← BioTransfer GEO Dataset Finder
GEO series

AXL phosphosite mapping links FAK1 and YAP1 signaling to erlotinib resistance in EGFR-mutant lung cancer

GSE333379 Homo sapiens (human) RNA-seq (gene expression) 29 samples Submitted 2026/05/27 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 29 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
29 samples: 29 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.
Summary (written by the submitters)
Targeted therapies for receptor tyrosine kinases are effective but invariably limited by drug resistance. In EGFR-mutant lung cancer, AXL activation drives resistance to erlotinib by restoring cell survival and migration. To map these signaling mechanisms, we generated a panel of lung adenocarcinoma PC9 cell lines with phenylalanine substitutions at each intracellular AXL tyrosine residue.
Read the full summary ↓
By integrating phosphorylation data with phenotypic changes via multivariate modeling, we found that AXL signaling organizes into two clusters enriched for Abl1 and SFK substrate motifs. A peptide specificity screen identified FAK1 as a top proximal substrate of AXL. Downstream, AXL-mediated YAP1 activation was found to sustain drug tolerance, while combined inhibition eliminated persister cells in vitro. These AXL and YAP pathways correlate with disease progression and poor clinical outcomes in erlotinib-treated patients. Collectively, this study dissects the specific signaling regulators by which AXL drives erlotinib resistance.
This dataset
Open GSE333379 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 GSE333379_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 PRJNA1012098 and SRA study SRP458077. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
29 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 29 sample titles: 29 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
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.