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The development of techniques for oncogenomic analyses such as array comparative genomic hybrid- ization, messenger RNA expression arrays and mutational screens have come to the fore in modern cancer research. Studies utilizing these techniques are able to highlight panels of genes that are altered in cancer. However, these candidate cancer genes must then be scrutinized to reveal whether they contribute to oncogenesis or are coin- cidental and non-causative. We present a computa- tional method for the prioritization of candidate (i) proto-oncogenes and (ii) tumour suppressor genes from oncogenomic experiments. We constructed computational classifiers using different combina- tions of sequence and functional data including sequence conservation, protein domains and inter- actions, and regulatory data. We found that these classifiers are able to distinguish between known cancer genes and other human genes. Furthermore, the classifiers also discriminate candidate cancer genes from a recent mutational screen from other human genes. We provide a web-based facility through which cancer biologists may access our results and we propose computational cancer gene classification as a useful method of prioritizing can- didate cancer genes identified in oncogenomic studies.

Prioritization of candidate cancer genes—an aid to oncogenomic studies