Systematic perturbation screens provide comprehensive resources for the elucidation of cancer driver genes. The perturbation of many genes in relatively few cell lines in such functional screens necessitates the development of specialized computational tools with sufficient statistical power. Here we developed APSiC (Analysis of Perturbation Screens for identifying novel Cancer genes) to identify genetic drivers and effectors in perturbation screens even with few samples. Applying APSiC to the shRNA screen Project DRIVE, APSiC identified well-known and novel putative mutational and amplified cancer genes across all cancer types and in specific cancer types. Additionally, APSiC discovered tumor-promoting and tumor-suppressive effectors, respectively, for individual cancer types, including genes involved in cell cycle control, Wnt/β-catenin and hippo signalling pathways. We functionally demonstrated that LRRC4B, a putative novel tumor-suppressive effector, suppresses proliferation by delaying cell cycle and modulates apoptosis in breast cancer. We demonstrate APSiC is a robust statistical framework for discovery of novel cancer genes through analysis of large-scale perturbation screens. The analysis of DRIVE using APSiC is provided as a web portal and represents a valuable resource for the discovery of novel cancer genes.

中文翻译：

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通过深度 shRNA 扰动筛选分析，系统鉴定新型癌症基因


系统扰动筛选为阐明癌​​症驱动基因提供了全面的资源。在这种功能筛选中，相对较少的细胞系中许多基因的扰动需要开发具有足够统计能力的专门计算工具。在这里，我们开发了 APSiC（用于识别新型癌症基因的扰动屏幕分析），即使样本很少，也可以在扰动屏幕中识别遗传驱动因素和效应因素。 APSiC 将 APSiC 应用于 shRNA 筛选 Project DRIVE，在所有癌症类型和特定癌症类型中识别出众所周知的新型推定突变和扩增癌症基因。此外，APSiC 还分别发现了针对个体癌症类型的肿瘤促进和肿瘤抑制效应子，包括参与细胞周期控制、Wnt/β-catenin 和 hippo 信号通路的基因。我们在功能上证明了 LRRC4B（一种假定的新型肿瘤抑制效应物）通过延迟细胞周期来抑制增殖并调节乳腺癌细胞凋亡。我们证明 APSiC 是一个强大的统计框架，可通过分析大规模扰动屏幕来发现新的癌症基因。使用 APSiC 进行的 DRIVE 分析以门户网站的形式提供，代表了发现新型癌症基因的宝贵资源。