Back

Loss of tristetraprolin activates NF-κB induced phenotypic plasticity and primes transition to lethal prostate cancer

Morel, K. L.; Hamid, A. A.; Falcon, B. G.; Singh, J. N.; Linder, S.; Bergman, A. M.; van der Poel, H. G.; Hofland, I.; Bekers, E. M.; Trostel, S.; Wilkinson, S.; Ku, A. T.; Burkhart, D. L.; Kim, M.; Kim, J.; Plummer, J. T.; You, S.; Sowalsky, A. G.; Zwart, W.; Sweeney, C. J.; Ellis, L.

2023-03-26 cancer biology
10.1101/2022.08.05.500896 bioRxiv
Show abstract

Phenotypic plasticity is a hallmark of cancer and increasingly realized as a mechanism of resistance in androgen indifferent prostate tumors. It is critical to identify mechanisms and actionable targets driving phenotypic plasticity. Here, we report that loss of tristetraprolin (TTP, gene ZFP36), an RNA binding protein that regulates mRNA stability increases NF-{kappa}B activation and is associated with higher rates of aggressive disease and early recurrence in primary prostate cancer (PCa). We examined the clinical and biological impact of ZFP36 loss combined with PTEN loss, a known driver of PCa. Combined loss of PTEN and ZFP36 expression was associated with increased risk of recurrence in multiple independent primary PCa cohorts, and significantly reduced overall survival and time to progression following castration in genetically engineered mouse models. ZFP36 loss alters the cell state that is driven by PTEN loss, demonstrated by positive enrichment of gene sets including EMT, inflammation, TNF/NF-{kappa}B, IL6-JAK/STAT3. ZFP36 loss also induces enrichment of multiple gene sets involved in cell migration, chemotaxis, and proliferation. Use of the NF-{kappa}B inhibitor dimethylaminoparthenolide induced significant therapeutic responses in tumors with PTEN and ZFP36 co-loss and reversed castration resistance. This work identifies a novel molecular mechanism driving phenotypic plasticity and castration resistance through loss of ZFP36 expression, that can be reversed by inhibition of NF-{kappa}B activity.

Matching journals

The top 6 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.