Zhu Zhou, Ihle Nathan T, Rejto Paul A, Zarrinkar Patrick P
Oncology Research Unit, Pfizer Worldwide Research & Development, La Jolla Laboratories, 10777 Science Center Drive, San Diego, CA, 92121, USA.
BMC Genomics. 2016 Jun 13;17:455. doi: 10.1186/s12864-016-2807-y.
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.
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.
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.
跨大型细胞系面板进行的全基因组功能基因组筛选为发现可能带来下一代靶向治疗的肿瘤脆弱性提供了丰富资源。其数据分析通常侧重于识别那些在各种预定义基因背景下敲低后能增强反应的基因，而这些分析受生物学复杂性以及我们知识的不完整性所限。因此，我们引入一种互补的数据挖掘策略，以识别在细胞系子集或异常值组中具有异常敏感性的基因，从而在不预先假设潜在依赖生物学的情况下进行无偏分析。
尽管未使用先验知识来推动分析，但具有异常特征的基因强烈且特异性地富集了那些已知与癌症及相关生物学过程相关的基因。识别异常反应者（异常值）不仅可能带来新的治疗干预候选基因，还可能带来用于伴随精准医学策略的肿瘤适应症和反应生物标志物。几种肿瘤抑制因子具有异常敏感性模式，支持并推广了肿瘤抑制因子可发挥依赖于背景的致癌作用这一观点。
本文所述的异常值分析的新应用展示了一种系统的数据驱动分析策略，用于解读大规模功能基因组数据以发现肿瘤学靶点和精准医学。
