Mutant p53 protein accumulation is selectively targetable by proximity-inducing drugs

Sadagopan, Ananthan; Carson, Maximilian; Zamurs, Eriks J.; Garaffo, Nicholas; Chang, Heng-Jui; Schreiber, Stuart L.; Meyerson, Matthew; Gibson, William J.

doi:10.1038/s41589-025-02051-7

Download PDF

Article
Open access
Published: 03 November 2025

Mutant p53 protein accumulation is selectively targetable by proximity-inducing drugs

Nature Chemical Biology (2025)Cite this article

25k Accesses
4 Citations
37 Altmetric
Metrics details

Subjects

A Publisher Correction to this article was published on 12 November 2025

This article has been updated

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 this approach with a molecule that binds the Y220C mutant of p53 and concentrates a PLK1 inhibitor in cells harboring TP53^Y220C mutations. The resulting bifunctional molecule promotes formation of a p53^Y220C–PLK1 ternary complex, mislocalizes PLK1, inhibits PLK1 activity, elicits selective G2/M arrest and induces apoptosis in TP53^Y220C cells while sparing wild-type TP53 cells. These data exemplify a potentially generalizable framework for targeting TP53 missense mutations by leveraging mutant p53 protein abundance to induce cell death, independent of p53’s transcriptional activity.

Pharmacological reactivation of p53 in the era of precision anticancer medicine

Article 15 December 2023

TP53: the unluckiest of genes?

Article Open access 23 October 2024

Cell differentiation modifies the p53 transcriptional program through a combination of gene silencing and constitutive transactivation

Article Open access 21 January 2023

Main

TP53 is the most mutated gene in human cancer (36% according to the Pan-Cancer Analysis of Whole Genomes, 37% according to The Cancer Genome Atlas (TCGA) and 39% according to the American Association for Cancer Research GENIE project)^1,2,3. TP53 mutations have been shown to induce resistance to cytotoxic chemotherapy and are associated with significantly worse prognosis across nearly all cancers^4,5,6. In total, TP53-mutant cancers are estimated to be associated with ~46% of cancer deaths (Supplementary Fig. 1; analysis across cBioPortal^7,8). In the past, attempts have been made to therapeutically target TP53-mutant cancers in several ways⁹: delivery of wild-type (WT) TP53 genes to cells with gene therapy or mRNA^10,11, immunotherapy^12,13,14 or small-molecule refolders of p53 protein^15,16. Attempts to enact these strategies have invariably failed, and the activity of purported mutant-agnostic p53 refolders has consistently been shown to be off-target^17,18. Furthermore, small-molecule refolding of p53 may not be possible for contact mutants of p53 specifically affecting residues that interact with the negatively charged phosphate backbone of DNA. New generalizable strategies are needed to specifically kill cancer cells harboring these mutations.

In contrast to mutations of most tumor suppressor genes, such as APC, RB1 or PTEN, TP53 mutations are typically missense mutations. These mutations act as dominant negative mutations that disallow DNA binding at consensus sequences and, therefore, poison the tetramer¹⁹. Because they act as dominant negatives, they function as a shorter evolutionary path to inactivating most p53 functions within the cells that harbor these mutations^20,21,22. p53 typically acts as a central signal integrator for various cellular stress signals such as hypoxia, DNA damage or excessive oncogenic signaling^23,24. When these mutations occur, the mechanisms that normally sense the above cellular stressors are typically intact and jointly work to activate p53 by increasing its abundance and promoting its nuclear translocation. Meanwhile the mutant p53 that accumulates is impotent to drive the transcription of its own destroyer, MDM2. The negative feedback loop that usually serves to tightly regulate p53 protein is broken and the half-life (t_1/2) of p53 stretches from its typical ~5–20 min to several hours. Thus, p53 protein accumulates in cells with TP53 missense mutations^{25,26,27,28,29}.

Several recent examples have revealed the powerful effects of gain-of-function-induced proximity pharmacology such as transcriptional and epigenetic chemical inducers of proximity, velcrins, regulated induced proximity-targeting chimeras (RIPTACs) or cancer mutation-specific effectors of targeted protein degradation^30,31,32,33. Application of gain-of-function approaches might provide a generalizable method to target deadly cancers associated with mutant TP53. Here, we develop gain-of-function small molecules that target the abundance of p53 protein, a characteristic of cancers harboring TP53 missense mutants, to selectively kill cancer cells with high levels of mutant p53.

Results

TP53-mutant cells lack synthetic lethal dependencies

Large-scale functional genomic studies have recently been undertaken to systematically determine the genetic vulnerabilities of nearly 1,000 cancer cell lines^34,35. We sought to test the hypothesis that there exist synthetic lethal interactions with TP53-mutant cancer cells. We performed a genome-wide analysis of CRISPR dependencies in cells with TP53 mutations versus WT cell lines. Unfortunately, there is no genetic dependency that is enriched in cancer cells with TP53 loss-of-function mutations (Fig. 1a), consistent with a prior report²², nor any profiled small molecules selectively killing TP53-mutant cells (Extended Data Fig. 1a,b). One might instead hope that a subset of proteins are enriched in cells with TP53 mutations, particularly cell surface proteins that might be amenable to targeting with a number of effector modalities such as chimeric antigen receptor T cells or antibody–drug conjugates. Unfortunately, no such cell-surface proteins are enriched in TP53-mutant cancers (Fig. 1b and Extended Data Fig. 1c).

**Fig. 1: p53 protein abundance is the only genetic or proteomic distinction between *TP53*-mutant and WT cancer cells.**

TP53-mutant cells strongly upregulate p53 protein

Quantitative proteomics of the Cancer Cell Line Encyclopedia (CCLE)³⁶ shows that the only protein whose abundance is significantly increased in TP53-mutant cancers is p53 itself (Fig. 1b; Δ(z-scored abundance)_Mut-WT > 2, q < 0.05). p53 protein is expressed at a low level in TP53 WT cancer cells but is elevated in cells with TP53 missense mutations (Fig. 1c; >2-fold increase in mean reverse-phase protein array (RPPA) signal), for both monoallelic and biallelic mutations, as well as structural (for example, Y220C) and contact (for example, R273H) mutants (Extended Data Fig. 1d,e). Correspondingly, immunohistochemistry of p53 in tumor tissues shows abundant staining in cancer cells but not adjacent normal tissue (Fig. 1d). Taken together, these data demonstrate that the only genetic dependency or substantial proteomic difference between TP53-mutant cells and WT cells, detected to date, is the overabundance of p53 protein in TP53-mutant cells.

The increased abundance of the p53 protein in cancer cells with TP53 missense mutations provides a unique therapeutic opportunity: a p53 concentration-dependent toxin could selectively kill TP53-mutant cancer cells in a way that is generalizable to multiple TP53 missense mutations.

Bifunctional molecules kill cells with p53 overabundance

We, therefore, hypothesized that, if the abundance of p53 protein could be translated into a proportionate cell death signal, cells with TP53 missense mutations could be selectively killed. We hypothesized that bifunctional small molecules consisting of a p53 binder and a small-molecule toxin may be able to concentrate the toxin in cells overexpressing mutant TP53 (ref. ³³). Unfortunately, no high-affinity ligands for WT p53 exist. In the absence of such ligands, we used the Halo tag as a surrogate for a small-molecule binder of p53. Mutant p53-R273H was stably expressed in 293T cells fused to a Halo tag and mCherry for visualization. The fusion protein showed nuclear expression similar to native p53 (Extended Data Fig. 2a).

To leverage the abundance of p53 protein for therapeutic purposes, a bifunctional molecule would ideally be designed such that its binding partner is less abundant than overexpressed mutant p53 protein and highly essential for cell proliferation. To determine which cytotoxin to append to a potential bifunctional molecule, we compared CRISPR gene essentiality scores across Dependency Map (DepMap)³⁷ and absolute protein abundance profiled in OpenCell³⁸ (Fig. 2a). We identified five targets with high essentiality and low abundance: DNA2, WEE1, LRR1, PRELID1 and PLK1 (Fig. 2a and Methods). Of these, WEE1 and PLK1 have ligands previously used to synthesize bifunctional small molecules for targeted protein degradation^39,40,41. We functionalized WEE1 inhibitor adavosertib⁴² (half-maximal inhibitory concentration (IC₅₀) = 5.2 nM for cell-free WEE1 inhibition) and an analog of PLK1 inhibitor BI2536 (ref. ⁴³) (IC₅₀ = 0.83 nM for cell-free PLK1 inhibition), both of which display potent antiproliferative effects across DepMap (adavosertib median IC₅₀: 348 nM, BI2536 median IC₅₀: 11.8 nM; Extended Data Fig. 2b,c), with a Halo tag ligand and PEG2 linker, yielding the bifunctional small molecules Halo-PEG2-adavosertib (1) and Halo-PEG2-BI2536 (2) (Fig. 2b and Extended Data Fig. 2d).

**Fig. 2: Bifunctional molecules liganding p53 fusion proteins selectively inhibit proliferation of *TP53*-mutant cells.**

Halo-PEG2-BI2536 inhibited proliferation of 293T cells stably expressing Halo–p53-R273H (full length (FL))–mCherry at doses between 20 nM and 500 nM, with little impact on the proliferation of parental 293T cells (Fig. 2c,d; p53-R273H 293T IC₅₀: 23 nM, parental 293T IC₅₀: 1,143 nM). When competing Halo–p53-R273H (FL)–mCherry 293T cells with parental 293T cells in the presence of Halo-PEG2-BI2536 (200 nM), we observed a 1,200-fold decline in the population of mCherry⁺ cells after 1 week (0.04% versus 47.94% for DMSO control; Fig. 2e). These results were reproducible in a competition assay between the parental TP53-null (TP53 homozygous deletion) Calu-1 cell line and Calu-1 cells stably expressing Halo–p53-R273H (FL)–mCherry (Extended Data Fig. 2e; half-maximal effective concentration (EC₅₀) = 13 nM). Furthermore, treatment of Halo–p53-R273H (FL)–mCherry 293 T cells with low doses of Halo-PEG2-BI2536 resulted in selection for low expressors of the fusion protein (Extended Data Fig. 2a). Halo-PEG2-adavosertib also selectively inhibited the proliferation of Halo–p53-R273H (FL)–mCherry 293T cells in the competition assay at doses between 100 nM and 1 μM (Extended Data Fig. 2f; EC₅₀ = 41 nM).

To assess the therapeutic window obtained by this strategy, we established Calu-1 cell lines stably expressing Halo–p53-R273H (FL)–mCherry and mTagBFP2 from the same transcript using 2A peptides. We established versions of this cell line for TP53 WT and TP53^R273H. We confirmed that these cell lines displayed approximately equal expression of the transgene by reverse transcription (RT)–qPCR for the transcript and fluorescence-activated cell sorting (FACS) for mTagBFP2 (Fig. 2f; mean [TP53 WT mRNA]/[TP53^R273H mRNA] = 1.14; mean mTagBFP2 mean fluorescence intensity (MFI) p53 WT/p53-R237H = 0.77). We then compared expression of Halo–p53-R273H (FL)–mCherry between the cell lines using FACS for mCherry and found 30.2-fold greater expression of Halo–p53-R273H (FL)–mCherry compared to Halo–p53 WT (FL)–mCherry (Fig. 2g). We next assessed whether the 30-fold increase in p53 protein abundance was targetable. Indeed, Halo-PEG2-BI2536 had no effect on the proliferation of Calu-1 p53 WT cells at doses ≤ 2 μM. However, in Calu-1 p53-R237H cells, we observed notable viability declines starting at 125 nM, with an IC₅₀ = 402 nM after 4 days of treatment. BI2536 alone lacked a differential proliferation effect (Fig. 2h). We repeated this experiment for the TP53^Y220C mutation. Calu-1 TP53^Y220C cells displayed 18.6-fold greater mCherry expression than p53 WT counterparts with similar mTagBFP2 expression levels, corresponding to a ~4-fold therapeutic window for Halo-PEG2-BI2536 (Extended Data Fig. 2g,h). This change in protein abundance is consistent with the reported p53-Y220C in cellulo t_1/2 (that is, in the presence of cellular chaperones) of 10–13 h from cycloheximide chase experiments⁴⁴ (compared to the WT p53 t_1/2 of minutes in quiescent cells), implying that Y220C destabilizes an active conformation of p53 rather than reducing p53 protein abundance.

Prior reports suggested that the enhanced efficacy of similar bifunctional compounds can occur because of the accumulation of small molecules in cells expressing high concentrations of the protein target³³. We, therefore, modeled reaction–diffusion kinetics of Halo-PEG2-BI2536 using ordinary differential equations (Methods) to gain more insights into the enhanced toxicity observed in the presence of a ligandable p53 fusion protein. Previously reported increases in p53 protein t_1/2 upon mutation greatly influence the expected accumulation of the bifunctional small molecule^28,29. In particular, an increase in t_1/2 from the WT p53 t_1/2 of 16 min to a mutant p53 t_1/2 of 17 h is expected to increase final intracellular concentration of the small molecule at steady state by 2–3 orders of magnitude depending on the initial concentration (Fig. 2i). Together, these data suggest that, if a bifunctional molecule combining a cytotoxic molecule with a small-molecule ligand of p53 were developed, the high protein abundance of mutant p53 would allow for differential cell killing.

Bifunctional molecules selectively kill p53-Y220C⁺ cells

To assess the impact of bifunctional molecules containing p53-binding and cytotoxic moieties in an endogenous cellular context, we synthesized bifunctional molecules that bind to p53-Y220C. This mutant indirectly inhibits DNA binding activity through the loss of DNA-binding domain (DBD) thermal stability⁴⁵. The removal of a bulky tyrosine residue, creating a pocket, and its replacement with a ligandable cysteine make it an attractive target for small-molecule refolders meant to restore transcriptional activity^{46,47,48,49,50,51}, as well as bifunctional cytotoxic strategies. We initially used acrylamide KG5 as the p53-Y220C binder in a PLK1-directed bifunctional; KG5 is a carbazole-based covalent fragment liganding > 95% of p53-Y220C at 10 μM (ref. ⁴⁹). We synthesized KG5-PEG4-BI2536 (3) and did not observe any difference in cell survival between 293T cells stably expressing Halo–p53-Y220C ΔTAD–mCherry (transactivation domain deleted to remove confounding protein stabilization effects) and parental 293T cells in an 8-day competition assay (Fig. 3 and Extended Data Fig. 3a).

**Fig. 3: p53-Y220C–PLK1 bifunctional molecules selectively inhibit proliferation of *TP53*^Y220C-mutant cells.**

We surveyed the patent literature and identified a p53-Y220C-binding pharmacophore consisting of an o-anisidine moiety linked to an indole and piperidine (US20230024905A1, US10138219B2, WO2021262483A1, WO2021061643A1, WO2022213975A1 and WO2023016434A1). These binders are exemplified by PMV6 (4), a compound identified in a structure–activity relationship series by PMV Pharma, with close similarity to the recently disclosed structure of the investigational compound, rezatapopt, which has a K_d of ~2.5 nM for p53-Y220C (PMV6, p. 96: US20230024905A1)⁵². PMV6 induces an 8 °C thermal shift of p53-Y220C (p. 21: US20230024905A1). The cocrystal structure with p53-Y220C showed that the compound binds to a shallow groove created by the Y220C substitution, with the piperidine facing the solvent. To construct a bifunctional molecule containing PMV6 and BI2536, we functionalized PMV6 at the solvent-exposed piperidine, which was functionalized in other analogs that continued to bind p53-Y220C using a thermal shift assay (compounds 7 and 10, p. 21 and 96: US20230024905A1).

We synthesized PMV6-PEG4-BI2536 (5, p53-01; Fig. 3a) and confirmed that p53-01 but not binders PMV6 or BI2536 alone induce ternary complex formation between p53-Y220C and PLK1 using a NanoBiT assay in 293T cells cotransfected with mEGFP–PLK1–SmBiT and NLS–LgBiT–p53-Y220C (DBD) (Fig. 3b and Extended Data Fig. 3b; EC₅₀ = 1.4 μM). As an alternative method to demonstrate ternary complex formation, we assessed protein colocalization upon compound treatment^53,54,55,56. Live-cell imaging of 293T cells cotransfected with mEGFP–PLK1–SmBiT and Halo–p53-Y220C ΔTAD–mCherry revealed diffuse PLK1 localization throughout the cell, with strong enrichment in an extranuclear region (possibly the centrosome as previously reported⁵⁷), whereas p53-Y220C was constitutively nuclear. In nonmitotic cells, p53-01 treatment resulted in PLK1 nuclear enrichment and colocalization with p53-Y220C, whereas p53-Y220C also entered the extranuclear region colocalizing with PLK1. In untreated mitotic cells, p53-Y220C appeared localized to chromatin, whereas PLK1 was localized elsewhere in the cell. Upon p53-01 treatment, PLK1 became colocalized with p53-Y220C on chromatin; binders alone had no impact on localization (Fig. 3c and Extended Data Fig. 3c). We speculate that this mislocalization could disrupt the function of PLK1 as a mitotic kinase beyond simple steric blockade of its active site.

We performed an 8-day growth competition experiment between Halo–p53-Y220C ΔTAD–mCherry and parental 293T cells and observed strong selection against p53-Y220C⁺ cells (mCherry⁺) when p53-01 was dosed between 250 nM and 2 μM (E_max: 21.5-fold decline in the mCherry⁺ fraction; EC₅₀ = 443 nM). We did not observe any activity from nonfunctionalized binders or control compounds, including PMV6, BI2536, AP1867-C8-BI2536 (6) or selinexor. We also did not observe any activity of p53-01 in either 293T or Calu-1 cells expressing Halo–p53-R273H (FL)–mCherry (Fig. 3d and Extended Data Fig. 2e). The differential antiproliferative activity of p53-01 in Halo–p53-Y220C ΔTAD–mCherry 293T cells was validated with crystal violet staining (Fig. 3e and Extended Data Fig. 3d). An analogous bifunctional molecule constructed with a WEE1 inhibitor (7, PMV6-PEG4-adavosertib) was unable to selectively inhibit proliferation of p53-Y220C⁺ cells (Extended Data Fig. 3e,f).

We next monitored for induction of apoptosis using caspase 3/7 glo and annexin–PI staining. p53-01 but not either binder alone differentially induced apoptosis in Halo–p53-Y220C ΔTAD–mCherry versus parental 293T cells (Fig. 3f,g and Extended Data Fig. 3g,h). To confirm the effects of our system were dependent on protein dosage, we used a doxycycline-inducible Halo–p53-Y220C ΔTAD–mCherry construct in 293T cells and were able to increase the potency of p53-01 approximately twofold without impacting the potency of either PMV6 or BI2536 (Extended Data Fig. 3i). To confirm that the effects were mediated by compound binding to p53-Y220C, we repeated the Halo–p53-Y220C ΔTAD–mCherry and parental 293T competition experiment using a tenfold excess of PMV6, observing partial rescue (p53-01 alone at 250 nM: 8.9-fold decline in mCherry⁺ percentage; p53-01 + PMV6 at 2.5 μM: 2.1-fold decline in mCherry⁺ percentage; P < 0.001; Fig. 3h).

PLK1 activity is critical for mitotic progression and its inhibitor, BI2536, was previously shown to induce G2/M arrest and genome doubling⁵⁸. We monitored DNA content following treatment of synchronized Halo–p53-Y220C ΔTAD–mCherry and parental 293T cells with p53-01 or binders for 1 day. Low-dose p53-01 (0.75 μM) resulted in the accumulation of Halo–p53-Y220C ΔTAD–mCherry 293T cells with 4 N DNA content, leaving parental cells unaffected, whereas a higher dose (1.5 μM) induced genome doubling (accumulation of cells with 8 N DNA content) in ~60% of Halo–p53-Y220C ΔTAD–mCherry 293T but <10% of parental cells, phenocopying the high-dose BI2536 (10 nM) treatment (Fig. 3i). To confirm the effect was because of differential PLK1 inhibition, we performed western blots for PLK1 substrates including cyclin B1 (which accumulates upon PLK1 inhibition) and using an antibody to the phospho-PLK1-binding motif (PLK1-binding partners and substrates). p53-01 once again phenocopied BI2536 specifically in Halo–p53-Y220C ΔTAD–mCherry 293T but not in parental cells, with PMV6 having no effect (Fig. 3j). Together, these data indicate that a bifunctional molecule that both binds to p53-Y220C and binds to and inhibits PLK1 can selectively inhibit proliferation of cells expressing TP53^Y220C because of enhanced PLK1 inhibition, leading to mitotic arrest.

Activity is p53-Y220C dependent without reactivation

We next analyzed whether the mechanism of action for p53-01 is distinct from that for PMV6, a reactivator of p53-Y220C. We performed RNA sequencing (RNA-seq) of Huh7 hepatocellular carcinoma cells (homozygous TP53^Y220C/Y220C) treated with p53-01 (4 μM) and PMV6 (4 μM) for 1 day. We observed strong induction of a p53 signature by PMV6, including >10-fold upregulation of p53 targets such as MDM2, CDKN1A, GDF15 and PUMA (BBC3) and downregulation of genes essential for proliferating cells such as TOP2A. Manual inspection of a set of high-confidence p53 transcriptional targets and gene set enrichment analysis (GSEA) of a larger group of consensus p53 targets confirmed strong upregulation of these genes by PMV6 (normalized enrichment score (NES) = 2.68) but not p53-01 (NES = 1.33) (Fig. 4a–c and Extended Data Fig. 4a). Thus, p53-01 does not appear to act through a transcriptional mechanism of p53 reactivation.

**Fig. 4: p53-Y220C–PLK1 bifunctionals are active in endogenous settings and do not reactivate p53.**

We subsequently established p53-Y220C⁺ cell lines stably expressing a p53 luciferase reporter⁵⁹. In Huh7 cells (TP53^Y220C/Y220C), PMV6 was able to induce activity of the reporter at doses of 1.25–10 μM (EC₅₀ = 2.4 μM), whereas p53-01 and BI2536 had no such effect. We repeated the p53 reporter experiments in the p53-Y220C⁺ endometrial cancer cell line MFE319, where no induction of luciferase activity for any compound (p53-01, PMV6 or BI2536) was observed (Fig. 4d). Note that MFE319 has both TP53^Y220C and TP53^R273C mutations reported in the CCLE dataset³⁵. Analysis of paired RNA-seq reads in this cell line revealed that the mutations were in trans (Extended Data Fig. 4b). It is possible that the additional dominant negative TP53 mutation in this cell line prevents p53 reactivation by p53-Y220C refolders (for example, PMV6), plausibly because of poisoning of the tetramer even in the presence of correctly functioning p53-Y220C proteins. The strategy presented in this paper, using p53-Y220C to concentrate a toxin in cells, is unlikely to be impacted by the additional TP53^R273C mutation. This would result in a ~20% greater scope of treatable cancers for PLK1-p53-Y220C bifunctional molecules compared to p53-Y220C reactivators (Fig. 4e,f; as determined by analysis of TP53^Y220C-mutant cancers in TCGA⁶⁰ with additional TP53 mutations).

Lastly, we wanted to assess whether the effects of p53-01 in MFE319 were dependent on its interaction with p53-Y220C. Indeed, we were able to partially compete the decline in cell viability induced by p53-01 in MFE319 using PMV6 (Fig. 4g). Additionally, we established MFE319 TP53-knockout cells by stably expressing sgTP53–Cas9–EGFP. In competition with parental MFE319 cells, p53-01 was able to significantly enrich an EGFP⁺ population at doses between 800 nM and 1.25 μM. BI2536 and PMV6 had no such activity (Fig. 4h). As further evidence of on-target activity, we cultured Halo–p53-Y220C ΔTAD–mCherry 293T cells for 3 weeks in the presence of low doses of p53-01 (≤250 nM) and observed the expansion of an mCherry⁻ population (Fig. 4i; 6.9-fold expansion of mCherry⁻ fraction at 250 nM). Furthermore, p53-01 trended toward superior activity across a panel of p53-Y220C⁺ cancer cell lines compared to p53-Y220C⁻ counterparts (Extended Data Fig. 4c,d). Altogether, these data indicate that p53-01 has activity in cell lines expressing TP53^Y220C, distinct from PMV6 (not through p53 reactivation), and its activity is dependent on its interaction with p53-Y220C.

Linker optimization improves efficacy of bifunctionals

We created a library of nine PMV6-BI2536 bifunctional compounds with varying linker lengths (4.33–22.22 Å, compounds 5 and 8–15) and assessed their efficacy in competition assays between parental and Halo–p53-Y220C ΔTAD–mCherry 293 T cells (Table 1). We observed no consistent effect of linker length on compound efficacy in general, although shorter alkyl linkers outperformed longer alkyl linkers. PEG(2,4,6) linkers all performed similarly. The most potent compound, PMV6-C3-BI2536 (10, β-alanine linker), was over 1.5 orders of magnitude more potent than p53-01 in the competition assay (PMV6-C3-BI2536 EC₅₀: 25 nM, p53-01 EC₅₀: 850 nM; Fig. 5a,b). Activity of compounds strongly correlated with their ability to form ternary complexes in the mEGFP–PLK1–SmBiT and NLS–LgBiT–p53-Y220C (DBD) NanoBiT system (Fig. 5a,c). These data indicate that substantial improvements to the efficacy of p53-Y220C–PLK1 bifunctional compounds are possible going forward.

**Fig. 5: Linker optimization yields more potent p53-Y220C–PLK1 bifunctionals.**

Table 1 Effect of linker on activity of p53-Y220C–PLK1 bifunctionals

Full size table

Discussion

TP53 mutations remain the dominant mutation associated with death from human cancers⁵. While there have been prior reports of mutation-agnostic small-molecule refolders of p53, these molecules have later been found to lack the desired activity^17,18. Our strategy reveals one critical insight: p53-targeted drugs need not restore native p53 function but can instead use differential p53 protein abundance or mutant-specific ligands to bring about a TP53-mutant selective therapy. Here, we use the high intracellular concentration of the missense mutant p53 protein to induce cancer-selective cell death. Our work is conceptually aligned with RIPTACs but uses p53 as a target protein instead of lineage oncogenes such as androgen receptor³³. Selectivity arises from the pathognomonic overabundance of missense mutant p53 (a broken MDM2 feedback loop) rather than lineage expression and selectivity can be made mutant specific using a Y220C binder, enabling killing without restoring canonical p53 transcriptional activity.

The molecules in this report are far from pharmacologically optimized. We anticipate that more potent and selective molecules may be achieved in the future. Because the bivalent compounds presented here include a nonspecific small-molecule toxin, they are able to induce toxicity in cells that do not bear TP53 mutations when administered at sufficiently high doses. Future generations of molecules may have no toxicity in the absence of p53 proteins by working like other molecular glues, using mutant p53 as a ‘presenter protein’ whose surface may form a complementary interaction with essential components of cellular machinery. The prior observation that members of the manumycin polyketide family of natural products are molecular glues between p53 and UBR7 (ref. ⁶¹) indicates that molecular glues involving p53 are indeed possible.

It is important to recognize that the compounds we describe are only capable of binding to the Y220C mutant of p53, which possesses a unique cavity not found on the native p53 protein or other mutants. More work will be needed to extend this strategy to other missense mutants. Future p53-selective therapies may bind to protein folds found on WT p53 proteins⁶² and use elevated p53 protein abundance to generate compounds that selectively target the majority of cancers carrying TP53 missense mutations. Because most sources of cell stress can induce the accumulation of p53, the optimal pan-p53-selective compounds are likely to be covalent and dosed in pulses such that the therapeutic window between cancer and normal tissue is maximized. PLK1 inhibition in cells with high levels of WT p53 may be tolerated as these cells could be growth arrested. Other future compounds may bind selectively to mutant p53 proteins whose surface allows for selective small-molecule binding. Furthermore, targeting certain essential genes may promote the accumulation of mutant p53 protein, as was shown for WEE1 degraders recently⁶³, possibly enabling larger therapeutic windows.

If clinical-grade compounds that can specifically kill cells with increased p53 abundance can be generated, they may enjoy roles outside of cancer therapy. In one example, p53 is highly expressed in senescent cells and deficiency of MDM2 causes a progeroid syndrome in humans^64,65. Future p53-targeting compounds of this class could function as senolytics.

New targeted therapies are inevitably followed by the emergence of on-target and off-target resistance mechanisms. As we demonstrated, cancer cells evolved resistance by decreasing mutant p53 protein expression upon chronic exposure to intermediate doses of these compounds. These results reflect the observation that antigen loss follows exposure to cytotoxic agents whose activity depends on antigen presence. Nonetheless, we hope that, in the fullness of time, gain-of-function p53-selective small molecules will allow for sufficient specific killing of cancer cells such that, in combination with other therapies, a greater fraction of persons will achieve meaningful remissions.

Methods

General methods

Measurements and experiments were performed in triplicate unless otherwise specified. All statistical tests were two-tailed; heteroscedastic t-tests were used for pairwise comparisons unless otherwise specified. Points with error bars are plotted as the mean ± s.d. unless otherwise specified; lines in box plots represent the first quartile to 1.5× the interquartile range (IQR), the first quartile, the median, the third quartile and the third quartile to 1.5× the IQR.

Cancer genomics analyses

CRISPR dependency scores, RPPA z-scored protein expression, RNA-seq, TP53 mutation calls and compound sensitivity data were downloaded from the DepMap portal (https://depmap.org/portal/)³⁷. Human cell surface proteins (n = 1,492) were previously reported⁶⁷. Protein abundance in 293T cells was downloaded from OpenCell³⁸. Immunohistochemistry images were downloaded from the Human Protein Atlas⁶⁶. To identify genes that were both essential and had low protein abundance, we filtered to those with mean Chronos scores ≤ −2 and with protein abundance ≤ 920 nM (p53 protein abundance in 293T cells). We ranked the remaining genes on the basis of log₁₀(protein concentration) + mean Chronos score and identified those with the lowest score using this metric.

MFE319 TP53 mutation analysis

MFE319 paired RNA-seq reads were downloaded from the CCLE (SRR8615235)³⁵. The reads were aligned directly to the p53 major isoform mRNA transcript fasta (NM_000546.6) using STAR⁶⁸. The resulting BAM file was visualized in Integrative Genomics Viewer⁶⁹. Read pairs spanning both mutant residues (Y220C and R273C) were used to infer the mutations were in trans.

Modeling of compound accumulation

We assume that the cell membrane is permeable to the bifunctional small molecule, allowing it to freely diffuse. We also assume that p53 is in greater abundance than the other target bound by the bifunctional small molecule, such that the binding of the other target does not contribute meaningfully to intracellular trapping of the molecule. We define the following variables: [M_A0], the initial concentration of the bifunctional small molecule outside of the cell (which is a parameter we vary); [M_B0] = 0, the initial concentration of the bifunctional small molecule inside the cell; [P₀] = 1 × 10⁻⁶ M, the initial intracellular concentration of p53 (defined according to abundance from OpenCell³⁸); [MP₀] = 0, the initial intracellular concentration of the molecule–p53 complex. We also define constants for diffusion (k_diff = 1 × 10³ s⁻¹), binding of a pharmaceutically optimized bifunctional molecule to p53 (k_bind = 8.7 × 10⁵ M⁻¹ s⁻¹), dissociation of a pharmaceutically optimized bifunctional molecule from p53 (k_unbind = 1 × 10⁻⁶ s⁻¹) and p53 protein t_1/2 (which is a parameter we vary).

We define the following rates:

d[M_A]/dt = 0

d[M_B]/dt = −k_diff[M_B] + k_diff[M_A] − k_bind[M_B][P] + k_unbind[MP]

d[MP]/dt = k_bind[M_B][P] − k_unbind[MP] − [MP] × (ln 2)/(t_1/2)

d[P]/dt = −k_bind[M_B][P] + k_unbind[MP] − [P] × (ln 2)/(t_1/2) + r_{p53 production}

Note that the constant rate of p53 protein production, r_{p53 production}, was estimated using the concentration of p53 missense mutant protein at steady state in a cell.

d[p53]/dt = protein production rate − protein degradation rate = 0

d[p53_missense]/dt = protein production rate − [p53_missense] × ln(2)/t_{1/2-p53missense} = 0

Protein production rate = [p53_missense] × ln(2)/t_{1/2-p53missense}, with t_{1/2-p53missense} = 24 h

We ran an ODE solver in R (package: deSolve) using these parameters and calculated final intracellular ([M_B] + [MP]) and extracellular ([M_A]) bifunctional small-molecule concentrations at t = 600 h.

Cell culture

The 293T (CRL-3216), Calu-1 (HTB-54), BxPC-3 (CRL-1687), HepG2 (HB-8065), DU145 (HTB-81), LNCaP (CRL-1740) and MCF7 cells (HTB-22) were obtained from the American Type Culture Collection; the MFE319 (ACC 423) and MFE296 (ACC 419) cells were obtained from the German Collection of Microorganisms and Cell Cultures; the Huh7 (JCRB0403) and NUGC-3 (JCRB0822) cells were obtained from the Japanese Collection of Research Bioresources. All cell lines were cultured in DMEM supplemented with 10% FBS, 100 IU per ml penicillin and 100 μg ml⁻¹ streptomycin at 37 °C in 5% CO₂.

Plasmids

Plasmids were ordered as codon-optimized entry vectors from TWIST (pTwist-ENTR). This includes the following constructs: Halo–p53-R273H (FL)–mCherry, Halo–p53-Y220C (ΔTAD)–mCherry, Halo–p53-Y220C (FL)–mCherry, Halo–p53 WT (FL)–mCherry–2A–mTagBFP2-V5, Halo–p53-R273H (FL)–mCherry–2A–mTagBFP2-V5, Halo–p53-Y220C (FL)–mCherry–2A–mTagBFP2-V5, NLS–LgBiT–p53-Y220C (DBD) and mEGFP–PLK1–SmBiT. p53-Y220C was ordered with stabilizing substitutions (M133L;V203A;N239Y;N268D)⁴⁵ in all cases except for the Halo–p53-Y220C (FL)–mCherry–2A–mTagBFP2-V5 construct. Plasmids were Gateway-cloned into lentiviral EF1a expression vector pLEX307 or pLIX403 using LR clonase II (Invitrogen). The p53 reporter (Addgene, 90363)⁵⁹ and Cas9–EGFP plasmid (Addgene, 82416) were purchased from Addgene⁷⁰. DNA oligos encoding the TP53 sgRNA (top strand: CACCGCAGAATGCAAGAAGCCCAGA, bottom strand: AAACTCTGGGCTTCTTGCATTCTGC) were ordered from Azenta and restriction-cloned into the Cas9–EGFP plasmid. Sequences were verified through PlasmidSaurus whole-plasmid sequencing.

Transient transfection and stable cell line creation

Plasmids were transfected into 293T cells using TransIT-LT1 transfection reagent following the manufacturer’s protocol. Lentivirus was generated transfecting psPAX2 (Addgene, 12260), pMD2.G (Addgene, 12259) and the cloned pLEX307/pLIX403 plasmid (Addgene, 41392) (3:3:2 ratio) into 293T cells. Lentivirus was collected 2 days after transfection and stable cell lines were established infecting with filtered lentivirus and polybrene (10 μg ml⁻¹). Cells were switched to selection medium 2 days after infection.

CellTiter-Glo (CTG)

Cells were plated in 384-well plates (typically 1,500 cells per well) with varying concentrations of compounds, with a total volume of 50 μl per well. CTG reagent was added (25 μl) to the solutions, mixed and read out using an EnVision 2105 multimode plate reader.

Crystal violet

The 293T cells were plated in six-well or 12-well plates at low density (~10% confluence) and treated with compound for the indicated duration. Cells were rinsed once with PBS and then stained with crystal violet (0.5% m/v) in 20% methanol–water for 10 min. Cells were washed with water five times and air-dried overnight.

Flow-cytometry-based competition assays

Cells with and without a fluorescent marker (for example, Halo–p53–mCherry) were mixed and the resulting solution was plated in six-well plates or 10-cm dishes at low density; compound was added at varying concentrations. After reaching confluence, cells were passaged and retreated with compound if needed. Otherwise, cells were trypsinized, washed and resuspended in complete growth medium in 96-well plates or tubes for flow cytometry. Untreated cell mixtures were used to establish gates separating the fluorophore^+/− populations. Analysis was conducted on a CytoFLEX LX flow cytometer.

NanoBiT assay

The 293T cells were seeded in six-well plates and cotransfected with NLS–LgBiT–p53-Y220C (DBD) and mEGFP–PLK1–SmBiT (1:1). After 1 day, the cells were passaged into 384-well plates and treated with varying concentrations of compound. Then, 1 day after treatment, nanoluciferase substrate was added to the cells and luminescence was monitored using an EnVision 2105 multimode plate reader.

Colocalization assay and microscopy

The 293T cells were seeded in six-well plates and cotransfected with Halo–p53-Y220C ΔTAD–mCherry and mEGFP–PLK1–SmBiT (1:1). After 1 day, the cells were passaged into 96-well plates in FluoroBrite DMEM for microscopy. The cells were imaged with the Opera Phenix Plus high-content screening system (PerkinElmer) before compound treatment and as a time series following compound treatment.

Caspase 3/7 glo

Cells were plated in 384-well plates (typically 1,500 cells per well) with varying concentrations of compounds with a total volume of 50 μl per well. After 1 day, caspase 3/7 glo reagent (Promega) was added following the manufacturer’s protocol and read out using an EnVision 2105 multimode plate reader.

Annexin–PI staining

Cells were plated in six-well plates and treated with varying concentrations of compounds for 1 day. Cells were stained with annexin V–AF488 and PI following the manufacturer’s instructions (Thermo Fisher, V13241) and analyzed by flow cytometry (Beckman CytoFLEX LX), using untreated cells to establish gates.

Cell-cycle analysis

Cells were split into a 10-cm dish at low confluency (20%) and grown overnight. Cells were synchronized using overnight treatment with palbociclib (150 nM), washed and split into a six-well plate for treatment with compound for 24 h. Cells were harvested by trypsinization, washed twice with cold 1× PBS, fixed by dropwise addition of ice-cold 70% ethanol while vortexing and incubated overnight (4 °C). Fixed samples were centrifuged at 1,000g for 5 min and 70% ethanol was removed. Cells were washed twice with 1× PBS + 1% BSA before DNA labeling with 1 µg ml⁻¹ DAPI for 10 min at room temperature, before being analyzed for DNA content by flow cytometry (Beckman CytoFLEX LX).

Western blotting

Cells were split into a 10-cm dish at low confluency (20%) and grown overnight. Cells were synchronized with double thymidine block. Cells were treated with thymidine (2 mM) overnight and released into fresh medium for 8.5 h and then retreated with thymidine (2 mM) overnight. Cells were split into a six-well plate and released into medium containing compound for 8.5 h. Cells were pelleted and protein lysates were created using RIPA buffer containing Halt phosphatase and protease inhibitor (Thermo Fisher, 78440). Lysates were clarified by centrifugation (21,000g for 15 min) and the supernatant was denatured in 1× Lamelli buffer containing DTT by heating at 95 °C for 5 min. The resulting protein was loaded onto a Tris–acetate gel, transferred onto a PVDF membrane and treated with primary antibodies: cyclin B1 (D5C10, Cell Signaling Technologies, 12231T; 1:1,000) and phospho-PLK-binding motif (D73F6, Cell Signaling Technologies, 5243T; 1:1,000). The membrane was stained with IRDye 800CW goat anti-rabbit IgG secondary antibody (Licor Biosciences; 1:20,000) and imaged on an Odyssey DLx.

Huh7 RNA-seq and analysis

Huh7 cells were passaged into six-well plates and treated with PMV6 (4 μM), p53-01 (4 μM) or DMSO for 24 h. Cells were washed once with cold PBS. Subsequently, TRIzol (Invitrogen) was added to cells and, following the manufacturer’s protocol, RNA was extracted. RNA concentration was monitored using a Qubit fluorometer (Thermo Fisher) and RNA integrity was analyzed using an Agilent Bioanalyzer. The NEBNext Ultra II RNA library prep kit (Illumina) was used to prepare an RNA-seq library. A NovaSeq 6000 machine (Illumina) was used for paired-end 150-bp RNA-seq. STAR/RSEM^68,71 was used to align RNA-seq reads to the GENCODE v38 transcript ref.⁷² and generate a count matrix. Raw counts were rounded and DESeq2 was used for downstream analysis⁷³. A preranked GSEA⁷⁴ was performed using the DESeq2 t statistic (versus DMSO-treated cells) as a rank metric using the Fischer p53 targets geneset⁷⁵.

P53 reporter assay

Huh7 and MFE319 cell lines stably expressing a p53 reporter (Addgene, 90363)⁵⁹ were generated. Cells were plated in 384-well plates (1,500 cells per well) and treated with varying concentrations of compound. Then, 16 h after treatment, firefly luciferase substrate was added to the cells and luminescence was monitored using an EnVision 2105 multimode plate reader.

RT–qPCR

Calu1 cells stably expressing Halo–p53 (FL)–mCherry–2A–mTagBFP2-V5 were passaged into six-well plates. RNA was extracted using a RNeasy Plus mini kit (Qiagen) following the manufacturer’s instructions. RNA (500 ng) was reverse-transcribed to complementary DNA (cDNA) using SuperScript VILO master mix following the manufacturer’s instructions (total volume: 20 μl). The resulting solution was diluted 1:5 with water and 2 μl of cDNA was used in each qPCR reaction in 384-well PCR plates (AB1384W). Then, 2× Power SYBR green PCR master mix (Fisher) and primers (150 nM final concentration) were added to a total volume of 15 μl. Primers used were as follows: Halo–p53 (FL)–mCherry–2A–mTagBFP2-V5 forward, CAGGACGGCTGCCTTATTTA; Halo–p53 (FL)–mCherry–2A–mTagBFP2-V5 reverse, AGACGGCAGATCGCAATATC; GAPDH forward, GTCTCCTCTGACTTCAACAGCG; GAPDH reverse, ACCACCCTGTTGCTGTAGCCAA. Amplification was performed following the manufacturer’s protocol on a QuantStudio 7 Flex real-time PCR system.

Chemical synthesis

The synthesis and characterization of the small molecules reported in this paper are described in Supplementary Note 1.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The data supporting the findings of this study are available within the paper and its Supplementary Information. RNA-seq raw data (FASTQ, BAM) were deposited to Dropbox (https://www.dropbox.com/scl/fo/1c2c9wvccret30y0j8x7w/AKw3XZ5S0YjUnMws3Me6gOM?rlkey=n83j73hitpkamvar9pyfeknoz&st=pye3w9kw&dl=0). Should any raw data files be needed in another format, they are available from the corresponding author upon reasonable request. Source data are provided with this paper.

Change history

12 November 2025
A Correction to this paper has been published: https://doi.org/10.1038/s41589-025-02095-9

References

ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium Pan-cancer analysis of whole genomes. Nature 578, 82–93 (2020).
Article Google Scholar
Donehower, L. A. et al. Integrated analysis of TP53 gene and pathway alterations in The Cancer Genome Atlas. Cell Rep. 28, 1370–1384 (2019).
Article CAS PubMed PubMed Central Google Scholar
AACR Project GENIE Consortium AACR Project GENIE: powering precision medicine through an international consortium. Cancer Discov. 7, 818–831 (2017).
Article Google Scholar
Ozaki, T. & Nakagawara, A. Role of p53 in cell death and human cancers. Cancers 3, 994–1013 (2011).
Article CAS PubMed PubMed Central Google Scholar
Petitjean, A., Achatz, M. I. W., Borresen-Dale, A. L., Hainaut, P. & Olivier, M. TP53 mutations in human cancers: functional selection and impact on cancer prognosis and outcomes. Oncogene 26, 2157–2165 (2007).
Article CAS PubMed Google Scholar
Robles, A. I. & Harris, C. C. Clinical outcomes and correlates of TP53 mutations and cancer. Cold Spring Harb. Perspect. Biol. 2, a001016 (2010).
Article PubMed PubMed Central Google Scholar
Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).
Article PubMed Google Scholar
Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013).
Article PubMed PubMed Central Google Scholar
Hassin, O. & Oren, M. Drugging p53 in cancer: one protein, many targets. Nat. Rev. Drug Discov. 22, 127–144 (2023).
Article CAS PubMed Google Scholar
Wilson, J. M. Gendicine: the first commercial gene therapy product. Hum. Gene Ther. 16, 1014–1015 (2005).
Article CAS PubMed Google Scholar
Kong, N. et al. Synthetic mRNA nanoparticle-mediated restoration of p53 tumor suppressor sensitizes p53-deficient cancers to mTOR inhibition. Sci. Transl. Med. 11, eaaw1565 (2019).
Article CAS PubMed PubMed Central Google Scholar
Chikamatsu, K. et al. Generation of anti-p53 cytotoxic T lymphocytes from human peripheral blood using autologous dendritic cells. Clin. Cancer Res. 5, 1281–1288 (1999).
CAS PubMed Google Scholar
Speetjens, F. M. et al. Induction of p53-specific immunity by a p53 synthetic long peptide vaccine in patients treated for metastatic colorectal cancer. Clin. Cancer Res. 15, 1086–1095 (2009).
Article CAS PubMed Google Scholar
Hsiue, E. H.-C. et al. Targeting a neoantigen derived from a common TP53 mutation. Science 371, eabc8697 (2021).
Article CAS PubMed PubMed Central Google Scholar
Foster, B. A., Coffey, H. A., Morin, M. J. & Rastinejad, F. Pharmacological rescue of mutant p53 conformation and function. Science 286, 2507–2510 (1999).
Article CAS PubMed Google Scholar
Bykov, V. J. N. et al. Restoration of the tumor suppressor function to mutant p53 by a low-molecular-weight compound. Nat. Med. 8, 282–288 (2002).
Article CAS PubMed Google Scholar
Fujihara, K. M. et al. SLC7A11 is a superior determinant of APR-246 (eprenetapopt) response than mutation status. Mol. Cancer Ther. 20, 1858–1867 (2021).
Article CAS PubMed Google Scholar
Rippin, T. M. et al. Characterization of the p53-rescue drug CP-31398 in vitro and in living cells. Oncogene 21, 2119–2129 (2002).
Article CAS PubMed Google Scholar
de Vries, A. et al. Targeted point mutations of p53 lead to dominant-negative inhibition of wild-type p53 function. Proc. Natl Acad. Sci. USA 99, 2948–2953 (2002).
Article PubMed PubMed Central Google Scholar
Kern, S. E. et al. Oncogenic forms of p53 inhibit p53-regulated gene expression. Science 256, 827–830 (1992).
Article CAS PubMed Google Scholar
Milner, J. & Medcalf, E. A. Cotranslation of activated mutant p53 with wild type drives the wild-type p53 protein into the mutant conformation. Cell 65, 765–774 (1991).
Article CAS PubMed Google Scholar
Giacomelli, A. O. et al. Mutational processes shape the landscape of TP53 mutations in human cancer. Nat. Genet. 50, 1381–1387 (2018).
Article CAS PubMed PubMed Central Google Scholar
Harris, S. L. & Levine, A. J. The p53 pathway: positive and negative feedback loops. Oncogene 24, 2899–2908 (2005).
Article CAS PubMed Google Scholar
Vousden, K. H. & Lane, D. P. p53 in health and disease. Nat. Rev. Mol. Cell Biol. 8, 275–283 (2007).
Article CAS PubMed Google Scholar
Midgley, C. A. & Lane, D. P. p53 protein stability in tumour cells is not determined by mutation but is dependent on MDM2 binding. Oncogene 15, 1179–1189 (1997).
Article CAS PubMed Google Scholar
Maki, C. G., Huibregtse, J. M. & Howley, P. M. In vivo ubiquitination and proteasome-mediated degradation of p53(1). Cancer Res. 56, 2649–2654 (1996).
CAS PubMed Google Scholar
Haupt, Y., Maya, R., Kazaz, A. & Oren, M. MDM2 promotes the rapid degradation of p53. Nature 387, 296–299 (1997).
Article CAS PubMed Google Scholar
Giaccia, A. J. & Kastan, M. B. The complexity of p53 modulation: emerging patterns from divergent signals. Genes Dev. 12, 2973–2983 (1998).
Article CAS PubMed Google Scholar
Finlay, C. A. et al. Activating mutations for transformation by p53 produce a gene product that forms an hsc70–p53 complex with an altered half-life. Mol. Cell. Biol. 8, 531–539 (1988).
CAS PubMed PubMed Central Google Scholar
Gourisankar, S. et al. Rewiring cancer drivers to activate apoptosis. Nature 620, 417–425 (2023).
Article CAS PubMed PubMed Central Google Scholar
Lee, S. et al. Velcrin-induced selective cleavage of tRNALeu^TAA by SLFN12 causes cancer cell death. Nat. Chem. Biol. 19, 301–310 (2023).
Article CAS PubMed Google Scholar
Basu, A. A. et al. Harnessing the FBXW7 somatic mutant R465C for targeted protein degradation. J. Am. Chem. Soc. 147, 6108–6115 (2025).
Article CAS PubMed Google Scholar
Raina, K. et al. Regulated induced proximity targeting chimeras—RIPTACs—a heterobifunctional small molecule strategy for cancer selective therapies. Cell Chem. Biol. 31, 1490–1502 (2024).
Article CAS PubMed PubMed Central Google Scholar
Behan, F. M. et al. Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens. Nature 568, 511–516 (2019).
Article CAS PubMed Google Scholar
Ghandi, M. et al. Next-generation characterization of the cancer cell line encyclopedia. Nature 569, 503–508 (2019).
Article CAS PubMed PubMed Central Google Scholar
Nusinow, D. P. et al. Quantitative proteomics of the cancer cell line encyclopedia. Cell 180, 387–402 (2020).
Article CAS PubMed PubMed Central Google Scholar
Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576 (2017).
Article CAS PubMed PubMed Central Google Scholar
Cho, N. H. et al. OpenCell: endogenous tagging for the cartography of human cellular organization. Science 375, eabi6983 (2022).
Article CAS PubMed PubMed Central Google Scholar
Hu, R. et al. Identification of a selective BRD4 PROTAC with potent antiproliferative effects in AR-positive prostate cancer based on a dual BET/PLK1 inhibitor. Eur. J. Med. Chem. 227, 113922 (2022).
Article CAS PubMed Google Scholar
Mu, X., Bai, L., Xu, Y., Wang, J. & Lu, H. Protein targeting chimeric molecules specific for dual bromodomain 4 (BRD4) and Polo-like kinase 1 (PLK1) proteins in acute myeloid leukemia cells. Biochem. Biophys. Res. Commun. 521, 833–839 (2020).
Article CAS PubMed Google Scholar
Aublette, M. C., Harrison, T. A., Thorpe, E. J. & Gadd, M. S. Selective Wee1 degradation by PROTAC degraders recruiting VHL and CRBN E3 ubiquitin ligases. Bioorg. Med. Chem. Lett. 64, 128636 (2022).
Article CAS PubMed Google Scholar
Hirai, H. et al. Small-molecule inhibition of Wee1 kinase by MK-1775 selectively sensitizes p53-deficient tumor cells to DNA-damaging agents. Mol. Cancer Ther. 8, 2992–3000 (2009).
Article CAS PubMed Google Scholar
Lénárt, P. et al. The small-molecule inhibitor BI 2536 reveals novel insights into mitotic roles of polo-like kinase 1. Curr. Biol. 17, 304–315 (2007).
Article PubMed Google Scholar
Lv, F. L. et al. Cabozantinib selectively induces proteasomal degradation of p53 somatic mutant Y220C and impedes tumor growth. J. Biol. Chem. 301, 108167 (2025).
Article CAS PubMed PubMed Central Google Scholar
Joerger, A. C., Ang, H. C. & Fersht, A. R. Structural basis for understanding oncogenic p53 mutations and designing rescue drugs. Proc. Natl Acad. Sci. USA 103, 15056–15061 (2006).
Article CAS PubMed PubMed Central Google Scholar
Boeckler, F. M. et al. Targeted rescue of a destabilized mutant of p53 by an in silico screened drug. Proc. Natl Acad. Sci. USA 105, 10360–10365 (2008).
Article CAS PubMed PubMed Central Google Scholar
Bauer, M. R. et al. A structure-guided molecular chaperone approach for restoring the transcriptional activity of the p53 cancer mutant Y220C. Future Med. Chem. 11, 2491–2504 (2019).
Article CAS PubMed PubMed Central Google Scholar
Bauer, M. R. et al. Targeting cavity-creating p53 cancer mutations with small-molecule stabilizers: the Y220X paradigm. ACS Chem. Biol. 15, 657–668 (2020).
Article CAS PubMed PubMed Central Google Scholar
Guiley, K. Z. & Shokat, K. M. A small molecule reacts with the p53 somatic mutant Y220C to rescue wild-type thermal stability. Cancer Discov. 13, 56–69 (2023).
Article CAS PubMed Google Scholar
Stephenson Clarke, J. R. et al. Discovery of nanomolar-affinity pharmacological chaperones stabilizing the oncogenic p53 mutant Y220C. ACS Pharm. Transl. Sci. 5, 1169–1180 (2022).
Article CAS Google Scholar
Kabir, M. et al. Acetylation targeting chimera enables acetylation of the tumor suppressor p53. J. Am. Chem. Soc. 145, 14932–14944 (2023).
Article CAS PubMed PubMed Central Google Scholar
Dumble, M. et al. Abstract LB006: PC14586: the first orally bioavailable small molecule reactivator of Y220C mutant p53 in clinical development. Cancer Res. 81, LB006 (2021).
Article Google Scholar
Shao, Q., Doung, T. N., Park, II., Orr, L.M. & Nomura, D. K. Targeted Protein Localization by Covalent 14-3-3 Recruitment. J. Am. Chem. Soc. 146, 24788–24799 (2024).
Kumar, S. & Nabet, B. A chemical magnet: approaches to guide precise protein localization. Bioorg. Med. Chem. 102, 117672 (2024).
Article CAS PubMed PubMed Central Google Scholar
Gibson, W. J. et al. Bifunctional small molecules that induce nuclear localization and targeted transcriptional regulation. J. Am. Chem. Soc. 145, 26028–26037 (2023).
Article CAS PubMed PubMed Central Google Scholar
Ng, C. S. C., Liu, A., Cui, B. & Banik, S. M. Targeted protein relocalization via protein transport coupling. Nature 633, 941–951 (2024).
Kishi, K., van Vugt, M. A. T. M., Okamoto, K.-I., Hayashi, Y. & Yaffe, M. B. Functional dynamics of Polo-like kinase 1 at the centrosome. Mol. Cell. Biol. 29, 3134–3150 (2009).
Article CAS PubMed PubMed Central Google Scholar
Steegmaier, M. et al. BI 2536, a potent and selective inhibitor of polo-like kinase 1, inhibits tumor growth in vivo. Curr. Biol. 17, 316–322 (2007).
Article CAS PubMed Google Scholar
O’Connell, D. J. et al. Simultaneous pathway activity inference and gene expression analysis using RNA sequencing. Cell Syst. 2, 323–334 (2016).
Article PubMed PubMed Central Google Scholar
Cancer Genome Atlas Research Network et al. The Cancer Genome Atlas pan-cancer analysis project. Nat. Genet. 45, 1113–1120 (2013).
Article PubMed Central Google Scholar
Isobe, Y. et al. Manumycin polyketides act as molecular glues between UBR7 and P53. Nat. Chem. Biol. 16, 1189–1198 (2020).
Article CAS PubMed PubMed Central Google Scholar
Klett, T. et al. Evaluation of a covalent library of diverse warheads (CovLib) binding to JNK3, USP7, or p53. Drug Des. Dev. Ther. 18, 2653–2679 (2024).
Article CAS Google Scholar
Razumkov, H. et al. Discovery of CRBN-dependent WEE1 molecular glue degraders from a multicomponent combinatorial library. J. Am. Chem. Soc. 146, 31433 (2024).
Article CAS PubMed PubMed Central Google Scholar
Rufini, A., Tucci, P., Celardo, I. & Melino, G. Senescence and aging: the critical roles of p53. Oncogene 32, 5129–5143 (2013).
Article CAS PubMed Google Scholar
Lessel, D. et al. Dysfunction of the MDM2/p53 axis is linked to premature aging. J. Clin. Invest. 127, 3598–3608 (2017).
Article PubMed PubMed Central Google Scholar
Thul, P. J. & Lindskog, C. The Human Protein Atlas: a spatial map of the human proteome. Protein Sci. 27, 233–244 (2018).
Article CAS PubMed Google Scholar
Bausch-Fluck, D. et al. A mass spectrometric-derived cell surface protein atlas. PLoS ONE 10, e0121314 (2015).
Article PubMed PubMed Central Google Scholar
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Article CAS PubMed Google Scholar
Robinson, J. T. et al. Integrative Genomics Viewer. Nat. Biotechnol. 29, 24–26 (2011).
Article CAS PubMed PubMed Central Google Scholar
Walter, D. M. et al. Systematic in vivo inactivation of chromatin-regulating enzymes identifies Setd2 as a potent tumor suppressor in lung adenocarcinoma. Cancer Res. 77, 1719–1729 (2017).
Article CAS PubMed PubMed Central Google Scholar
Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).
Article CAS PubMed PubMed Central Google Scholar
Frankish, A. et al. GENCODE: reference annotation for the human and mouse genomes in 2023. Nucleic Acids Res. 51, D942–D949 (2023).
Article CAS PubMed Google Scholar
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Article PubMed PubMed Central Google Scholar
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Article CAS PubMed PubMed Central Google Scholar
Fischer, M., Grossmann, P., Padi, M. & DeCaprio, J. A. Integration of TP53, DREAM, MMB-FOXM1 and RB-E2F target gene analyses identifies cell cycle gene regulatory networks. Nucleic Acids Res. 44, 6070–6086 (2016).
Article CAS PubMed PubMed Central Google Scholar

Download references

Acknowledgements

This work was supported by a Hertz Foundation Fellowship (A.S.), a Herchel Smith Graduate Fellowship (A.S.), a Svenson Fellowship (W.J.G.), the Lubin Scholar Award (W.J.G.), the Briger Foundation for Oncology Research Award (W.J.G), the National Cancer Institute’s Cancer Target Discovery and Development Network (grant number U01CA217848, S.L.S), National Cancer Institute grant R35CA197568 (M.M.) and an American Cancer Society Research Professorship (M.M.). We thank J. Ostrem, C. Harmange-Magnani, S. Singh, P. Singh and J. Knapp for helpful discussions.

Author information

These authors contributed equally: Ananthan Sadagopan, William J. Gibson.
These authors jointly supervised this work: Stuart L. Schreiber, Matthew Meyerson, William J. Gibson.

Authors and Affiliations

Broad Institute of MIT and Harvard, Cambridge, MA, USA
Ananthan Sadagopan, Nicholas Garaffo, Heng-Jui Chang, Stuart L. Schreiber, Matthew Meyerson & William J. Gibson
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
Ananthan Sadagopan, Maximilian Carson, Eriks J. Zamurs, Nicholas Garaffo, Matthew Meyerson & William J. Gibson
Division of Medical Sciences, Harvard Medical School, Boston, MA, USA
Ananthan Sadagopan & Nicholas Garaffo
Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
Stuart L. Schreiber
Department of Medicine, Harvard Medical School, Boston, MA, USA
William J. Gibson

Authors

Ananthan Sadagopan
View author publications
Search author on:PubMed Google Scholar
Maximilian Carson
View author publications
Search author on:PubMed Google Scholar
Eriks J. Zamurs
View author publications
Search author on:PubMed Google Scholar
Nicholas Garaffo
View author publications
Search author on:PubMed Google Scholar
Heng-Jui Chang
View author publications
Search author on:PubMed Google Scholar
Stuart L. Schreiber
View author publications
Search author on:PubMed Google Scholar
Matthew Meyerson
View author publications
Search author on:PubMed Google Scholar
William J. Gibson
View author publications
Search author on:PubMed Google Scholar

Contributions

A.S. and W.J.G. conceptualized the strategy. A.S. performed most of the experiments in the paper including the chemical synthesis and molecular biology. M.C. assisted with the immunoblotting. E.J.Z. and H.-J.C. assisted in the synthesis of compounds. N.G. assisted with the initial G2/M arrest assays. W.J.G. assisted computational analyses, and with the initial viability competition, rescue, and resistance assays. S.L.S., M.M. and W.J.G. supervised the project.

Corresponding authors

Correspondence to Stuart L. Schreiber, Matthew Meyerson or William J. Gibson.

Ethics declarations

Competing interests

A patent application naming A.S., W.J.G. and M.M. as inventors has been filed by the Broad Institute covering aspects of this work. W.J.G. is on the scientific advisory board and has received consulting fees from Esperion therapeutics and consulting fees from Belharra therapeutics, Boston Clinical Research Institute, Faze Medicines, ImmPACT-Bio and nference. M.M. reports consultant, advisory board and equity for DelveBio and Isabl, research funding from Janssen and Bayer Pharmaceuticals, equity in Bayer and patents licensed to LabCorp and Bayer. S.L.S. is the founding CEO of Arena BioWorks, is a shareholder and serves on the Board of Directors of Kojin Therapeutics, is a shareholder and advises Jnana Therapeutics, Kisbee Therapeutics, Belharra Therapeutics, Magnet Biomedicine, Exo Therapeutics, Eikonizo Therapeutics and Replay Bio, advises Vividion Therapeutics, Eisai, Ono Pharma Foundation and F-Prime Capital Partners and is a Novartis Faculty Scholar. Except for the patent application, competing interests listed above are outside the submitted work. The other authors declare no competing interests.

Peer review

Peer review information

Nature Chemical Biology thanks the anonymous reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 No small molecules specifically kill p53 mutant cancer cells or cell surface receptors upregulated on TP53 mutant cells.

a, Mean log₂(fold-change) for compounds in the PRISM repurposing library (23Q2) on the viability of TP53 mutant (y-axis) and TP53 wild-type cell lines (x-axis) across DepMap. MDM2 inhibitors are boxed. There are no molecules >2-fold more toxic on average to p53^Mut vs. p53^WT cells in the PRISM repurposing library. b, Mean area under the curve (AUC) for compounds in the CTD2 library on the viability of TP53 mutant (y-axis) and TP53 wild-type cell lines (x-axis) across DepMap. There are no molecules with Δ(Mean AUC)_Mut-WT < −0.5 in CTD2 screen. c, Mean mRNA expression (log₂(TPM + 1)) for genes encoding cell surface proteins for TP53 mutant (y-axis) and TP53 wild-type cell lines across DepMap. d, p53 protein abundance (log₂(RPPA Signal)) versus TP53 hotspot mutation status (WT, monoallelic, biallelic) across DepMap. e, p53 protein abundance (log₂(RPPA Signal)) versus TP53 hotspot mutation status for select hotspot mutations (R273H, Y220C) across DepMap.

Extended Data Fig. 2 Halo bifunctionals selectively kill cells overexpressing Halo-p53.

a, Brightfield and mCherry imaging of Halo-p53^R273H(FL)-mCherry 293 T cells treated with Halo-PEG2-BI2536 (top row: 0.3 nM, bottom two rows: 20 nM) for 8 days. Scale bar is shown (50 μm). Experiment performed in duplicate; representative replicate shown. b, ln(IC₅₀) for adavosertib in DepMap (PRISM OncRef), c, ln(IC₅₀) for BI-2536 in DepMap (GDSC1). d, Structure of Halo-PEG2-adavosertib. e, Competition of Halo-p53^R273H(FL)-mCherry vs. parental Calu-1 cells with p53-01, BI2536, AP1867-C8-BI2536, or Halo-PEG2-BI2536 for 7 days; analyzed for mCherry expression by flow cytometry; one replicate was performed. f, Competition of Halo-p53^Y220CΔTAD-mCherry vs. parental 293 T cells with p53-01 or Halo-PEG2-adavosertib for 8 days; analyzed for mCherry expression by flow cytometry. Experiment performed in duplicate; representative replicate shown. g, Calu-1 cell lines stably expressing Halo-p53^WT(FL)-mCherry-2A-mTagBFP2-V5 or Halo-p53^Y220C(FL)-mCherry-2A-mTagBFP2-V5 were established. mTagBFP2 and mCherry expression was assessed by FACS, and mean fluorescence intensity is plotted. N = 3 measurements/condition. h, Cell viability of Calu-1 cell lines established in panel g treated with Halo-PEG2-BI2536 (left) or BI-2536 (right) measured by cell-titer-glo after 4 days. N = 6 technical replicates/condition.

Source data

Extended Data Fig. 3 Cell killing is specific to p53-01.

a, Structure of KG5-PEG4-BI2536. b, Normalized nanoluciferase signal one day after dose titration of PMV6 and BI-2536 in 293 T cells co-transfected with NLS-LgBiT-p53^Y220C(DBD) and mEGFP-PLK1-SmBiT. N = 4 technical replicates/condition. c, Live cell imaging of 293 T cells co-transfected with Halo-p53^Y220CΔTAD-mCherry and mEGFP-PLK1-SmBiT and treated with DMSO, PMV6, or BI-2536 for 16 h. Two replicates of this experiment were performed; a representative replicate is shown. Scale bar is shown (10 μm). d, Crystal violet staining of 293 T cells stably expressing Halo-p53^Y220CΔTAD-mCherry versus parental 293 T cells treated with PMV6 or BI-2536, analyzed after 5 days. Experiment performed in triplicate; representative replicate shown. e, Structure of PMV6-PEG4-adavosertib. f, Competition of Halo-p53^Y220CΔTAD-mCherry vs. parental 293 T cells with p53-01 or PMV6-PEG4-adavosertib for 10 days; analyzed for mCherry expression by flow cytometry. N = 3 measurements/condition. g, Proportion of late apoptotic cells (Annexin+, PI+) following one day treatment with p53-01, BI-2536, or PMV6 at indicated concentrations in Halo-p53^Y220CΔTAD-mCherry vs. parental 293 T cells. n.s. not significant, **P < 0.01, ***P < 0.001 by t-test. N = 3 measurements/condition. h, Caspase-3/7 glo of Halo-p53^Y220CΔTAD-mCherry vs. parental 293 T cells treated with BI-2536 or PMV6 after one day. N = 4 technical replicates/condition. i, Normalized viability of 293 T cells expressing a dox inducible Halo-p53^Y220CΔTAD-mCherry treated with p53-01, PMV6, or BI-2536 for 4 days, induced with dox at various concentrations the day before beginning compound treatment (0 ng/mL, 100 ng/mL, 1000 ng/mL). N = 4 technical replicates/condition.

Source data

Extended Data Fig. 4 p53-01 is active in endogenous settings.

a, Difference in mRNA expression (log₂FC) between TP53 WT and mutant cell lines in DepMap for all genes. P-values computed by t-test. Fischer census p53 targets are colored in blue. b, Analysis of RNA-seq reads aligned to p53 transcript sequence in MFE319 and visualized in IGV. Paired reads boxed in red span residues 220-273 demonstrate the Y220C and R273C mutations are present in trans. c, Distribution of log(IC₅₀) of p53-01 across a panel of Y220C+ (MFE319, NUGC-3, MFE296, BxPC-3, Huh7) and Y220C- cell lines (MCF7, LNCaP, DU145, HepG2) after 4 day treatment. P-value computed by heteroscedastic t-test; n.s., not significant. d, Individual viability-dose curves for p53-01 across a panel of Y220C+ (MFE319, NUGC-3, MFE296, BxPC-3, Huh7) and Y220C- cell lines (MCF7, LNCaP, DU145, HepG2) after 4 day treatment. Cell lines in red are Y220C+, those in black are Y220C-. N = 4 technical replicates/condition.

Source data

Supplementary information

Supplementary Information

Supplementary Fig. 1 and Note 1.

Reporting Summary

Source data

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 3

Uncropped blots.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 4

Statistical source data.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Cite this article

Sadagopan, A., Carson, M., Zamurs, E.J. et al. Mutant p53 protein accumulation is selectively targetable by proximity-inducing drugs. Nat Chem Biol (2025). https://doi.org/10.1038/s41589-025-02051-7

Download citation

Received: 04 February 2025
Accepted: 17 September 2025
Published: 03 November 2025
Version of record: 03 November 2025
DOI: https://doi.org/10.1038/s41589-025-02051-7

Subjects

Abstract

Similar content being viewed by others

Main

Results

TP53-mutant cells lack synthetic lethal dependencies

TP53-mutant cells strongly upregulate p53 protein

Bifunctional molecules kill cells with p53 overabundance

Bifunctional molecules selectively kill p53-Y220C+ cells

Activity is p53-Y220C dependent without reactivation

Linker optimization improves efficacy of bifunctionals

Discussion

Methods

General methods

Cancer genomics analyses

MFE319 TP53 mutation analysis

Modeling of compound accumulation

Cell culture

Plasmids

Transient transfection and stable cell line creation

CellTiter-Glo (CTG)

Crystal violet

Flow-cytometry-based competition assays

NanoBiT assay

Colocalization assay and microscopy

Caspase 3/7 glo

Annexin–PI staining

Cell-cycle analysis

Western blotting

Huh7 RNA-seq and analysis

P53 reporter assay

RT–qPCR

Chemical synthesis

Reporting summary

Data availability

Change history

12 November 2025

References

Acknowledgements

Author information

Authors and Affiliations

Contributions

Corresponding authors

Ethics declarations

Competing interests

Peer review

Peer review information

Additional information

Extended data

Supplementary information

Source data

Rights and permissions

About this article

Cite this article

Share this article

Search

Quick links

Bifunctional molecules selectively kill p53-Y220C⁺ cells