Abstract IA06: A precision medicine framework for brain tumors

The current paradigm in precision cancer medicine is predicated on the identification of actionable genetic alterations that can be targeted with high-affinity inhibitors. However, less than 25% of aggressive malignancies present with actionable alterations and not all of these respond to targeted therapy. Additionally, many of those that initially respond eventually relapse to drug-resistant disease, which is ultimately lethal. As a result, there is a critical and impending need to complement the actionable mutation paradigm with approaches targeting critical and more universal tumor dependencies that are not necessarily represented by genes harboring activating mutations (i.e., non-oncogene dependencies (1, 2)). We have developed methods for the systematic identification and validation of tumor specific dependencies (or master regulators), including both individual genes (3) and synergistic gene combinations (4-6) that can be targeted pharmacologically. We will discuss the use of these methodologies for the identification of such tumor dependencies in human glioblastoma, including from tumor and from single cell profiles, as well as the discovery of the upstream genetic determinants of master regulators aberrant activity (7). Finally, we will discuss a new precision medicine study that is enrolling 260 patients across 6 distinct rare or incurable tumors, including glioblastoma and anaplastic meningioma, to identify the optimal drug or drug-combination for treatment based on matching patient specific tumor dependencies with drug mechanism of action elucidated de novo from large scale perturbational data. References: 1. Schreiber SL et al. Towards patient-based cancer therapeutics. Nat Biotechnol. 2010;28(9):904-6. PMCID: 2939009. 2. Luo J et al. Principles of cancer therapy: oncogene and non-oncogene addiction. Cell. 2009;136(5):823-37. PMCID: 2894612. 3. Chudnovsky Y et al. ZFHX4 interacts with the NuRD core member CHD4 and regulates the glioblastoma tumor-initiating cell state. Cell Rep. 2014;6(2):313-24. PMCID: 4041390. 4. Aytes A et al. Cross-species regulatory network analysis identifies a synergistic interaction between FOXM1 and CENPF that drives prostate cancer malignancy. Cancer Cell. 2014;25(5):638-51. PMCID: 4051317. 5. Carro MS et al. The transcriptional network for mesenchymal transformation of brain tumours. Nature. 2010;463(7279):318-25. PMCID: 4011561. 6. Piovan E, Yu J, Tosello V, Herranz D, Ambesi-Impiombato A, Da Silva AC, et al. Direct reversal of glucocorticoid resistance by AKT inhibition in acute lymphoblastic leukemia. Cancer Cell. 2013;24(6):766-76. PMCID: 3878658. 7. Chen JC, Alvarez MJ, Talos F, Dhruv H, Rieckhof GE, Iyer A, et al. Identification of Causal Genetic Drivers of Human Disease through Systems-Level Analysis of Regulatory Networks. Cell. 2014;159(2):402-14. PMCID: 4194029. Citation Format: Andrea Califano. A precision medicine framework for brain tumors. [abstract]. In: Proceedings of the AACR Special Conference: Advances in Brain Cancer Research; May 27-30, 2015; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2015;75(23 Suppl):Abstract nr IA06.