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In situ detection of PD1–PD-L1 interactions as a functional predictor for response to immune checkpoint inhibition in NSCLC.

GSE283829 Homo sapiens (human) RNA-seq (gene expression) 27 samples Submitted 2025/01/17 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 27 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
27 samples: 16 × 105691 vs 11 × 105796Groups 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
In Situ Detection of Programmed Cell Death Protein 1 and Programmed Death Ligand 1 Interactions as a Functional Predictor for Response to Immune Checkpoint Inhibition in NSCLCJournal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2025
Summary (written by the submitters)
Introduction: Immune checkpoint inhibitors (ICIs) have transformed lung cancer treatment, yet their effectiveness seem restricted to certain patient subsets. Current clinical stratification on the basis of programmed death ligand 1 (PD-L1) expression offers limited predictive value. Given the mechanism of action, directly detecting spatial programmed cell death protein 1 (PD1)–PD-L1 interactions might yield more precise insights into immune responses and treatment outcomes.
Read the full summary ↓
Methods: We applied a second-generation in situ proximity ligation assay to detect PD1–PD-L1 interactions in diagnostic tissue samples from 16 different cancer types, a tissue microarray with surgically resected early-stage NSCLC, and finally diagnostic biopsies from 140 patients with advanced NSCLC with and without ICI treatment. RNA sequencing analysis was used to identify potential resistance mechanisms. Results: In the early-stage NSCLC, only approximately half of the cases with detectable PD-L1 and PD1 expression exhibited PD1–PD-L1 interactions, with significantly lower levels in EGFR-mutated tumors. Interaction levels varied across cancer types, aligning with reported ICI response rates. In ICI-treated patients with NSCLC, higher PD1–PD-L1 interactions were linked to complete responses and longer survival, outperforming standard PD-L1 expression assays. Patients who did not respond to ICIs despite high PD1–PD-L1 interactions exhibited additional expression of stromal immune mediators (EOMES, HAVCR1/TIM-1, JAML, FCRL1). Conclusion: Our study proposes a diagnostic shift from static biomarker quantification to assessing active immune pathways, providing more precise ICI treatment. This functional concept applies to tiny lung biopsies and can be extended to further immune checkpoints. Accordingly, our results indicate concerted ICI resistance mechanisms, highlighting the need for combination diagnostics and therapies.
Published in
In Situ Detection of Programmed Cell Death Protein 1 and Programmed Death Ligand 1 Interactions as a Functional Predictor for Response to Immune Checkpoint Inhibition in NSCLC
Lindberg A, Muhl L, Yu H et al. · Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer 2025 · PMID 39743139 · doi:10.1016/j.jtho.2024.12.026
This dataset
Open GSE283829 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 GSE283829_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 PRJNA1195840 and SRA study SRP550210. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39743139) ↗
Study design
16 × 105691 vs 11 × 105796

Supports a between-group comparison across 27 samples.

How we read this: 2 replicated groups read from 27 sample titles; they account for 27 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
More Lung cancer datasets

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