TROP2/claudin program mediates immune exclusion to impede checkpoint blockade in breast cancer
RNA-seq (gene expression) · mouse · 10 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
- 10 samples: 5 × 4T1, KO, vs 5 × 4T1, WT,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.
Read the full summary ↓
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE334497_RAW.tar (it can run to tens of gigabytes).
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.
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 PRJNA1475339 and SRA study SRP707135. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 10 samples.
How we read this: 2 replicated groups read from 10 sample titles; they account for 10 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.
- GSM9789678 4T1, KO, rep1 4T1, KO,
- GSM9789679 4T1, KO, rep2 4T1, KO,
- GSM9789680 4T1, KO, rep3 4T1, KO,
- GSM9789681 4T1, KO, rep4 4T1, KO,
- GSM9789682 4T1, KO, rep5 4T1, KO,
- GSM9789683 4T1, WT, rep1 4T1, WT,
- GSM9789684 4T1, WT, rep2 4T1, WT,
- GSM9789685 4T1, WT, rep3 4T1, WT,
- GSM9789686 4T1, WT, rep4 4T1, WT,
- GSM9789687 4T1, WT, rep5 4T1, WT,
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- GSE300288 Single-cell and spatial transcriptomics analysis of tobacco-associated lung adenocarcinoma development, and in response to targeting inflammation alone or in combination with immune checkpoint blockade in precancerous phase [scRNA-seq] 31 samples shares checkpoint, blockade
- GSE252597 Combination LIGHT overexpression and checkpoint blockade disrupts the tumor immune environment eradicating colorectal liver metastases 24 samples shares checkpoint, blockade
- GSE310218 Dietary methionine restriction primes T cell metabolism for activation and tumor inhibition and enhances the efficacy of immune checkpoint blockade 21 samples shares checkpoint, blockade
- GSE290464 Noncanonical role of NCOR1 as a facilitator of DNA mismatch repair and its deficiency sensitizes cancers to immune checkpoint blockade therapy 18 samples shares checkpoint, blockade
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.