Correlation between Oncogenic Mutations and Parameter Sensitivity of the Apoptosis Pathway Model

Ouyang Q (2014) Correlation between Oncogenic Mutations and Parameter Sensitivity of the Apoptosis Pathway Model. PLoS Comput Biol 10(1): e1003451. doi:10.1371/journal.pcbi.1003451 Correlation between Oncogenic Mutations and Parameter Sensitivity of the Apoptosis Pathway Model Jia Chen 0 Haicen Yue 0 Qi Ouyang 0 Sheng Zhong, University of California, San Diego, United States of America 0 1 Center for Quantitative Biology and Peking-Tsinghua Center for Life Sciences at Peking University , Beijing , China , 2 School of Physics and the State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, Peking University , Beijing , China One of the major breakthroughs in oncogenesis research in recent years is the discovery that, in most patients, oncogenic mutations are concentrated in a few core biological functional pathways. This discovery indicates that oncogenic mechanisms are highly related to the dynamics of biologic regulatory networks, which govern the behaviour of functional pathways. Here, we propose that oncogenic mutations found in different biological functional pathways are closely related to parameter sensitivity of the corresponding networks. To test this hypothesis, we focus on the DNA damage-induced apoptotic pathway-the most important safeguard against oncogenesis. We first built the regulatory network that governs the apoptosis pathway, and then translated the network into dynamics equations. Using sensitivity analysis of the network parameters and comparing the results with cancer gene mutation spectra, we found that parameters that significantly affect the bifurcation point correspond to high-frequency oncogenic mutations. This result shows that the position of the bifurcation point is a better measure of the functionality of a biological network than gene expression levels of certain key proteins. It further demonstrates the suitability of applying systems-level analysis to biological networks as opposed to studying genes or proteins in isolation. - Funding: The National Science Foundation of China (11074009, 10721463), www.nsfc.gov.cn/Portal0/default152.htm; the Ministry of Science and Technology of China (2009CB918500,2012AA02A702), www.most.gov.cn. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. Cancer is one of the most important diseases affecting human health today [1]. Although cancer is considered a genetic disease [2], with a variety of oncogenes and tumour suppressor genes identified, the specific genomic alterations vary wildly between and within cancer types. In 2008, three high-throughput cancer genomic studies reported that cancer gene mutations are concentrated in a limited number of core cellular pathways and regulatory processes [35]. This discovery suggests that oncogenesis is highly related to the dynamics of biologic regulatory networks, which govern the behaviour of functional pathways. Clearly, to understand the mechanisms underlying oncogenesis, we need to take a systems and dynamics approach. A number of studies have proposed a network-based approach to investigate oncogenesis. For example, Torkamani and Schork identified functionally related gene modules targeted by somatic mutation in cancer [6]; Cerami et al. proposed an automated network analysis approach to identify candidate oncogenic processes [7]. A more recent approach by Stites et al. sought to explain mutations in Ras pathway, which are commonly found in cancer, by investigating the steady state concentrations of cellular proteins in parameters changes [8]. In this paper, we propose a new way to identify high-frequency gene mutations in cancer cells. We reason that because gene mutations may affect the activities of their corresponding proteins in a biological regulatory network, they can be considered as perturbations of the systems dynamics. Therefore, those mutations that qualitatively affect biological network function should correspond to mutation hot spots in cancer. From a dynamics point of view, a qualitative change in a system relates to bifurcationsoncogenic mutations should therefore significantly affect certain bifurcation points. One of the hallmarks of cancer is evasion of apoptosis; in fact p53 mutations are found in most human cancers [9]. We therefore chose the DNA damage-induced p53-centered apoptosis pathway, as an example, to evaluate our hypothesis. We evaluated the sensitivity of bifurcation points to different network parameters, and compared the results with the cancer gene mutation spectrum. We found that parameters that significantly affect the bifurcation points corresponded to high-frequency oncogenic mutations. This study investigates the mutation spectrum found in cancer cells and provides a useful tool for predicting oncogenic mutations. Network description and model building We focused on the apoptotic pathway that responds to sustained DNA damage, induced by the chemotherapeutic compound, etoposide [10,11]. A recent study showed that while low-dose etoposide induces oscillations in p53 levels, caspase3 levels remain low, and most cells survive; in contrast, high-dose etoposide induces a monotonic increase in p53 concentration, followed by a rapid increase in caspase3 with most cells undergoing Among complex genetic diseases affecting humans, cancer is a major cause of death. In 2008, a genome-wide analysis of hundreds of tumour samples showed that oncogenic mutations are concentrated in a few core functional pathways, revealing a new conceptual framework for cancer biology research, where the role of oncogenic mutations and oncogenic mechanisms are addressed from a network perspective. We therefore propose a new way of identifying high-frequency gene mutations in cancer: gene mutations may affect their corresponding proteins activity in the biological regulatory network and can be considered as perturbations of the dynamical system. Therefore, mutations that induce qualitative changes in biological networks should correspond to high-frequency mutations in cancer. This concept can help us identify and understand the function of genes that play an important role in oncogenesis, thereby allowing targeted and effective design of gene-based therapy in cancer. apoptosis [11]. This experiment further justifies the use of p53 in our model. A schematic of the corresponding regulatory network, which is a modification of the p53 DNA damage response network established and analysed by Li et al. [12], is shown in Figure 1. Nuclear p53 induces mdm2 transcription, while MDM2 antagonizes p53 by promoting multistep ubiquitination and proteasome-dependent degradation of p53 [13,14]. In unstressed cells, p53 is kept at a low concentration by its negative regulator MDM2. DNA damage reduces the binding affinity between p53 and MDM2 by inducing phosphorylation of p53 and MD (...truncated)