Identifying biomarkers predictive of cancer cell response to drug treatment constitutes one of the main challenges in precision oncology. Recent large-scale cancer pharmacogenomic studies have opened new avenues of research to develop predictive biomarkers by profiling thousands of human cancer cell lines at the molecular level and screening them with hundreds of approved drugs and experimental chemical compounds. Many studies have leveraged these data to build predictive models of response using various statistical and machine learning methods. However, a common pitfall to these methods is the lack of interpretability as to how they make predictions, hindering the clinical translation of these models. To alleviate this issue, we used the recent logic modeling approach to develop a new machine learning pipeline that explores the space of bimodally expressed genes in multiple large in vitro pharmacogenomic studies and builds multivariate, nonlinear, yet interpretable logic-based models predictive of drug response. The performance of this approach was showcased in a compendium of the three largest in vitro pharmacogenomic datasets to build robust and interpretable models for 101 drugs that span 17 drug classes with high validation rates in independent datasets. These results along with in vivo and clinical validation support a better translation of gene expression biomarkers between model systems using bimodal gene expression. Significance: A new machine learning pipeline exploits the bimodality of gene expression to provide a reliable set of candidate predictive biomarkers with a high potential for clinical translatability.

中文翻译：

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癌症患者的双峰基因表达为药物敏感性提供了可解释的生物标志物

识别预测癌细胞对药物治疗反应的生物标志物是精准肿瘤学的主要挑战之一。最近的大规模癌症药物基因组学研究开辟了新的研究途径，通过在分子水平上分析数千种人类癌细胞系并用数百种批准的药物和实验化合物对其进行筛选，开发预测性生物标志物。许多研究利用这些数据，使用各种统计和机器学习方法来构建反应预测模型。然而，这些方法的一个常见缺陷是它们如何进行预测缺乏可解释性，阻碍了这些模型的临床转化。为了缓解这个问题，我们使用最近的逻辑建模方法开发了一种新的机器学习管道，该管道在多个大型体外药物基因组研究中探索双峰表达基因的空间，并构建预测药物反应的多变量、非线性但可解释的基于逻辑的模型。该方法的性能在三个最大的体外药物基因组数据集概要中得到展示，为涵盖 17 个药物类别的 101 种药物构建稳健且可解释的模型，在独立数据集中具有高验证率。这些结果以及体内和临床验证支持使用双峰基因表达在模型系统之间更好地翻译基因表达生物标志物。意义：