Outlier analysis of functional genomic profiles enriches for oncology targets and enables precision medicine
Outlier analysis of functional genomic profiles enriches for oncology targets and enables precision medicine
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DOI：
10.1186/s12864-016-2807-y
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发表时间：
2016-06-13
期刊：
影响因子：
4.4
通讯作者：
Zarrinkar, Patrick P.
中科院分区：
文献类型：
--
作者：
Zhu, Zhou;Ihle, Nathan T.;Zarrinkar, Patrick P.
背景：跨大细胞系面板的基因组规模的功能基因组筛选为发现肿瘤脆弱性提供了丰富的资源，这些脆弱性可能导致下一代靶向治疗。他们的数据分析通常侧重于识别基因，这些基因的敲除增强了在各种预先定义的遗传背景下的反应，这些遗传背景受到生物学复杂性以及我们知识的不完整性的限制。因此，我们引入了一种互补的数据挖掘策略来识别细胞系子集或离群组中具有特殊敏感性的基因，从而允许进行无偏分析，而无需任何关于依赖的潜在生物学基础的先验假设。结果：尽管没有先验知识用于驱动分析，但具有离群点特征的基因强烈且特定地富含那些已知与癌症和相关生物学过程相关的基因。识别特殊的应答者(离群值)不仅可能为治疗干预带来新的候选者，而且还可能为伴随的精确医学策略带来肿瘤适应症和反应生物标记物。几种肿瘤抑制基因具有离群值敏感性模式，支持并推广了肿瘤抑制基因可以发挥上下文相关的致癌作用的概念。结论：本文描述的离群值分析的新应用展示了一种系统化和数据驱动的分析策略，用于破译大规模功能基因组数据，用于肿瘤靶点和精确药物的发现。
Background: Genome-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.Results: Genes 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.Conclusions: The 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.