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. 2026 Jan 6;215(2):49. doi: 10.1007/s10549-025-07865-6

Inhibition of GPX4 induces the death of p53-mutant triple-negative breast cancer cells

William M Tahaney 1,2, Amanda Lanier 2, Jing Qian 2, Cassandra L Moyer 2, Nghi Nguyen 3, Yanxia Ma 2, Jamal Hill 2, Reid T Powell 3, Clifford C Stephan 3, Peter J A Davies 3, Abhijit Mazumdar 2, Powel H Brown 1,2,✉
PMCID: PMC12774971  PMID: 41493644

Abstract

Background

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer characterized by high rates of tumor protein 53 (TP53) mutation and with limited targeted therapies. Despite being clinically advantageous, direct targeting of mutant TP53 has been challenging. Therefore, we hypothesized that p53-mutant TNBC cells rely upon other potentially targetable survival pathways.

Methods

In vitro and in silico screens were used to identify drugs that induced preferential death in TP53-mutant cells. The effect of the ferroptosis inducer ML-162 was tested both in vitro and in vivo and the mechanism of cell death following ML-162 treatment or GPX4 knockout was determined.

Results

High-throughput drug screening demonstrated that TP53-mutant TNBCs are highly sensitive to peroxidase, cell cycle, cell division, and proteasome inhibitors. We further characterized the effect of the ferroptosis inducer ML-162 and demonstrated that ML-162 induces preferential ferroptosis in TP53-mutant TNBC cells. Treatment of TP53-mutant xenografts with ML-162 suppressed tumor growth and increased lipid peroxidation in vivo. Testing ferroptosis inducers demonstrated TP53-missense mutant, and not TP53-null or wild-type cells, were more sensitive to ferroptosis, and expression of mutant TP53 genes in p53-null cells sensitized cells to ML-162 treatment.

Conclusions

This study demonstrates that TP53-mutant TNBC cells have unique survival pathways that can be effectively targeted. Our results illustrate the intrinsic vulnerability of TP53-mutant TNBCs to ferroptosis and highlight GPX4 as a potential target for the precision treatment of TP53-mutant TNBC.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10549-025-07865-6.

Keywords: TNBC, p53, TP53, GPX4, Ferroptosis, Gain-of-function

Background

Triple-negative breast cancer (TNBC) represents 15–20% of all breast cancer diagnoses, has a poor prognosis, and is treated predominantly with chemotherapy [1, 2]. Though TNBCs exhibit a wide range of mutations, one defining feature is the high frequency of mutations in the Tumor Protein 53 gene (TP53), reported between 80 and 90% [3]. The p53 protein plays crucial roles in the control of signals balancing cellular growth, survival, and death [4, 5]. However, in the presence of TP53 mutations, cells lose the tumor suppressive functions of p53 and can gain oncogenic functions [6, 7]. Therefore, mutant p53 is an attractive therapeutic target in TNBC.

Unfortunately, TP53 mutations have been difficult to target directly [8, 9]. While several drugs attempting direct targeting of the mutant p53 protein have been developed, only APR-246 (Prima-1Met, Eprenetapopt) has reached phase III clinical trials [10]. An alternative to directly targeting mutant p53 is to target other proteins that a cell carrying a mutant TP53 gene may rely upon for survival. We hypothesized that specific proteins or pathways are critical for the survival of TP53-mutant, but not TP53-wild type breast cancers.

We conducted an integrated screen of small molecules with known protein targets to identify drugs that induced preferential death or growth suppression in TP53-mutant cells, yielding six drugs, representing a ferroptosis inducer, and cell cycle, cell division, and proteasome inhibitors. Due to its effect against a large proportion of TP53-mutant cells and ability to induce death, we further studied the ferroptosis inducer ML-162 and its proposed protein target Glutathione Peroxidase 4 (GPX4). While ML-162 was initially thought to directly target GPX4, recent work by Cheff and colleagues found that ML162 inhibits GPX4 through an indirect mechanism dependent on direct binding to TXNRD1 [11]. We determined that ML-162 exhibits highest efficacy in TP53-mutant TNBCs, and that inhibition or knockout of GPX4 induces greater ferroptosis in TP53-missense mutant as compared to TP53-null or TP53-wild-type cells. We further demonstrated that ML-162 treatment decreases tumor burden of TP53-mutant xenografts in vivo and increases tumor lipid peroxidation. These studies demonstrate that GPX4 is a promising target for the treatment of TP53-mutant TNBCs and highlight the ability of our screening approach to identify targets for classically undruggable proteins, providing a pathway to identify potential treatments for difficult to treat cancers.

Methods

Dataset access

METABRIC and TCGA Breast datasets were accessed through cBioPortal [3, 12–19]. Area under the curve (AUC) values were retrieved from the CTD2 Data Portal [20, 21]. TP53 mutational data was extracted from the Cancer Cell Line Encyclopedia (CCLE) portal [22].

Cell lines and culture methods

Cal51 cells were obtained from DSMZ. Sum149PT cells were a generous gift from Dr. Naoto Ueno (MD Anderson, Houston, TX). All other cell lines were obtained from ATCC (Supplementary Table 1). Generation of sgGPX4 and TP53-mutant cell lines is described in Supplementary Methods. For all Dox-inducible cell lines, Tet-system approved FBS (Clontech) was used. TP53 DNA-resequencing of cell lines was performed through the MD Anderson Advanced Technology Genomics core. Cell line identity was confirmed with STR DNA fingerprinting, as previously described [23].

In vitro drug screening

Briefly, cells were seeded in 384-well plates (Greiner), incubated at 37 °C for 24 h, and treated with 10uM, 1uM, or 0.1 uM of each drug in duplicate for 72 h. The drug library was curated from three separate drug libraries: the BROAD “Informer Set” of drugs as published by Seashore-Ludlow et al.[21], Texas A&M IBT’s NCI Custom Clinical Library, and the PB Library, a collection of drugs of interest to the Brown lab. In total, this collection totaled 453 unique compounds, with known and diverse mechanisms of action, represented according to PANTHER classification in Supplementary Figure 1 [24]. Viability was determined with CellTiterGlo (Promega). Data processing is described in detail in Supplementary Methods.

In silico drug screening

Cancer Therapeutics Response Portal (CTRPv2.0) Informer Set AUC values representing 486 drugs were retrieved from the CTD2 Data Portal and integrated with TP53 mutational data from the CCLE. AUC values were normalized to the maximum theoretical value. An AUC ≤ 0.5 in at least one TP53-mutant cell lines was required for progression into the counter screen. A difference of > 0.1 between average TP53-wild-type AUC and average TP53-mutant AUC was considered a candidate in silico drug.

Cell proliferation assays

Cells were treated with drugs at indicated concentrations for 72 h. Plates were fixed with paraformaldehyde, stained with DAPI, and imaged with an ImageXpress Pico (Molecular Devices). Results were either reported as fold growth or Hafner’s growth rate [25]. Resulting values were fit into logistic regression models using Prism 9.2 (GraphPad), from which IC50 and AUC values were extracted.

DRAQ7 cell death assay

MDA-MB-468 cells were treated with indicated drugs for 24 h. Cells were co-stained live with DRAQ7 (300 nM) and Hoechst 33,342 (10uM) for 20 min, imaged at 4X, and counted using CellReporterXpress Software (Molecular Devices).

Annexin V/PI flow cytometry

Cells were treated with DMSO, ML-162 (500 nM, 24 h), or staurosporine (1uM, 3 h), then stained with Annexin-V and PI (Invitrogen) and analyzed with a Gallios 561 (Beckman Coulter).

Western blotting

Western blots were performed as described [26]. Primary antibodies were: GPX4 (Abcam, ab125066, 1:1000), TP53 (Santa Cruz, sc-126, 1:1000, 1:5000), Caspase 3 (Cell Signaling Technology, 14220, 1:1000), Caspase 7 (Cell Signaling Technology, 12872, 1:1000), Caspase 9 (Cell Signaling Technology, 9508, 1:1000), Actin (Sigma, SAB4301137, 1:4000), GAPDH (Sigma-Aldrich, G8795, 1:2000), and Vinculin (Sigma, 05–386, 1:4000).

Brightfield cell imaging

Cells were seeded in 6-well plates. Treatments and incubations were performed as indicated, following which cells were imaged live at 20X with an Eclipse Ti (Nikon) using NIS Elements Software v3.2 (Nikon).

Small molecule death inhibition assay

Cells were pre-treated with DMSO or 10uM inhibitors Z-VAD-fmk, Necrostain-1, or Ferrostatin-1 (Cayman Chemical) for 24 h, following which cells were treated with ML-162 with or without inhibitors for 72 h. Plates were fixed, stained with DAPI, and imaged at 4 × with an ImageXpress Pico (Molecular Devices).

C11-BODIPY581/591 fluorescent imaging

Cells were seeded into 96-well optical plates (Nunc) and grown for 24 h at 37 °C with or without FS-1. Cells were treated with 100 nM ML-162, with or without FS-1 for 6 h, then with 5 µM C11-BODIPY581/591 (Molecular Probes) in HBBS for 30 min at 37 °C. Fluorescence intensity was calculated using CellReporterXpress Software (Molecular Devices).

Mouse experiments

All animal experiments were performed with M.D. Anderson Institutional Animal Care and Use Committee (IACUC)-approved protocols. Cells were injected into the mammary fat pads of nude mice (5 × 106 or 7.5 × 105 cells). Mice were randomized (at 50–100 mm3) into groups to receive ML-162 (50 mg/kg) or vehicle (DMSO), injected intratumorally 5 day per week. Tumor sizes were measured and volume was calculated with the formula: Volume = (width2 × length)/2. Tumor growth rates were calculated with log-transformed linear regression.

H&E and immunohistochemistry

Tumor sections were processed for immunohistochemical (IHC) staining, as previously described [27] using anti-Ki67 (Thermo Scientific, prediluted), anti-cleaved caspase 3 (Thermo Scientific, prediluted), or anti-4-hydroxynonenal (R&D Systems, 1:1000).

q-RT-PCR

RNA was extracted from cell pellets using the RNeasy Mini Kit (74,104; Qiagen) according to manufacturer instructions. Reverse transcription was performed, followed by qPCR using an ABI 7500 System (Applied Biosystems). Assays were run in technical quadruplicate. Data shown are displayed as mean ± SD of three biologic replicates. Gene expression was calculated using the comparative Ct method, relative to 18 s (2−ΔΔCt method). All TaqMan assays used can be found in Supplementary Methods.

Data analysis and statistical considerations

Z′ was calculated as in Zhang et al. [28]. A Z′ of > 0.5 was required for each cell line replicate to be used. Fold DRAQ7+ was calculated as the ratio between drug and DMSO treated percent positivities. Fold growth of cells was compared using a mixed-effects model with a Geisser-Greenhouse correction. Slopes of tumor growth were calculated with log10-transformed linear regression, then compared with Student’s t-test. Kaplan–Meier curves were compared with log-rank analysis. All other experimental significance was determined with Student’s t-test. For all experiments, a p-value of < 0.05 was considered statistically significant.

Results

Integrated high throughput drug screening of breast cancer cell lines

To identify drugs that induce death or suppress growth of TP53-mutant and not TP53-wild type breast cancer cells, we used in vitro and in silico drug screening (Fig. 1A) with a 453 drug library (Supplementary Figure 1 and Supplementary Table 2).

Fig. 1.

Fig. 1

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Integrated drug screening identifies drugs that induce death or suppress growth of TP53-mutant breast cancers. A Workflow for integrated high-throughput drug screens. B Hits from primary in vitro screen. C Hits from counter in vitro screen. D Hits from primary in silico screen. E Hits from counter in silico screen. F Integration of in vitro and in silico drug screens and identified common drug hits. G Summary of identified candidate drugs, drug activity, and known protein targets (GPX4—Glutathione Peroxidase 4; CHEK1—Checkpoint Kinase 1; CHEK2—Checkpoint Kinase 2; WEE1—WEE1 G2 Checkpoint Kinase; PSMB5—Proteasome 20S Subunit Beta 5; KIF11—Kinesin Family Member 11)

The in vitro screen began by screening the drug library at three concentrations against eight TP53-mutant TNBC cell lines (Supplementary Table 3) using the ATP luminescence assay CellTiterGlo as a readout. This yielded 67 drugs as capable of inducing death or suppressing growth in TP53-mutant TNBC cells (Fig. 1B, Supplementary Figure 2A–C). We then counter-screened these drugs against 5 TP53-wild type breast and breast cancer cells. By determining the difference in area-under-the-curve (AUC) between TP53-mutant and TP53-wild type cells, we identified 13 drugs as inducing preferential growth suppression or death induction of TP53-mutant breast cancer cells (Fig. 1C, Supplementary Figure 2D, Supplementary Table 3, the 13 drugs are listed in the left column in bold).

In parallel, we accessed dose–response data from the Cancer Therapeutics Response Portal dataset (CTRP v2.0, Supplementary Table 4) and TP53 mutational statuses from the Cancer Cell Line Encyclopedia (CCLE) listed in Supplementary Table 5. Examining data from 19 TP53-mutant breast cancer cell lines, we identified 70 drugs as capable of inducing death or suppressing growth of TP53-mutant breast cancer (Fig. 1D, Supplementary Figure 2E–F). We then conducted a counter screen of the 70 candidate drugs against 6 TP53-wild type breast cancer cell lines as described above, which identified 18 drugs as preferentially inducing the death or suppressing the growth of TP53-mutant breast cancer cells (Fig. 1E, Supplementary Table 6, the 18 drugs are listed in the left column in bold).

The results from the in vitro and in silico screens were integrated to identify shared protein targets and pathways using BioVenn [29]. Six drugs were identified in both screens, including (Fig. 1F) one peroxidase inhibitor (ML-162), two cell cycle inhibitors (AZD7762 and MK-1775), one proteasome inhibitor (Ixazomib), and two cell division inhibitors (Docetaxel and SB-743921), as shown in Fig. 1G. Drugs excluded after integration are listed in Supplementary Table 7.

Identification of ML-162 as inducing preferential death in TP53-mutant TNBC cell lines

We then generated dose–response curves of these six drugs and compared the difference in AUC between TP53-mutant and TP53-wild type breast cancer cells (Fig. 2A). ML-162, AZD7762, MK-1775, and ixazomib demonstrated a significant decrease of AUC in TP53-mutant, as compared to TP53-wild-type, cell lines (Fig. 2B).We also found ML-162 and MK-1775 to have a significant difference in IC50 values between TP53-mutant and TP53-wild type cell lines (Fig. 2C). Finally, we used DRAQ7 and Hoechst co-staining to determine that all drugs, except docetaxel, induced cell death (Fig. 2D). As ML-162 yielded the greatest differential in AUC and IC50 values and induced cell death, we chose to further characterize the effect of ML-162 and its target protein GPX4 on the survival of TP53-mutant breast cancer.

Fig. 2.

Fig. 2

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Confirmation of drug screen identifies ML-162 as inducing death in p53-mutant triple-negative breast cancer. A 5-log dose–response curves of identified drugs in p53-mutant (red/orange) and p53-wild type (blue) immortalized normal and cancerous breast cell lines (n = 4). Comparison of B AUC and C IC50 values between p53-wild type and p53-mutant cells. D Fold DRAQ7+ values for drug treated MDA-MB-468 cells, with staurosporine (1uM, 3Hr) as positive control (n = 3). AUC and IC50 values were compared between p53 mutational statuses and fold DRAQ7+ values were compared between drug and DMSO treatments, all with Student’ t-test. For all comparisons, a p value of < 0.05 was considered statistically significant (*/**/***p < 0.05/0.01/0.001)

GPX4 expression does not correlate with ML-162 sensitivity

As ML-162 is a direct inhibitor of GPX4, we examined the expression of GPX4 in clinical datasets. Using the METABRIC and TCGA datasets, we determined that TNBC tumors and TP53-mutant tumors have lower mRNA expression of GPX4 than non-TNBC tumors (Supplementary Figure 3A). We further examined the METABRIC dataset by PAM50 classification and found that basal and claudin-low subtypes expressed significantly lower levels of GPX4 (Supplementary Figure 3B). However, we did not observe a significant difference in GPX4 protein expression between p53-mutant and p53-wild type breast cancer cell lines (Supplementary Figure 3C). To determine if ML-162 sensitivity is due to altered GPX4 protein expression, we performed a correlation between GPX4 expression and ML-162 IC50 for breast cancer cell lines and determined there was not a significant correlation (Supplementary Figure 3D). Taken together, these data indicate that differences in ML-162 sensitivity are not due to GPX4 expression.

ML-162 exhibits higher potency in TP53-mutant TNBCs

To better understand the effects of GPX4 inhibition in breast cancer, we first determined the effect of ML-162 treatment on cell growth. We determined that ML-162 did not have a significant effect on growth of TP53-wild type cell lines (Fig. 3A) but induced potent growth suppression of TP53-mutant TNBC cells (Fig. 3B). The data show that the number of cells in the ML-162 treated TP53-mutant TNBC cell lines drops below the initial number plated, consistent with the finding that ML-162 treatment induces cell death. These data suggest that ML-162 exhibits preferential potency in TP53-mutant TNBCs when compared to TP53-wild type breast cancer cell lines.

Fig. 3.

Fig. 3

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ML-162 exhibits preferential potency in TP53-mutant triple-negative breast cancer. Cell growth curves of A TP53-wild type immortalized normal breast and breast cancer cell lines and B cell growth curves of TP53-mutant TNBC cell lines treated with 100 nM ML-162. N = 3 for all experiments. Fold growth of cells was compared using a mixed-effects model with Geisser-Greenhouse correction, and p < 0.05 was considered statistically significant (***/****p < 0.001/0.0001)

ML-162 treatment and GPX4 knockout induce ferroptosis in TNBC

As ML-162 was found to induce cell death, we examined the effect of GPX4 inhibition on Annexin V positivity and observed a significant increase after ML-162 treatment in MDA-MB-468 cells (Fig. 4A) and additional TP53-mutant TNBC cell lines (Supplementary Figure 4A). To determine if cell death was due to apoptosis, we tested expression of cleaved caspases after ML-162 treatment. Interestingly, ML-162 treatment did not induce these traditional apoptotic death markers (Fig. 4B). We then examined the cellular morphology, finding that while staurosporine-treated cells exhibited characteristic apoptotic cell shrinkage and membrane blebbing, ML-162 treated cells were markedly enlarged (Fig. 4C, Supplementary Figure 4B).

Fig. 4.

Fig. 4

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GPX4 inhibition induces ferroptosis in TP53-mutant triple-negative breast cancer. A Quantification of Annexin V positivity after treatment with DMSO, Staurosporine (1uM, 3Hr), or ML-162 (500 nM, 24Hr). n = 3. B Immunoblot of intact and cleaved caspases in MDA-MB-468 cells after treatment with DMSO, ML-162 (50, 100, 500 nM), or Staurosporine (1uM, 3Hr). C Representative brightfield images of MDA-MB-468 after treatment with DMSO, Staurosporine (1uM, 3Hr), or ML-162 (500 nM, 24Hr). Scale bar = 50 μm. D Growth rate of TP53-mutant TNBC cell lines after co-treatment with ML-162 and death inhibitors (n = 4). E Growth of MDA-MB-468 sgGPX4 cells with and without Dox and FS-1 co-treatment (n = 3). Immunoblot demonstrates GPX4 knockout. F Representative images and quantification of C11-BODIPY581/591 staining in MDA-MB-468 cells after treatment with ML-162 (100 nM, 6Hr) and/or FS-1 (10uM, 24 Hr).n = 3, scale bar = 20 μm. Significance of differences between Annexin V positivity or C11 fluorescence was determined with Student’s t-test. Fold growth of cells was compared using a mixed-effects model with Geisser-Greenhouse correction. For all comparisons, a p-value of < 0.05 was considered statistically significant (**/***/****p < 0.01/0.001/0.0001)

To investigate the mechanism of cell death induced by ML-162, we co-treated TP53-mutant TNBC cells with ML-162 and small molecule inhibitors of cell death, including Z-VAD(OMe)-FMK (Z-VAD, apoptosis inhibitor), Necrostatin-1 (Nec-1, necrosis inhibitor), and Ferrostatin-1 (FS-1, ferroptosis inhibitor). As shown in Fig. 4D, only FS-1 was able to reduce sensitivity to ML-162, as indicated by a shift in the dose response curve and an increase in IC50 values. As shown in Supplementary Figure 4C, ML-162 treatment induced ferroptosis (as shown by increased C11-BODIPY staining), which was reversed by the ferroptosis inhibitor, FS-1 (Supplemental Figure 4C).

As shown in Supplementary Figure 5, the IC50 is shifted only with the addition of FS-1 in the three TP53-mutant cell lines, although due to the high variability of the IC50 measurements, this shift in IC50 was statistically significant only in the HCC1143 cell line (Supplementary Figure 5). These results are consistent with the conclusion that ML-162 induces ferroptosis in TP53-mutant TNBC cell lines (Fig. 4D, Supplementary Figures 4 and 5).

Fig. 5.

Fig. 5

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ML-162 reduces in vivo triple-negative breast cancer xenograft growth and induces lipid peroxidation. A Growth curves of MDA-MB-468 xenografts treated with vehicle (left) or ML-162 (right). B Analysis of MDA-MB-468 tumor growth slopes compared between DMSO and ML-162 treated tumors. C Growth curves of MDA-MB-231 xenografts treated with vehicle (left) or ML-162 (right). D Analysis of MDA-MB-231 tumor growth slopes compared between DMSO and ML-162 treated tumors. E Representative MDA-MB-231 xenograft IHC images and quantification of Ki67, Cleaved Caspase 3, and 4-HNE staining after treatment with vehicle or ML-162. F Quantification of Ki67, Cleaved Caspase 3, and 4-HNE staining after vehicle or ML-162 treatment (n = 5). Comparisons between vehicle and ML-162 treatment were compared with Student’s t-test, and a p value of < 0.05 was considered statistically significant

We next used CRISPR-Cas9 to create an inducible GPX4 knockout cell line to confirm the effect of ML-162 was due to GPX4 inhibition (Supplementary Figure 6A). We determined that Dox-treatment induced death of sgGPX4, but not control, cells (Supplementary Figure 6B). We then examined the morphology of these cells and determined that GPX4 knockout cells exhibited the same phenotype as ML-162 treated cells (Supplementary Figure 6C). Finally, we tested whether treatment with FS-1 could rescue the death induction of GPX4 knockout. As shown in Fig. 4E and Supplementary Figure 6D, Dox-treated GPX4 knockout cells died, while cotreatment with FS-1 rescued the lethality of GPX4 knockout, demonstrating that loss of GPX4 in TP53-mutant TNBCs induces ferroptosis. To further confirm this induction of ferroptosis, we used the fluorescent lipid peroxidation probe C11-BODIPY581/591 to image cells after ML-162 treatment or GPX4 knockout. C11-BODIPY581/591 is oxidized through lipid peroxidation, upon which its excitation spectra shift from red to green. Treatment with DMSO or FS-1 resulted in low levels of C11 oxidation, whereas ML-162 treatment yielded a significant induction of C11 oxidation, which was reversed upon co-treatment with FS-1 (Fig. 4F). We further observed that ML-162 induces C11-BODIPY581/5 oxidation in three additional TP53-mutant TNBC cell lines, which is ablated with FS-1 co-treatment (Supplementary Figure 4C). Collectively, these data demonstrate that ML-162 treatment or GPX4 knockout induces ferroptosis in TP53-mutant TNBCs.

As Cheff and colleagues [11] proposed that the action of ML-162 was primarily mediated through TXNRD1 binding, and not a direct effect of GPX4 inhibition, we investigated the effects of TXNRD1 knockdown in TP53-mutant TNBC cells (Supplementary Figure 7). Using two siRNAs directed against TXNRD1 and a non-targeting siRNA control, we found that knockdown of TXNRD1 inhibited growth of TP53-mutant TNBC cells (Supplementary Figure 7A–B). However, TXNRD1 knockdown did not appear to induce death since the cell number did not drop below the plating baseline (below fold growth of 1.0), as was seen with ML-162 treatment (as seen in Fig. 3). In addition, the growth inhibition seen with TXNRD1 knockdown was not due to ferroptosis, as there was no rescue of growth inhibition via treatment with Ferrostatin-1 (see Supplementary Figure 7C). Finally, as shown in Supplementary Figure 7D, ML-162 treatment caused increased growth inhibition even after TXNRD1 knockdown, demonstrating that the growth inhibition and cell death induced by ML-162 is not solely due to TXNRD1 inhibition.

ML-162 suppresses TP53-mutant TNBC xenograft growth in vivo

In order to determine the effect of ML-162 treatment in vivo, we tested the effect of ML-162 on the growth of TP53-mutant MDA-MB-468 TNBC xenografts. Mice were treated five times weekly with intratumoral vehicle or 50 mg/kg ML-162 and tumor growth was monitored. As demonstrated in Fig. 5A, tumor growth was slower in ML-162 than in vehicle treated tumors. Next, tumor volumes were log10 transformed and fit with linear regression. Comparison of slopes indicated a significant decrease in the growth rate of tumors treated with ML-162 compared to vehicle (Fig. 5B). We also tested the effect of ML-162 on TP53-mutant MDA-MB-231 xenografts and observed a significant reduction of tumor burden in ML-162 treated mice (Fig. 5C, D).

We then analyzed MDA-MB-231 tumors with immunohistochemistry (Fig. 5E, F). We found that tumor cell proliferation, indicated by Ki67 staining, and induction of apoptosis, determined by cleaved caspase 3, were not significantly different between groups. To confirm that in vivo activity of ML-162 was due to ferroptosis, we stained 4-hydroxynonenal (4-HNE), a ferroptosis byproduct, and found it was significantly increased in ML-162 treated tumors when compared to vehicle treated tumors. These results demonstrate that ML-162 is capable of decreasing TP53-mutant TNBC xenograft tumor burden by inducing ferroptosis.

Expression of missense-mutant p53 sensitizes cells to ferroptosis

To further examine the role of TP53 mutational status in sensitivity to ferroptosis, we tested ML-162 and three additional ferroptosis inducing compounds – GPX4 inhibitors ML-210 and 1S-3R-RSL3 and System Xc inhibitor erastin – against a panel of TP53-missense mutant, TP53-null, and TP53-wild type breast cancer cells (Fig. 6A). We quantified differences in these dose–response curves by analyzing both AUC (Fig. 6B) and drug IC50 (Fig. 6C). Using both methods, TP53-missense mutant cells were more sensitive to all compounds than the TP53-wild type and TP53-null cells, indicating this sensitivity may be due to a TP53 mutant gain of function effect. To determine if expression of mutant p53 was sufficient to induce sensitivity to GPX4 inhibition, we created a series of Dox-inducible TP53-mutants in the TP53-null TNBC cell line MDA-MB-436, including p53-R248Q and p53-R273H (Fig. 6D). To determine how expression of mutant p53 altered sensitivity to ML-162, we examined cell growth following treatment with Dox and ML-162. As demonstrated in Fig. 6E and Supplementary Figure 8, expression of either p53-R248Q or p53-R273H was sufficient to sensitize TNBC cells to ML-162 treatment, as demonstrated by a shift (to the left) in the dose response curves (Fig. 6E) and a statistically significant reduction in IC50 values (Supplementary Figure 8). These data collectively demonstrate that TP53 missense mutant TNBCs are sensitive to ferroptosis, and that TP53 mutational status contributes to this sensitivity.

Fig. 6.

Fig. 6

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Expression of missense-mutant p53 sensitizes cells to ferroptosis. A 5-log dose–response curves of GPX4 inhibitors ML-162, RSL3, ML-210, and System Xc inhibitor Erastin in p53-mutant (red/orange), p53-null (green) and p53-wild type (blue) immortalized normal breast and breast cancer cell lines (n = 4). Comparison of B AUC and C IC50 values between p53-wild type/null and p53-mutant cells. D Immunoblot of p53 protein in MDA-MB-436 pInducer20 p53-R248Q and p53-R273H cell lines with and without Dox treatment. E Growth of MDA-MB-468 p53-R248Q and p53-R273H cells in the presence of ML-162 with and without Dox treatment (n = 4). AUC and IC50 values were compared between p53 statuses with Student’s t-test. For all comparisons, a p value of < 0.05 was considered statistically significant (***/****p < 0.001/0.0001)

Role of lipid hydroperoxides in the induction of ferroptosis by ML-162

Ferroptosis is reliant upon lipid metabolism. Given that TP53-mutant TNBC cells exhibit increased sensitivity to ferroptosis, we investigated the expression of lipid metabolism enzymes upstream of GPX4 which produce lipid hydroperoxides (LOOH) from lipids (LH) (see Fig. 7A and Supplementary Figure 9). Using METABRIC and TCGA data, we determined that mRNA expression of several enzymes in this pathway, including FADS1, FADS2, and ACSL4, is higher in tumors with TP53 mutation, as compared to those without TP53 mutation. Furthermore, we found that induction of R273H-mutant p53 (Supplementary Figure 10) in the MDA-MB-436 p53-null cell line resulted in a significant increase in ACSL4 mRNA expression, as well as an increase in FADS1 and GPX4 expression that was not statistically significant (Fig. 7B). These data indicate that mutant p53 may increase expression of enzymes involved in the synthesis of lipid hydroperoxides, the substrate of ferroptosis.

Fig. 7.

Fig. 7

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Proposed mechanism of sensitivity to ferroptosis according to TP53-mutational status. A mRNA expression of enzymes in the lipid peroxidation pathway upstream of GPX4 in the METABRIC dataset, separated by TP53 mutational status. B RNA expression of lipid metabolism enzymes was determined using q-RT-PCR following doxycycline induction (1 µg/mL, 72 h) of R273H-mutant p53 in MDA-MB-436 p53 null cells. ALOX15 RNA expression is not shown, as it was not detectable. Student’s t-tests were used to determine significance. C Growth of breast cancer cells treated with DMSO, ALOX15 small molecule inhibitor PD-146176, and/or ML-162 (n = 3). D Proposed mechanism of ferroptosis in TP53-mutant breast cancer. Statistical significance of mRNA expression and percent growth differences were determined with Student’s t-test. For all comparisons, a p-value of < 0.05 was considered statistically significant (**/***p < 0.01/0.001)

As the synthesis of LOOH may promote ferroptosis, we hypothesized that inhibition of lipid hydroperoxide synthesis (as achieved by inhibition ALOX15, the enzyme which catalyzes the final step of LOOH synthesis) would rescue the death-inductive effect of ML-162. Thus, we co-treated cells with ML-162 and the ALOX15 small molecule inhibitor PD-146176. Addition of PD-146176 was able to significantly rescue ML-162-induced death in TP53-mutant TNBC cell lines, indicating that production of lipid hydroxides is necessary for the ML-162-mediated induction of ferroptosis (Fig. 7C). These data highlight the importance of lipid metabolism in TP53-mutant breast cancer.

Discussion

In this study, we identified multiple drugs targeting proteins critical for the growth and survival of TP53-mutant breast cancer. We further studied the ferroptosis inducer ML-162 and demonstrated ML-162 treatment or GPX4 knockout induced preferential death in TP53-mutant triple-negative breast cancers by the induction of ferroptosis. In vivo, ML-162 treatment was shown to decrease the growth of TP53-mutant TNBC xenografts and induce ferroptotic cell death. Furthermore, TP53-missense mutant cells were found to be more sensitive to ferroptosis than TP53-null or wild type cells, and expression of a mutant TP53 gene was sufficient to sensitize TP53-null TNBC cells to ML-162. Finally, this study highlighted that TP53-mutant TNBCs harbor increased expression of lipid metabolizing enzymes upstream of GPX4, that expression of mutant-p53 increased expression of some lipid metabolism enzymes, and that production of lipid hydroxides correlates with sensitivity to ferroptosis.

Our proposed model for the role of GPX4 in breast cancer is shown in Fig. 7D. In TP53-wild type non-TNBC, cells exhibit low levels of lipid hydroperoxide (LOOH) production. These LOOH are reduced to LOH via GPX4, preventing the formation of lipid radicals (LO˙). Upon GPX4 inhibition, LOOH become LO˙, but do not reach a level sufficient to induce ferroptosis. In TP53-mutant TNBC cells, there is greater production of LOOH. Upon ML-162 treatment or GPX4 knockdown, this results in a large generation of LO˙, which induces ferroptosis and subsequent cell death.

In addition to ferroptosis, this paper highlights other molecular vulnerabilities of TP53-mutant TNBC, including proliferation, cell division, and protein turnover pathways. Cell proliferation inhibitors AZD7762 and MK-1775, cell cycle checkpoint kinase and WEE1 kinase inhibitors, respectively, have been previously demonstrated to regulate survival of TP53-mutant cancers [30–33]. These kinases function at the G2/M checkpoint, the sole checkpoint for DNA repair in TP53-mutant cancers [34]. We also identified cell division inhibitors, such as the microtubule inhibiting chemotherapy agent docetaxel and KIF11 inhibitor SB-743921 as preferentially effective in TP53-mutant breast cancers. Both of these inhibit cell division, causing accumulated DNA damage, further highlighting the importance of DNA damage repair in the G2 phase of the cell cycle. Finally, we identified the proteasome inhibitor ixazomib as having greater activity in TP53-mutant breast cancers. The effect of proteasome inhibition in TP53-mutant cancers is still unclear, with some reports stating effect from proteasome inhibitors is TP53-independent, while others share our findings of a mutant TP53-dependent activity [35–39].

While studies have been conducted on the effect of GPX4 inhibition and ferroptotic induction in various cancer subtypes, the specific role of TP53-mutation in the sensitivity of TNBC to ferroptosis has not previously been known. A recent publication by Verma et al. [40] showed basal breast tumors are more sensitive to ferroptotic induction than are ER-positive cells, but did not explore the role of p53 in this finding. Similarly, Thompson et al. demonstrated that different TP53 mutations can increase sensitivity to ferroptosis, though no clear mechanism linking TP53 mutation and ferroptosis was demonstrated [41]. The most well-studied mechanism linking TP53-mutant status and ferroptosis was proposed by Liu et al., where it was demonstrated mutant p53 can entrap NRF2 and subsequently repress SLC7A11 expression in esophageal cancers, resulting in ferroptosis [42]. In addition, Yuan et al. demonstrated that induction of ferroptosis in glioma cells is mediated by p62 which enhances p53-dependent suppression of NRF2 [43]. Future studies will be needed to determine the downstream effects of TP53 mutational status on lipid and ferroptosis-related gene expression in the context of both breast cancer and other TP53-mutant cancers.

Conclusions

We conducted an integrated high-throughput drug screen and demonstrated that TP53-mutant, but not TP53-wild type, breast cancer cells, are sensitive to ferroptosis inducers, proteasome, cell cycle, and cell division inhibitors. Induction of ferroptosis through ML-162 treatment or GPX4 knockout induces death of TP53-mutant TNBC cells, demonstrating that GPX4 is critical for the survival of TP53-mutant TNBC and a potential therapeutic target in these aggressive cancers. Finally, this study highlights a drug screening strategy to identify vulnerabilities of classically undruggable targets, providing a pathway to develop more effective treatments for triple-negative breast cancer.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (TIF 211 kb) (211.7KB, tif)
Supplementary file2 (TIF 317 kb) (317.5KB, tif)
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Supplementary file10 (TIF 971 kb) (971.8KB, tif)
Supplementary file11 (DOCX 38 kb) (38.6KB, docx)
Supplementary file12 (XLSX 405 kb) (405.2KB, xlsx)

Acknowledgements

We would like to thank Sam Tillinger and Michelle Savage for assisting in the submission, and the MD Anderson Flow Cytometry and Cellular Imaging Core Facility for their assistance with these experiments and their data analysis. We additionally thank CPRIT, the NCI, the Susan G Komen Foundation, the Charles Cain Endowment, and the Breast Cancer Research Foundation for the financial support of this research.

Abbreviations

4-HNE

4-Hydroxynonenal

ALOX15

Arachidonate 15-Lipoxygenase

AUC

Area Under the Curve

CCLE

Cancer Cell Line Encyclopedia

ER

Estrogen Receptor

FS-1

Ferrostatin-1

GPX4

Glutathione Peroxidase 4

HER2

Human Epidermal Growth Factor Receptor 2

LH

Lipid

LO

Lipid Radical

LOH

Lipid Alcohol

LOOH

Lipid Hydroperoxide

METABRIC

Molecular Taxonomy of Breast Cancer International Consortium

Nec-1

Necrostatin-1

p53, TP53

Tumor Protein 53

PANTHER

Protein Analysis Through Evolutionary Relationships

PR

Progesterone Receptor

STR

Short Tandem Repeat

TCGA

The Cancer Genome Atlas

TNBC

Triple-Negative Breast Cancer

Z-VAD

Z-VAD(OMe)-FMK

Author contributions

WMT: Conceptualization, data curation, conduction of experiments, formal analysis, methodol-ogy, manuscript writing, and manuscript editing. AL: Conduction of experiments, methodology, formal analysis, manuscript writing, manuscript editing. JQ: Conduction of experiments, formal analysis, methodology, manuscript writing, manuscript editing. CLM: Conduction of experi-ments, formal analysis, manuscript editing. NN: Conduction of experiments, methodology, man-uscript editing. YM: Methodology, manuscript editing. JH: Conduction of experiments, meth-odology, manuscript editing. RTP: Resources, conceptualization, data curation, conduction of experiments, formal analysis, methodology, and manuscript editing. CCS: Resources, conceptu-alization, methodology, and manuscript editing. AM: Conceptualization, data curation, conduc-tion of experiments, formal analysis, methodology, manuscript writing, and manuscript editing. PJAD: Resources, conceptualization, methodology, and manuscript editing. PHB: Resources, conceptualization, methodology, manuscript writing, and manuscript editing.

Funding

This work was funded by a CPRIT Core Grant (RP150578, N.N., R.T.P, C.C.S., P.J.A.D.), an NCI Core Grant (CA016672), a Susan G Komen Promise Grant (KG081694, P.H.B.), a Komen SAB grant (KG081694, P.H.B.), the Charles Cain Endowment grant (P.H.B.), and a Breast Cancer Research Foundation grant (P.H.B.).

Availability of data and materials

mRNA expression of METABRIC and TCGA Breast Datasets are available at https://www.cbioportal.org. PANTHER classifications are available from www.pantherdb.com. CTD2 Data Portal is found at https://ocg.cancer.gov/programs/ctd2/data-portal. CCLE mutational data are available at https://portals.broadinstitute.org/ccle. Additional data supporting the findings are available within the article, and from the corresponding author on reasonable request.

Declarations

Conflict of interest

P. Brown served as a Scientific Advisory Board Member for the Susan G. Komen for the Cure Foundation (until 2017), and Dr. Brown is a holder of GeneTex stock (less than 1% of the total company stock); neither of these relate to this publication. All remaining authors declare no actual, potential, or perceived conflict of interest that would prejudice the impartiality of this article.

Consent for publication

Not applicable.

Ethics approval and consent to participate

All animal experiments were performed using protocols approved by the M.D. Anderson Institutional Animal Care and Use Committee (IACUC).

Footnotes

Publisher's Note

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

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file1 (TIF 211 kb) (211.7KB, tif)
Supplementary file2 (TIF 317 kb) (317.5KB, tif)
Supplementary file3 (TIF 157 kb) (157.5KB, tif)
Supplementary file4 (TIF 377 kb) (377.1KB, tif)
Supplementary file5 (TIF 403 kb) (403.3KB, tif)
Supplementary file6 (TIF 1189 kb) (1.2MB, tif)
Supplementary file7 (TIF 690 kb) (690.1KB, tif)
Supplementary file8 (TIF 385 kb) (385.8KB, tif)
Supplementary file9 (TIF 510 kb) (510.3KB, tif)
Supplementary file10 (TIF 971 kb) (971.8KB, tif)
Supplementary file11 (DOCX 38 kb) (38.6KB, docx)
Supplementary file12 (XLSX 405 kb) (405.2KB, xlsx)

Data Availability Statement

mRNA expression of METABRIC and TCGA Breast Datasets are available at https://www.cbioportal.org. PANTHER classifications are available from www.pantherdb.com. CTD2 Data Portal is found at https://ocg.cancer.gov/programs/ctd2/data-portal. CCLE mutational data are available at https://portals.broadinstitute.org/ccle. Additional data supporting the findings are available within the article, and from the corresponding author on reasonable request.

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