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METTL5 Blockade Enhances Anti-Tumor Immune Response via Inducing Neoantigen Generation

GSE299761 Mus musculus (mouse) RNA-seq (gene expression) 19 samples Submitted 2026/02/18 Platform GPL28330Platform GPL24247Platform GPL30215
At a glance

RNA-seq (gene expression) · mouse · 19 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Mus musculus (mouse)
Samples
19 samples: 5 × TCR-seq, B16-F10 melanoma cells, Mettl5 KO, vs 4 × TCR-seq, B16-F10 melanoma cells, scrambled control (SCR) , vs 2 × Riboseq, B16-F10 melanoma cells, Mettl5 KO, vs 2 × RNAseq, B16-F10 melanoma cells, Mettl5 KO,Groups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Summary (written by the submitters)
Tumor neoantigens play a pivotal role in immunotherapy through eliciting tumor-specific immune responses. However, their clinical application is limited by the restricted repertoire of naturally occurring neoantigens, which undergoes continuous immune editing within the tumor microenvironment. To address this critical challenge, we explored translational regulation as a reversible and safer strategy to augment neoantigen production.
Read the full summary ↓
Through systematic analysis of translational regulatory factors associated with immunotherapy response, we identified METTL5 as a key modulator of anti-tumor immunity. METTL5 depletion in syngeneic tumor models demonstrated significant enhancement of endogenous anti-tumor immunity across distinct MHC-I haplotypes, with synergistic effects observed when combined with immune checkpoint blockade. Based on evolutionary conservation of ribosomal decoding center modifications and established mechanisms from prokaryotic studies, we propose that METTL5-mediated m6A modification at the small ribosomal subunit decoding center may contribute to translational fidelity regulation. Mechanistically, integrated MHC-I immunoprecipitation-mass spectrometry and ribosome profiling revealed the identification of non-canonical peptides, including those derived from novel unannotated open reading frames (nuORFs), which provide previously absent neoantigen sources for tumor cells. T-cell receptor sequencing further demonstrated significantly enhanced clonal diversity and tumor-specific reactivity of infiltrating T cells in METTL5-deficient tumors. Therapeutic vaccination strategies targeting METTL5-deficiency-induced neoantigens, including both peptide-based and mRNA vaccines, exhibited partial anti-tumor efficacy in murine tumor models. This study establishes METTL5 as a critical translational regulator that restricts neoantigen diversity and proposes a novel combinatorial strategy to enhance immunotherapy through translational control.
This dataset
Open GSE299761 in finder → Find more mouse datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE299761_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1276454 and SRA study SRP596339. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
5 × TCR-seq, B16-F10 melanoma cells, Mettl5 KO, vs 4 × TCR-seq, B16-F10 melanoma cells, scrambled control (SCR) , vs 2 × Riboseq, B16-F10 melanoma cells, Mettl5 KO, vs 2 × RNAseq, B16-F10 melanoma cells, Mettl5 KO,

Supports a between-group comparison across 13 samples.

How we read this: 4 replicated groups read from 19 sample titles; they account for 13 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
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