While the advent of targeted therapies has promised to revolutionize the success of cancer treatment, a critical review of clinical response rates provides a sobering perspective on the challenge at hand. New levels of discovery innovation are urgently required to redress this enormous medical need. Focusing on primary imatinib resistance in chronic myeloid leukemia, we demonstrate that the systems‐based modeling and analysis of clinico‐molecular information from responder and non‐responder patients can provide unique molecular insights into the sources of therapy resistance. By then utilizing these models to prioritize therapeutic drug space, the strategy elaborates rational multi‐component therapies to potentially redress the issue. Moreover, by hypothesizing that co‐medications may perturb the activity of resistance model components, we utilize prescription information to “probe” the functional network in search of key resistance associated targets. Not only does this method provide a novel approach to the analysis of patient information, it also permits the repositioning of drugs from independent therapeutic areas within predicted combinatorial regimens. Taken together, our results provide an exciting new approach to the computational analysis of clinico‐molecular data, and suggest a future where rationally designed theranostic‐linked combination therapies could significantly address the non‐responder problem.

Citation Information: Mol Cancer Ther 2009;8(12 Suppl):A34.