Abstract：
: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.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Rao JS,Liu Hdoi
10.1038/s41598-017-15590-4subject
Has Abstractpub_date
2017-11-09 00:00:00pages
15169issue
1issn
2045-2322pii
10.1038/s41598-017-15590-4journal_volume
7pub_type
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