bioRxivCancer BiologyPreprint
R-loop-induced p21 expression following CDC73, CTR9, and PAF1 loss protects cancer cells against replicative catastrophe following WEE1 inhibition
L. van Bijsterveldt, H. B. Landsverk, V. Näshe, S. C. Durley, S. Sarkar, R. G. Syljuasen, T. C. Humphrey
Abstract
WEE1 inhibitors have now advanced into clinical studies as monotherapy or in combination with chemoradiotherapy in TP53, RAS, BRAF, and SETD2 mutation carriers across several tumour types, yet mechanisms of resistance are still poorly understood. Here, we further elucidate the mechanisms by which AZD1775, the most potent WEE1 inhibitor, kills cells and reveal additional genetic interactions that can result in resistance, but could be used to optimise its clinical utility. We identified RNA Polymerase II-associated factor 1 (PAF1) complex members, CDC73, CTR9, and PAF1 as major determinants of WEE1-inhibitor sensitivity in isogenic SETD2-positive and negative cell lines. PAF1-knockdown cells resist higher doses of the WEE1 inhibitor, which we show is due to reduced DNA damage induction ({gamma}H2AX) and delayed G1 checkpoint activation, ultimately protecting cells against replicative catastrophe. Investigations into the molecular mechanisms responsible for PAF1-mediated resistance identify involvement of R-loops and subsequent activation of the cyclin-dependent kinase inhibitor p21Cip1/Waf1, which in addition to causing prolonged G1 arrest in the following cell cycle, also regulates CDK activity, therefore limiting replication. These results provide evidence that the PAF1 complex and p21 are important regulators of proliferation under increased DNA replication stress and their expression levels might be useful biomarkers to predict clinical response to WEE1 inhibitors and other ribonucleotide reductase inhibitors.
- organism: human
- assay: bulk RNA-seq
- assay: DNA methylation
- research focus: drug response / drug discovery
- research focus: cell type annotation
- research focus: differential expression
- approach: experimental assay
- approach: statistical model
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