ReviewBioinformatics and cancer target discovery
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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