Glioblastoma multiforme (GBM) is the deadliest and most frequently occurring malignant brain tumor in the U.S. Despite its high mortality, the complex and heterogenous nature of GBM has left the treatment landscape largely unchanged since 2005. Identifying synergistic treatment combinations that can simultaneously target multiple tumor vulnerabilities is a logical way forward, yet the sheer number of possible drug combinations complicates these efforts. With an average success rate of approximately 3.4%, average cost per enrolled patient of $59,500, and average time-to-market of 14 years for oncology drugs, using an empirical approach to test even a fraction of potential combinations represents an extremely costly and time-consuming proposition. The systems genetic analysis (SYGNAL) transcription pipeline provides an alternative to the “trial-and-error” drug development method by using integrated multiomics and clinical patient data to prioritize drug combinations based on causal and mechanistic inference. To estimate the potential value of SYGNAL’s ability to expedite the clinical trial process, we developed a decision-analytic framework incorporating cost-effectiveness and value-of-information analyses. The primary focus of this framework is on the differential number of trials required to identify a (cost-)effective drug combination using SYGNAL compared to the standard empirical approach. Analysis results will provide insight into the potential role that tools like SYGNAL can play in reversing the historical trend of increasing R&D cost per approved oncology drug by identifying and bringing synergistic drug combinations to market sooner.