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Nucleotide metabolism in cancer cells fuels a UDP-driven macrophage cross-talk promoting immunosuppression and immunotherapy resistance [RNAseq_meta_analysis]

GSE196786 Mus musculus (mouse) RNA-seq (gene expression) 36 samples Submitted 2024/03/28 Platform GPL17021
At a glance

RNA-seq (gene expression) · mouse · 36 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
36 samples
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)
The project aims to identify metabolic genes and pathways associated with immunotherapy resistance. Methods: 4x10e6 Panc02, 1x10e6 MC38 or 2x10e6 CT26 cells were injected subcutaneously (s.c.) in the right flank of the mouse in a final suspension of 200μl PBS. Tumor volumes were measured at least three times per week. At the indicated time points, mice were randomized and treated intraperitoneally (i.p.) with 10 mg/kg α-PD-1 (BioXcell) or α-CTLA4 or control IgG from rat serum (Sigma-Aldrich).
Read the full summary ↓
At end-stage, tumor-bearing mice were sacrificed by cervical dislocation and RNA was isolated from tumor tissue using TRIzol (Life Technologies). Starting from total RNA, poly-adenylated fragments were isolated, reverse transcribed and converted into indexed sequencing libraries using the Illumina TruSeq RNA sample preparation kit V2 according to the manufacturer’s instructions. The first 50 bases of these libraries were sequenced on a HiSeq 2500 system (Illumina, San Diego, CA). Raw sequencing reads were mapped to the transcriptome and the mouse reference genome (GRCm38/mm10) using TopHat 2.0 and Bowtie2.0 (Langmead and Salzberg, 2012). Mapped reads were assigned to ensemble gene IDs with the HTSeq software package. Transcriptome profiling was performed in three different murine cancer models (MC38, CT26 and panc02) treated with PD-1, CTLA-4 or vehicle Our bulk tumor transcriptomic profiles of responsive (MC38), low responsive (CT26) and non-responsive (PancO2) murine tumor models were integrated with publicly available pretreatment transcriptomic datasets of patients responding and resistant to immune checkpoint inhibitors, such as α-CTLA-4 and α-PD-1 (Van Allen- et al., Science 2015; Hugo et al., Cell 2016; Ascierto et al., Cancer Imm Research 2016). All these datasets were interpreted by using our in-house BIOMEX platform (Goveia et al., EMBO Mol Med 2016), which focuses on metabolic profiles. Metabolic genes were ranked, prioritizing the most consistently upregulated genes associated with resistance to immuno checkpoint inhibitors (ICIs).
This dataset
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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 GSE196786_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 PRJNA807316 and SRA study SRP359960. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Samples in this study

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