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Downregulation of the FTO m6A RNA demethylase promotes EMT-mediated progression of epithelial tumors and sensitivity to Wnt inhibitors

Nature Cancer volume 2, pages 611–628 (2021)Cite this article

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Abstract

Post-transcriptional modifications of RNA constitute an emerging regulatory layer of gene expression. The demethylase fat mass- and obesity-associated protein (FTO), an eraser of N6-methyladenosine (m6A), has been shown to play a role in cancer, but its contribution to tumor progression and the underlying mechanisms remain unclear. Here, we report widespread FTO downregulation in epithelial cancers associated with increased invasion, metastasis and worse clinical outcome. Both in vitro and in vivo, FTO silencing promotes cancer growth, cell motility and invasion. In human-derived tumor xenografts (PDXs), FTO pharmacological inhibition favors tumorigenesis. Mechanistically, we demonstrate that FTO depletion elicits an epithelial-to-mesenchymal transition (EMT) program through increased m6A and altered 3′-end processing of key mRNAs along the Wnt signaling cascade. Accordingly, FTO knockdown acts via EMT to sensitize mouse xenografts to Wnt inhibition. We thus identify FTO as a key regulator, across epithelial cancers, of Wnt-triggered EMT and tumor progression and reveal a therapeutically exploitable vulnerability of FTO-low tumors.

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Fig. 1: FTO loss enhances breast tumorigenesis.
Fig. 2: FTO loss promotes breast cancer cell motility and metastases.
Fig. 3: FTO loss promotes tumor progression in several cancers.
Fig. 4: FTO loss elicits an EMT program.
Fig. 5: FTO controls Wnt signaling in cancer.
Fig. 6: Wnt signaling is regulated by FTO-dependent m6A demethylation.
Fig. 7: In vitro Wnt inhibition restores EMT regulation in FTO-depleted cells.
Fig. 8: FTO-low tumors are sensitive to Wnt inhibitor therapy.

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Data availability

Sequencing data that support the findings of this study have been deposited in the GEO repository under accession number GSE128582. Human cancer data (bladder urothelial carcinoma, breast invasive carcinoma, cervical squamous cell carcinoma and endocervical adenocarcinoma, glioblastoma multiforme, kidney chromophobe, lung adenocarcinoma and lung squamous cell carcinoma, prostate adenocarcinoma, thyroid carcinoma and uterine corpus endometrial carcinoma) were derived from the TCGA Research Network (http://cancergenome.nih.gov/). RNA-seq data from TCGA were obtained as normalized FPKM counts from GDC (https://portal.gdc.cancer.gov/repository), and PAM50 subtype information was extracted from UCSC Xena (https://xenabrowser.net). METABRIC expression and clinical data61 were downloaded through the European Genome-Phenome Archive (EGA) (http://www.ebi.ac.uk/ega/, accession number EGAS00000000083). Previously published microarray data that were reanalyzed here are available under accession codes GSE9195, GSE10780, GSE10810, GSE12276, GSE19615, GSE20711 and GSE21653. Reannotation and processing of Affymetrix Human Genome U133 Plus 2.0 array data were performed as previously detailed76.

The hg19 reference transcriptome (version 85) was obtained from the Ensembl portal (ensembl.org). Hallmark gene sets were obtained from the MsigDB database (https://www.gsea-msigdb.org/gsea/msigdb). Human poly(A) sites were obtained from PolyA_DB2 (http://polya.umdnj.edu/PolyA_DB2).

For motility assays related to Figs. 2a,b, 3c,e and 7b and Extended Data Figs. 2a,b,d,e, 3c,f and 4d, independent repeats have been deposited on the Figshare repository (https://doi.org/10.6084/m9.figshare.14602932, https://doi.org/10.6084/m9.figshare.14602944, https://doi.org/10.6084/m9.figshare.14602953, https://doi.org/10.6084/m9.figshare.14602965, https://doi.org/10.6084/m9.figshare.14602971 and https://doi.org/10.6084/m9.figshare.14602986). Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Code availability

Code supporting this study is available at a dedicated GitHub repository (https://github.com/martinBizet/m6A_FTO_cancer).

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Acknowledgements

J.J. was supported by the Belgian ‘Fonds de la Recherche Scientifique’ (FNRS) postdoctoral fellowship, E. Collignon was supported by the L’Oréal ‘For Women In Science’ fellowship and by the Belgian FNRS and Y.J. was supported by the China Scholarship Council (CSC) postdoctoral fellowship. C.A.W., M.B., B.H. and G.D. were supported by the Belgian FNRS. N.K.S. was supported by the ULB Foundation. F.F. is a ULB Professor. F.F.’s lab was funded by grants from the FNRS and Télévie, the ‘Action de Recherche Concertée’ (ARC; AUWB-2018-2023 ULB-No 7), Wallon Region grants (U-CAN-REST, INTREPID), an FNRS Welbio grant, the ULB Foundation and the Belgian Foundation Against Cancer (FCC 2016-086 FAF-F/2016/872). P.C. and A.C. are Senior Research Associate and Research Director at the FNRS, respectively. C.S. is a research director at the FNRS and his lab is supported by grants from the FNRS. P.A.L. was supported by the National Institutes of Health (GM 058843). Trace is supported by the ‘Belgian Foundation Against Cancer’ and, as a part of the EurOPDX infrastructure, by EDIReX a Horizon 2020 grant agreement 731105. PET–CT acquisition was performed at the nuMix-CMMI, which is supported by the European Regional Development Fund (ERDF) and the Walloon Region.

Author information

Author notes

  1. These authors contributed equally: Jana Jeschke, Evelyne Collignon, Clémence Al Wardi, Mohammad Krayem.

Authors and Affiliations

  1. Laboratory of Cancer Epigenetics, Faculty of Medicine, ULB-Cancer Research Center (U-CRC), Université Libre de Bruxelles (ULB), Brussels, Belgium

    Jana Jeschke, Evelyne Collignon, Clémence Al Wardi, Martin Bizet, Yan Jia, Emilie Calonne, Bouchra Hassabi, Rachel Deplus, Pascale Putmans, Gaurav Dube, Nitesh Kumar Singh & François Fuks

  2. Laboratory of Oncology and Experimental Surgery, Institut Jules Bordet, ULB, Brussels, Belgium

    Mohammad Krayem, Zéna Wimana, Renato Morandini & Ghanem Ghanem

  3. Department of Breast Oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China

    Yan Jia

  4. Molecular Immunology Laboratory, Institut Jules Bordet, ULB, Brussels, Belgium

    Soizic Garaud & Karen Willard-Gallo

  5. Department of Nuclear Medicine, Institut Jules Bordet, ULB, Brussels, Belgium

    Zéna Wimana

  6. Department of Pathology, Maastricht UMC, Maastricht, the Netherlands

    Alexander Koch

  7. Laboratory of Medical Chemistry, GIGA Stem Cells, University of Liège, Liège, Belgium

    Kateryna Shostak & Alain Chariot

  8. Trace, LKI Leuven Cancer Institute, KU Leuven, Leuven, Belgium

    Lara Rizzotto & Eleonora Leucci

  9. Rieveschl Laboratories for Mass Spectrometry, Department of Chemistry, University of Cincinnati, Cincinnati, OH, USA

    Robert L. Ross & Patrick A. Limbach

  10. Breast Cancer Translational Research Laboratory, Institut Jules Bordet, U-CRC, ULB, Brussels, Belgium

    Christine Desmedt, Yacine Bareche, Françoise Rothé & Christos Sotiriou

  11. Pathophysiology of Breast Cancer Team, Université de Paris, INSERM U976, HIPI, Paris, France

    Jacqueline Lehmann-Che & Luis Teixeira

  12. Breast Disease Unit and Molecular Oncology Unit, AP-HP, Hôpital Saint-Louis, Paris, France

    Jacqueline Lehmann-Che & Luis Teixeira

  13. Université Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, UMR9020-UMR-S 1277, CANTHER, Lille, France

    Martine Duterque-Coquillaud & Xavier Leroy

  14. Department of Pathology, CHU Lille, Université Lille, Lille, France

    Xavier Leroy

  15. Biobix, Laboratory of Bioinformatics and Computational Genomics, Ghent University, Ghent, Belgium

    Gerben Menschaert

  16. Department of Gastroenterology & Hepatology, Chinese PLA General Hospital, Beijing, China

    Mingzhou Guo

  17. Laboratory of Cancer Signaling, GIGA Stem Cells, University of Liège, Liège, Belgium

    Pierre Close

  18. WELBIO, University of Liège, Liège, Belgium

    Pierre Close & Alain Chariot

  19. Laboratory of RNA Cancer Biology, Department of Oncology, LKI, KU Leuven, Leuven, Belgium

    Eleonora Leucci

  20. College of Chemistry and Molecular Sciences, Wuhan University, Wuhan, China

    Bi-Feng Yuan

  21. Laboratory for Molecular Cancer Biology, VIB, KU Leuven, Leuven, Belgium

    Jean-Christophe Marine

  22. Laboratory for Molecular Cancer Biology, Department of Oncology, KU Leuven, Leuven, Belgium

    Jean-Christophe Marine

  23. WELBIO, Université Libre de Bruxelles (ULB), Brussels, Belgium

    François Fuks

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  1. Jana Jeschke
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J.J., E. Collignon and F.F. designed experiments and interpreted data. J.J., E. Collignon, C.A.W. and Y.J. performed in vitro phenotypic assays. E. Calonne performed m6A-seq and paired-end RNA-seq experiments. C.A.W., J.J., E. Collignon, B.H., P.P., K.S., P.C. and A.C. performed RNAi and overexpression experiments as well as dot blots, western blots and RT–qPCR. S.G., C.D., Y.B., F.R., J.L.C., M.D.C., X.L., L.T., K.W.G. and C.S. provided human biopsies/data and helped with analysis of these samples. P.A.L., R.L.R. and B.F.Y. performed mass spectrometry experiments. M.K., R.M., R.D., M.G. and G.G. performed mouse xenograft experiments. Z.W. analyzed and interpreted the FDG PET experiments. L.R., E.L. and J.C.M. designed and performed PDX experiments. M.B., G.D., N.K.S., A.K. and G.M. performed bioinformatics analyses. F.F. directed the study and J.J., E. Collignon and F.F. wrote the manuscript.

Corresponding author

Correspondence to François Fuks.

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Competing interests

F.F. is a cofounder of Epics Therapeutics, Belgium. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 FTO downregulation promotes tumorigenesis in breast cancer.

a, FTO expression in the Affymetrix data meta-analysis cohort (left, 171 normal vs. 812 tumor breast tissues, p = 5e-50). RT-qPCR quantification of FTO in an in-house cohort (right, 9 normal vs. 47 tumor breast tissues, p = 0.005), normalized to ACTB and SDHA. The box defines the IQR split by the median, with whiskers extending to the most extreme values within 1.5×IQR beyond the box. P values calculated by two-tailed t-test. b, Quantification of m6A levels by mass spectrometry in paired human normal and tumor breast samples (n = 6 per group, pairs connected with line, red dots represent the group medians, two-tailed paired t-test, p = 0.046). c, Validation of FTO depletion by two shRNAs in SKBR3 cells by RT-qPCR (upper panel, mean + SD, pRNAi#1 = 6e-10; pRNAi#2 = 0.0045, nRNAi#1 = 7; nRNAi#2 = 3), normalized to ACTB and SDHA) and western blotting (lower panel, representative of n = 3 independent replicates with histone H3 as loading control). P values calculated by two-tailed paired t-test. d, Light microscopy imaging of mammospheres formed by RNAi Ctrl and RNAi FTO SKBR3 cells with scale bar of 100 µm (representative of n = 3 independent experiments). e, Effects of FTO knockdown on colony-forming capacity (left, P = 0.001) and mammosphere-forming capacity (right, P = 0.02) in SKBR3 cells with a second shRNA (RNAi FTO #2). Data from n = 3 technical replicates (mean + SD) within a single experiment, representative of 3 biologically independent experiments provided as Source Data. P values calculated by two-tailed t-test. f, Representative western blot of doxycycline-induced overexpression of wild-type FTO (FTO-WT) and of a catalytically inactive FTO mutant (FTO-MUT) in SKBR3 cells (upper panel) with β-actin as loading control (n = 3). Effect of FTO-WT and FTO-MUT overexpression on colony formation and mammosphere formation in SKBR3 cells (bottom). Data from n = 3 technical replicates (mean + SD) within a single experiment, representative of 3 and 2 biologically independent experiments, respectively, provided as Source Data. P values calculated by two-tailed t-test, as indicated. g, FTO depletion by shRNA in breast cancer MCF7 cells (n = 6) by RT-qPCR. Data as mean + SD, normalized to ACTB and SDHA. P values calculated by two-tailed paired t-test (p = 4e-6).

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Extended Data Fig. 2 Depletion of FTO promotes breast cancer cell motility and metastases.

a-b, Real-time chemotactic invasion (a) and migration (b) of RNAi FTO (red, shRNA #1 and #2, respectively) and RNAi control SKBR3 cells (green). c, Wound scratch assay performed with RNAi Ctrl and RNAi FTO SKBR3 cells. Representative pictures (left) with quantification of wound closure (as percentage of the width at 0 h, right). Data from a single experiment, representative of n = 2 biologically independent experiments provided as Source Data. d, Cell viability by MTT assay (left, mean + SD, n = 5, two-tailed paired t-test, p = 0.37) and real-time proliferation (right) of FTO-depleted and control SKBR3 cells. e, Western blotting of FTO depletion and rescue by overexpression of wild-type FTO (FTO-WT) or a catalytically inactive FTO mutant (FTO-MUT) in SKBR3 cells (upper panel, n = 3 with β-actin as loading control) and real-time chemotactic migration (lower panel). f, Expression of FTO in PDX shown by immunochemistry. Representative image of tissue sections with scale bar of 250 µm (n = 3). g, Metabolism imaging with 18F-FDG PET of subiliac lymph nodes xenograft mice bearing RNAi Ctrl (black, n = 4) or RNAi FTO (red, n = 5) SKBR3 tumors. Representative images (9 weeks after inoculation) are shown (pathological uptake in LN encircled; physiological uptake in bladder (Bl)). The box defines the IQR split by the median, with whiskers extending to the most extreme values within 1.5×IQR beyond the box. P-value calculated by two-tailed t-test (P = 0.0061). h, Kaplan-Meier based on FTO expression and disease-specific survival in the breast cancer METABRIC cohort (n(high FTO) = 660, n(low FTO) = 661, logrank test, P = 7e-4). i, Forest plot showing association between FTO expression and distant-metastasis-free survival in breast cancer. Data shown as hazard ratio (log2), with 95% confidence interval, for both the aggregate KM Plot data set (‘overall’, presented in Fig. 2f) and individual cohorts. The number of samples (n) and P-values (calculated by two-sided logrank tests, in bold if P < 0.05) are indicated for each cohort. Data for (a), (b), (d), (e) are from n = 3 technical replicates (mean + SD) within a single experiment, representative of 3 biologically independent experiments (except for (e): 2 replicates). Independent repeats have been deposited on the Figshare repository (DOI: 10.6084/m9.figshare.14602965).

Source data

Extended Data Fig. 3 FTO depletion in prostate, cervical and lung cancers.

a, FTO depletion by shRNA in PC3 prostate cancer cells by RT-qPCR (left). Mean + SD from 3 independent experiments, normalized to ACTB and SDHA. P value calculated by two-tailed paired t-test (P = 0.004). Representative western blot (right) of FTO depletion in PC3 cells (n = 3) with β-actin as loading control. b, Nude mice were injected subcutaneously with PC3 prostate cells and tumors was monitored every two days (n = 6 tumors per group; mean ± SEM, p = 0.0002, two-way ANOVA). c, Real-time chemotactic invasion of RNAi FTO (red) and RNAi control PC3 cells (green). d, FTO depletion by shRNA in Ca Ski cervical cancer cells (left, n = 3, P = 0.009) and H1650 lung cancer cells (right, n = 3, p = 0.0004) by RT-qPCR. Data as mean + SD, normalized to ACTB and SDHA. P values calculated by two-tailed paired t-test. e, Effects of FTO knockdown on tumorsphere-forming capacity in Ca Ski (left, P = 0.02) and H1650 cells (right, p = 0.008). Data from n = 3 technical replicates (mean + SD) within a single experiment, representative of 2 biologically independent experiments provided as Source Data. P values calculated by two-tailed t-test. f, Real-time chemotactic migration of FTO-depleted (red) and control (green) H1650 cells. g-h, Kaplan-Meier curves showing the overall survival of patients with either high-FTO or low-FTO lung tumors (n(high FTO) = 333, n(low FTO) = 326, logrank test, P = 0.0073) (g) or uterine tumors (n(high FTO) = 310, n(low FTO) = 232, logrank test, P = 0.048) (h). Motility data in (c), (f) from n = 3 technical replicates (mean + SD) within a single experiment, representative of 2 and 3 biologically independent experiments, respectively. Independent repeats have been deposited on the Figshare repository (DOI: 10.6084/m9.figshare.14602971).

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Extended Data Fig. 4 FTO downregulation promotes EMT in several cancers.

a, Representative light microscopy imaging of RNAi Ctrl or RNAi FTO SKBR3 cells (n = 3 independent experiments). Cells displaying elongated morphology and extended pseudopodia are indicated with arrows. Scale bars represent 50μm. b, Representative western blot of mesenchymal EMT markers in FTO-depleted (RNAi FTO) and control (RNAi Ctrl) SKBR3 cells (n = 3) with HDAC1 as loading control. c, Immunochemistry staining of EMT markers in SKBR3 xenografted tumors (from Fig. 1e at endpoint). Percent VIM and FN1 positive cells (n = 3 fields × 3 tumors per group; mean + SEM). P-values calculated by two-tailed t-test (pVIM = 0.0002; pFN1 < 0.0001). d, Real-time chemotactic migration of FTO-depleted and control SKBR3 cells in the absence and presence of TGFβ. Data from n = 3 technical replicates (mean + SD) within a single experiment, representative of 3 biologically independent experiments. Independent repeats have been deposited on the Figshare repository (DOI: 10.6084/m9.figshare.14602986). e, Relative gene expression of mesenchymal (VIM, MMP2) and epithelial (FSTL3) EMT marker genes, as estimated by RT-qPCR in SKBR3 cells following RNAi of FTO, treated or not with TGFβ. Mean + SD from n = 3 independent experiments, normalized to ACTB and SDHA. P values calculated by two-tailed paired t-test as indicated. f, Representative western blot of mesenchymal EMT markers FN1 and SLUG in FTO-depleted and control PC3 cells (n = 3) with β-actin and histone H3 as loading controls. g, Relative gene expression of mesenchymal EMT marker genes, as estimated by RT-qPCR in MCF7 (left, n = 4 except for nFN1 = 3) and H1650 (right, n = 3 except for nSLUG = 4) cells following RNAi of FTO. Mean + SD, normalized to ACTB and SDHA. P values calculated by two-tailed t-test. Representative western blot of mesenchymal EMT markers FN1, CDH1 and SLUG in FTO-depleted and control MCF7 (left) and H1650 (right) cells (n = 3) with HSP90 and HDAC1 as loading controls. h, Immunochemistry staining of VIM in PC3 xenografted tumors (from Fig. 3c at endpoint). Percent VIM positive cells (n = 3 fields × 3 tumors per group; mean + SEM). P-value calculated by two-tailed t-test (p = 0.0008). Scale bars represent 250μm.

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Extended Data Fig. 5 FTO loss upregulates Wnt/β-catenin signaling in breast and prostate cancers.

a, Numbers of m6A peaks and related transcripts identified by m6A-seq in RNAi Ctrl and RNAi FTO SKBR3 cells (upper panel, two independent biological replicates). Scatterplot displaying differentially m6A-methylated peaks (lower panel, hyper and hypo = peaks with increased and decreased m6A in FTO-depleted cells, respectively). b-d, Visualization of the IP m6A signal in RNAi FTO and RNAi Ctrl SKBR3 cells at the DVL3, FZD1, SOX11, MARK2, CSNK1G2 (b), CTNNB1 (c), and WNT5A (d) transcripts. e, Representative western blot of β-catenin in RNAi FTO #2 and RNAi Ctrl #2 SKBR3 cells with β-actin as loading control (n = 3). f, Representative western blot of cytoplasmic and nuclear β-catenin in RNAi FTO and RNAi Ctrl SKBR3 cells (n = 3). Loading controls: β-Tubulin (cytoplasmic) and histone H3 (nuclear). Wnt3a treatment as positive control for nuclear translocation of β-catenin. g, Representative immunofluorescence staining of β-catenin (red) and DAPI (blue) in RNAi Ctrl and RNAi FTO SKBR3 (n = 3). Scale bars represent 20μm. h, Relative gene expression of Wnt/β-catenin targets measured by RT-qPCR in SKBR3 cells. Mean + SD from n = 6 independent replicates (except for n(TCF1) = 5), normalized to ACTB and SDHA. Two-tailed paired t-tests: PTCF1 < 0.0001; PAXIN2 = 0.0001; PNMYC=0.001. i, Western blot of total β-catenin in RNAi FTO vs. RNAi Ctrl from SKBR3 xenografted tumors in mice (n = 4 per group) with β-actin as loading control. j, Numbers of m6A peaks and related transcripts identified in three human breast cancer biopsies by m6A-seq (upper left panel). The m6A motif (upper right) retrieved from sample BC1 includes the consensus motif DRACH. Bar graph displaying the distribution of m6A peaks relatively to transcriptomic regions (lower panel, representative of sample BC1). k, m6A IP signal at transcripts related to the Wnt/β-catenin signaling (CSNK1G2, MARK2, AXIN1 and FZD1), in three human breast cancer biopsies.

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Extended Data Fig. 6 Regulation of FTO targets.

a, Numbers of m6A peaks and related transcripts identified by m6A-seq in RNAi Ctrl and RNAi FTO PC3 cells (upper panel, two independent biological replicates). Schematic representation of m6A/FTO mRNA targets (red asterisks) identified within the Wnt/β-catenin signaling pathway (lower panel). b, Visualization of the IP m6A signal in RNAi FTO and RNAi Ctrl PC3 cells at the WNT3A and GSK3B transcripts. c, Representative immunochemistry staining of β-catenin in PC3 xenografted tumors with scale bar at 250 µm. Percent β-catenin positive cells (right, n = 3 fields × 3 tumors per group; mean + SD). Two-tailed t-test, P = 0.005. d, Relative quantification of the CSNK1D levels in RNAi FTO and RNAi Ctrl SKBR3 cells displayed in the western blot shown in Fig. 6a. e, Western blot showing the level of CSNK1D in an independent biological replicate of RNAi FTO and RNAi Ctrl SKBR3 cells (left, with HDAC1 as loading control, related to Fig. 6a). Relative quantification of CSNK1D levels (right). Western blot data in (d-e) are representative of n = 3 biologically independent experiments and normalized to HDAC1.

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Extended Data Fig. 7 FTO-low tumors are sensitive to WNT inhibitor therapy.

a, Relative gene expression of Wnt/β-catenin target genes AXIN2 and TCF1, as estimated by RT-qPCR, in SKBR3 cells following RNAi of FTO, treated or not with iCRT3. Mean + SD from 3 independent experiments, normalized to ACTB and SDHA. P values calculated by two-tailed t-test, as indicated. b, Wnt/β-catenin transcriptional activity, as measured by TOPFlash β-catenin/TCF-LEF reporter assay performed on SKBR3 cells following RNAi of FTO, in the absence and presence of iCRT3 (mean + SD, n = 5, iCRT3 doses as indicated). P values calculated by two-tailed t-test, as indicated. c, The migration capacity, as shown in Fig. 7b, was quantified by the slope of the migration curves and corrected for cell viability (as measured by MTT assay in Fig. 7c, from 2 biologically independent experiments). P-values calculated by two-tailed t-test (p(RNAi Ctrl)=0.58, p(RNAi FTO) = 0.018). d, Levels of the mesenchymal EMT marker proteins FN1, VIM and SNAIL in RNAi Ctrl and RNAi FTO SKBR3 cells in the absence and presence of iCRT3 (n = 3) with histone H3 and β-actin as loading controls. e, Levels of FN1 protein in FTO-depleted and control tumors derived from SKBR3 xenografts treated with iCRT3 or vehicle (DMSO), with β-actin as loading control (n = 3 independent experiments). f, Nude mice growing tumors from RNAi Ctrl (left) or RNAi FTO (right) SKBR3 cells were treated with LGK974 (n = 5 tumors per group, mean ± SEM). P values calculated by two-way ANOVA (p(RNAi Ctrl)=0,69; p(RNAi FTO) = 0.48). g, Nude mice growing tumors from RNAi Ctrl (left) or RNAi FTO (right) PC3 cells were treated with or without Trastuzumab (n = 5 tumors per group, mean ± SEM). P values calculated by two-way ANOVA, (p(RNAi Ctrl)<0.0001; p(RNAi FTO) < 0.0001).

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Jeschke, J., Collignon, E., Al Wardi, C. et al. Downregulation of the FTO m6A RNA demethylase promotes EMT-mediated progression of epithelial tumors and sensitivity to Wnt inhibitors. Nat Cancer 2, 611–628 (2021). https://doi.org/10.1038/s43018-021-00223-7

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