Cell signaling heterogeneity is modulated by both cell-intrinsic and -extrinsic mechanisms: An integrated approach to understanding targeted therapy

Abstract

During the last decade, our understanding of cancer cell signaling networks has significantly improved, leading to the development of various targeted therapies that have elicited profound but, unfortunately, short-lived responses. This is, in part, due to the fact that these targeted therapies ignore context and average out heterogeneity. Here, we present a mathematical framework that addresses the impact of signaling heterogeneity on targeted therapy outcomes. We employ a simplified oncogenic rat sarcoma (RAS)-driven mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase-protein kinase B (PI3K-AKT) signaling pathway in lung cancer as an experimental model system and develop a network model of the pathway. We measure how inhibition of the pathway modulates protein phosphorylation as well as cell viability under different microenvironmental conditions. Training the model on this data using Monte Carlo simulation results in a suite of in silico cells whose relative protein activities and cell viability match experimental observation. The calibrated model predicts distributional responses to kinase inhibitors and suggests drug resistance mechanisms that can be exploited in drug combination strategies. The suggested combination strategies are validated using in vitro experimental data. The validated in silico cells are further interrogated through an unsupervised clustering analysis and then integrated into a mathematical model of tumor growth in a homogeneous and resource-limited microenvironment. We assess posttreatment heterogeneity and predict vast differences across treatments with similar efficacy, further emphasizing that heterogeneity should modulate treatment strategies. The signaling model is also integrated into a hybrid cellular automata (HCA) model of tumor growth in a spatially heterogeneous microenvironment. As a proof of concept, we simulate tumor responses to targeted therapies in a spatially segregated tissue structure containing tumor and stroma (derived from patient tissue) and predict complex cell signaling responses that suggest a novel combination treatment strategy.

Patient-specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies

“…Considering a family of models allows us to account for cell signaling heterogeneity for both estimated parameters values and model predictions (Kim et al, 2018). This is currently taken into account when comparing models and when making predictions of efficacious therapy.…”

Multi-Omics Profiling of the Tumor Microenvironment: Paving the Way to Precision Immuno-Oncology

“…Mathematical models can unravel mechanisms of tumorigenesis and make predictions, which can be experimentally validated ( 70 , 71 ). Dynamic models have been used to describe intracellular signaling to understand resistance and suggest personalized therapies ( 72 – 74 ), and they can be extended to study also extra-cellular signaling ( 75 ). Cell-cell interactions can be modeled considering individual cells as agents, thus providing a spatial description of the system ( 76 ), or as black-boxes with a certain input-output behavior ( 77 ).…”

Using neural networks to bridge scales in cancer: Mapping signaling pathways to phenotypes

“…Our approach was also previously used to investigate the impact of the microenvironment on cancer growth and evolution (42)(43)(44)(45)(46), where the neural networks were embedded into an individual cell based model, hybrid cellular automata model, which allows for mutation in the cancer cells and subsequent selection by the microenvironment. A recent study utilizing similar approach highlighted the impact of targeted therapy on cell signaling heterogeneity (47).…”