Table_3_In silico Prioritization of Transporter–Drug Relationships From Drug Sensitivity Screens.XLSX

The interplay between drugs and cell metabolism is a key factor in determining both compound potency and toxicity. In particular, how and to what extent transmembrane transporters affect drug uptake and disposition is currently only partially understood. Most transporter proteins belong to two protein families: the ATP-Binding Cassette (ABC) transporter family, whose members are often involved in xenobiotic efflux and drug resistance, and the large and heterogeneous family of solute carriers (SLCs). We recently argued that SLCs are collectively a rather neglected gene group, with most of its members still poorly characterized, and thus likely to include many yet-to-be-discovered associations with drugs. We searched publicly available resources and literature to define the currently known set of drugs transported by ABCs or SLCs, which involved ∼500 drugs and more than 100 transporters. In order to extend this set, we then mined the largest publicly available pharmacogenomics dataset, which involves approximately 1,000 molecularly annotated cancer cell lines and their response to 265 anti-cancer compounds, and used regularized linear regression models (Elastic Net, LASSO) to predict drug responses based on SLC and ABC data (expression levels, SNVs, CNVs). The most predictive models included both known and previously unidentified associations between drugs and transporters. To our knowledge, this represents the first application of regularized linear regression to this set of genes, providing an extensive prioritization of potentially pharmacologically interesting interactions.

药物与细胞代谢之间的相互作用是决定化合物药效与毒性的关键因素。具体而言，跨膜转运蛋白如何以及在何种程度上影响药物摄取与处置，目前仍仅部分为人所了解。绝大多数转运蛋白隶属于两大蛋白家族：ATP结合盒（ATP-Binding Cassette, ABC）转运蛋白家族，其成员多参与外源性物质外排与药物耐药性形成；以及庞大且具有高度异质性的溶质载体（solute carriers, SLCs）家族。我们此前曾提出，SLCs整体上属于被严重忽视的基因类群，其多数成员的功能仍未得到充分表征，因此很可能包含诸多尚未被发现的药物相关关联。我们通过检索公开可用的资源与文献，确定了目前已知的由ABC或SLC转运的药物集合，涵盖约500种药物与100余种转运蛋白。为拓展该药物集合，我们随后挖掘了目前规模最大的公开药物基因组学数据集——该数据集包含约1000株经分子注释的癌细胞系及其对265种抗癌化合物的响应数据，并使用正则化线性回归模型（弹性网（Elastic Net）、套索回归（LASSO）），基于SLC与ABC的组学数据（基因表达水平、单核苷酸变异（Single Nucleotide Variants, SNVs）、拷贝数变异（Copy Number Variants, CNVs））预测药物响应。预测性能最优的模型同时涵盖了已知与此前未被发现的药物-转运蛋白关联。据我们所知，这是首次将正则化线性回归应用于该类基因集合的研究，实现了对潜在药理学相关相互作用的大规模优先级排序。