High-throughput CRISPR-Cas9 knockout screens are widely used to evaluate gene essentiality in cancer research. Here we introduce a probabilistic modeling framework, Analysis of CRISPR-based Essentiality (ACE), that accounts for multiple sources of variation in CRISPR-Cas9 screens and enables new statistical tests for essentiality. We show using simulations that ACE is effective at predicting both absolute and differential essentiality. When applied to publicly available data, ACE identifies known and novel candidates for genotype-specific essentiality, including RNA m6-A methyltransferases that exhibit enhanced essentiality in the presence of inactivating TP53 mutations. ACE provides a robust framework for identifying genes responsive to subtype-specific therapeutic targeting.

中文翻译：

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ACE：用于表征 CRISPR 筛选中基因水平重要性的概率模型

高通量 CRISPR-Cas9 敲除筛选广泛用于评估癌症研究中的基因重要性。在这里，我们介绍了一个概率建模框架，即基于 CRISPR 的必要性分析 (ACE)，它解释了 CRISPR-Cas9 筛选中的多种变异来源，并启用了新的必要性统计测试。我们使用模拟表明 ACE 在预测绝对重要性和微分重要性方面是有效的。当应用于公开可用的数据时，ACE 确定了基因型特异性重要性的已知和新候选物，包括在存在失活 TP53 突变时表现出增强的重要性的 RNA m6-A 甲基转移酶。ACE 为识别对亚型特异性治疗靶向有反应的基因提供了一个强大的框架。