Tumor-targeted IL-10 combined with IL-2 amplifies anti-tumor immunity
RNA-seq (gene expression) · mouse · 3 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
- 3 samples: 3 samples, each with a distinct title — no replicated groupsGroups 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 GSE342584_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 PRJNA1508234 and SRA study SRP724526. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 3 sample titles: 3 distinct titles with little repetition. 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.
- GSM9933588 Tumor, CD45+ CD3-, untreated
- GSM9933589 Tumor, CD45+ CD3-, Combo treated
- GSM9933590 Tumor, CD45+ CD3+, Combo treated
- GSE283694 Bacterial immunotherapy leveraging IL-10R hysteresis for both phagocytosis evasion and tumor immunity revitalization [RNA-seq] 21 samples shares il-10, immunity
- GSE283696 Bacterial immunotherapy leveraging IL-10R hysteresis for both phagocytosis evasion and tumor immunity revitalization [CUT&Tag] 33 samples shares il-10, immunity
- GSE250205 A PD-1-targeted, receptor-masked IL-2 immunocytokine that engages IL-2Rα strengthens T cell-mediated anti-tumor therapies 32 samples shares il-2, anti-tumor
- GSE222227 Induced regulatory B cells stably expressing IL-10 cure CNS autoimmunity by targeting microglia 22 samples shares il-10, immunity
- GSE283695 Bacterial immunotherapy leveraging IL-10R hysteresis for both phagocytosis evasion and tumor immunity revitalization [ATACseq] 11 samples shares il-10, immunity
- GSE293874 Anchored screening identifies transcription factor blueprints underlying dendritic cell diversity and subset-specific anti-tumor immunity [mouse bulk RNA-seq] 167 samples shares anti-tumor, immunity
- GSE278990 Single-cell screens identify ADAM12 as a fibroblast checkpoint impeding anti-tumor immunity 73 samples shares anti-tumor, immunity
- GSE253444 Combining TIGIT blockade with MDSC inhibition hinders breast cancer bone metastasis by activating anti-tumor immunity 55 samples shares anti-tumor, immunity
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.