GEO series

Microarray study to understand acquired resistance to EGFR-targeted therapy in lung cancer

GSE38310 Homo sapiens (human) Expression microarray 18 samples Submitted 2012/05/30 Platform GPL6947

At a glance

Expression microarray · human · 18 samples

What it is: Expression microarrayGene activity read on a chip (the older technology that RNA-seq replaced).
Organism: Homo sapiens (human)
Topic: Lung cancerThis series is part of our curated briefing on it.
Samples: 18 samples
Data you can get: A processed expression table (series matrix) and the array files, from NCBI.

Summary (written by the submitters)

Activating mutations of EGFR have been characterized as important mechanisms for carcinogenesis in a subset of EGFR-dependent non-small cell lung cancers (NSCLC). EGFR tyrosine kinase inhibitors (TKI), such as erlotinib and gefitinib, have dramatic clinical effects on EGFR-addicted lung cancers and are used as first-line therapy for EGFR-mutant tumors.

Read the full summary ↓

However, eventually all tumors acquire secondary resistance to the drugs and progress. We established a model to better understand mechanisms of acquired resistance. NCI- HCC827 cells are EGFR-mutant and highly erlotinib-sensitive. In this study we exposed HCC827 cells to increasing concentrations of erlotinib and two highly erlotinib-resistant subclones were developed (ER3 and T15-2). In these subclones no acquired alterations of EGFR or MET were found. We hereby performed a gene expression microarray studies to understand changes that might explain mechanisms of resistance. Through these studies we demonstrated in one resistant clone (ER3) overexpression of AXL, a tyrosine kinase implicated in imatinib and lapatinib resistance.

This dataset

Open GSE38310 in finder → Find more human microarray datasets → Lung cancer landscape →

Download · which file do I need?

Direct links to NCBI, 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.

All supplementary files ↗

3 · The original raw files

Whole study · RAW.tar

Everything the authors deposited as one archive (GSE38310_RAW.tar), including the raw array or instrument files. Can be large; the picker in step 2 takes just the samples you need.

Whole study · RAW.tar ↓

What the files usually look like: For arrays the series matrix is normally the normalised expression table you want; the raw array files (CEL and similar) are in the RAW.tar.

Also filed as BioProject PRJNA167692. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 22751098) ↗

Samples in this study

The sample list for this study is not cached yet. Press Load samples and it will be fetched from NCBI.

+ 18 more — browse all 18 samples with per-sample file links →

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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.