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The dopamine transporter antagonist vanoxerine inhibits G9a and suppresses cancer stem cell functions in colon tumors

Nature Cancer volume 5, pages 463–480 (2024)Cite this article

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Abstract

Cancer stem cells (CSCs), functionally characterized by self-renewal and tumor-initiating activity, contribute to decreased tumor immunogenicity, while fostering tumor growth and metastasis. Targeting G9a histone methyltransferase (HMTase) effectively blocks CSC functions in colorectal tumors by altering pluripotent-like molecular networks; however, existing molecules directly targeting G9a HMTase activity failed to reach clinical stages due to safety concerns. Using a stem cell-based phenotypic drug-screening pipeline, we identified the dopamine transporter (DAT) antagonist vanoxerine, a compound with previously demonstrated clinical safety, as a cancer-specific downregulator of G9a expression. Here we show that gene silencing and chemical antagonism of DAT impede colorectal CSC functions by repressing G9a expression. Antagonizing DAT also enhanced tumor lymphocytic infiltration by activating endogenous transposable elements and type-I interferon response. Our study unveils the direct implication of the DAT–G9a axis in the maintenance of CSC populations and an approach to improve antitumor immune response in colon tumors.

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Fig. 1: Multiparametric high-throughput phenotypic drug-screening pipeline identifies VXN as a repurposed compound displaying cancer-selective growth inhibition effects.
Fig. 2: Multi-omic characterization of VXN cancer-selective effect on cell functions.
Fig. 3: DAT distribution across human colorectal intratumor heterogeneity.
Fig. 4: Vanoxerine alters stem-like functions in colon cancer cells by blocking DAT.
Fig. 5: VXN inhibits G9a expression in CRCs via the AKT–Nur77 axis.
Fig. 6: VXN alters colorectal CSC functions in patient-derived tumor organoids.
Fig. 7: VXN alters colorectal CSC activity in vivo.
Fig. 8: Epigenetic changes induced by VXN suppress immune evasion in colorectal tumors.

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Data availability

All sequencing data generated in this study and supporting these findings were deposited at Gene Expression Omnibus (GEO) under accession codes GSE154057, GSE207032, GSE226324 and GSE226377. Single-cell RNA-seq data that support the findings of this study are available at GEO under accession code GSE132465. Reposited ChIP-seq data were re-analyzed in this study and the original dataset is available in GEO under accession code GSE82131. The human colon adenocarcinoma dataset used to derive mRNA stem cell indexes was from the TCGA Research Network at http://cancergenome.nih.gov/. Other databases/datasets used in this study include the PRISM drug repurposing portal (https://depmap.org/repurposing), the RSCB PDB database (4XP4), the Signaling Pathways Project online platform (https://www.signalingpathways.org/ominer), the ENCODE project database (https://www.encodeproject.org/), the ChIP Atlas database (https://chip-atlas.org/), the PerMM web portal (https://permm.phar.umich.edu) and the BioGrid database (https://thebiogrid.org/). Source data for Figs. 1–8 and Extended Data Figs. 1–10 are provided as Source Data files. All other data supporting the findings of this study are available from the corresponding author on reasonable request. Source data are provided with this paper.

Code availability

All the code used in this study is available at https://gitlab.com/ohri/the-dopamine-transporter-antagonist-vanoxerine-inhibits-g9a-and-suppresses-cancer-stem-cell-functions-in-colon-tumors.

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Acknowledgements

This work was supported by grants from the Cancer Research Society (22778, 24039 and 942363 to Y.D.B.), the Ontario Ministry of Research, Innovation and Science (ER17-13-012 to Y.D.B.), the CIHR (PJT-17354 and 201302MFE-300637-197755 to Y.D.B.) and the Natural Sciences and Engineering Research Council (RGPIN-2018-06521 and DGECR-2018-00029 to Y.D.B.). The GEMb facility is supported by CFI-36490, CFI-37607 and CFI-36940. We thank M. Bhatia (McMaster University) and J.-F. Beaulieu (Université de Sherbrooke) for sharing key cell models. We thank C. Porter and G. Palidwor (the Ottawa Bioinformatics Core Facility, OHRI, RRID SCR_022466) for computational analysis, as well as C. Van Oostende-Triplet at the University of Ottawa Cell Biology and Image Acquisition Core Facility (RRID SCR_021845). Collection of human tissue for this study was made possible by the Global Tissue Consent and Collection Program at the OHRI and C. O’Brien at the Toronto General Hospital Research Institute. We thank T. J. Collins for critical input on high-throughput screening and L. A. Sabourin and S. Delisle for providing Ad-Cre particles. We thank F. M. Desrochers and A. Chatterji for technical assistance. The graphical elements of Figs. 1, 2, 4–7 and Extended Data Figs. 8 and 9 were generated using BioRender.

Author information

Authors and Affiliations

  1. Department of Cellular and Molecular Medicine, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada

    Christopher J. Bergin, Aïcha Zouggar, Amanda Mendes da Silva, Tanguy Fenouil, Joshua R. Haebe, Angelique N. Masibag, Gautam Agrawal, Muhammad S. Shah, Tamara Sandouka, Mario Tiberi & Yannick D. Benoit

  2. Inserm U1052, CNRS UMR5286, Centre de Recherche en Cancérologie de Lyon, Université Claude Bernard Lyon 1, Lyon, France

    Tanguy Fenouil

  3. Institut de Pathologie Multisite des Hospices Civils de Lyon, Site Est, Groupement Hospitalier Est, Bron, France

    Tanguy Fenouil

  4. University of Ottawa Brain and Mind Research Institute, Ottawa, Ontario, Canada

    Mario Tiberi

  5. Neuroscience Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada

    Mario Tiberi

  6. Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada

    Rebecca C. Auer & Michele Ardolino

  7. Department of Surgery, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada

    Rebecca C. Auer

  8. Center for Cancer Therapeutics, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada

    Rebecca C. Auer & Michele Ardolino

  9. Centre for Infection, Inflammation and Immunity, University of Ottawa, Ottawa, Ontario, Canada

    Rebecca C. Auer & Michele Ardolino

  10. School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada

    Yannick D. Benoit

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Contributions

C.J.B. and A.Z. designed and performed experiments, analyzed/interpreted data and wrote the paper. A.M.dS. performed experiments and analyzed and interpreted data. T.F. performed experiments and tissue pathological analysis. J.R.H., A.N.M., G.A. and T.S. performed experiments. M.S.S. analyzed data. M.T. provided results interpretation and wrote the paper. R.C.A. provided primary tissue samples and results interpretation. M.A. designed experiments, performed experiments, provided results interpretation and wrote the paper. Y.D.B. supervised the project, designed experiments, performed experiments, provided results interpretation and wrote the paper.

Corresponding author

Correspondence to Yannick D. Benoit.

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Competing interests

Relatives of Y.D.B. hold shares in Gibiex Biotech. M.A. received monetary compensation from Alloy Therapeutics for consulting. M.A. is under a contract agreement to perform sponsored research with Actym Therapeutics and Dragonfly Therapeutics. Neither consulting nor sponsored research is related to the present article. The other authors declare no competing interests.

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Nature Cancer thanks Toshiro Sato 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 Supporting information on the high-throughput phenotypic assay used to identify cancer-selective inhibitors of H3K9me2 deposition.

a, OCT4 staining on t-hESCs upon drug treatments (5 μM, 48 h) and DMSO control (left panel, scale bar: 50 μm). Flutamide, clomiphene, and colistin sulfate are example of drugs tested for loss of pluripotency (n = 2 biological replicates, 1 independent experiment). Quantification of OCT4-positive cells vs. counts scored from a validation set of 142 compounds relative to DMSO control (right panel). Values are expressed as log2 fold-changes (L2FC). Dashed box indicates loss-of-pluripotency hits (cut off: L2FC > -1.0). b, H3K9me2 immunostaining on t-hESCs treated with loss-of-pluripotency inducers (5 μM, 48 h, n = 2 biological replicates from 2 independent experiments, scale bar: 100 μm). Thioridazine and niclosamide are examples of drugs further tested for H3K9me2 deposition. The G9a inhibitor BIX-01294 (1 μM, 48 h) was used as positive control. c, Quantification of H3K9me2 immunostaining signal in t-hESC cells vs. cell count scored for each loss-of-pluripotency inducer relative to DMSO. CWP232228 (100 nM, 48 h) and BIX-01294 (1 μM, 48 h) were used as positive controls for cell count and H3K9me2 inhibition, respectively (dashed lines) (n = 2 biological replicates from 2 independent experiments). Prioritized hits (vanoxerine (VXN), niclosamide, clomiphene) were identified in circles. d, Heat map of the mean immunodetection signal from assessing total 5-methylated cytosine (5-meC) and histone marks H3K9me2, H3K9me3, and H3K27me3 levels in t-hESCs treated with different candidates vs. DMSO control (5 μM, 48 h, n = 3 biological replicates from 3 independent experiments, color scale: Log2 fold change signal vs. vehicle, cut off: >0.25). BIX-02492 (1 μM) and DZNep (1 μM) (S-adenosylhomocysteine synthesis and EZH2 inhibitor) were used as positive controls. e, Cancer-selective toxicity assessment for clomiphene and niclosamide using the human CRC cell lines HT29 (n = 4 biological replicates), SW480 (n = 3 biological replicates), and HCT116 (n = 3 biological replicates) vs. normal intestinal progenitor line HIEC (n = 6 biological replicates). Data from 1 independent experiment were best-fitted using a nonlinear curve fitting model. f, Dose–response cell growth experiment evaluating the EC50 of VXN in HT29 cells for 48 h vs. 72 h treatments (n = 4 biological replicates from 1 independent experiment). g, Cell growth analysis comparing 2.5μM VXN treatments over 72 h vs. control in 3 independent cell lines from human colorectal, melanoma, ovarian, prostate, liver, lung, breast, glioblastoma, and kidney tumors (Data from 1 independent experiment, number of biological replicates (n) and p values are indicated on graphs, mean values ± SEM, unpaired multiple t-tests). See also Fig. 1, Supplementary Tables 1–3, Supplementary Note 1.

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Extended Data Fig. 2 Transcriptomic profiling of cancer-selective effects of vanoxerine treatments.

a, Fold change comparison plot of differentially expressed genes upon VXN treatments (10 μM, 48 h) vs. DMSO in HT29 and HIEC cells (n = 2 biological replicates from 1 independent RNA-seq experiment, p < 0.05). “HT29_VXN” represents the cancer-selective transcriptional signature to VXN treatments. b, GSEA plots showing a downregulation of genes overexpressed in human colorectal adenomas (Sabates) within the VXN cancer-selective signature in CRC cell. Such a relationship is not observed in VXN-treated HIEC cells (vs. DMSO). The expression of all SABATES genes highlighted by GSEA are presented in a heat map (RNA-seq data from a, Log2 fold change expression vs. DMSO, NES and NOM p value were calculated by the GSEA Software version 4.0.3). c, Heat map representing the expression of individual genes from curated lists of intestinal differentiation and progenitor (undifferentiated) markers in HIEC and HT29 cells treated with either VXN (10 μM, 48 h) or DMSO control (RNA-seq data from a, z score of FPKM values). d, Enrichment plots for the GO_CANONICAL _WNT_SIGNALING_PATHWAY and the DOUGLAS_BMI1_TARGETS_DN (downregulated genes in BMI1-knockdown cells) signatures from GSEA performed on differentially expressed genes following VXN treatment (vs. DMSO) in HT29 and HIEC cells (RNA-seq data from a, NES and NOM p value were calculated by the GSEA Software version 4.0.3). e, Changes of mRNA expression observed for common colon CSC markers upon VXN treatments in HT29 cells (10 μM, 48 h) vs. DMSO controls (Log2 fold change expression, RNA-seq data from a, q values calculated using the Benjamini–Hochberg’s multiple correction testing method are indicated on graph). f, Transcriptional stem cell indexes calculated using one-class logistic regression machine-learning algorithm for t-hESC (CSC-like, n = 2 biological replicates from 1 independent RNA-seq experiment), HIEC (normal intestinal, RNA-seq data from a), HCT116 (CRC, n = 1 biological replicate from GSM2891824) and HT29 (CRC, RNA-seq data from a) vs. the mRNAsi distribution curve of the colon adenocarcinoma (COAD) patient cohort (TCGA: n = 275 patients). g, GSEA plots showing a positive correlation between genes selectively modulated by VXN in HT29 cells (vs. DMSO, p < 0.05) and gene signatures characteristic of a response to type-1 interferon. Such a profile is not observed at a statistically significant level in VXN-treated HIEC cells (vs. DMSO) (NES and NOM p value were calculated by the GSEA Software version 4.0.3). The expression of all type-1 interferon response genes highlighted by GSEA is presented in a heat map (RNA-seq data from a, Log2 fold change expression) following VXN treatments (vs. DMSO) in HIEC and HT29 cells. See also Fig. 2, Supplementary Tables 4–5.

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Extended Data Fig. 3 Comparison of vanoxerine response to other pharmacological compounds.

a, Correlation study of VXN sensitivity across the PRISM repurposing primary screen 19Q3 (top-6 entries). The epigenetic drug romidepsin significantly correlates with the VXN response (Pearson correlation (r) coefficient, two-tailed p values are presented). b, Correlation plots of the chemotherapy drug idarubicin and the nootropic compound IDRA-21 vs. VXN for cell growth inhibition in 33 human CRC cell lines (n = 33 cell lines, Pearson r test, two-tailed p values indicated on graphs). c, Structure of VXN second-pass metabolite GBR-12935 and its impact on HT29 (CRC) and HIEC (normal) cell viability at 1.25, 2.5, and 5μM vs. DMSO (0μM) (Data from 3 independent experiments, number of biological replicates (n) for each condition is indicated on graphs, mean values ± SEM, ***: p = 0.0008). d, Correlation plots of benztropine-mesylate vs. VXN for cell growth inhibition in 33 human CRC and 108 lung cancer cell lines (CRC: n = 33 cell lines, lung: n = 108 cell lines, Pearson r test). Dashed box represents the expected distribution zone for correlated responses. e, Heat maps of mRNA expression for documented H3K9me and 5-meC regulators in HIEC and HT29 cells treated with VXN (10 μM, 48 h) vs. DMSO (n = 2 biological replicates from 1 independent RNA-seq experiment, z score of FPKM values). Adjusted p values (q values) calculated based on Benjamini–Hochberg’s multiple correction testing method. f, Expression of G9a (EHMT2) (Data from 2 independent qPCR experiments, DMSO: n = 3 biological replicates, VXN: HIEC n = 3 and HT29 n = 5 biological replicates, ***: p = 0.0002, unpaired two-tailed t-test) and GLP (EHMT1) (RNA-seq data from e, normalized counts) encoding mRNA were measured following VXN treatments in HIEC and HT29 cells (mean values ± SEM). g, Western blot analysis comparing G9a levels in HT29 (cancer) and HIEC (normal) cells. When detectable, G9a levels were decreased by VXN in HIEC cells. Actin was used as loading control (n = 3 biological replicates from 3 independent experiments). h, Fold change comparison plot of differentially expressed genes upon VXN (10 μM, 48 h) or BIX-1294 (1 μM, 48 h) treatments vs. DMSO in t-hESCs (n = 2 biological replicates from 1 independent RNA-seq experiment, p < 0.05, Benjamini–Hochberg’s multiple correction test and false discovery estimation). i, Heat map showing mRNA expression of documented H3K9me and 5-meC regulators in t-hESCs treated with VXN (10 μM, 48 h) or BIX-01294 (1μM, 48 h) vs. DMSO controls. Arrows indicating downregulation by VXN but not by BIX. (RNA-seq data from h, z score of FPKM values). j, GSEA enrichment plots showing correlations between genes significantly modulated by VXN in t-hESC and apoptosis (HALLMARK_APOPTOSIS), proliferation (E2F_TARGETS), pluripotency (WONG), MYC (HALLMARK_MYC), p53 (HALLMARK_P53), and colorectal cancer (GRADE_COLON_AND_RECTAL_CANCER_UP) signatures (RNA-seq data from h, NOM p < 0.05, NES and NOM p value were calculated by the GSEA Software version 4.0.3). k, Bubble plot of transcription factor binding sites (UCSC_TFBS) enrichment in genes from top-50 upregulated G9a/H3K9me2 target genes upon VXN treatments in HT29 and HIEC cells (ChIP-seq: n = 1 sample series, RNA-seq: n = 2 biological replicates, 1 independent experiment, one-sided p values for Fisher’s Exact test available in Source Data). See also Fig. 2, Supplementary Tables 4–6, and Supplementary Note 2.

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Extended Data Fig. 4 Characterization of DAT as the main target of VXN in colon cancer.

a, Docking poses of VXN and cocaine interaction with DAT X-ray crystallographic structure (PDB 4xp1). The binding pocket surface is highlighted according to electrostatic properties (top). b, qPCR analysis of SLC6A3 mRNA expression in bulk, heterogeneous organoids and CSC-enriched fractions from primary CRC samples (n = 3 biological replicates from 1 patient sample (Patient #92) in 2 independent experiments, mean values ± SEM, ***: p < 0.0001, unpaired two-tailed t-test). HIEC (n = 4 biological replicates) and t-hESCs (n = 4 biological replicates) were used as low and high-expressing controls for SLC6A3, respectively. c, UMAP plot of clustering analysis from scRNA-seq profiling (n = 23 CRC patient samples). Each cluster represents a cell type-specific signature. Histograms are showing transcript levels of SLC6A3 and LGR5-expressing epithelial and stromal cells across primary CRC tumors (T) and normal colon (N) samples. d, Representative micrograph of crypts from human normal colonic tissue showing the absence of DAT in E-cadherin stained epithelial cells (n = 3 independent donors, scale bar: 100 μm). e, Representative single-cell protein signal observed for total histone H3 (cell occupancy control), DAT, phospho-H3S10, CD133 (colon CSC marker), chromogranin A (enteroendocrine cell marker), and neurofilament (NEFL, neuronal marker) on scWest chips loaded with either patient-derived CSC-enriched spheroids (n = 5 CRC patient samples) or M17 neuroblastoma cells (n = 3 biological replicates). f, Single-cell protein quantification of DAT and CD133-positive cells within primary bulk colorectal adenocarcinoma samples. Scatter-plots are showing observed fluorescence intensity per individual cell. DAT-positive are highlighted in red on CD133 plots (n = 3 samples, Patients #409 and #397 presented, 3 independent experiments). g, Single-cell protein quantification of DAT-positive cells in CD133-sorted fractions from primary colorectal adenocarcinomas. Scatter-plots are showing observed fluorescence intensity per individual cell (n = 2 CRC patient samples from 2 independent experiments). h, SLC6A3 mRNA levels in HCT116 cells transduced with pInducer10-RFP vector containing a non-silencing scrambled control or an SLC6A3-targeting shRNA. shRNA expression was induced by doxycycline and qPCR measurements were performed on 90-95% RFP-positive populations (n = 6 biological replicates from 2 independent experiments, mean values ± SEM, ***: p = 0.0007, unpaired two-tailed t-test). I, Representative micrographs of pInducer10-RFP shCTRL and shSLC6A3-transduced HT29 cells immunostained for DAT. The punctate intracellular staining corresponding to DAT is reduced in RFP-positive shSLC6A3 cells (n = 3 biological replicates from 2 independent experiments, scale bar: 30 μm). j, SLC6A3 siRNA knockdown decreases growth in HT29 and HCT116 cells, with no additive effects when combined with VXN treatments (48 h, 5 μM) (1 independent experiment, biological replicates (n) and p values indicated on the graphs, mean values ± SEM, one-way ANOVA with Tukey’s multiple comparisons test). Knockdown efficiency vs. scramble control (siCTRL) was confirmed by western blot (n = 3 biological replicates from 3 independent experiments). k, Clonogenic spheroid formation experiment using HT29 cells transduced with shCTRL and shSLC6A3 pInducer10-RFP system and plated as single cells in Elplasia® microwell plates. Spheroid formation frequency and spheroid size in Dox-induced shSLC6A3 (RFP-positive) HT29 cells are presented vs. shRNA controls (n = 18 biological replicates from 3 independent experiments, mean values ± SEM, ***: p<0.0001, unpaired two-tailed t-test). l, Clonogenic organoid formation assay using siSLC6A3 knockdown HCT116 cells (vs. siCTRL). Transient knockdown of SLC6A3 significantly reduces HCT116 organoid formation frequency (n = 3 biological replicates from 3 independent experiments, mean values ± SEM, ***: p = 0.0002, unpaired two-tailed t-test). See also Figs. 3–4, and Supplementary Note 3.

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Extended Data Fig. 5 Relationship between the impact of vanoxerine on cancer cells and the dopamine pathway.

a, Cell growth experiment measuring the impact of increasing doses of dopamine (0.02 – 20 μM, 48 h) on HT29 cell sensitivity to VXN treatments (10 μM, 48 h, vs. DMSO control) (n = 4 biological replicates from 2 independent experiments, ***: p < 0.0001, **: p = 0.0023, *: p = 0.0493, one-way ANOVA with Bonferroni’s multiple comparisons test). b, ELISA measuring dopamine levels in cell lysates and conditioned growth media from HIEC, HT29, t-hESCs, and primary human colorectal CSC (CCSC) cultures treated with VXN (10 μM, 48 h) or DMSO control. Complete growth media were also tested (n = 3 biological replicates from 1 independent experiment, color scale: 1/absorbance 450 nm). Dopamine (DA) standard solutions were used as positive controls (8 – 530 nM). c, mRNA levels of putative VXN targets previously documented in the literature (SLC6A3: HIEC n = 4 and HT29 n = 3 biological replicates from 3 independent experiments. hERG/KCNA5, sigma receptor-1, and sigma receptor-2: n = 2 biological replicates from 1 independent RNA-seq experiment), as well as dopamine receptors (n = 2 biological replicates from 1 independent RNA-seq experiment) in HIEC (normal) and HT29 (CRC) cells (mean values ± SEM, ***: p < 0.0001, unpaired two-tailed t-test). d, Dose–response experiments measuring the effect of the sigma receptor-1/2 agonist ditolylguanidine (DTG, n = 4 biological replicates) and the sigma receptor-2 antagonist SM-21 (HT29 n = 3 and HIEC n = 4 biological replicates) on HT29 and HIEC cell growth (48 h, data from 2 independent experiments, mean values ± SEM, p values indicated on graphs, two-way ANOVA with Bonferroni’s multiple comparisons test). e, Cell growth experiment comparing the effect of VXN (10 μM, 48 h) to the dopamine receptor D2 antagonist haloperidol (10 μM, 48 h), in the presence or absence of dopamine (10 μM) vs. DMSO control (n = 7 biological replicates from 1 independent experiment, mean values ± SEM, ***: p < 0.0001, **: p = 0.0041, one-way ANOVA with Bonferroni’s multiple comparisons test).

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Extended Data Fig. 6 Impact of vanoxerine on intracellular signaling mechanisms in colon cancer cells.

a, GSEA showing significant enrichment of differentially expressed genes from shSLC6A3 (vs. shCTRL) and VXN-treated (10 μM, 48 h,vs. DMSO) HCT116 cells (n = 3 biological replicates from 2 independent RNA-seq experiments), and VXN-treated HT29 and HIEC cells (vs. DMSO, n = 2 biological replicates from 1 independent RNA-seq experiments) in AKT/mTOR-related transcriptional signatures. Bubble plot represents normalized enrichment scores (NES, color scale) and NOM p values (circle size) for each gene signature (NES and NOM p values calculated by the GSEA Software version 4.0.3). b, Expression of PI3K/AKT/mTOR target genes (from signatures in a) in shCTRL (DMSO and VXN 10 μM, 48 h) and shSLC6A3 (DMSO) HCT116 cells. mRNA levels are presented as FPKM values (n = 3 biological replicates from 2 independent experiments, mean values ± SEM, p values indicated on graphs, one-tailed paired t-test). c, Extended kinome profiling data on 6 h, 12 h, and 24 h VXN-treated HT29 cells (10 μM) for the phosphorylation state of MAPK, JAK/STAT, p53, and Src family kinases (n = 4 biological replicates from 2 independent experiments, *: p < 0.05, **: p = 0.0019, ***: p < 0.001, exact p values are in Source Data, one-way ANOVA with Bonferroni’s multiple comparisons test, color scale: Log2 fold change signal vs. vehicle). d, Western blot analysis of total and phospho-AKT levels (T308: n = 3 biological replicates, S473: n = 4 biological replicates) in HT29 SLC6A3 knockdown vs. shCTRL cells. SLC6A3 knockdown was confirmed by western blotting, and actin was used as a loading control (Data from 2 independent experiments). e, STRING analysis performed on the DAT interactome from the BioGRID database plus hits from the kinome profiling experiments in HT29 cells. Shell-connecting edges are based on interaction confidence (medium threshold: 0.4), and Kmean clustering analysis was performed to identify functional clusters related to the AKT-mTOR pathway and colorectal cancer (Cluster-1), dopamine processing (Cluster-2), and microtubules (Cluster-3). Dashed edges represent supported inter-cluster interactions. f, Western blot analysis of active and total β-catenin, and E-cadherin in VXN-treated (10 μM, 48 h vs. DMSO) and shSLC6A3 (vs. shCTRL) HT29 cells. OD signal quantification of active vs. total β-catenin and E-cadherin vs. actin is presented in bar graphs (n = 3 biological replicates from 1 independent experiment, mean values ± SEM, ***: p = 0.0007, *: p = 0.0120, unpaired two-tailed t-test). g, High-content immunofluorescence imaging analysis of E-cadherin and β-catenin distribution in DMSO (n = 4 biological replicates), VXN-treated (10 μM, 48 h, n = 5 biological replicates), shCTRL (n = 4 biological replicates), and shSLC6A3 (n = 4 biological replicates) HT29 cells. Signal colocalization analysis suggests increased adherens junction assembly in VXN-treated cells (4 independent experiments) but not in SLC6A3 knockdown cells (1 independent experiment). Mean values ± SEM, ***: p = 0.0004, unpaired two-tailed t-test, scale bar: 30 μm). See also Fig. 4, and Supplementary Table 9.

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Extended Data Fig. 7 Impact of vanoxerine on EHMT2 promoter regulation in colon cancer cells.

a, Western blot analysis of total Nur77 levels in HCT116 cells treated with DMSO, AZD5362 (AKTi, 2.5 μM), and VXN (5μM) over 24 h (n = 2 biological replicates from 1 independent experiment). Actin was used as a loading control. b, Expression of target genes transactivated by Nur77. mRNA levels in DMSO and VXN-treated HT29 and HIEC cells are presented (relative expression vs. DMSO, n = 2 biological replicates from 1 RNA-seq experiment). c, SPP analysis of ChIP-seq data from the ChIP Atlas database integrating MACS2 binding scores of different transcription factors around the transcription start site of EHMT2 ( + /- 10 kb). Each dot corresponds to an independent biological sample. Dashed boxes highlight candidate transcription factors enriched at the EHMT2 promoter in several biological samples and representing potential regulators of G9a expression (CTCF, CEBPβ, and ZNF143). d, ChIP analysis of CEBPβ and ZNF143 transcription factor enrichment at the EHMT2 proximal promoter in HCT116 cells treated with VXN (5 μM, 24 h) vs. DMSO controls (n = 6 biological replicates from 4 independent experiments, mean values ± SEM, p values indicated on graph, unpaired two-tailed t-test). e, RRBS data from the ENCODE project database showing 5-methylated cytosine distribution around the EHMT2 transcription start site in human normal and neoplastic tissues (Each row corresponds to 1 independent biological sample). f, Western blot analysis of G9a levels in pLenti CTRL (empty) and pLenti-G9a-transduced HCT116 cells treated with DMSO or VXN (10 μM, 48 h). Relative G9a vs. actin OD ratios are presented in the bar graph (n = 3 biological replicates from 2 independent experiments, mean values ± SEM, **: p = 0.0090, unpaired multiple t-tests). See also Fig. 5.

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Extended Data Fig. 8 Vanoxerine inhibits tumor-initiating function in patient-derived colon cancer tissue samples.

a, Western blot analysis of G9a levels and H3K9me2 deposition following 48 h of VXN (5 μM) or BIX-01294 (1 μM) treatment (vs. DMSO controls) in HT29 (2D cultures) and patient-derived CSC-enriched spheroids (n = 4 biological replicates from 3 independent experiments). Actin was used as a loading control. Optical density signal ratios for G9a and H3K9me2 vs. loading control are presented. b, Representative whole-well brightfield imaging of patient-derived CRC organoids from primary organoid series treated with increasing doses of VXN (7 days, 0.25–10μM) vs. DMSO controls (n = 5 CRC patient samples, Patient #92 is presented). c, Relative size assessment of primary organoids treated with VXN (7 days, 0.25–10μM) vs. DMSO (n = 5 CRC patient samples from 5 independent experiments, mean values ± SEM, ***: p < 0.0001, one-way ANOVA with Dunnett’s multiple comparisons test). d, Representative whole-well brightfield imaging of secondary patient-derived CRC organoids from the primary series treated with 0.25 and 0.5 μM of VXN vs. DMSO controls (n = 5 CRC patient samples, Patient #181 is presented). e, Patient-specific response in secondary organoid formation assays for VXN-treated primary samples (0.25, 0.5, and 1μM) vs. DMSO controls (n = 5 CRC patient samples). f, Representative whole-well brightfield imaging of donor-derived normal colonic organoids from primary and secondary plating series, treated with increasing doses of VXN (7 days, 0.25 - 1μM) vs. DMSO controls (n = 5 biological replicates from 1 healthy tissue sample in 5 independent experiments). g, Secondary normal colonic organoid formation frequency observed upon VXN (0.25–10 μM, 7 days) vs. DMSO controls (n = 5 biological replicates from 1 healthy tissue sample in 5 independent experiments, *: p = 0.0112, one-way ANOVA with Dunnett’s multiple comparisons test). Box plot center line corresponds to median, and whiskers represent min to max values. h, qPCR analysis of colon CSC markers as well as SLC6A3 and EHMT2 in MACS-sorted CD133Low and CD133High fractions isolated from CSC-enriched spheroids derived from 2 independent primary colon adenocarcinomas (PROM1: CD133High: n = 8, CD133Low: n = 9), LGR5: CD133High: n = 5, CD133Low: n = 6, SLC6A3: CD133High: n = 8, CD133Low: n = 9, EHMT2: CD133High: n = 6, CD133Low: n = 8). n. corresponds to biological replicates from 2 independent experiments, p values are indicated on graphs, mean values ± SEM, unpaired two-tailed t-test). i, Mean spheroid size measured from clonogenic experiments using CD133High (n = 12 biological replicates) and CD133Low (n = 11 biological replicates) fractions, treated with VXN (0.5 μM, 48 h) or DMSO control (Data from 2 CRC patient samples, in 11 independent experiments, mean values ± SEM, ***: p < 0.0001, unpaired two-tailed t-test). j, Targeting strategy for the generation of patient-derived LGR5-GFP CRC organoids using a CRISPR-Cas9 knock-in system, where construct integration and endogenous LGR5 expression is visualized via an RFP and a GFP reporter, respectively. EF1-RFP-Puro cassette was removed via transduction with Cre-expressing adenoviral particles (Ad-Cre). PCR reactions using primer pairs a/b and c/e detected knock-in of the 5′ and 3′ arms, respectively, in organoids treated with ad-Cre. Primer pair d/e detected the RFP-puro selection cassette in organoids not treated with Ad-Cre. Non-engineered parental patient sample was used as control (n = 3 biological replicates from 1 CRC patient sample, in 3 independent experiments). k, CRC patient-derived LGR5-GFP cells were used in clonogenic organoid formation assay, treated with VXN (0.5 and 1.0 μM) or DMSO, and analyzed by high-content imaging for GFP signal at day 0, day-7, day 10, and day 14. Representative images of CRC LGR5-GFP organoids at each stage of the experiment (VXN 0.5 and 1.0 μM vs. DMSO, image acquisition at 4X, n = 6 biological replicates from 4 independent experiments, scale bar: 30μm). l, Quantification of “LGR5-positive” primary organoids across all counted structures at day 0, day-7, day 10, and day 14 for DMSO and VXN (0.5 and 1 μM, 7 days) treatments (n = 6 biological replicates from 4 independent experiments, mean values ± SEM, p values indicated on graph, two-way ANOVA with Dunnett’s multiple comparisons test). m, Box plot (left) showing LGR5-GFP secondary organoid formation frequency observed for VXN (0.5 and 1 μM, 7 days) and DMSO-treated groups (n = 6 biological replicates from 4 independent experiments, ***: p<0.0001, one-way ANOVA with Dunnett’s multiple comparisons test). Box plot center line corresponds to median, and whiskers represent min to max values. Violin plot (right) representing integrated GFP intensity per organoid was measured by high-content imaging in residual secondary organoids from each group (DMSO: n = 1518 organoids, VXN 0.5 μM: n = 287 organoids, VXN 1 μM: n = 216 organoids). Dashed line within violin plots corresponds to median. See also Fig. 6, and Supplementary Table 8.

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Extended Data Fig. 9 Impact of in vivo vanoxerine treatments on H3K9me2 deposition, tumor development, and normal tissue architecture.

a, Dose–response curves of VXN impact on the growth of mouse MC38 (C57BL/6) and CT26 (BALB/c) cell lines. Concentrations ranging from 0.32 to 20 μM were used over 48 h (Data from 3 independent experiments, number of biological replicates per dose/condition (n) is indicated on graphs, points represent mean values ± SEM) vs. DMSO control. Data were best-fitted using a nonlinear curve fitting model, and the calculated EC50 is presented for each cell line. b, Assessment of SLC6A3 mRNA levels (qPCR) in parental 2D cultured and primary in vivo MC38 tumors. GAPDH was used as housekeeping gene (n = 6 biological replicates from 3 independent experiments, ***: p = 0.0002, unpaired two-tailed t-test). c, Volcano plots representing modulation of gene expression in VXN-treated (25 mg/kg, 10 days) primary MC38 tumors vs. vehicle (saline) controls (n = 2 biological replicates from 1 independent RNA-seq experiment). Dashed lines represent p value < 0.05 and ±1.0 log2 fold change (L2FC) (Benjamini–Hochberg’s multiple correction test and false discovery estimation). Clustering analysis of transcriptional responses to VXN (V) and vehicle (CTRL, C) treatments in each tumor sample is presented as an inset. d, Western blot analysis of G9a levels in primary syngeneic in vivo tumors (MC38-C57BL/6) following 10 days of VXN treatments (25 mg/kg) vs. vehicle controls. Actin was used as loading control (n = 3 independent tumor pairs from 2 independent experiments, mean values ± SEM, *: p = 0.0307, unpaired two-tailed t-test). e, Flow cytometry analysis of mouse stem cell antigen Sca-1 and CD44 levels in single-cell dissociated mouse primary tumors (MC38-C57BL/6) from 10-day VXN-treated and vehicle control recipients (n = 3 biological replicates from 1 independent experiment). Histograms show decreases in both stemness markers across live cell populations following VXN treatments. f, Schematic of immunofluorescence-based histological analysis performed on CRC tumors resected from mouse primary recipients (VXN vs. vehicle) and visualized by high-content imaging (HCI). g, Representative HCI-acquired images from mouse primary tumor histological sections stained for H3K9me2 and DAPI. Automated scoring of H3K9me2-positive cells across the whole scans are presented for VXN (n = 14 tumors) and vehicle-treated (n = 26 tumors) mouse recipients (fluorescence signal vs. HCI mask are presented, 3 independent experiments, scale bar: 2 mm). h, Western blot analysis of H3K9me2 levels in primary syngeneic in vivo tumors (MC38-C57BL/6) following 10 days of VXN treatments (25 mg/kg) vs. vehicle controls. Total histone H3 was used as loading control (n = 4 independent tumor pairs from 2 independent experiments, mean values ± SEM, *: p = 0.0262, unpaired two-tailed t-test). i, Measurement of residual tumor volume detected in secondary mouse recipients at experimental endpoint from VXN (n = 3 tumors) and control (n = 12 tumors) groups. CT26-BALB/c secondary tumor sizes are presented in mm3 (Data from 2 independent experiments, mean values ± SEM, **: p = 0.0067, unpaired two-tailed t-test). Representative imaging of normal intestine tissue architecture and homeostasis where j, crypt proliferative cells are immunostained with anti-Ki-67, and k, E-cadherin and alpha-smooth muscle actin respectively mark the epithelial sheath and myofibroblasts in VXN (25 mg/kg) and vehicle-treated animals (C57BL/6, 10 days, scale bar in j: 50μm. Scale bar in k: 100 μm). Average proliferative cell counts and villus length for both experimental groups are presented in bar graphs (Number of biological units (n: crypts and villi) from 3 independent mice per condition is indicated on graphs, mean values ± SEM). See also Fig. 7, and Supplementary Table 10.

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Extended Data Fig. 10 Suppression of cancer stem cell activity by vanoxerine enhances CRC antitumor immune response in vivo.

a, Differential expression of transposable elements (TEs) in HCT116 cells treated with VXN (10 μM, 48 h) vs. DMSO controls (n=3 biological replicates from 2 independent RNA-seq experiments, p < 0.05). b, Distribution of significantly and commonly upregulated TEs in VXN (10 μM) and BIX-01294 (1 μM) treated t-hESCs (vs. vehicle) across different TE classes (n = 2 independent tumor pairs from 1 independent RNA-seq experiment). c, Gene ontology analysis performed on differentially expressed genes from VXN in vivo-treated CRC tumors (vs. vehicle) showing significant enrichment of GO categories linked to T cells and anticancer immune response (RNA-seq data from b, exact p values from hypergeometric test available in Source Data). d, GSEA enrichment plots showing a positive correlation between genes within the COATES_MACROPHAGE_M1_VS_M2_UP and FOSTER_TOLERANT _MACROPHAGES _UP signatures and genes upregulated by VXN treatments in CRC tumors in vivo (RNA-seq data from b, NES and NOM p value were calculated by the GSEA Software version 4.0.3). e, f, Heat map representing the expression of individual genes from the GOTZMANN_EPITHELIAL_TO_ MESENCHYMAL_TRANSITION_UP _DOWN gene sets in vehicle and VXN-treated (25 mg/kg, 10 days) CRC tumors (RNA-seq data from b, color scale: Log2 normalized counts). g, Representative pictures of hematoxylin and eosin staining showing lymphocyte infiltration in VXN-treated CRC tumors vs. vehicle controls. Tissue sections were analyzed by a certified pathologist, and arrowheads indicate the presence of lymphocytes (n = 3 independent tumor pairs, 2 independent experiments, scale bar: 50 μm). h, Representative fluorescence micrograph of anti-CD8a immunostaining in vehicle and VXN-treated (25 mg/kg, 10 days) CRC tumor sections. Nuclei were stained using DAPI (n = 3 independent tumor pairs, 2 independent experiments, scale bar: 50 μm). i, Representative HCI-acquired images of vehicle and VXN in vivo-treated mouse primary tumors co-stained for T cell CD8a and macrophage Iba-1 markers. Nuclei were counterstained using DAPI (Scale bar: 1 mm). CD8a and Iba-1-positive cell counts are presented in the bar graph (n = 3 independent tumor pairs, 2 independent experiments, mean values ± SEM, *: p = 0.0117, ***: p = 0.0007, unpaired multiple t-tests). j, qPCR analysis of T cell markers in bulk CT26-BALB/c tumors excised from vehicle and VXN-treated mouse primary recipients (n = 5 independent tumor pairs (except Il12b: n = 4, and cd74: n = 3), dots on bar graphs represent independent PCR experiments: bars are mean values ± SEM, p values are indicated on graphs, unpaired two-tailed t-test). k, qPCR analysis of T cell markers in bulk CT26-BALB/c tumors excised from control IgG and anti-PD-L-injected mouse primary recipients (n = 3 independent tumor pairs from 3 independent experiments, mean values ± SEM, p values indicated on graphs, unpaired two-tailed t-test). See also Fig. 8, and Supplementary Tables 5, 10–11.

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Bergin, C.J., Zouggar, A., Mendes da Silva, A. et al. The dopamine transporter antagonist vanoxerine inhibits G9a and suppresses cancer stem cell functions in colon tumors. Nat Cancer 5, 463–480 (2024). https://doi.org/10.1038/s43018-024-00727-y

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