Combining genomic and network characteristics for extended capability in predicting synergistic drugs for cancer

By Yi Sun, Zhen Sheng, Chao Ma, Kailin Tang, Ruixin Zhu, Zhuanbin Wu, Ruling Shen, Jun Feng, Dingfeng Wu, Danyi Huang and 4 more. 2015

115 citations recorded by the source corpus.

The identification of synergistic chemotherapeutic agents from a large pool of candidates is highly challenging. Here, we present a Ranking-system of Anti-Cancer Synergy (RACS) that combines features of targeting networks and transcriptomic profiles, and validate it on three types of cancer. Using data on human β-cell lymphoma from the Dialogue for Reverse Engineering Assessments and Methods consortium we show a probability concordance of 0.78 compared with 0.61 obtained with the previous best algorithm. We confirm 63.6% of our breast cancer predictions through experiment and literature, including four strong synergistic pairs. Further in vivo screening in a zebrafish MCF7 xenograft model confirms one prediction with strong synergy and low toxicity. Validation using A549 lung cancer cells shows similar results. Thus, RACS can significantly improve drug synergy prediction and markedly reduce the experimental prescreening of existing drugs for repurposing to cancer treatment, although the molecular mechanism underlying particular interactions remains unknown. Predicting combinations of chemotherapeutic drugs that act synergistically is challenging. Here the authors take a computational approach to predict synergistic pairs, validate novel pairs using several cancer cell lines, and assess toxicity in a zebrafish xenograft model.

Subfields: Computational Theory and Mathematics, Molecular Biology

Citations in the corpus

• Network-based prediction of drug combinations (2019, unknown): “To be sure, several machine learning-based “ black-box ” models have been developed to predict drug combinations 10 – 12 , offering a modest increase in accuracy 10 over random guesses 11 .”
• Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities (2018, unknown): “Drug-drug interactions are defined through the concepts of synergy and antagonism [283, 284] and are quantified biologically by measuring the dose-effect curves [285, 286] or cell viability [287, 288, 289, 290, 280, 291, 292].”
• Review of Drug Repositioning Approaches and Resources (2018, unknown): “In this section, two types of network-based approaches are reviewed: networkbased cluster approaches [22-24, 26] and network-based propagation approaches [27, 29, 31, 32, 50].”