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IFITM3-MET interaction drives osimertinib resistance through AKT pathway activation in EGFR-mutant non–small cell lung cancer II

GSE289619 Homo sapiens (human) RNA-seq (gene expression) 32 samples Submitted 2025/12/03 Platform GPL34284
At a glance

RNA-seq (gene expression) · human · 32 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
32 samples: 32 × FFPE tumor sectionGroups 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
IFITM3-MET interaction drives osimertinib resistance through AKT pathway activation in EGFR-mutant non-small cell lung cancerMolecular cancer · 2025
Summary (written by the submitters)
Despite an initial favorable response of EGFR mutant non–small cell lung cancer (NSCLC) to osimertinib, an EGFR tyrosine kinase inhibitor (TKI), resistance inevitably develops. We here performed transcriptomics analysis of pretreatment specimens and identified IFITM3 as a gene specifically upregulated in tumors that develop early resistance to osimertinib.
Read the full summary ↓
Immunohistochemistry confirmed patients with IFITM3-positive tumors experienced a shorter progression-free survival. Spatial transcriptomics and other analyses further revealed that IFITM3 expression was increased in response to cytokines derived from the tumor microenvironment (TME) during osimertinib treatment. IFITM3 was found to promote the development of osimertinib resistance through interaction with MET and activation of the AKT pathway. Furthermore, combined treatment with a MET inhibitor suppressed the development of osimertinib resistance in a mouse model. Our findings thus reveal that upregulation of IFITM3 represents a previously unrecognized mechanism of osimertinib resistance, and they suggest that targeting the IFITM3-MET axis may improve treatment outcomes.
Published in
IFITM3-MET interaction drives osimertinib resistance through AKT pathway activation in EGFR-mutant non-small cell lung cancer
Ibusuki R, Iwama E, Shimauchi A et al. · Molecular cancer 2025 · PMID 41152910 · doi:10.1186/s12943-025-02493-6
This dataset
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.

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 GSE289619_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1223832 and SRA study SRP563969. Searching any of these in the dataset finder brings you back here.

Study design
32 × FFPE tumor section

Supports a between-group comparison across 32 samples.

How we read this: 1 replicated groups read from 32 sample titles; they account for 32 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

Search all human RNA-seq datasets in GEO →

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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.