p53 protein abundance is a therapeutic window across TP53 mutant cancers and is targetable with proximity inducing small molecules
Sadagopan, A.; Garaffo, N.; Chang, H.-J.; Schreiber, S. L.; Meyerson, M.; Gibson, W. J.
Show abstract
TP53 mutant cancers are associated with approximately half of cancer deaths. The most common mechanism of p53 inactivation involves missense mutations. Such mutations in TP53 result in a robust upregulation of the p53 protein. Here, we demonstrate an induced proximity approach to selectively kill TP53 mutant cells. This approach uses the increased abundance of p53 protein in TP53 mutant cancer cells to concentrate toxic molecules in these cells. We demonstrate the first generalizable strategy using a small molecule to selectively kill TP53 mutant cells. This molecule binds the Y220C mutant of p53 and concentrates a PLK1 inhibitor in cells harboring TP53 Y220C mutations. Together, these data demonstrate that the abundance of p53 protein provides a therapeutic window for TP53 missense mutant cancers that can be translated into a cell death signal using proximity-inducing small molecules.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- VIPER-TACs leverage viral E3 ligases for disease-specific targeted protein degradation 96%
- Inducible mismatch repair streamlines forward genetic approaches to target identification of cytotoxic small molecules 96%
- Identification of structurally diverse FSP1 inhibitors that sensitize cancer cells to ferroptosis 95%
Similar papers in this journal
- Designer small molecule control system based on Minocycline induced disruption of protein-protein interaction. 96%
- Controlled protein activities with viral proteases, antiviral peptides, and antiviral drugs 95%
- Multivalent peptide ligands to probe the chromocenter microenvironment in living cells 94%
Similar papers in this journal
"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.