APSiC: Analysis of Perturbation Screens for the Identification of Novel Cancer Genes

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 (<em>A</em>nalysis of <em>P</em>erturbation <em>S</em>creens for <em>i</em>dentifying novel <em>C</em>ancer genes) to identify genetic and non-genetic drivers in perturbation screens even with few samples. Applying APSiC to the shRNA screen Project DRIVE, APSiC identified well-known, pan-cancer genetic drivers, novel putative genetic drivers known to be dysregulated in specific cancer types and the context dependency of mRNA-splicing between cancer types. Additionally, APSiC discovered a median of 28 and 35 putative non-genetic oncogenes and tumor suppressor genes, respectively, for individual cancer types, including genes involved in genome stability maintenance and cell cycle. We functionally demonstrated that <em>LRRC4B, </em>a putative novel non-genetic tumor suppressor gene, 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.系统性扰动筛选为阐明癌症驱动基因提供了全面的研究资源。在这类功能筛选中，针对少量细胞系开展大量基因扰动实验的研究范式，亟需开发具备足够统计效力的专用计算工具。本研究开发了APSiC（Analysis of Perturbation Screens for identifying novel Cancer genes，即「新型癌症基因扰动筛选分析工具」），可在样本量有限的扰动筛选数据中鉴定遗传型与非遗传型癌症驱动因子。将APSiC应用于短发卡RNA（short hairpin RNA，shRNA）筛选项目DRIVE后，其不仅鉴定出了公认的泛癌遗传驱动基因，还发现了在特定癌症类型中存在表达失调的潜在新型遗传驱动基因，以及不同癌症类型间mRNA剪接的上下文依赖性特征。此外，APSiC针对单种癌症类型分别鉴定出了中位数为28个的潜在非遗传型致癌基因，以及35个的潜在非遗传型肿瘤抑制基因，其中包含参与基因组稳定性维持与细胞周期调控的基因。本研究通过功能实验验证了潜在新型非遗传型肿瘤抑制基因LRRC4B，其可通过阻滞细胞周期抑制乳腺癌细胞增殖，并调控细胞凋亡过程。研究结果表明，APSiC是一款稳健的统计分析框架，可通过大规模扰动筛选数据挖掘新型癌症基因。本研究将基于APSiC的DRIVE项目分析结果部署为在线门户网站，可为新型癌症基因的挖掘提供宝贵的研究资源。