GEO series

Oral ENPP1 inhibitor designed using generative AI as next generation STING modulator for solid tumors

GSE277788 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2025/04/13 Platform GPL24247

At a glance

RNA-seq (gene expression) · mouse · 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: Mus musculus (mouse)
Samples: 12 samples: 12 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.
Paper: Oral ENPP1 inhibitor designed using generative AI as next generation STING modulator for solid tumorsNature communications · 2025

Summary (written by the submitters)

NCOMMS-24-54326: Despite the STING-type-I interferon pathway playing a key role in effective anti-tumor immunity, the therapeutic benefit of direct STING agonists appears limited. In this study, we used several artificial intelligence techniques and patient-based multi-omic data to show that Ectonucleotide Pyrophosphatase/Phosphodiesterase 1 (ENPP1), which hydrolyzes STING-activating cyclic GMP-AMP (cGAMP), is a safer and more effective STING-modulating target than direct STING agonism in multiple solid tumors.

Read the full summary ↓

We then leveraged our generative chemistry artificial intelligence-based drug design platform to facilitate the design of ISM5939, an orally bioavailable ENPP1-selective inhibitor capable of stabilizing extracellular cGAMP and activating bystander antigen-presenting cells without inducing either toxic inflammatory cytokine release or tumor-infiltrating T-cell death. In murine syngeneic models across cancer types, ISM5939 synergizes with targeting the PD-1/PD-L1 axis and genotoxic chemotherapy in suppressing tumor growth with good tolerance. Our findings provide new evidence supporting ENPP1 as a novel innate immune checkpoint across solid tumors and reports the first AI design-aided ENPP1 inhibitor, ISM5939, as a cutting-edge STING modulator for cancer therapy, paving a new path for immunotherapy advancements.

Published in

Oral ENPP1 inhibitor designed using generative AI as next generation STING modulator for solid tumors

Pu C, Cui H, Yu H et al. · Nature communications 2025 · PMID 40410143 · doi:10.1038/s41467-025-59874-0

This dataset

Open GSE277788 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 GSE277788_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 PRJNA1164192 and SRA study SRP534212. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40410143) ↗

Study design

12 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 12 sample titles: 12 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.

Samples in this study

GSM8529754 G1-tumor-8h-4-1
GSM8529755 G1-tumor-8h-5-1
GSM8529756 G1-tumor-8h-6-1
GSM8529757 G2-tumor-8h-4-1
GSM8529758 G2-tumor-8h-5-1
GSM8529759 G2-tumor-8h-6-1
GSM8529760 G3-tumor-8h-1-1
GSM8529761 G3-tumor-8h-2-1
GSM8529762 G3-tumor-8h-3-1
GSM8529763 G6-tumor-8h-1-1
GSM8529764 G6-tumor-8h-2-1
GSM8529765 G6-tumor-8h-3-1

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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.