Back

A Sequential Triple-Drug Strategy for Selective Targeting of p53-Mutant Cancers

Bakin, A. V.; Alruwaili, M.; Guo, Y.; Zonneville, J.; Melendy, T.; Straubinger, R. M.; Foster, B. A.; Rajan, P.; Withers, H.; Chatley, S.; Iyer, R.; Fountzilas, C.

2025-11-07 cancer biology
10.1101/2025.11.06.687028 bioRxiv
Show abstract

The tumor suppressor TP53 gene (p53) is mutated in most human malignancies; however, existing treatment options are largely ineffective, lack selectivity, and cause toxic side effects. To address these clinical problems, we developed a sequential triple-drug strategy for p53 mutant cancer cells. Here we show that a combination of a thymidine analogue (TAS102) plus PARP inhibitor (PARPi) promotes formation of DNA double-strand breaks (DSBs) and G2-arrest specifically in p53 mutant cancer cells. Transcriptome analysis revealed that TAS102-PARPi treatment of p53 mutant cells did not repress DNA replication but activated DSB repair and blocked the mitotic program, consistent with G2-arrest. In contrast, TAS102-PARPi treatment of normal p53 wild-type cells resulted in a temporal G1-arrest and rapid recovery of cell cycle capacity after drug withdrawal. In p53 mutant cancer cells, subsequent blocking of a G2-checkpoint kinase, such as WEE1, released these G2-arrested cells into mitosis, leading to massive cell death. Delayed administration of a G2-kinase inhibitor provides time for p53 wild-type cells to repair DNA, thereby minimizing toxicity to normal tissues. This sequential triple-drug strategy exhibited robust efficacy in preclinical models of colorectal and pancreatic cancers and was well tolerated in mice. Together, our findings illustrate a promising triple-drug strategy for targeting p53 mutant malignancies.

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.