MYBL2 Drives Prostate Cancer Plasticity: Inhibiting its Transcriptional Target CDK2 for RB1-Deficient Neuroendocrine Prostate Cancer.
Beatriz GermanSarah Abou AlaiwiKun-Lin HoJagpreet S NandaMarcos A S FonsecaDeborah L BurkhartAnjali V SheahanHannah E BergomKatherine L MorelHimisha BeltranJustin H HwangMatthew L FreedmanKate LawrensonLeigh EllisPublished in: Cancer research communications (2024)
Phenotypic plasticity is a recognized mechanism driving therapeutic resistance in prostate cancer (PCa) patients. While underlying molecular causations driving phenotypic plasticity have been identified, therapeutic success is yet to be achieved. To identify putative master regulator transcription factors (MR-TF) driving phenotypic plasticity in PCa, this work utilized a multiomic approach using genetically engineered mouse models of prostate cancer combined with patient data to identify MYBL2 as a significantly enriched transcription factor in PCa exhibiting phenotypic plasticity. Genetic inhibition of Mybl2 using independent murine PCa cell lines representing phenotypic plasticity demonstrated Mybl2 loss significantly decreased in vivo growth as well as cell fitness and repressed gene expression signatures involved in pluripotency and stemness. Because MYBL2 is currently not druggable, a MYBL2 gene signature was employed to identify cyclin-dependent kinase-2 (CDK2) as a potential therapeutic target. CDK2 inhibition phenocopied genetic loss of Mybl2 and significantly decreased in vivo tumor growth associated with enrichment of DNA damage. Together, this work demonstrates MYBL2 as an important MR-TF driving phenotypic plasticity in PCa. Further, high MYBL2 activity identifies PCa that would be responsive to CDK2 inhibition.
Keyphrases
- prostate cancer
- transcription factor
- radical prostatectomy
- cell cycle
- gene expression
- genome wide
- dna damage
- stem cells
- dna methylation
- end stage renal disease
- magnetic resonance
- single cell
- chronic kidney disease
- mouse model
- epithelial mesenchymal transition
- cell death
- machine learning
- genome wide identification
- cancer therapy
- climate change
- drug delivery
- risk assessment
- mesenchymal stem cells
- big data
- human health
- pi k akt
- cell cycle arrest
- patient reported