Abstract

Primary liver cancer represents a major health problem. It comprises hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC), which differ markedly with regards to their morphology, metastatic potential and responses to therapy. However, the regulatory molecules and tissue context that commit transformed hepatic cells towards HCC or ICC are largely unknown. Here we show that the hepatic microenvironment epigenetically shapes lineage commitment in mosaic mouse models of liver tumorigenesis. Whereas a necroptosis-associated hepatic cytokine microenvironment determines ICC outgrowth from oncogenically transformed hepatocytes, hepatocytes containing identical oncogenic drivers give rise to HCC if they are surrounded by apoptotic hepatocytes. Epigenome and transcriptome profiling of mouse HCC and ICC singled out Tbx3 and Prdm5 as major microenvironment-dependent and epigenetically regulated lineage-commitment factors, a function that is conserved in humans. Together, our results provide insight into lineage commitment in liver tumorigenesis, and explain molecularly why common liver-damaging risk factors can lead to either HCC or ICC.

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Data availability

The data and code that support the findings of this study are available from the corresponding author on reasonable request. Source data for graphs showed in Figs. 4, 5 and Extended Data Figs. 1, 410 are available in the online version of this paper. Data from ChIP–seq experiments are available at the Sequence Read Archive (SRA) under the accession number SRP136997. Whole scans of western blots are depicted in Supplementary Fig. 1, and the gating strategy for flow cytometry is depicted in Supplementary Fig. 2.

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Acknowledgements

We thank E. Rist, P. Schiemann, C. Fellmeth, C.-J. Hsieh, D. Heide and J. Hetzer for technical help or assistance. We thank A. Weber for providing TLR2 and TLR4 knockout mice and W. S. Alexander and The Walter and Eliza Hall Institute of Medical Research for providing Mlklfl/fl mice. The Cas9n–p19Arf sgRNA vector was provided by W. Xue. We thank the c.ATG facility of Tuebingen University and CeGaT Tuebingen for exome sequencing and data analysis. This work was supported by the ERC Consolidator Grant ‘CholangioConcept’ (to L.Z.), the German Research Foundation (DFG): grants FOR2314, SFB685, SFB/TR209 and the Gottfried Wilhelm Leibniz Program (to L.Z.). Further funding was provided by the German Ministry for Education and Research (BMBF) (e:Med/Multiscale HCC), the German Universities Excellence Initiative (third funding line: ‘future concept’), the German Center for Translational Cancer Research (DKTK), the German-Israeli Cooperation in Cancer Research (DKFZ-MOST) (to L.Z.) and the Intramural Research Program of the Centre for Cancer Research, National Cancer Institute, National Institutes of Health (to X.W.W.). The group of O.B. is supported by grants from ANR-BMFT, Fondation ARC pour la recherche sur le Cancer, INSERM, and the National Cancer Institute of the National Institutes of Health under Award Number R01CA136533. O.B. is a CNRS fellow.

Reviewer information

Nature thanks E. Guccione, E. Pikarsky and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Author notes

  1. These authors contributed equally: Marco Seehawer, Florian Heinzmann

Affiliations

  1. Department of Internal Medicine VIII, University Hospital Tuebingen, Tuebingen, Germany

    • Marco Seehawer
    • , Florian Heinzmann
    • , Luana D’Artista
    • , Jule Harbig
    • , Lisa Hoenicke
    • , Sabrina Klotz
    • , Tae-Won Kang
    • , Rishabh Chawla
    •  & Lars Zender
  2. Department of Physiology I, Institute of Physiology, Eberhard Karls University Tuebingen, Tuebingen, Germany

    • Marco Seehawer
    • , Florian Heinzmann
    • , Luana D’Artista
    • , Jule Harbig
    • , Lisa Hoenicke
    • , Sabrina Klotz
    • , Tae-Won Kang
    • , Rishabh Chawla
    •  & Lars Zender
  3. Institut Pasteur, Nuclear Organization and Oncogenesis Unit, Department of Cell Biology and Infection, Paris, France

    • Pierre-François Roux
    • , Lucas Robinson
    • , Grégory Doré
    • , Nir Rozenblum
    •  & Oliver Bischof
  4. INSERM, U993, Paris, France

    • Pierre-François Roux
    • , Lucas Robinson
    • , Grégory Doré
    •  & Oliver Bischof
  5. Equipe Labellisée Fondation ARC pour la recherche sur le cancer, Villejuif, France

    • Pierre-François Roux
    • , Lucas Robinson
    • , Grégory Doré
    •  & Oliver Bischof
  6. Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA

    • Hien Dang
    •  & Xin Wei Wang
  7. Institute of Laboratory Animal Science University of Zurich, University of Zurich, Schlieren, Switzerland

    • Thorsten Buch
  8. RWTH University Hospital Aachen, Department of Gastroenterology, Digestive Diseases and Intensive Care Medicine (Department of Medicine III), Aachen, Germany

    • Mihael Vucur
    •  & Tom Luedde
  9. Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Vienna, Austria

    • Mareike Roth
    •  & Johannes Zuber
  10. Institute of Pathology, University of Tuebingen, Tuebingen, Germany

    • Bence Sipos
  11. Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany

    • Thomas Longerich
  12. Division of Chronic Inflammation and Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany

    • Mathias Heikenwälder
  13. Translational Gastrointestinal Oncology Group, German Consortium for Translational Cancer Research (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany

    • Lars Zender

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Contributions

The study was designed by L.Z., M.S. and F.H with support from O.B. T.B. provided TLR KO (TLR2, 3, 4, 7 and 9 KO) mice. Mouse experiments, western blots, qRT–PCR, immunohistochemistry, immunofluorescence, vector generation and cell culture work were conducted and analysed by M.S., F.H. and L.Z. L.D. performed and analysed flow cytometry experiments, J.H. performed immunohistochemistry, immunofluorescence and mouse sampling, L.H. crossed ROSAmT/mG × Alb-cre × p19Arf−/− mice, S.K. and T.-W.K. conducted mouse experiments, R.C. subcloned vectors and performed knockdown experiments. Histopathological analyses were performed by T.Lo. and B.S. Human ICC and HCC samples were collected and analysed by H.D. and X.W.W. ChIP–seq, ATAC-seq, transcriptome and integrative analyses were performed by P.-F.R., O.B., G.D., N.R., L.R., M.R., J.Z. and M.H. M.V. and T.Lu. generated the Alb-cre × Mlklfl/fl mice and conducted MLKL western blot analyses. L.Z. supervised the overall execution of experiments and analysed data. The manuscript was written by L.Z. with support from, M.S., F.H. and O.B.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Lars Zender.

Extended data figures and tables

  1. Extended Data Fig. 1 Tumour phenotype depends on the delivery method of oncogene encoding transposons.

    a, Schematic representation of transposon vectors encoding Myc and NRASG12V (pCaMIN) or Myc and Akt1 (pCaMIA) and a plasmid encoding the SB13 transposase. b, c, Representative micrographs of H&E staining of HDTV- or Epo-derived tumours. Scale bars, 100 µm. d, Histopathological scoring and quantification of tumours developed after hydrodynamic delivery of oncogene encoding transposons. e, Histopathological scoring and quantification of tumours developed after transposon delivery via in vivo electroporation. f, Representative image of native fluorescence microscopy of liver cryosections from ROSAmT/mG × Alb-cre × p19Arf−/− mice. In such mice, activation of the albumin promoter induces excision of a red fluorescence marker gene (mTomato) together with a stop codon flanked by loxP sites, thus resulting in a colour switch from red to green fluorescence (membrane-bound GFP). In this model, only fully differentiated hepatocytes (with high albumin promoter activity and therefore high levels of Cre expression) were able to induce the switch from red to green fluorescence, whereas liver cells with low albumin promoter activity such as embryonic hepatocytes or oval cells or liver progenitor cells were unable to accomplish such a colour change. Shown is mGFP expression in hepatocytes (green) and mTomato expression in bile duct cells or endothelial cells (red) (n = 3). Scale bar, 100 µm. g, h, Representative H&E staining images of tumours 4 weeks after HDTV (g) or Epo (h) transfection of the pCaMIN vector in ROSAmT/mG × Alb-cre × p19Arf−/− mice (n = 4). Scale bars, 100 µm. i, Representative images of DAPI-positive (blue), K19-positive (red) and native GFP-positive (green) hepatocytes in ICC derived from pCaMIN electroporated ROSAmT/mG × Alb-cre × p19Arf−/− mice (n = 6, left). Scale bars, 100 µm (left) and 20 µm (right). Data are from one experiment. j, qPCR analysis with transposon-specific primers on DNA isolated from HDTV- or Epo-induced tumours using (SB13) showed an approximately 1.5-fold increased transposon integration compared to tumours triggered by hydrodynamic delivery (HDTV). Epo-induced tumours using the SB10 transposase show equal transposon integration levels compared to HDTV-derived tumours with SB13 (n = 3). NS, not significant (P = 0.074); *P = 0.0011, Student’s two-sided t-test. Data are mean ± s.d. k, Representative images of H&E, K19 or HNF4α staining of Epo-induced tumours transfected using pCaMIN and SB10 (n = 3). Scale bars, 100 µm. Source Data

  2. Extended Data Fig. 2 Exome sequencing reveals recurrent mutations in HCC and ICC.

    a, Purification of epithelial components from HCC or ICC derived from pCaMIN electroporated p19Arf−/− mice and normal liver tissue as a control using laser capture microdissection (LCM) (n = 3 per group). Scale bars, 100 µm. b, Exome sequencing revealed recurrent mutations (in red), in which 12 mutations were found in at least 2 samples in 3 analysed HCC (left) and 3 ICC (right) tissues. c, Schematic outline of transposon vectors expressing Myc and NRASG12V (pCaMIN) and mutated (259G>T) Fam72a cDNA (bottom), which were co-delivered into p19Arf−/− mice. d, Immunohistochemical analysis of tumour tissue for K19 expression (n = 3 per group). Scale bar, 100 µm.

  3. Extended Data Fig. 3 Characterization of early and pre-tumorigenic phase after Epo- or HDTV-mediated oncogene delivery.

    a, Immunohistochemical analysis of p19Arf−/− deficient liver sections 5 days after Epo- or HDTV-mediated transposon delivery, showing micro-tumours in H&E (top) and Epo-derived K19-positive, HNF4α-negative ICCs (middle and bottom left panel) as well as HDTV-derived HNF4α-positive, K19-negative HCCs (middle and bottom right panel, indicated by white arrowheads) (n = 3). Scale bars, 100 µm. b, Schematic outline of the experimental approach (left) and representative macroscopic liver photographs 3 days after hydrodynamic (HDTV) or Epo delivery of the pCaMIN and SB13 vectors into p19Arf−/− mouse livers. Macroscopically visible liver damage (left) as well as eosinonophilic areas indicating microscopic liver damage (right) are shown on H&E-stained liver sections (n = 4). Original magnification, ×200.

  4. Extended Data Fig. 4 Immune composition does not contribute to lineage commitment in liver cancer.

    a, Representative micrographs of αSMA immunohistochemistry (top) and quantification (bottom) 3 days after Epo and HDTV treatment in p19Arf−/− livers and quantification (n = 2). Scale bars, 100 µm. Data are mean ± s.d. b, Representative micrographs of F4/80 immunofluorescence (top) and quantification (bottom) 3 days after Epo and HDTV treatment in p19Arf−/− livers (n = 3). Scale bar, 100 µm. NS, P = 0.500, Student’s two-sided t-test. Data are mean ± s.d. c, Flow cytometry analysis showing the efficiency of clodronate in depleting Kupffer cells (CD45+F4/80+) after lipopolysaccharide (LPS) treatment (n = 3). Bottom, representative micrographs of HNF4α and K19 immunostaining analysis of Epo-induced tumours with and without Kupffer cell depletion (n = 3). Scale bar, 100 µm. d, Quantifications of liver-infiltrating immune cells from Fig. 4a, b. B220 P = 0.6255, CD3 P = 0.7649, Ly6G P = 0.3966, MHCII P = 0.9889, Student’s two-sided t-test. Data are mean ± s.d. e, Quantification of T cells (CD45+CD3+, P = 0.2622), T-helper cells (CD45+CD3+CD8CD4+, P = 0.960) and killer T cells (CD45+CD3+CD8+CD4, P = 0.0914) (n = 6). P values determined by Student’s two-sided t-test. Data are mean ± s.d. f, Quantification of monocytic immature myeloid cells (moIMC; CD11b+Gr1−lowLy6c+F4/80, P = 0.0750), neutrophilic immature myeloid cells (NeuIMC; CD11b+Gr1+Ly6cF480, P = 0.2483) and macrophages (CD11b+Gr1Ly6cF4/80+, P = 0.1744) (n = 3). P values determined by Student’s two-sided t-test. Data are mean ± s.d. Source Data

  5. Extended Data Fig. 5 Induction of hepatocyte cell death after HDTV or Epo.

    a, Representative micrographs of TUNEL (red) and DAPI (blue) staining in livers of ROSAmT/mG × Alb-cre × p19Arf−/− mice with native membrane GFP (green) in hepatocytes 3 days after Epo or HDTV transfection (n = 3). Scale bars, 100 µm. b, Ripk3 mRNA expression in p19Arf−/− livers 3 days after HDTV delivery of pCaMIN compared to Epo delivery of pCaMIN, determined by qRT–PCR (n = 4). *P = 0.0485, Student’s two-sided t-test. Data are mean ± s.d. c, Representative immunhistochemistry of RIPK3 in livers 3 days after Epo or HDTV treatment (n = 3). Scale bars, 100 µm. Source Data

  6. Extended Data Fig. 6 Necroptotic cell death affects the hepatic microenvironment and tumorigenesis.

    a, Representative TUNEL (green) and DAPI (blue) staining in liver sections from mice with (n = 4) or without (n = 3) Nec-1 pre-treatment 3 days after Epo transfection. Scale bar, 100 µm. b, Quantification of TUNEL-positive cells from mice with (n = 4) or without (n = 3) Nec-1 pre-treatment 3 days after Epo transfection. *P = 0.0264, Student’s two-sided t-test. Data are mean ± s.d. c, Western blot analysis for the apoptosis marker cleaved caspase 3 in liver lysates from livers with (n = 4) or without (n = 3) Nec-1 pre-treatment 3 days after Epo transfection. d, Western blot analysis for MLKL and pMLKL in liver lysates from livers with (n = 4) or without (n = 3) Nec-1 pre-treatment 3 days after Epo transfection. e, Immunohistochemistry quantification of B220 (P = 0.7745), CD3 (P = 0.9809), Ly6G (P = 0.0075) or MHCII (P = 0.0994) in livers with or without Nec-1 pre-treatment 3 days after Epo transfection (n = 3). P values determined by Student’s two-sided t-test. Data are mean ± s.d. f, Magnification of photographs depicted in Fig. 4k, right. Quantification of HNF4α-positive cells in Epo-induced tumours with or without Nec-1 pre-treatment (n = 4). *P = 0.0407, Student’s two-sided t-test. Data are mean ± s.d. g, Western blot analysis of MLKL on lysates from hepatocytes isolated via perfusion from Mlklfl/fl × Alb-cre−/− or Mlklfl/fl × Alb-cre+/− mice. The experiment was done once with two independent Mlklfl/fl × Alb-cre+/− mice and one Mlklfl/fl × Alb-cre−/− mouse). h, Western blot analyses for MLKL, pMLKL and vinculin on lysates from Mlklfl/fl × Alb-cre+/− mice 3 days after Epo treatment. Depicted blot is as shown in Fig. 4d (bottom), with an additional lane showing the pMLKL signal obtained in Mlklfl/fl × Alb-cre−/− mice 3 days after Epo treatment. The experiment was performed twice with similar results. i, Quantification of the duration until tumour size exceeds 0.5 cm after Epo delivery of pCaMIN in p19Arf−/− mice or pCaMIN plus Cas9n and sgRNA against p19Arf in wild-type mice (n = 7). NS, P = 0.0913, Student’s two-sided t-test. Data are mean ± s.d. j, Immunohistochemistry quantification of B220 (P = 0.9220), CD3 (P = 0.1577), Ly6G (P = 0.2375) or MHCII (P = 0.3870) in liver sections from Mlklfl/fl × Alb-cre−/− or Mlklfl/fl × Alb-cre+/− mice 3 days after Epo treatment (n = 3). P values determined by Student’s two-sided t-test. Data are mean ± s.d. k, qPCR-based necroptosis-associated cytokine profile measured on mRNA isolated from livers of Mlklfl/fl × Alb-cre−/− or Mlklfl/fl × Alb-cre+/− mice 3 days after Epo treatment. Overlapping downregulated cytokines with Nec-1-treated mice are indicated in green (compare to Fig. 4g). From the 11 cytokines that were found to be suppressible by Nec-1 treatment (Fig. 4g), the expression of 6 was found to be attenuated in Epo-treated MLKL-deficient livers as compared to wild-type livers. This difference might be explained by Nec-1-mediated inhibition of RIPK1-dependent signalling in cells other than hepatocytes. This could also explain why Nec-1 treatment reduced the Ly6G-positive cells in Epo livers (compare to Extended Data Fig. 6e), whereas MLKL deficiency had no effect on the numbers of Ly6G-positive cells after Epo treatment (compare to Extended Data Fig. 6j) (n = 2). Data are fold change of the mean from each group. l, Quantification of HNF4α-positive cells in liver sections of Epo-induced tumours in Mlklfl/fl × Alb-cre−/− or Mlklfl/fl × Alb-cre+/− mice (n = 5). *P = 0.0381, Student’s two-sided t-test. Data are mean ± s.d. m, Representative photograph of HNF4α and K19 staining of pCaMIN Epo-derived tumours in Mlkl wild-type × Alb-cre+/− mice (n = 2). n, Representative micrographs of pRIPK3 immunohistochemistry in tissue sections from sham-operated or bile duct ligated livers of Arfp19−/− mice (n = 3 each). Scale bars, 100 µm. o, Western blot analyses for MLKL and pMLKL on liver lysates from sham-operated or bile duct ligated Arfp19−/−mice (n = 3 each). Source Data

  7. Extended Data Fig. 7 Necroptosis signatures are found in primary human liver carcinomas.

    a, Transcriptomic patterns of apoptosis- (n = 84) or necroptosis- (n = 10) related genes in patients with HCC and ICC (n = 199) analysed via hierarchal clustering analysis. b, Gene expression of RIPK3 in ICC and HCC patient samples from the TIGER-LC cohort24(n = 199). P < 0.0001, Student’s two-sided t-test. Data are mean ± s.d. c, Western blot analysis for MLKL and pMLKL in lysates from TLR-knockout and p19Arf−/− mouse livers 3 days after Epo treatment. The experiment was performed once (n = 4 mice each). d, Immunohistochemistry quantification of B220 (P = 0.6698), CD3 (P = 0.2846), Ly6G (P = 0.9362) or MHCII (P = 0.6734) in livers from TLR5-knockout or syngeneic wild-type mice 3 days after Epo treatment (n = 3). P values determined by Student’s two-sided t-test. Data are mean ± s.d. e, qPCR-based cytokine profile of necroptosis-associated pattern in TLR5-knockout or syngeneic wild-type mice 3 days after Epo treatment (n = 2). Data are fold change of the mean from each group. f, Quantification of HNF4α-positive cells in Epo-induced tumours in TLR KO (TLR2, 3, 4, 7 and 9-knockout) (n = 3) or syngeneic wild-type (n = 4) mice. *P = 0.0255, Student’s two-sided t-test. Data are mean ± s.d. g, Representative micrographs of HNF4α and K19 staining on sections from tumours triggered by pCaMIN Epo delivery in TLR2 and TLR4 knockout or syngeneic wild-type mice (n = 5). Scale bar, 100 µm. Source Data

  8. Extended Data Fig. 8 Generation and analysis of clonally derived cell lines from HDTV or Epo tumours.

    a, Immunocytochemistry of isolated single cell lines of HDTV-derived HCC and Epo-derived ICC tumours. Depicted are representative co-staining images of K19 (red) and DAPI (blue). Scale bars, 100 µm. Experiment was performed twice with similar results. b, Schematic outline of the generation of clonal cell lines of Epo and HDTV tumours for subcutaneous injection into immunodeficient Rag2−/− mice. c, Representative micrographs of sections from subcutaneously grown HCC (see b; top) and ICC (bottom) with H&E (left) and K19 (right) staining. These data show that both HCC and ICC phenotypes are stably maintained even after in vitro passaging and in vivo retransplantation procedures in mice (n = 3). Scale bars, 100 µm. d, Bi-clustering of pairwise Pearson’s correlations based on normalized ATAC-seq fragment pseudo-counts for differentially accessible areas in ICC (n = 4 single cell clones) and HCC (n = 4 single cell clones). e, f, qRT–PCR analysis for Tbx3 (e) or Prdm5 (f) in mouse HCC or ICC cells (n = 4 single cell clones each). ***P = 0.0004, ****P < 0.0001, Student’s two-sided t-test. Data are mean ± s.d. Source Data

  9. Extended Data Fig. 9 Influence of PRDM5 and TBX3 on tumour phenotype.

    a, Representative micrographs of immunostaining for HNF4α or K19 on tumour sections after Epo delivery of pCaMIN transposon vector co-expressing control shRNA (shRen) and full-length Tbx3 (pCAMINshRen + Tbx3 Epo) or pCaMIN vector co-expressing Prdm5 shRNA and full-length Tbx3 (pCAMINPrdm5_1 + Tbx3 Epo) (n = 3). Scale bars, 100 µm. b, Representative micrograph of tumours induced by Epo delivery of pCaMIN and Tbx3 overexpression in ROSAmT/mG × Alb-cre × p19Arf−/− mice showing DAPI (blue) and mGFP (green) positivity (n = 6). Scale bar, 100 µm. c, qRT–PCR analysis for Tbx3 in mouse HCC cells stably expressing shRNAs targeting Tbx3 (shTbx3_1 and shTbx_2; n = 3). Data are mean ± s.d. d, qRT–PCR analysis for Prdm5 in mouse ICC cells stably expressing shRNAs targeting Prdm5 (shPrdm5_1 and shPrdm5_2; n = 2). Data are mean ± s.d. Source Data

  10. Extended Data Fig. 10 Direct and indirect changes of Tbx3 and Prdm5 targets and pathways.

    a, b, ChIP–seq density heat map for two biological replicates in the global set of reproducible peaks detected for Tbx3 (a) and Prdm5 (a) following the irreproducible discovery rate workflow (a and b, left) and corresponding ATAC-seq signal (a and b, right). Peaks are ranked according to the average ChIP–seq signal across replicates. The data are expressed as normalized reads per million mapped reads (RPM). The signal is shown 5 kb upstream and downstream of the centre of the ChIP–seq peaks. c, d, Heat maps depicting gene expression changes after Tbx3 shRNA-mediated (c) and Prdm5 shRNA-mediated (d) suppression. Only direct Tbx3 and Prdm5 targets are shown. Data are expressed as z-score. For each transcription factor (TBX3 or PRDM5), n = 4 cases (2 shRNAs per target, biological duplicates for each) and n = 2 controls (1 control shRNA in duplicate), two-sided moderated t-statistics. e, f, Heat maps depicting gene expression changes after Tbx3 (e) and Prdm5 (f) shRNA-mediated stable knockdown. Each knockdown experiment was performed in established cell lines from two different clones using two different shRNAs. In these heat maps, both direct and indirect Tbx3 and Prdm5 ChIP–seq-derived gene targets are shown. Differentially regulated genes were separated into direct or indirect Tbx3 or Prdm5 targets based on the presence or absence of proximal ChIP–seq peaks (<100 kb from the TSS or inside the gene body of deregulated genes). Data are expressed as row Z-score. For each transcription factor (TBX3 or PRDM5), n = 4 cases (2 shRNAs per target, biological duplicates for each) and n = 2 controls (1 control shRNA in duplicate), two-sided moderated t-statistics. g, Functional over-representation map depicting MSigDB canonical pathways associated to all/direct target/indirect target genes perturbed after Tbx3 and Prdm5 knockdown. The size of dots is proportional to the P value based on the hypergeometric distribution obtained when testing for over-representation, and their colour denotes whether the term is enriched for up or downregulated gene list. These data show regulation of distinct downstream pathways between Tbx3 (for example, biological oxidation, developmental biology) and Prdm5 (for example, extracellular matrix organization, collagen formation or Erbb signalling) (n = 4 cases; 2 shRNAs per target, biological duplicates for each, and n = 2 controls; 1 control shRNA in duplicate). h, qRT–PCR analysis of epigenetic modifiers from livers 3 days after Epo or HDTV treatment. All significantly regulated genes are shown (n = 3). P values determined by Student’s two-sided t-test. Data are fold changes of the mean. Source Data

Supplementary information

  1. Supplementary Figures

    This file contains Supplementary figures 1 and 2. Supplementary figure 1 shows the uncropped western blots: loading controls were always blotted on the same membranes. Supplementary figure 2 contains the representative gating strategy for flow cytometry used in this project

  2. Reporting Summary

  3. Supplementary Table

    This file contains Supplementary table 1: microarray data of knockdown experiments. It shows a list of gene expression data of ICC single cell clones upon Prdm5 knockdown (n=4) or ICC controls (n=2) and HCC single cell clones upon Tbx3 knockdown (n=4) or HCC controls (n=2)

  4. Supplementary Table

    This file contains Supplementary table 2: Oligonucleotid sequences. It shows a list of qPCR Primer, cloning Primer, shRNA sequences and sgRNA sequences

  5. Supplementary Table

    This file contains Supplementary table 3: Antibodies used for flow cytometry. It shows a list of all antibodies used for flow cytometry in this study

  6. Supplementary Table

    This file contains Supplementary table 4: Apoptosis and necroptosis gene signatures. It shows a list of genes reflecting apoptosis (n=84) or necroptosis (n=10) signatures in human samples

  7. Supplementary Table

    This file contains Supplementary table 5: ATAC and microarray data. It shows a list of gene expression data and ATAC-seq accessibility data in ICC (n=4) and HCC (n=4) cells

Source data

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DOI

https://doi.org/10.1038/s41586-018-0519-y

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