Relevance of transportome among the mechanisms of chemoresistance in hepatoblastoma

Relevance of transportome among the mechanisms of chemoresistance in hepatoblastoma Candela Cives-Losada a,b , Maitane Asensio a,b , Oscar Briz a,b , Luis Miguel Chinchilla-T´ abora c , María Manuela Barranco b,d , ´ Alvaro del Río-´ Alvarez b,d , Maria Luz Martinez-Chantar b,e , Matias A. Avila b,f , Stefano Cairo g , Carolina Armengol b,d , Jose J.G. Marin a,b,1 , Rocio I.R. Macias a,b,1,* a Experimental Hepatology and Drug Targeting (HEVEPHARM), Institute for Biomedical Research of Salamanca (IBSAL), University of Salamanca, 37007 Salamanca, Spain b Center for the Study of Liver and Gastrointestinal Diseases (CIBEREHD), Carlos III National Institute of Health, 28029 Madrid, Spain c Pathology Service, University Hospital and IBSAL, 37007 Salamanca, Spain d Childhood Liver Oncology Group, Translational Program in Cancer Research (CARE), Germans Trias i Pujol Research Institute (IGTP), 08916 Badalona, Spain e Liver Disease Laboratory, Center for Cooperative Research in Biosciences (CICbioGUNE), Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain f Instituto de Investigaciones Sanitarias de Navarra IdiSNA, Hepatology Laboratory, Solid Tumors Program, CIMA, CCUN, University of Navarra, 31008 Pamplona, Spain g Champions Oncology, Rockville, MD 20850, USA ARTICLE INFO Keywords: Multidrug resistance Pediatric liver cancer Prognosis Resistome Tumor heterogeneity Sensitization ABSTRACT Approximately 20 % of hepatoblastomas (HBs) exhibit a poor response to conventional chemotherapy due to mechanisms of chemoresistance (MOCs), such as reduced intracellular drug accumulation. This study evaluated the role of transportome in the multidrug resistance (MDR) of HB. Paired HB and adjacent liver tissue samples (n =19) and HB-derived cell lines (HepG2, HuH6) were analyzed for their resistome characterization at mRNA (RTqPCR, Taqman Low-Density Array, sequencing) and protein (western blot, immunohistochemistry, immunofluorescence) levels. Cell viability (MTT test) proliferation and migration (holographic microscopy) were determined. The impact of short-term (72 h) and long-term (>10 months) exposure of HB cells to cisplatin or doxorubicin on the transportome was investigated. Solute carrier (SLC) family of transporters showed minor relevance in HB MDR, while drug export pumps, particularly MRP2, were associated with poor response to chemotherapy. Exposure of HB cells to doxorubicin or cisplatin up-regulated MDR1, MRP1 and MRP2. In cells with induced persistent chemoresistance, the expression of genes involved in other MOCs, and epigenetic machinery was altered. Chemoresistant cells showed cross-resistance to several anticancer drugs but maintained sensitivity to cabozantinib. In conclusion, drug export pumps, but not SLC uptake transporters, are key contributors to HB chemoresistance. Cabozantinib emerges as a potential therapeutic option for HBs resistant to conventional chemotherapy. Abbreviations: 5-FU, 5-fluorouracil; ABC, ATP-binding cassette; AFP, α -fetoprotein; calcein-AM, calcein acetoxymethyl ester; CF, 5(6)-carboxyfluorescein diacetate; CisPt, cisplatin; CR, cisplatin resistant; Doxo, doxorubicin; DR, doxorubicin resistant; FBS, fetal bovine serum; FTC, fumitremorgin C; HB, hepatoblastoma; HCC, hepatocellular carcinoma; IC 50 , half-maximal inhibitory concentration; IF, immunofluorescence; IHC, immunohistochemistry; IQR, interquartile range; MDR, multidrug resistance; MOC, mechanism of chemoresistance; N.D., not detected; NR, non-responder; NT, non-tumor tissue; OS, overall survival; PRETEXT, PRETreatment and EXTension of the tumor; qPCR, quantitative PCR; R, responder; ROC, receiver operating characteristic; RNA-seq, RNA sequencing; RT, reverse transcription; SLC, solute carrier; TKI, tyrosine kinase inhibitor; TLDA, Taqman Low-Density Array; WT, wild type. * Corresponding author at: Department of Physiology and Pharmacology, University of Salamanca, Campus Miguel de Unamuno, E.D. Lab B-17, 37007-Salamanca, Spain. E-mail address: [email protected] (R.I.R. Macias). 1 Both authors contributed equally as senior authors to this work. Contents lists available at ScienceDirect Biochemical Pharmacology journal homepage: www.elsevier.com/locate/biochempharm https://doi.org/10.1016/j.bcp.2025.116914 Received 22 December 2024; Received in revised form 13 March 2025; Accepted 27 March 2025 Biochemical Pharmacology 237 (2025) 116914 Available online 2 April 2025 0006-2952/© 2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ). 1. Introduction Hepatoblastoma (HB) is the most common liver cancer in children. In contrast to hepatocellular carcinoma (HCC), which generally affects adults with underlying cirrhosis, HB develops in the absence of apparent liver disease [1]. Although rare, with an incidence of approximately two cases per million children, its occurrence has risen globally at a rate higher than that of other pediatric cancers in recent decades [2]. Most cases are diagnosed within the first three years of life [3], typically through physical examination, elevated α -1-fetoprotein (AFP) levels (present in ≈90 % of cases) [3], imaging studies, and histological analysis. Histopathologically, HB reflects different stages of liver development [4]. Well-differentiated fetal HBs have an epithelial phenotype, whereas immature forms such as embryonal component usually display aggressive behavior, metastasis, and chemoresistance [2,5]. Genomic, transcriptomic, epigenetic and metabolomic analyses have facilitated the molecular classifications of HB. Cairo et al. [5] identified two subtypes of this liver cancer (C1, C2) based on the expression profile of 16 genes, with C1 linked to mature hepatocyte markers and C2 to liver progenitor cell markers. Hooks et al. [6] further divided C2 into two subgroups C2A and C2B, differing in the expression of four genes (HSD17B6, ITGA6, TOP2A, and VIM). Recent epigenetic studies proposed two clusters, Epi-CA and Epi-CB, based on DNA methylation profiles and an integrative molecular risk stratification of HB [7]. Clinically, HB is stratified using CHIC stratification [8] that is based on the PRETEXT (from PRETreatment and EXTension of the tumor) algorithm, which assesses tumor extension (I-IV) to guide personalized therapeutic strategies [9]. Treatment options include: chemotherapy (neoadjuvant/adjuvant), surgical resection, and liver transplantation [10]. Although most patients with HB respond to standard pharmacological treatment, based mainly on a combination of doxorubicin and cisplatin [11], ≈20 % of patients exhibit aggressive, chemoresistant tumors with poor prognosis [12,13], and ≈12 % of patients initially considered in complete remission are prone to relapse [14]. Additional prognostic factors included in the CHIC stratification are age at diagnosis, serum levels of AFP, PRETEXT stage, and the presence of metastases. Molecular features, such as C2-pure subtype and epigenetic alterations (Epi-CB subtypes), are also associated with worse outcomes [15]. The lack of response of HB to pharmacological treatment involves complex mechanisms of chemoresistance (MOCs), which can be inherently present in HB cells or develop after exposure to antitumor drugs, conferring multidrug resistance (MDR). MOCs have been classified into seven groups depending on whether they account for: a reduction in the intracellular drug content, thereby limiting its ability to reach its target, which can result from decreased expression or function of plasma membrane solute carriers (SLCs) responsible for drug uptake (MOC-1a) or from increased expression of ATP-binding cassette (ABC) transporters involved in drug export (MOC-1b). Additionally, a lower proportion of active drug within tumor cells may arise due to alterations in drugmetabolizing enzymes, either through reduced expression of those involved in pro-drug activation or increased expression of inactivating enzymes (MOC-2). Chemoresistance can also occur due to modifications in the expression or function of molecular targets (MOC-3); enhanced DNA repair mechanisms counteracting drug-induced damage (MOC-4), and dysregulated apoptotic pathways (MOC-5), either via reduced proapoptotic factors (MOC-5a) or up-regulated pro-survival genes (MOC5b). Further contributing factors include alterations in the tumor microenvironment (MOC-6) and phenotypic transformations, such as epithelial-mesenchymal transition (EMT) or the acquisition of stemness characteristics (MOC-7) [16]. Changes in the expression levels of genes accounting for these MOCs, the so-called resistome, and the presence of genetic variants in these genes can influence drug response and, hence, patient outcomes [16,17]. This study aimed to characterize the HB resistome, with a focus on the transportome (accounting for MOC-1) and to evaluate the impact of chemotherapy on gene expression. Additionally, the existence of cross-resistance and the identification of collateral drug sensitivity were investigated in chemoresistant HB cell models developed for this purpose. 2. Materials and methods 2.1. Human samples Samples from surgically resected HBs and paired adjacent nontumor tissue (n =19) were obtained from the Childhood Liver Oncology Group at the Germans Trias i Pujol Research Institute (IGTP) in Badalona, Spain. The study was conducted in compliance with national regulations, and the protocol received approval from the Ethics Committees for Clinical Research of IGTP and Salamanca University Hospital (Ref. 2020-02-42). To utilize biological samples in biomedical research, written informed consent was obtained from patients or their legally authorized representatives. Relevant clinical and pathological information is summarized in Table 1. All patients received neoadjuvant chemotherapy, and the reduction in serum AFP levels served as a surrogate marker for chemotherapy response. Patients were categorized as responders (R) when the percentage of reduction of AFP values after treatment was ≥90 %, while those with a lower percentage were classified as non-responders (NR). 2.2. Chemicals 5(6)-carboxyfluorescein diacetate (CF), cisplatin, diclofenac, doxorubicin, epirubicin, etoposide, 5-fluorouracil (5-FU), fumitremorgin C (FTC), irinotecan, mitoxantrone, probenecid, rhodamine 123, verapamil, vinblastine, and vincristine were obtained from Sigma-Aldrich (Merck, Madrid). Calcein acetoxymethyl ester (calcein-AM) was purchased from Invitrogen (Thermo Fisher, Madrid). Oxaliplatin was obtained from Acros Organics (Thermo Fisher). Cabozantinib, idarubicin, lenvatinib, PFI-2, regorafenib, and sorafenib were acquired from Selleckchem (Deltaclon, Madrid). All chemicals were of ≥98 % purity. CM272, a dual inhibitor of G9a and DNA methyltransferase (DNMT) activity, was synthesized as previously published [18]. 2.3. Cell cultures Human HB cell lines HepG2 (HB-8065) and HuH6 cells (JCRB0401) were obtained from the American Type Culture Collection (ATCC) (LGC Standards, Barcelona, Spain) or the Japanese Collection of Research Bioresources Cell Bank (Osaka, Japan), respectively. HepG2 cells were cultured in MEM medium (Sigma-Aldrich) supplemented with fetal bovine serum (FBS, 10 %), penicillin/streptomycin/amphotericin B (Thermo Fisher), 1 mM sodium pyruvate (Sigma-Aldrich), and 26 mM sodium bicarbonate (Sigma-Aldrich). HuH6 cells were cultured in DMEM medium (Sigma-Aldrich) supplemented with FBS (10 %), penicillin, streptomycin (Thermo Fisher), and GlutaMAX (Thermo Fisher) (passages 15–40). Drug resistant sublines (HuH6/CR for cisplatin resistance and HepG2/DR for doxorubicin resistance) were developed by exposing wild type (WT) cells to increasing drugs concentrations over 10 months; from 0.1 to 2 µM cisplatin and from 10 to 200 nM doxorubicin. Cell morphology, viability, and growth rate were monitored during resistance development. Resistant cells were maintained in drug-containing culture media unless stated otherwise (passages 40–80). Cell lines were verified to be mycoplasma-free throughout the experiments using the Mycoplasma Gel Form Kit (Biotools B&M Labs, Madrid). 2.4. Determination of cell viability and cell proliferation rate To determine cytostatic activity, 7,500 cells/well (HepG2-WT and HepG2/DR), 4,000 cells/well (HuH6), and 8,000 cells/well (HuH6/CR) C. Cives-Losada et al. Biochemical Pharmacology 237 (2025) 116914 2 were seeded in 96-well plates. After 24 h, cells were exposed to several concentrations of compounds for 72 h. Cell viability was measured using MTT-formazan or sulforhodamine B assays, as previously reported [19], and proliferation rates were assessed via real-time digital holographic cytometry with a HoloMonitor® Live Cell Imaging System (Phase Holographic Imaging PHI AB, Lund, Sweden). 2.5. Clonogenic and cell migration assays Cell migration was analyzed using wound healing assays. Monolayer wounds were captured at various time points using phase holographic microscopy. Colony formation assays were performed by seeding 1000 cells per well into 6-well plates and culturing them for 15 days (HepG2) or 10 days (HuH-6), with media changes every 2–3 days. Colonies were fixed for 5 min using a methanol: acetic acid solution (7:1 ratio) at room temperature and stained with 0.05 % crystal violet in 25 % methanol for 15 min. After washing with water to remove the excess of crystal violet, colonies were counted, and their area was calculated using ImageJ imaging software (NIH, MD, USA). 2.6. Transport assays Cells in suspension were incubated at 37 ◦C for 30 min in 100 μ l of “transport buffer” (96 mM NaCl, 5.3 mM KCl, 0.8 mM MgSO 4 , 1.1 mM KH 2 PO 4 , 1.8 mM CaCl 2 , 11 mM glucose, and 50 mM HEPES, pH 7.40 adjusted with Tris) containing fluorescent substrates of ABC proteins: rhodamine 123 for MDR1, calcein-AM for MRP1 and MRP2, CF for MRP3, MRP4, and MRP5, and mitoxantrone for BCRP. This loading period was terminated by diluting with 900 μ l of substrate-free medium, with or without transporter inhibitors (verapamil for MDR1, probenecid for MRP1 and MRP2, diclofenac for MRP3, MRP4, and MRP5, and FTC for BCRP), and intracellular fluorescence was determined by flow cytometry in a FACSCalibur flow cytometer (BD Biosciences, Madrid). Efflux of the preloaded substrate occurred in the diluted medium before measuring the intracellular substrate content again. The export activity of ABC proteins was determined by calculating the difference in intracellular content between the end of the loading phase (30 min) and the completion of the efflux phase (30 min). Efflux was quantified as the percentage of the total substrate initially loaded. 2.7. Quantification of gene expression Total RNA was isolated using RNA mini-spin columns treated with RNase-free DNase I with the illustraRNAspin Mini RNA Isolation Kit (GE Healthcare, Madrid). Reverse transcription (RT) was performed using random primers and SuperScript VILO cDNA Synthesis kit (Invitrogen) or High-Capacity cDNA Reverse Transcription kit (Applied Biosystems, Thermo Fisher). Quantitative PCR (qPCR) was conducted using gene-specific primers spanning exon-exon junctions in the target mRNA (Table 2). Reactions were performed in single-tube assays with AmpliTaq Gold DNA polymerase and the SYBR Green I detection kit on a QuantStudio™ 3 RealTime PCR System (Applied Biosystems) or using Taqman Low-Density Arrays (TLDAs) in an ABI Prism 7900HT Sequence Detection System (Applied Biosystems). Thermal cycling conditions were the following: single cycles at 50 ◦C for 2 min and at 95 ◦C for 10 min, and 40 cycles at 95 ◦C for 15 s and at 60 ◦C for 60 s. The mRNA abundance was normalized based on ROTH2 content for single-tube qPCR, while ACTB was used for TLDA analyses. 18S rRNA was used as a quality control check. 2.8. Determination of gene expression by high-throughput techniques RNA sequencing (RNA-Seq) in cell lines was performed at the University of Salamanca Sequencing Service. After checking the integrity of the RNA using the TapeStation bioanalyser (Agilent Technologies, Santa Clara CA), mRNA libraries were prepared using 1 µg of total RNA and the ‘mRNA HyperPrep-Kit.Illumina’ method (Roche, Basel, Switzerland), according to the manufacturer’s specifications. Sequencing was performed on a NovaSeq 6000 configured with 2x100bp and 50 M paired end reads. RNA-Seq data was analyzed by the University of Salamanca’s Bioinformatics Unit through the alignment of reads and quantification of gene expression using DEXSeq and EdgeR methods for exon counting. High-throughput proteomic analyses of cell lysates were conducted using liquid chromatography-mass spectrometry (LC-MS) on a timsTOF Pro with PASEF instrument (Bruker Daltonics) coupled online to an Evosep ONE. A total of 200 ng of sample was directly loaded onto an analytical column (EVOSEP) and separated at a flow rate of 300 nL/min using a 44-minute gradient. Protein identification and quantification were performed with PEAKS X software (Bioinformatics Solutions). Peptide quantity normalization and batch effect correction were carried out using the proBatch R package. 2.9. Immunoblotting and immunofluorescence assays Immunoblotting analyses of cell lysates, prepared in RIPA buffer, were performed using 7.5–10 % SDS-PAGE gels loading twenty to fifty µg of protein homogenate per lane. In some cases, samples were denatured by heating at 100 ◦C for 5 min. Subsequently, proteins were transferred onto nitrocellulose membranes. Blots were probed with primary antibodies (Table 3). Appropriate horseradish peroxidaselinked secondary antibodies (Invitrogen) were diluted 1:2000. An enhanced chemiluminescence detection system (Hybond ECL; GE Healthcare) was used for band visualization in a LAS-4000 image analysis system (Fujifilm, TDI, Madrid). Immunofluorescence (IF) staining was performed on cells cultured on coverslips, which were fixed and permeabilized with ice-cold methanol or, in the case of BCRP, with 4 % paraformaldehyde (PFA) followed by 0.1 % Triton-X. The cells were subsequently incubated with primary antibodies (Table 3). The secondary antibodies conjugated to Alexa Fluor-488 or Alexa Fluor-594 (Thermo Fisher) were diluted at a 1:2000 ratio. Nuclei were counterstained with DAPI (Sigma-Aldrich). Confocal laser-scanning microscopy was performed using a Leica TCS SP2 confocal microscope. Immunohistochemistry (IHC) was carried out at the Pathology Service of the Salamanca University Hospital Complex using formalin-fixed paraffin-embedded (FFPE) sections from eight HBs. The process included antigen retrieval at pH 6.0 and incubation with primary antibodies (Table 3) for 40 min in a Leica Biosystems BOND-III Fully Automated IHC and ISH Stainer. Slides were counterstained with hematoxylin (Palex, Madrid) and mounted using an aqueous mounting medium. Imaging was performed at ×200 magnification using the dotSlide virtual image system (Olympus, Tokyo, Japan) at the Comparative Table 1 Clinical and pathologic characteristics of HB patients. Characteristics Values Age, months (mean/range) 14.5/3.8–65 Sex (Male/Female) 11/8 Serum AFP, ng/mL (range) 18,000–2,186,461 Histology (Epithelial/Fetal/Mixed/Full regression) 6/1/11/1 Molecular subclass (C1/C2) 9/10 Tumor stage  PRETEXT stage (I/II/III/IV) 1/7/7/4 Metastasis at diagnosis (Y/N) 3/16 Multifocality (Y/N) 6/13 Vascular invasion (Y/N) 4/15 Pre-surgical chemotherapy (Y/N) 19/0 Response to chemotherapy (Y/N) 14/5 SIOPEL protocol (3/4/6) 5/5/9 Follow-up, months (mean/range) 47.1/3.5–96.5 Dead of the disease 5 % AFP, alpha-fetoprotein; PRETEXT, pretreatment extent of disease; Y/N, Yes/No. C. Cives-Losada et al. Biochemical Pharmacology 237 (2025) 116914 3 Molecular Pathology Unit of Salamanca Centro de Investigaci´ on del C´ ancer. Images were acquired using Olympus software (DotSlide, OlyVIA, Olympus). 2.10. Statistical analysis Data processing and statistical analyses were performed using Microsoft Office Excel (version 365) and GraphPad Prism 8. For normally distributed data, results were expressed as mean ±SEM, and statistical significance between groups was assessed using parametric tests. Student’s t-tests were applied for comparisons between two sample groups, with paired or unpaired tests used as appropriate. For nonnormally distributed data, results were presented as median and interquartile range (IQR), and the statistical significance of the differences between groups was determined using nonparametric tests. Specifically, Wilcoxon signed-rank tests were employed for paired samples, while Mann-Whitney U tests were used for unpaired comparisons. The performance of transportome gene expression in predicting HB patients’ response, encompassing accuracy, sensitivity and specificity, was assessed through binormal Receiver Operating Characteristic (ROC) curves using online software (https://www.rad.jhmi.edu/jeng/javara d/roc/JROCFITi.html) from Johns Hopkins University School of Medicine, MD, US. 3. Results 3.1. Transportome characterization in HB clinical samples In this study, we focused on transporters involved in the uptake and efflux of drugs used in HB. Specifically, we explored the role of drug uptake transporters OATP1B1, OATP1B3, OATP2B1, OCT1, OCT3 and CTR1 (SLCO1B1, SLCO1B3, SLCO2B1, SLC22A1, SLC22A3 and SLC31A1 genes, respectively), and drug efflux pumps MDR1, MRP1-5 and BCRP (ABCB1, ABCC1-5 and ABCG2 genes, respectively). mRNA expression was quantified by RT-qPCR in 19 paired samples of HB and adjacent nontumor tissue (NT) samples (Fig. 1). RHOT2 served as a normalizer due to its consistent expression in HB. Patients included in this cohort had received cisplatin and doxorubicin-based chemotherapy before sample collection. Table 1 presents the clinical and pathological characteristics of patients. The expression levels of all uptake transporters, except for SLC22A3, were lower in HB than in NT. Among these genes, SLCO1B3 and SLC22A1 exhibited the lowest mRNA expression in HB (Fig. 1A). The remaining transporters showed moderate to high expression levels in HB. All analyzed pumps were highly expressed in HB (Fig. 1B), which supports their potential role in chemoresistance, despite some differences with NT were found. Thus, compared to NT, there was a significant increase in mRNA levels of ABCC5 and a trend in those of ABCC1 and ABCC4 in HB, while ABCB1, ABCC2, and ABCG2 were downregulated. Regarding HB subtypes, the expression levels of SLCO1B1, SLCO2B1, and SLC22A1 were significantly lower in C2 tumors compared to C1 tumors (Fig. 1A). However, no significant differences were observed in export pumps (Fig. 1B). Regarding treatment response, no significant differences in any uptake transporter between responders (R) and nonresponders (NR) were found, and only a trend toward lower expression of SLC22A1 in NR was seen (Fig. 1A). However, the expression of the export pumps was significantly higher in NR (Fig. 1B). Next, we analyzed the relationship between changes in the transportome and the lack of response to chemotherapy, indirectly measured by decreased serum AFP levels following pharmacological treatment. Considering the low expression of uptake transporters and high expression of drug export pumps as predictors of chemoresistance, SLC22A1 (Fig. 2A) and ABCC2 (Fig. 2B) exhibited the highest values of specificity (71.4 and 85.7 %, respectively) and sensitivity (57.1 and 78.6 %, respectively), while the other transporters showed poor values (Fig. 2C). Although OCT1 transports doxorubicin and cisplatin, this transporter was not further investigated due to low expression in most tumors. Concerning transport proteins, besides expression levels, their correct function requires their localization in the plasma membrane of tumor cells; we performed an IHC analysis of the most relevant Table 2 Forward (F) and reverse (R) primers used to determine the expression levels of the genes of interest by RT-qPCR. Gene Protein Primers Type Amplicon (bp) Accesion number ABCB1 MDR1 GCGCGAGGTCGGAATGGAT F 198 NM_000927 CCATGGATGATGGCAGCCAAAGTT R ABCC1 MRP1 CCGCTCTGGGACTGGAATGT F 215 NM_004996 GTGTCATCTGAATGTAGCCTCGGT R ABCC2 MRP2 TGAAGAGGAAGCCACAGTCCATGA F 171 NM_000392 TTCAGATGCCTGCCATTGGACCTA R ABCC3 MRP3 CCAAGTTCTGGGACTCCAACCTG F 160 NM_003786 ATGATGTAGCCACGACAATGGTGC R ABCC4 MRP4 TGCAAGGGTTCTGGGATAAAGA F 141 NM_005845 CTTTGGCACTTTCCTCAATTAACG R ABCC5 MRP5 CGTGAACTGCAGAAGACTAGAGAGACT F 128 NM_005688 GGCACACGATGGACAGGATGA R ABCG2 BCRP CCCAGGCCTCTATAGCTCAGATCATT F 161 NM_004827 CACGGCTGAAACACTGCTGAAACA R RHOT2 RHOT2 CTGCGGACTATCTCTCCCCTC F 151 NM_138769 AAAAGGCTTTGCAGCTCCAC R SLC22A1 OCT1 TGCAGACAGGTTTGGCCGT F 187 NM_003057 GCCCGAGCCAACAAATTCTGTGAT R SLC22A3 OCT3 CATCGTCAGCGAGTTTGACCTTGT F 139 NM_021977 GTAAATGACGATCCTGCCATACCTGTCT R SLC31A1 CTR1 GGAAATTCTTGCCCAACTAAACCCAG F 277 NM_001859 TCCGCCTCCTAGGTTCAAGTGATT R SLCO1B1 OATP1B1 GCATCACCTGAGATAGTGGGAAAAGGTT F 104 NM_006446 GGAGTCTCCCCTATTCCACGAAGCATAT R SLCO1B3 OATP1B3 GATTCAAGATGTTCTTGGCAGCCCT F 136 NM_019844 CCATCAATTAAACCAGCAAGAGAAGAGGA R SLCO2B1 OATP2B1 GAAGGGCAAGGACTCTCCCTCTA F 87 NM_007256 GTCAGGTTTGGTGCAATCTGGACT R   bp, base pairs; F, forward; R, reverse. C. Cives-Losada et al. Biochemical Pharmacology 237 (2025) 116914 4 transporter in HB chemoresistance based on the results described above, specifically MRP2 in eight paired tumor and non-tumor samples, four from the R group and four from the NR group. For comparison, we also analyzed MDR1 and MRP1. As expected, MDR1 and MRP2 were localized in the canalicular membrane of the hepatocytes and in the apical plasma membrane of cholangiocytes. We also found intracellular labeling for MRP1 in hepatocytes and cholangiocytes as it is less expressed and typically localized in the basolateral membrane of hepatocytes (Fig. 3A-D). Considerable inter-individual variability was found in IHC analysis results of HB samples. Here, we present representative images of a tumor included in the responder group (R) with low MDR1, MRP1, and MRP2 staining (Fig. 3E-H), together with another one included in the non-responder group (NR), with high staining of these pumps (Fig. 3I-L). 3.2. Modulation of the transportome in HB cells following short-term exposure to antitumor drugs Since the samples included in the study were from previously treated children, it was not possible to characterize the transportome profile in naïve patients nor investigate the effect of drug exposure. As an alternative, we used in vitro models, specifically, HB-derived cells HepG2 and HuH6. Dose-response curves for the cytotoxic effects of cisplatin and doxorubicin revealed a similar pattern for both cell lines (data not shown). We conducted a short-term exposure (72 h) of HepG2 and HuH6 to cisplatin and doxorubicin at near their half-maximal inhibitory concentrations (IC 50 , 5 µM and 0.1 µM, respectively) to evaluate transportome changes (Figs. 4 and 5). Uptake transporters (Fig. 4) were expressed at negligible levels (SLCO1B1, and SLCO1B3 in both cell lines, and SLC22A3 in HuH6 cells), or showed very low expression (SLC22A1 in HuH6 cells). Short-term exposure to doxorubicin or cisplatin had no significant effect on the mRNA levels of uptake transporters in HepG2 cells (Fig. 4A). In HuH6 cells, a modest up-regulation was observed in response to pharmacological stress (Fig. 4B), which would not be consistent with decreased sensitivity. In contrast, both drugs significantly up-regulated efflux transporters in HepG2 (Fig. 5A) and HuH6 (Fig. 5D) cells, with the exception of ABCC4 and ABCC5. Western blot analysis (Fig. 5B and 5E) and functional assays (Fig. 5C and 5F) confirmed these findings, although the functional changes were less pronounced. 3.3. Generation of chemoresistant HB cells HB cells with persistent resistance to either doxorubicin or cisplatin were established by exposing HepG2 and HuH6 cells to stepwise increasing concentrations of each drug for at least 10 months. After this selection process, HepG2 cells tolerant to 200 nM doxorubicin (HepG2/ DR) (Fig. 6A), and HuH6 cells capable of growing in the presence of 2 µM cisplatin (HuH6/CR) (Fig. 6B) were obtained. Accordingly, the IC 50 values for chemoresistant cells were markedly increased (4-fold and 50fold, respectively) compared to their parental wild-type cells. Interestingly, the resistance persisted even after removing the pharmacological pressure (DRand CR-, respectively). The sensitivity of HepG2/DR cells to doxorubicin remained unchanged 10 days after being cultured in the absence of this drug, showing minimal recovery after an additional 18day period. The sensitivity of HuH6/CR cells to cisplatin persisted for at least 28 of culturing the cells with the cisplatin-free medium. Besides chemoresistance, other characteristics of malignancy were observed in the generated cells. Thus, cell proliferation rate was slower in HepG2-DR than in wild-type HepG2 cells. This difference was not abolished by culturing the chemoresistant cells in the absence of doxorubicin for up to 28 days (Fig. 7A). Cell migration, as determined by the wound-healing test, was also slower in HepG2-DR cells (Fig. 7A). Moreover, these cells formed fewer and smaller colonies than their parental wild-type cells (Fig. 7A). Similar phenotypic changes were seen in HuH6-CR cells. Besides slower cell proliferation and cell migration (Fig. 7B), the colony formation was also less efficient in HuH6-CR cells as compared with wild-type HuH6 cells (Fig. 7B). 3.4. Changes in the resistome of chemoresistant HB cells To characterize the resistome of chemoresistant cells, the expression of a panel of 93 MOC genes was determined by RT-qPCR using TLDAs (Fig. 8). Regarding drug transporters (MOC-1), some genes involved in drug uptake, such as SLC29A2, SLC47A1, and SLCO4A1, showed lower expression in HepG2/DR cells(Fig. 8A). In contrast, a marked increase in the expression of the drug export pump ABCB1 was found. Other genes encoding doxorubicin exporters, such as ABCC1 and ABCC2, were moderately up-regulated. Moreover, some enzymes that participate in drug metabolism were down-regulated, while others had higher expression in HepG2/DR cells (MOC-2). In the MOC-3 group, we found that doxorubicin target TOP2A was down-regulated in resistant cells. Several genes involved in DNA repair processes (MOC-4) were upregulated in HepG2-DR cells; ERCC1, MLH1, and PMS1 stood out among them. Among pro-apoptotic genes (MOC-5a), only PTEN, which was already poorly expressed in wild-type HepG2 cells, was downregulated in HepG2/DR cells. In contrast, several genes favoring cell survival (MOC-5b) were up-regulated in chemoresistant cells. No change in MOC-6 genes associated with changes in tumor microenvironment was found. Interestingly, an increased expression of several genes involved in the acquisition of stem cell characteristics (MOC-7) was seen. Changes in the transportome of HuH6/CR cells were also detected (Fig. 8B). These were down-regulation of nucleoside uptake transporters (mainly SLC29A2) (MOC-1a) and up-regulation of several export pumps (MOC-1b), including ABCB1 and ABCC2. The up-regulation of some drug-metabolizing enzymes (MOC-2) was found in chemoresistant cells. Regarding changes in molecular targets (MOC-3), marked upregulations (e.g., TOP2A) and down-regulations (e.g., PDGFRA) were observed in chemoresistant cells. Genes involved in DNA repair processes (MOC-4), which are particularly relevant in resistance to cisplatin Table 3 Primary antibodies and conditions used for protein detection by Western blot, immunofluorescence or immunohistochemistry. Antigen Host Manufacturer Catalog# Dilution Western blot     MDR1 Mouse Thermo Fisher C219 1:1000 MRP1 Rat Enzo Life Sciences MRPr1 1:500 MRP2 Mouse Enzo Life Sciences M2III6 1:1000 MRP3 Rabbit Sigma-Aldrich M0318 1:500 MRP4 Rat Abcam M4I-10 1:1000 MRP5 Rat OriGene Technologies M5I-10 1:50 BCRP Mouse Abcam BXP-21 1:500 α -tubulin Mouse Sigma-Aldrich DM1A 1:1000 Immunofluorescence     MDR1 Mouse Thermo Fisher C219 1:25 MRP1 Rat Enzo Life Sciences MRPr1 1:40 MRP2 Mouse Enzo Life Sciences M2III6 1:33 MRP3 Mouse Abcam M3III9 1:50 MRP4 Goat Novus NB1001471 1:50 MRP5 Rat OriGene Technologies M5I-10 1:25 BCRP Mouse Abcam BXP-21 1:50 Na + /K + -ATPase Mouse Abcam M7-PB-E9 1:100 Na + /K + -ATPase Rabbit Abcam EP1845Y 1:100 Immunohistochemistry     MDR1 Rabbit Cell Signaling Technology E1Y7B 1:50 MRP1 Mouse Santa Cruz QCRL-1 1:50 MRP2 Mouse Enzo M2III-5 1:25 C. Cives-Losada et al. Biochemical Pharmacology 237 (2025) 116914 5 Fig. 1. Relative mRNA levels of drug uptake transporters (A) and drug export pumps (B) in paired samples (n =19) of hepatoblastoma (HB) tumor (T) and adjacent non-tumor tissue (NT) measured by RT-qPCR. Squares are mean ±SEM. *, p <0.05, compared with NT tissue using the Wilcoxon test for paired samples. The above values of gene expression were classified into two groups of HB defined by a 16-gene signature (C1 or C2). Alternatively, the tumors were classified into those that responded (R) or did not respond (NR) to standard chemotherapy as determined by the reduction of serum alpha-fetoprotein levels. Values are shown as the median and IQR of individual data points (circles). *, p <0.05, comparing C1 (n =9) vs. C2 (n =10) tumors or R (n =14) vs. NR (n =5) tumors using the Mann-Whitney U test. C. Cives-Losada et al. Biochemical Pharmacology 237 (2025) 116914 6 due to the mechanism of action of this drug, were up-regulated in HuH6/ CR cells (e.g., ERCC1). Regarding genes involved in activating apoptosis (MOC-5a) or promoting cell survival (MOC-5a) marked down-regulation and up-regulation, respectively, of relevant genes were not found. The expression of genes associated with chemoresistance due to changes in tumor microenvironment (MOC-6) was not altered in chemoresistant cells. Among genes that promote the acquisition of stem cell characteristics (MOC-7), the expression of ALDH1A1, CD44 and TGFB1 was significantly increased in HuH6/CR cells. Given the focus of the present study on the role of ABC export pumps in HB chemoresistance, the subcellular localization of these transporters in chemoresistant cells was analyzed by IF (Fig. 9). In HepG2/DR cells, MDR1, MRP1, and MRP4 were mainly located in the plasma membrane, while MRP2 and MRP5 were mostly intracellular. In contrast, in HuH6/ CR cells, MRP1, MRP4, MRP5, and BCRP were mainly found in the plasma membrane, whereas MRP2-associated signals were intracellular, and MDR1 was not detected. The results obtained by qPCR were compared with those obtained in HepG2 (WT vs. DR) and HuH6 (WT vs. CR) cells by RNA-Seq and highthroughput proteomics (Table 4). The three techniques used revealed some similarities and discrepancies, as well as differences and shared changes between cell lines. 3.5. Changes in the expression of epigenetic genes in chemoresistant HB cells Given the relevance of impaired epigenetic control in HB biology, the expression of profile of more than 400 genes involved in this process (erasers, readers, scaffolds, and writers) was analyzed by RNA-Seq. This study revealed differential expression of several genes in chemoresistant cells compared with their wild-type counterparts (Table 5). While some changes were observed in both cell lines (e.g., PRDM7 up-regulation), others showed opposite patterns (e.g., HR was down-regulated in HepG2/DR cells but up-regulated in HuH6/CR cells). Moreover, the Fig. 2. Maximum likelihood estimation of binormal receiver operating characteristic (ROC) curve to validate the SLC22A1 (A) or ABCC2 (B) expression performance in predicting response of hepatoblastoma (HB) patients to chemotherapy. Analysis of ROC curves evaluating the predictive capacity of drug uptake and efflux transporter expression in tumors for the response of HB patients to chemotherapy (C). Patients (n =19) were categorized as responders or non-responders based on standard clinical criteria, defining a positive response as achieving a >90 % reduction in serum alpha-fetoprotein values post-treatment. C. Cives-Losada et al. Biochemical Pharmacology 237 (2025) 116914 7 expression of several other genes was significantly altered in only one cell line. Specifically, HDAC, UTY, CHD5, PAX6, SFMBT2, EID1, MECOM, PRDM6, PRDM8, RAG1 and SETD7 were significantly upregulated in HepG2/DR compared to HepG2/WT. Among the genes differentially expressed in HuH6/CR versus HuH6/WT, PADI2, PHF19, NAIP1L2, SNAI2, and PARP10 were up-regulated, while USP44, MLLT3, and PDP1 were down-regulated. 3.6. Evaluation of cross-resistance and lateral sensitivity in chemoresistant HB cells As a final step of the characterization of chemoresistant cells we investigated the existence of cross-resistance to other antitumor drugs commonly used to treat this cancer (Fig. 10). The results revealed that, in general, HepG2/DR and HuH6/CR cells were more resistant to anthracyclines, and etoposide than their wild-type counterparts. Interestingly, we found cross-resistance to cisplatin in HepG2/DR cells and to doxorubicin in HuH6/CR cells. In contrast, no cross-resistance against 5FU and Vinca alkaloids was seen (Fig. 10, Table 6). While HepG2/DR cells showed no cross-resistance to any tested TKIs used to treat HCC, HuH6/CR were resistant to sorafenib, regorafenib, and lenvatinib. Interestingly, the response to cabozantinib was unaltered in both chemoresistant cells. Besides, as an attempt to modulate chemoresistance, based on results of marked impact of chemoresistance in the expression of epigenetic genes, we evaluated the effect of inhibitors of epigenetic factors such CM-272, able to inhibit histone methyltransferase G9a [20], and lysine methyltransferase SETD7 (PFI-2) [21] in the response of HB cells to doxorubicin and cisplatin. In both HepG2 (Fig. 10A) and HuH6 (Fig. 10B) cells, either sensitive or resistant to these drugs, the treatment with CM-272 and PFI-2 for 72 h did not induce enhanced cytotoxic response. 4. Discussion Since it is well recognized that changes in the expression and function of drug transport proteins have an important impact on the chemoresistance of major liver and gastrointestinal tumors in adults [22], but their contribution to the MDR phenotype of HB has been less explored, this study addresses this gap by characterizing the expression of drug uptake and export transporters in HB clinical samples from patients who had received cisplatin and doxorubicin prior undergoing surgery. Most uptake transporters were downregulated in HB tissue in comparison with paired surrounding NT tissue. Similar findings were previously reported in a small number of untreated patient samples for the organic cation transporter OCT1 (gene SLC22A1) [23], which can transport doxorubicin and has been associated with poor response of patients with HCC and cholangiocarcinoma to several anticancer drugs [24,25]. Moreover, the low expression of functional variants of other drug uptake transporters has been related to weak response to chemotherapy [26]. Regarding ABC drug export pumps, ABCC5 expression was higher in HB than in NT. MRP5 is involved in the resistance to anthracyclines [27] and cisplatin [28]. There is also a trend for MRP1 (ABCC1 gene) expression to be elevated in HB. Although high levels of both pumps have been associated with poor prognosis in other solid tumors, such as MRP1 in neuroblastoma [29] and MRP5 in metastatic colorectal cancer [30], in our cohort of HB patients, both genes showed low sensitivity values as predictive markers of treatment response. The expression of other ABC pumps involved in doxorubicin and cisplatin efflux, such as ABCB1, ABCC2, and ABCG2 was elevated in HB but, surprisingly, lower than that in NT tissue. Other authors reported an increased expression of MDR1, MRP1 and BCRP in HB after being treated with cisplatin and doxorubicin, and no changes in MRP2 [31–33], while BCRP down-regulation has been reported in other tumors treated with platinum derivatives [34]. The high expression of MRP2 in tumors with poor responses to cisplatin [35,36] and doxorubicin [37] suggests that this transporter may be a prognostic marker in HB upon IHC analysis. This would determine whether the protein is expressed and well localized in the plasma membrane, as has been reported in HCC [25]. Besides intrinsic resistance mechanisms already present in naïve cancer cells, the resistome is modified upon exposure to antitumor Fig. 3. Representative images of histological sections of adjacent non-tumor infant liver tissue (A-D) and two selected hepatoblastoma (HB) tumors, one with good response to chemotherapy (HB-R) and one with poor response to chemotherapy (HB-NR), stained with hematoxylin-eosin (A, E, I) or after immunohistochemical staining for MDR1 (B, F, J), MRP1 (C, G, K) or MRP2 (D, H, L). In all the cases, slides were counterstained with hematoxylin. Arrows point canalicular expression (3B and 3D). Scale bar: 50 µm. C. Cives-Losada et al. Biochemical Pharmacology 237 (2025) 116914 8 agents [38]. Considering the ethical limitation to investigate this process by collecting HB samples before and after the treatment, two in vitro approaches were used to simulate the impact on the resistome, and more specifically on the transportome, upon acute and long-term exposure to chemotherapy. The most relevant finding was the up-regulation of drug export pumps, such as MDR1, MRP1 MRP2 and MRP5, whose association with the lack of response to anthracyclines has been previously described in liver cancers [22,39], including HB [32,40]. Besides changes in the transportome (MOC-1), chemoresistant cells also showed differential expression in genes involved in other MOCs, such as up-regulation of enzymes involved in phase II of drug metabolism, like GSTs, which has been associated with resistance to doxorubicin and cisplatin [41,42]. Alterations in the expression of doxorubicin’s target topoisomerase II could also contribute to the lack of response to this anthracycline [16,43], whereas changes in genes involved in DNA repair are particularly important in cisplatin resistance [16,44]. Moreover, in our investigation of chemoresistant cells we also found alterations in the epigenetic machinery, such as SETD7 upFig. 4. Effect of short-term exposure of HepG2 (A) and HuH6 (B) cells to doxorubicin or cisplatin on the expression of drug uptake transporters. Cells were cultured in the absence (Control) or presence of either 0.1 µM doxorubicin (+Doxo) or 5 µM cisplatin (+CisPt) (close to their IC 50 ) for 72 h. Relative expression was determined by RT-qPCR as mRNA levels as the percentage of those of the housekeeping gene, RHOT2. Values are mean ±SEM of 3 independent experiments performed in duplicate wells. *, p <0.05, compared to Control by paired t-test. C. Cives-Losada et al. Biochemical Pharmacology 237 (2025) 116914 9 Table 4 (continued) C. Cives-Losada et al. Biochemical Pharmacology 237 (2025) 116914 16 proliferation rate suggests that cabozantinib’s mechanism of action extends beyond proliferation inhibition but may involve alternative vulnerabilities in resistant cells. Indeed, cabozantinib is the only of the assayed TKIs that targets c-MET [50]. Notably, c-MET is commonly overexpressed in HB and has been implicated in the activation of prosurvival pathways [51]. Our RNA-Seq analysis confirmed high MET expression in both parental HepG2 and HuH6 cells, with no significant changes in resistant cells. Accordingly, c-MET inhibition by cabozantinib may account for the sensitivity of resistant cells to this drug, although other contributing factors cannot be excluded. This finding is highly relevant, from a clinical perspective, because it recommends further investigation aimed at using cabozantinib as a therapeutic option for chemoresistant HB tumors. In conclusion, drug export pumps, but not SLC uptake transporters, play an important role in the lack of response of HB to conventional chemotherapy. Moreover, cabozantinib emerges as a potential alternative anticancer drug to be used in non-responding patients. CRediT authorship contribution statement Candela Cives-Losada: Writing – review & editing, Writing – original draft, Project administration, Methodology, Investigation, Formal analysis, Data curation. Maitane Asensio: Writing – review & editing, Methodology, Investigation, Formal analysis, Data curation. Oscar Briz: Writing – review & editing, Writing – original draft, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Luis Miguel Chinchilla-T´ abora: Writing – review & editing, Methodology. María Manuela Barranco: Writing – review & editing, Resources. ´ Alvaro del Río- ´ Alvarez: Writing – review & editing, Resources. Maria Luz MartinezChantar: Writing – review & editing, Resources. Matias A. Avila: Writing – review & editing, Resources. Stefano Cairo: Writing – review & editing, Resources. Carolina Armengol: Writing – review & editing, Resources, Investigation, Funding acquisition. Jose J.G. Marin: Writing – review & editing, Writing – original draft, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization. Rocio I.R. Macias: Writing – review & Expression fold-change of genes involved in mechanisms of chemoresistance (MOC) in doxorubicin-resistant (DR) HepG2 cells and cisplatin-resistant (CR) Huh6 cells as compared with their respective parental cell line (wild-type, WT). The expression levels were determined by RNA-Seq. RT-qPCR using microfluidic cards or TLDAs. and high-throughput proteomics. N.D., not detected. The red background indicates fold-change values ≥2 (moderate), ≥5 (high), or ≥10 (very high). The blue background indicates decreased expression with fold-change expression levels of ≤0.5. Table 5 Changes in the expression of genes involved in epigenetics, classified as erasers, readers, scaffolds, and writers in resistant hepatoblastoma cells, determined by RNASeq. HepG2 HuH6 WT DR Fold-change WT CR Fold-change Erasers       HDAC9 0.08 0.77 a 10.3 0.20 0.49 2.5 HR 11.4 4.2 a 0.4 0.08 2.7 a 33.5 PADI2 0.01 0.03 2.7 0.54 58.7 a 109 USP44 0.36 0.37 1.0 1.2 0.40 a 0.3 UTY >0.01 0.19 a 1860 13.1 20.1 1.5 Readers       CHD5 0.46 2.3 a 5.0 0.94 2.7 2.9 MLLT3 11.2 7.5 0.7 104.0 24.5 a 0.2 PAX6 1.0 8.6 a 8.4 0.68 0.79 1.2 PDP1 3.8 9.8 2.6 61.9 25.7 a 0.4 PHF19 34 20 0.6 10.8 73.1 a 6.8 SFMBT2 0.05 0.34 a 6.6 6.5 9.4 1.4 Scaffolds       EID1 0.01 0.41 a 40.8 35.9 44.5 1.2 NAP1L2 0.06 0.31 4.8 0.09 2.0 a 21.1 SNAI2 0.35 1.15 3.3 0.01 0.26 a 24.0 Writers       MECOM 0.09 0.43 a 4.7 14.7 14.9 1.0 PARP10 33.9 41.8 1.2 1.2 14.9 a 12.9 PRDM6 0.02 0.16 a 8.4 3.7 3.4 0.9 PRDM7 0.04 1.04 a 25.3 0.04 0.57 a 16.4 PRDM8 0.05 0.27 a 5.9 0.056 0.013 0.2 RAG1 0.06 0.33 a 5.5 1.6 3.2 2.1 SETD7 1.9 17.8 a 9.3 38 72 1.9 The expression levels (FPKM) were determined in three different cultures of HepG2 cells resistant to doxorubicin (HepG2/DR) and HuH6 cells resistant to cisplatin (HuH6/CR), as well as their corresponding wild-type (WT) cells using RNA-Seq. Only genes with considerable expression levels (>0.1) and significant differences (>2 fold) compared to parental wild-type (WT) cells are depicted. a , p <0.05 as compared with WT cells. C. Cives-Losada et al. Biochemical Pharmacology 237 (2025) 116914 17 (caption on next page) C. Cives-Losada et al. Biochemical Pharmacology 237 (2025) 116914 18 editing, Writing – original draft, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This study was supported by Spanish Ministry of Economy, Industry and Competitiveness (grant number SAF2016-75197-R), CIBEREHD (grant numbers CB06/04/0023; EHD22PI01), Fondo de Investigaciones Sanitarias, Instituto de Salud Carlos III, Spain, co-funded by the European Regional Development Fund/European Social Fund, “Investing in your future” (grant numbers PI19/00819 and PI22/00526); “Junta de Castilla y Leon” (SA113P23); Fundaci´ o Marato TV3 (grant number 201916-31), Spain; and AECC Scientific Foundation (grant number IGTP-AECC_2022-042/PRYCO223102ARME), Spain. C.C.-L. was supported by a predoctoral contract and its complementary mobility grant funded by the Ministry of Science and Innovation Spain (BOE-B-201772875). The authors express their gratitude to Mrs. Carmen Rodríguez Gonz´ alez for her assistance with immunohistochemical studies, Mrs. Emilia Flores for her contributions as a laboratory technician, and Mrs. Mariar Franco for her support with administrative tasks. Data availability Data will be made available on request. References [1] J. Feng, G. Polychronidis, U. Heger, G. Frongia, A. Mehrabi, K. Hoffmann, Incidence trends and survival prediction of hepatoblastoma in children: a population-based study, Cancer Commun. (Lond) 39 (1) (2019) 62. [2] K. Ng, D.B. Mogul, Pediatric liver tumors, Clin. Liver Dis. 22 (4) (2018) 753–772. [3] B. Lucas, S. Ravishankar, I. Pateva, Pediatric primary hepatic tumors: diagnostic considerations, Diagnostics (Basel) 11 (2) (2021). [4] D.F. Calvisi, A. Solinas, Hepatoblastoma: current knowledge and promises from preclinical studies, Transl. Gastroenterol. Hepatol. 5 (2020) 42. [5] S. Cairo, C. Armengol, A. De Reynies, Y. Wei, E. Thomas, C.A. Renard, A. Goga, A. Balakrishnan, M. Semeraro, L. Gresh, M. Pontoglio, H. Strick-Marchand, F. Levillayer, Y. Nouet, D. Rickman, F. Gauthier, S. Branchereau, L. Brugieres, V. Laithier, R. Bouvier, F. Boman, G. Basso, J.F. Michiels, P. Hofman, F. ArbezGindre, H. Jouan, M.C. Rousselet-Chapeau, D. Berrebi, L. Marcellin, F. Plenat, D. Zachar, M. Joubert, J. Selves, D. Pasquier, P. Bioulac-Sage, M. Grotzer, M. Childs, M. Fabre, M.A. Buendia, Hepatic stem-like phenotype and interplay of Wnt/beta-catenin and Myc signaling in aggressive childhood liver cancer, Cancer Cell 14 (6) (2008) 471–484. [6] K.B. Hooks, J. Audoux, H. Fazli, S. Lesjean, T. Ernault, N. Dugot-Senant, T. LesteLasserre, M. Hagedorn, B. Rousseau, C. Danet, S. Branchereau, L. Brugieres, S. Taque, C. Guettier, M. Fabre, A. Rullier, M.A. Buendia, T. Commes, C.F. Grosset, A.A. Raymond, New insights into diagnosis and therapeutic options for proliferative hepatoblastoma, Hepatology 68 (1) (2018) 89–102. [7] J. Carrillo-Reixach, L. Torrens, M. Simon-Coma, L. Royo, M. Domingo-Sabat, J. Abril-Fornaguera, N. Akers, M. Sala, S. Ragull, M. Arnal, N. Villalmanzo, S. Cairo, A. Villanueva, R. Kappler, M. Garrido, L. Guerra, C. Sabado, G. Guillen, M. Mallo, D. Pineyro, M. Vazquez-Vitali, O. Kuchuk, M.E. Mateos, G. Ramirez, M. L. Santamaria, Y. Mozo, A. Soriano, M. Grotzer, S. Branchereau, N.G. de Andoin, B. Lopez-Ibor, R. Lopez-Almaraz, J.A. Salinas, B. Torres, F. Hernandez, J.J. Uriz, M. Fabre, J. Blanco, C. Paris, V. Bajciova, G. Laureys, H. Masnou, A. Clos, C. Belendez, C. Guettier, L. Sumoy, R. Planas, M. Jorda, L. Nonell, P. Czauderna, B. Morland, D. Sia, B. Losic, M.A. Buendia, M.R. Sarrias, J.M. Llovet, C. Armengol, Epigenetic footprint enables molecular risk stratification of hepatoblastoma with clinical implications, J. Hepatol. 73 (2) (2020) 328–341. [8] R.L. Meyers, R. Maibach, E. Hiyama, B. Haberle, M. Krailo, A. Rangaswami, D. C. Aronson, M.H. Malogolowkin, G. Perilongo, D. von Schweinitz, M. Ansari, D. Lopez-Terrada, Y. Tanaka, R. Alaggio, I. Leuschner, T. Hishiki, I. Schmid, K. Watanabe, K. Yoshimura, Y. Feng, E. Rinaldi, D. Saraceno, M. Derosa, P. Czauderna, Risk-stratified staging in paediatric hepatoblastoma: a unified analysis from the Children’s Hepatic tumors International Collaboration, Lancet Oncol. 18 (1) (2017) 122–131. [9] D.C. Aronson, P. Czauderna, R. Maibach, G. Perilongo, B. Morland, The treatment of hepatoblastoma: its evolution and the current status as per the SIOPEL trials, J. Indian Assoc. Pediatr. Surg. 19 (4) (2014) 201–207. Fig. 10. Effect of drugs used to treat hepatoblastoma on the viability of wild-type (WT) and doxorubicin-resistant (DR) HepG2 cells (A) and cisplatin-resistant (CR) HuH6 cells (B). The impact of epigenetic modulators (CM272 and PFI-2) on the toxic effect of doxorubicin in HepG2 (A) and HuH6 (B) cells was assayed. Cell viability was determined using the MTT-formazan test after incubating the cells with the drugs for 72 h. In experiments with epigenetic modulators, these drugs were added to the culture 72 h before adding doxorubicin or cisplatin. Values are expressed as the percentage of viability in non-treated cells. They are shown as mean ±SEM of 4–6 independent experiments performed in triplicate wells. *, p <0.05, comparing WT with chemoresistant cells by Student’s t-test. No difference (p >0.05) between values with and without epigenetic modulators was found. Table 6 Cytostatic effect of antitumor drugs used in firstand second-line treatment of hepatoblastoma in wild-type (WT) HuH6 and HepG2 cells and after long-term exposure to cisplatin (CR) or doxorubicin (DR). IC 50 RQ IC 50 RQ HepG2 WT HepG2/ DR HuH6 WT HuH6/ CR Anthracyclines ( μ M)       Doxorubicin 0.17 ± 0.02 8.7 ± 2.3 a 51.1 0.08 ±0.02 0.12 ± 0.02 1.6 Epirubicin 0.3 ± 0.0 9.3 ± 0.8 a 31.0 0.04 ±0.01 0.05 ± 0.00 1.3 Idarubicin 0.06 ± 0.02 0.65 ± 0.24 a 10.2 0.04 ±0.01 0.09 ± 0.01 a 2.0 Platinum derivatives ( μ M)       Cisplatin 7.7 ± 0.9 20.6 ± 2.4 a 2.7 8.6 ± 0.6 31.3 ± 1.3 a 3.7 Oxaliplatin 4.8 ± 1.5 6.8 ± 1.7 1.4 1.7 ± 0.5 7.9 ± 3.2 4.8 Nitrogen-base analogs ( μ M)       5-Fluorouracil >50 >50 1.0 9.1 ± 0.9 9.2 ± 1.3 1.0 Camptothecins ( μ M)       Irinotecan 2.7 ± 0.6 9.9 ± 0.1 a 3.7 17.6 ±2.6 20.7 ± 3.2 1.2 Podophylotoxins ( μ M)       Etoposide 9.3 ± 2.5 >40 a 4.0 4.7 ± 1.8 14.4 ± 3.5 a 3.0 Vinca alkaloids (nM)       Vinblastine >25,000 >25,000 1.0 0.7 ± 0.1 2.2 ± 0.3 a 3.2 Vincristine >20,000 >20,000 1.0 0.6 ± 0.2 0.6 ± 0.3 1.0 Tyrosine kinase inhibitors ( μ M)       Cabozantinib 5.2 ± 2.3 5.7 ± 1.4 1.1 6.0 ± 0.4 8.6 ± 1.4 1.4 Lenvatinib >20 >20 1.0 0.5 ± 0.0 >10 a > 20 Regorafenib 2.0 ± 0.2 2.5 ± 0.7 1.2 1.3 ± 0.2 6.7 ± 1.2 a 5.3 Sorafenib 4.6 ± 0.6 4.2 ± 0.5 0.9 1.5 ± 0.2 3.9 ± 0.1 a 2.7 The half-maximal inhibitory concentration (IC 50 ) was calculated after incubating the cells with increasing concentrations of the drugs for 72 h and determining cell viability by MTT-formazan assay. Values are means ±SEM of at least 3 independent experiments performed in triplicate wells. RQ, relative sensitivity to drugs compared to that of WT cells. a , p <0.05, compared to WT cells by Student’s t-test. C. Cives-Losada et al. Biochemical Pharmacology 237 (2025) 116914 19 [10] T. Yang, R.S. Whitlock, S.A. Vasudevan, Surgical management of hepatoblastoma and recent advances, Cancers (Basel) 11 (2019). [11] R. Venkatramani, J.E. Stein, A. Sapra, Y. Genyk, V. Jhaveri, M. Malogolowkin, L. Mascarenhas, Effect of neoadjuvant chemotherapy on resectability of stage III and IV hepatoblastoma, Br. J. Surg. 102 (1) (2015) 108–113. [12] S. Cairo, C. Armengol, M.A. Buendia, Activation of Wnt and Myc signaling in hepatoblastoma, Front. Biosci. (Elite Ed.) 4 (1) (2012) 480–486. [13] A.D. Trobaugh-Lotrario, R.L. Meyers, A.F. O’Neill, J.H. Feusner, Unresectable hepatoblastoma: current perspectives, Hepat. Med. 9 (2017) 1–6. [14] E. Natarajan, C. Auerbach, R. Cheron, F. Pashankar, Sustained remission after maintenance irinotecan in patient with multiply relapsed hepatoblastoma, J. Pediatr. Hematol. Oncol. 42 (7) (2020) e659–e661. [15] S. Cairo, C. Armengol, R. Maibach, B. Haberle, K. Becker, J. Carrillo-Reixach, C. Guettier, C. Vokuhl, I. Schmid, M.A. Buendia, S. Branchereau, D. von Schweinitz, R. Kappler, A combined clinical and biological risk classification improves prediction of outcome in hepatoblastoma patients, Eur. J. Cancer 141 (2020) 30–39. [16] J.J.G. Marin, C. Cives-Losada, M. Asensio, E. Lozano, O. Briz, R.I.R. Macias, Mechanisms of anticancer drug resistance in hepatoblastoma, Cancers (Basel) 11 (2019). [17] J.J. Marin, O. Briz, M.J. Monte, A.G. Blazquez, R.I. Macias, Genetic variants in genes involved in mechanisms of chemoresistance to anticancer drugs, Curr. Cancer Drug Targets 12 (4) (2012) 402–438. [18] E. San Jose-Eneriz, X. Agirre, O. Rabal, A. Vilas-Zornoza, J.A. Sanchez-Arias, E. Miranda, A. Ugarte, S. Roa, B. Paiva, A. Estella-Hermoso de Mendoza, R. M. Alvarez, N. Casares, V. Segura, J.I. Martin-Subero, F.X. Ogi, P. Soule, C. M. Santiveri, R. Campos-Olivas, G. Castellano, M.G.F. de Barrena, J.R. RodriguezMadoz, M.J. Garcia-Barchino, J.J. Lasarte, M.A. Avila, J.A. Martinez-Climent, J. Oyarzabal, F. Prosper, Discovery of first-in-class reversible dual small molecule inhibitors against G9a and DNMTs in hematological malignancies, Nat. Commun. 8 (2017) 15424. [19] A. Sanchez-Martin, P. Sanchon-Sanchez, M.R. Romero, J.J.G. Marin, O. Briz, Impact of tumor suppressor genes inactivation on the multidrug resistance phenotype of hepatocellular carcinoma cells, Biomed. Pharmacother. 165 (2023) 115209. [20] A. Claveria-Cabello, J.M. Herranz, M.U. Latasa, M. Arechederra, I. Uriarte, A. Pineda-Lucena, F. Prosper, P. Berraondo, C. Alonso, B. Sangro, J.J. Garcia Marin, M.L. Martinez-Chantar, S. Ciordia, F.J. Corrales, P. Francalanci, R. Alaggio, J. Zucman-Rossi, E. Indersie, S. Cairo, M. Domingo-Sabat, L. Zanatto, P. SanchoBru, C. Armengol, C. Berasain, M.G. Fernandez-Barrena, M.A. Avila, Identification and experimental validation of druggable epigenetic targets in hepatoblastoma, J. Hepatol. 79 (4) (2023) 989–1005. [21] D. Barsyte-Lovejoy, F. Li, M.J. Oudhoff, J.H. Tatlock, A. Dong, H. Zeng, H. Wu, S. A. Freeman, M. Schapira, G.A. Senisterra, E. Kuznetsova, R. Marcellus, A. AllaliHassani, S. Kennedy, J.P. Lambert, A.L. Couzens, A. Aman, A.C. Gingras, R. AlAwar, P.V. Fish, B.S. Gerstenberger, L. Roberts, C.L. Benn, R.L. Grimley, M. J. Braam, F.M. Rossi, M. Sudol, P.J. Brown, M.E. Bunnage, D.R. Owen, C. Zaph, M. Vedadi, C.H. Arrowsmith, (R)-PFI-2 is a potent and selective inhibitor of SETD7 methyltransferase activity in cells, PNAS 111 (35) (2014) 12853–12858. [22] J.J.G. Marin, M.J. Monte, R.I.R. Macias, M.R. Romero, E. Herraez, M. Asensio, S. Ortiz-Rivero, C. Cives-Losada, S. Di Giacomo, J. Gonzalez-Gallego, J.L. Mauriz, T. Efferth, O. Briz, Expression of chemoresistance-associated ABC proteins in hepatobiliary, pancreatic and gastrointestinal cancers, Cancers (Basel) 14 (14) (2022). [23] P. Martinez-Becerra, J. Vaquero, M.R. Romero, E. Lozano, C. Anadon, R.I. Macias, M.A. Serrano, N. Grane-Boladeras, L. Munoz-Bellvis, L. Alvarez, B. Sangro, M. Pastor-Anglada, J.J. Marin, No correlation between the expression of FXR and genes involved in multidrug resistance phenotype of primary liver tumors, Mol. Pharm. 9 (6) (2012) 1693–1704. [24] E. Herraez, E. Lozano, R.I. Macias, J. Vaquero, L. Bujanda, J.M. Banales, J.J. Marin, O. Briz, Expression of SLC22A1 variants may affect the response of hepatocellular carcinoma and cholangiocarcinoma to sorafenib, Hepatology 58 (3) (2013) 1065–1073. [25] A. Geier, R.I. Macias, D. Bettinger, J. Weiss, H. Bantel, D. Jahn, R. Al-Abdulla, J. J. Marin, The lack of the organic cation transporter OCT1 at the plasma membrane of tumor cells precludes a positive response to sorafenib in patients with hepatocellular carcinoma, Oncotarget 8 (9) (2017) 15846–15857. [26] J.J.G. Marin, M.A. Serrano, E. Herraez, E. Lozano, S. Ortiz-Rivero, L. Perez-Silva, M. Reviejo, O. Briz, Impact of genetic variants in the solute carrier (SLC) genes encoding drug uptake transporters on the response to anticancer chemotherapy, Cancer Drug Resist. 7 (2024) 27. [27] K. Engle, G. Kumar, Cancer multidrug-resistance reversal by ABCB1 inhibition: a recent update, Eur. J. Med. Chem. 239 (2022) 114542. [28] T. Oguri, T. Isobe, T. Suzuki, K. Nishio, Y. Fujiwara, O. Katoh, M. Yamakido, Increased expression of the MRP5 gene is associated with exposure to platinum drugs in lung cancer, Int. J. Cancer 86 (1) (2000) 95–100. [29] M. Haber, J. Smith, S.B. Bordow, C. Flemming, S.L. Cohn, W.B. London, G. M. Marshall, M.D. Norris, Association of high-level MRP1 expression with poor clinical outcome in a large prospective study of primary neuroblastoma, J. Clin. Oncol. 24 (10) (2006) 1546–1553. [30] P. Gazzaniga, A. Gradilone, A. Petracca, C. Nicolazzo, C. Raimondi, R. Iacovelli, G. Naso, E. Cortesi, Molecular markers in circulating tumour cells from metastatic colorectal cancer patients, J. Cell Mol. Med. 14 (8) (2010) 2073–2077. [31] S. Warmann, M. Hunger, B. Teichmann, P. Flemming, K.F. Gratz, J. Fuchs, The role of the MDR1 gene in the development of multidrug resistance in human hepatoblastoma: clinical course and in vivo model, Cancer 95 (8) (2002) 1795–1801. [32] T. Oue, A. Yoneda, S. Uehara, H. Yamanaka, M. Fukuzawa, Increased expression of multidrug resistance-associated genes after chemotherapy in pediatric solid malignancies, J. Pediatr. Surg. 44 (2) (2009) 377–380. [33] S. Vander Borght, J. van Pelt, H. van Malenstein, D. Cassiman, M. Renard, C. Verslype, L. Libbrecht, T.A. Roskams, Up-regulation of breast cancer resistance protein expression in hepatoblastoma following chemotherapy: a study in patients and in vitro, Hepatol. Res. 38 (11) (2008) 1112–1121. [34] S. Ota, G. Ishii, K. Goto, K. Kubota, Y.H. Kim, M. Kojika, Y. Murata, M. Yamazaki, Y. Nishiwaki, K. Eguchi, A. Ochiai, Immunohistochemical expression of BCRP and ERCC1 in biopsy specimen predicts survival in advanced non-small-cell lung cancer treated with cisplatin-based chemotherapy, Lung Cancer 64 (1) (2009) 98–104. [35] X.Y. Liu, S.P. Liu, J. Jiang, X. Zhang, T. Zhang, Inhibition of the JNK signaling pathway increases sensitivity of hepatocellular carcinoma cells to cisplatin by down-regulating expression of P-glycoprotein, Eur. Rev. Med. Pharmacol. Sci. 20 (6) (2016) 1098–1108. [36] P.V. Korita, T. Wakai, Y. Shirai, Y. Matsuda, J. Sakata, M. Takamura, M. Yano, A. Sanpei, Y. Aoyagi, K. Hatakeyama, Y. Ajioka, Multidrug resistance-associated protein 2 determines the efficacy of cisplatin in patients with hepatocellular carcinoma, Oncol. Rep. 23 (4) (2010) 965–972. [37] Y. Cui, J. Konig, J.K. Buchholz, H. Spring, I. Leier, D. Keppler, Drug resistance and ATP-dependent conjugate transport mediated by the apical multidrug resistance protein, MRP2, permanently expressed in human and canine cells, Mol. Pharmacol. 55 (5) (1999) 929–937. [38] A. Alisi, W.C. Cho, F. Locatelli, D. Fruci, Multidrug resistance and cancer stem cells in neuroblastoma and hepatoblastoma, Int. J. Mol. Sci. 14 (12) (2013) 24706–24725. [39] Z. Su, G. Liu, T. Fang, Y. Wang, H. Zhang, S. Yang, J. Wei, Z. Lv, L. Tan, J. Liu, Silencing MRP1-4 genes by RNA interference enhances sensitivity of human hepatoma cells to chemotherapy, Am. J. Transl. Res. 8 (6) (2016) 2790–2802. [40] S.W. Warmann, H. Heitmann, B. Teichmann, K.F. Gratz, P. Ruck, M. Hunger, J. Fuchs, Effects of P-glycoprotein modulation on the chemotherapy of xenotransplanted human hepatoblastoma, Pediatr. Hematol. Oncol. 22 (5) (2005) 373–386. [41] M. Zou, X. Hu, B. Xu, T. Tong, Y. Jing, L. Xi, W. Zhou, J. Lu, X. Wang, X. Yang, F. Liao, Glutathione S‑transferase isozyme alpha 1 is predominantly involved in the cisplatin resistance of common types of solid cancer, Oncol. Rep. 41 (2) (2019) 989–998. [42] R.N. Ganapathi, M.K. Ganapathi, Mechanisms regulating resistance to inhibitors of topoisomerase II, Front. Pharmacol. 4 (2013) 89. [43] P. Bader, J. Fuchs, M. Wenderoth, D. von Schweinitz, D. Niethammer, J.F. Beck, Altered expression of resistance associated genes in hepatoblastoma xenografts incorporated into mice following treatment with adriamycin or cisplatin, Anticancer Res. 18 (4C) (1998) 3127–3132. [44] Z.H. Siddik, Cisplatin: mode of cytotoxic action and molecular basis of resistance, Oncogene 22 (47) (2003) 7265–7279. [45] L. Gao, J. Zhang, Q. Long, Y. Yang, Y. Li, G. Li, P. Pu, S. Tong, Y. He, Q. Li, Y. Chen, Y. Liu, X. Kong, SETD7 promotes metastasis of triple-negative breast cancer by YY1 lysine methylation, Biochim. Biophys. Acta Mol. basis Dis. 1869 (7) (2023) 166780. [46] J. Lv, Q. Wu, K. Li, K. Bai, H. Yu, J. Zhuang, H. Sun, H. Yang, X. Yang, Q. Lu, Lysine N-methyltransferase SETD7 promotes bladder cancer progression and immune escape via STAT3/PD-L1 cascade, Int. J. Biol. Sci. 19 (12) (2023) 3744–3761. [47] C. Varamo, C. Peraldo-Neia, P. Ostano, M. Basirico, C. Raggi, P. Bernabei, T. Venesio, E. Berrino, M. Aglietta, F. Leone, G. Cavalloni, Establishment and characterization of a new intrahepatic cholangiocarcinoma cell line resistant to gemcitabine, Cancers (Basel) 11 (4) (2019). [48] R. Marayati, L.L. Stafman, A.P. Williams, L.V. Bownes, C.H. Quinn, J.M. Aye, J. E. Stewart, K.J. Yoon, J.C. Anderson, C.D. Willey, E.A. Beierle, PIM kinases mediate resistance to cisplatin chemotherapy in hepatoblastoma, Sci. Rep. 11 (1) (2021) 5984. [49] B.A. Aguilar, The efficacy of art therapy in pediatric oncology patients: an integrative literature review, J. Pediatr. Nurs. 36 (2017) 173–178. [50] A.B. El-Khoueiry, D.L. Hanna, J. Llovet, R.K. Kelley, Cabozantinib: an evolving therapy for hepatocellular carcinoma, Cancer Treat. Rev. 98 (2021) 102221. [51] R. Purcell, M. Childs, R. Maibach, C. Miles, C. Turner, A. Zimmermann, M. Sullivan, HGF/c-Met related activation of β-catenin in hepatoblastoma, J. Exp. Clin. Cancer Res. 30 (1) (2011) 96. C. Cives-Losada et al. Biochemical Pharmacology 237 (2025) 116914 20