Abstract B2-40: Many-attractor models of signaling dynamics and heterogeneity in acute myeloid leukemia gene regulatory networks

Introduction and purpose of the study: Significant progress has been made in biological network reconstruction methods in recent years, with much emphasis placed on investigating the topology of gene regulatory networks (GRNs). While studying a network9s topology can provide useful biological information, a dynamical model for how genes and proteins exchange information is needed in order to understand and predict a cell9s response to stimuli such as drugs which inhibit the activity of a protein. Two thirds of patients with acute myeloid leukemia (AML) have an unfavorable prognosis. This is in part due to the high tumor cell heterogeneity in AML, which is often the ultimate cause of drug resistance and relapse in patients. Mathematical models of signaling dynamics in AML which account for the heterogeneity of clonally derived cells in a tumor could be valuable new tools for designing effective, original therapies. Heterogeneity can be described in signaling by defining nonlinear models with multiple attractor states. Novel computational methods: We present two mathematical signaling models that encode real gene expression measurements as attractors in a directed AML GRN. Gene expression profiles obtained from RNA-seq in normal progenitor and AML cells data are used to define a set of robust gene expression profiles corresponding to different clonal states. The first model is Boolean, and is based on an asymmetric Hopfield model with multiple memory patterns (Szedlak et al. 2014). The second model uses continuous expression values in which the elements of the GRN interact like oscillators characterized by multi-stability. The equations of motion for the oscillators are defined in such a way that the expression profiles for the RNA-seq data match the multiple steady states of the signaling network. Summary of new data: We examine the sensitivity of normal and cancer attractors to single gene perturbations and to real gene-inhibiting drugs in both models. For the network topology we use a recently developed network that is specific for AML, specifically AML 2.3 (Ong et al. 2014). Attractors are defined using RNA-seq data from AML cells and hematopoietic controls (Macrae et al. 2013). According to the Hopfield model, we found that ELAVL1, GATA1, IRX5, MYOG, RXRA, and TFEB are genes in AML which, when inhibited, strongly destabilize the cancer attractor. In the continuum model, we explicitly included interactions between drugs currently in AML clinical trials and their targets. Focusing on known targets of lenalidomide and sorafenib, we found that FLT1, KDR, and PDGFRB are associated with the strongest sensitivity to perturbations. In addition, we found that besides the direct targets of these drugs (BRAF, RAF1, FLT4, KDR, FLT3, PDGFRB, KIT, FGFR1, RET, FLT1 for sorafenib, and TNF, TNFSF11, CDH5, PTGS2, CRBN for lenalidomide), RPS18, RPS11, RPS3, and TPT1 are also strongly indirectly down-regulated. Conclusions: We developed two novel methods of network signaling that encode gene expression patterns of cells under varying conditions as attractor states. The methods were applied to AML to identify a set of genes whose perturbation is associated with strong deviations from cancer conditions. Citation Format: Anthony D. Szedlak, Giovanni Paternostro, Carlo Piermarocchi. Many-attractor models of signaling dynamics and heterogeneity in acute myeloid leukemia gene regulatory networks. [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 B2-40.