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T Cell Receptor-mimic STAR Conveys Superior Sensitivity over CAR in Targeting Tumors with Low-density Neoantigens

GSE279137 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 144 samples Submitted 2024/10/31 Platform GPL24676
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 144 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Homo sapiens (human)
Topic
Breast cancerThis series is part of our curated briefing on it.
Samples
144 samples: 48 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.
Summary (written by the submitters)
Targeting tumor-specific neoantigens is promising for cancer immunotherapy, yet their ultra-low expression on tumor cells poses significant challenges for T cell therapies. Here, we found that chimeric antigen receptors (CARs) exhibited 10-100 times lower sensitivity compared to T cell receptors (TCRs) when targeting p53R175H common neoantigen.
Read the full summary ↓
To enhance CAR functionality, we introduce T cell receptor fusion construct (TRuC) and synthetic TCR and antigen receptor (STAR). Our data demonstrate that STAR, which incorporates TCR-mimic antibody fragments and complete TCR signaling machinery, optimally reproduces antigen sensitivity of TCRs. STAR outperforms both CAR and TRuC in redirecting both CD8+ and CD4+ T cells to recognize HLA class I neoantigens. In vitro, human primary T cells engineered with STAR kill multiple cancer cell lines with low neoantigen density better than CAR-T and TRuC-T cells. In tumor mouse models, STAR-T cells outperform CAR-T and TRuC-T cells in controlling neoantigen-low breast cancer and leukemia. Taken together, our findings highlight severe defects in CAR sensitivity and introduce STAR as a more sensitive synthetic receptor, providing a new framework for T cell-based immunotherapy targeting tumors with low neoantigen density.
This dataset
Open GSE279137 in finder → Find more human RNA-seq datasets → Breast cancer landscape →
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 GSE279137_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1170959 and SRA study SRP537583. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
48 conditions, mostly in triplicate
ATAC-seq for CD4-28zCAR-100nM Experiment ×3 ATAC-seq for CD4-28zCAR-10nM Experiment ×3 ATAC-seq for CD4-28zCAR-1uM Experiment ×3 ATAC-seq for CD4-28zCAR-noStimulation Experim… ×3 ATAC-seq for CD4-STAR-100nM Experiment ×3 ATAC-seq for CD4-STAR-10nM Experiment ×3 ATAC-seq for CD4-STAR-1uM Experiment ×3 ATAC-seq for CD4-STAR-noStimulation Experiment ×3 +40 more

Supports a between-group comparison across 144 samples.

How we read this: 48 replicated groups read from 144 sample titles; they account for 144 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

+ 104 more — browse all 144 samples with per-sample file links →

More Breast cancer datasets

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.