Abstract：
BACKGROUND: Predicting cancer drug response requires a comprehensive assessment of many mutations present across a tumor genome. While current drug response models generally use a binary mutated/unmutated indicator for each gene, not all mutations in a gene are equivalent.
RESULTS: Here, we construct and evaluate a series of predictive models based on leading methods for quantitative mutation scoring. Such methods include VEST4 and CADD, which score the impact of a mutation on gene function, and CHASMplus, which scores the likelihood a mutation drives cancer. The resulting predictive models capture cellular responses to dabrafenib, which targets BRAF-V600 mutations, whereas models based on binary mutation status do not. Performance improvements generalize to other drugs, extending genetic indications for PIK3CA, ERBB2, EGFR, PARP1, and ABL1 inhibitors. Introducing quantitative mutation features in drug response models increases performance and mechanistic understanding.
METHODS: Code and example datasets are available at https://github.com/pgwall/qms.
RESULTS: Here, we construct and evaluate a series of predictive models based on leading methods for quantitative mutation scoring. Such methods include VEST4 and CADD, which score the impact of a mutation on gene function, and CHASMplus, which scores the likelihood a mutation drives cancer. The resulting predictive models capture cellular responses to dabrafenib, which targets BRAF-V600 mutations, whereas models based on binary mutation status do not. Performance improvements generalize to other drugs, extending genetic indications for PIK3CA, ERBB2, EGFR, PARP1, and ABL1 inhibitors. Introducing quantitative mutation features in drug response models increases performance and mechanistic understanding.
METHODS: Code and example datasets are available at https://github.com/pgwall/qms.
摘要：
背景：预测癌症药物反应需要对整个肿瘤基因组中存在的许多突变进行全面评估。虽然目前的药物反应模型通常使用每个基因的二元突变/未突变指标，不是所有的基因突变都是相同的.
结果：这里，我们构建并评估了一系列基于领先的定量突变评分方法的预测模型。这些方法包括VEST4和CADD，对突变对基因功能的影响进行评分，和CHASMplus,对突变导致癌症的可能性进行评分。由此产生的预测模型捕获了细胞对dabrafenib的反应，针对BRAF-V600突变，而基于二元突变状态的模型则没有。性能改进推广到其他药物，扩展PIK3CA的遗传适应症，ERBB2,EGFR,PARP1和ABL1抑制剂。在药物反应模型中引入定量突变特征可提高性能和机理理解。
方法：代码和示例数据集可在https://github.com/pgwall/qms获得。
结果：这里，我们构建并评估了一系列基于领先的定量突变评分方法的预测模型。这些方法包括VEST4和CADD，对突变对基因功能的影响进行评分，和CHASMplus,对突变导致癌症的可能性进行评分。由此产生的预测模型捕获了细胞对dabrafenib的反应，针对BRAF-V600突变，而基于二元突变状态的模型则没有。性能改进推广到其他药物，扩展PIK3CA的遗传适应症，ERBB2,EGFR,PARP1和ABL1抑制剂。在药物反应模型中引入定量突变特征可提高性能和机理理解。
方法：代码和示例数据集可在https://github.com/pgwall/qms获得。
