A regularized functional regression model enabling transcriptome-wide dosage-dependent association study of cancer drug response.
A regularized functional regression model enabling transcriptome-wide dosage-dependent association study of cancer drug response.
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DOI：
10.1371/journal.pcbi.1008066
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发表时间：
2021-01
影响因子：
4.3
通讯作者：
Park J
中科院分区：
文献类型：
--
作者：
Koukouli E;Wang D;Dondelinger F;Park J
癌症治疗可能是高毒性的，并且通常只有一部分患者群体将从给定的治疗中受益。肿瘤基因组成在癌症药物敏感性中起重要作用。我们怀疑基因表达标记物可用作治疗选择或剂量调整的决策辅助。使用来自癌症药物敏感性基因组学（GDSC）项目的体外癌细胞系剂量反应和基因表达数据，我们建立了一个剂量变化回归模型。与现有方法不同，这使我们能够估计与基因表达的剂量依赖性关联。我们将转录组学特征作为剂量不变协变量纳入回归模型，并假设其效应随剂量水平平滑变化。使用两阶段变量选择算法（变量筛选，然后是惩罚回归）来识别与不同剂量的药物反应相关的遗传因素。我们评估我们的方法使用模拟研究的有效性，重点是选择的调整参数和交叉验证的预测精度评估。我们进一步将该模型应用于来自在不同剂量水平下应用于不同癌细胞系的五种BRAF靶向化合物的数据。我们强调了所选基因和药物反应之间的关联的剂量依赖性动力学，我们进行了途径富集分析，以表明所选基因在与肿瘤发生和DNA损伤反应相关的途径中发挥重要作用。肿瘤细胞系使科学家能够在实验室环境中测试抗癌药物。将细胞暴露于浓度增加的药物，并测量药物反应或存活细胞的量。通常，药物反应通过单个数字总结，例如50%细胞死亡时的浓度（IC 50）。为了避免依赖于这样的总结措施，我们采用了一种函数回归方法，将剂量-反应曲线作为输入，并使用它们来寻找药物反应的生物标志物。我们的方法的一个主要优点是它描述了生物标志物对药物反应的影响如何随药物剂量而变化。这对于确定最佳治疗剂量和预测未知药物-细胞系组合的药物反应曲线是有用的。我们的方法通过使用正则化来扩展到大量的生物标志物，并且与现有文献相比，通过在未经测试的剂量下考虑药物反应来选择信息量最大的基因。我们使用癌症药物敏感性基因组学项目的数据来鉴定其表达与药物反应相关的基因，从而证明了其价值。我们表明，选定的基因概括了先前的生物学知识，属于已知的癌症途径。
Cancer treatments can be highly toxic and frequently only a subset of the patient population will benefit from a given treatment. Tumour genetic makeup plays an important role in cancer drug sensitivity. We suspect that gene expression markers could be used as a decision aid for treatment selection or dosage tuning. Using in vitro cancer cell line dose-response and gene expression data from the Genomics of Drug Sensitivity in Cancer (GDSC) project, we build a dose-varying regression model. Unlike existing approaches, this allows us to estimate dosage-dependent associations with gene expression. We include the transcriptomic profiles as dose-invariant covariates into the regression model and assume that their effect varies smoothly over the dosage levels. A two-stage variable selection algorithm (variable screening followed by penalized regression) is used to identify genetic factors that are associated with drug response over the varying dosages. We evaluate the effectiveness of our method using simulation studies focusing on the choice of tuning parameters and cross-validation for predictive accuracy assessment. We further apply the model to data from five BRAF targeted compounds applied to different cancer cell lines under different dosage levels. We highlight the dosage-dependent dynamics of the associations between the selected genes and drug response, and we perform pathway enrichment analysis to show that the selected genes play an important role in pathways related to tumorigenesis and DNA damage response. Tumour cell lines allow scientists to test anticancer drugs in a laboratory environment. Cells are exposed to the drug in increasing concentrations, and the drug response, or amount of surviving cells, is measured. Generally, drug response is summarized via a single number such as the concentration at which 50% of the cells have died (IC50). To avoid relying on such summary measures, we adopted a functional regression approach that takes the dose-response curves as inputs, and uses them to find biomarkers of drug response. One major advantage of our approach is that it describes how the effect of a biomarker on the drug response changes with the drug dosage. This is useful for determining optimal treatment dosages and predicting drug response curves for unseen drug-cell line combinations. Our method scales to large numbers of biomarkers by using regularization and, in contrast with existing literature, selects the most informative genes by accounting for drug response at untested dosages. We demonstrate its value using data from the Genomics of Drug Sensitivity in Cancer project to identify genes whose expression is associated with drug response. We show that the selected genes recapitulate prior biological knowledge, and belong to known cancer pathways.