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Farnesyltransferase inhibition overcomes the adaptive resistance to targeted therapies in oncogene-addicted non-small cell lung cancer II

GSE249721 Homo sapiens (human) RNA-seq (gene expression) 44 samples Submitted 2024/05/17 Platform GPL21697
At a glance

RNA-seq (gene expression) · human · 44 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
44 samples: 14 conditions, mostly in triplicateGroups 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
Farnesyltransferase inhibition overcomes oncogene-addicted non-small cell lung cancer adaptive resistance to targeted therapiesNature communications · 2024
Summary (written by the submitters)
Drug-tolerance has emerged as one of the major non-genetic adaptive processes driving resistance to targeted therapy (TT) in non-small cell lung cancer (NSCLC). However, the kinetics and sequence of molecular events governing this adaptive response remain poorly understood. Here, we performed transcriptomic profiling by RNAseq in a panel of EGFR-mutant NSCLC cell lines (PC9, HCC4006, H3255 and HCC827) that were previously subcloned to minimize the presence of potential pre-existing resistant cells.
Read the full summary ↓
Cells were treated by either erlotinib (1 µM) or osimertinib (1 µM) for a short period (24h), until drug-tolerance (between 7 and 21 days), and until development of fully resistant proliferative cells (RPC).
Published in
Farnesyltransferase inhibition overcomes oncogene-addicted non-small cell lung cancer adaptive resistance to targeted therapies
Figarol S, Delahaye C, Gence R et al. · Nature communications 2024 · PMID 38937474 · doi:10.1038/s41467-024-49360-4
This dataset
Open GSE249721 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 GSE249721_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 PRJNA1050239 and SRA study SRP476773. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38937474) ↗
Study design
14 conditions, mostly in triplicate
H3255_Erlo_21j ×3 H3255_NT ×3 H4006_CT_24h ×3 H4006_Erlo_24h ×3 H4006_Erlo_DTC ×3 H4006_Erlo_DTEC ×3 HCC827Osimertinib11d ×3 HCC827Untreated ×3 +10 more

Supports a between-group comparison across 40 samples.

How we read this: 14 replicated groups read from 44 sample titles; they account for 40 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

+ 4 more — browse all 44 samples with per-sample file links →

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.