IEEE J Biomed Health Inform. 2021 Nov;25(11):4229-4237. doi: 10.1109/JBHI.2021.3100424. Epub 2021 Nov 5.
The identification of mutation markers and the selection of appropriate treatment for patients with specific genome mutations are important steps in the development of targeted therapies and the realization of precision medicine for human cancers. To investigate the baseline characteristics of drug sensitivity markers and develop computational methods of mutation effect prediction, we presented a manually curated online-based database of mutation Markers for anti-Cancer drug Sensitivity (dbMCS). Currently, dbMCS contains 1271 mutations and 4427 mutation-disease-drug associations (3151 and 1276 for sensitivity and resistance, respectively) with their PubMed indexed articles. By comparing the mutations in dbMCS with the putative neutral polymorphisms, we investigated the characteristics of drug sensitivity markers. We found that the mutation markers tend to significantly impact on high-conservative regions both in DNA sequences and protein domains. And some of them presented pleiotropic effects depending on the tumor context, appearing concurrently in the sensitivity and resistance categories. In addition, we preliminarily explored the machine learning-based methods for identifying mutation markers of anti-cancer drug sensitivity and produced optimistic results, which suggests that a reliable dataset may provide new insights and essential clues for future cancer pharmacogenomics studies. dbMCS is available at http://bioinfo.aielab.cc/dbMCS/.
鉴定突变标记物并为具有特定基因组突变的患者选择合适的治疗方法是开发针对癌症的靶向治疗和实现精准医学的重要步骤。为了研究药物敏感性标记物的基线特征并开发突变效应预测的计算方法，我们提出了一个基于人工编辑的在线癌症药物敏感性突变标记物数据库（dbMCS）。目前，dbMCS 包含 1271 个突变和 4427 个突变-疾病-药物关联（分别有 3151 个和 1276 个与敏感性和耐药性相关），并附有其 PubMed 索引文章。通过将 dbMCS 中的突变与假定的中性多态性进行比较，我们研究了药物敏感性标记物的特征。我们发现，药物敏感性标记物倾向于在 DNA 序列和蛋白质结构域中高度保守的区域产生显著影响。其中一些标记物根据肿瘤背景呈现出多效性，同时出现在敏感性和耐药性类别中。此外，我们初步探索了基于机器学习的识别抗癌药物敏感性突变标记物的方法，并取得了乐观的结果，这表明可靠的数据集可能为未来的癌症药物基因组学研究提供新的见解和重要线索。dbMCS 可在 http://bioinfo.aielab.cc/dbMCS/ 上获取。
