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LXRα activation and Raf inhibition trigger lethal lipotoxicity in liver cancer

Nature Cancer volume 2, pages 201–217 (2021)Cite this article

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Abstract

The success of molecular therapies targeting specific metabolic pathways in cancer is often limited by the plasticity and adaptability of metabolic networks. Here we show that pharmacologically induced lipotoxicity represents a promising therapeutic strategy for the treatment of hepatocellular carcinoma (HCC). LXRα-induced liponeogenesis and Raf-1 inhibition are synthetic lethal in HCC owing to a toxic accumulation of saturated fatty acids. Raf-1 was found to bind and activate SCD1, and conformation-changing DFG-out Raf inhibitors could disrupt this interaction, thereby blocking fatty acid desaturation and inducing lethal lipotoxicity. Studies in genetically engineered and nonalcoholic steatohepatitis-induced HCC mouse models and xenograft models of human HCC revealed that therapies comprising LXR agonists and Raf inhibitors were well tolerated and capable of overcoming therapy resistance in HCC. Conceptually, our study suggests pharmacologically induced lipotoxicity as a new mode for metabolic targeting of liver cancer.

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Fig. 1: Sorafenib activates LXR signaling in HCC.
Fig. 2: Sorafenib-mediated induction of LXRα signaling contributes to its therapeutic efficacy.
Fig. 3: Raf counteracts the therapeutic efficacy of LXR activation in HCC.
Fig. 4: Therapy comprising LXRα activation and Raf inhibition is effective in therapy-resistant mouse HCCs in vivo.
Fig. 5: LXRα activation and Raf inhibition result in a toxic accumulation of saturated fatty acids in liver cancer cells.
Fig. 6: Raf-1 influences intracellular lipid composition in HCC cells by stabilizing SCD1 protein levels.
Fig. 7: DFG-out Raf inhibitors disrupt the Raf-1–SCD1 protein complex in HCC.
Fig. 8: Pharmacologically induced lipotoxicity is effective in human HCC.

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Data availability

The data that support the findings of this study are available from the corresponding authors upon reasonable request. For the e:Med-HCC-1 clinical study (NCT02372162), all data can be requested for research purposes from Michael Bitzer (michael.bitzer@uni-tuebingen.de). Deep sequencing (RNA-seq) data that support the findings of this study have been deposited in the Gene Expression Omnibus (GEO) under accession codes GSE162322 and GSE162324. For the molecular modeling studies, all data are deposited in the Zenodo repository (https://doi.org/10.5281/zenodo.3253878, under accession code 3253878) and can also be requested for research purposes from Antti Poso (antti.poso@uef.fi). Associated raw data for Figs. 1b,c and 2b and Extended Data Fig. 3d are provided as supplementary tables. The gating strategy for flow cytometry is depicted in Extended Data Figs. 2a,b and 6f–h. Source data are provided with this paper.

References

  1. Schulze, A. & Harris, A. L. How cancer metabolism is tuned for proliferation and vulnerable to disruption. Nature 491, 364–373 (2012).

    Article  CAS  PubMed  Google Scholar 

  2. Luengo, A., Gui, D. Y. & Vander Heiden, M. G. Targeting metabolism for cancer therapy. Cell Chem. Biol. 24, 1161–1180 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Vriens, K. et al. Evidence for an alternative fatty acid desaturation pathway increasing cancer plasticity. Nature 566, 403–406 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Llovet, J. M. et al. Hepatocellular carcinoma. Nat. Rev. Dis. Primers 2, 16018 (2016).

    Article  PubMed  Google Scholar 

  5. Rosmorduc, O. & Fartoux, L. HCC and NASH: how strong is the clinical demonstration? Clin. Res. Hepatol. Gastroenterol. 36, 202–208 (2012).

    Article  CAS  PubMed  Google Scholar 

  6. Anstee, Q. M., Reeves, H. L., Kotsiliti, E., Govaere, O. & Heikenwalder, M. From NASH to HCC: current concepts and future challenges. Nat. Rev. Gastroenterol. Hepatol. 16, 411–428 (2019).

    Article  PubMed  Google Scholar 

  7. Yau, T., Chan, P., Epstein, R. & Poon, R. T. Evolution of systemic therapy of advanced hepatocellular carcinoma. World J. Gastroenterol. 14, 6437–6441 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Llovet, J. M., Montal, R., Sia, D. & Finn, R. S. Molecular therapies and precision medicine for hepatocellular carcinoma. Nat. Rev. Clin. Oncol. 15, 599–616 (2018).

    Article  PubMed  Google Scholar 

  9. Raoul, J. L. et al. Systemic therapy for intermediate and advanced hepatocellular carcinoma: sorafenib and beyond. Cancer Treat. Rev. 68, 16–24 (2018).

    Article  CAS  PubMed  Google Scholar 

  10. Finn, R. S. et al. Atezolizumab plus bevacizumab in unresectable hepatocellular carcinoma. N. Engl. J. Med. 382, 1894–1905 (2020).

    Article  CAS  PubMed  Google Scholar 

  11. Finn, R. S. et al. Pembrolizumab as second-line therapy in patients with advanced hepatocellular carcinoma in KEYNOTE-240: a randomized, double-blind, phase III trial. J. Clin. Oncol. 38, 193–202 (2020).

    Article  CAS  PubMed  Google Scholar 

  12. Rudalska, R. et al. In vivo RNAi screening identifies a mechanism of sorafenib resistance in liver cancer. Nat. Med. 20, 1138–1146 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Dauch, D. et al. A MYC−Aurora kinase A protein complex represents an actionable drug target in p53-altered liver cancer. Nat. Med. 22, 744–753 (2016).

    Article  CAS  PubMed  Google Scholar 

  14. Kang, T. W. et al. Senescence surveillance of pre-malignant hepatocytes limits liver cancer development. Nature 479, 547–551 (2011).

  15. Seehawer, M. et al. Necroptosis microenvironment directs lineage commitment in liver cancer. Nature 562, 69–75 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Peet, D. J., Janowski, B. A. & Mangelsdorf, D. J. The LXRs: a new class of oxysterol receptors. Curr. Opin. Genet. Dev. 8, 571–575 (1998).

    Article  CAS  PubMed  Google Scholar 

  17. Hong, C. & Tontonoz, P. Liver X receptors in lipid metabolism: opportunities for drug discovery. Nat. Rev. Drug Discov. 13, 433–444 (2014).

    Article  CAS  PubMed  Google Scholar 

  18. Schultz, J. R. et al. Role of LXRs in control of lipogenesis. Genes Dev. 14, 2831–2838 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Peet, D. J. et al. Cholesterol and bile acid metabolism are impaired in mice lacking the nuclear oxysterol receptor LXRα. Cell 93, 693–704 (1998).

    Article  CAS  PubMed  Google Scholar 

  20. Pencheva, N., Buss, C. G., Posada, J., Merghoub, T. & Tavazoie, S. F. Broad-spectrum therapeutic suppression of metastatic melanoma through nuclear hormone receptor activation. Cell 156, 986–1001 (2014).

    Article  CAS  PubMed  Google Scholar 

  21. Janowski, B. A. et al. Structural requirements of ligands for the oxysterol liver X receptors LXRα and LXRβ. Proc. Natl Acad. Sci. USA 96, 266–271 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Liu, L. et al. Sorafenib blocks the RAF/MEK/ERK pathway, inhibits tumor angiogenesis, and induces tumor cell apoptosis in hepatocellular carcinoma model PLC/PRF/5. Cancer Res. 66, 11851–11858 (2006).

    Article  CAS  PubMed  Google Scholar 

  23. Wilhelm, S. M. et al. BAY 43-9006 exhibits broad spectrum oral antitumor activity and targets the RAF/MEK/ERK pathway and receptor tyrosine kinases involved in tumor progression and angiogenesis. Cancer Res. 64, 7099–7109 (2004).

    Article  CAS  PubMed  Google Scholar 

  24. Griffett, K. et al. The LXR inverse agonist SR9238 suppresses fibrosis in a model of non-alcoholic steatohepatitis. Mol. Metab. 4, 353–357 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Xue, W. et al. CRISPR-mediated direct mutation of cancer genes in the mouse liver. Nature 514, 380–384 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Terasaka, N. et al. T-0901317, a synthetic liver X receptor ligand, inhibits development of atherosclerosis in LDL receptor-deficient mice. FEBS Lett. 536, 6–11 (2003).

    Article  CAS  PubMed  Google Scholar 

  27. Waizenegger, I. C. et al. A novel RAF kinase inhibitor with DFG-out-binding mode: high efficacy in BRAF-mutant tumor xenograft models in the absence of normal tissue hyperproliferation. Mol. Cancer Ther. 15, 354 (2016).

    Article  CAS  PubMed  Google Scholar 

  28. Faivre, S., Demetri, G., Sargent, W. & Raymond, E. Molecular basis for sunitinib efficacy and future clinical development. Nat. Rev. Drug Discov. 6, 734–745 (2007).

    Article  CAS  PubMed  Google Scholar 

  29. Villa, G. R. et al. An LXR–cholesterol axis creates a metabolic co-dependency for brain cancers. Cancer Cell 30, 683–693 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Chen, K. F. et al. Activation of phosphatidylinositol 3-kinase/Akt signaling pathway mediates acquired resistance to sorafenib in hepatocellular carcinoma cells. J. Pharmacol. Exp. Ther. 337, 155–161 (2011).

    Article  CAS  PubMed  Google Scholar 

  31. Porstmann, T. et al. SREBP activity is regulated by mTORC1 and contributes to Akt-dependent cell growth. Cell Metab. 8, 224–236 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Kay, H. Y. et al. Nrf2 inhibits LXRα-dependent hepatic lipogenesis by competing with FXR for acetylase binding. Antioxid. Redox Signal. 15, 2135–2146 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Holohan, C., Van, S. S., Longley, D. B. & Johnston, P. G. Cancer drug resistance: an evolving paradigm. Nat. Rev. Cancer 13, 714–726 (2013).

    Article  CAS  PubMed  Google Scholar 

  34. Bovenga, F., Sabba, C. & Moschetta, A. Uncoupling nuclear receptor LXR and cholesterol metabolism in cancer. Cell Metab. 21, 517–526 (2015).

    Article  CAS  PubMed  Google Scholar 

  35. Harriman, G. et al. Acetyl-CoA carboxylase inhibition by ND-630 reduces hepatic steatosis, improves insulin sensitivity, and modulates dyslipidemia in rats. Proc. Natl Acad. Sci. USA 113, E1796–E1805 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Pommier, A. J. et al. Liver X receptor activation downregulates AKT survival signaling in lipid rafts and induces apoptosis of prostate cancer cells. Oncogene 29, 2712–2723 (2010).

    Article  CAS  PubMed  Google Scholar 

  37. Nguyen, T. T. T. et al. Activation of LXR receptors and inhibition of TRAP1 causes synthetic lethality in solid tumors. Cancers https://doi.org/10.3390/cancers11060788 (2019).

  38. Wang, D., Wei, Y. & Pagliassotti, M. J. Saturated fatty acids promote endoplasmic reticulum stress and liver injury in rats with hepatic steatosis. Endocrinology 147, 943–951 (2006).

    Article  CAS  PubMed  Google Scholar 

  39. Ricchi, M. et al. Differential effect of oleic and palmitic acid on lipid accumulation and apoptosis in cultured hepatocytes. J. Gastroenterol. Hepatol. 24, 830–840 (2009).

    Article  CAS  PubMed  Google Scholar 

  40. Zambo, V. et al. Lipotoxicity in the liver. World J. Hepatol. 5, 550–557 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Hetz, C. The unfolded protein response: controlling cell fate decisions under ER stress and beyond. Nat. Rev. Mol. Cell Biol. 13, 89–102 (2012).

    Article  CAS  PubMed  Google Scholar 

  42. McCullough, K. D., Martindale, J. L., Klotz, L. O., Aw, T. Y. & Holbrook, N. J. Gadd153 sensitizes cells to endoplasmic reticulum stress by down-regulating Bcl2 and perturbing the cellular redox state. Mol. Cell. Biol. 21, 1249–1259 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Holze, C. et al. Oxeiptosis, a ROS-induced caspase-independent apoptosis-like cell-death pathway. Nat. Immunol. 19, 130–140 (2018).

    Article  CAS  PubMed  Google Scholar 

  44. Souers, A. J. et al. ABT-199, a potent and selective BCL-2 inhibitor, achieves antitumor activity while sparing platelets. Nat. Med. 19, 202–208 (2013).

    Article  CAS  PubMed  Google Scholar 

  45. Ntambi, J. M. & Miyazaki, M. Recent insights into stearoyl-CoA desaturase-1. Curr. Opin. Lipidol. 14, 255–261 (2003).

    Article  CAS  PubMed  Google Scholar 

  46. Zhang, X. et al. Liver X receptor activation increases hepatic fatty acid desaturation by the induction of SCD1 expression through an LXRα–SREBP1c-dependent mechanism. J. Diabetes 6, 212–220 (2014).

    Article  CAS  PubMed  Google Scholar 

  47. Mziaut, H., Korza, G. & Ozols, J. The N terminus of microsomal Δ9 stearoyl-CoA desaturase contains the sequence determinant for its rapid degradation. Proc. Natl Acad. Sci. USA 97, 8883–8888 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Kato, H., Sakaki, K. & Mihara, K. Ubiquitin–proteasome-dependent degradation of mammalian ER stearoyl-CoA desaturase. J. Cell Sci. 119, 2342–2353 (2006).

    Article  CAS  PubMed  Google Scholar 

  49. Peng, S. B. et al. Inhibition of RAF isoforms and active dimers by LY3009120 leads to anti-tumor activities in RAS or BRAF mutant cancers. Cancer Cell 28, 384–398 (2015).

    Article  CAS  PubMed  Google Scholar 

  50. Shao, W. et al. Antitumor properties of RAF709, a highly selective and potent inhibitor of RAF kinase dimers, in tumors driven by mutant RAS or BRAF. Cancer Res. 78, 1537–1548 (2018).

    Article  CAS  PubMed  Google Scholar 

  51. Gibney, G. T. & Zager, J. S. Clinical development of dabrafenib in BRAF mutant melanoma and other malignancies. Expert Opin. Drug Metab. Toxicol. 9, 893–899 (2013).

    Article  CAS  PubMed  Google Scholar 

  52. King, A. J. et al. Demonstration of a genetic therapeutic index for tumors expressing oncogenic BRAF by the kinase inhibitor SB-590885. Cancer Res. 66, 11100–11105 (2006).

    Article  CAS  PubMed  Google Scholar 

  53. Yeh, T. C. et al. Biological characterization of ARRY-142886 (AZD6244), a potent, highly selective mitogen-activated protein kinase kinase 1/2 inhibitor. Clin. Cancer Res. 13, 1576–1583 (2007).

    Article  CAS  PubMed  Google Scholar 

  54. Agianian, B. & Gavathiotis, E. Current insights of BRAF inhibitors in cancer. J. Med. Chem. 61, 5775–5793 (2018).

    Article  CAS  PubMed  Google Scholar 

  55. Xin, Z. et al. Discovery of piperidine-aryl urea-based stearoyl-CoA desaturase 1 inhibitors. Bioorg. Med. Chem. Lett. 18, 4298–4302 (2008).

    Article  CAS  PubMed  Google Scholar 

  56. Sebolt-Leopold, J. S. & Herrera, R. Targeting the mitogen-activated protein kinase cascade to treat cancer. Nat. Rev. Cancer 4, 937–947 (2004).

    Article  CAS  PubMed  Google Scholar 

  57. Wolf, M. J. et al. Metabolic activation of intrahepatic CD8+ T cells and NKT cells causes nonalcoholic steatohepatitis and liver cancer via cross-talk with hepatocytes. Cancer Cell 26, 549–564 (2014).

    Article  CAS  PubMed  Google Scholar 

  58. Nwosu, Z. C. et al. Liver cancer cell lines distinctly mimic the metabolic gene expression pattern of the corresponding human tumours. J. Exp. Clin. Cancer Res. 37, 211 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Preziosi, M. E. et al. In vivo screen identifies LXR agonism potentiates sorafenib killing of hepatocellular carcinoma. Preprint at bioRxiv https://doi.org/10.1101/668350 (2019).

  60. Kirchgessner, T. G. et al. Pharmacological characterization of a novel liver X receptor agonist with partial LXRα activity and a favorable window in nonhuman primates. J. Pharmacol. Exp. Ther. 352, 305–314 (2015).

    Article  PubMed  Google Scholar 

  61. Carlson, C. M., Frandsen, J. L., Kirchhof, N., McIvor, R. S. & Largaespada, D. A. Somatic integration of an oncogene-harboring Sleeping Beauty transposon models liver tumor development in the mouse. Proc. Natl Acad. Sci. USA 102, 17059–17064 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Zender, L. et al. Identification and validation of oncogenes in liver cancer using an integrative oncogenomic approach. Cell 125, 1253–1267 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Watnick, R. S., Cheng, Y. N., Rangarajan, A., Ince, T. A. & Weinberg, R. A. Ras modulates Myc activity to repress thrombospondin-1 expression and increase tumor angiogenesis. Cancer Cell 3, 219–231 (2003).

    Article  CAS  PubMed  Google Scholar 

  64. Pelossof, R. et al. Prediction of potent shRNAs with a sequential classification algorithm. Nat. Biotechnol. 35, 350–353 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Fellmann, C. et al. An optimized microRNA backbone for effective single-copy RNAi. Cell Rep. 5, 1704–1713 (2013).

    Article  CAS  PubMed  Google Scholar 

  66. Naito, Y., Hino, K., Bono, H. & Ui-Tei, K. CRISPRdirect: software for designing CRISPR/Cas guide RNA with reduced off-target sites. Bioinformatics 31, 1120–1123 (2015).

    Article  CAS  PubMed  Google Scholar 

  67. Doench, J. G. et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR–Cas9. Nat. Biotechnol. 34, 184–191 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Ran, F. A. et al. Genome engineering using the CRISPR–Cas9 system. Nat. Protoc. 8, 2281–2308 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Feoktistova, M., Geserick, P. & Leverkus, M. Crystal violet assay for determining viability of cultured cells. Cold Spring Harb. Protoc. 2016, prot087379 (2016).

    Article  Google Scholar 

  70. Mehlem, A., Hagberg, C. E., Muhl, L., Eriksson, U. & Falkevall, A. Imaging of neutral lipids by oil red O for analyzing the metabolic status in health and disease. Nat. Protoc. 8, 1149–1154 (2013).

    Article  PubMed  Google Scholar 

  71. Bligh, E. G. & Dyer, W. J. A rapid method of total lipid extraction and purification. Can. J. Biochem. Physiol. 37, 911–917 (1959).

    Article  CAS  PubMed  Google Scholar 

  72. Babicki, S. et al. Heatmapper: web-enabled heat mapping for all. Nucleic Acids Res. 44, W147–W153 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Mendez, J. & Stillman, B. Chromatin association of human origin recognition complex, Cdc6, and minichromosome maintenance proteins during the cell cycle: assembly of prereplication complexes in late mitosis. Mol. Cell. Biol. 20, 8602–8612 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Friesner, R. A. et al. Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein–ligand complexes. J. Med. Chem. 49, 6177–6196 (2006).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank E. Rist, P. Schiemann, A. Heinrich, E. Enzinger, O. Hihn, W. Albrecht, L. Herrmann, P. Wenk, F. Iglauer, S. Gerold, M. A. Jarboui, U. Koppenhoefer, S. Poths, F. J. Hilke, D. Quetting, M. Riebold and all members of the Zender department for fruitful discussions and/or technical assistance. Furthermore, we thank M. Eilers and J. Zuber for providing vectors, the c.ATG facility of Tuebingen University for sequencing analyses and the CSC-IT Center for Science, Finland, for computational resources. This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) (FOR2314 (project number 267467939; D.D., L.Z., A.S., N.P., B.P., S.L., M.T.R., A.P., N.P.M.), SFB-TR209-314905040 (D.D., L.Z., N.P.M., M.B.), SFB-TR240 (L.Z., B.P.), Gottfried Wilhelm Leibniz Program (L.Z.)), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s excellence strategy EXC 2180-390900677 (Image Guided and Functionally Instructed Tumor Therapies (iFIT) (L.Z., B.P., D.D., S.L., N.P., M.B., A.P., N.P.M.)) and the German Ministry for Education and Research (BMBF) (eMed/Multiscale HCC (L.Z., B.P., N.P.M., M.B.), Neuroallianz (S.L.)). Further funding was provided by the Landesstiftung Baden-Wuerttemberg (Improve CRC (D.D., S.L., L.Z.)), the European Research Council (CholangioConcept (L.Z.)), the German Center for Translational Cancer Research (DKTK (S.L., L.Z.)) and the Ministry of Science, Research and Arts of Baden-Wuerttemberg (MWK-BW (S.L., M.B.)). Furthermore, S.L. is supported by the Robert Bosch Foundation and the Interfaculty Centre for Pharmacogenomics and Pharma Research (ICEPHA), and B.P. is supported by the Werner Siemens Foundation and the DFG (project number 403462768).

Author information

Author notes

  1. Marteinn T. Snaebjornsson & Almut Schulze

    Present address: German Cancer Research Center (DKFZ), Heidelberg, Germany

Authors and Affiliations

  1. Department of Medical Oncology and Pneumology, University Hospital Tuebingen, Tuebingen, Germany

    Ramona Rudalska, Jule Harbig, Sabrina Klotz, Stefan Zwirner, Liudmyla Taranets, Florian Heinzmann, Thales Kronenberger, Wei Cui, Luana D’Artista, Elias Einig, Martina Hinterleitner, Agata Dylawerska, Tae-Won Kang, Antti Poso, Nikita Popov, Lars Zender & Daniel Dauch

  2. Theodor Boveri Institute, Biocenter, University of Wuerzburg, Wuerzburg, Germany

    Marteinn T. Snaebjornsson, Werner Schmitz & Almut Schulze

  3. Department of Pharmaceutical Chemistry, University of Tuebingen, Tuebingen, Germany

    Michael Forster & Stefan Laufer

  4. German Cancer Research Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany

    Michael Forster, Tae-Won Kang, Lars Zender & Daniel Dauch

  5. School of Pharmacy, University of Eastern Finland, Kuopio, Finland

    Antti Poso

  6. iFIT Cluster of Excellence EXC 2180 ‘Image-Guided and Functionally Instructed Tumor Therapies’, University of Tuebingen, Tuebingen, Germany

    Antti Poso, Nisar P. Malek, Michael Bitzer, Stefan Laufer, Bernd J. Pichler, Nikita Popov, Lars Zender & Daniel Dauch

  7. Institute of Pathology, University Hospital Wuerzburg, Wuerzburg, Germany

    Mathias T. Rosenfeldt

  8. Department of Gastroenterology, Hepatology and Infectiology, University Hospital Tuebingen, Tuebingen, Germany

    Nisar P. Malek & Michael Bitzer

  9. Tuebingen Center of Academic Drug Discovery (TüCAD2), Tuebingen, Germany

    Stefan Laufer

  10. Werner Siemens Imaging Center, University of Tuebingen, Tuebingen, Germany

    Bernd J. Pichler

  11. Comprehensive Cancer Center (CCCM), University Hospital Wuerzburg, Wuerzburg, Germany

    Almut Schulze

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  1. Ramona Rudalska
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Contributions

The study was designed by D.D. and L.Z. The research was conducted by R.R., J.H., M.T.S., S.K., S.Z., L.T., F.H., M.F., W.C., L.d’A., E.E., M.H., W.S., A.D., T.-W.K. and D.D. Histopathological analyses were performed by M.T.R. Human HCC samples were collected by M.B. Molecular modeling was conducted by T.K. and A.P. N.P.M., S.L., B.P., N.P. and A.S. contributed to research design and supervised experiments. D.D. and L.Z. supervised all research. The manuscript was written by D.D. and L.Z.

Corresponding authors

Correspondence to Lars Zender or Daniel Dauch.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Cancer thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 The treatment response of sorafenib in MycOE;NrasG12V-driven HCCs is dependent on LXRα activation.

a,b, Treatment of murine, MycOE;NrasG12V-driven HCCs with sorafenib. Representative pictures (a) and H+E staining (b) of MycOE;NrasG12V HCCs in wildtype mice upon a 5-week therapy with sorafenib or the corresponding carrier (n = 3 mice per group). Scale bars, 1 cm, 100 µm. c, Survival of mice with MycOE;NrasG12V HCCs under sorafenib or carrier treatment (Kaplan-Meier curve, statistical significance was calculated using a log-rank test, n = 6 mice per group). d, Overview of LXRß structure with the coactivator peptide from NCOA1 (dark blue) in the AF2 region and sorafenib structure, docked in the ligand-binding pocket (orange). e, Suggested binding mode of sorafenib proposed by molecular modelling into the ligand-binding pocket of LXRß (PDB ID: 1PQ6). Residues contributing towards the interaction are colored by property, hydrophobic side chains are purple and hydrogen bonds are cyan. f, Schematic outline of an LXRα-binding saturation assay. Pulldown of LXRα was performed upon incubation with different concentrations of 3H-sorafenib. This experiment was side-by-side conducted in the presence of 320 µM unlabeled sorafenib to determine unspecific binding. g, Representative western blot analysis of LXRα in individual MycOE;NrasG12V;gN1h3 tumors (cropped blot images, MycOE;NrasG12V;gNC control: n = 3 mice, the western blot was repeated twice with similar results). α-tubulin was used as a loading control. Unprocessed images of the blots and numerical source data are provided as source data files.

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Extended Data Fig. 2 Inhibition of Raf improves the therapeutic effect of LXRα activation in liver cancer.

a,b, Gating strategy to quantify LXR activity in HCC cells. FACS analysis of 10 µM sorafenib-treated MycOE;NrasG12V;Cdkn2aARF–/–;p-LXRE-GFP cells. c, LXRE reporter assay in MycOE;NrasG12V;Cdkn2aARF–/–;p-LXRE-GFP cells that were treated for 3 days with different concentrations of sorafenib or T0901317 (GFP measurements in individual cells, values represent mean ± SD, n = 3 independent experiments, nonlinear regression curves, variable slope). d, Reporter assay to compare LXR activation by sorafenib or T0901317 in vivo. Representative photographs of tumor sections stained for native GFP and native RFP (+DAPI) after intrahepatic delivery of pT-CaM-LXRE-G, pT-CaRIN and pSB13 and treatment with carrier, sorafenib or T0901317 (3 d, n = 4 mice per group). Scale bar, 100 µm. e, Establishing Nr1h3 or Nr1h2 gene knockout in MycOE;NrasG12V;Cdkn2aARF–/– HCC cells by transient transfection of cells with pX-458-GFP expressing the corresponding gRNAs and generation of single cell clones. f,g, Representative western blot analysis of LXRα (f) and LXRβ (g) in MycOE;NrasG12V;Cdkn2aARF–/– single cell clones upon transient transfection with pX-458-GFP expressing the corresponding gRNAs (single cell clones, cropped blot images, n = 2 independent experiments). Vinculin and Histone H3 were used as loading controls. h, Treatment of MycOE;NrasG12V;Cdkn2aARF–/–gNr1h3, MycOE;NrasG12V;Cdkn2aARF–/–gNr1h2 or MycOE;NrasG12V;Cdkn2aARF–/–gNC cells for 4 days with T0901317/sorafenib or carrier (quantification of viable cells by crystal violet staining, values represent mean ± SD, n = 3 independent experiments, statistical significance was calculated using two-tailed Student´s t test). i, Treatment of MycOE;NrasG12V;Cdkn2aARF–/– cells for 4 days with sorafenib and/or 24-S hydroxycholesterol, GW3965 or LXR-623 (quantification of viable cells by crystal violet staining, values represent mean ± SD, n = 3 independent experiments, statistical significance was calculated using two-tailed Student´s t test). Unprocessed images of the blots and numerical source data are provided as source data files.

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Extended Data Fig. 3 Sorafenib resistant, MycOE;Akt1Myr-driven murine HCCs exert reduced LXRα activity.

a, Co-delivery of a transposon construct encoding for oncogenic Myc and myristoylated Akt1 (Akt1Myr) and pSB13 into C57BL/6 wildtype mice via hydrodynamic tail vein injection. b, Representative western blot analysis of P-Mek1/2 (S217/221) and Mek1/2 in MycOE;Akt1Myr tumors 6 h after treatment with sorafenib or BI-882370 (cropped blot images, n = 2 mice per condition). α-tubulin was used as a loading control. c, Survival of mice with MycOE;Akt1Myr HCCs under sorafenib or carrier treatment (Kaplan-Meier curve, statistical significance was calculated using a log-rank test, n = 6 mice per group). d, mRNA levels of Nr1h3, Abcg1, Abcg5, Abcg8 and Apoe in MycOE;Akt1Myr tumors (compared to MycOE;NrasG12V HCCs, mRNAseq data, n = 3 mice per group, statistical significance was calculated using two-tailed Student´s t test). e, Representative western blot analysis of Akt-1 and LXRα in MycOE;Akt1Myr;Cdkn2aARF–/– and MycOE;NrasG12V;Cdkn2aARF–/– cells (cropped blot images, n = 3 independent experiments). β-actin was used as a loading control. f, LXRE reporter assay in MycOE;Akt1Myr;Cdkn2aARF–/–;p-LXRE-GFP and MycOE;NrasG12V;Cdkn2aARF–/–;p-LXRE-GFP cells (GFP measurements in individual cells, values represent mean ± SD, n = 5 independent experiments, statistical significance was calculated using two-tailed Student´s t test). g, mRNA levels of Nfe2l2 in MycOE;Akt1Myr tumors (compared to MycOE;NrasG12V HCCs, mRNAseq data, n = 3 mice per group, statistical significance was calculated using two-tailed Student´s t test). h, Representative western blot analysis of Nrf2 in MycOE;Akt1Myr;Cdkn2aARF–/– and MycOE;NrasG12V;Cdkn2aARF–/– cells (cropped blot images, n = 3 independent experiments). α-tubulin was used as a loading control. i, Representative western blot analysis of Nrf2 in MycOE;Akt1Myr;Cdkn2aARF–/–shNfe2l2 or MycOE;Akt1Myr;Cdkn2aARF–/–shNC cells upon 4 days of treatment with doxycycline (cropped blot images, n = 3 independent experiments). α-tubulin was used as a loading control. j, LXRE reporter assay in MycOE;Akt1Myr;Cdkn2aARF–/–;p-LXRE-GFP cells 4 days after transient transfection with pT-CaR-shNfe2l2.2406, pT-CaR-shNfe2l2.2272, or pT-CaR-shNC (GFP measurements in RFP positive, individual cells, values represent mean ± SD, n = 5 independent experiments, statistical significance was calculated using two-tailed Student´s t test). k, LXRE reporter assay in MycOE;Akt1Myr;Cdkn2aARF–/–;p-LXRE-GFP cells upon 3 days of treatment with sorafenib or T0901317 (GFP measurements in individual cells, values represent mean ± SD, n = 3 independent experiments, statistical significance was calculated using two-tailed Student´s t test). Unprocessed images of the blots and numerical source data are provided as source data files.

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Extended Data Fig. 4 A combination of LXR activation and Raf inhibition blocks MycOE;Akt-1Myr-driven HCC development.

a, Quantification of MycOE;Akt1Myr tumor nodules from mice that were treated for 7 weeks with sorafenib, BI-882370 or carrier +/− T0901317 (values represent mean ± SD, n = 4 mice per group, statistical significance was calculated using two-tailed Student´s t test). b-d, Treatment of MycOE;Akt1Myr;gNr1h3 or MycOE;Akt1Myr;gNC HCCs with T0901317/sorafenib or carrier. Representative pictures of livers upon a 6-week therapy (c, n = 3 mice per group) and survival of mice under treatment (d, Kaplan-Meier curve, statistical significance was calculated using a log-rank test, n = 6 mice per group). Scale bars, 1 cm. Numerical source data are provided as source data files.

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Extended Data Fig. 5 A T0901317/sorafenib combinatorial therapy influences lipid metabolism in liver cancer.

a, Representative pictures of murine Yap1;NrasG12V;Trp53-/- tumors that were treated for 2 weeks with T0901317/sorafenib, carrier, T0901317 or sorafenib and stained with H+E (n = 3 mice per group). Scale bars, 100 µm. b, Representative pictures of MycOE;NrasG12V tumors that were treated for 3 weeks with T0901317/sorafenib, carrier, T0901317 or sorafenib and stained with Oil red O (n = 3 mice per group). Scale bars, 50 µm. c, Representative western blot analysis of Srebp1c and Acetyl-CoA Carboxylase1 (ACACA) in MycOE;NrasG12V;Cdkn2aARF–/– cells upon 3 days of treatment with sorafenib, T0901317 or a combination thereof (cropped blot images, n = 2 independent experiments). α-tubulin was used as a loading control. d, Treatment of MycOE;NrasG12V;Cdkn2aARF–/– cells with the ACACA inhibitor ND-630 (quantification of viable cells by crystal violet staining, values represent mean ± SD, n = 3 independent experiments, statistical significance was calculated using two-tailed Student´s t test). e, Treatment of MycOE;NrasG12V;Cdkn2aARF–/– cells with T0901317/sorafenib +/− cholesterol/methyl-β-cyclodextrin (cholesterol/mbc) (quantification of viable cells by crystal violet staining, values represent mean, n = 6 cell independent experiments, statistical significance was calculated using two-tailed Student´s t test). f, Schematic overview describing the role of Raf in fatty acid metabolism in liver cancer. Unprocessed images of the blots and numerical source data are provided as source data files.

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Extended Data Fig. 6 A T0901317/sorafenib therapy results in lipid peroxidation, an ER stress response and macrophage activation in HCCs.

a, Quantification of lipid peroxidation in MycOE;NrasG12V;Cdkn2aARF–/– cells, upon 3 days of treatment with sorafenib, T0901317 or a combination thereof (by analysing the ratio of green and red fluorescence, values represent mean, ± SD, n = 3 cell cultures per condition, statistical significance was calculated using two-tailed Student´s t test, the analysis was repeated twice with similar results). b, Representative western blot analysis of uncleaved or cleaved Atf6, BiP/HSPA5, P-IRE1α (S724) and Xbp-1s in MycOE;NrasG12V;Cdkn2aARF–/– cells after 3 days of treatment with T0901317, sorafenib or a combination thereof (cropped blot images, n = 3 independent experiments). α-tubulin was used as a loading control. c, Representative western blot analysis of eIF2α, P-eIF2α (S51), GADD34, BiP/HSPA5 and P-IRE1α (S724) in MycOE;NrasG12V;Cdkn2aARF–/– cells after 3 days of treatment with T0901317, BI-882370 or a combination thereof (cropped blot images, n = 3 independent experiments). α-tubulin and Vinculin were used as loading controls. d, Representative western blot analysis of MLKL, P-MLKL (S345), RIPK3, P-RIPK3 (S232) and AIF (nuclear and cytoplasmic fraction) in MycOE;NrasG12V;Cdkn2aARF–/– cells after 3 days of treatment with T0901317, sorafenib or a combination thereof (cropped blot images, n = 3 independent experiments). Vinculin and Histone H3 were used as loading controls. e, FACS based analysis of myeloid cells in MycOE;NrasG12V HCCs upon treatment with T0901317/sorafenib or the corresponding monotherapies (3 d). f-h, Flow cytometry gating strategy. The following populations are identified: immature myeloid cells (iMC: CD11b+,Ly6G+/High,F4/80−, fraction 1), monocyte-macrophages (Mac-mono: CD11b+,Ly6G+,F4/80High, fraction 2) and Kupffer cells (Ku: CD11b+,Ly6G+,F4/80High, fraction 3). Monocyte and macrophages are further distinguished/labeled as Ly6C+ (fraction 2a) or Ly6C− (fraction 2b). Samples were gated on viable leukocytes by DAPI exclusion and doublets were excluded using height versus area dot plots. (shown are representative pseudocolor plots, n = 3 mice per group). i,j, Results of FACS analysis in MycOE;NrasG12V HCCs upon 3 days of treatment with T0901317/sorafenib, T0901317, sorafenib or carrier. Charts show the percentage mean ± SD of monocyte-macrophages (Mac−mono: CD11b+,Ly6G+,F/480+, i), Kupffer cells (Ku: CD11b+,Ly6G+,F4/80High, i) and immature myeloid cells (iMC: CD11b+, j) (n = 3 mice per group, statistical significance was calculated using two-tailed Student´s t test, the experiment was repeated with similar results). k, Treatment of MycOE;NrasG12V;Cdkn2aARF–/– cells for 4 days with T0901317 and/or the Bcl-2 inhibitor ABT-199 (quantification of viable cells by crystal violet staining, values represent mean ± SD, n = 3 independent experiments, statistical significance was calculated using two-tailed Student´s t test). Unprocessed images of the blots and numerical source data are provided as source data files.

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Extended Data Fig. 7 Raf-1 stabilizes SCD1 protein in liver cancer.

a, Quantification of western blot analysis (shown in Fig. 6b) to quantify relative SCD1 expression in MycOE;NrasG12V;Cdkn2aARF–/– cells upon 3 days of treatment with sorafenib, T0901317 or a combination thereof (quantified with ImageJ, only samples from the same blot were compared, values represent mean ± SD, n = 6 independent experiments, statistical significance was calculated using two-tailed Student´s t test, α-tubulin was used as a loading control). b, Quantification of western blot analysis (shown in Fig. 6c) to quantify relative SCD1 expression in MycOE;NrasG12V;Cdkn2aARF–/– cells upon treatment with T0901317 (2 d) and sorafenib (1-3 h) (quantified with ImageJ, only samples from the same blot were compared, values represent mean ± SD, n = 6 independent experiments, statistical significance was calculated using two-tailed Student´s t test, α-tubulin was used as a loading control). c, Representative western blot analysis of SCD1 in HLE cells upon treatment with T0901317 (2 d) and sorafenib (1-3 h) (cropped blot images, n = 3 independent experiments). α-tubulin was used as a loading control. d-f, Representative western blot analysis of SCD1 in MycOE;NrasG12V;Cdkn2aARF–/– cells upon treatment with T0901317 (2 d) and the DFG-out RAF inhibitors RAF-265 (d) and RAF-709 (e) or the DFG-in DFG-in RAF inhibitor SB590885 (f) for 1 to 3 h (cropped blot images, n = 2 independent experiments). α-tubulin was used as a loading control. g, Representative western blot analysis of MEK and p-MEK1/2 (Ser217/221) in MycOE;NrasG12V;Cdkn2aARF–/– cells upon 6 h of treatment with sorafenib, BI-882370, dabrafenib or SB590885 (cropped blot images, n = 2 independent experiments). α-tubulin was used as a loading control. h, Representative western blot analysis of SCD1 in MycOE;NrasG12V;Cdkn2aARF–/–;Raf1WT cells upon treatment with T0901317 (2 d) and sorafenib for 1 to 3 h (cropped blot images, n = 2 independent experiments). α-tubulin was used as a loading control. i, Representative western blot analysis of Raf-1 in MycOE;NrasG12V;Cdkn2aARF–/–;shRaf1 or MycOE;NrasG12V;Cdkn2aARF–/–;shNC cells, 4 days upon treatment with doxycycline (cropped blot images, n = 2 independent experiments). j, Treatment of MycOE;NrasG12V;Cdkn2aARF–/– cells with carrier, T0901317, the MEK1/2 inhibitor PD0325901 or a combination thereof (quantification of viable cells by crystal violet staining, values represent mean ± SD, n = 3 independent experiments, statistical significance was calculated using two-tailed Student´s t test). Unprocessed images of the blots and numerical source data are provided as source data files.

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Extended Data Fig. 8 A lipotoxic therapy did not induce adverse metabolic effects in murine livers.

a, Analysing potential liver damage in C57BL/6 wildtype mice upon a 3-week T0901317/sorafenib treatment. b, Liver-to-body ratio of mice upon a 3-week treatment with T0901317/sorafenib or carrier (values represent mean ± SD, n = 3 mice per group, statistical significance was calculated using two-tailed Student´s t test). c, Representative pictures of murine livers upon a 3-week treatment with T0901317/sorafenib or carrier that were stained with H+E, Oil red O, Ki67, cleaved caspase 3, TUNEL and Sirius red (n = 3 mice per group). TUNEL and cleaved caspase 3 positive control = Intraperitoneal injection of Jo2 antibody. Sirius red positive control = Bi-weekly intraperitoneal injections of CCl4 for 6 weeks. Ki67 positive control = MycOE;NrasG12V HCC. Scale bars, 100 µm. d, Analysing potential liver damage in C57BL/6 wildtype mice upon 6 months of T0901317/sorafenib treatment. e, Liver-to-body ratio of mice upon a 6-month treatment with T0901317/sorafenib or carrier (values represent mean ± SD, n = 6 mice per group, statistical significance was calculated using two-tailed Student´s t test). f, Representative pictures of murine livers upon a 6-month treatment with T0901317/sorafenib or carrier that were stained with H + E, Oil red O, Ki67, cleaved caspase 3, TUNEL and Sirius red (n = 6 mice per group). Scale bars, 100 µm. g,h, Weight development of mice during 2 weeks (g) or 6 months (h) of treatment with T0901317/sorafenib or carrier (values represent mean ± SD, n = 3 (g) or 6 (h) mice per group, statistical significance was calculated using two-tailed Student´s t test). Numerical source data are provided as source data files.

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Extended Data Fig. 9 A lipotoxic therapy reduced NASH triggered HCC development and did not induce adverse metabolic effects in NASH livers.

a, Weight development of wildtype mice that were fed with choline deficient high fat diet (CD-HFD) or normal diet (ND) (values represent mean ± SD, n = 8 mice per group, statistical significance was calculated using two-tailed Student´s t test). b, Analysing tumor development and potential liver damage in NASH mice upon a 3-week T0901317/sorafenib treatment. c, Quantification of tumor nodules in mice that were fed for 12 months with CD-HFD and treated for 3 weeks with T0901317/sorafenib, sorafenib or carrier (values represent mean ± SD, n = 7 mice per group, statistical significance was calculated using two-tailed Student´s t test). d, Liver-to-body ratio of NASH mice upon a 3-week treatment with T0901317/sorafenib or carrier (values represent mean ± SD, n = 3 mice per group, statistical significance was calculated using two-tailed Student´s t test). e, Representative pictures of murine NASH livers upon a 3-week treatment with T0901317/sorafenib or carrier that were stained with H+E, Oil red O, Ki67, cleaved caspase 3, TUNEL and Sirius red (n = 3 mice per group). Scale bars, 100 µm. f, Weight development of NASH mice during treatment with T0901317/sorafenib or carrier (values represent mean ± SD, n = 3 mice per group, statistical significance was calculated using two-tailed Student´s t test). Numerical source data are provided as source data files.

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Extended Data Fig. 10 Comparison of human HCC cells and weight development of tumor bearing mice.

a, Representative western blot analysis of Nrf2, Akt-1 and LXRα in different human HCC cells (cropped blot images, n = 3 independent experiments). β-actin was used as a loading control. b, LXRE reporter assay in different human HCC cells upon lentiviral infection of pL-LXRE-GFP (GFP measurements in individual cells; values represent mean ± SD, n = 5 independent experiments, statistical significance was calculated using two-tailed Student´s t test). c, Weight development of CB17.Cg-PrkdcscidLystbg/Crl mice upon subcapsular injection of HuH7 cells into the liver and treatment with T0901317/sorafenib or carrier (values represent mean ± SD, n = 6 mice per group statistical significance was calculated using two-tailed Student´s t test). Unprocessed images of the blots and numerical source data are provided as source data files.

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Supplementary information

Reporting Summary

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Rudalska, R., Harbig, J., Snaebjornsson, M.T. et al. LXRα activation and Raf inhibition trigger lethal lipotoxicity in liver cancer. Nat Cancer 2, 201–217 (2021). https://doi.org/10.1038/s43018-020-00168-3

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