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Published in final edited form as: Oncogene. 2025 Sep 6;44(42):4075–4086. doi: 10.1038/s41388-025-03562-2

Targeting FSP1 to induce ferroptosis in chromophobe renal cell carcinoma

Samer Salem 1, Tiegang Han 1, Michel Alchoueiry 1, Nadine Mahmoud 1, Wafaa Bzeih 1, Joelle Chami 1, Damir Khabibullin 1, Hadi Mansour 1, Yan Tang 1, Thai H Ho 2, Jessalyn M Ubellacker 3, Carmen Priolo 1, Elizabeth P Henske 1,✉
PMCID: PMC13045003  NIHMSID: NIHMS2136551  PMID: 40914768

Abstract

There are no proven therapies for metastatic or unresectable Chromophobe Renal Cell Carcinoma (ChRCC). ChRCC is characterized by high glutathione levels and hypersensitivity to ferroptosis, an iron-dependent form of cell death characterized by peroxidation of polyunsaturated fatty acids. The underlying mechanisms leading to ferroptosis hypersensitivity are unknown. Ferroptosis suppressor protein (FSP1) is a glutathione-independent suppressor of ferroptosis whose role in ChRCC is unexplored. In The Cancer Genomic Atlas (TCGA), we find that ChRCC exhibits the second highest upregulation of FSP1 relative to healthy organ out of all cancers, and that higher FSP1 expression correlates with poorer patient outcomes. We also define a ferroptosis signature combining FSP1 and Solute Carrier Family 7 Member 11 (SLC7A11) that predicts patient survival across all TCGA tumor types. Data queried from the Dependency Map and the Cancer Target Discovery and Development indicate that high FSP1 expression correlates with resistance to cell death induced by disruption of glutathione homeostasis via inhibition of glutathione peroxidase 4 (GPX4) or SLC7A11. Studies using ChRCC cell lines in vitro reveal that genetic inhibition of GPX4 or FSP1 individually does not induce substantial cell death, while inhibition of both results in near-complete loss of viability. Consistent with these genetic data, combining pharmacologic inhibition of GPX4 or SLC7A11 with inhibition of FSP1 demonstrates synergistic loss of viability. Strikingly, inhibition of FSP1 alone in vivo is sufficient to decrease ChRCC tumor growth by 69%, consistent with recent studies in lung and colorectal cancer showing similar effects. Taken together, these data establish FSP1 as targetable vulnerability in ChRCC.

INTRODUCTION

Chromophobe renal cell carcinoma (ChRCC) represents 5–7% of all renal cell carcinomas and is the third most common subtype [1, 2]. It originates from the mitochondria-rich intercalated cells located in the collecting duct system of the kidney [3–6]. ChRCC is characterized by a low mutational burden compared to other cancer types [7, 8]. Thirty-two percent of ChRCC tumors have mutations in tumor protein p53 (TP53), 9% have mutations in phosphatase and tensin homolog (PTEN), and 23% have mutations in mechanistic target of rapamycin kinase (MTOR), TSC complex subunit 1 (TSC1), TSC complex subunit 2 (TSC2), or NRAS. Forty percent of ChRCC in The Cancer Genome Atlas (TCGA) have no identifiable driver mutations. ChRCC tumors are characterized by a marked accumulation of abnormal mitochondria, exhibiting the highest expression of 13 mitochondrial DNA genes out of all tumors in the TCGA, and 13% of tumors have mutations in the mitochondrially-encoded NADH oxidoreductase core subunit 5 (ND5) [7, 9, 10]. Whole chromosomal losses are common in ChRCC, most commonly affecting chromosomes 1, 2, 6, 10, 13, 17, and 21 [11]. In contrast to clear cell renal cell carcinoma (ccRCC), ChRCC has inconsistent responses to tyrosine kinase inhibition and immune checkpoint blockade [12–14], and median overall survival of patients with metastatic disease remains low at 27 months [15].

Ferroptosis is an iron-catalyzed, reactive oxygen species (ROS)-dependent type of cell death first described in 2012 [16] that is distinct from other types of cell death, including apoptosis, pyroptosis, and necroptosis. Ferroptosis is characterized by peroxidation of membrane phospholipids, leading to loss of cellular integrity and cell death [16–19]. Membrane polyunsaturated phospholipids, in particular, are highly susceptible to peroxidation due to the presence of bisallylic hydrogens which facilitates hydrogen abstraction by free radicals and formation of lipid peroxyl radicals and subsequent propagation of lipid peroxidation [20–23]. Neoplastic cells exhibit high reactive oxygen stress, making ferroptosis a therapeutic target in multiple cancers, including lymphoma and clear cell renal cell carcinoma [24–30].

Glutathione, a tripeptide of glutamate, glycine, and cysteine, plays a major role in ferroptosis resistance. The availability of cysteine is rate-limiting for glutathione synthesis [31]. System Xc−, which imports L-cysteine, is composed of 2 subunits, Solute Carrier Family 7 Member 11 (SLC7A11) and SLC3A2 [32–35]. GPX4 (Glutathione Peroxidase 4) couples the oxidation of reduced glutathione to the reduction and scavenging of lipid peroxides [26] (Fig. 1A). Our lab and others previously showed that glutathione is markedly elevated in ChRCC (50–100 times higher than matched normal kidney) [36, 37]. ChRCC-derived cells are hypersensitive to ferroptotic cell death induced by genetic and pharmacologic disruption of glutathione homeostasis via inhibition of GPX4 or SLC7A11 [38], leading to tumor cell death in vitro and in vivo. The ChRCC-derived cell line UOK276 is >80 times more sensitive to the SLC7A11 inhibitor imidazole ketone erastin (IKE) [25] than ccRCC-derived 786–0 cells [38], which is remarkable given that ccRCC is highly sensitive to ferroptosis [29, 30].

Fig. 1. FSP1 expression is upregulated in chromophobe renal cell carcinoma and correlates with survival.

Fig. 1

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A Key molecular mechanisms involved in ferroptosis regulation. Ferroptosis is a reactive oxygen species (ROS)-mediated, iron-catalyzed form of cell death characterized by lipid peroxidation and loss of membrane integrity. SLC7A11 imports cystine, which is converted to cysteine and supports the synthesis of glutathione. GPX4 (Glutathione Peroxidase 4) couples the oxidation of glutathione to the reduction of lipid peroxides, preventing ferroptosis. FSP1 (Ferroptosis Suppressor Protein 1) reduces Coenzyme Q10 (CoQ10) and Vitamin K, enabling them to function as lipid peroxide-trapping antioxidants, thereby inhibiting ferroptosis. B The Cancer Genome Atlas (TCGA) data showing the ratio of FSP1 mRNA expression in tumors compared to their corresponding healthy organ. UCEC Uterine Corpus Endometrial Carcinoma, KICH Kidney Chromophobe, LIHC Liver Hepatocellular Carcinoma, LUAD Lung Adenocarcinoma, SKCM Skin Cutaneous Melanoma, CHOL Cholangiocarcinoma, CESC Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma, ESCA Esophageal Carcinoma, STAD Stomach Adenocarcinoma, READ Rectum Adenocarcinoma, KIRC Kidney Renal Clear Cell Carcinoma, KIRP Kidney Renal Papillary Cell Carcinoma, LUSC Lung Squamous Cell Carcinoma, HNSC Head and Neck Squamous Cell Carcinoma, PAAD Pancreatic Adenocarcinoma, BLCA Bladder Urothelial Carcinoma, COAD Colon Adenocarcinoma, THCA Thyroid Carcinoma, PRAD Prostate Adenocarcinoma, PCPG Pheochromocytoma and Paraganglioma, SARC Sarcoma, BRCA Breast Invasive Carcinoma, THYM Thymoma. The three kidney cancers (KICH, KIRC, KIRP) are highlighted in red. C TCGA data showing the expression of FSP1 in ChRCC tumors compared to matched normal kidney. Each dot represents a patient sample. Line represents the median. Statistical significance was determined using the Mann–Whitney U test. ****p < 0.0001. D TCGA data showing the expression of FSP1 in ccRCC tumors compared to matched normal kidney. Each dot represents a patient sample. Line represents the median. Statistical significance was determined using the Mann–Whitney U test. ***p = 0.0001. E TCGA data showing the expression of FSP1 in pRCC tumors compared to matched normal kidney. Line represents the median. Statistical significance was determined using the Mann–Whitney U test. P-value not significant. F Kaplan–Meyer plot of ChRCC TCGA data showing overall patient survival stratified by FSP1 mRNA expression (high vs. low) using the median expression as the cutoff. Hazard Ratio (HR) = 4.4 (95% CI : 0.9–21.4). p = 0.03. G Kaplan–Meyer plot of ccRCC TCGA data showing overall patient survival stratified by a Combined FSP1 and SLC7A11 Ferroptosis Signature (high vs. low) using the median expression as the cutoff. Hazard Ratio (HR) = 1.6 (95% CI : 1.2–2.2). p = 0.004. H Kaplan–Meyer plot of Pan-TCGA TCGA data showing overall patient survival stratified by a Combined FSP1 and SLC7A11 Ferroptosis Signature (high vs. low) using the median expression as the cutoff. Hazard Ratio (HR) = 1.4 (95% CI : 1.3–1.5). p < 0.001.

FSP1 (Ferroptosis Suppressor Protein 1) is a recently described cellular suppressor of ferroptosis [39, 40]. This flavoprotein contains an oxidoreductase domain and acts by reducing co-enzyme Q10 and vitamin K to their active antioxidant form, allowing them to scavenge lipid peroxides [39–42]. Multiple FSP1 inhibitors have been developed, including iFSP1, icFSP1, viFSP1, and FSEN1 [40, 43–46]. icFSP1 and FSEN1 have adequate pharmacokinetics in mice and have shown efficacy in vivo [44, 45]. Importantly, unlike Gpx4 knockout mice [47], which exhibit severe phenotypes and early lethality, Fsp1 knockout mice are viable and display no known defects [41, 48, 49], suggesting a favorable safety profile. The role of FSP1 in ChRCC remains unexplored.

Here, we show that FSP1 is upregulated in ChRCC, and that high levels of FSP1 are associated with worse patient outcomes. We also find that a ferroptosis-related gene signature consisting of FSP1 and SLC7A11 predicts patient survival in both the ccRCC TCGA and the pan-TCGA datasets. Co-targeting of FSP1 and GPX4, or FSP1 and SLC7A11, using genetic knockdown or pharmacologic inhibition, shows synergy and induces near complete cell death in ChRCC-derived cells. We also show that inhibiting FSP1 in vivo leads to decreased ChRCC tumor growth, a first for any cancer. Taken together, these data establish FSP1 as a unique and targetable dependency in ChRCC.

METHODS

Cell culture

UOK276 cells (RRID: CVCL_LC28) were provided by Dr. Marston Linehan at the National Cancer Institute [50]. RCJ-T1/T2/M were provided by Dr. Thai H. Ho [51, 52]. HEK293T (RRID: CVCL_0063), HeLa (RRID: CVCL_0030) and 786-O (RRID: CVCL_1051) cells were purchased from American Type Culture Collection (Manassas, VA). Cells were tested for Mycoplasma contamination monthly using a commercial detection kit (Lonza, Allendale, NJ; Catalog #: LT07–318). All cells were cultured in Dulbecco’s modified Eagle medium (DMEM) containing 4.5 g/L glucose supplemented with 10% fetal bovine serum (FBS), 100 μg/mL penicillin, and 100 μg/mL streptomycin at 37 °C in a humidified incubator in an atmosphere of 5% CO2.

siRNA transfection

siRNA transfections were performed using Lipofectamine RNAiMax (ThermoFisher Scientific, Waltham, MA; 13778150). The following Silencer Select siRNA reagents from ThermoFisher were used: GPX4 (s6111), FSP1 (s39571), and non-targeting control (4390844).

shRNA transfection

Stable knockdown of FSP1 was achieved using shRNA lentiviral transduction. Lentiviral particles carrying shFSP1 (Sigma-Aldrich, Burlington, MA; TRCN0000064426) or non-targeting control shRNA (Sigma-Aldrich; SHC005) were produced in HEK293T cells via co-transfection with psPAX2 and pMD2.G plasmids using Fugene 6 (Promega, Madison, WI; E2691). After 48 h, viral supernatants were collected, filtered, and used to transduce UOK276 cells in the presence of 8 μg/mL polybrene (Sigma-Aldrich; TR-1003–50UL). Stable knockdown clones were selected with 2 μg/mL puromycin (Thermo Fisher Scientific, A1113803) and validated by immunoblotting.

Treatment with icFSP1, FSEN1, RSL3, and IKE

Treatment with icFSP1 (Cayman Chemical, Ann Arbor, MI; 39347), FSEN1 (Cayman Chemical, 38025), RSL3 (Sigma-Aldrich, SML2234), and IKE (Cayman Chemical, 27088) was performed for 48 h, and viability was assessed using crystal violet staining. For co-treatment of siCTRL and siFSP1 cells with icFSP1 and FSEN1, 48 h after transfection with siRNA, cells were plated onto 96-well plates at 1 × 104 cells per well and allowed to adhere overnight. The following day, the media was replaced with fresh media, and cells were treated with icFSP1 or FSEN1 for an additional 48 h. For pharmacologic co-treatment experiments, cells were plated onto 96-well plates at 1 × 104 cells per well and allowed to adhere overnight. The next day, media was removed and replaced with fresh media, followed by treatment with icFSP1, FSEN1, RSL3, and/or IKE for 48 h.

Drug combination synergy analysis

ZIP (Zero Interaction Potency) synergy model was employed using the synergy finder R package. Dose-response matrices were obtained from viability assays conducted in a panel of cell lines. Contour plots were generated using Plot2DrugContour to visualize the ZIP synergy landscapes across all tested dose combinations, with color gradients representing antagonism (blue), additivity (white), and synergy (red).

In vitro viability assays using Crystal Violet staining

Cells were fixed with 10% formalin for 10 min, stained with 0.05% (w/v) crystal violet in distilled water for 20 min, washed three times by submerging the plates in clean tap water and air dried. Crystal violet was solubilized using methanol. The absorbance was measured with a CLARIOstar Plus plate reader at 540 nm (BMG Labtech, Ortenberg, Germany).

Protein extraction and western blot analysis

Cells were washed with ice-cold PBS, scraped, and lysed on ice with 1X RIPA buffer. Lysates were normalized by concentration and resolved on 4–12% Bis-Tris gel. The following antibodies were used at 1:1000 dilution and purchased from Cell Signaling Technology unless otherwise specified: Actin (4970), Vinculin (4650), FSP1 (1:500) (24972), GPX4 (abcam, Waltham, MA; ab125066).

Animal studies

All animal studies were performed in accordance with protocols approved by the Brigham and Woman’s Hospital Institutional Animal Care and Use Committee (Protocol # 2019N000124). Six-week-old female athymic nude mice (Charles River, Wilmington, MA; Strain Code 490) were injected subcutaneously in the flank with 5 million cells after randomization. Each group consisted of 10 mice. The investigator was not blinded afterwards during tumor measurement. Tumor size was measured using an electronic caliper twice per week and calculated using the formula: 0.5 × length × width2. When tumor size reached 1000 mm3, mice were humanely euthanized.

TCGA data analysis

RNA sequencing and clinical data for Chromophobe Renal Cell Carcinoma (ChRCC) and other tumor types were obtained from The Cancer Genome Atlas (TCGA) using the Genomic Data Commons Data Portal (https://portal.gdc.cancer.gov). Normalized RNA-seq expression values (RSEM) were used to assess FSP1 expression levels, pan-cancer expression patterns, and correlation with patient survival. Overall survival analyses were conducted using Kaplan–Meier survival curves, and statistical significance was assessed using log-rank tests. Hazard ratios were estimated using Cox proportional hazards models. Multivariate survival analyses using Cox proportional hazards regression models was performed. Time-to-event data were modeled with overall survival as the outcome, defined by time in months and vital status. Covariates included experimental groups (gene expression and tumor stage).

DepMap analysis

Genetic dependency data were retrieved from the Cancer Dependency Map (DepMap) database (https://depmap.org), specifically from RNAi (Achilles+DRIVE+Marcotte, DEMETER2) and CRISPR (DepMap Public 24Q4+Score, Chronos) datasets.

Cancer target discovery and development (CTD2) analysis

The results published here are partially based upon data generated by the Cancer Target Discovery and Development (CTD2) Network (https://www.cancer.gov/ccg/research/functional-genomics/ctd2) established by the National Cancer Institute’s Center for Cancer Genomics. Pharmacogenomic data were obtained from the Cancer Dependency Map (DepMap) database (https://depmap.org), specifically from the Drug Sensitivity AUC dataset generated by CTD2 Network. Gene expression–drug response correlations were analyzed to assess the role of FSP1 in ferroptosis resistance to Erastin, an inhibitor of SLC7A11.

Statistical analysis

Sample sizes were based on effect sizes observed in pilot studies and prior publications. Homogeneity of variance for every comparison was assessed using GraphPad Prism’s F-test for two-group analyses and the Brown–Forsythe test for ≥3 groups; and appropriate statistical tests were used for every comparison. All analyses were conducted using GraphPad Prism 10 and R (version 4.3.2). Statistical significance was defined as p ≤ 0.05. Survival analyses were performed using the R packages survival (version 3.5–7), survminer (version 0.5.0), and survRM2 (version 1.0–4).

RESULTS

Ferroptosis suppressor protein 1 (FSP1) expression is upregulated in ChRCC and correlates with patient survival

Previous work has established that ChRCC-derived cells are hypersensitive to ferroptosis induction via genetic or pharmacologic inhibition of SLC7A11 or pharmacologic inhibition of GPX4 [38]. However, the overall mechanisms regulating ferroptosis in ChRCC remain poorly understood.

Data from The Cancer Genome Atlas (TCGA) shows that, among the 23 cancer types with available RNAseq data for both neoplastic and matched normal tissue, ChRCC exhibits the 2nd highest level of FSP1 expression relative to matched normal tissue (Fig. 1B). FSP1 is 1.76-fold higher in ChRCC compared to normal kidney (median RSEM = 742 vs 423, p-value < 0.0001) (Fig. 1C) [53]. In comparison, ccRCC, which is also hypersensitive to ferroptosis [26, 29, 30] but less sensitive than ChRCC [38], shows a 1.20-fold upregulation of FSP1 compared to matched normal kidney (median RSEM = 429 vs 358, p-value = 0.0001) (Fig. 1D). In papillary renal cell carcinoma, FSP1 is 1.16-fold upregulated relative to matched normal kidney (median RSEM = 517 vs 446, p-value non-significant) (Fig. 1E).

We next examined how FSP1 mRNA expression levels in tumors influenced overall survival (OS) in ChRCC patients. TCGA patients were stratified into two groups based on FSP1 mRNA expression: the top 50th percentile (high expression) and the bottom 50th percentile (low expression). High FSP1 mRNA expression in tumors correlated with poorer median OS, with a hazard ratio of 4.4 (p = 0.03) (Fig. 1F). Because FSP1 mRNA expression was not predictive of survival in ccRCC, pRCC, or pan-TCGA data, we next asked if a signature of combining both FSP1 and SLC7A11 mRNA could predict patient survival. In ccRCC TCGA patients, we observed that a high ferroptosis resistance signature, defined by elevated FSP1 and SLC7A11 expression, was significantly associated with worse overall survival (HR = 1.6, p = 0.004) (Fig. 1G). Next, we analyzed this combined ferroptosis resistance signature in a pan-cancer setting using TCGA datasets. Across all tumor types, patients with high FSP1 and SLC7A11 expression exhibited significantly shorter OS compared to those with lower expression (HR = 1.4, p < 0.001) (Fig. 1H). We then performed multivariable Cox regression models incorporating tumor stage as covariate. These analyses show that high FSP1/SLC7A11 signature was associated with worse overall survival in the pan-TCGA (Supplementary Fig. S1B). In ChRCC there was a similar trend that did not reach statistical significance (p = 0.11) due to low number of patients (n = 31 per group) (Supplementary Fig. S1A).

Taken together, these results show that FSP1 is upregulated in ChRCC. They also suggest that elevated FSP1 tumor expression is associated with worse ChRCC patient prognosis, pointing towards an important role for FSP1 in ChRCC tumor progression and/or metastasis.

FSP1 expression correlates with resistance to ferroptosis induced by cysteine uptake disruption and inhibition of GPX4

In the Dependency Map (DepMap) RNAi (Achilles+DRIVE+Marcott) dataset, which combines three large genome-wide screens [54–56] and a total of 707 cell lines, we find that FSP1 is the fourth highest gene out of 16,836 whose expression is positively associated with resistance to cell death induced by shRNA inhibition of GPX4, with a Z-score of 1.48 (Fig. 2A). SLC7A11 was ranked 21st, with a Z-score of 1.2.

Fig. 2. High FSP1 mRNA expression correlates with resistance to inhibition of GPX4 and SLC7A11.

Fig. 2

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A Dependency Map (DepMap) data illustrating the correlation between gene expression levels and resistance to GPX4 inhibition by RNA interference (RNAi). The dataset integrates the Achilles, DRIVE, and Marcotte projects. The y-axis represents Z-scores. Each dot represents a specific gene. FSP1 and SLC7A11 are indicated in red. B DepMap data from Project Score illustrating the correlation between gene expression levels and resistance to GPX4 CRISPR-Cas9 inhibition. The y-axis represents Z-scores. Each dot represents a specific gene. FSP1 is highlighted in red. C Cancer Target Discovery and Development (CTD2) data illustrating the correlation between gene expression levels and resistance to the SLC7A11 inhibitor Erastin. The y-axis represents Z-scores. Each dot represents a specific gene. FSP1 and SLC7A11 are highlighted in red.

In the DepMap’s Score Project [57], genome-scale CRISPR–Cas9 screens were performed in 1178 human cancer cell lines across 30 cancer types. We find that FSP1 is the 27th highest gene out of 17,916 whose expression at is positively associated with resistance to cell death induced by CRISPR-Cas9 knockout of GPX4, with a Z-score of 1.26 (Fig. 2B).

Finally, we examined the Cancer Target Discovery and Development (CTD2) dataset [58], which includes 814 cancer cell lines. FSP1 was the 60th highest gene out of the 19,098 the genes whose expression positively correlated with resistance to Erastin, with a Z-score of 2.67. SLC7A11 ranked 25th in this dataset, with a Z-score of 2.90 (Fig. 2C).

Taken together, these data suggest that FSP1 upregulation contributes to resistance to ferroptotic cell death.

Genetic inhibition of FSP1 sensitizes ChRCC-derived cells to ferroptotic cell death when combined with glutathione homeostasis disruption

Given that FSP1 is upregulated in human ChRCC, and that high FSP1 expression is associated with resistance to ferroptotic cell death in the CTD2 and DepMap datasets, we next asked whether knockdown of FSP1 could potentiate induction of ferroptosis when combined with glutathione homeostasis disruption.

GPX4 and FSP1 were inhibited using siRNA in two human ChRCC-derived cell lines (Fig. 3A), UOK276 [50] and RCJ-T2 [51, 52], and cell survival monitored using crystal violet staining. Five days after transfection, siRNA-mediated inhibition of GPX4 or FSP1 individually did not induce substantial cell death, while combined inhibition of GPX4 and FSP1 resulted in almost complete loss of viability. For UOK276 cells, siRNA-mediated knockdown of GPX4 did not affect viability, and knockdown of FSP1 led to a mean viability of 90% that of control siRNA (p = 0.0061). Double knockdown of GPX4 and FSP1 led to 8% mean viability (p < 0.0001) (Fig. 3B). For RCJ-T2 cells, the mean viability for the GPX4 knockdown group was 49% that of control siRNA (p < 0.0001) and the mean viability of FSP1 knockdown group was 91% that of control siRNA (p = 0.0013). Similarly to UOK276 cells, the double knockdown of GPX4 and FSP1 in RCJ-T2 cells led to 13% viability compared to the control siRNA group (p < 0.0001) (Fig. 3B).

Fig. 3. FSP1 inhibition synergizes with disruption of glutathione homeostasis to induce ferroptotic cell death in ChRCC cell lines.

Fig. 3

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A Western blot showing the level of FSP1 and GPX4 in UOK276 and RCJ-T2 cells following siRNA-mediated knockdown 120 h after transfection. Vinculin is a loading control. B Crystal violet viability assay in UOK276 and RCJ-T2 cells with siRNA-mediated knockdown of GPX4 and/or FSP1 (8 biological replicates per condition) at 120 h. Cells were treated with DMSO, necrostatin-1 (2 μM), Z-VAD-FMK (5 μM), or Ferrostatin-1 (1 μM) starting 72 h after transfection. Statistical significance was determined using ordinary one-way ANOVA. **p < 0.01, ****p < 0.0001. C Western blot showing the level of GPX4 in UOK276 and RCJ-T2 cells following knockdown using siRNA at 120 h. Vinculin and Actin are loading controls. D Crystal violet viability assay in UOK276 and RCJ-T2 cells following siRNA-mediated knockdown of GPX4 at 120 h. Cells were treated with DMSO, icFSP1 (10 μM), or FSEN1 (10 μM) starting 72 h after transfection (6 biological replicates per condition for UOK276, 8 biological replicates per condition for RCJ-T2). Statistical significance was determined using two-way ANOVA. ****p < 0.0001.

To confirm that this cell death induced by double knockdown of GPX4 and FSP1 was via ferroptosis, we treated with the ferroptosis inhibitor ferrostatin-1, the necroptosis inhibitor necrostatin-1, or the apoptosis inhibitor Z-Vad-FMK. Cell viability was completely rescued by ferrostatin-1, but not by necrostatin-1 or Z-Vad-FMK (Fig. 3B), consistent with ferroptotic cell death.

Pharmacologic inhibition of FSP1 sensitizes ChRCC-derived cells to ferroptotic cell death when combined with genetic or pharmacologic inhibition of GPX4

Human FSP1 can be pharmacologically inhibited using the small molecules icFSP1 [44] and FSEN1 (Ferroptosis Sensitizer 1) [45]. To determine if pharmacologic inhibition mirrors the effects of genetic inhibition of FSP1, UOK276 cells were treated with these inhibitors 3 days after transduction with GPX4 siRNA. In UOK276 cells, siRNA-mediated GPX4 knockdown alone led to a mean viability of 76% compared to siCTRL group (p < 0.0001), while combination of siGPX4 with 10 μM icFSP1 or FSEN1 for 48 h led to a mean viability of 21% and 29% compared to siCTRL (p < 0.0001), respectively (Fig. 3C, D). Consistent with previous studies which demonstrate in vitro decreases in viability only in the absence of GPX4 [39, 40], treating siCTRL cells with icFSP1 or FSEN1 did not impact viability. In RCJ-T2 cells, siRNA-mediated GPX4 knockdown alone led to a mean viability of 58% compared to siCTRL group (p < 0.0001), while combination of siGPX4 with 10 μM icFSP1 or FSEN1 for 48 h led to a mean viability of 17% and 18% compared to siCTRL (p < 0.0001), respectively (Fig. 3C, D). Treating siCTRL cells with icFSP1 or FSEN1 did not impact viability.

Next, the combination of the GPX4 inhibitor RSL3 [59] and FSEN1 was tested in UOK276 (Fig. 4A, B) and RCJ-T2 cells (Fig. 4C, D) (with the ccRCC-derived cell line 786–0 (Fig. 4E) and cervical carcinoma HeLa (Fig. 4F) cells used as comparisons) using 96 different concentrations (from 0 to 300 nM for RSL3 and from 0 to 20 μM for FSEN1). The combination of the two drugs led to near-complete loss of cell viability. For example, in UOK276 cells, an RSL3 concentration of 26 nM alone and FSEN1 concentration of 20 μM alone did not significantly impact viability compared to DMSO control, respectively. However, the combination of RSL3 and FSEN1 led to 23% viability compared to control (p < 0.0001) (Fig. 4B). In RCJ-T2 cells, an RSL3 concentration of 39 nM alone and FSEN1 concentration of 20 μM alone led to 87% cell viability compared to DMSO control, while combinatorial treatment with RSL3 and FSEN1 led to 10% viability compared to control (p = 0.0125) (Fig. 4D). ChRCC-derived cell lines (Fig. 4A, C) showed increased overall sensitivity to these drug combinations compared to 786–0 cells and HeLa cells (Fig. 4E, F). ZIP (Zero Interaction Potency) analysis also showed synergistic interactions (Supplementary Fig. S2A–D).

Fig. 4. FSP1 inhibition with FSEN1 sensitizes cells to GPX4 inhibition to induce ferroptosis in ChRCC cell lines.

Fig. 4

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A, B Crystal violet viability assay in UOK276 cells after 48 h of treatment with increasing concentrations of RSL3 (0–300 nM) and FSEN1 (0–20 μM). A Matrix showing UOK276 cell viability at different concentrations (3 biological replicates per dose). B Bar graph of cell viability at 26 nM RSL3 and 20 μM FSEN1. Statistical significance was determined using ordinary one-way ANOVA. ****p < 0.0001; DMSO vs. RSL3 (26 nM) + FSEN1 (20 μM) (95% CI: 0.53–1.01). C, D Crystal violet viability assay in RCJ-T2 cells after 48 h of treatment with increasing concentrations of RSL3 (0–300 nM) and FSEN1 (0–20 μM). C Matrix showing RCJ-T2 cell viability at different concentrations (3 biological replicates per dose). D Bar graph of cell viability at 39 nM RSL3 and 20 μM FSEN1. Statistical significance was determined using a Kruskal–Wallis test. *p < 0.0125. Crystal violet viability assay performed in (E) ccRCC-derived 786–0 and (F) HeLa cells after 48 h of treatment with increasing concentrations of RSL3 (0–300 nM) and FSEN1 (0–20 μM) (3 biological replicates per dose).

Pharmacologic inhibition of FSP1 sensitizes ChRCC-derived cells to ferroptotic cell death when combined with pharmacologic disruption of glutathione homeostasis

Given the synergy demonstrated by dual inhibition of FSP1 and GPX4 or FSP1 and SLC7A11, we next validated these results using different combinations of small molecule inhibitors for FSP1 and GPX4. UOK276 and RCJ-T2 cells were treated with the FSP1 inhibitor icFSP1 [44] and the GPX4 inhibitor RSL3 for 48 h (Fig. 5A–D). icFSP1 concentrations ranged from 0 to 20 μM, and RSL3 concentration ranged from 0 to 300 nM. In UOK276 cells, an RSL3 concentration of 26 nM alone led to 76% viability (p < 0.0001), and an icFSP1 concentration of 20 μM alone led to 102% cell viability (p-value non-significant) compared to DMSO control, while their combination led to 0% viability compared to control (p < 0.0001) (Fig. 5B). In RCJ-T2 cells, an RSL3 concentration of 18 nM alone and FSEN1 concentration of 20 μM did not significantly impact cell viability compared to DMSO control, while their combination led to 32% viability compared to control (p = 0.0001). (Fig. 5D). ZIP analysis also showed synergistic interactions (Supplementary Fig. S3A, B).

Fig. 5. FSP1 inhibition with icFSP1 sensitizes cells to GPX4 and SLC7A11 inhibition to induce cell death in ChRCC cell lines.

Fig. 5

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A, B Crystal violet viability assay in UOK276 cells after 48 h of treatment with increasing concentrations of RSL3 (0–300 nM) and icFSP1 (0–20 μM). A Matrix showing UOK276 cell viability at different concentrations (3 biological replicates per dose). B Bar graph of cell viability at 26 nM RSL3 and 20 μM icFSP1. Statistical significance was determined using ordinary one-way ANOVA. ****p < 0.0001; DMSO vs. RSL3 (26 nM) + icFSP1 (20 μM) (95% CI: 0.97–1.02). C, D Crystal violet viability assay in RCJ-T2 cells after 48 h of treatment with increasing concentrations of RSL3 (0–300 nM) and icFSP1 (0–20 μM). C Matrix showing UOK276 cell viability at different concentrations (3 biological replicates per dose). D Bar graph of cell viability at 18 nM RSL3 and 20 μM icFSP1. Statistical significance was determined using ordinary one-way ANOVA. ***p < 0.001, ****p < 0.0001; DMSO vs. RSL3 (18 nM) + icFSP1 (20 μM) (95% CI :0.42–0.93). E, F Crystal violet viability assay in UOK276 cells after 48 h of treatment with increasing concentrations of IKE (0–2000 nM) and icFSP1 (0–20 μM). E Matrix showing UOK276 cell viability at different concentrations (3 biological replicates per dose). F Bar graph of cell viability at 177 nM IKE and 20 μM icFSP1. Statistical significance was determined using ordinary one-way ANOVA. *p < 0.05, ****p < 0.0001; DMSO vs. IKE (177 nM) + icFSP1 (20 μM)(95% CI :0.72–1.11). G, H Crystal violet viability assay in RCJ-T2 cells after 48 h of treatment with increasing concentrations of IKE (0–2000 nM) and icFSP1 (0–20 μM). G Matrix showing RCJ-T2 cell viability at different concentrations (3 biological replicates per dose). H Bar graph of cell viability at 48 nM IKE and 20 μM icFSP1. Statistical significance was determined using ordinary one-way ANOVA. ****p < 0.0001; DMSO vs. IKE (48 nM) + icFSP1 (20 μM)(95% CI : 0.76–1.19).

To determine if pharmacologic inhibition of FSP1 is also effective in the presence of pharmacologic inhibition of SLC7A11, UOK276 and RCJ-T2 cells were treated with the SLC7A11 inhibitor IKE (0–2 μM) and the FSP1 inhibitor icFSP1 (0–20 μM) for 48 h (Fig. 5E–G). In UOK276 cells, at an IKE concentration of 177 nM, mean cell viability was 76% compared to DMSO control. An icFSP1 concentration of 20 μM led to 88% viability compared to control. Together, however, these doses of IKE and icFSP1 led to almost complete cell death (8% viability) compared to DMSO control-treated cells (p < 0.0001) (Fig. 5F). Similarly, in RCJ-T2 cells, at an IKE concentration of 48 nM and an icFSP1 concentration of 20 μM alone did not significantly impact cell viability compared to control. Together, however, these doses of IKE and icFSP1 led to 0% viability compared to DMSO control-treated cells (p < 0.0001) (Fig. 5F). ZIP analysis also showed synergistic interactions (Supplementary Fig. S3C, D).

These results indicate that pharmacologic inhibition of FSP1 potently induces ferroptosis in ChRCC-derived cell lines when combined with disruption of glutathione homeostasis via either GPX4 or SCL7A11 pharmacologic inhibition (Fig. 1A).

FSP1 inhibition alone is sufficient to suppress ChRCC tumor growth in vivo

Finally, to determine the impact of FSP1 inhibition on ChRCC tumor growth, we generated two stable UOK276 clones (#426 and #664) with FSP1 knockdown using shRNA (Fig. 6A). FSP1 knockdown did not significantly affect in vitro cell proliferation, confirming our previous results (Fig. 6B). We then injected these cells into the flank of athymic nude mice to form subcutaneous tumors. Remarkably, and in contrast to the in vitro data, knockdown of FSP1 alone was sufficient to suppress tumor growth. Mean tumor volume at day 60 was 285 mm3 in the shFSP1 #426 group and 247 mm3 in the shFSP1 #664 compared to 943 mm3 in the shCTRL group (p < 0.01) (Fig. 6D). At the time of tumor excision (62 days), mean tumor weight was 114 mg in the shFSP1 #426 group and 68 mg in the shFSP1 #664 compared to 450 mg in the shCTRL group (p < 0.05 and p < 0.001, respectively) (Fig. 6E). These findings confirm that FSP1 plays a critical role in ChRCC tumor growth.

Fig. 6. FSP1 knockdown reduces UOK276 tumor growth in vivo.

Fig. 6

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A Western blot analysis validating FSP1 knockdown in UOK276 cells transduced with shFSP1 #426 and shFSP1 #664 compared to shCTRL. Vinculin serves as a loading control. B Cell proliferation assay showing relative proliferation of UOK276 cells transduced with shCTRL (black), shFSP1 #426 (magenta), or shFSP1 #664 (teal) over time (24, 48, 72, and 96 h). Data are presented as mean ± SD. Statistical significance was determined using ordinary one-way ANOVA. P-value not significant. C Tumor growth curves of UOK276 tumor with shCTRL (black), shFSP1 #426 (magenta), or shFSP1 #664 (teal). Statistical significance was determined using a Kruskal–Wallis test at the final time point. **p < 0.01, ns non-significant. Data are presented as mean ± SEM. D Representative image of excised tumors at the time of sacrifice (62 days), comparing shCTRL, shFSP1 #426, and shFSP1 #664. E Excised tumor weight at the time of sacrifice (day 62). Data are presented as mean ± SD. Statistical significance was determined using a Kruskal–Wallis test. *p < 0.05, ***p < 0.001.

DISCUSSION

Here, we show that FSP1 is highly expressed in ChRCC compared to other cancer types in the TCGA, with ChRCC having the second FSP1 highest level relative to matched normal tissue. In the TCGA ChRCC dataset – but not in ccRCC, pRCC, or pan-TCGA data – higher levels of FSP1 expression correlate with decreased overall survival in ChRCC patients. Using a combined FSP1 and SLC7A11 expression signature, we also show that higher levels of FSP1 and SLC7A11 correlate with decreased patient overall survival in ccRCC and pan-TCGA data. Using empirical data from DepMap and CTD2, we build on previous findings [39, 40] and show that high FSP1 expression correlates with resistance to ferroptosis induced by pharmacologic inhibition of SLC7A11 as well as and genetic inhibition of either GPX4 or SLC7A11. GPX4 or FSP1 knockdown in ChRCC-derived cell lines results in partial cell death. In contrast, dual genetic inhibition of GPX4 and FSP1 in vitro induces near-complete cell death in ChRCC. Inhibitors of other forms of cell death including apoptosis and necroptosis did not rescue this cell death whereas the cell death was rescuable with the ferroptosis inhibitor, ferrostatin-1. Next, we used two small molecule inhibitors of FSP1 (icFSP1 and FSEN1) to demonstrate that pharmacologic inhibition of FSP1 phenocopies the cell death observed with genetic inhibition of FSP1 in the context of GPX4-deficiency. At concentrations where FSP1 and GPX4 or SLC7A11 inhibitors alone do not cause substantial cell death, combinatorial inhibition led to near complete cell death in ChRCC-derived cells. Remarkably, our in vivo data indicates that inhibiting FSP1 alone is sufficient to decrease ChRCC tumor growth, in contrast to the in vitro setting – adding to a growing body of preclinical evidence supporting FSP1 as a viable therapeutic target [60, 61]. Taken together, these findings highlight the potential therapeutic value of targeting FSP1 in ChRCC in preclinical models and thus hold promise for FSP1 as a potential therapeutic target in patients with ChRCC.

Our findings that FSP1 mRNA levels are elevated in ChRCC and negatively correlate with patient overall survival in ChRCC suggest that FSP1 plays an important role in tumor progression. This is further corroborated by data from the DepMap and CTD2 showing that high FSP1 expression correlates with resistance to ferroptotic cell death induced by GPX4 and SLC7A11 inhibition. Previous work has demonstrated that cells encounter high levels of cellular oxidative stress when metastasizing through the blood, leading to ferroptotic cell death [62, 63]. We hypothesize that FSP1 upregulation enhances the survival of circulating ChRCC tumor cells, leading to metastasis.

Two pharmacologic inhibitors of FSP1, icFSP1 and FSEN1, replicate the effects observed with genetic knockdown of FSP1. Their combination with GPX4 or SLC7A11 inhibitors (RSL3 and IKE, respectively) induces robust ferroptotic cell death, at concentrations that do not cause significant cell death alone. The promising pharmacokinetics and safety profiles of these two inhibitors in mice supports their potential as therapeutic agents for ChRCC and other ferroptosis-sensitive cancers, although they have not yet been tested in humans.

Our in vivo data demonstrate that FSP1 inhibition alone is sufficient to suppress tumor growth in ChRCC xenografts. This contrasts with our in vitro findings where dual targeting of FSP1 with GPX4 or SLC7A11 was required for robust ferroptotic cell death. This discrepancy highlights the potential influence of the tumor microenvironment (TME) on ferroptosis susceptibility. We hypothesize that the in vivo TME imposes metabolic and oxidative stress. These environmental pressures may increase the reliance of ChRCC cells on FSP1-mediated lipid peroxide detoxification, priming ChRCC tumors for ferroptotic death upon FSP1 inhibition. While FSP1 knockdown significantly reduced tumor growth in vivo, residual tumor growth was still observed. Our experiments were conducted in immunodeficient mice, limiting our ability to assess the contribution of immune-mediated enhancement of ferroptosis. Given the well-established bidirectional crosstalk between the immune response and ferroptosis [64–68], we hypothesize that targeting FSP1 in immunocompetent hosts may elicit a more robust anti-tumor effect.

The discovery of FSP1’s role in ferroptosis resistance in ChRCC opens avenues for further investigation into its mechanistic interactions with coenzyme Q10 and vitamin K pathways. Initial reports on the role of FSP1 in ferroptosis suppression focused on its reduction of co-enzyme Q10, which scavenges lipid peroxide radicals [39, 40]. Subsequently, FSP1 was also found to play an anti-ferroptotic role by reducing vitamin K [41]. These finding were in line with the known roles of co-enzyme Q10 and vitamin K as radical trapping antioxidants in ChRCC. It remains unclear whether FSP1 acts predominantly on co-enzyme Q10 or vitamin K. Future studies are needed to determine the relative abundance of coenzyme Q10 and vitamin K in the ChRCC cellular pool, and how these distinct substrates affect FSP1’s ferroptosis-suppressing role in ChRCC. While humans can synthesize co-enzyme Q10 through the mevalonate pathway, they cannot synthesize Vitamin K de novo, relying on dietary sources and the gut microbiota instead. Given the striking abundance of mitochondria in ChRCC [69], and the role co-enzyme Q10 plays as part of the electron transport chain, we hypothesize that co-enzyme Q10 plays an important role in FSP1-mediated ferroptosis suppression in ChRCC. Understanding this mechanism could guide therapeutic interventions. For instance, targeting co-enzyme Q10 synthesis might act in a “double-hit fashion” in ChRCC, targeting both mitochondrial metabolic defects and disrupting the ability of the tumor cells to defend against ROS and ferroptosis.

ChRCC exhibits the highest rate of mitochondrial DNA mutations in the TCGA, with 18% of patients harboring mutations in electron transport chain complex I genes [7, 70]. Notably, both ChRCC [7, 38, 69, 70] and Hürthle cell carcinoma [71–74] share a distinct phenotype characterized by abnormal mitochondrial accumulation and heightened susceptibility to ferroptosis. Given that electron transport chain dysfunction is a well-established driver of reactive oxygen species (ROS) generation [75], it is plausible that lipid peroxidation at the mitochondrial membrane itself acts as the trigger for ferroptosis in ChRCC. Multiple sites of lipid peroxidation initiation and propagation have been identified beyond the plasma membrane [23, 76–80], further supporting this possibility. We hypothesize, therefore, that mitochondria-derived ROS drives ferroptosis susceptibility in ChRCC, with ferroptosis initiating at the mitochondrial membrane. Our results also provide a framework for evaluating FSP1 as a therapeutic target in other ferroptosis-sensitive cancers, such as ccRCC [26, 29, 30], Hürthle cell carcinoma [74], lymphoma [25, 26], pancreatic adenocarcinoma [81], and triple negative breast cancer [82].

In summary, FSP1 is upregulated in ChRCC, and higher expression correlates with lower median overall survival and higher resistance to ferroptotic cell death. Inhibiting FSP1 represents a unique and promising therapeutic avenue in ChRCC. Inducing ferroptosis by targeting SLC7A11 or GPX4, and simultaneously blocking FSP1 has synergistic efficacy in ChRCC. In vivo, genetic inhibition of FSP1 alone is sufficient to block ChRCC tumor growth, which has not yet been reported for other cancer types. Thus, ChRCC may have a unique dependency on FSP1 to protect against ferroptotic cell death. This makes FSP1 inhibition a promising therapeutic avenue in ChRCC, compared to other cancer types where GPX4 and other regulators drive key dependencies against ferroptotic cell death.

Supplementary Material

Supplemental information

Supplementary information The online version contains supplementary material available at https://doi.org/10.1038/s41388-025-03562-2.

ACKNOWLEDGEMENTS

This work was supported by the DF/HCC Kidney Cancer SPORE, the Tuttle Family, DOD CDMRP (KC220094P1) to Carmen Priolo, Ludwig Cancer Center at Harvard and NIH NCI 1R01CA282202 (Jessalyn M. Ubellacker), and NIH-R01CA271503, NIH-R01CA224917, and Hollings Cancer Center’s Cancer Center Support Grant P30 CA138313 (Thai H. Ho).

Footnotes

COMPETING INTERESTS

The authors declare no competing interests.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

All methods were carried out in accordance with relevant guidelines and regulations. Animal experiments were approved by the Brigham and Women’s Hospital Institutional Animal Care and Use Committee (Protocol # 2019N000124). This study did not involve human participants, and therefore informed consent was not required.

DATA AVAILABILITY

The datasets analyzed in this study were obtained from publicly available sources, including The Cancer Genome Atlas (TCGA) via the Genomic Data Commons Data Portal (https://portal.gdc.cancer.gov), The Cancer Dependency Map (DepMap) (https://depmap.org), and The Cancer Target Discovery and Development (CTD2) Network, accessed via DepMap. No code was generated in this study.

Further details on data processing are available upon request.

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Associated Data

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

Supplementary Materials

Supplemental information
NIHMS2136551-supplement-Supplemental_information.pdf (556.9KB, pdf)

Data Availability Statement

The datasets analyzed in this study were obtained from publicly available sources, including The Cancer Genome Atlas (TCGA) via the Genomic Data Commons Data Portal (https://portal.gdc.cancer.gov), The Cancer Dependency Map (DepMap) (https://depmap.org), and The Cancer Target Discovery and Development (CTD2) Network, accessed via DepMap. No code was generated in this study.

Further details on data processing are available upon request.

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