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WNTinib is a multi-kinase inhibitor with specificity against β-catenin mutant hepatocellular carcinoma

Nature Cancer volume 4, pages 1157–1175 (2023)Cite this article

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Abstract

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide. β-Catenin (CTNNB1)-mutated HCC represents 30% of cases of the disease with no precision therapeutics available. Using chemical libraries derived from clinical multi-kinase inhibitor (KI) scaffolds, we screened HCC organoids to identify WNTinib, a KI with exquisite selectivity in CTNNB1-mutated human and murine models, including patient samples. Multiomic and target engagement analyses, combined with rescue experiments and in vitro and in vivo efficacy studies, revealed that WNTinib is superior to clinical KIs and inhibits KIT/mitogen-activated protein kinase (MAPK) signaling at multiple nodes. Moreover, we demonstrate that reduced engagement on BRAF and p38α kinases by WNTinib relative to several multi-KIs is necessary to avoid compensatory feedback signaling—providing a durable and selective transcriptional repression of mutant β-catenin/Wnt targets through nuclear translocation of the EZH2 transcriptional repressor. Our studies uncover a previously unknown mechanism to harness the KIT/MAPK/EZH2 pathway to potently and selectively antagonize CTNNB1-mutant HCC with an unprecedented wide therapeutic index.

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Fig. 1: Chemical genetic screens in tumor organoids yield WNTinib as a selective antagonist of CTNNB1-mutated HCC.
Fig. 2: WNTinib reduces EZH2 phosphorylation to drive the suppression of essential gene networks in CTNNB1-mutated HCC.
Fig. 3: WNTinib depends on EZH2 relocalization to chromatin for its activity in CTNNB1-mutated HCC.
Fig. 4: WNTinib utilizes unique polypharmacology to regulate the EZH2–WNT axis.
Fig. 5: WNTinib utilizes unique polypharmacology to regulate the EZH2–WNT axis.
Fig. 6: KIT is a critical target for WNTinib’s MoA.
Fig. 7: BRAF and p38 kinases are critical anti-targets for WNTinib.
Fig. 8: WNTinib outperforms clinical compounds across in vivo models of HCC.

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Data availability

All data needed to evaluate the conclusions in the present study are in the paper and/or its supplementary materials. CUT&RUN and RNA-seq data that support the findings of the present study have been deposited in the GEO under accession nos. GSE213518 (subseries GSE213515 and GSE213517). MS data have been deposited in the JPOST Repository with the identifier (JPST001111; PXID: PXD024958). Source data are provided with this paper. All other data supporting the findings of the present study are available from the corresponding author on reasonable request.

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Acknowledgements

Research reported in this publication was supported in part by the National Cancer Institute (NCI) of the National Institutes of Health (NIH: grant no. R01 CA256480-01) and an ISMMS seed fund to E.G. We thank the Tisch Cancer Institute for the use of the services and facilities supported by the NCI Cancer Center Support Grant (no. P30 CA196521). E.G. and A.C.D. thank the Mark Foundation for the Cancer Research Aspire award. E.G., A.C.D. and J.L. thank Alex’s Lemonade Stand Foundation for Childhood Cancer for support. M.S. was supported by an NCI training grant (no. T32CA078207). A.R. was supported by an NCI training grant, NCI F32 fellowship and K99 fellowship (grant nos. T32CA078207-16A1, F32CA247414-01 and 1K99CA273538-01). M.D. is a recipient of the T32 fellowship (grant no. 5T32GM062754). We are grateful for support from the NIH for grant nos. R01AI143295 (to C.M.R.) and R01AA027327 (to Y.P.D.J.). Further support was provided by the Robertson Therapeutic Development Fund (to E.M. and C.M.R.) and the Center for Basic and Translational Research on Disorders of the Digestive System through the generosity of the Leona M. and Harry B. Helmsley Charitable Trust (to E.M.). A.L. is supported by a Damon Runyon-Rachleff Innovation Award (no. DR52-18), Pfizer Emerging Science Fund, NIH/NCI R37 Merit Award (no. R37CA230636) and NIH/NCI (grant no. R01 CA256480-01). The Dar laboratory (M.D., A.P.S., Z.M.K. and A.C.D.) were supported by innovation awards from the NIH (no. 1DP2CA186570-01) and Damon Runyan Rachleff Foundation, as well as NIH grants (nos. R01CA227636, R01CA258736 and R01 CA256480-01). A.C.D. thanks the following for their support as a the Pew-Stewart Scholar in Cancer Research, Young Investigator of the Pershing-Square Sohn Cancer Research Alliance and Mark Foundation Aspire Awardee. J.J. acknowledges the support by grants (nos. R01CA218600, R01CA230854 and R01CA268519) from the NIH. This work utilized the NMR Spectrometer Systems at Mount Sinai acquired with funding from NIH SIG grants (nos. 1S10OD025132 and 1S10OD028504). J.M.L. is supported by grants from the NIH (nos. R01 DK128289-01), HUNTER-CRUK/AECC/FAIRC (ref. no. C9380/A26813), Samuel Waxman Cancer Research Foundation, the Spanish National Health Institute (MICINN, grant no. PID2019-105378RB-I00) and the Generalitat de Catalunya (AGAUR, grant no. SGR-1358). J.A.F. is supported by the University of Barcelona (PREDOCS-UB) and the Societat Catalana de Digestologia. This research was conducted with the support of the Biorepository and Pathology Core at ISMMS. We thank Dr. Rachel Brody and team members for this support. We thank the ISMMS Liver Cancer Program’s surgeons, Drs. Myron Schwartz, Parissa Tabrizian, Ganesh Gunasekaran and Umut Sarpel. We also thank Drs. Meritxell Huch and Laura Broutier for their initial help with setting up organoid cultures.

Author information

Author notes

  1. These authors contributed equally: Alex Rialdi, Mary Duffy, Alex P. Scopton, Ernesto Guccione, Arvin C. Dar.

Authors and Affiliations

  1. Center for Therapeutics Discovery, Department of Oncological Sciences and Pharmacological Sciences, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Alex Rialdi, Mary Duffy, Alex P. Scopton, Frank Fonseca, Julia Nanyi Zhao, Megan Schwarz, Slim Mzoughi, Elisa Arceci, Austin Meadows, Denis Torre, Kyna Reyes, Zaigham M. Khan, Kevin Mohammed, Xufen Yu, Jian Jin, Ernesto Guccione & Arvin C. Dar

  2. Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Alex Rialdi, Julia Nanyi Zhao, Megan Schwarz, Pedro Molina-Sanchez, Slim Mzoughi, Elisa Arceci, Austin Meadows, Marina Ruiz de Galarreta, Denis Torre, Kyna Reyes, Felix Rosemann, Amaia Lujambio, Ernesto Guccione & Arvin C. Dar

  3. Liver Cancer Program, Division of Liver Diseases, Department of Medicine, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Alex Rialdi, Julia Nanyi Zhao, Megan Schwarz, Pedro Molina-Sanchez, Marina Ruiz de Galarreta, Denis Torre, Augusto Villanueva, Josep M. Llovet, Amaia Lujambio & Ernesto Guccione

  4. Center for OncoGenomics and Innovative Therapeutics, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Alex Rialdi, Frank Fonseca, Julia Nanyi Zhao, Megan Schwarz, Slim Mzoughi, Elisa Arceci, Austin Meadows, Denis Torre, Kevin Mohammed & Ernesto Guccione

  5. Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Mary Duffy, Frank Fonseca, Julia Nanyi Zhao, Megan Schwarz, Elisa Arceci, Jordi Abril-Fornaguera, Denis Torre, Felix Rosemann & Amaia Lujambio

  6. The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Pedro Molina-Sanchez, Marina Ruiz de Galarreta, Amaia Lujambio & Ernesto Guccione

  7. Translational Research in Hepatic Oncology, Liver Unit, IDIBAPS, Hospital Clinic, University of Barcelona, Barcelona, Spain

    Jordi Abril-Fornaguera & Josep M. Llovet

  8. Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, Singapore

    Yan Ting Lim, Manikandan Lakshmanan, Ravisankar Rajarethinam, Soo Yong Tan & Radoslaw M. Sobota

  9. Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, USA

    Kevin Mohammed

  10. Bioinformatics for Next Generation Sequencing Shared Resource Facility, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Xuedi Wang, Dan Hasson & Ernesto Guccione

  11. Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Soo Yong Tan

  12. Division of Hematology and Medical Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Augusto Villanueva

  13. Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY, USA

    Eleftherios Michailidis, Ype P. De Jong & Charles M. Rice

  14. Laboratory of Biochemical Pharmacology, Department of Pediatrics, School of Medicine, Emory University, Atlanta, GA, USA

    Eleftherios Michailidis

  15. Division of Gastroenterology and Hepatology, Weill Cornell Medicine, New York, NY, USA

    Ype P. De Jong

  16. Department of Biological Cancer, University of California Irvine, Orange, CA, USA

    Ivan Marazzi

  17. Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain

    Josep M. Llovet

  18. Program in Chemical Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Arvin C. Dar

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  1. Alex Rialdi
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  2. Mary Duffy
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Contributions

A.R., M.D., A.S., E.G. and A.C.D. were responsible for the overall design of the project. A.R., M.D., M.S., F.F., P.M.-S., M.R.d.G., S.M., E.A., J.A., A.M., K.R., Y.T.L., K.M., X.Y., F.R., Z.M.K., M.L., R.R. and E.M. acquired the experimental data. X.W., A.R., J.N.Z., D.T., D.H. and E.G. did the computational and omics analyses. A.C.D. conceived and designed the compound libraries. A.P.S. designed compound libraries and chemical syntheses. A.R., M.D., A.S., M.S., P.M.-S., M.R.d.G., K.R., Y.T.L., F.R., X.Y., Z.M.K., M.L., R.R., S.Y.T., J.J., A.V., E.M., Y.P.D.J., C.M.R., I.M., J.M.L., R.M.S., A.L., E.G. and A.C.D. generated reagents and provided scientific inputs. A.C.D., A.L. and E.G. supervised the research.

Corresponding authors

Correspondence to Amaia Lujambio, Ernesto Guccione or Arvin C. Dar.

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Competing interests

A.R., M.D., A.P.S., S.M., P.M.S., A.L., E.G. and A.C.D. are inventors on a patent application describing the use of WNTinib in CTNNB1-mutant tumors (patent no. 63/108,728). A.C.D. and A.P.S. are inventors on a patent describing WNTinib composition of matter (patent no. 16/325,218). The Guccione laboratory received research funds from Prelude Therapeutics (for unrelated projects). E.G. is a cofounder of Immunoa Pte.Ltd. A.C.D. is a cofounder, shareholder, consultant and advisory board member of Nested Therapeutics. A.C.D. and E.G. are cofounders, shareholders, consultants and advisory board members of Prometeo Therapeutics.The Jin laboratory received research funds from Celgene Corporation, Levo Therapeutics, Inc., Cullgen, Inc. and Cullinan Oncology, Inc. J.J. is a cofounder and equity shareholder in Cullgen, Inc. and a consultant for Cullgen, Inc., EpiCypher, Inc. and Accent Therapeutics, Inc. J.M.L. receives research support from Eisai Inc, Bayer HealthCare Pharmaceuticals, Ipsen and consulting fees from Eisai Inc, Merck, Eli Lilly, Bayer HealthCare Pharmaceuticals, Genentech, Roche, AstraZeneca, Bristol-Myers Squibb, Ipsen, Glycotest, Exelixis, Mina alpha and Boston Scientific. The remaining authors declare no competing interests.

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Nature Cancer thanks Lars Zender and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Chemical genetic screens in tumor organoids yield WNTinib as a selective antagonist of CTNNB1-mutated HCC.

a, The base structures of sorafenib and regorafenib were used as starting points for kinase inhibitor development. b, The required perfluoroalkyl-substituted aniline building blocks were obtained in a single step from aniline. c, In cases where isocyanates were not commercially available, the required acyl imidazole intermediates were generated in situ from an aniline and N,N’-carbonyldiimidazole. d, In the final inhibitor generating step, the urea linker component was formed via reaction of a commercially available isocyanate or in situ formed acyl imidazole with a core aniline corresponding to sorafenib (X = H) or regorafenib (X = F). e, Key interactions and predicted binding pose of sorafenib and regorafenib analogs. Points of diversification are highlighted as X and R, which are specified for each analog in panel d. f-i, IC50 curves of WNTinib (f), 8-50-2 (g), sorafenib (h), and regorafenib (i) in murine HCC organoids used in Fig. 1a. N = 3 independent experiments [mean, SEM]. j. Caspase activity in CTNNB1-mutant models treated with WNTinib at 1 μM for 3 days. N = 3 independent experiments [mean, SEM]. Extended data associated with Fig. 1.

Source data

Extended Data Fig. 2 WNTinib reduces EZH2 phosphorylation to drive the suppression of essential gene networks in CTNNB1-mutated HCC.

a, Principal component analysis (PCA) of phosphoproteomics displaying MYC-CTNNB1 and MYC-Tp53 tumor organoids treated or not with WNTinib for 4 or 24 hours, as related to Fig. 2a. b. Volcano plots depicting phosphoproteomic changes elicited by WNTinib in MYC-CTNNB1 tumor organoids (left) or MYC-Tp53 tumor organoids (right) as compared to DMSO. Inset: pathway enrichment terms associated with significantly regulated phosphoproteins. WNTinib was used at 1 μM for 4 hours. N = 2 independent experiments. Gene-centered, differentially expressed phosphosites were determined by using estimates of variance-mean dependence with a Benjamini-Hochberg FDR correction. c-d, Interaction networks and pathway enrichment for the significantly up and downregulated phosphoproteins in the MYC-CTNNB1 (c) and MYC-Tp53 (d) models treated with WNTinib used at 1 μM for 24 hours. Strength of interactions denoted by STRING P value. Proteins driving pathway enrichment shown in boxes. e-f, Clustered heatmap of the combined score for kinase-substrate predictions (a higher combined score indicates a stronger kinase-substrate prediction) for the top substrates (y-axis) and kinases (x-axis) modulated by WNTinib in MYC-CTNNB1 (e) and MYC-Tp53 (f) tumor organoids. Pathway enrichment for both kinases and substrates was done using STRING. Substrates driving enrichment shown in boxes; EZH2 highlighted in red. Extended data associated with Fig. 2.

Extended Data Fig. 3 WNTinib reduces EZH2 phosphorylation to drive the suppression of essential gene networks in CTNNB1-mutated HCC.

a-b, PCA of transcriptomics (a) displaying MYC-CTNNB1 tumor organoids treated or not with WNTinib for 1 hour, 24 hours, or 7 days, as related to Fig. 2b. N = 2 independent experiments. b, Volcano plots depicting transcriptomic changes elicited by WNTinib as related to panel a. Inset: pathway enrichment terms associated with significantly regulated transcripts. c-d, PCA of transcriptomics (c) displaying MYC-Tp53 tumor organoids treated or not with WNTinib for 1 hour, 24 hours, or 7 days, as related to Fig. 2b. N = 2 independent experiments. d, Volcano plots depicting transcriptomic changes elicited by WNTinib as related to panel c. Inset: pathway enrichment terms associated with significantly regulated transcripts. For panels b, d – differential events were identified using two-sided Wald tests with Benjamini-Hochberg multiple testing corrections. Extended data associated with Fig. 2.

Extended Data Fig. 4 WNTinib reduces EZH2 phosphorylation to drive the suppression of essential gene networks in CTNNB1-mutated HCC.

a, Heat maps and average profile plots for MYC-CTNNB1 tumor organoids (right) or MYC-Tp53 tumor organoids (left) displaying CUT&RUN H3K27me3 signal around the TSS/TES ( + /- 3 kb) of genes unchanged by WNTinib in the MYC-CTNNB1 tumor organoids (RNAseq – 7 days; N = 3767). Genes were chosen for similar expression levels to WNTinib-modulated genes. b, As in a, but displaying H3K27me3 levels at genes belonging to the Multicellular Organism Development Pathway in WNTinib-treated MYC-CTNNB1 tumor organoids. c, Representative genome browser tracks of H3K27me3 levels in WNTinib-treated MYC-CTNNB1 tumor organoids. Top: examples belong to Wnt Signaling Pathway; Bottom: example of a housekeeping gene. d, ChIP-qPCR enrichment of H3K27me3 at the promoters of genes shown in panel c. MYC-CTNNB1 tumor organoids were treated with DMSO, sorafenib (10 μM), or WNTinib (1 μM) for the indicated times. N = 3 independent experiments [mean, SEM]. P values; Sox7 *.00698 (1 day), *.0085 (3 day), *.01045 (7 day); Wnt11 *.02699 (1 day), *.02624 (3 day), *.02432 (7 day); Wnt7a *.01741 (1 day), *.01263 (3 day), ***.00045 (7 day); as calculated with two-tailed paired t-tests. Extended data associated with Fig. 2.

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Extended Data Fig. 5 WNTinib depends on EZH2 relocalization to chromatin for its activity in CTNNB1-mutated HCC.

a, Schematic of phospho-site regulation of EZH2 by WNTinib in the MYC-CTNNB1 tumor organoids. Predicted kinases for each site shown in boxes. b, Western blot depicting the modulation of pT367 EZH2 by WNTinib (1 μM) or sorafenib (10 μM) in the four tumor organoid models used in Fig. 1a. Tumor organoids were treated for 24 hours. c-d, Cytoplasmic and nuclear fractions of total and pT367 EZH2 from MYC-CTNNB1 organoids (c) or HEPG2 cells (d) treated with DMSO, sorafenib (10 μM organoids, 5 μM cells), or WNTinib (1 μM organoids, .5 μM cells) for 24 hours. Tubulin and histone H3 used as fractionation controls. e, IC50 curves for WNTinib (left) or sorafenib (right) in MYC-CTNNB1 tumor organoids depleted for EZH2 (with two independent shRNA targeting EZH2). Inset: western blot depicting depletion efficiency. N = 3 independent experiments [mean, SEM]. f, Western blot depicting total EZH2 degradation as related to Fig. 3g. g, WNT reporter expression levels in MYC-CTNNB1 organoids treated with WNTinib (1 μM), GSK343 (1 μM), or MS1943 (1 μM) alone or in combination. Values obtained from three biological replicates [mean, SEM, n = 3]. Significant differences between groups indicated by asterisks. * P < .05, ** P < .005, as calculated with two-tailed, paired t-tests. h, RNA expression levels of genes in MYC-CTNNB1 tumor organoids depleted for EZH2 and treated with DMSO, sorafenib (10 μM), or WNTinib (1 μM). Genes are classified as being described PRC2 targets or not. N = 3 independent experiments [mean, SEM]. Significant differences between groups indicated by asterisks. * P < .05, ** P < .005, *** P < .0005 as calculated with two-tailed, paired t-tests. Exact P values listed in source data. Western blot results were independently validated at least two times. Extended data associated with Fig. 3.

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Extended Data Fig. 6 WNTinib utilizes unique polypharmacology to regulate the EZH2-WNT axis.

a, Kinome selectivity for sorafenib, regorafenib, 8-50-2, and WNTinib. Y-axis indicates the number of kinases that each compound inhibits at >65%, as profiled using KINOMEscan. b-e, Trees depicting the kinome inhibition profiles of WNTinib (b), 8-50-2 (c), sorafenib (d), and regorafenib (e). f, Time course (1 to 24 hours) of signaling perturbations on pT367 EZH2. HEPG2 cells were treated with DMSO, sorafenib (5 μM) or WNTinib (.5 μM). Western blot measures endogenous proteins as indicated. g, Supernatant transfer experiment in HEPG2 cells. Cells were first treated with DMSO, sorafenib (5 μM), WNTinib (.5 μM), or vemurafenib (10 μM) for 48 hours. Supernatants were harvested and applied to fresh cells for the indicated times and pT367 EZH2 levels were measured. h, IC50 curves and i, Heatmap depicting IC50 values associated with each treatment condition in Fig. 5d-e. HEPG2 cells were transduced with constructs encoding constitutively active MEK (S218D, S222D) or MKK6 (S207E, T211E). Parental HEPG2 cells used as control comparison. Cells were treated for 3 days. Values obtained from three biological replicates [mean, SEM, n = 3]. j, WNT reporter expression levels in HEPG2 transduced as in panel h and treated or not with WNTinib (.5 μM) for 24 hours. Parental HEPG2 cells used as control comparison. N = 3 independent experiments [mean, SEM]. Significant differences between groups indicated by asterisks. * P < .05, as calculated with two-tailed paired t-tests. P values; *.0291 (WNTinib), *.0181 (MEK WNTinib), *.0352 (MKK6 WNTinib). Western blot results were independently validated at least two times. Extended data associated with Figs. 4 and 5.

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Extended Data Fig. 7 KIT is a critical target for WNTinib’s MoA.

a, IC50 curves and b, Heatmap depicting IC50 values associated with each treatment condition in Fig. 6e-f. HEPG2 cells were transduced with a doxycycline (DOX)-inducible construct encoding constitutively active cKIT (V559D, T670I). Cells not treated with DOX used as control comparison. Cells were treated for 3 days. Values obtained from three biological replicates [mean, SEM, n = 3]. c, Growth curves for MYC-CTNNB1 tumor organoids (left) or MYC-Tp53 tumor organoids (right) depleted for KIT using shRNA. Depletion efficiency represented in panel d. N = 3 independent experiments [mean, SEM]. Significant differences between curves (as compared to untreated) indicated by asterisks. *** P < .0005, as calculated with a two-way ANOVA with Tukey test for multiple comparisons (F (4, 20) = 38.99). d, pT367 EZH2 and H3K27me3 modulation in shKIT MYC-CTNNB1 tumor organoids and MYC-Tp53 tumor organoids. Western blot measures endogenous proteins as indicated. e, IC50 curves for WNTinib in MYC-CTNNB1 tumor organoids depleted for KIT. Organoids were treated for 3 days. N = 3 independent experiments [mean, SEM]. For panel c, P values; ***<.0001 (shKit-1, shKit-2). Extended data associated with Fig. 6.

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Extended Data Fig. 8 BRAF and p38 kinases are critical anti-targets for WNTinib.

a, IC50 curves and b, Heatmap depicting IC50 values associated with each treatment condition in Fig. 7a-b. HEPG2 cells were transduced with constructs encoding drug resistant p38 (T106M) or BRAF (T529N) kinases. Parental HEPG2 cells used as control comparison. Cells were treated for 3 days. N = 3 independent experiments [mean, SEM]. c, Total and pT367 EZH2 levels in HEPG2 cells transduced with constructs encoding wildtype or drug resistant p38 (T106M) or BRAF (T529N) kinases. Parental HEPG2 cells used as control comparison. Cells were treated with DMSO, sorafenib (5 μM), or regorafenib (10 μM) for 1 hour. Endogenous antibodies used to assess overexpression of mutant constructs. d, Total and pT367 EZH2 levels in HEPG2 cells treated with DMSO, sorafenib, vemurafenib, or WNTinib at increasing doses (.01 μM to 30 μM) for 1 hour. Western blot results were independently validated at least two times. Extended data associated with Fig. 7.

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Extended Data Fig. 9 WNTinib outperforms clinical compounds across in vivo models of HCC.

a-b, Dose escalation of WNTinib in BALB/c mice (a) and BALB/c/nude mice (b). Animals were dosed every day via oral gavage for 2 weeks. Percentage change in body weight noted in brackets (N = 3 animals per group; mean, SEM). c. Histological images (H&E) of liver, small & large intestine, and kidney from BALB/c mice treated with WNTinib at either 60 mg/kg or 120 mg/kg via oral gavage for 14 days. d, Images of C57BL/6 J mice treated with either vehicle or WNTinib for extended periods of time. WNTinib-treated animals present mosaic patterns of grey hair. e, Quantitative PCR expression of apoptotic genes in tumors derived from mice in panel Fig. 7e (N = 3 per group). *** P < .0005, as calculated with two-tailed paired t-tests. Exact P values listed in source data. For panel c, representative images are shown (n = 3). Extended data associated with Fig. 8.

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Supplementary Tables 1–18.

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Source Data Figs. 1 and 3–8

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Rialdi, A., Duffy, M., Scopton, A.P. et al. WNTinib is a multi-kinase inhibitor with specificity against β-catenin mutant hepatocellular carcinoma. Nat Cancer 4, 1157–1175 (2023). https://doi.org/10.1038/s43018-023-00609-9

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