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Inhibition of salt inducible kinases reprograms T cells and antitumor immunity in ovarian cancer

GSE273961 Mus musculus (mouse) Single-cell RNA-seq 9 samples Submitted 2026/03/31 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 9 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Mus musculus (mouse)
Samples
9 samples: 4 × BeCon tumor cells from BeCon-bearing B6 mice treated with vehicle control, vs 4 × BeCon tumor cells from BeCon-bearing B6 mice treated with YKL-05- 099,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.
Paper
Inhibition of salt-inducible kinases reprograms T cells and antitumor immunity in ovarian cancerNature immunology · 2026
Summary (written by the submitters)
Patients with metastatic high grade serous ovarian carcinoma (HGSOC) are often unresponsive to immunotherapies due to a hostile tumor microenvironment (TME). To identify the main drivers of immunosuppression, we performed an all-human functional screen of 4,292 druggable small molecules in the presence of malignant ascites from patients with HGSOC.
Read the full summary ↓
Salt-inducible kinases (SIK) emerged as top targets that block human T cell antitumor activity. We saw that when cultured with cell-free patient ascites, primary human T cells express high levels of SIK. Administration of SIK inhibitors, YKL-05-099 or ARN-3236, consistently demonstrated therapeutic efficacy and survival advantages in syngeneic mouse models of resistant HGSOC. The encouraging outcomes from pharmaceutical inhibition of SIK were recapitulated by genetic depletion of Sik2/Sik3 in immunocompetent mice bearing HGSOC. In contrast, SIK inhibition in T cell-depleted hosts was ineffective, establishing T cell-centered antitumor immunity as the primary mechanism of action. Mechanistically, bulk transcriptomic analysis of tumor cells from SIK inhibitor-treated mice showed reduced disease progression along with upregulation of interferon-stimulated genes, while single-cell transcriptomic analysis of matched immune TME populations demonstrated increased infiltration of CD8 and CD4 T cells with enhanced cytotoxicity and effector cytokine function. Molecular studies in primary T cells identified in vivo target genes responsible for SIK-induced dysfunction including upregulation of TXNIP (Thioredoxin- Interacting Protein) and ZFP36L2 (ZFP36 Ring Finger Protein Like 2), negative regulators of effector cell metabolism and IFN-g cytokine production. We also observed downregulation of LYST and VPS37B, critical in cytotoxic granzyme biogenesis and trafficking. In contrast to PD-1 blockade alone that does not improve survival, combination of SIK inhibitors with immune checkpoint blockers significantly increased survival of mice with HGSOC. While T cells are the essential targets of SIK inhibitors, we noted that SIK inhibition boosted innate immunity including enhancing antigen presentation by dendritic cells. Spatial proteomic analysis (CODEX) in the presence of SIK inhibition further revealed a shift from immunosuppressive to immunostimulatory cellular neighborhoods. Our studies identify SIK as a key driver of immunosuppression in HGSOC. Treatment with SIK inhibitor alone or combined with PD-1 blockade may increase the survival of patients with ovarian cancer.
Published in
Inhibition of salt-inducible kinases reprograms T cells and antitumor immunity in ovarian cancer
Dong H, Ray A, Rotter LK et al. · Nature immunology 2026 · PMID 42162294 · doi:10.1038/s41590-026-02512-8
This dataset
Open GSE273961 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 GSE273961_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1144154 and SRA study SRP524148. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42162294) ↗
Study design
4 × BeCon tumor cells from BeCon-bearing B6 mice treated with vehicle control, vs 4 × BeCon tumor cells from BeCon-bearing B6 mice treated with YKL-05- 099,

Supports a between-group comparison across 8 samples.

How we read this: 2 replicated groups read from 9 sample titles; they account for 8 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
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