The identification of synergistic chemotherapeutic agents from a large pool of candidates is highly challenging.
Researchers 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 they show a probability concordance of 0.78 compared with 0.61 obtained with the previous best algorithm.
Authors confirm 63.6% of the 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.