Outlier analysis of functional genomic profiles enriches for oncology targets and enables precision medicine

BMC Genomics

Research Article

Nathan T. Ihle¹  Zhou Zhu¹  Paul A. Rejto¹  Patrick P. Zarrinkar¹ 

[1] Oncology Research Unit, Pfizer Worldwide Research & Development, La Jolla Laboratories, 10777 Science Center Drive, 92121, San Diego, CA, USA;

关键词: Outlier analysis;    Functional genomics;    Oncology;    Cancer;    Target identification;    Precision medicine;    Oncogene addiction;    Synthetic lethality;   

DOI  :  10.1186/s12864-016-2807-y

 received in 2015-11-06, accepted in 2016-05-27,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundGenome-scale functional genomic screens across large cell line panels provide a rich resource for discovering tumor vulnerabilities that can lead to the next generation of targeted therapies. Their data analysis typically has focused on identifying genes whose knockdown enhances response in various pre-defined genetic contexts, which are limited by biological complexities as well as the incompleteness of our knowledge. We thus introduce a complementary data mining strategy to identify genes with exceptional sensitivity in subsets, or outlier groups, of cell lines, allowing an unbiased analysis without any a priori assumption about the underlying biology of dependency.ResultsGenes with outlier features are strongly and specifically enriched with those known to be associated with cancer and relevant biological processes, despite no a priori knowledge being used to drive the analysis. Identification of exceptional responders (outliers) may not lead only to new candidates for therapeutic intervention, but also tumor indications and response biomarkers for companion precision medicine strategies. Several tumor suppressors have an outlier sensitivity pattern, supporting and generalizing the notion that tumor suppressors can play context-dependent oncogenic roles.ConclusionsThe novel application of outlier analysis described here demonstrates a systematic and data-driven analytical strategy to decipher large-scale functional genomic data for oncology target and precision medicine discoveries.

【 授权许可】

CC BY   
© The Author(s). 2016

【 图 表 】

Fig. 6