Abstract Understanding the complexity of a cancer signaling network contributed to the discovery of potential targets for therapeutic intervention. Mathematical and computational models have been developed and investigated to predict the molecular dynamics during cancer development and drug treatment. Recent studies analyzing large-scale patient data, however, showed that genetic alterations are dramatically different among patients even for the same type of cancer, and that different therapeutic strategies should be employed for the patients. To resolve this problem using Boolean network modeling and analysis, we have integrated genome-wide data of glioblastoma patients from TCGA database and constructed an individualized network model for each patient. We defined three attractor states that represent different cell fates: proliferation, cell cycle arrest, and apoptosis. We then applied the information of patient-specific genetic alterations to the Boolean model by adjusting the interaction weights of the mutated molecules, and investigated the change of attractors for each patient. Finally, we have investigated the targeted therapy for each patient by performing perturbation analysis of the individualized network model to find the optimal drug combination. Together, our study provides a new insight into the impact of genetic alterations on the drug responses and the patient-specific therapeutic strategies. Acknowledgements: This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea Government, the Ministry of Science, ICT & Future Planning (2014R1A2A1A10052404, 2013M3A9A7046303, and 2010-0017662) and by the KUSTAR-KAIST Institute, Korea, under the R&D program supervised by the KAIST. Citation Format: Hwang-Yeol Lee, Kwang-Hyun Cho. The development of an individualized Boolean network model for targeted anticancer therapy of glioblastoma. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B2-28.