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Glycosaminoglycan-driven lipoprotein uptake protects tumours from ferroptosis

Nature volume 644, pages 799–808 (2025)Cite this article

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Abstract

Lipids are essential components of cancer cells due to their structural and signalling roles1. To meet metabolic demands, many cancers take up extracellular lipids2,3,4,5; however, how these lipids contribute to cancer growth and progression remains poorly understood. Here, using functional genetic screens, we identify uptake of lipoproteins—the primary mechanism for lipid transport in circulation—as a key determinant of ferroptosis sensitivity in cancer. Lipoprotein supplementation robustly inhibits ferroptosis across diverse cancer types, primarily through the delivery of α-tocopherol (α-toc), the most abundant form of vitamin E in human lipoproteins. Mechanistically, cancer cells take up lipoproteins through a pathway dependent on sulfated glycosaminoglycans (GAGs) linked to cell-surface proteoglycans. Disrupting GAG biosynthesis or acutely degrading surface GAGs reduces lipoprotein uptake, sensitizes cancer cells to ferroptosis and impairs tumour growth in mice. Notably, human clear cell renal cell carcinomas—a lipid-rich malignancy—exhibit elevated levels of chondroitin sulfate and increased lipoprotein-derived α-toc compared with normal kidney tissue. Together, our study establishes lipoprotein uptake as a critical anti-ferroptotic mechanism in cancer and implicates GAG biosynthesis as a therapeutic target.

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Fig. 1: Lipoprotein delivery of α-toc promotes cancer cell resistance to ferroptosis.
Fig. 2: Cancer cells require GAG biosynthesis for lipoprotein uptake, ferroptosis resistance and tumour growth.
Fig. 3: Cell-surface GAGs linked to proteoglycans drive lipoprotein uptake and ferroptosis resistance in tumours.
Fig. 4: GAG-mediated lipoprotein uptake in human ccRCC tissues and PDXs.

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Data availability

All data and raw gel images are included with the Article and its Supplementary Information. Raw metabolomics files for untargeted lipidomics experiment are available at the NIH Common Fund’s National Metabolomics Data Repository website, the Metabolomics Workbench (https://metabolomicsworkbench.org), where they have been assigned project ID ST003870. The data can be accessed directly through its project (https://doi.org/10.21228/M8254R). Analysed untargeted lipidomics data are provided in Supplementary Table 1. Raw files for MS analysis of GAGs are available at GlycoPost74 under project ID GPST000580. Raw sequencing files for RNA-seq experiments have been deposited at the Gene Expression Omnibus (GSE296260). Processed sequencing data, such as CRISPR screen gene scores and RNA-seq analysis of proteoglycan expression, are provided in Supplementary Tables 3–6. TCGA KIRC data can be accessed online (https://portal.gdc.cancer.gov/projects/TCGA-KIRC). Restrictions are in place in accordance with the IRB for providing patient samples. All other materials are available on request from the corresponding author. Source data are provided with this paper.

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Acknowledgements

We thank the members of the Garcia-Bermudez laboratory for input; S. Morrison for melanoma PDXs; and N. Bardeesy and T. Papagiannakopoulos for providing KP mouse cell lines (HY15549, Tom2); the patients who provided their tissue for research; the staff at the UT Southwestern Metabolic Phenotyping Core, supported by a NIH grant (P30-DK127984), and the UT Southwestern Whole Brain Microscopy Facility for assistance; and P. Azadi and the members of the UGA Analytical Services & Training Core for providing instrumentation to perform the glycosaminoglycan analyses (supported by NIH grant R24GM137782). J.G.-B. is supported by NIH (R00CA248838, DP2GM159178), the Cancer Prevention and Research Institute of Texas (CPRIT RR210059) and the American Cancer Society (ACS RSG-24-1255384-01). R.J.D. is supported by the Howard Hughes Medical Institute Investigator Program and NIH (R35CA220449, P50CA196516). D.C. is funded by an NCI TCB T32 (T32CA291654). S.-C.H. holds a Human Frontiers Postdoctoral Fellowship (HFSP-LT0006/2024-L). D.B. was supported by NIH (F31CA239330). G.S. (R35GM130385) and L.C. (R01CA285336) are supported by the by NIH. D.A.P. is funded by the Natural Sciences and Engineering Council of Canada (RGPIN-2022-05058) and the Canada Foundation for Innovation. I.C. is supported by the NIH (R01CA271496) and DoD (HT9425-23-1-0866). R.J.W. is supported by the NIH (R35GM150736, R21HL167091). K.B. is supported by the NIH/NCI (R01CA273233, U54CA261701) and a Mark Foundation Emerging Leader Award. ccRCC PDX experiments, V.T.-T., B.J. and J.B. were supported by a National Institutes of Health Kidney Specialized Programs of Research Excellence (SPORE) (P50CA196516). We acknowledge the assistance of the University of Texas Southwestern Tissue Management Shared Resource, a shared resource at the Simmons Comprehensive Cancer Center, which is supported in part by the National Cancer Institute under award number P30CA142543.

Author information

Author notes

  1. These authors contributed equally: Dylan Calhoon, Lingjie Sang

Authors and Affiliations

  1. Children’s Medical Center Research Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Dylan Calhoon, Lingjie Sang, Fubo Ji, Divya Bezwada, Sheng-Chieh Hsu, Feng Cai, Nathaniel Kim, Renfei Wu, Anastasia Pimentel, Bailey Brooks, Ana Paulina Serrano, Daniel L. Cassidy, Ling Cai, Ralph J. DeBerardinis & Javier Garcia-Bermudez

  2. Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA

    Amrita Basu & Ryan J. Weiss

  3. Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA

    Konnor La & Kıvanç Birsoy

  4. Quantitative Biomedical Research Center, Peter O’Donnell School of Public Health, University of Texas Southwestern, Dallas, TX, USA

    Ling Cai

  5. Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Vanina Toffessi-Tcheuyap, Brooklyn Jackson & James Brugarolas

  6. Kidney Cancer Program, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Vanina Toffessi-Tcheuyap, Brooklyn Jackson & James Brugarolas

  7. Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Ontario, Canada

    Maryam E. Moussa & Derek A. Pratt

  8. Scripps Center for Metabolomics, The Scripps Research Institute, La Jolla, CA, USA

    Winnie Uritboonthai, Linh Truc Hoang & Gary Siuzdak

  9. Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Meghana Kolli & Ian Corbin

  10. Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Vitaly Margulis

  11. Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, USA

    Ryan J. Weiss

  12. Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Ralph J. DeBerardinis

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  1. Dylan Calhoon
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Contributions

J.G.-B. and D.C. conceived the project and designed the experiments. D.C. and L.S. performed most of the experiments, with assistance from F.J., D.B., S.-C.H., N.K., R.W., A.P. and A.P.S.; R.J.W. and A.B. performed MS analyses of GAGs. K.L. and L.C. conducted computational analyses. V.T.-T., D.L.C., B.J. and J.B. provided PDX fresh tissues. V.M., R.J.D., D.B. and B.B. provided human ccRCC and adjacent kidney specimens. F.C. performed targeted metabolomic analyses. W.U., L.T.H. and G.S. conducted and analysed untargeted lipidomic experiments. M.E.M. and D.A.P. performed liposome autoxidation FENIX assays. M.K. and I.C. assisted with experiments involving the substitution of the nonpolar lipid core of lipoproteins and the loading of lipoproteins with isotope-labelled cholesterol. J.G.-B. wrote the manuscript with input from K.B. All of the authors reviewed the manuscript.

Corresponding author

Correspondence to Javier Garcia-Bermudez.

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Competing interests

K.B. is scientific advisor to Nanocare Pharmaceuticals and Atavistik Bio. R.J.D. is a founder at Atavistik Bio and serves on the scientific advisory boards of Atavistik Bio, Agios Pharmaceuticals, Faeth Therapeutics, General Metabolics and Vida Ventures. The other authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 The lipid fraction of lipoproteins renders GPX4 dispensable in cancer cells.

a, b. Cellular uptake of DiI-labelled LDL (a) or HDL (b) in HeLa cells, quantified by median PE fluorescence intensity (left) and shown in representative histograms (right). Cells treated with DiI-LDL or DiI-HDL (blue) are compared to untreated controls (grey). c. Differential gene scores in HeLa cells cultured in lipoprotein-depleted serum (LPDS) with or without lipoprotein supplementation. Anti-ferroptotic genes and lipid metabolism genes are highlighted. d. Individual sgRNA scores (log2) for GPX4 in the CRISPR screen in HeLa cells under indicated conditions. e, f. Immunoblot analysis of GPX4 in human (e) or mouse (f) isogenic cell lines with sgControl or GPX4/Gpx4 knockout (KO). GAPDH is included as loading control. g. Proliferation (log2 doublings, 5 days) of indicated GPX4-deficient cell lines in vitro either untreated or supplemented with cholesterol (5 μg/mL), LDL (50 μg/mL), HDL (50 μg/mL) or Fer-1 (1 μM). h. Uncropped co-autoxidation of STY-BODIPY (1 μM) and liposomal egg phosphatidylcholine with cholesterol, initiated by DTUN (0.2 mM), and inhibited by 50 μg/mL LDL, HDL, reconstituted HDL with Tristearin (rHDL-TG(18:0)3), or PMC (4 μM). i. Experimental approach to replace the non-polar lipid core of native lipoproteins. Native lipoproteins are first stripped of their non-polar core, and then reconstituted with tristearin (TG(18:0)3). j. Proliferation (log2 doublings, 5 days) of indicated GPX4-deficient cell lines in vitro either untreated or supplemented with cholesterol (5 μg/mL), combined native LDL and HDL (50 μg/mL), combined tristearin-reconstituted LDL and HDL (rLDL+rHDL, 50 μg/mL), or Fer-1 (1 μM). a, b, g, j: Bars represent mean ± s.d.; a, b, g, j: n = 3 biological replicates. Statistics by two-sided unpaired t-tests as indicated or compared to untreated cells (a, b, g, j). For gel source data, see Supplementary Fig. 1.

Source Data

Extended Data Fig. 2 The antioxidant effect of lipoproteins is independent of MUFAs and vitamin K.

a. Proliferation (log2 doublings, 5 days) of indicated GPX4_KO cell lines in vitro either untreated or supplemented with Fer-1 (1 μM), α-toc (10 μM), vitamin K2 (10 μM), or oleic acid (OA, 250 μM). b. Proliferation (log2 doublings, 5 days) of indicated GPX4-deficient cell lines in vitro left untreated or supplemented with α-toc, vitamin D3, or 7-DHC at indicated concentrations. c. Proliferation (log2 doublings, 5 days) of A-498 cells in vitro either treated with ferroptosis inducers ML162 (left) or erastin (right), with or without α-toc, vitamin D3, or 7-DHC at indicated concentrations. d. Cellular lipid oxidation ratio using BODIPY-C11 in A-498 cells untreated or treated with ML162 (125 nM) ± LDL, HDL, α-toc (10 μM), vitamin K2 (10 μM), vitamin D3 (10 μM), CoQ10 (10 μM), OA (250 μM) or Fer-1 (1 μM). e. Cellular lipid oxidation ratio using BODIPY-C11 in A-498 cells ± LDL, HDL, α-toc (10 μM), vitamin K2 (10 μM), OA (250 μM) or Fer-1 (1 μM). f. Cellular lipid oxidation ratio using BODIPY-C11 in Karpas299 cells untreated or treated with ML162 (125 nM) ± LDL, HDL, α-toc (10 μM), vitamin K2 (10 μM), vitamin D3 (10 μM), vitamin A (10 μM), CoQ10 (10 μM), OA (250 μM) or Fer-1 (1 μM). g. Schematic of genes required for cellular utilization of antioxidant lipids carried by lipoproteins. h. Immunoblot of ACSL3 and AIFM2 in the indicated cell lines transduced with a sgControl, sgACSL3 or sgAIFM2. GAPDH is the loading control. i, j. Cellular lipid oxidation ratio using BODIPY-C11 in Karpas299 (left) or A-498 (right) cell lines transduced with sgACSL3 (i) or sgAIFM2 (j) versus sgControl under GPX4 inhibition (ML162: 75 nM for Karpas299, 175 nM for A-498) and HDL supplementation. For all LDL and HDL experiments, 50 ug/mL was used. a-c, Bars represent mean ± s.d.; d-f, i, j, bars represent median; a-f, i, j, n = 3 biological replicates. Statistics by two-sided unpaired t-tests as indicated or compared to untreated cells (a-c, e) or ML162 treated cells (d, f). For gel source data, see Supplementary Fig. 1.

Source Data

Extended Data Fig. 3 α-Toc is the primary lipid driving lipoprotein-mediated antioxidant protection.

a. Rank of significantly altered lipid metabolites (detected in negative mode) in B16 cells treated with HDL versus LDL. b. LC/MS analysis of eight vitamin E isoforms in HeLa cells supplemented with human LDL and HDL (50 µg/mL each), shown as fold change relative to lipoprotein-depleted cells. c. LC/MS analysis of eight vitamin E isoforms in HeLa cells treated with LDL or HDL (50 µg/mL), shown as fold change relative to lipoprotein-depleted cells. d. Relative metabolite peak area for eight vitamin E isoforms in plasma from mice fed a control or Vitamin E-deficient diet. e. Proliferation (log2 doublings, 5 days) of Karpas299 in vitro untreated or treated with ML162 (125 nM) ± 50 ug/mL of human HDL, HDL isolated from mice fed a vitamin E-deficient diet, HDL isolated from mice fed a vitamin E-sufficient diet, or Fer-1 (1 μM). f. Representative tumours of Karpas299, HeLa, and B16 cells implanted subcutaneously in mice fed a control or vitamin E-deficient diet. g. Quantification of circulating plasma triglycerides (TGs, mg/dL) and HDL (mg/dL) in mice fed a control or a vitamin E-deficient diet. h. Schematic depicting generation of Ttpa-liver specific knockout mice to evaluate depletion of α-toc in driving tumour growth in mice. i. Immunoblot of TTPA in the liver of mice injected with liver-specific AAV8-sgControl or AAV8-sgTtpa. Ponceau staining is used as loading control. j. Plasma α-toc levels from Cas9 knock-in mice injected with liver-specific AAV8-sgControl or AAV8-sgTtpa 4 weeks after AAV injection. k. Quantification of 4-HNE H-scores in B16 tumours grown in control or liver-specific Ttpa knockout mice. l. Tumour weights from control and liver-specific Ttpa knockout mice implanted with B16 cells. m. Mass spectrometry analysis of vitamin E isoforms α-tocotrienol and δ-tocotrienol in plasma from mice fed a standard mouse diet or a Vitamin E-sufficient diet. Data is presented as relative metabolite peak area. b-e, g, k, m: Bars represent mean ± s.d.; j, l: Boxes represent median, first/third quartiles; whiskers are range. b, c, e, n = 3 biological replicates; d: n = 4 biological replicates; g: n = 7 biological replicates; j: n = 5 biological replicates; k: n = 14 representative fields; l: n = 10-12 biological replicates; m: n = 3-4 biological replicates. Statistics by: i) two-sided unpaired t-tests as indicated or compared to untreated cells (b, c), control-diet mice (d, g), or to ML162-treated cells (e); or ii) by one-way ANOVA followed by a Kruskal–Wallis nonparametric test (a). For gel source data, see Supplementary Fig. 1.

Source Data

Extended Data Fig. 4 Loss of UGDH sensitizes cancer cells to ferroptosis in vitro.

a. Gene essentiality plot showing genes scores in untreated (x-axis) and ML210-treated (GPX4 inhibition) Karpas299 cells. b. Differential gene scores in Karpas299 cells ML210-treated CRISPR screen. Negative scores represent genes whose loss potentiates ML210 toxicity; positive scores represent genes whose loss provides resistance to ML210. GAG biosynthesis, canonical anti-ferroptotic, and lipid metabolism genes are highlighted. c. Differential gene scores in Karpas299 cells for DiI-LDL uptake CRISPR screen. Negative scores represent genes whose loss reduce cellular DiI-LDL uptake. GAG biosynthesis and canonical lipoprotein uptake genes are highlighted. d. Individual sgRNA scores (log2) for UGDH in untreated and ML210-treated conditions from Karpas299 screen. e. Individual sgRNA scores (log2) for UGDH in high and low DiI-LDL uptake populations from Karpas299 screen. f. Immunoblot of UGDH in the indicated isogenic cell lines transduced with sgUGDH and expressing or not a sgRNA-resistant UGDH cDNA. GAPDH included as a loading control. g. Proliferation (log2 doublings, 5 days) of indicated HeLa (left) or Caki-2 (right) cell lines transduced with sgControl or sgUGDH under the indicated concentrations of ML162. h. Cellular lipid oxidation ratio using BODIPY-C11 in A-498 (left) or Karpas299 (right) UGDH_KO cell lines expressing a sgRNA resistant UGDH cDNA under GPX4 inhibition (ML162: 200 nM for Karpas299, 250 nM for A-498) and Fer-1 supplementation (1 μM). i. Proliferation (log2 doublings, 5 days) of indicated UGDH-deficient cell lines expressing a sgRNA resistant UGDH cDNA or an empty vector under the indicated concentrations of erastin. g, i: Bars represent mean ± s.d.; h: bars represent median; g-i: n = 3 biological replicates. Statistics by two-sided unpaired t-tests compared to untreated empty vector transduced cells (g-i). For gel source data, see Supplementary Fig. 1.

Source Data

Extended Data Fig. 5 UGDH expression is a major determinant of LDL and HDL uptake in cancer cells.

a. Proliferation (log2 doublings, 5 days) of indicated UGDH-deficient cell lines expressing a sgRNA resistant UGDH cDNA or an empty vector under treatment with the indicated concentrations of ferrous ammonium citrate (FAC). b. Immunoblot of TFRC (top) and IRP1 (bottom) in the indicated UGDH_KO cell lines expressing a sgRNA resistant UGDH cDNA or an empty vector treated or not with FAC (0.1 mg/mL). GAPDH included as a loading control. c. Immunoblot of GPX4 (left), SLC7A11 (centre), and AIFM2 (right) in the indicated UGDH_KO cell lines expressing a sgRNA resistant UGDH cDNA or an empty vector. GAPDH included as a loading control. d. Histograms of DiI-LDL uptake in Karpas299 UGDH_KO cells expressing sgRNA-resistant UGDH cDNA or vector control. e, f. Cellular uptake of DiI-labelled LDL (e) or HDL (f) in the indicated Karpas299 (left), A-498 (centre) and HeLa (right) UGDH_KO cells expressing a sgRNA resistant UGDH cDNA or an empty vector, quantified by median PE fluorescence intensity. g. Mass spectrometry analysis of isotope labelled ([3-13C]Cholesterol, left) and unlabelled (right) cholesterol in A-498 UGDH_KO cells expressing a sgRNA resistant UGDH cDNA or an empty vector cultured in the presence of native LDL (grey) or 13C-Cholesterol-labelled LDL for 4 h (dark blue) and 8 h (light blue). Data is presented as fold of the control LDL condition. a, e-g, Bars represent mean ± s.d.; a, e-g, n = 3 biological replicates. Statistics by two-sided unpaired t-tests compared to empty vector transduced cells (a, e-g). For gel source data, see Supplementary Fig. 1.

Source Data

Extended Data Fig. 6 GAG sulfation and xylose synthesis are essential for lipoprotein-mediated ferroptosis resistance in lymphoma cells.

a. Schematic showing that GAG sulfation depends on PAPSS1-derived 3′-phosphoadenylylsulfate and its Golgi import via SLC35B2. b. Individual sgRNA scores (log2) for PAPSS1 in untreated and ML210-treated conditions (left) or in high and low DiI-LDL populations (right) of the Karpas299 CRISPR screens. c. Immunoblot of PAPSS1 in Karpas299 cells transduced with a sgControl or sgPAPSS1. GAPDH included as a loading control. d. Quantification of heparan sulfate (HS) in Karpas299 cells transduced with a sgControl or sgPAPSS1. e. Quantification of total sulfate per glycosaminoglycan disaccharide in Karpas299 cells transduced with a sgControl or sgPAPSS1. f. Proliferation (log2 doublings, 5 days) of Karpas299 cells transduced with a sgControl or sgPAPSS1 under the indicated concentrations of ML162. g. Schematic showing UGDH-driven synthesis of UDP-GlcUA and its conversion by UXS1 into UDP-xylose, the initiating sugar of the GAG linker to proteoglycans. h. Individual sgRNA scores (log2) for UXS1 in the presence and absence of ML210 (left), or in high and low DiI-LDL populations (right) of Karpas299 CRISPR screens. i. Sanger sequencing of UXS1 gene exon 9 in Karpas299 parental cells (WT) or transduced with sgUXS1. j. Quantification of total milligrams of HS per gram of protein in Karpas299 cells transduced with a sgControl or sgUXS1. k. Proliferation (log2 doublings, 5 days) of Karpas299 cells transduced with a sgControl or sgUXS1 under the indicated concentrations of ML162. l. Cellular uptake of DiI-LDL in Karpas299 cells transduced with a sgControl, sgUGDH, sgPAPSS1 or sgUXS1, quantified by median PE fluorescence intensity. d-f, j-l, Bars represent mean ± s.d.; d-f, j-l, n = 3 biological replicates. Statistics by two-sided unpaired t-tests compared to sgControl cells (d-f, j-l). For gel source data, see Supplementary Fig. 1.

Source Data

Extended Data Fig. 7 The anti-ferroptotic effect of UGDH is essential for tumour growth.

a. Representative images of subcutaneous tumours of HeLa UGDH_KO cells expressing a sgRNA resistant UGDH cDNA or an empty vector. b. Tumour weights resulting from implantation of the indicated UGDH_KO cell lines in immunocompromised mice. c. Representative IHC images (20X magnification) of CD45, a pan-immune marker, in tumours resulting from subcutaneous implantation of Karpas299 cells expressing a sgRNA-resistant UGDH cDNA or an empty vector. Scale bars, 100 μm. d. Quantification of CD45 H-scores in tumours described in (c). e. Immunoblot of UGDH in the indicated isogenic cell lines transduced with a doxycycline-inducible shGFP or a shUGDH construct ± doxycycline treatment (doxy, 1 μg/mL) for 24 h. GAPDH included as a loading control. f. Representative images of subcutaneous tumours of Karpas299 cells expressing a doxycycline-inducible shRNA against either GFP (control) or UGDH implanted in mice given sucrose-water with or without doxycycline (doxy, 2 g/L). g. Tumour weights resulting from implantation of Karpas299 cells expressing a doxycycline-inducible shRNA against either GFP (control) or UGDH in immunocompromised mice given sucrose-water with or without doxycycline (doxy, 2 g/L). h. Tumour weights resulting from implantation of the indicated UGDH_KO cell lines expressing a sgRNA-resistant UGDH cDNA treated with daily intraperitoneal injection of vehicle or Lip-1. i. Fold change in tumour weight relative to vehicle-treated empty vector controls for Karpas299 UGDH_KO cells expressing UGDH cDNA or an empty vector in mice treated with vehicle or Lip-1. b, g-i: Boxes represent median, first and third quartiles, and whiskers are range; b, g-i: n = 10 biological replicates. Statistics by two-sided unpaired t-tests compared to empty vector transduced cells (b, d), shGFP-expressing cells (g), or vehicle-treated mice (h, i). For gel source data, see Supplementary Fig. 1.

Source Data

Extended Data Fig. 8 Contribution of GAGs and lipoprotein receptors to lipoprotein uptake and tumour growth.

a. Immunoblot of LDLR, SCARB1, and UGDH in HeLa cells transduced with the indicated sgRNAs. GAPDH included as a loading control. b, c. Fold change in the uptake of DiI-labelled LDL (b) and HDL (c) of the indicated HeLa cells relative to sgControl-transduced cells. d. Immunoblot of LRP8 in 786-O cells transduced with a sgControl or sgLRP8. ACTINB included as a loading control. e. Fold change in the uptake of DiI-LDL (left) and DiI-HDL (right) of the indicated 786-O cells relative to sgControl-transduced cells. f. Proliferation (log2 doublings, 5 days) of the indicated 786-O cells under ML162 treatment. g. Immunoblot of LDLR and SCARB1 in HeLa LDLR_KO (left) and SCARB1_KO (right) cells expressing a sgRNA-resistant LDLR, SCARB1 cDNA or empty vector. ACTINB and vinculin included as loading controls. h. Fold change in the uptake of DiI-LDL and DiI-HDL by the indicated HeLa cells relative to cells expressing an empty vector. i. Proliferation (log2 doublings, 5 days) of LDLR_KO and SCARB1_KO cells expressing the indicated cDNAs or an empty vector (grey) under the indicated concentrations of ML162. j. Representative images of tumours resulting from implantation of HeLa UGDH-KO, LDLR_KO, or SCARB1_KO cells expressing the indicated cDNAs or empty vector. k. Fold change in tumour weight relative to isogenic KO tumours for HeLa UGDH_KO, LDLR_KO, or SCARB1_KO cells expressing the indicated cDNAs or empty vector. b, c, e, f, h, i: Bars represent mean ± s.d.; k: Boxes represent median, first and third quartiles, and whiskers are range; b, c, e, f, h, i: n = 3 biological replicates; k: n = 10 biological replicates. Statistics by two-sided unpaired t-tests compared to sgControl-expressing cells (b, c, e, f) or empty vector transduced cells (h, i, k). For gel source data, see Supplementary Fig. 1.

Source Data

Extended Data Fig. 9 The proteoglycan VCAN modestly increases resistance to ferroptosis and lipoprotein uptake in cancer cells.

a. Schematic of CRISPR screens in 786-O (ccRCC) and Karpas299 (lymphoma) cells transduced with a proteoglycan-focused sgRNA library (55 genes). 786-O cells were subjected to a proliferation-based screen in the presence or absence of the GPX4 inhibitor, ML162 (left). Karpas299 cells were subjected to flow cytometry-based cell sorting for high and low fluorescent populations after DiI-LDL treatment. b. Differential gene scores from the 786-O CRISPR screen under ML162 treatment relative to untreated cells. Negative scores indicate genes whose loss sensitizes cells to ML162. UGDH served as a positive control. c. Differential gene scores in high DiI-LDL uptake versus low uptake CRISPR screen in Karpas299 cells. Negative scores indicate genes whose loss reduces LDL uptake. UGDH served as a positive control. d. Quadrant map showing overlapping hits from both screens; genes essential in both are highlighted, with VCAN emerging as a shared hit. e. Sanger sequencing of VCAN gene exon 9 in Karpas299 parental cells (WT) or transduced with sgVCAN. f. Proliferation (log2 doublings, 5 days) of 786-O transduced with a sgControl or sgVCAN under the indicated concentrations of ML162. g. Proliferation (log2 doublings, 5 days) of Karpas299 cells transduced with a sgControl or sgVCAN under the indicated concentrations of ML162. h. Cellular uptake of DiI-LDL in the indicated Karpas299 cells transduced with a sgControl or sgVCAN, quantified as median PE intensity. i. Tumour weights resulting from implantation of Karpas299 cells transduced with a sgControl or sgVCAN in mice. f-h: Bars represent mean ± s.d.; i: Boxes represent median, first and third quartiles, and whiskers are range. f-h, n = 3 biological replicates; i, n = 10 biological replicates. Statistics by two-sided unpaired t-tests compared to sgControl-expressing cells (f-i).

Source Data

Extended Data Fig. 10 Cell-surface sulfated GAGs promote lipoprotein uptake.

a, b. Total cell surface HS (a) or CS (b) measured in A-498 cells treated or not with heparinases (0.1 U/mL) or chondroitinase (0.1 U/mL). c, d. Fold change in DiI-labelled LDL (c) and HDL (d) uptake of A-498 cells upon treatment with heparinases (0.1 U/mL), chondroitinase (0.1 U/mL), or both (blue), relative to uptake of untreated cells. e. Total cell surface hyaluronic acid (HA) measured in A-498 UGDH_KO cells expressing an UGDH cDNA or empty vector. f. Total cell surface HA (left), HS (middle), and CS (right) measured in A-498 cells treated or not with hyaluronidase (5 U/mL). g. Fold change in DiI-LDL uptake of A-498 cells treated with hyaluronidase (5 U/mL) or combined heparinases/chondroitinase (0.1 U/mL) relative to uptake of untreated cells. h. Representative image of HS immunostaining in A-498 cells treated or not with combined heparinases/chondroitinase (0.1 U/mL). Nuclei stained with DAPI. Scale bars, 20 μm. i. Representative image of Laurdan staining in A-498 cells treated or not with combined heparinases/chondroitinase (0.1 U/mL). Generalized polarization (GP) values represent membrane rigidity (higher value, red) or fluidity (lower value, blue). j. Histogram of GP values from images from experiment in (i). k. Fold change in pHrodo-transferrin uptake in A-498 cells treated or not with combined heparinases/chondroitinase (0.1 U/mL). l. Histogram of DiI-LDL uptake in cell suspension of 786-O tumours resected from mice treated or not with combined heparinases/chondroitinase. m. Uptake of DiI-LDL in the tumour suspension in (l). n, o. Representative IHC images (n) (20x magnification) and H-score quantification (o) of CD45 in Karpass299 tumours injected daily with HBSS or combined enzymes (10 U/mL). Scale bars, 100 μm. a-g, k, m, o: Bars represent mean ± s.d.; a-g, k, m: n = 3 biological replicates; o: n = 25 representative fields. Statistical significance determined by two-sided unpaired t-tests as indicated or compared to untreated cells (a-d, f-g, k, m, o) or empty vector expressing cells (e).

Source Data

Extended Data Fig. 11 Proteoglycan expression in human ccRCCs and the role of the GAG-lipoprotein axis in ferroptosis resistance.

a. Violin plot showing the relative levels of cholesterol in ccRCC patient tissues compared to paired adjacent kidney. b. Violin plot showing the concentration (ng/mL) of α-toc in the plasma of ccRCC patients and healthy donors. c. Violin plot showing the relative levels of total HS per gram of protein in ccRCC patient tissues compared to paired adjacent kidney. d. Heatmap showing expression of individual proteoglycan genes in ccRCC tumours and paired adjacent kidney. Each sample was assigned a proteoglycan score, based on their expression of all proteoglycans. Relevant proteoglycans are highlighted in blue. e. Rank of proteoglycans by fold change in gene expression in ccRCC tumours relative to paired adjacent kidney. Relevant proteoglycans are highlighted in blue. f. Fold change in DiI-LDL uptake by melanoma PDX cell suspensions treated with heparinases and chondroitinase (1 U/mL of both, dark blue) relative to untreated samples (grey). Each dot represents a different PDX. f: Bars represent mean ± s.d.; a: n = 20 biological replicates; b: n = 8 biological replicates; c: n = 18 biological replicates; f, n = 3 different melanoma PDXs. Statistical significance was determined by a two-sided unpaired t-test compared to non-cancer samples (a, b, c), or untreated cells (f).

Source Data

Supplementary information

Supplementary Figs. 1 and 2

The uncropped western blots and the flow cytometry gating strategies.

Reporting Summary

Supplementary Table 1

Rank of significantly changed lipids between HDL-treated and LDL-treated B16 cells.

Supplementary Table 2

List of sgRNA and shRNA sequences.

Supplementary Table 3

Differential gene scores for highly focused genetic screen in HeLa cells.

Supplementary Table 4

Differential gene scores for metabolism-focused genetic screens.

Supplementary Table 5

Differential gene scores for proteoglycan-focused genetic screens.

Supplementary Table 6

Proteoglycan gene expression in ccRCC tumours relative to paired adjacent kidney tissue from RNA-seq analysis.

Supplementary Table 7

Demographic, clinical and pathological details of patients with ccRCC.

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Calhoon, D., Sang, L., Ji, F. et al. Glycosaminoglycan-driven lipoprotein uptake protects tumours from ferroptosis. Nature 644, 799–808 (2025). https://doi.org/10.1038/s41586-025-09162-0

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