Mutant CTNNB 1 and histological heterogeneity define metabolic subtypes of hepatoblastoma

Research Article19 September 2017Open Access Source DataTransparent process Mutant CTNNB1 and histological heterogeneity define metabolic subtypes of hepatoblastoma Stefania Crippa Stefania Crippa Swiss Institute for Experimental Cancer Research, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland Search for more papers by this author Pierre-Benoit Ancey Pierre-Benoit Ancey Swiss Institute for Experimental Cancer Research, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland Search for more papers by this author Jessica Vazquez Jessica Vazquez Swiss Institute for Experimental Cancer Research, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland Search for more papers by this author Paolo Angelino Paolo Angelino Bioinformatics Core Facility, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland Search for more papers by this author Anne-Laure Rougemont Anne-Laure Rougemont Division of Clinical Pathology, Geneva University Hospitals, Geneva, Switzerland Search for more papers by this author Catherine Guettier Catherine Guettier Department of Pathology, Hôpital Bicêtre, HUPS, Assistance Publique-Hôpitaux de Paris, INSERM U1193, Faculté de Médecine, Université Paris Sud, Paris, France Search for more papers by this author Vincent Zoete Vincent Zoete Swiss Institute of Bioinformatics, Lausanne, Switzerland Search for more papers by this author Mauro Delorenzi Mauro Delorenzi Bioinformatics Core Facility, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland Ludwig Center for Cancer Research and Department of Oncology, University of Lausanne, Lausanne, Switzerland Search for more papers by this author Olivier Michielin Olivier Michielin Swiss Institute of Bioinformatics, Lausanne, Switzerland Search for more papers by this author Etienne Meylan Corresponding Author Etienne Meylan [email protected] orcid.org/0000-0002-0899-2230 Swiss Institute for Experimental Cancer Research, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland Search for more papers by this author Stefania Crippa Stefania Crippa Swiss Institute for Experimental Cancer Research, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland Search for more papers by this author Pierre-Benoit Ancey Pierre-Benoit Ancey Swiss Institute for Experimental Cancer Research, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland Search for more papers by this author Jessica Vazquez Jessica Vazquez Swiss Institute for Experimental Cancer Research, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland Search for more papers by this author Paolo Angelino Paolo Angelino Bioinformatics Core Facility, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland Search for more papers by this author Anne-Laure Rougemont Anne-Laure Rougemont Division of Clinical Pathology, Geneva University Hospitals, Geneva, Switzerland Search for more papers by this author Catherine Guettier Catherine Guettier Department of Pathology, Hôpital Bicêtre, HUPS, Assistance Publique-Hôpitaux de Paris, INSERM U1193, Faculté de Médecine, Université Paris Sud, Paris, France Search for more papers by this author Vincent Zoete Vincent Zoete Swiss Institute of Bioinformatics, Lausanne, Switzerland Search for more papers by this author Mauro Delorenzi Mauro Delorenzi Bioinformatics Core Facility, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland Ludwig Center for Cancer Research and Department of Oncology, University of Lausanne, Lausanne, Switzerland Search for more papers by this author Olivier Michielin Olivier Michielin Swiss Institute of Bioinformatics, Lausanne, Switzerland Search for more papers by this author Etienne Meylan Corresponding Author Etienne Meylan [email protected] orcid.org/0000-0002-0899-2230 Swiss Institute for Experimental Cancer Research, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland Search for more papers by this author Author Information Stefania Crippa1,‡, Pierre-Benoit Ancey1,‡, Jessica Vazquez1, Paolo Angelino2, Anne-Laure Rougemont3, Catherine Guettier4, Vincent Zoete5, Mauro Delorenzi2,6, Olivier Michielin5 and Etienne Meylan *,1 1Swiss Institute for Experimental Cancer Research, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland 2Bioinformatics Core Facility, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland 3Division of Clinical Pathology, Geneva University Hospitals, Geneva, Switzerland 4Department of Pathology, Hôpital Bicêtre, HUPS, Assistance Publique-Hôpitaux de Paris, INSERM U1193, Faculté de Médecine, Université Paris Sud, Paris, France 5Swiss Institute of Bioinformatics, Lausanne, Switzerland 6Ludwig Center for Cancer Research and Department of Oncology, University of Lausanne, Lausanne, Switzerland ‡These authors contributed equally to this work *Corresponding author. Tel: +41 21 693 7247; Fax: +41 21 693 7210; E-mail: [email protected] EMBO Mol Med (2017)9:1589-1604https://doi.org/10.15252/emmm.201707814 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Hepatoblastoma is the most common malignant pediatric liver cancer. Histological evaluation of tumor biopsies is used to distinguish among the different subtypes of hepatoblastoma, with fetal and embryonal representing the two main epithelial components. With frequent CTNNB1 mutations, hepatoblastoma is a Wnt/β-catenin-driven malignancy. Considering that Wnt activation has been associated with tumor metabolic reprogramming, we characterized the metabolic profile of cells from hepatoblastoma and compared it to cells from hepatocellular carcinoma. First, we demonstrated that glucose transporter GLUT3 is a direct TCF4/β-catenin target gene. RNA sequencing enabled to identify molecular and metabolic features specific to hepatoblastoma and revealed that several glycolytic enzymes are overexpressed in embryonal-like compared to fetal-like tumor cells. This led us to implement successfully three biomarkers to distinguish embryonal from fetal components by immunohistochemistry from a large panel of human hepatoblastoma samples. Functional analyses demonstrated that embryonal-like hepatoblastoma cells are highly glycolytic and sensitive to hexokinase-1 silencing. Altogether, our findings reveal a new, metabolic classification of human hepatoblastoma, with potential future implications for patients' diagnosis and treatment. Synopsis The fetal and embryonal subtypes of hepatoblastoma (HB), a rare pediatric liver cancer, are shown to represent two distinct metabolic subtypes, potentially offering new avenues for diagnosis or treatment of this malignancy. RNA sequencing from human liver tumor cell lines highlights signatures of carbohydrate transport and metabolism in HB cells compared to cells from hepatocellular carcinoma. HB subtype analyses demonstrate that embryonal-like HB cells display enhanced glycolysis, whereas fetal-like HB cells have stronger capacity for β-oxidation of fatty acids. Glucose transporter GLUT3 is a Wnt/β-catenin target gene whose protein product, strongly expressed in embryonal-like HB cells, drives glucose uptake and glycolysis. Glycolysis-associated proteins GLUT3 and LDHB are detected in 38 and 86% of HB tumor cases with embryonal components, but only in 0 and 46% of pure fetal HBs. Gluconeogenesis- and glycogenolysis-associated protein G6PC is detected in 30% of HB tumor cases with embryonal components, and in 93% of pure fetal HB cases. Introduction Hepatoblastoma (HB) and hepatocellular carcinoma (HCC) are the first and the second most common pediatric malignant liver tumors representing about 1–2% of cancers in children. HB is detected in very young children between the ages of 2 months and 3 years, while HCC occurs most frequently in children between 10 and 16 years old and is the predominant type of adult liver cancer. Fetal and embryonal HBs represent the two main epithelial subtypes, together with small cell-undifferentiated, pleomorphic poorly differentiated, cholangioblastic and epithelial macrotrabecular patterns (Lopez-Terrada et al, 2014). Pure fetal with low mitotic activity has the most favorable outcome and may be treated by surgery alone (Czauderna et al, 2014). Components of the Wnt/β-catenin pathway are frequently mutated and overactive in solid malignancies promoting tumor development (Clevers & Nusse, 2012). In HB, a driving proto-oncogene, and the most recurrently mutated gene, with 50–90% frequency, is CTNNB1 that encodes β-catenin (Koch et al, 1999; Cairo et al, 2008; Lopez-Terrada et al, 2009; Eichenmuller et al, 2014). CTNNB1 mutations in HB are located at exon 3, in a region of β-catenin important for its degradation by the proteasome (Aberle et al, 1997). Hence, in-frame deletions or missense mutations within exon 3 are gain-of-function mutations, resulting in a degradation-resistant β-catenin protein that accumulates in the nucleus, binds the TCF/LEF transcription factor, and drives the activation of target genes. Interestingly, in HB, large deletions in CTNNB1, which encompass exon 3 and part of exon 4, were reported exclusively in pure fetal tumor histotypes, whereas CTNNB1 mutations in embryonal HB are small mutations confined to exon 3 (Lopez-Terrada et al, 2009). Although it is unclear whether and how different deletions affect β-catenin activity, the differential gene expression profiles in HB subtypes raise the possibility of context and time-dependent activation of β-catenin, leading to enriched expression of Wnt and stem cell-related genes in embryonal tumors, and activation of hepatic differentiation program in fetal tumors (Cairo et al, 2008; Lopez-Terrada et al, 2009). Recently, Wnt signaling was shown to increase glycolysis through the upregulation of pyruvate dehydrogenase kinase 1 (PDK1) and other glycolytic genes (Pate et al, 2014). This highlights an important contribution of the Wnt pathway for reprogramming the cellular energetics of tumor cells. It is becoming increasingly appreciated that different tumor types exhibit different metabolic activities depending on the tissue of origin and oncogenic mutations. Indeed, differences in glucose and glutamine metabolism were recently uncovered between MYC- and MET-induced liver tumors and between MYC-induced liver and lung cancer (Yuneva et al, 2012). Hence, the identification of tumor subtypes that differ metabolically could help to guide new diagnosis and treatment approaches. In this study, we highlighted molecular and metabolic differences between HB and HCC and characterized the expression of metabolic genes in pediatric liver cancer. This led us to define two different metabolic subtypes of HB with particular diagnostic biomarkers. Results Molecular characterization of HB cell lines In an effort to comprehend molecular differences between two cancers that originate from the same tissue, HCC and HB, we profiled four HCC (Huh-1, Hep3B, HLE, and HLF) and four HB (Hep-U2, Huh-6, HepG2, and Hep293TT) cell lines using RNA sequencing (RNAseq). Principal component and heatmap analysis showed that the cell lines clustered by pairs (Fig 1A and B). Specifically, the two most epithelial HCCs (Huh-1 and Hep3B), as defined by CDH1high, VIMlow, and GLUT3low (Masin et al, 2014), clustered together, separately from the two most mesenchymal ones (HLE and HLF), as defined by CDH1low, VIMhigh, and GLUT3high, which clustered together, too. Within the HB group, Huh-6 clustered with Hep-U2 and HepG2 with Hep293TT (Fig 1A). Gene expression analysis revealed signatures of cytokine signaling enriched in the HCC group. In contrast, the HB group was enriched for signatures of carbohydrate transport and metabolism (Fig 1C), suggesting HBs differ from HCCs in sugar uptake and usage. Figure 1. Gene profiling reveals enhanced carbohydrate uptake and metabolism in tumor cells from HB compared to HCC Multidimensional scaling (MDS) plot of the expression levels derived from the RNAseq. Distance between sample labels indicates similarity. Samples form four distinct groups according to the four cell line types. Heatmap of the enrichment scores from the single-sample GSEA. Each column of the heatmap shows a cell line, while the rows represent gene sets. The plotted gene sets are the top 50 differentially enriched sets in HCC versus HB cell lines ranked by FDR. Color scale in the heatmap represents scores standardized across rows. Heatmap of the enrichment scores from the single-sample GSEA. Each column of the heatmap shows a cell line, while the rows represent gene sets. The plotted gene sets are those that have been found as significantly enriched or depleted in HCC versus HB cell lines at a false discovery ratio (FDR) < 0.05. Differential enrichment is assessed on the ssGSEA enrichment scores with the limma package. Color scale in the heatmap represents scores standardized across rows. Expression levels from RNAseq of the different glucose transporters differentially expressed in all HB compared to all HCC cell lines. Data show means ± s.d. (n = 4). P-values were determined by Mann–Whitney test. Download figure Download PowerPoint For this reason, we determined whether the differences between the two groups of liver cancer cells (HCC and HB) could be associated with different utilization of genes involved in glucose metabolism. We found a strong enrichment for glucose transporter expression in HB cells (Fig 1D). Among them, GLUT3 was robustly expressed in HBs compared to HCCs, with Huh-6 and Hep-U2 exhibiting the highest levels (Figs 1D and 2A, and Dataset EV1). We recently reported that SLC2A3 encoding for GLUT3 is induced by ZEB1 during epithelial–mesenchymal transition (EMT) in tumor cells from non-small cell lung cancer (NSCLC) and HCC (Masin et al, 2014). However, when we compared HLE, a mesenchymal HCC cell line (Masin et al, 2014), with our panel of four cell lines derived from human HB, GLUT3 expression was stronger in each of the HB cells (Fig 2A). Unfortunately, we could not maintain Hep-U2 in culture so our data on this cell line is limited to gene expression analysis. Immunocytochemistry (ICC) confirmed the strongest GLUT3 protein expression and membrane localization in Huh-6, followed by HepG2, Hep293TT, and HLE (Fig 2B). Huh-1, an epithelial HCC cell line, was negative. We then decided to explore whether this difference in GLUT3 expression is linked to a differential glucose uptake and utilization in the seven tumor cell lines. First, we monitored glucose uptake and found Huh-6 displaying a very high glucose consumption compared to every other cells (Fig 2C). We further explored whether this difference in glucose uptake may have consequences on the metabolic program of the different cells. Glycolysis and oxidation were determined through measurements of the extracellular acidification rate (ECAR) and oxygen consumption rate (OCR), respectively. The HB cell lines displayed a high oxidative and a low glycolytic profile (Fig 2D). Importantly, Huh-6 cells had a particular profile within HB, with a strong glycolytic metabolism (Fig 2D). The Huh-6 glycolytic profile was further confirmed using an independent assay based on the oxidative activity of mitochondria, showing the high glycolytic rate of those cells compared to all other cell lines (Fig 2E). To better characterize the glycolytic potential of the different cell lines, we performed sequential addition of glucose and 2-deoxyglucose (DG) after 24-h glucose starvation. As a result, we found that Huh-6 exhibited the highest glucose response in comparison with all other tested cell lines (Fig 2F). Interestingly, we also observed a response to glucose in Hep293TT confirming the glycolytic potential of these cells that we previously found with MitoTracker (Fig 2E). In contrast, we did not observe a clear response to glucose in the other tested cell lines. To understand the different metabolic profiles within HBs, we tested whether the less glycolytic cells, HepG2 and to a lesser extent Hep293TT, may have a higher fatty acid (FA) oxidation capacity. We measured OCR after addition of palmitate–BSA or BSA alone (vehicle control) after 24 h in limited medium (no glucose, 1% FBS). Only HepG2 consumed palmitate, while there was no response for Huh-6 (Fig 2G). A trend for increased consumption in Hep293TT was also detected, although this did not reach significance. Hence, decreased glucose use indeed correlates with increased FA use in HB cells. To understand better the molecular basis for FA consumption in the different cell lines, we took advantage of our RNAseq data to monitor the expression of CPT1A (carnitine palmitoyltransferase 1A), responsible for exogenous fatty acid incorporation into mitochondria. This gene was expressed in fetal-like cells with HepG2 displaying the strongest expression (Datasets EV1 and EV2), but was almost not expressed in embryonal-like Huh-6. In order to explore more precisely FA metabolism in the different HB cell subtypes, we used Etomoxir, a CPT1A inhibitor, which blocks exogenous fatty acid consumption. Only HepG2 oxygen consumption was affected by Etomoxir, revealing the highest exogenous fatty acid dependence in this cell type (Fig 2H). Figure 2. Identification of two HB metabolic subtypes Expression of GLUT3 in the different cell lines. Data show means ± s.d. (n = 3). P-values were determined by Mann–Whitney test. Immunocytochemistry of GLUT3 on the indicated cell lines. Dashed squares highlight a zoom on a cell. Scale bar: 20 μm. 2-Deoxy-d-[3H]glucose (DG) incorporation was measured in the indicated HB cell lines. Data show means ± s.d. (n = 5) of glucose uptake (nmol) normalized to protein concentration. Huh-6 values were significantly different (*P < 0.05, Mann–Whitney test) to each other cell lines. The highest P-value is indicated. Seahorse analysis of lactate production (ECAR) and oxygen consumption (OCR) in HB cell lines. Analysis was done on 10 measurements per sample and was performed 3 times. Data show means ± s.d. P-values were determined by Mann–Whitney test. Flow cytometry analysis of the oxidative state (oxy) with MitoTracker Red (MTRed) and Green (MTGreen). Data are normalized to total mitochondrial mass (MTRed/MTGreen). Glycolytic state (gly) was calculated as 1 – (MTRed/MTGreen). Huh-6 values were significantly different (****P < 0.0001) to each other cell lines. Data show means ± s.d. (n = 5). P-values were determined by Mann–Whitney test. After 24 h of glucose starvation, 10 mM glucose was added to the wells followed by 2-DG to block glycolysis at a concentration of 50 mM. Sequential measurements of ECAR from 10 replicates after the injections were done. Data show means ± s.e.m. After 24 h in limited medium, BSA-palmitate or BSA was added just before seahorse experiment; 5 sequential measurements of OCR from 10 replicates were done. Data show means ± s.d. P-values were determined by Mann–Whitney test. After 24 h in limited medium, BSA-palmitate or BSA was added just before seahorse experiment in presence or in absence of 10 mM Etomoxir. Five sequential measurements of OCR from 10 replicates were done. Data show means ± s.d. P-values were determined by Mann–Whitney test. Download figure Download PowerPoint To characterize the specific profile of Huh-6 within HBs, we examined broadly genes of glucose metabolism whose expression is, like that of GLUT3, significantly different between Huh-6 and the other HB cell lines. In total, we found nine differentially expressed genes implicated in glycolysis or the reverse biological process, gluconeogenesis. Specifically, in addition to GLUT3, three genes coding for enzymes of glycolysis, HK1 (coding for hexokinase 1), PFKP (phosphofructokinase, platelet type), and LDHB (lactate dehydrogenase B), were significantly more elevated in Huh-6. In contrast, genes promoting gluconeogenesis, PPARGC1A (PGC-1α), an important liver gluconeogenesis transcriptional co-activator (Herzig et al, 2001; Yoon et al, 2001), AQP9 (aquaporin-9), GK (glycerol kinase), and G6PC (glucose-6-phosphatase), were all less abundant in Huh-6 and Hep-U2 (Fig 3A, Appendix Fig S1A, Datasets EV1 and EV2), supporting the increased glycolysis of these cells compared to other HB cells. As notable exception, HepG2 and Hep293TT cells expressed HK2 to a significantly higher level than Huh-6 and Hep-U2. This is interesting, as HK2 is often overexpressed in adult solid tumors (Patra et al, 2013; Guo et al, 2015), which is compatible with a more differentiated state of those cells, while HK1 expression is strong specifically in the developing mouse liver (Appendix Fig S1B). Figure 3. The glycolytic profile of HB cell lines is correlated to β-catenin mutation Real-time PCR using specific probes for glycolytic (HK1, PFKP, LDHB, HK2) and gluconeogenesis (AQP9, GK, G6PC, PPARGC1A) genes. Data show means ± s.d. (n = 3). P-values were determined by Mann–Whitney test. Cytoplasmic and nuclear protein extracts isolated from the indicated cell lines were analyzed for β-catenin expression by Western blot. Histograms of the RNAseq read counts for the CTNNB1 region in the embryonal and fetal cell lines. Real-time PCR on GLUT3 after siGLUT3 or siCtrl transfection. Data show means ± s.d. (n = 3). P-values were determined by Mann–Whitney test. Glucose uptake assay in HepG2 and Huh-6 after transfection with siGLUT3 or siCtrl. Data show means ± s.d. (n = 5). P-values were determined by Mann–Whitney test. Seahorse glycolytic assay on the three hepatoblastoma cell lines after transfection with siGLUT3 or siCtrl. Sequential measurements of ECAR from 10 replicates after the injections were done. Data show means ± s.d. Multiple t-test was used. Source data are available online for this figure. Source Data for Figure 3 [emmm201707814-sup-0008-SDataFig3.pdf] Download figure Download PowerPoint Because of the singularity of Huh-6 cells within HBs for utilization of glucose and because HB is a β-catenin-driven cancer, we wanted to know whether the Huh-6 cell line could represent a different subtype of HB at the molecular level. Western blotting of β-catenin, frequently mutated in HBs, with an antibody recognizing the C-terminus, revealed two bands for HepG2 and Hep293TT, but only one for the other liver tumor cells including Huh-6 (Fig 3B). In contrast, a second antibody recognizing the N-terminus failed to detect the shortest form specific to HepG2 and Hep293TT, suggesting this variant lacks some amino acids located near the N-terminus (Fig 3B). Because HepG2 is known to carry a large CTNNB1 deletion of exon 3 and part of exon 4, and Huh-6 a point mutation within exon 3 (Koch et al, 1999), we hypothesized that Hep293TT carries a similar deletion than HepG2. To identify the exact CTNNB1 mutations, we analyzed the exon coverage profile using the RNAseq reads. This revealed that (i) Huh-6 has a GGA to GTA point mutation at codon 34, resulting in a G to V amino acid substitution, (ii) Hep-U2 carries a small deletion confined to exon 3, and (iii) both HepG2 and Hep293TT have a large deletion, typical of fetal HB subtypes, encompassing exon 3 and part of exon 4 (Fig 3C). PCR-based genomic amplification confirmed the genomic deletions (Fig EV1A). Mutations in Hep-U2 and Huh-6 are reminiscent of embryonal HB. Additionally, real-time PCR and ICC showed a stronger expression of LIN28A, expressed in undifferentiated tissues and important for embryonic stem cells (Tan et al, 2014), in Huh-6 and Hep-U2 compared to HepG2 and Hep293TT. In contrast, CLDN1, which codes for claudin-1, a tight junction protein enriched in the fetal compared to the embryonal components of HB (Halasz et al, 2006), was more elevated in HepG2 and Hep293TT (Fig EV1B–D and Dataset EV1). Finally, we analyzed the top 200 differentially expressed genes between the two cell clusters using Gene Ontology (http://geneontology.org/). This revealed biological processes of development, morphogenesis, and embryogenesis from the genes up in Huh-6 and Hep-U2, compatible with a block in early development or undifferentiated stage. In contrast, the same analysis highlighted metabolic processes from the genes up in HepG2 and Hep293TT, reminiscent of the physiological function of differentiated hepatocytes (Datasets EV3 and EV4). Hence, Hep-U2 and Huh-6 classify as poorly differentiated embryonal-like HB, whereas HepG2 and Hep293TT represent more differentiated, fetal-like HB. Click here to expand this figure. Figure EV1. Molecular characterization of embryonal and fetal-like HB cells Schematic representation of the primers used to amplify the genomic region spanning exon 3 to exon 4 of the CTNNB1 gene (left panel). Genomic DNA was isolated from the indicated cells lines and amplified by PCR. A PCR product of 640 bp was obtained from the full-length CTNNB1 gene. The appearance of a 124-bp band in HepG2 and Hep293TT indicated a heterozygous deletion of CTNNB1 gene (right panel). Real-time PCR analysis for the expression of LIN28A in the indicated cell lines. Data show means ± s.d. (n = 3). P-values were determined by Mann–Whitney test. Immunocytochemistry of Lin28A on the indicated cell lines. Dashed squares highlight a zoom on a cell. Scale bar: 20 μm. Expression analysis (RPKM) of the fetal marker claudin-1 (CLDN1) in the HB cell lines. Download figure Download PowerPoint We then explored the impact of GLUT3 in the different cell lines with GLUT3 siRNA, which strongly decreased its expression (~80% decrease) (Fig 3D). Using this approach, we first found that GLUT3 was essential for glucose uptake (Fig 3E) in Huh-6 but not in HepG2. Second, we performed a glycolysis assay using seahorse. Huh-6 glycolytic capacity was altered by the reduction of GLUT3 while HepG2 was insensitive to GLUT3 decrease (Fig 3F). Finally, Hep293TT exhibited an intermediate response in absence of GLUT3 correlating well with their partial glycolytic phenotype observed with MitoTracker and seahorse experiments (Fig 2E and F). In order to test the validity of our cell line-based classification in human samples, we used immunohistochemistry and stained 20 post-chemotherapy HB surgical specimens, which have been histologically classified and scored for their embryonal/fetal status by two pathologists (CG and ALR) using standard H&E or HPS staining (Dataset EV5). Because we identify GLUT3 as strongly expressed in embryonal-like cells, we assessed the ability of GLUT3 staining to distinguish embryonal and fetal tumors. While staining for GLUT3 was never observed in healthy liver, in tumor samples GLUT3 was exclusively expressed in a minority of the cells from embryonal foci (mild 1+ cytoplasmic reactivity, and mild 1+ or moderate 2+ membranous staining in 5/13 cases), or from the squamous component (mild 1+ or moderate 2+ staining in all three cases) (Figs 4A–C and EV2A–F). The latter finding prompted us to assess CK5/6 reactivity, a squamous epithelium and basal cell marker, in all HB cases displaying a histologically obvious squamous component, and/or reactivity to GLUT3. As expected, foci of squamous differentiation were reactive to CK5/6. Moreover, in one of four HBs with no obvious squamous differentiation, CK5/6 highlighted a basal-like cell component in areas reactive to GLUT3 (Fig EV2D–F). Importantly, the fetal cells did not stain for GLUT3 in any of the HBs with a fetal component (Fig 4A and C). Chemotherapy causes a decrease in GLUT3 expression (Watanabe et al, 2010), (Fig EV2G), suggesting GLUT3 expression is underestimated in post-chemotherapy HB samples. Thus, GLUT3 is a marker able to distinguish embryonal from fetal tumors. Figure 4. The embryonal component of human HB tumors stains positive for GLUT3 Representative H&E or GLUT3 staining of human HB tumor sections. Scale bars: 200 μm (original magnification ×40). Representative GLUT3 IHC staining of two human HB tumor sections at high magnification showing the embryonal component positive for GLUT3 expression. Stars: fetal component; arrows: embryonal component. Scale bars: 100 μm (original magnification ×100). Contingency table between the absence/presence of the staining and the embryonal/fetal status. Chi-square test was used. Download figure Download PowerPoint Click here to expand this figure. Figure EV2. Representative histological and immunohistochemistry findings A–F. GLUT3 reactivity was observed in the embryonal and squamous components only. Standard HPS stain shows squamous differentiation foci (A), confirmed by a CK5/6 immunostain (B and E). In the two tumors shown (C and F), focal mild 1+ cytoplasmic, and moderate 2+ membranous GLUT3 reactivity is seen. The embryonal component of the second tumor (D-F) shows a serpiginous architecture, with foci of tumor necrosis (star). Scale bars = 100 μm, except for (A) (scale bar = 50 μm). G. Relative expression of GLUT3, LDHB, and HK1 in Huh-6 cells treated or not for 24 h with cisplatin (cis). Data show means ± s.d. (n = 4). P-values were determined by Mann–Whitney test. Download figure Download PowerPoint GLUT3 is a target gene of the Wnt/β-catenin pathway Because HB is a β-catenin-driven malignancy, with c-Myc and YAP also contributing to disease development (Shachaf et al, 2004; Tao et al, 2014; Lehmann et al, 2016), we hypothesized one of them to be implicated in the su