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Baseline tumor features and peripheral blood immune dynamics that underlie efficacy in MSS metastatic colorectal cancer treated with regorafenib, ipilimumab, and nivolumab

GSE289583 Homo sapiens (human) RNA-seq (gene expression) 14 samples Submitted 2026/02/01 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 14 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
14 samples: 8 × Biopsy collected at Baseline from Pt vs 6 × Biopsy collected at 4wks from PtGroups 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
Baseline Tumor Features and Systemic Immune Dynamics Underlying Efficacy in MSS Metastatic Colorectal Cancer Treated with Regorafenib, Ipilimumab, and NivolumabCancer immunology research · 2026
Summary (written by the submitters)
Microsatellite-stable metastatic colorectal cancer (MSS mCRC) remains resistant to conventional immunotherapies, yet in a phase I trial the combination of regorafenib, ipilimumab, and nivolumab (RIN) demonstrated a 27.6% overall response rate and a median overall survival of 20 months, particularly benefiting patients without liver metastases.
Read the full summary ↓
Correlative studies of tumor biopsies from eight patients and peripheral blood samples from 29 patients revealed that good responders had a more immunogenic tumor microenvironment (TME) at baseline, with enhanced cellular proliferation, active DNA repair pathways, and elevated STING pathway expression, along with a Th1-skewed cytokine profile in peripheral blood. Poor responders exhibited dysregulated inflammation, vascular remodeling, and T cells showing pathogen-response gene signatures indicative of chronic inflammation; in those with liver metastases, T cells showed immune senescence and metabolic hyperactivation. After RIN therapy, good responders displayed increased TME immune cell infiltration, upregulation of immune-related genes, marked expansion of previously low-frequency TCR clones in CD8 T cells, heightened naive T cell proliferation, and activation of immune and metabolic pathways. Poor responders initially showed transient increases in bulk CD8 T cells, NK cells, Th1 cytokine production, and reduced exhaustion markers but did not sustain meaningful TCR repertoire expansion. These findings underscore the potential of RIN to reshape the immunological landscape of MSS mCRC by modulating both TME and systemic immunity, highlighting the importance of predictive biomarkers to guide personalized therapeutic strategies, particularly for overcoming resistance in liver metastases.
Published in
Baseline Tumor Features and Systemic Immune Dynamics Underlying Efficacy in MSS Metastatic Colorectal Cancer Treated with Regorafenib, Ipilimumab, and Nivolumab
Ye J, Wang C, Egelston CA et al. · Cancer immunology research 2026 · PMID 41719220 · doi:10.1158/2326-6066.CIR-25-0243
This dataset
Open GSE289583 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 GSE289583_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 PRJNA1223462 and SRA study SRP563860. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41719220) ↗
Study design
8 × Biopsy collected at Baseline from Pt vs 6 × Biopsy collected at 4wks from Pt

Supports a between-group comparison across 14 samples.

How we read this: 2 replicated groups read from 14 sample titles; they account for 14 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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