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Selective Loss of RB in Resistant EGFR Mutant Lung Adenocarcinomas that Transform to SCLC [CGH]

GSE64765 Homo sapiens (human) Genome variation profiling by SNP array 6 samples Submitted 2015/01/30 Platform GPL16237
At a glance

human · 6 samples

Organism
Homo sapiens (human)
Samples
6 samples
Data you can get
A processed table (series matrix) and supplementary files from NCBI.
Summary (written by the submitters)
The study was designed to identify the molecular changes that occur in EGFR mutant NSCLCs that become resistant to TKI by transforming to SCLC. Tyrosine kinase inhibitors (TKIs) are effective treatments for non-small cell lung cancers (NSCLCs) with epidermal growth factor receptor (EGFR) mutations. However, they do not lead to cures, and, on average, relapse occurs after one year of continuous treatment.
Read the full summary ↓
In a subset of patients, a fundamental histological transformation from NSCLC to small cell lung cancer (SCLC) is observed in the resistant cancers, but the molecular changes associated with this transformation remain unknown. Analysis of a cohort of tumor samples and cell lines derived from resistant EGFR mutant patients with SCLC transformation revealed that RB is lost in 100% of these cases, but rarely in those that remain NSCLC. Global changes in gene expression, including increased neuroendocrine marker expression and absence of EGFR expression, are observed in cancers that transformed to SCLC. Consistent with their genetic and epigenetic similarities to classical SCLC, cell lines derived from resistant EGFR mutant SCLC biopsies are substantially more sensitive to ABT-263 treatment compared to those derived from resistant EGFR mutant NSCLCs. Together, these findings suggest that despite developing initially as EGFR mutant adenocarcinomas, this subset of resistant cancers ultimately take on many of the molecular and phenotypic characteristics of classical SCLC.
This dataset
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.

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 (GSE64765_RAW.tar), including the raw array or instrument files. Can be large; the picker in step 2 takes just the samples you need.

Also filed as BioProject PRJNA271817. Searching any of these in the dataset finder brings you back here.

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