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Squalene accumulation in cholesterol auxotrophic lymphomas prevents oxidative cell death

Abstract

Cholesterol is essential for cells to grow and proliferate. Normal mammalian cells meet their need for cholesterol through its uptake or de novo synthesis1, but the extent to which cancer cells rely on each of these pathways remains poorly understood. Here, using a competitive proliferation assay on a pooled collection of DNA-barcoded cell lines, we identify a subset of cancer cells that is auxotrophic for cholesterol and thus highly dependent on its uptake. Through metabolic gene expression analysis, we pinpoint the loss of squalene monooxygenase expression as a cause of cholesterol auxotrophy, particularly in ALK+ anaplastic large cell lymphoma (ALCL) cell lines and primary tumours. Squalene monooxygenase catalyses the oxidation of squalene to 2,3-oxidosqualene in the cholesterol synthesis pathway and its loss results in accumulation of the upstream metabolite squalene, which is normally undetectable. In ALK+ ALCLs, squalene alters the cellular lipid profile and protects cancer cells from ferroptotic cell death, providing a growth advantage under conditions of oxidative stress and in tumour xenografts. Finally, a CRISPR-based genetic screen identified cholesterol uptake by the low-density lipoprotein receptor as essential for the growth of ALCL cells in culture and as patient-derived xenografts. This work reveals that the cholesterol auxotrophy of ALCLs is a targetable liability and, more broadly, that systematic approaches can be used to identify nutrient dependencies unique to individual cancer types.

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Fig. 1: Identification of cholesterol auxotrophic cancer cell lines using a barcode-based competition assay.
Fig. 2: ALK+ ALCLs are auxotrophic for cholesterol owing to a lack of SQLE expression.
Fig. 3: In ALK+ ALCLs LDLR is upregulated and is a potential therapeutic target.
Fig. 4: Squalene accumulation rewires membrane lipid composition and protects ALCLs from ferroptosis.

Data availability

Source data for barcoding experiment in Fig. 1 are provided as Supplementary Table 1. Gene correlation comparing RNA-sequencing data of ALK and ALK+ patients (Fig. 2i) is included in Supplementary Table 2. Gene scores of CRISPR screens in Fig. 3 and Extended Data Fig. 4 are provided as Supplementary Table 4. Clinical data of the PDXs used in Fig. 3f and Extended Data Fig. 6c are provided as Supplementary Table 5. Source Data for Figs. 14 and Extended Data Figs. 110 are available with the online version of the paper. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

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Acknowledgements

We thank all members of the Birsoy laboratory for helpful suggestions. We also thank R. Sordella for DD-Cas9 lentiviral plasmid, and R. Milne and J. Ersching for LDLR hybridoma cells. This research was supported by an EMBO long-term fellowship (EMBO ALTF 887-2016) to J.G.-B.; and by grants from the NIH (R01 CA103866 and R37 AI47389) and Department of Defense (W81XWH-07-0448) to D.M.S. AIRC Special Program in Clinical Molecular Oncology (10007), SCOR LLS and the Sandra and Edward Meyer Cancer Center PDTX Shared Resource supported G.I. D.M.S is an investigator of the HHMI and ACS Research Professor. K.B. was supported by K22 (1K22CA193660), DP2 (DP2 CA228042-01), Irma-Hirschl Trust Scholarship, Chapman-Perelman MMRF grant, AACR NextGen Grant, and is a Pew-Stewart, Searle, Sidney Kimmel and Basil O’Connor Scholar.

Reviewer information

Nature thanks M. Conrad and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Affiliations

Authors

Contributions

K.B. and J.G.-B. conceived the project and designed the experiments. J.G.-B. performed most of the experiments with help from L.B. and R.G. L.B. performed CRISPR screens. L.B. and B.T. assisted with tumour xenograft experiments. E.C.B. and B.Y. performed cell competition experiments. K.L. performed computational analysis. Y.S. and W.M. designed SRS chemical imaging and generated images. G.I. and D.F. provided primary and patient-derived tumour models and performed immunohistochemistry. S.H.C., C.L. and E.F. performed lipidomics. K.B. and J.G.-B. wrote the manuscript with edits from D.M.S.

Corresponding author

Correspondence to Kıvanç Birsoy.

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The authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Extracellular cholesterol dependence of cancer cell lines.

a, Representative bright-field micrographs of U-937 cells cultured with the indicated concentrations of cholesterol and LDL. b, Relative fold change in cell number of indicated cell lines cultured for five days with LPDS in the presence or absence of free cholesterol relative to LDL-replete serum. c, Relative fold change in cell number of indicated cell lines grown for five days under LPDS with or without free cholesterol (1, 5 or 10 μg ml−1) relative to LDL-replete serum. d, Fold change in cell viability of cholesterol auxotrophic cancer cell lines grown for five days with LPDS in the presence or absence of cholesterol or oleic acid (OA), relative to LPDS supplemented with free cholesterol. e, Reported alterations in copy number or driver mutations in oncogenic EGFR/Ras and PI3K pathways of cancer cell lines used in the DNA barcode-based competition assay. f, Heat map showing mRNA expression levels of cholesterol metabolism genes in LDL-dependent and -independent cancer cell lines. Colour bar indicates scale (log2 transformed). g, SQLE and HSD17B7 mRNA levels in indicated cell lines relative to cholesterol prototroph cell line NCI-H524. mRNA levels were measured using a real-time PCR assay. RPL0 is used as a control. h, Immunoblotting of SQLE in SNU-1 cell lines transduced with a control vector or an SQLE cDNA. Actin is included as a loading control. i. Schematic depicting squalene synthesis from acetate. In bd and g, bars represent mean ± s.d. For bd, n = 3 biologically independent samples. For g, n = 2 biologically independent samples. Statistical significance was measured by two-tailed unpaired t-test.

Source Data

Extended Data Fig. 2 Promoter hypermethylation of the SQLE gene and accumulation of squalene in lipid droplets of ALK+ ALCLs.

a, Raman spectra of squalene (blue dashed), cholesterol (red dashed) and lipid droplets in Karpas299 parental cell (blue solid), and lipid droplets in Karpas299 cell expressing SQLE cDNA (red solid). Lipid droplets were identified in bright field and targeted in the confocal Raman microspectrometer. Arrows indicate squalene-specific Raman peak. b, Representative bright-field image, SRS image obtained at cell lipid background (1,372 cm−1) and fluorescence of Nile Red staining (for lipid droplets) in Karpas299 cells. c, Heat map showing the DNA methylation ratio for the indicated genomic region containing SQLE promoter for indicated cancer cell lines. Chromosomal position range and strand is indicated. Colour bar indicates scale. d, SQLE promoter methylation ratio of control (grey) and SQLE-deficient (blue) cancer cell lines. The boxes represent the median and the first and third quartiles, and the whiskers represent the minimum and maximum data points still within 1.5 of the interquartile range (n = 67 independent genomic positions per sample). e, Fold change in SQLE mRNA expression levels of indicated cell lines after treatment with decitabine (500 nM for 4 days) or 5-azacytidine (5-Aza, 1 μM for 6 days), relative to untreated cells (mean ± s.d., n = 3 biologically independent samples).

Source Data

Extended Data Fig. 3 Lack of SQLE expression in primary ALK+ ALCLs.

a, List of most-upregulated and -downregulated genes from differential expression analysis of primary ALK+ primary samples compared to ALK samples. The student t-test statistic of each gene is calculated and used as a ranking metric (n = 17 biologically independent ALK samples, 5 biologically independent ALK+ samples). b, Fold change in SQLE mRNA expression levels of primary ALK+ ALCLs relative to primary ALK ALCLs, using actin as a control. The boxes represent the median and the first and third quartiles, and the whiskers represent the minimum and maximum of all data points. Statistical significance was determined by two-tailed unpaired t-test. c, Immunoblotting of SQLE and ALK in indicated PDX and cell line models. Actin was used as the loading control. PTCL, peripheral T cell lymphoma. d, Immunohistochemical staining of SQLE in ALK+ and ALK ALCL primary tumour samples. e, Immunohistochemical staining of SQLE in Karpas299 xenograft tumours transduced with a control or SQLE cDNA. Representative images are shown. f, Immunoblotting of SQLE of indicated cell lines (top). Relative fold change in cell viability of the indicated ALK+ (Karpas299) and ALK cell lines (TLBR-1 and ALK PDX cell line) grown for five days under LPDS with or without free cholesterol relative to LDL-replete serum (bottom) (mean ± s.d., n = 3 biologically independent samples). g, Immunoblotting of STAT3, phospho-STAT3 and SQLE in indicated cell lines after 72-h treatment with crizotinib (200 nM). Actin was used as a loading control. h, Immunoblotting of STAT3 and phospho-STAT3 3 days after transduction of Ba/F3 with a dead kinase version of the NPM–ALK fusion (NPM-ALK DK) or with oncogenic NPM-ALK cDNA. i, SQLE mRNA levels of Ba/F3 and ALK ALCL cell lines two or seven days after transduction with NPM-ALK DK or NPM-ALK, relative to levels in NPM–ALK dead kinase. mRNA levels were quantified with a real-time PCR assay using β-actin as a control (mean ± s.d., n = 3–4 biologically independent samples). In b, the boxes represent the median, and the first and third quartiles, and the whiskers represent the minimum and maximum of all data points. In f and h, bars represent mean ± s.d. For f and h, n = 3 biologically independent samples. Statistical significance was determined by two-tailed unpaired t-test.

Source Data

Extended Data Fig. 4 LDLR is an essential gene for the growth of ALK+ ALCLs.

a, Squalene and lanosterol abundance of Karpas299 and DEL cell lines in the absence or presence of SQLE cDNA or after incubation for 24 h with an SQLEi  (1 μM). b, Gene essentiality scores for control or SQLE-expressing DEL cell line. Pearson correlation coefficients are indicated. Red dot denotes LDLR. c, Gene essentiality scores for cholesterol-prototroph HEL cell lines in the presence or absence of an SQLEi (1 μM). Pearson correlation coefficients are indicated. Red dot denotes LDLR. d, LDLR guide scores of the indicated cell lines in the presence or absence of SQLE inhibitor. e, Immunoblots for LDLR and SQLE in control and SQLE-cDNA-expressing Karpas299 cells infected with sgAAVS1 or sgLDLR virus in the presence or absence of Shield-1 (250 nM; left). Relative fold change in cell viability of indicated cancer cell lines grown in the absence and presence of Shield-1 for 5 days (right). f, Immunoblotting of LDLR in ALK+ ALCL lines transduced with an inducible sgLDLR vector in the presence or absence of Shield-1 (250 nM). Actin is used as a loading control. g, Relative fold change in cell viability of control or SQLE-expressing DEL cell lines transduced with sgAAVS1 or sgLDLR after five days of growth. h, Gene essentiality scores for untreated or cholesterol-supplemented Karpas299 cell line. Red dot denotes LDLR. i, LDLR guide scores in Karpas299 cell lines expressing a control vector or SQLE cDNA in the presence or absence of cholesterol supplementation. j, Coomassie blue staining of control IgG and LDLR monoclonal antibodies used in proliferation assays. k, Relative fold change in cell viability of indicated cancer cell lines grown for five days in the presence of the indicated amounts of IgG or a monoclonal antibody against LDLR. l, Relative fold change in cell viability of DEL cell lines transduced with a control vector or an SQLE cDNA grown for five days in the presence of the indicated amounts of IgG or an anti-LDLR monoclonal antibody compared to cells grown in the absence of both. In a, d, e, g, i, k and l bars represent mean ± s.d. For a, e, g, i, and k, n = 3 biologically independent samples. For d and i, n = 5 independent LDLR-targeting sgRNAs. Statistical significance was determined by two-tailed unpaired t-test.

Source Data

Extended Data Fig. 5 Upregulation of the LDL-cholesterol uptake pathway in ALK+ ALCLs.

a, Dil-LDL uptake in the indicated cell lines. Results were normalized to protein levels (mean ± s.d., n = 2 biologically independent samples). b, mRNA expression levels of LDLR (log) in cell lines from CCLE database compared to that of ALK+ ALCL lines (mean ± s.d., n = 1,010 independent cell lines for CCLE collection, 5 independent cell lines for ALCL). c, Expression levels of Niemann-Pick C1 protein (NPC1) mRNA (log) in cell lines from CCLE database compared to that of ALK+ ALCL lines (mean ± s.d., n = 1,010 independent cell lines for CCLE collection, 5 independent cell lines for ALCL). d, Immunoblot of LDLR in the indicated primary PDXs (top). Immunoblotting of NPC1 in control and ALK+ ALCL cell lines (bottom). Actin is included as a loading control. PTCL, peripheral T cell lymphoma. e, Immunoblotting of SREBP-2 (non-cleaved and cleaved forms) in cytoplasmic and nuclear fractions of indicated cell lines expressing a vector or an SQLE cDNA. The cells were incubated for 24 h in medium containing either FBS (−) or LPDS (+). GAPDH and histone H3 were used as cytoplasmic and nuclear loading controls respectively.

Source Data

Extended Data Fig. 6 Squalene accumulation leads to resistance of SQLE-null cells to ferroptosis inducers.

a, Immunoblots of FDFT1 and SQLE in the indicated Karpas299 cell lines. Actin was used as the loading control. b, SRS imaging of squalene for indicated Karpas299 cells. Grey image shows cellular background (1,372cm−1), squalene image (pseudo-coloured yellow hot, 1,386 cm−1; left). SRS spectra integrating intensity from lipid droplet with Raman peak of squalene (1,386 cm−1; right) (mean ± s.d., n = 3 biologically independent samples). Error bar represent standard deviation from multiple lipid droplets in at least three cells. c, sgRNA competition assay using a pool of five control (sgControl) and five FDFT1-targeting (sgFDFT1) sgRNAs in indicated PDXs. Transduced cells were injected subcutaneously to NSG mice to generate tumours. Subsequent to four weeks of growth, genomic DNA was collected to measure sgRNA abundance by deep sequencing. Average guide scores of tumours were calculated and graphed. d, Relative fold change in cell viability of indicated Karpas299 lines treated with or without ML162 (20 nM, top) or RSL3 (30 nM, bottom) in the presence or absence of an SQLE inhibitor (1 μM) for 5 days. e, Fold change in cell viability relative to untreated cells of indicated Karpas299 lines treated with or without ML162 (120 nM) for 2 days (top). Representative bright-field micrographs of indicated Karpas299 cells after two days of indicated treatments (bottom). f, Immunoblotting of FDFT1 in the indicated DEL and SUP-M2 cell lines. Actin is used as a loading control (left). Relative fold change in cell viability of control, FDFT1-null and rescued DEL and SUP-M2 cell lines in the presence and absence of ML162 (20 nM) after 5 days. g, Immunoblotting of FDFT1 in the indicated HEC1B and SNU-1 cell lines. Actin is used as a loading control (top). Squalene abundance of the indicated cell lines (middle). Relative fold change in cell viability of control and FDFT1-null HEC1B and SNU-1 cell lines in the presence and absence of ML162 (200 nM for HEC1B lines, 1 μM for SNU-1 cell lines) and grown for 5 days. In cg, bars represent mean ± s.d. For c, n = 5 independent sgRNAs targeting a control region or LDLR gene. For dg, n = 3 biologically independent samples. Statistical significance was determined by two-tailed unpaired t-test.

Source Data

Extended Data Fig. 7 Blocking squalene accumulation sensitizes ALCLs to a GPX4 inhibitor (ML162) and erastin.

a, Mevalonate pathway in mammalian cells and fates of the side reactions. Reactions catalysed by HMGCR, COQ2, FDFT1 and SQLE, and chemical inhibitors of these enzymes, are indicated. b, Relative abundance of squalene and coenzyme Q10 in Karpas299 treated for 24 h with atorvastatin (1 μM), 4-nitrobenzoate (4-NB, 1 mM) or zaragozic acid (ZA, 20 μM) to untreated. c, Relative fold change in cell viability of Karpas299 cells treated with erastin (1 μM), atorvastatin (1 μM), 4-nitrobenzoate (4-NB, 1 mM), zaragozic acid (ZA, 20 μM) or a combination of two of them after 5 days to untreated. d, Relative fold change in cell viability compared to untreated cells of Karpas299 cells treated with ML162 (25 nM), atorvastatin (1 μM), 4-nitrobenzoate (4-NB, 1 mM), zaragozic acid (ZA, 20 μM) or a combination of 2 of them after 5 days. In bd bars represent mean ± s.d. For bd, n = 3 biologically independent samples.

Source Data

Extended Data Fig. 8 Loss of SQLE decreases sensitivity of cancer cell lines to ferroptosis inducers.

a, Immunoblotting of SQLE in the indicated cell lines transduced with a vector or sgSQLE. Actin is used as a loading control. b, Squalene abundance in the indicated cell lines. c, Relative fold change in cell viability of control and sgSQLE-expressing cell lines in the presence and absence of ML162 (500 nM for Jurkat lines, 200 nM for RPMI 8226 and SU-DHL-8 cell lines) grown for 5 days. d, Relative fold change in cell viability of Karpas299 parental cells supplemented with the indicated concentrations of exogenous squalene to untreated cells. e, Relative fold change in cell viability of Karpas299 parental or FDFT1 null cells expressing a vector, SQLE cDNA or FDFT1 cDNA treated with or without ML162, squalene or both, to untreated cells. In be bars represent mean ± s.d. For be, n = 3 biologically independent samples. Statistical significance was determined by two-tailed unpaired t-test.

Source Data

Extended Data Fig. 9 Inhibition of PUFA synthesis prevents ferroptotic cell death in ALCLs.

a, Immunoblotting of ACSL4 in the indicated Karpas299 cells. Actin is used as a loading control (top). Relative fold change in cell viability of indicated Karpas299 cell lines in the presence or absence of ML162 (20 nM) and Fer-1 (1 μM) for 5 days (bottom) (mean ± s.d., n = 3 biologically independent samples). Statistics, two-tailed unpaired t-test. b, Correlation of mRNA levels of ACSL4 with SQLE (left) and ALK (right) in primary ALCLs dataset (n = 22 biologically independent samples). c, Correlation of mRNA levels of ACSL4 with SQLE in CCLE dataset (n = 935 independent cell lines). d, Lipid peroxidation assessed by flow cytometry measuring C11-BODIPY fluorescence of indicated Karpas299 cell lines after an 18-h treatment with ML162 (200 nM). Representative data from one of three experiments are shown. e, Lipid peroxidation assessed by flow cytometry measuring C11-BODIPY fluorescence of indicated Karpas299 cell lines after an 18-h treatment in the presence and absence of ML162 (200 nM) and Fer-1 (1 μM).

Source Data

Extended Data Fig. 10 Squalene accumulation rewires membrane phospholipid composition.

a, Unbiased lipidomic analysis of Karpas299 FDFT1-null cell line relative to its rescued isogenic counterpart expressing FDFT1 cDNA. Fold change (log2) in metabolite abundance was graphed and membrane phospholipids containing saturated and polyunsaturated fatty acids are indicated. b, Heat map showing fold changes (log2) in indicated phosphatidylcholines (PCs) and phosphatidylethanolamines (PEs) of Karpas299 cells cultured for 24 h in the absence or presence of ZA (zaragozic acid, 20 μM) and ML162 (200 nM) relative to untreated cells. Triplicates of each condition are shown. Colour bar indicates log2 change in abundance. c, Heat map showing fold changes (log2) in indicated phosphatidylethanolamines (PEs) of indicated Karpas299 cell lines cultured for 24 h with Fer-1 (1 μM) and ML162 (200 nM). Triplicates of each condition are shown. Colour bar indicates log2 change in abundance. d, Relative fold change in cell viability of HEL, KMS-26 and Jurkat cell lines expressing vector or an sgRNA targeting FDFT1 in the presence or absence of ML162 (20 nM) for 5 days. e, Immunoblotting of GPX4 in indicated Karpas299 cell lines expressing a vector, SQLE cDNA, FDFT1 cDNA or an sgRNA targeting FDFT1. Actin is used as a loading control. f, Coenzyme Q10 abundance of indicated Karpas299 cell lines relative to parental cells expressing a control vector. In d and f, bars represent mean ± s.d. For d and f, n = 3 biologically independent samples. Statistical significance was determined by two-tailed unpaired t-test.

Source Data

Supplementary information

Supplementary Figure 1

This file contains the uncropped gels and the gating strategy used in FACS experiments

Reporting Summary

Supplementary Table 1

Barcode sequences and their abundance in lipoprotein-depleted competition assay

Supplementary Table 2

Genes ranked by differential expression analysis of primary ALK+ ALCL primary samples compared to ALK samples

Supplementary Table 3

sgRNA sequences of the highly focused human metabolism CRISPR library

Supplementary Table 4

CRISPR screen gene scores in cholesterol auxotroph and prototroph cell lines

Supplementary Table 5

Patient Derived Xenografts clinical information

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Garcia-Bermudez, J., Baudrier, L., Bayraktar, E.C. et al. Squalene accumulation in cholesterol auxotrophic lymphomas prevents oxidative cell death. Nature 567, 118–122 (2019). https://doi.org/10.1038/s41586-019-0945-5

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