Back

NUAK2 is a therapeutically tractable regulator of RNA splicing and tumor progression in neuroendocrine prostate cancer

Mehraj, U.; Maimekov, U.; Manzoor, S.; Cordova, E.; Patel, M.; Mancera-Ortiz, I. Y.; Howell, S.; Davis-Gilbert, Z. W.; Wang, M.-E.; Chen, M.; Park, J. W.; Wang, Y.; Armstrong, A. J.; Huang, J.; Drewry, D. H.; Mitrofanova, A.; Macias, E.

2025-11-13 cancer biology
10.1101/2025.11.12.687734 bioRxiv
Show abstract

Prostate cancer (PC) remains the second leading cause of cancer-related mortality in men. The emergence of treatment-emergent neuroendocrine prostate cancer (NEPC) arising from androgen receptor (AR) pathway inhibition poses a significant clinical challenge. Here, we report that NUAK family kinase 2 (NUAK2) is an actionable therapeutic target in NEPC. NUAK2 expression is markedly elevated in NEPC patient specimens and preclinical models, and its genetic or pharmacologic inhibition suppresses NEPC tumor growth. The FDA-approved CDK4/6 inhibitor trilaciclib exerts potent inhibition of NUAK2, leading to marked tumor suppression alone and enhanced efficacy in combination with carboplatin. Integrated phospho-target and interactome analyses demonstrate that NUAK2 engages core spliceosome components to regulate pre-mRNA splicing. As proof of principle, we validated that NUAK2 inhibition perturbs pre-mRNA splicing of EZH2 and TTK leading to reduced translation. Collectively, these findings establish NUAK2 as a clinically actionable regulator of RNA splicing and tumor progression in NEPC, revealing a novel mechanism by which trilaciclib exerts antitumor activity in NEPC. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=180 SRC="FIGDIR/small/687734v1_ufig1.gif" ALT="Figure 1"> View larger version (91K): org.highwire.dtl.DTLVardef@1bb3e77org.highwire.dtl.DTLVardef@21857org.highwire.dtl.DTLVardef@18c2f26org.highwire.dtl.DTLVardef@6ba82e_HPS_FORMAT_FIGEXP M_FIG C_FIG

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.