a n a ly s i s Recent therapeutic successes have renewed interest in drug combinations, but experimental screening approaches are costly and often identify only small numbers of synergistic combinations. The DREAM consortium launched an open challenge to foster the development of in silico methods to computationally rank 91 compound pairs, from the most synergistic to the most antagonistic, based on gene-expression profiles of human B cells treated with individual compounds at multiple time points and concentrations. Using scoring metrics based on experimental dose-response curves, we assessed 32 methods (31 community-generated approaches and SynGen), four of which performed significantly better than random guessing. We highlight similarities between the methods. Although the accuracy of predictions was not optimal, we find that computational prediction of compound-pair activity is possible, and that community challenges can be useful to advance the field of in silico compound-synergy prediction. Recent success in the study of synergistic combinations, such as the use of CHK1 inhibitors in combination with several DNA damaging agents 1 or of the PARP inhibitor olaparib in combination with the PI3K inhibitor BKM120 (ref. 2), have generated significant interest in the systematic screening of compound pairs to identify synergistic pairs for combination therapy. Compound synergy can be measured by multiple endpoints, including reducing or delaying the development of resistance to treatment 3 (for instance by abrogating the emergence of resistant clones 4-6 ), improving overall survival 7, 8 or lowering toxicity by decreasing individual compound dose 9 . Similarly, at the molecular level, synergistic interactions can be implemented by several distinct mechanisms. For instance, a compound may sensitize cells to another compound by regulating its absorption and distribution, modulating the cell's growth properties 10 , inhibiting compound degradation 11 , inhibiting pathways that induce resistance 6 or reducing the other compound's toxicity 12 . When used in combination, two compounds may elicit one of three distinct responses: (i) additive, when the combined effect is equivalent to the sum of the independent effects; (ii) synergistic, when the combined effect is greater than additive; and (iii) antagonistic, when the combined effect is smaller than additive. The goal of combination therapy is thus to attain a synergistic or at least an additive yet complementary effect. Most approaches to identify synergistic compound pairs are still exploratory 13, 14 . In cancer research, synergy assays are usually performed by treating cell lines in vitro with all possible compound combinations from a diverse library or with candidate combinations selected on the basis of mechanistic principles. Unfortunately, such experimental screens impose severe limits on the practical size of compound diversity libraries. Computational methods to predict compound synergy can potentially complement high-throughput synergy screens, but the few that have been published lack rigorous experimental validation or are appropriate only for compounds that modulate well-studied molecular pathways 15 or that are equivalent to previously established combinations 16 . Current algorithms are not generalizable to arbitrary compound combinations unless molecular profile data following compound-pair treatment are available 17 , which is clearly impractical. Thus, there is a need for new methods to predict compound synergy from molecular profiles of single compound activity, as well as for assays designed to objectively and systematically evaluate the accuracy and specificity of such predictions. To address this issue, the DREAM Challenges initiative (an effort run by a community of researchers that poses fundamental questions in systems biology and translational science in the form of a community computational challenge to predict the activity of pairs of compounds
Although these methods performed significantly better than chance, none was near-optimal REF .
A community computational challenge to predict the activity of pairs of compounds
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Biology Medicine