Back

Targeting replication stress in neuroblastoma by exploiting the synergistic potential of second generation RRM2 and CHK1 inhibitors

Nelen, I. H.; Leys, S.; Bekaert, S.-L.; De Vloed, F.; Martens, F.; Raj, A. P. E.; Ohmura, S.; Vleminckx, L.; Grunewald, T. G. P.; Van Roy, N.; De Wilde, B.; Speleman, F.; Van Hemelryk, A.; Depestel, L.; Durinck, K.

2025-03-02 cancer biology
10.1101/2025.02.26.640375 bioRxiv
Show abstract

Tumor cells often cope with elevated levels of replication stress (RS) causing increased dependency on ATR-CHK1 signalling. We previously presented RRM2, the regulatory component of the ribonucleotide reductase (RNR) enzyme, as novel dependency in neuroblastoma, in keeping with its role in RS resistance. We identified strong synergism for combined RRM2-CHK1 inhibition using the iron chelator triapine and prexasertib respectively. To obtain direct RNR targeting, we evaluated a novel inhibitor, TAS1553, specifically disrupting the RNR complex in this study. Treatment with TAS1553 impedes cell growth and induces enhanced RS, DNA damage and apoptosis. We demonstrated strong synergism between TAS1553 the CHK1 inhibitors prexasertib and SRA737 in both NB and sarcoma cell lines, underscoring the broad clinical potential of combinatorial RRM2-CHK1 inhibition. Transcriptome profiling demonstrated strong overlap between the different RRM2-CHK1 treatments and revealed differential expression of RNA splicing components, opening new perspectives for combination treatments using splicing inhibitors. Altogether, this study paves the way for further preclinical testing of second generation RRM2 and CHK1 inhibitors such as TAS1553 and SRA737 in neuroblastoma and sarcomas.

Published in Cell Death & Disease (predicted rank #14) · training set

Matching journals

The top 11 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.