Abstract 1309: Network as a biomarker to predict drug candidates: Mapping driver dysregulated target networks onto pharmacologic data-derived drug networks identifies cardiac glycosides as the potential treatment of Group 3 medulloblastomas
Authors: Lei Huang, Sarah Garrett Injac, Xiaonan Li, Adesina Adekunle, Hong Zhao, Ching Lau, Stephen Wong
Journal: Cancer Research (2018)

Abstract Background: Medulloblastoma is a heterogeneous group of tumors that consists of four subtypes with distinct genomic signatures. Two of these subgroups are defined by a single dysfunctional signaling pathway, WNT and SHH respectively, which has raised the prospect of taking a rational target based approach to the development of new therapies. Conversely, the other 2 sub-types, Groups 3 and 4, which compose 60-65% of total medulloblastoma cases, are associated with much more complicated genetic, epigenetic and genomic contents and display significant intragroup heterogeneity. In addition, Group 3 are associated with the worst prognosis among all the subgroups and are frequently metastatic at presentation which makes the need for novel approaches to drug discovery for these tumors particularly acute. Results: We developed a computational systems biology method that incorporates novel algorithms for driver signaling network identification (DSNI) and drug functional network-(DFN) based drug repositioning to integrate multiple types of genomics profiles for Group 3 MB patients with human cancer signaling pathways resources and gene expression profiles of 1,309 drugs in CMAP with drug structure information and effects. By applying the DSNI-DFN method on Groups 3 MB data we identified five members of the cardiac glycoside family, a group of Na/K pump inhibitors best known for their role in the treatment of heart failure, as potential inhibitors of driver dysregulated network of the Groups 3 MB. We were subsequently able to validate this finding in cell culture where all 5 compounds lead to significant growth inhibition of Group 3 derived medulloblastoma cell lines. Members of the cardiac glycoside family showed efficacy in vivo in animal models inhibiting proliferation. Digoxin significantly prolonged survival in both PDOX models of Group 3 medulloblastoma (ICb-2055) at a dose which resulted in plasma trough levels similar to those targeted in cardiac patients receiving digoxin. Digoxin-treated mice (n=10) had a median survival of 180 days compared to a median survival of 102 in untreated controls (n=8) (P&amp;lt;0.0001). Furthermore, in the Group 3 model digoxin showed survival extension comparable to ionizing radiation which represents a major component of the current standard care for medulloblastoma. Taken together, these data demonstrate that digoxin has a strong in vivo anti-tumor effect against preformed PDOX tumors derived from Groups 3 medulloblastoma. Conclusions: Our findings represent both an exciting potential new therapy for Group 3 medulloblastoma and validation of an approach that can be applied to identify driver dysregulated networks and predict drugs for a wide variety of genomically complex tumors. Citation Format: Lei Huang, Sarah Garrett Injac, Xiaonan Li, Adesina Adekunle, Hong Zhao, Ching Lau, Stephen Wong. Network as a biomarker to predict drug candidates: Mapping driver dysregulated target networks onto pharmacologic data-derived drug networks identifies cardiac glycosides as the potential treatment of Group 3 medulloblastomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1309.