Paper Info
Title
TANDEM: a two-stage approach to maximize interpretability of drug response models based on multiple molecular data types.
Abstract
Motivation: Clinical response to anti-cancer drugs varies between patients. A large portion of this variation can be explained by differences in molecular features, such as mutation status, copy number alterations, methylation and gene expression profiles. We show that the classic approach for combining these molecular features (Elastic Net regression on all molecular features simultaneously) results in models that are almost exclusively based on gene expression. The gene expression features selected by the classic approach are difficult to interpret as they often represent poorly studied combinations of genes, activated by aberrations in upstream signaling pathways. Results: To utilize all data types in a more balanced way, we developed TANDEM, a two-stage approach in which the first stage explains response using upstream features (mutations, copy number, methylation and cancer type) and the second stage explains the remainder using downstream features (gene expression). Applying TANDEM to 934 cell lines profiled across 265 drugs (GDSC1000), we show that the resulting models are more interpretable, while retaining the same predictive performance as the classic approach. Using the more balanced contributions per data type as determined with TANDEM, we find that response to MAPK pathway inhibitors is largely predicted by mutation data, while predicting response to DNA damaging agents requires gene expression data, in particular SLFN11 expression.
Year
DOI
Venue
2016
10.1093/bioinformatics/btw449
BIOINFORMATICS
Field
DocType
Volume
Gene,Regression,Biology,Elastic net regularization,Gene expression,DNA,Methylation,Signal transduction,Bioinformatics,Genetics,Mutation
Journal
32
Issue
ISSN
Citations 
17
1367-4803
2
PageRank 
References 
Authors
0.38
2
4
Name
Order
Citations
PageRank
Nanne Aben121.06
Daniel J. Vis2180.89
Magali Michaut318314.79
Lodewyk F A Wessels433722.28