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STING agonism overcomes STAT3-mediated immunosuppression and adaptive resistance to PARP inhibition in ovarian cancer

GSE218024 Mus musculus (mouse) RNA-seq (gene expression) 24 samples Submitted 2025/11/14 Platform GPL25723
At a glance

RNA-seq (gene expression) · mouse · 24 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
24 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
STING agonism overcomes STAT3-mediated immunosuppression and adaptive resistance to PARP inhibition in ovarian cancerJournal for immunotherapy of cancer · 2023
Summary (written by the submitters)
Background: PARP inhibition (PARPi) has demonstrated potent therapeutic efficacy in patients with BRCA-mutant ovarian cancer. However, acquired resistance to PARPi remains a major challenge in the clinic. Methods: PARPi-resistant ovarian cancer mouse models were generated by long-term treatment of olaparib in syngeneic Brca1-deficient ovarian tumors.
Read the full summary ↓
STAT3-mediated immunosuppression was investigated in vitro by co-culture experiments and in vivo by analysis of immune cells in the TME of human and mouse PARPi-resistant tumors. Whole genome transcriptome analysis was performed to assess the anti-tumor immunomodulatory effect of STING (stimulator of interferon genes) agonists on myeloid cells in the TME of PARPi-resistant ovarian tumors. A STING agonist was used to overcome STAT3-mediated immunosuppression and acquired PARPi resistance in syngeneic and PDX models of ovarian cancer. Results: In this study, we uncover an adaptive resistance mechanism to PARP inhibition mediated by tumor associated macrophages (TAMs) in the tumor microenvironment (TME). Markedly increased populations of pro-tumor macrophages are found in BRCA-deficient ovarian tumors that rendered resistance to PARPi in both murine models and patients. Mechanistically, PARP inhibition elevates the STAT3 signaling pathway in tumor cells, which in turn promotes pro-tumor polarization of TAMs. STAT3 ablation in tumor cells mitigates polarization of pro-tumor macrophages and increases tumor-infiltrating T-cells upon PARP inhibition. These findings are corroborated in patient-derived, PARPi-resistant BRCA1-mutant ovarian tumors. Importantly, STING agonists reshape the immunosuppressive TME by reprogramming myeloid cells and overcome the TME-dependent adaptive resistance to PARPi in ovarian cancer. This effect is further enhanced by addition of PD-1 blockade. Conclusions: We elucidate an adaptive immunosuppression mechanism rendering resistance to PARPi in BRCA1-mutant ovarian tumors. This is mediated by enrichment of pro-tumor TAMs propelled by PARPi-induced STAT3 activation in tumor cells. We also provide a new strategy to reshape the immunosuppressive TME with STING agonist and overcome PARPi resistance in ovarian cancer.
Published in
STING agonism overcomes STAT3-mediated immunosuppression and adaptive resistance to PARP inhibition in ovarian cancer
Ding L, Wang Q, Martincuks A et al. · Journal for immunotherapy of cancer 2023 · PMID 36609487 · doi:10.1136/jitc-2022-005627
This dataset
Open GSE218024 in finder → Find more mouse 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 GSE218024_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 PRJNA901942 and SRA study SRP408070. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 36609487) ↗
Study design
6 conditions, mostly in triplicate
PBM tumo ×6 PBM-R tumo ×6 CD11b+ cells, control ×3 CD11b+ cells, olaparib ×3 CD11b+ cells, MSA-2 ×3 CD11b+ cells, olaparib + MSA-2 ×3

Supports a between-group comparison across 24 samples.

How we read this: 6 replicated groups read from 24 sample titles; they account for 24 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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