Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer

High-throughput drug screening has facilitated the discovery of drug combinations in cancer. Many existing studies adopted a full matrix design, aiming for the characterization of drug pair effects for cancer cells. However, the full matrix design may be suboptimal as it requires a drug pair to be combined at multiple concentrations in a full factorial manner. Furthermore, many of the computational tools assess only the synergy but not the sensitivity of drug combinations, which might lead to false positive discoveries. We proposed a novel cross design to enable a more cost-effective and simultaneous testing of drug combination sensitivity and synergy. We developed a drug combination sensitivity score (CSS) to determine the sensitivity of a drug pair, and showed that the CSS is highly reproducible between the replicates and thus supported its usage as a robust metric. We further showed that CSS can be predicted using machine learning approaches which determined the top pharmaco-features to cluster cancer cell lines based on their drug combination sensitivity profiles. To assess the degree of drug interactions using the cross design, we developed an S synergy score based on the difference between the drug combination and the single drug dose-response curves. We showed that the S score is able to detect true synergistic and antagonistic drug combinations at an accuracy level comparable to that using the full matrix design. Taken together, we showed that the cross design coupled with the CSS sensitivity and S synergy scoring methods may provide a robust and accurate characterization of both drug combination sensitivity and synergy levels, with minimal experimental materials required. Our experimental-computational approach could be utilized as an efficient pipeline for improving the discovery rate in high-throughput drug combination screening, particularly for primary patient samples which are difficult to obtain. Author summary Cancer is one of the main causes of death worldwide. Although new treatment strategies have been achieved, they still have limited efficacy as cancer cells can easily develop drug resistance. To achieve more sustainable therapies to treat cancer, we need multi-targeted drug combinations that can inhibit cancer cells more effectively and synergistically. However, the increasing number of possible drug combinations makes a full matrix design unfeasible, even with automated drug screening instruments. Therefore, we proposed a novel cross design to access drug combinations more efficiently. We further developed a drug combination sensitivity score (CSS) that is tailored for the cross design to quantify the efficacy of a drug combination. Using public datasets, we showed that the CSS is a robust metric and highly predictive with an accuracy comparable to the experimental replicates. We also developed a CSS-based synergy score to assess the degree of drug interaction and showed its capability to correctly identify synergistic and antagonistic drug combinations. Taken together, we showed that the cross design and its scoring methods allow a more systematic and cost-effective evaluation of drug combinations. The proposed experimental and computational techniques are expected to be widely applicable in the field of drug combination discovery.