Costello James C, Heiser Laura M, Georgii Elisabeth, Gönen Mehmet, Menden Michael P, Wang Nicholas J, Bansal Mukesh, Ammad-ud-din Muhammad, Hintsanen Petteri, Khan Suleiman A, Mpindi John-Patrick, Kallioniemi Olli, Honkela Antti, Aittokallio Tero, Wennerberg Krister, Collins James J, Gallahan Dan, Singer Dinah, Saez-Rodriguez Julio, Kaski Samuel, Gray Joe W, Stolovitzky Gustavo
1] Howard Hughes Medical Institute, Boston University, Boston, Massachusetts, USA. [2] Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA. [3] [4].
1] Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA. [2].
Nat Biotechnol. 2014 Dec;32(12):1202-12. doi: 10.1038/nbt.2877. Epub 2014 Jun 1.
Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.
从基因组信息预测最佳治疗策略是精准医学的核心目标。在此，我们专注于基于在人类乳腺癌细胞系中测量的一组基因组、表观基因组和蛋白质组分析数据集来预测药物反应。通过美国国立癌症研究所（NCI）与逆向工程评估与方法对话（DREAM）项目之间的合作，我们总共分析了44种药物敏感性预测算法。表现最佳的方法对非线性关系进行建模并纳入了生物通路信息。我们发现基因表达微阵列始终能为各个分析数据集提供最佳预测能力；然而，通过纳入多个独立数据集，性能得到了提升。我们讨论了表现最佳的方法——贝叶斯多任务多核学习（Bayesian multitask MKL）背后的创新点，并提供了所有方法的详细描述。本研究建立了药物敏感性预测的基准，并确定了可用于开发新方法的途径。
