Abstract
Cancer is a complex disease involving multiple genomic alterations that disrupt the dynamic response of signaling networks. The heterogeneous nature of cancer, which results in highly variable drug response, is a major obstacle to developing effective cancer therapy. Previous studies of cancer therapeutic response mostly focus on static analysis of genome-wide alterations, thus they are unable to unravel the dynamic, network-specific origin of variation. Here we present a network dynamics-based approach to integrate cancer genomics with dynamics of biological network for drug response prediction and design of drug combination. We select the p53 network as an example and analyze its cancer-specific state transition dynamics under distinct anticancer drug treatments by attractor landscape analysis. Our results not only enable stratification of cancer into distinct drug response groups, but also reveal network-specific drug targets that maximize p53 network-mediated cell death, providing a basis to design combinatorial therapeutic strategies for distinct cancer genomic subtypes.
Highlights
Cancer is a complex disease involving multiple genomic alterations that disrupt the dynamic response of signaling networks
Cancer cell can be viewed as a rewired network due to endogenous perturbations resulting from genomic alterations, which subsequently leads to modifications of signaling networks and their dynamic responses[11,15,16,17,18]
Cancer cell lines are described by differentially wired networks with distinct network topology resulted from their genomic alterations
Summary
Cancer is a complex disease involving multiple genomic alterations that disrupt the dynamic response of signaling networks. The acquired large genomic data sets were used to establish a model to predict a relationship between drug sensitivity and genomic alterations of specific cancer cells as well as to identify response biomarkers to cancer therapeutics[6,10] This approach is primarily based on analyzing genomic alterations at the molecular level and may help preclinical stratification of patients for more effective anticancer drug treatment. We extended the attractor landscape analysis of network to a large cancer cell panel by combining it with comprehensive genomic alteration profiles of these cancer cells to characterize cancer subtypes and developed a computational framework to evaluate drug efficacies and synergistic effects as a function of genotype. Our study established a novel computational framework to predict anticancer drug response based on cancer genotypes, which could be employed to design more effective, cancer-specific combinatorial therapy
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