Abstract 5097: Clinical trial outcomes for drug combination therapies can be predicted by modeling independent action using cancer cell line screens

Background: In many cancer settings, it is evident that combination drug therapies show increased efficacy compared to monotherapies. However, the development of new combination therapies is slowed by the fact that it is infeasible to experimentally evaluate the vast number of possible drug combinations. In an effort to overcome this problem, we developed a computational method which uses monotherapy screening data in cell lines to estimate the efficacy of combinations of two or more chemotherapy drugs. When validated against published phase 3 clinical cancer trial results, our method is capable of predicting clinical trial success or failure with a high degree of sensitivity and specificity. Methods and Results: We processed existing high-throughput in vitro drug screening datasets (i.e. GDSC and CTRPv2) to obtain fitted dose response curves for more than 600,000 drug-cell line pairs, including data for 695 unique compounds and 1347 unique cell lines. We then used this dataset to develop a method of estimating the efficacy of combinations of two or more drugs. While previous efforts to predict drug combination efficacy have largely focused on dose-agnostic estimates of drug synergy, we modeled dose-dependent estimates of independent drug action, where each patient treated with a drug combination only derives benefit from the single drug in the combination to which they are most sensitive. For method validation, we systematically searched clinicaltrials.gov to identify completed, phase 3 cancer clinical trials that evaluated drug combinations for which we could estimate efficacy and which had at least 50 patients per trial arm. Of the >9,000 clinical trials considered, 37 trials (11 successful and 26 unsuccessful) met our inclusion criteria. Using clinically determined plasma concentrations for each drug, we then predicted relative efficacy for each of the experimental drug combinations versus the control group therapies in these trials. These predictions were clustered into two groups (predicted high benefit vs low benefit) using k-means clustering, and these clusters were compared to whether or not the clinical trials were considered a success by the original study investigators. Our method correctly classified 84% of the trials, with a sensitivity of 73% and a specificity of 88%. Furthermore, efficacy predictions for combinations containing erlotinib were dependent on EGFR mutation status, suggesting that this method may be useful for identifying molecular subtypes which are sensitive to a given combination therapy. Conclusion: Clinically meaningful predictions of drug combination efficacy can be generated from single-agent, in vitro drug screens under the assumption of independent drug action. This enables efficacy predictions to be made for >150,000 2-drug combinations and millions of 3- and 4-drug combinations using the existing GDSC and CTRPv2 datasets. Citation Format: Alexander Ling, R. Stephanie Huang. Clinical trial outcomes for drug combination therapies can be predicted by modeling independent action using cancer cell line screens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 5097.