Computational cell fate modelling for discovery of rewiring in apoptotic network for enhanced cancer drug sensitivity

Meta-Dynamic Network Modelling for Biochemical Networks

ODE modelling requires accurate knowledge of parameter and state variable values to deliver accurate and robust predictions. Parameters and state variables, however, are rarely static and immutable entities, especially in a biological context. This observation undermines the predictions made by ODE models that rely on specific parameter and state variable values and limits the contexts in which their predictions remain accurate and useful. Meta-dynamic network (MDN) modelling is a technique that can be synergistically integrated into an ODE modelling pipeline to assist in overcoming these limitations. The core mechanic of MDN modelling is the generation of a large number of model instances, each with a unique set of parameters and/or state variable values, followed by the simulation of each to determine how parameter and state variable variation affects protein dynamics. This process reveals the range of possible protein dynamics for a given network topology. Since MDN modelling is integrated with traditional ODE modelling, it can also be used to investigate the underlying causal mechanics. This technique is particularly suited to the investigation of network behaviors in systems that are highly heterogenous or systems wherein the network properties can change over time. MDN is a collection of principles rather than a strict protocol, so in this chapter, we have introduced the core principles using an example, the Hippo-ERK crosstalk signalling network.

Regulation of dual specificity phosphatases in breast cancer during initial treatment with Herceptin: a Boolean model analysis

Background

25% of breast cancer patients suffer from aggressive HER2-positive tumours that are characterised by overexpression of the HER2 protein or by its increased tyrosine kinase activity. Herceptin is a major drug used to treat HER2 positive breast cancer. Understanding the molecular events that occur when breast cancer cells are exposed to Herceptin is therefore of significant importance. Dual specificity phosphatases (DUSPs) are central regulators of cell signalling that function downstream of HER2, but their role in the cellular response to Herceptin is mostly unknown. This study aims to model the initial effects of Herceptin exposure on DUSPs in HER2-positive breast cancer cells using Boolean modelling.

Results

We experimentally measured expression time courses of 21 different DUSPs between 0 and 24 h following Herceptin treatment of human MDA-MB-453 HER2-positive breast cancer cells. We clustered these time courses into patterns of similar dynamics over time. In parallel, we built a series of Boolean models representing the known regulatory mechanisms of DUSPs and then demonstrated that the dynamics predicted by the models is in agreement with the experimental data. Furthermore, we used the models to predict regulatory mechanisms of DUSPs, where these mechanisms were partially known.

Conclusions

Boolean modelling is a powerful technique to investigate and understand signalling pathways. We obtained an understanding of different regulatory pathways in breast cancer and new insights on how these signalling pathways are activated. This method can be generalized to other drugs and longer time courses to better understand how resistance to drugs develops in cancer cells over time.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0534-5) contains supplementary material, which is available to authorized users.

Predicting and Overcoming Chemotherapeutic Resistance in Breast Cancer

Our understanding of breast cancer and its therapeutic approach has improved greatly due to the advancement of molecular biology in recent years. Clinically, breast cancers are characterized into three basic types based on their immunohistochemical properties. They are triple-negative breast cancer, estrogen receptor (ER) and progesterone receptor (PR)-positive-HR positive breast cancer, and human epidermal growth factor receptor 2 (HER2)-positive breast cancer. Even though these subtypes have been characterized, assessment of a breast cancer's receptor status is still widely used to determine whether or not a targeted therapy could be applied. Moreover, drug resistance is common in all breast cancer types despite the different treatment modalities applied. The development of resistance to different therapeutics is not mutually exclusive. It seems that tumor could be resistant to multiple treatment strategies, such as being both chemoresistant and monoclonal antibody resistant. However, the underlying mechanisms are complicated and need further investigation. In this chapter, we aim to provide a brief review of the different types of breast cancer and their respective treatment strategies. We also review the possible mechanisms of potential drug resistance associated with each treatment type. We believe that a better understanding of the drug resistance mechanisms can lead to a more effective and efficient therapeutic success.

Knowledge-guided fuzzy logic modeling to infer cellular signaling networks from proteomic data

Modeling of signaling pathways is crucial for understanding and predicting cellular responses to drug treatments. However, canonical signaling pathways curated from literature are seldom context-specific and thus can hardly predict cell type-specific response to external perturbations; purely data-driven methods also have drawbacks such as limited biological interpretability. Therefore, hybrid methods that can integrate prior knowledge and real data for network inference are highly desirable. In this paper, we propose a knowledge-guided fuzzy logic network model to infer signaling pathways by exploiting both prior knowledge and time-series data. In particular, the dynamic time warping algorithm is employed to measure the goodness of fit between experimental and predicted data, so that our method can model temporally-ordered experimental observations. We evaluated the proposed method on a synthetic dataset and two real phosphoproteomic datasets. The experimental results demonstrate that our model can uncover drug-induced alterations in signaling pathways in cancer cells. Compared with existing hybrid models, our method can model feedback loops so that the dynamical mechanisms of signaling networks can be uncovered from time-series data. By calibrating generic models of signaling pathways against real data, our method supports precise predictions of context-specific anticancer drug effects, which is an important step towards precision medicine.

Generalized logical model based on network topology to capture the dynamical trends of cellular signaling pathways

Background

Cellular responses to extracellular perturbations require signaling pathways to capture and transmit the signals. However, the underlying molecular mechanisms of signal transduction are not yet fully understood, thus detailed and comprehensive models may not be available for all the signaling pathways. In particular, insufficient knowledge of parameters, which is a long-standing hindrance for quantitative kinetic modeling necessitates the use of parameter-free methods for modeling and simulation to capture dynamic properties of signaling pathways.

Results

We present a computational model that is able to simulate the graded responses to degradations, the sigmoidal biological relationships between signaling molecules and the effects of scheduled perturbations to the cells. The simulation results are validated using experimental data of protein phosphorylation, demonstrating that the proposed model is capable of capturing the main trend of protein activities during the process of signal transduction. Compared with existing simulators, our model has better performance on predicting the state transitions of signaling networks.

Conclusion

The proposed simulation tool provides a valuable resource for modeling cellular signaling pathways using a knowledge-based method.

Targeted Therapies for Triple-Negative Breast Cancer: Combating a Stubborn Disease

Triple-negative breast cancers (TNBCs) constitute a heterogeneous subtype of breast cancers that have a poor clinical outcome. Although no approved targeted therapy is available for TNBCs, molecular-profiling efforts have revealed promising molecular targets, with several candidate compounds having now entered clinical trials for TNBC patients. However, initial results remain modest, thereby highlighting challenges potentially involving intra- and intertumoral heterogeneity and acquisition of therapy resistance. We present a comprehensive review on emerging targeted therapies for treating TNBCs, including the promising approach of immunotherapy and the prognostic value of tumor-infiltrating lymphocytes. We discuss the impact of pathway rewiring in the acquisition of drug resistance, and the prospect of employing combination therapy strategies to overcome challenges towards identifying clinically-viable targeted treatment options for TNBC.