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Iron-mediated ferroptosis impairs CAR-T cell function and antitumor efficacy

Nature Cancer (2026) Cite this article

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Abstract

Long-term persistence of chimeric antigen receptor (CAR)-T cells is essential for durable therapeutic efficacy but the mechanisms underlying CAR-T cell dysfunction remain unclear. On the basis of integrated analyses of clinical samples from participants with multiple myeloma and acute lymphoblastic leukemia treated with CAR-T cells, we show that rapid expansion of CAR-T cells after infusion is followed by a ‘diminution’ phase characterized by ferroptosis-associated features and elevated serum iron levels. In preclinical cancer models in female mice and ex vivo culture systems, excess intracellular iron impaired CAR-T cell function. Mechanistically, iron promoted ferroptosis by increasing mitochondrial reactive oxygen species and lipid peroxidation, in part through acyl-CoA synthetase long-chain family member 4 (ACSL4)-associated lipid remodeling. Targeting ferroptosis, particularly through genetic ablation of ACSL4 in CAR-T cells, substantially enhanced antitumor efficacy. Together, these findings identify iron-driven ferroptosis as a determinant of CAR-T cell dysfunction and a targetable barrier to durable CAR-T efficacy.

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Fig. 1: CAR-T cells in individuals exhibit ferroptotic features.
The alternative text for this image may have been generated using AI.
Fig. 2: Fer-1 enhances the in vivo antitumor efficacy of CAR-T cells.
The alternative text for this image may have been generated using AI.
Fig. 3: Serum-derived iron overload induces ferroptosis in CAR-T cells in vivo.
The alternative text for this image may have been generated using AI.
Fig. 4: Ferroptotic CAR-T cells induced by iron exhibit impaired function.
The alternative text for this image may have been generated using AI.
Fig. 5: Intracellular iron overload induces mitochondrial damage and ROS accumulation.
The alternative text for this image may have been generated using AI.
Fig. 6: Iron activates ACSL4 in CAR-T cells to trigger ferroptosis.
The alternative text for this image may have been generated using AI.
Fig. 7: Targeting ACSL4 rescues iron-induced impairments in CAR-T cells in both hematologic and solid tumor models.
The alternative text for this image may have been generated using AI.

Data availability

The scRNA-seq raw data from four participants with MM were deposited to the Genome Sequence Archive for Human (GSA-Human) of the China National Center for Bioinformation under accession code HRA002359 and are publicly available. The scRNA-seq data for CAR-T, CAR-T + FC and CAR-T + FC + Fer-1 treatments were deposited to GSA-Human under accession code HRA005275 and are available under controlled access because of participant privacy and human genetic resource regulations.

The complete quantitative matrix and annotation output for the untargeted serum metabolomics analysis are provided in Supplementary Table 5. The complete quantitative matrix and annotation output for the targeted lipidomics and targeted lipid peroxidation metabolite analyses are provided in Supplementary Table 7. The remaining data are available within the article and its Supplementary Information and/or from the corresponding authors upon request. Source data are provided with this paper.

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Acknowledgements

We thank Tissue Bank Biotechnology for metabolomic sequencing support and Y. Huang (Core Facilities, School of Medicine, Zhejiang University) and S. Liu (Shanghai Universal Biotech) for assistance with cell sorting and data analysis.

Funding

This work was supported by the National Natural Science Foundation of China (82130003, 92268117, 82161138028 and 82000149 to P.X.Q.; 82270234 to Y.X.H.; 82270235 to M.M.Z.; 82341205 and 82341206 to D.R.W.; 82200250 to D.L.K.; 82570196 and 82200162 to L.Y.), the Zhejiang Province ‘Jianbing Lingyan + X’ Science and Technology Program (2021C03010 to H.H.; 2025C02075 to M.M.Z.), the National Key Research and Development Program of China (2024YFA1107102 to P.X.Q.), the Natural Science Foundation of Zhejiang Province (LR25H080001 to D.R.W.), the Key R&D Program of Zhejiang (2024SSYS0024, 2024SSYS0023 and 2024SSYS0025 to P.X.Q.), the Department of Science and Technology of Zhejiang Province (2023R01012 to P.X.Q.) and the Fundamental Research Funds for the Central Universities (226-2024-00007 to P.X.Q.). P.X.Q. gratefully acknowledges support from the K.C. Wong Education Foundation. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Delin Kong, Tingting Yang, Mengyu Zhao, Lin Yang, Ruirui Jing.

Authors and Affiliations

  1. Bone Marrow Transplantation Center, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China

    Delin Kong, Tingting Yang, Lin Yang, Youqin Feng, Zhouning Lin, Shi Han, Cong Wei, Rongrong Chen, Qiqi Zhang, Jiazhen Cui, Tianning Gu, Xiujian Wang, Xia Li, Ye Meng, Mingming Zhang, Linghui Zhou, Houli Zhao, Ruimin Hong, Simao Huang, Mi Shao, Xiaohui Si, Yingli Han, Xiangjun Zeng, Dawei Huo, Meng Zhang, Kejia Hu, Zenan Cen, Haiqiong Zheng, Guoqing Wei, Dongrui Wang, Yongxian Hu & He Huang

  2. Liangzhu Laboratory, Zhejiang University, Hangzhou, China

    Delin Kong, Tingting Yang, Lin Yang, Youqin Feng, Shaohui Shi, Zhouning Lin, Shi Han, Cong Wei, Rongrong Chen, Qiqi Zhang, Jiazhen Cui, Tianning Gu, Xiujian Wang, Xia Li, Ye Meng, Mingming Zhang, Linghui Zhou, Houli Zhao, Ruimin Hong, Simao Huang, Mi Shao, Xiaohui Si, Yingli Han, Xiangjun Zeng, Dawei Huo, Meng Zhang, Kejia Hu, Zenan Cen, Haiqiong Zheng, Guoqing Wei, Dongrui Wang, Yongxian Hu, Pengxu Qian & He Huang

  3. Institute of Hematology, Zhejiang University, Hangzhou, China

    Delin Kong, Tingting Yang, Lin Yang, Youqin Feng, Zhouning Lin, Shi Han, Cong Wei, Rongrong Chen, Qiqi Zhang, Jiazhen Cui, Tianning Gu, Xiujian Wang, Xia Li, Ye Meng, Mingming Zhang, Linghui Zhou, Houli Zhao, Ruimin Hong, Simao Huang, Mi Shao, Xiaohui Si, Yingli Han, Shufen Wang, Xiangjun Zeng, Dawei Huo, Meng Zhang, Kejia Hu, Zenan Cen, Haiqiong Zheng, Guoqing Wei, Dongrui Wang, Yongxian Hu, Pengxu Qian & He Huang

  4. Zhejiang Province Engineering Research Center for Stem Cell and Immunity Therapy, Hangzhou, China

    Delin Kong, Tingting Yang, Lin Yang, Youqin Feng, Zhouning Lin, Shi Han, Cong Wei, Rongrong Chen, Qiqi Zhang, Jiazhen Cui, Tianning Gu, Xiujian Wang, Xia Li, Ye Meng, Mingming Zhang, Linghui Zhou, Houli Zhao, Ruimin Hong, Simao Huang, Mi Shao, Xiaohui Si, Yingli Han, Xiangjun Zeng, Dawei Huo, Meng Zhang, Kejia Hu, Zenan Cen, Haiqiong Zheng, Guoqing Wei, Dongrui Wang, Yongxian Hu, Pengxu Qian & He Huang

  5. Department of Hematology, Zhejiang Cancer Hospital, Hangzhou, China

    Mengyu Zhao

  6. Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China

    Mengyu Zhao

  7. Department of Hematology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China

    Ruirui Jing, Xiaolin Yuan & Shufen Wang

  8. Center for Stem Cell and Regenerative Medicine and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China

    Pengxu Qian

Authors
  1. Delin Kong
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  2. Tingting Yang
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Contributions

D.L.K., P.X.Q. and H.H. designed the overall study and wrote the manuscript. Y.X.H. and D.R.W. designed the clinical detection protocol. D.L.K., T.T.Y., M.Y.Z., L.Y., R.R.J., Y.Q.F., R.R.C., Z.N.L., S.H., C.W. and Q.Q.Z. performed the experiments. S.H.S., Y.M., J.Z.C., T.N.G. and X.J.W. performed the bioinformatic analysis. L.H.Z., H.L.Z., R.M.H., S.M.H., M.S., X.H.S., Y.L.H., S.F.W., K.J.H., X.J.Z., Z.N.C., D.W.H., M.Z., X.L. and H.Q.Z. discussed the results and manuscript. X.L.Y., M.M.Z. and G.Q.W. assisted with the study. D.R.W., P.X.Q. and H.H. supervised the study. All authors approved the manuscript for submission and publication.

Corresponding authors

Correspondence to Dongrui Wang, Yongxian Hu, Pengxu Qian or He Huang.

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Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Cancer thanks Paul Maciocia and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Multi-omics analyses of CAR-T cells from clinical participants.

a, Related to Fig. 1a: Quality control of scRNA-seq. Violin plots showing mitochondrial gene percentage, UMI counts (nCount_RNA), and gene counts (nFeature_RNA) after filtering (n = 4 multiple myeloma participants). Violin plots show the distribution of the data; embedded box plots show the median (centre line), 25th and 75th percentiles (box bounds), and minimum to maximum values (whiskers). b and c, t-SNE plots of total PBMCs (b) and separately clustered CAR-T vs. non-CAR-T cells (c) from four participants at two time points during CAR-T treatment. d-f, t-SNE feature plots showing the expression of the indicated marker genes used for cell-type annotation (n = 4 participants). g, Proportions of CAR-T and non-CAR-T subsets at specified time points during treatment (n = 4 participants). h, Gene set variation analysis (GSVA) comparing CAR-T cells from the expansion and diminution phases (n = 4 participants). i, j, Flow cytometric analysis of lipid ROS. i, Comparison between CAR-T cells and matched T cells from multiple myeloma (MM, n = 8) and acute lymphoblastic leukemia (ALL, n = 7) participant samples at the diminution phase. j, Time-course analysis of lipid ROS levels in T cells from MM and ALL participants during CAR-T therapy (n = 8). Data in j are presented as mean ± s.e.m. P values were calculated by paired two-tailed Student’s t-test (i) or one-way ANOVA with Tukey’s multiple-comparisons test (j). Box plots (i) show the median (centre line), 25th and 75th percentiles (box bounds), and minimum to maximum values (whiskers).

Source data

Extended Data Fig. 2 Ferroptosis inhibitors promote CAR-T cell viability and functionality in vitro.

a-c, Expansion curves of CAR-T cells treated with ferrostatin-1 (Fer-1) (a), liproxstatin-1 (Lip-1) (b), or UAMC-3203 (UAM) (c) at indicated concentrations. Inhibitors were added every 48 h from day 6 post-CAR-T cell generation. d-i, Flow cytometric analyses of Tcm (central memory T cells, CD45RO+, CD62L+) and Tem (effector memory T cells, CD45RO+, CD62L−) subsets in CAR-T cells pretreated with Fer-1 (d, g), Lip-1 (e, h) or UAM (f, i) for 72 h starting at day 6. j-l, Flow cytometric analysis of T-cell differentiation based on CD45RA and CD27 expression in CAR-T cells (j) or in CD4+ and CD8+ subsets (k, l) pretreated with the indicated inhibitors. m, Expression of exhaustion markers (PD-1, TIM3 and LAG3) in CAR-T cells treated with different doses of Fer-1, Lip-1, and UAM: group A (1 μM, 100 nM, 5 nM), B (2 μM, 200 nM, 10 nM), and C (4 μM, 400 nM, 20 nM). n-p, Tumor growth in mice not treated with CAR-T cells. Bioluminescence kinetics (n), representative bioluminescence images (o) and Kaplan-Meier survival curves (p) of luciferase-expressing Nalm6 tumor-bearing mice treated with vehicle or the indicated inhibitors by intraperitoneal injection (n = 5 mice per group). Images were acquired at the indicated time points from the same animals. Data in a-c, g-i and k-m are shown as mean ± s.d. of 3 technical replicates from one representative donor. The experiment was repeated using one additional independent donor, with similar trends observed for panels a-m; the related quantification is available in the Source Data. Panels d-f and j show representative flow cytometry plots from one representative donor. No statistical analysis was performed for a-c, g-i and k-m. P values were calculated by two-way ANOVA with Dunnett’s multiple-comparisons test (n), log-rank (Mantel-Cox) test (p).

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Extended Data Fig. 3 Physiological levels of extracellular iron induce ferroptosis in CAR-T cells in vitro.

a, scRNA-seq analysis of iron-homeostasis gene expression in CD4+ and CD8+ CAR-T cells during the expansion and diminution phases (n = 4 participants). Color scale, mean expression; circle size, percentage of expressing cells. b, Volcano plot of differential serum metabolites between the two phases (threshold: log2FC > 1, adjusted P < 0.1; two-tailed paired Student’s t-test, n = 47). c, Iron concentrations in culture media, serum and ferric citrate (FC)-supplemented media measured by colorimetric assay. d, Intracellular iron in CAR-T cells treated with increasing FC doses. e,f, SYTOX Green-based Incucyte assay of cell-death kinetics after 72 h FC treatment; representative images (e) and fluorescence curves (f). Curves in f were generated from 6 technical replicate wells per group, with 9 fields acquired per well, from one representative donor. g, Lipid ROS in CAR-T cells treated with increasing FC doses. h, CAR-T expansion under varying FC concentrations. i,j, Differential sensitivity of early functional (pre-day 9) versus late dysfunctional (post-day 12) CAR-T cells to FC, assessed by viability (i, CCK-8) and lipid ROS (j). k, Schematic of inhibitor-based assessment of regulated cell death under serum-comparable iron using Z-VAD (apoptosis), NSA (necroptosis), VX765 (pyroptosis) and 3-MA (autophagy). l, Viability of FC-treated CAR-T cells with or without the indicated inhibitors. m-p, Fer-1 reduced lipid ROS (m) and MDA (n), restored redox balance (o), and rescued mitochondrial morphology (p), supporting ferroptosis under serum-level iron conditions. Scale bars, 5 μm and 2 μm (enlarged). q, r, Quantification of mitochondrial area (q) and mitochondrial length (Feret’s diameter; r) from transmission electron microscopy. Six independent electron micrographs were analyzed per group, and 25 mitochondria were quantified per group. Dots represent individual mitochondria. Statistical analyses were based on independent electron micrographs. s-u, Related to Fig. 3f: bioluminescence imaging (s), tumor growth (t) and survival (u) of tumor-bearing mice fed the indicated iron-modulating diets without CAR-T treatment (n = 5). v-y, Iron levels in serum (v) and organs (w-y) after 10 weeks on the indicated diets (n = 3 mice per group). Quantification data in c, d, g-j and l-o are shown as mean ± s.d. of 3 technical replicates from one representative donor. The experiment was repeated using one additional independent donor, with similar trends observed for panels c, d, g-j, l-o; the related quantification is available in the Source Data. Panels d, g and m show representative flow cytometry plots from one representative donor. No statistical analysis was performed for c, d, f, g-j and l-o. P values were calculated by one-way ANOVA with Tukey’s multiple-comparisons test (q,r,v-y), two-way ANOVA with Tukey’s multiple-comparisons test (t), or log-rank (Mantel-Cox) test (u).

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Extended Data Fig. 4 scRNA-seq of iron-treated CAR-T cells.

a-c, Violin plots displaying distributions of UMI counts (nCount_RNA, a), gene counts (nFeature_RNA, b), and the percentage of mitochondrial gene content (percent_mt, c) after quality control filtering. d, t-SNE plot of all single cells from the three indicated CAR-T treatment groups; the numbers of cells analyzed in each condition are indicated. e, Dot plot showing average expression levels and expression frequency (Pct.exp) of selected marker genes across eight identified clusters; color intensity reflects expression abundance. f, Differential gene expression analysis across all eight clusters comparing CAR-T + FC with CAR-T. Red and green dots indicate adjusted P values < 0.01 and > 0.01, respectively. g, Cytotoxicity scores visualized on t-SNE plots of CAR-T, CAR-T + FC and CAR-T + FC+Fer-1 cells. h and i, Gene expression profiles of iron transport/storage-related genes (h) and ferroptosis-related genes (i) across different clusters in CAR-T and CAR-T + FC groups. The numbers of cells contributing to each cluster are indicated. One sample was analyzed per condition. Violin plots show the distribution of the data; embedded box plots show the median (centre line), 25th and 75th percentiles (box bounds), and minimum to maximum values (whiskers) (a-c, h-i).

Extended Data Fig. 5 Knockdown of Iron-transporter genes impairs CAR-T cell viability.

a-d, Flow cytometry analyses of the proportion of CAR+ shRNA+ T cells at 8 days post-lentiviral infection. Plots are from one representative donor, with 3 technical replicate cultures per condition. e, Cell viability of CAR-T cells after knockdown of the indicated iron-homeostasis genes, assessed by CCK-8 assay. Data are shown as mean ± s.d. from 5 replicate cultures derived from one representative donor. No statistical analysis was performed. CAR+shRNA+ T cells were FACS-sorted prior to the assay. The experiment was repeated using one additional independent donor, with similar trends observed; the related quantification is available in the Source Data. f, Immunoblot analysis of knockdown efficiency for the indicated iron-homeostasis genes in CAR-T cells. Representative immunoblots from 3 independent experiments are shown. g, Related to Fig. 5c: GSEA of selected pathways comparing CAR-T cells treated with FC to untreated controls. One sample was analyzed per condition. h, Related to Fig. 5c: gene set enrichment analysis (GSEA) of selected pathways comparing CAR-T + FC+Fer-1 with CAR-T + FC. One sample was analyzed per condition.

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Extended Data Fig. 6 Mitochondrial damage impairs CAR-T fitness and function following iron exposure.

a and b, Immunofluorescence staining (a) and quantification (b) of mitochondria in early-stage/naive (day 9) and late-stage/exhausted (day 15) CAR-T cells. Dots in b represent individual cells quantified from one representative donor (n = 15 cells per condition). Scale bar, 2 μm. c and d, Flow cytometric analysis of mitochondrial membrane potential using MitoTracker (c) and TMRE (d) dyes in CAR-T cells from MM participants at the indicated time points (n = 3 participants). e and f, GSVA of upregulated (e) and downregulated (f) pathways based on scRNA-seq of CAR-T cells treated with FC compared to untreated controls (one sample was analyzed per condition). g and h, Bubble plots showing expression of genes associated with mitochondrial damage (g) and with ROS and glutathione metabolism (h). Bubble size indicates mean expression; bubble color indicates log2FC relative to CAR-T; one sample was analyzed per condition. i and j, Flow cytometric analysis of mitochondrial ROS (mitoSOX) (i) and total ROS (j) levels in Day 9 CAR-T cells with 48-hour iron treatment. k-n, OCR assays showing variations in total mitochondrial respiration (k), spare respiratory capacity (l), proton leak (m), and ATP production (n) in Day 9 CAR-T cells treated with FC for 24 h. o-s, CAR-T cells pretreated with the ROS scavengers GSH and NAC for 48 h were assessed for rescue of iron-induced ROS accumulation (o), exhaustion (p-r), cell loss (s), differentiation (t), and impaired cytotoxicity (u). v-w, Assessment of lysosomal content (v) and pH (w) in CAR-T cells in response to FC treatment by flow cytometry. Quantification data in j, k and l-w are shown as mean ± s.d. of 3 technical replicates from one representative donor. The experiment was repeated using one additional independent donor, with similar trends observed for panels i, j, o-w; the related quantification is available in the Source Data. Panels v and w show representative flow cytometry plots from one representative donor. No statistical analysis was performed for b, j, k and l-w. P values were calculated by paired two-tailed t test (c, d).

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Extended Data Fig. 7 Formation of peroxidizable lipids by ACSL4 in iron-treated CAR-T cells contributes to functional impairment.

a, Heatmap showing differentially expressed PUFA-derived lipid peroxidation products, including HETEs, HODEs and HDoHEs, across the indicated groups, determined by targeted metabolomics (n = 6 independently prepared samples per condition from one representative donor). b-h, Flow cytometric analyses of CAR-T activation markers CD25 (b and d), CD69 (c and e) and exhaustion markers (f-h) in ACSL4-knockout and control CAR-T cells with or without FC treatment. i-k, Serum cytokine levels in CRS mouse models treated with control CAR-T or ACSL4-KO CAR-T cells, measured by Luminex-based multiplex assay (n = 10 mice per group). Data in d, e are shown as mean ± s.d. of 3 technical replicates from one representative donor. The experiment was repeated using one additional independent donor, with similar trends observed for panels d, e; the related quantification is available in the Source Data. Panels b, c and f-h show representative flow cytometry plots from one representative donor. No statistical analysis was performed for d, e. Data in i-k are presented as mean ± s.e.m.; P values were calculated by unpaired two-tailed Student’s t-test.

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Extended Data Fig. 8 Representative flow cytometry gating strategies used in this study.

a, Gating strategy for analysis of CAR-T cell differentiation subsets and PD-1 expression in participant peripheral blood samples. Lymphocytes were gated, followed by singlets, CD3+ T cells and CAR-T cells; differentiation subsets were then defined on the basis of CD45RO and CD62L expression, and PD-1 was assessed. Applicable to Fig. 1b and Fig. 4a–c. b, Gating strategy for analysis of lipid ROS and labile iron in CAR-T cells from participant peripheral blood samples. Lymphocytes, singlets, CD3+ T cells and CAR-T cells were sequentially gated, followed by quantification of lipid ROS and labile iron signals. Applicable to Fig. 1h–k and Extended Data Fig. 1i, j. c, Gating strategy for functional analysis of CAR-T cells in mouse bone marrow and spleen samples; the same strategy was also used for in vitro CAR-T functional assays. After gating lymphocytes, singlets and live cells, CAR-T cells were analysed for differentiation status, lipid ROS, labile iron and functional or exhaustion-associated markers, including CD25, CD69, LAG3, PD-1 and TIM3. Applicable to Fig. 2i–r, Fig. 3j–o, Extended Data Fig. 3d, g, j, m, Extended Data Fig. 7b–h, Fig. 7d, Fig. 6d, e, k–o, and Fig. 4d, e, i, j, l, n, o. d, Multicolour flow cytometry gating strategy used primarily to assess phenotypic and functional changes in cultured CAR-T cells in vitro. Lymphocytes, singlets and CAR-T cells were sequentially gated, followed by CD4/CD8 subset separation, differentiation subset analysis based on CD45RO and CD62L, and detection of exhaustion markers including PD-1, LAG3 and TIM3. Applicable to Extended Data Fig. 2d–m, Extended Data Fig. 6p–u, and Fig. 4f–h, q–s, y.

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Kong, D., Yang, T., Zhao, M. et al. Iron-mediated ferroptosis impairs CAR-T cell function and antitumor efficacy. Nat Cancer (2026). https://doi.org/10.1038/s43018-026-01187-2

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