treatment of B-cell precursor acute lymphoblastic leukemia (B-ALL) and de novo GC resistance predicts relapse and poor clinical outcome in patients. Glucocorticoids induce B-ALL cell apoptosis through activation of glucocorticoid receptor (GR), a ligand-induced nuclear receptor transcription factor (TF). We previously identified disruptions to glucocorticoid receptor (GR)-bound cis-regulatory elements controlling TLE1 expression in GC-resistant primary B-ALL cells from patients. TLE1 is a GC-response gene up-regulated by steroids and functions as a canonical Wnt signaling repressor. To better understand the mechanistic relationship between GC signaling and canonical Wnt signaling, we performed diverse functional analyses that identified extensive crosstalk and mutual antagonism between these two signaling pathways in B-ALL. We determined that crosstalk and antagonism was driven by the binding of GR and the canonical Wnt signaling TFs LEF1 and TCF7L2 to overlapping sets of cis-regulatory elements associated with genes impacting cell death and cell proliferation, and was further accompanied by overlapping and opposing transcriptional programs. Our data additionally suggest that cis-regulatory disruptions at TLE1 are linked to GC resistance through a dampening of the GC response and GC-mediated apoptosis via enhanced canonical Wnt signaling. As a result of the extensive genomic and gene regulatory connectivity between these two signaling pathways, our data supports the importance of canonical Wnt signaling in mediating GC resistance in B-ALL.
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: gene regulatory network inference
- approach: statistical model
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