Context Sensitive Modeling of Cancer Drug Sensitivity.
Context Sensitive Modeling of Cancer Drug Sensitivity.
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DOI：
10.1371/journal.pone.0133850
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Pe'er D
中科院分区：
文献类型：
--
作者：
Chen BJ;Litvin O;Ungar L;Pe'er D
最近在大型癌症细胞系中筛选药物敏感性为开发预测药物反应的算法提供了宝贵的资源。由于更多的样本提供了更高的统计能力，大多数预测药物敏感性的方法都将多种癌症类型不加区别地集中在一起。然而，由于组织或癌症亚型的混杂效应，泛癌症结果可能会产生误导。另一方面，样本量小阻碍了对每种癌症类型的独立分析。为了平衡这种权衡，我们提出了CHER（上下文异质性启用回归），这是一种算法，通过选择预测性基因组特征并决定哪些应该和不应该在不同的癌症，组织和药物中共享来构建药物敏感性的预测模型。CHER提供的药物敏感性模型比类似的基于弹性网络的模型更准确。此外，CHER通过发现一组稀疏的共享和类型特异性基因组特征，提供了对潜在生物过程的更好洞察。
Recent screening of drug sensitivity in large panels of cancer cell lines provides a valuable resource towards developing algorithms that predict drug response. Since more samples provide increased statistical power, most approaches to prediction of drug sensitivity pool multiple cancer types together without distinction. However, pan-cancer results can be misleading due to the confounding effects of tissues or cancer subtypes. On the other hand, independent analysis for each cancer-type is hampered by small sample size. To balance this trade-off, we present CHER (Contextual Heterogeneity Enabled Regression), an algorithm that builds predictive models for drug sensitivity by selecting predictive genomic features and deciding which ones should—and should not—be shared across different cancers, tissues and drugs. CHER provides significantly more accurate models of drug sensitivity than comparable elastic-net-based models. Moreover, CHER provides better insight into the underlying biological processes by finding a sparse set of shared and type-specific genomic features.