Elsevier

Drug Discovery Today

Volume 9, Issue 18, 15 September 2004, Pages 795-802
Drug Discovery Today

Review
Bioinformatics and cancer target discovery

https://doi.org/10.1016/S1359-6446(04)03224-6Get rights and content

Abstract

The convergence of genomic technologies and the development of drugs designed against specific molecular targets provides many opportunities for using bioinformatics to bridge the gap between biological knowledge and clinical therapy. Identifying genes that have properties similar to known targets is conceptually straightforward. Additionally, genes can be linked to cancer via recurrent genomic or genetic abnormalities. Finally, by integrating large and disparate datasets, gene-level distinctions can be made between the different biological states that the data represents. These bioinformatics approaches and their associated methodologies, which can be applied across a range of technologies, facilitate the rapid identification of new target leads for further experimental validation.

Section snippets

Sequence analysis as a broad-based selection method

Although a wide variety of protein classes are involved in cancer, targets of interest fall into three general categories: secreted proteins, cell surface receptors and markers, and intracellular kinases. Secreted and cell-surface proteins, such as VEGF and epidermal growth factor receptor (EGFR), are essential for intercellular communication, in particular signaling of cell proliferation and angiogenesis, and they are physically accessible to monoclonal antibodies, which have proven to be

Gene expression profiling using microarrays

Many techniques have been developed for using microarray gene expression data to study various facets of cancer biology. Here we address some of the more common computational approaches that have found applications in target discovery and refer the reader to a recent review [9] for a general overview of microarray technology, experimental design and data processing issues.

Microarray analytical approaches generally fall into two categories: supervised and unsupervised learning. Supervised

Digital expression profiling using EST and SAGE

Gene expression profiling is not necessarily synonymous with microarrays. ‘Digital expression’ based on either expressed sequence tags (ESTs) or serial analysis of gene expression (SAGE) is complementary to microarrays and can be just as powerful. Both EST-derived expression and SAGE are based on the principle that the frequency of sequence tags sampled from a pool of cDNAs is directly proportional to the expression level of the corresponding gene (see Figure 2). Digital expression offers three

Cancer association from recurrent DNA amplification

Part of the multi-step process of tumor formation is a period of genomic instability usually resulting in regions of genomic copy number increase. Oncogenes such as c-myc have long been known to be associated with regions of high copy number, and mapping such amplicons in tumor cells has become a common method for searching for new oncogenes or determining which known oncogenes might be contributing to a particular cancer type. The methods for detecting recurrent DNA amplifications have

Cancer gene finding from variant analysis

As a genetic disease, cancer arises due to the accumulation of mutations in crucial genes that influence cell proliferation, differentiation and death. Identification of mutated genes that are causally implicated in oncogenesis is an important aspect of target discovery. Either loss-of-function mutations in tumor suppressors like p53 [45, 46] or gain-of-function mutations in oncogenes like BRAF [47] can play promoting roles in oncogenesis. Identification of mutations that occur predominately in

Conclusion

As a genetic disease, cancer leaves a trail of genetic markers accompanying tumorigenesis and cancer progression. Somatic mutations, genomic instability and altered gene expression patterns all provide possible ways to distinguish cancer from normal cells, and such distinctions can help us develop therapies that specifically target cancer cells. As an enabling technology, bioinformatics has evolved in many ways that enable us not only to identify players in cancer pathways but also to

Acknowledgements

The authors would like to thank Colin Watanabe, Thomas Wu and Paul Polakis for critical review of the manuscript and Allison Bruce for assistance in preparing the figures.

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