Model-based global sensitivity analysis as applied to identification of anti-cancer drug targets and biomarkers of drug resistance in the ErbB2/3 network

Abstract

High levels of variability in cancer-related cellular signalling networks and a lack of parameter identifiability in large-scale network models hamper translation of the results of modelling studies into the process of anti-cancer drug development. Recently global sensitivity analysis (GSA) has been recognised as a useful technique, capable of addressing the uncertainty of the model parameters and generating valid predictions on parametric sensitivities.Here we propose a novel implementation of model-based GSA specially designed to explore how multi-parametric network perturbations affect signal propagation through cancer-related networks. We use area-under-the-curve for time course of changes in phosphorylation of proteins as a characteristic for sensitivity analysis and rank network parameters with regard to their impact on the level of key cancer-related outputs, separating strong inhibitory from stimulatory effects. This allows interpretation of the results in terms which can incorporate the effects of potential anti-cancer drugs on targets and the associated biological markers of cancer. To illustrate the method we applied it to an ErbB signalling network model and explored the sensitivity profile of its key model readout, phosphorylated Akt, in the absence and presence of the ErbB2 inhibitor pertuzumab. The method successfully identified the parameters associated with elevation or suppression of Akt phosphorylation in the ErbB2/3 network. From analysis and comparison of the sensitivity profiles of pAkt in the absence and presence of targeted drugs we derived predictions of drug targets, cancer-related biomarkers and generated hypotheses for combinatorial therapy. Several key predictions have been confirmed in experiments using human ovarian carcinoma cell lines. We also compared GSA-derived predictions with the results of local sensitivity analysis and discuss the applicability of both methods. We propose that the developed GSA procedure can serve as a refining tool in combinatorial anti-cancer drug discovery.

Advocating the need of a systems biology approach for personalised prognosis and treatment of B-CLL patients

“…Note the different methods of sensitivity analysis used in different works [78,91,96,97]. Below we discuss the results of sensitivity analysis of PI3K/AKT pathway model carried in [96,97], which can be used to analyse drug targets within this network.…”

“…Sensitivity analysis of these models being a powerful method for analysing PI3K/AKT signaling in cancer was carried out in many works to identify fragile points in this pathway [78,91,96,97], to determine sensitivity of the pathway to different mutations involved [97], and to find promising drug targets [78,96,97]. Note the different methods of sensitivity analysis used in different works [78,91,96,97].…”

“…Development of large-scale models of the PI3K/AKT network, reported the response of the signaling network upon different cytokine stimuli [91] and elucidated the control mechanisms in AKT signaling cross talk with other pathways [92]. The application of the computational modeling of PI3K/AKT signaling in cancer research [93] is aimed at the description of activation signaling in different cancer cell lines [78], analysis of the roles of cancer-driven mutations in this pathway [78,94], modeling of the response of the proapoptotic signal to anticancer drugs [78,92], elucidation of mechanisms of drug resistance [95], identification of drug targets, [78,96], optimization of anticancer therapy, and also the development of combination therapy [78,97].…”

ATM in focus: A damage sensor and cancer target

“…Recently, several system biology attempts to elucidate the mechanism of drug resistance to chemotherapy in different cancer patients have been informative and uncovered underlying pathway alterations driving such drug resistance [177][178].…”

Quantification of Pathway Cross-talk Reveals Novel Synergistic Drug Combinations for Breast Cancer

“…Different approaches are available for predicting drug combinations for complex diseases, mostly including mathematical modeling, stochastic search techniques, as well as cell contextbased methods like global gene expression or targeted phosphoproteomics profiling (10)(11)(12)(13). More recently, network-based models have been proposed for identifying drug synergies and for examining the mechanisms of action of efficient combinations (14)(15)(16).…”