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Clinical and Immunological Responses to Combined JAK Inhibition and PD-1 immunotherapy for Lung Cancer Patients

GSE266219 Homo sapiens (human) RNA-seq (gene expression) 79 samples Submitted 2024/05/07 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 79 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)
Topic
Lung cancerThis series is part of our curated briefing on it.
Samples
79 samples: 79 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
Deciphering Cell Fate and Clonal Dynamics via Integrative Single-Cell Lineage ModelingbioRxiv : the preprint server for biology · 2025
Summary (written by the submitters)
Persistent inflammation driven by cytokines like type-one interferon (IFN-I) can cause immunosuppression. We show that administration of the JAK1 inhibitor itacitinib after anti-PD1 immunotherapy improves immune function and anti-tumor responses in mice, and results in high response rates (67%) in a phase-2 clinical trial for metastatic non-small cell lung cancer.
Read the full summary ↓
Patients who failed to respond to initial anti-PD1 immunotherapy but responded after addition of itacitinib had multiple features of poor immune function to anti-PD1 alone that improved after JAK inhibition. Itacitinib promoted CD8 T cell plasticity and therapeutic responses of exhausted- and effector-memory-like T cell clonotypes. Patients with persistent inflammation refractory to itacitinib showed progressive CD8 T cell terminal differentiation and progressive disease. Thus, JAK inhibition may improve the efficacy of anti-PD1 immunotherapy by pivoting T cell differentiation dynamics.
Published in
Deciphering Cell Fate and Clonal Dynamics via Integrative Single-Cell Lineage Modeling
Fu Y, Mathew D, Wang M et al. · bioRxiv : the preprint server for biology 2025 · PMID 40950163 · doi:10.1101/2025.09.01.673503
This dataset
Open GSE266219 in finder → Find more human RNA-seq datasets → Lung cancer landscape →
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 GSE266219_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 PRJNA1106397 and SRA study SRP505379. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40950163) ↗
Study design
79 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 79 sample titles: 79 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

+ 39 more — browse all 79 samples with per-sample file links →

More Lung cancer datasets

Search all human RNA-seq datasets in GEO →

Share this dataset

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.