Abstract
Tumour-induced mechanisms of immune evasion hinder immune response to cancer, particularly in melanoma. mRNA translation, by ensuring accurate protein synthesis, regulates cancer phenotypes and immune response, but the underlying mechanisms remain unclear. Here, we reveal how O-sialoglycoprotein endopeptidase (OSGEP), catalysing the tRNA modification N6-threonylcarbamoyladenosine (t6A), drives protein homeostasis in cancer cells to maintain T-cell exclusion and prevent anti-tumour immune response. t6A-deficient melanoma cells disrupt efficient cytoplasmic translation of ANN codons (trinucleotides with A in the first position and N = any nucleotide), causing specific protein aggregation and the formation of integrated stress response-dependent stress granules. We discovered that OSGEP loss triggers melanoma regression by relocating RIG-I to stress granules, leading to its pathway activation. As a result, T-cells are recruited to the tumour site and orchestrate an anti-tumour immune response. Finally, an OSGEP-driven gene signature in melanoma patients is associated with T-cell infiltration and improved overall survival. Together, our findings position t6A tRNA modification as a promising therapeutic target for melanoma treatment.
Data availability
RNA sequencing data using B16F10 cells are available under the accession number GSE286709. Quantitative proteomics data on protein aggregates are available on PRIDE under the number PXD059835 (https://www.ebi.ac.uk/pride/). Spatial transcriptomics dataset is available under the accession number GSE316760. All other sequencing data are deposited on GEO under the accession number GSE286704 for the ribosome profiling and GSE286715 for the RNAseq after Flag-RIG-IP (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE286715). Results are in part based on TCGA-SKCM data generated by the TCGA Research Network (http://cancergenome.nih.gov/). Source data are provided as a Source Data file. All other data supporting the findings of this study are available from the corresponding author on reasonable request (Pierre.Close@uliege.be). Source data are provided with this paper.
Code availability
TrimGalore: https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/ TrimGalore is a Perl wrapper based on two canonical tools (Cutadapt and FastQC) for adapters and quality-based trimming. TrimGalore’s usage improves consistency and repeatability of analysis. No newly generated code is presented. RSamtools: https://bioconductor.org/packages/release/bioc/html/Rsamtools.html. R package of Samtools was used for BAM files manipulations in this project (i.e: Metagene plot). No newly generated code is presented. GenomicAlignments: https://bioconductor.org/packages/release/bioc/html/GenomicAlignments.html R/Bioconductor package was used for the storage, manipulation and representation of short genomic alignments. In this project this package allowed us to perform the binning of genomic alignments for the ribosome stalling analysis. No newly generated code is presented. Pysam: https://github.com/pysam-developers/pysam: this used for the manipulation and storage of BAM/SAM files. Here, it was used to store BAM files for the tRNA pools analysis. No newly generated code is presented. ToppGene: https://toppgene.cchmc.org/: ToppGene suite is a canonical tool to perform Gene Ontology analysis, gene set enrichment and candidate gene prioritization. It was used for the RNA-sequencing data analysis. No newly generated code is presented. STAR: https://github.com/alexdobin/STAR: STAR is a canonical software for mapping RNA-seq reads to a reference genome. It was used for the RNA-seq and ribosome profiling in this project. No newly generated code is presented. DESeq2: https://bioconductor.org/packages/release/bioc/html/DESeq2.html: it was used for the normalization, visualization, analysis of RNA-seq data and differential expression analysis. Used for the RNA-sequencing and ribosome profiling analysis. No newly generated code is presented. EdgeR: https://bioconductor.org/packages/release/bioc/html/edgeR.html: R package developed for the differential expression analysis of RNA-sequencing data. It was used here for statistical analysis in the ribosome profiling analysis. No newly generated code is presented. GSVA: https://www.bioconductor.org/packages/release/bioc/html/GSVA.html: Gene Set Variation Analysis R package allowing gene set and pathway enrichment on single samples. No newly generated code is presented. Source data files are provided with this paper in supplementary data files. Gating strategies for FACS analyses is available in the corresponding supplementary figures.
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Acknowledgements
We thank Ghanem Ghanem and Ahmad Najem for the access to MM lines, and Lionel Larue for the access to M1014 cells. We are grateful to the GIGA-proteomics, imaging, genomics, bio-informatics and viral vector facilities for their assistance. This study was supported by the Belgian foundation against Cancer (2020-068; 2024-148), the Walloon Excellence in Life Sciences and Biotechnology (WEL Research Institute, WELBIO to P. Close), the FNRS (PDR T.0244.18; EOS O.0020.22), the University of Liege, and the “Foundation Leon Fredericq”. CD was supported by a FNRS Télévie grant. SD was supported by a FNRS research fellow grant, by a Cancer Research UK institute award (C5759/27412) and a Royal Society research grant (RGS/R2/252386), AB and FR are Research Associates, AC and PC are Research Directors at the FNRS, respectively. TB and AV were supported by the National Institute for Health and Care Research (NIHR) Manchester Biomedical Research Centre (BRC) (NIHR203308), Cancer Research UK RCCASF-May23/100001 Cancer Research UK Advanced Clinician Scientist, a core funded grant to the Cancer Research UK Manchester Institute (C5759/A27412), Melanoma Research Alliance and Rosetrees Trust Young Investigator Award (#825648). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. This article is based upon work from COST Action TRANSLACORE CA21154, supported by COST (European Cooperation in Science and Technology).
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C.D. designed, performed and analysed in vitro and in vivo experiments, and wrote the manuscript. C.S. assisted with orthotopic transplantation assays, FACS and analyses. C.C. and F.R. performed computational analyses of the RNA sequencing, Ribosome profiling, Proteomics and RNA immunoprecipitation sequencing data. C.M. and A.C. performed and analysed histological staining of tumour samples. A.B., N.A., D.H., R.V. performed immunoblotting experiments. N.E.-H. performed polysome sequencing. T.B. and A.V. generated and analysed the spatial transcriptomics dataset. L.M.-M. and F.R. performed ribosome profiling experiments. E.R. supported in vivo experiments. M.S.R. performed Nicoletti assays. M.L. performed computational analyses using TCGA patient data. J.U. and A.V. provided expertise for human clinical data. S.D. and P.C. supervised the work, designed and analysed experiments, and wrote the manuscript. P.C. acquired and secured funding. All authors discussed the results and commented the manuscript.
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P.C. and F.R. are co-founders and scientific advisors at THERAtRAME SA. All other authors declare no conflict of interest.
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Dziagwa, C., Seca, C., Capron, C. et al. Disruption of tRNA threonylation triggers RIG-I mediated anti-tumour immune response. Nat Commun (2026). https://doi.org/10.1038/s41467-026-69964-2
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DOI: https://doi.org/10.1038/s41467-026-69964-2
