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In vivo engineering tumor cells to a universal ‘all-in-one’ cancer vaccine with full antigen spectrum

GSE327399 Homo sapiens (human) Single-cell RNA-seq 18 samples Submitted 2026/07/15 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 18 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Samples
18 samples: 6 conditions, mostly in triplicateGroups 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.
Paper
In vivo engineering tumor cells to a universal "all-in-one" cancer vaccine with full antigen spectrumScience advances · 2026
Summary (written by the submitters)
Cancer vaccines offer a promising strategy to initiate de novo T cell responses or enhance existing ones, either functioning independently or synergizing with T cell-modulating therapeutics to reduce tumor burden. The clinical development of cancer vaccines faces challenges such as limited antigen coverage, insufficient antigen presentation, immune suppressive microenvironment, and the availability of personalized vaccines.
Read the full summary ↓
In this study, we developed a Universal ‘all-in-one’ Cancer cell-derived Vaccine (UniCVac) with comprehensive antigen spectrum coverage by programming tumor cells into antigen-presenting cells (APCs) through the co-delivery of CIITA, NLRC5, CD80, and IL-2. This reprogramming mimics the professional APC phenotype, providing simultaneous HLA-I and HLA-II antigen presentation, co-stimulation, and T cell proliferation signals. These tumor-derived UniCVac can directly activate both CD4+ and CD8+ T cells in vitro, independent of APCs. Additionally, their co-stimulation and T cell growth-stimulating capabilities result in superior CD4+ and CD8+ T cell activation and proliferation comparable to traditional APCs, with enhanced PI3K-AKT pathways activation. Single cell transcriptome analysis confirmed the similarity in cellular subtypes between UniCVac and traditional APCs activated T cells. In mouse models, the UniCVac vaccination reprogramed the tumor microenvironment from immunosuppressive to immune-permissive, induced robust CD4+ and CD8+ T cell expansion in both preventive and therapeutic tumor models and achieved complete tumor regression in vivo. Our approach provides a platform for the development of universal cancer vaccines with full antigen spectrum coverage and the ability to directly activate both CD4+ and CD8+ T cells, offering potential combinatorial opportunities with existing T cell-based immunotherapies against cancer.
Published in
In vivo engineering tumor cells to a universal "all-in-one" cancer vaccine with full antigen spectrum
Wang W, Huang Y, Zhang J et al. · Science advances 2026 · PMID 42418568 · doi:10.1126/sciadv.aee5201
This dataset
Open GSE327399 in finder → Find more human RNA-seq 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 GSE327399_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1450636 and SRA study SRP690208. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42418568) ↗
Study design
6 conditions, mostly in triplicate
bulk RNA-seq of human E7-TCR-T cells vaccinat… ×3 bulk RNA-seq of human E7-TCR-T cells vaccinat… ×3 bulk RNA-seq of human E7-TCR-T cells vaccinat… ×3 bulk RNA-seq of human HA-TCR-T cells vaccinat… ×3 bulk RNA-seq of human HA-TCR-T cells vaccinat… ×3 bulk RNA-seq of human HA-TCR-T cells vaccinat… ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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
Datasets on a similar topic

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