Elsevier

Toxicology Letters

Volume 158, Issue 1, 28 July 2005, Pages 20-29
Toxicology Letters

A novel method for generation of signature networks as biomarkers from complex high throughput data

https://doi.org/10.1016/j.toxlet.2005.02.004Get rights and content

Abstract

Traditionally, gene signatures are statistically deduced from large gene expression and proteomics datasets and have been applied as an experimental molecular diagnostic technique that is sensitive to experimental design and statistical treatment. We have developed and applied the approach of “signature networks” which overcomes some of the drawbacks of clustering methods. We have demonstrated signature network assembly, functional analysis and logical operations on the networks that can be generated. In addition, we have used this technique in a proof of concept study to compare the effect of differential drug treatment using 4-hydroxytamoxifen and estrogen on the MCF-7 breast cancer cell line from a previously published study. We have shown that the two compounds can be differentiated by the networks of interacting genes. Both networks consist of a core module of genes including c-Fos as part of c-Fos/c-Jun heterodimer and c-Myc which is clearly visible. Using algorithms in our MetaCore™ software we are able to subtract the 4-hydroxytamoxifen and estrogen networks to further understand differences between these two treatments and show that the estrogen network is assembled around the core with other modules essential for all phases of the cell cycle. For example, Cyclin D1 is present in networks for the estrogen treated cells from two separate studies. These signature networks represent an approach to identify biomarkers and a general approach for discovering new relationships in complex high throughput toxicology data.

Section snippets

Data annotation and software programming

An interactive, manually annotated database was derived from literature publications on proteins and small molecules (MetaCore™, GeneGo, St. Joseph, MI). This was developed with an Oracle version 9.2.0.4 Standard Edition (Oracle, Redwood Shores, CA) based architecture for the representation of biological functionality and integration of functional, molecular, or clinical information (Bugrim et al., 2004). Functional processes are the core objects in the database which can be of a different

Results

The original 2-D hierarchical clustering at 0.5, 2, 4, 12 and 24 h time points showed both compounds caused nearly identical expression patterns at early time points, featuring induction of cell cycle progression genes (Hodges et al., 2003) (Fig. 1A). Ultimately, E2 treatment leads to cell proliferation, while OHT treated cells largely remain arrested at G0. An additional supervised analysis (applicable when the distinct phenotypes are well defined (Quackenbush, 2001)) revealed E2 induced

Discussion

Traditionally, the functional organization of a biological system has been described in terms of pathways. Since Hartwell et al. (1999) hypothesized that many biological functions are carried out by discrete modules of physically interacting proteins (Hartwell et al., 1999), searching for interacting proteins and networks has become a key use for the functional analysis of microarrays. It is well documented that the outcome of microarray analysis is largely dependent upon statistical procedures

Acknowledgements

Dr. Craig Giroux (Wayne State University) and Dr. Christopher Bradfield (McCardle Cancer Institute) are acknowledged for their support and discussions. Our colleagues at GeneGo, Drs. Sergey Andreyev, Svetlana Sorokina, Tatyana Serebrijskaya, Roman Zuev, Andrej Ryabov, Eugene Kirillov, Eugene A. Rakhmatulin are sincerely acknowledged for their continuing contributions to software development and data annotation.

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