Pathway and Network Approaches for Identification of Cancer Signature Markers from Omics Data

The advancement of high throughput omic technologies during the past few years has made it possible to perform many complex assays in a much shorter time than the traditional approaches. The rapid accumulation and wide availability of omic data generated by these technologies offer great opportunities to unravel disease mechanisms, but also presents significant challenges to extract knowledge from such massive data and to evaluate the findings. To address these challenges, a number of pathway and network based approaches have been introduced. This review article evaluates these methods and discusses their application in cancer biomarker discovery using hepatocellular carcinoma (HCC) as an example

Analysis of signal transducers using flow cytometry is useful for detection of contractive and fluctuating signals

　Investigation of signal transduction mechanisms is important for development of therapy and understanding complex life systems. In this study, to establish a novel recognition method of signal transduction pathways, we investigated signal transducers using flow cytometry.　The flow cytometric measurement shows a mean phosphorylation level (mean of fluorescence intensity, MFI) and a deviation of the phosphorylation level (coefficient variation, CV) in a cluster of cells. As a model of signal pathways, Jurkat cells (T cell leukemia cell line) were stimulated with interleukin-21 or interferon-α, and signal transducers and activators of transcription (STATs) and extracellular signal-regulated kinase (ERK) 1/2 were measured using flow cytometry. Furthermore, peripheral blood was stimulated, and then various signal transducers of the lymphocytes and neutrophils were analyzed with MFI and CV.　After the stimulation, the increase of STATs MFI induced a temporal change of CV. On the other hand, the decrease of ERK1/2 phosphorylation accompanied the sustained increase of CV. Finally, we classified the signaling characters into five types using a combination of MFI and CV. These findings contribute to an explanation of the known relationship between signal transducers and stimulants on each cell subset. Therefore, this method may be useful to discover a causal relationship between stimulants and signal transducers in complex systems

Integrating Bayesian variable selection with Modular Response Analysis to infer biochemical network topology

Background: Recent advancements in genetics and proteomics have led to the acquisition of large quantitative data sets. However, the use of these data to reverse engineer biochemical networks has remained a challenging problem. Many methods have been proposed to infer biochemical network topologies from different types of biological data. Here, we focus on unraveling network topologies from steady state responses of biochemical networks to successive experimental perturbations. Results: We propose a computational algorithm which combines a deterministic network inference method termed Modular Response Analysis (MRA) and a statistical model selection algorithm called Bayesian Variable Selection, to infer functional interactions in cellular signaling pathways and gene regulatory networks. It can be used to identify interactions among individual molecules involved in a biochemical pathway or reveal how different functional modules of a biological network interact with each other to exchange information. In cases where not all network components are known, our method reveals functional interactions which are not direct but correspond to the interaction routes through unknown elements. Using computer simulated perturbation responses of signaling pathways and gene regulatory networks from the DREAM challenge, we demonstrate that the proposed method is robust against noise and scalable to large networks. We also show that our method can infer network topologies using incomplete perturbation datasets. Consequently, we have used this algorithm to explore the ERBB regulated G1/S transition pathway in certain breast cancer cells to understand the molecular mechanisms which cause these cells to become drug resistant. The algorithm successfully inferred many well characterized interactions of this pathway by analyzing experimentally obtained perturbation data. Additionally, it identified some molecular interactions which promote drug resistance in breast cancer cells. Conclusions: The proposed algorithm provides a robust, scalable and cost effective solution for inferring network topologies from biological data. It can potentially be applied to explore novel pathways which play important roles in life threatening disease like cancer.Science Foundation Ireland under Grant No. 06/CE/B1129 and European Union Grant PRIMES No. FP7-HEALTH-2011-278568.Deposited by bulk impor

Combining Network Modeling and Experimental Approaches to Predict Drug Combination Responses

Cancer is a lethal disease and complex at multiple levels of cell biology. Despite many advances in treatments, many patients do not respond to therapy. This is owing to the complexity of cancer-genetic variability due to mutations, the multi-variate biochemical networks within which drug targets reside and existence and plasticity of multiple cell states. It is generally understood that a combination of drugs is a way to address the multi-faceted drivers of cancer and drug resistance. However, the sheer number of testable combinations and challenges in matching patients to appropriate combination treatments are major issues. Here, we first present a general method of network inference which can be applied to infer biological networks. We apply this method to infer different kinds of networks in biological levels where cancer complexity resides-a biochemical network, gene expression and cell state transitions. Next, we focus our attention on glioblastoma and with pharmacological and biological considerations, obtain a ranked list of important drug targets in glioblastoms. We perform drug dose response experiments for 22 blood brain barrier penetrant drugs against 3 glioblastoma cell lines. These methods and experimental results inform a construction of a temporal cell state model to predict and experimentally validate combination treatments for certain drugs. We improve an experimental method to perform high throughput western blots and apply the method to discover biochemical interactions among some important proteins involved in temporal cell state transitions. Lastly, we illustrate a method to investigate potential resistance mechanisms in genome scale proteomic data. We hope that methods and results presented here can be adapted and improved upon to help in the discovery of biochemical interactions, capturing cell state transitions and ultimately help predict effective combination therapies for cancer