McCusker Jamie Patricia, Dumontier Michel, Yan Rui, He Sylvia, Dordick Jonathan S, McGuinness Deborah L
Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, USA.
Stanford Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA.
PeerJ Comput Sci. 2017 Feb 13;3:e106. doi: 10.7717/peerj-cs.106. eCollection 2017.
Metastatic cutaneous melanoma is an aggressive skin cancer with some progression-slowing treatments but no known cure. The omics data explosion has created many possible drug candidates; however, filtering criteria remain challenging, and systems biology approaches have become fragmented with many disconnected databases. Using drug, protein and disease interactions, we built an evidence-weighted knowledge graph of integrated interactions. Our knowledge graph-based system, ReDrugS, can be used via an application programming interface or web interface, and has generated 25 high-quality melanoma drug candidates. We show that probabilistic analysis of systems biology graphs increases drug candidate quality compared to non-probabilistic methods. Four of the 25 candidates are novel therapies, three of which have been tested with other cancers. All other candidates have current or completed clinical trials, or have been studied in in vivo or in vitro. This approach can be used to identify candidate therapies for use in research or personalized medicine.
转移性皮肤黑色素瘤是一种侵袭性皮肤癌，虽有一些减缓病情进展的治疗方法，但尚无已知的治愈方法。组学数据的爆炸式增长产生了许多潜在的候选药物；然而，筛选标准仍然具有挑战性，而且系统生物学方法已变得支离破碎，有许多互不关联的数据库。利用药物、蛋白质和疾病之间的相互作用，我们构建了一个综合相互作用的证据加权知识图谱。我们基于知识图谱的系统ReDrugS可通过应用程序编程接口或网络接口使用，并已生成25种高质量的黑色素瘤候选药物。我们表明，与非概率方法相比，系统生物学图谱的概率分析提高了候选药物的质量。这25种候选药物中有4种是新型疗法，其中3种已在其他癌症中进行了测试。所有其他候选药物都有正在进行或已完成的临床试验，或者已在体内或体外进行了研究。这种方法可用于识别用于研究或个性化医疗的候选疗法。
