Evaluating the molecule-based prediction of clinical drug responses in cancer
Evaluating the molecule-based prediction of clinical drug responses in cancer
复制标题
评估癌症临床药物反应的基于分子的预测。
DOI：
10.1093/bioinformatics/btw344
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发表时间：
2016-10-01
期刊：
影响因子：
5.8
通讯作者：
Gu, Jin
中科院分区：
文献类型：
--
作者：
Ding, Zijian;Zu, Songpeng;Gu, Jin
动机：基于分子的药物反应预测是精确肿瘤学的主要任务之一。最近，大规模的癌症基因组研究，如癌症基因组图谱(TCGA)，为评估分子数据对多种癌症类型的临床药物反应的预测作用提供了机会。结果：我们首次从TCGA收集药物治疗信息。有4种化疗药物的临床疗效记录超过180条。然后，我们开发了一个计算框架来评估基于分子的对四种药物临床反应的预测，并确定相应的分子签名。结果表明，在特定的癌症类型中，mRNA或miRNA的表达可以明显比随机分类器更好地预测药物反应。一些标志性基因参与药物反应相关的通路，如DNA修复通路中的DDB1和Notch信号通路中的DLL4。最后，我们将该框架应用于多种癌症类型的预测，发现基于miRNA表达的顺铂的预测性能得到了改善。临床药物反应数据和分子数据的综合分析为发现癌症的预测标记物提供了机会。这项研究为客观评价基于分子的临床药物反应预测提供了一个起点。
Motivation: Molecule-based prediction of drug response is one major task of precision oncology. Recently, large-scale cancer genomic studies, such as The Cancer Genome Atlas (TCGA), provide the opportunity to evaluate the predictive utility of molecular data for clinical drug responses in multiple cancer types.Results: Here, we first curated the drug treatment information from TCGA. Four chemotherapeutic drugs had more than 180 clinical response records. Then, we developed a computational framework to evaluate the molecule based predictions of clinical responses of the four drugs and to identify the corresponding molecular signatures. Results show that mRNA or miRNA expressions can predict drug responses significantly better than random classifiers in specific cancer types. A few signature genes are involved in drug response related pathways, such as DDB1 in DNA repair pathway and DLL4 in Notch signaling pathway. Finally, we applied the framework to predict responses across multiple cancer types and found that the prediction performances get improved for cisplatin based on miRNA expressions. Integrative analysis of clinical drug response data and molecular data offers opportunities for discovering predictive markers in cancer. This study provides a starting point to objectively evaluate the molecule-based predictions of clinical drug responses.