Li Ruowang, Kim Dokyoon, Wheeler Heather E, Dudek Scott M, Dolan M Eileen, Ritchie Marylyn D
Bioinformatics and Genomics program, Pennsylvania State University, University Park, Pennsylvania, USA.
Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
Pharmacogenomics J. 2019 Apr;19(2):178-190. doi: 10.1038/s41397-018-0024-6. Epub 2018 May 25.
Identifying genetic variants associated with chemotherapeutic induced toxicity is an important step towards personalized treatment of cancer patients. However, annotating and interpreting the associated genetic variants remains challenging because each associated variant is a surrogate for many other variants in the same region. The issue is further complicated when investigating patterns of associated variants with multiple drugs. In this study, we used biological knowledge to annotate and compare genetic variants associated with cellular sensitivity to mechanistically distinct chemotherapeutic drugs, including platinating agents (cisplatin, carboplatin), capecitabine, cytarabine, and paclitaxel. The most significantly associated SNPs from genome wide association studies of cellular sensitivity to each drug in lymphoblastoid cell lines derived from populations of European (CEU) and African (YRI) descent were analyzed for their enrichment in biological pathways and processes. We annotated genetic variants using higher-level biological annotations in efforts to group variants into more interpretable biological modules. Using the higher-level annotations, we observed distinct biological modules associated with cell line populations as well as classes of chemotherapeutic drugs. We also integrated genetic variants and gene expression variables to build predictive models for chemotherapeutic drug cytotoxicity and prioritized the network models based on the enrichment of DNA regulatory data. Several biological annotations, often encompassing different SNPs, were replicated in independent datasets. By using biological knowledge and DNA regulatory information, we propose a novel approach for jointly analyzing genetic variants associated with multiple chemotherapeutic drugs.
识别与化疗诱导毒性相关的基因变异是癌症患者个性化治疗的重要一步。然而，注释和解释相关基因变异仍然具有挑战性，因为每个相关变异都是同一区域许多其他变异的替代物。在研究多种药物相关变异的模式时，问题会更加复杂。在本研究中，我们利用生物学知识注释并比较了与对机制不同的化疗药物（包括铂类药物（顺铂、卡铂）、卡培他滨、阿糖胞苷和紫杉醇）细胞敏感性相关的基因变异。分析了来自欧洲（CEU）和非洲（YRI）血统人群的淋巴母细胞系中每种药物细胞敏感性全基因组关联研究中最显著相关的单核苷酸多态性（SNP）在生物途径和过程中的富集情况。我们使用更高级别的生物学注释来注释基因变异，以便将变异分组为更易于解释的生物模块。利用这些更高级别的注释，我们观察到与细胞系群体以及化疗药物类别相关的不同生物模块。我们还整合了基因变异和基因表达变量，以建立化疗药物细胞毒性的预测模型，并根据DNA调控数据的富集情况对网络模型进行优先级排序。几个通常包含不同SNP的生物学注释在独立数据集中得到了重复验证。通过利用生物学知识和DNA调控信息，我们提出了一种联合分析与多种化疗药物相关的基因变异的新方法。
