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

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.

Biomolecular Network-Based Synergistic Drug Combination Discovery

“…Several significantly different features between synergistic drug combinations and random drug combinations have been identified. The average shortest distance in PPI network of targets between synergistic drug combinations is significantly smaller than that in random drug combinations [68, 73, 79]. Also, dissimilarity of drug chemical structure for individual drug in drug combination is significantly associated with drug synergistic effect [80].…”

“…Sun et al constructed a model called Ranking-system of Anticancer Synergy (RACS) based on semi-supervised learning which was used to rank drug pairs according to their similarity to the labeled samples in a specified multifeature space [68]. Firstly, they performed feature selection to identify significantly different features between labeled samples and the unlabeled samples.…”

In-Silico Methodologies for Cancer Multidrug Optimization

“…They require a large amount of input data for model training compared with other methods such as statistical modelling methods or search algorithms. Integration of pharmacological and omic data types play key roles in successful machine learning methods prediction [12,16]. Therefore, machine learning methods could be an effective tool for the drug combination problem.…”

“…Sun et al [16] applied a semi-supervised method, namely Ranking-system of Anti-Cancer Synergy (RACS). Drug pairs were represented by a set of 14 pharmacological and genomic features different between labelled samples and unlabelled samples.…”

“…AstraZeneca-Sanger Drug Combination DREAM Challenge was launched using 85 cancer cell lines and 11,759 drug combination screening for 118 drugs [8]. The predictive models were designed to differentiate synergistic, additive and antagonistic combinations and predict new synergistic combinations in silico [9][10][11][12][13][14][15][16][17].…”