Discordancy Partitioning for Validating Potentially Inconsistent Pharmacogenomic Studies

The Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) are two major studies that can be used to mine for therapeutic biomarkers for cancers of a large variety. Model validation using the two datasets however has proved challenging. Both predictions and signatures...

Full description

Saved in:
Bibliographic Details
Published in:Scientific reports 2017-11, Vol.7 (1), p.15169-12, Article 15169
Main Authors: Rao, J. Sunil, Liu, Hongmei
Format: Article
Language:English
Subjects:
631/114/2415
631/67
Cancer
Datasets
Drug interaction
Drug interactions
Humanities and Social Sciences
multidisciplinary
Pharmacogenomics
Science
Science (multidisciplinary)
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) are two major studies that can be used to mine for therapeutic biomarkers for cancers of a large variety. Model validation using the two datasets however has proved challenging. Both predictions and signatures do not consistently validate well for models built on one dataset and tested on the other. While the genomic profiling seems consistent, the drug response data is not. Some efforts at harmonizing experimental designs has helped but not entirely removed model validation difficulties. In this paper, we present a partitioning strategy based on a data sharing concept which directly acknowledges a potential lack of concordance between datasets and in doing so, also allows for extraction of reproducible novel gene-drug interaction signatures as well as accurate test set predictions. We demonstrate these properties in a re-analysis of the GDSC and CCLE datasets.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-017-15590-4