GEO series

Reactivating cGAS-STING Signaling by Targeting SOS1 Enhances Antitumor Immunity in NRAS-Mutant Tumors

GSE290171 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2025/05/11 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 12 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: Homo sapiens (human)
Samples: 12 samples: 6 × dT) vs 3 × HT1080-siNC vs 3 × HT1080-siQ61KGroups 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.

Summary (written by the submitters)

NRAS is commonly mutated in several different types of human cancer. NRAS-mutant tumors are aggressive and associated with poor outcomes. Although the development of targeted therapies as well as immune checkpoint inhibitors has led to a substantial improvement in the overall survival of patients with NRAS wild-type tumors, current therapies for NRAS-mutant cancers are limited.

Read the full summary ↓

Here, analysis of RNA-sequencing data revealed a downregulation of the type I interferon response in NRAS-mutant tumors compared with NRAS wild-type tumors, which was associated with poor prognosis in NRAS-mutant melanoma. Knockdown of mutant NRAS, but not wild-type NRAS, enhanced the activation of cGAS-STING signaling. The formation of a trimeric complex between TBK1, STING, and IRF3, which is required for the activation and nuclear translocation, was blocked in NRAS-mutant tumor cells. Inactivation of innate immune signaling by mutant NRAS altered cytokine production, resulting in cell autonomous and non-cell autonomous signaling to prevent tumor cell death and in evasion of tumor immune surveillance. Screening of drugs for the ability to stimulate the release of CXCL10 revealed that SOS1 inhibitors reactivated cGAS-STING signaling in NRAS-mutant tumor cells. Importantly, combining SOS1 inhibitors with STING agonists maximized the activation of cGAS-STING signaling and elicited an increased antitumor immune response in vitro and in vivo. Overall, this study provides insights into the regulation of anti-tumor immunity by mutant NRAS and uncovers a potential strategy for treating NRAS-mutant cancer.

This dataset

Open GSE290171 in finder → Find more human 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 GSE290171_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 PRJNA1226513 and SRA study SRP565464. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

6 × dT) vs 3 × HT1080-siNC vs 3 × HT1080-siQ61K

Supports a between-group comparison across 12 samples.

How we read this: 3 replicated groups read from 12 sample titles; they account for 12 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

GSM8807575 HT1080-siNC-1 HT1080-siNC
GSM8807576 HT1080-siNC-2 HT1080-siNC
GSM8807577 HT1080-siNC-3 HT1080-siNC
GSM8807578 HT1080-siNC+poly(dA:dT)-1 dT)
GSM8807579 HT1080-siNC+poly(dA:dT)-2 dT)
GSM8807580 HT1080-siNC+poly(dA:dT)-3 dT)
GSM8807581 HT1080-siQ61K-1 HT1080-siQ61K
GSM8807582 HT1080-siQ61K-2 HT1080-siQ61K
GSM8807583 HT1080-siQ61K-3 HT1080-siQ61K
GSM8807584 HT1080-siQ61K+poly(dA:dT)-1 dT)
GSM8807585 HT1080-siQ61K+poly(dA:dT)-2 dT)
GSM8807586 HT1080-siQ61K+poly(dA:dT)-3 dT)

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GSE275534 Transcriptomic changes in signaling using NCI-H358 cells treated with a KRAS G12C or a SOS1::KRASi inhibitor 24 samples shares sos1
GSE255847 Combination of a SOS1-KRAS interaction inhibitor with a KRASG12C inhibitor combination can address intrinsic and acquired resistance leading to stronger and more durable response [Tru-seq] 22 samples shares sos1
GSE271143 METTL3-Mediated N6-methyladenosine mRNA Modification and cGAS-STING Pathway Activity in Kidney Fibrosis 127 samples shares cgas-sting

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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.