Abstract
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.
Highlights
Despite great advances in the understanding of cancer, there remains a major challenge to develop more effective anti-cancer treatments
Drug combination sensitivity score are supported by the FIMM-EMBL PhD program scholarship
We further developed a drug combination sensitivity score (CSS) that is tailored for the cross design to quantify the efficacy of a drug combination
Summary
Despite great advances in the understanding of cancer, there remains a major challenge to develop more effective anti-cancer treatments. To reach durable clinical responses, cancer patients who relapse and become refractory to standard chemotherapy need novel multi-targeted drug combinations which can effectively overcome the emergence of drug resistance [2,3,4]. A potential drug combination should achieve therapeutic efficacy at reduced dosages, and minimize the toxicity and other side effects associated with high doses of single drugs [5,6]. Two important properties for a drug combination must be evaluated: sensitivity and synergy. Sensitivity of a drug combination is defined as the level of treatment response, usually measured in the unit of percentage inhibition of cell viability or growth. Synergy of a drug combination is referred to the degree of drug interactions that contributes to the drug combination sensitivity independent of the single drug effects [7]
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