Application of DrVAEN in a non-small cell lung cancer dataset (GEO ID: GSE32989)

1. Dataset summary

In non-small cell lung cancer (NSCLC), Epithelial/mesenchymal transition (EMT) is associated with greater resistance to EGFR inhibitors. The original dataset of GSE32989 contains gene expression data for 69 NSCLC cell lines or NSCLC patients and were categorized into two groups based on a 76-gene EMT signature: Epithelial-like and Mesenchymal-like. We predicted response to the drug Erlotinib, an EGFR inhibitor, in these samples.

2. Get and prepare dataset

The original gene expression data can be downloaded from GEO. Users may follow the code in GitHub to preprocess the data, including ID mapping and dealing with genes with multiple probe sets.

3. Predict drug responses using DrVAEN.

Example input parameters can be found below.

4. Comparation of the drug response between the two groups (Epithelial-like vs Mesenchymal-like).