Chen Ruzhen, Wang Xun, Deng Xinru, Chen Lanhui, Liu Zhongyang, Li Dong
State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China.
Front Pharmacol. 2022 Jun 20;13:904909. doi: 10.3389/fphar.2022.904909. eCollection 2022.
Due to cancer heterogeneity, only some patients can benefit from drug therapy. The personalized drug usage is important for improving the treatment response rate of cancer patients. The value of the transcriptome of patients has been recently demonstrated in guiding personalized drug use, and the Connectivity Map (CMAP) is a reliable computational approach for drug recommendation. However, there is still no personalized drug recommendation tool based on transcriptomic profiles of patients and CMAP. To fill this gap, here, we proposed such a feasible workflow and a user-friendly R package-Cancer-Personalized Drug Recommendation (CPDR). CPDR has three features. 1) It identifies the individual disease signature by using the patient subgroup with transcriptomic profiles similar to those of the input patient. 2) Transcriptomic profile purification is supported for the subgroup with high infiltration of non-cancerous cells. 3) It supports drug efficacy assessment using drug sensitivity data on cancer cell lines. We demonstrated the workflow of CPDR with the aid of a colorectal cancer dataset from GEO and performed the validation of drug efficacy. We further assessed the performance of CPDR by a pancreatic cancer dataset with clinical response to gemcitabine. The results showed that CPDR can recommend promising therapeutic agents for the individual patient. The CPDR R package is available at https://github.com/AllenSpike/CPDR.
由于癌症的异质性，只有部分患者能从药物治疗中获益。个性化用药对于提高癌症患者的治疗有效率至关重要。患者转录组的价值最近已在指导个性化用药方面得到证实，而连通性图谱（CMAP）是一种可靠的药物推荐计算方法。然而，目前仍没有基于患者转录组谱和CMAP的个性化药物推荐工具。为填补这一空白，在此我们提出了这样一种可行的工作流程以及一个用户友好的R包——癌症个性化药物推荐（CPDR）。CPDR有三个特点。1）它通过使用转录组谱与输入患者相似的患者亚组来识别个体疾病特征。2）支持对非癌细胞高浸润的亚组进行转录组谱纯化。3）它支持使用癌细胞系的药物敏感性数据进行药物疗效评估。我们借助来自GEO的结直肠癌数据集展示了CPDR的工作流程，并进行了药物疗效验证。我们进一步通过一个对吉西他滨有临床反应的胰腺癌数据集评估了CPDR的性能。结果表明，CPDR可以为个体患者推荐有前景的治疗药物。CPDR R包可在https://github.com/AllenSpike/CPDR获取。
