Abstract：
Next-generation pathogenicity predictors are designed to identify pathogenic mutations in genetic disorders but are increasingly used to detect driver mutations in cancer. Despite this, their suitability for cancer is not fully established. Here we have assessed the effectiveness of next-generation pathogenicity predictors when applied to cancer by using a comprehensive experimental benchmark of cancer driver and neutral mutations. Our findings indicate that state-of-the-art methods AlphaMissense and VARITY demonstrate commendable performance despite generally underperforming compared to cancer-specific methods. This is notable considering that these methods do not explicitly incorporate cancer-related data in their training and have made concerted efforts to prevent data leakage from the human-curated training and test sets. Nevertheless, it should be mentioned that a significant limitation of using pathogenicity predictors for cancer arises from their inability to detect cancer potential driver mutations specific for a particular cancer type.
摘要：
下一代致病性预测因子被设计用于识别遗传疾病中的致病性突变，但越来越多地用于检测癌症中的驱动突变。尽管如此,它们对癌症的适用性尚未完全确定。在这里，我们通过使用癌症驱动因子和中性突变的综合实验基准来评估下一代致病性预测因子应用于癌症时的有效性。我们的发现表明，最先进的方法AlphaMissense和VARITY尽管与癌症特异性方法相比通常表现不佳，但仍表现出值得称赞的性能。考虑到这些方法在他们的训练中没有明确地结合癌症特异性信息,并且已经做出协同努力以防止数据从人类策划的训练和测试集泄漏,这是值得注意的。然而，应该提到的是,使用癌症致病性预测因子的显著限制是由于它们无法检测特定癌症类型的癌症潜在驱动突变.
