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

Yi Sun, Zhen Sheng, Chao Ma, Kailin Tang, Ruixin Zhu, Zhuanbin Wu, Ruling Shen, Jun Feng, Dingfeng Wu, Danyi Huang, Dandan Huang, Jian Fei (), Qi Liu () and Zhiwei Cao ()
Additional contact information
Yi Sun: School of Life Sciences and Technology, Tongji University
Zhen Sheng: School of Life Sciences and Technology, Tongji University
Chao Ma: School of Life Sciences and Technology, Tongji University
Kailin Tang: School of Life Sciences and Technology, Tongji University
Ruixin Zhu: School of Life Sciences and Technology, Tongji University
Zhuanbin Wu: Shanghai Research Center for Model Organisms
Ruling Shen: School of Life Sciences and Technology, Tongji University
Jun Feng: School of Life Sciences and Technology, Tongji University
Dingfeng Wu: School of Life Sciences and Technology, Tongji University
Danyi Huang: School of Life Sciences and Technology, Tongji University
Dandan Huang: School of Life Sciences and Technology, Tongji University
Jian Fei: School of Life Sciences and Technology, Tongji University
Qi Liu: School of Life Sciences and Technology, Tongji University
Zhiwei Cao: School of Life Sciences and Technology, Tongji University

Nature Communications, 2015, vol. 6, issue 1, 1-10

Abstract: Abstract 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.

Date: 2015
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/ncomms9481 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms9481

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/ncomms9481

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().