Abstract A13: A signaling pathway rationale for the design of combination therapies for cancer
Authors: Vera-Licona Paola , Zinovyev Andrei , Bonnet Eric , Kupperstein Inna , Kel-Margoulis Olga , Kel-Margoulis Alexander , Dubois Thierry , Tucker Gordon , Barillot Emmanuel 
Published: August 2014

Introduction: Cellular functions and activities are regulated by complex networks of signal transduction and regulatory pathways. Cancer diseases are attributed to abnormal behavior in these networks. Thus a critical problem in drug discovery and biomedical research is the identification of intervention strategies to repress a pathological behavior within these signal transduction networks while minimizing side-effects. Accordingly, combination or multicomponent interventions are necessary to cope with two of the fundamental challenges in targeted therapies: redundancy and multifunctionality of biological networks [1]. Furthermore, due to the inherent structural complexity of the networks in question, systemic and efficient methods to tackle such endeavor are necessary in particular when addressing large networks.

Materials and Methods: In this work, we introduce OCSANA (Optimal Combinations of Intervention Strategies for Network Analysis), freely available software for the efficient identification and prioritization of optimal combinations of intervention strategies to induce the blockage of signaling from specified source nodes to specified targets. In addition, OCSANA identifies effects with respect to specified off-target nodes (side effects) to further optimize the combinations of intervention points. Our method is purely based on the topological structure of the signaling pathways (signed directed graph structure). We implemented OCSANA within the Cytoscape's plugin BiNoM -Biological Network Manager [2], to facilitate the assembling, usage and analysis of signaling networks in standard systems biology formats (such as Systems Biology Markup Language [SBML] and Biological Pathway Exchange [BioPAX]).

Results: We have applied OCSANA to identify combination therapies from our cohort of human epidermal growth factor receptor overexpressing (Her2+) breast cancer tissues. We used transcriptome microarrays to compare Her2+ data with that obtained from normal breast tissue samples. We identified and assembled a signal transduction network of more than 2,500 nodes and 3,800 interactions that includes master regulators of the ERBB family pathways together with less expected molecular mechanisms, potentially involved in the molecular pathology of HER2+ breast cancer. With the assembled signal transduction network and with the aid of OCSANA, we tested in silico scenarios like those of the blocking effects of existent targeted therapies such as trastuzumab and lapatinib. We identified additional complementary combinations of therapeutic intervention points and validated our theoretical predictions with recently published literature.

Conclusions: This work represents a cancer systems biology approach aiming at the discovering of combination of intervention therapies from a systematic analysis of signal transduction pathways. The suitability of our approach is demonstrated by a couple of in silico predictions validated in recent literature.

Citation Format: Paola Vera-Licona, Andrei Zinovyev, Eric Bonnet, Inna Kupperstein, Olga Kel-Margoulis, Alexander Kel-Margoulis, Thierry Dubois, Gordon Tucker, Emmanuel Barillot. A signaling pathway rationale for the design of combination therapies for cancer [abstract]. In: Proceedings of the AACR Special Conference on Chemical Systems Biology: Assembling and Interrogating Computational Models of the Cancer Cell by Chemical Perturbations; 2012 Jun 27-30; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2012;72(13 Suppl):Abstract nr A13.