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Open Access Research

Preliminary evidence of different selection pressures on cancer cells as compared to normal tissues

Katie Ovens1 and Christopher Naugler2*

Author Affiliations

1 Bachelor of Health Sciences Program, Faculty of Medicine, Room G503, O’Brien Centre for the BHSc, 3330 Hospital Drive NW, Calgary, AB, T2N 4N1, Canada

2 Calgary Laboratory Services and Department of Pathology and Laboratory Medicine, University of Calgary, C414, Diagnostic and Scientific Centre, 9, 3535 Research Road NW, Calgary, AB, T2L 2K8, Canada

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Theoretical Biology and Medical Modelling 2012, 9:44  doi:10.1186/1742-4682-9-44

Published: 12 November 2012

Abstract

Background

Cancer is characterized by both a high mutation rate as well as high rates of cell division and cell death. We postulate that these conditions will result in the eventual mutational inactivation of genes not essential to the survival of the cancer cell, while mutations in essential genes will be eliminated by natural selection leaving molecular signatures of selection in genes required for survival and reproduction. By looking for signatures of natural selection in the genomes of cancer cells, it should therefore be possible to determine which genes have been essential for the development of a particular cancer.

Methods

We provide a proof of principle test of this idea by applying a test of neutrality (Nei-Gojobori Z-test of selection) to 139 cancer-related nucleotide sequences obtained from GenBank representing 46 cancer-derived genes.

Results

Among cancer associated sequences, 10 genes showed molecular evidence of selection. Of these 10 genes, four showed molecular evidence of selection in non-cancer transcripts. Among non-cancer associated sequences, eight genes showed molecular evidence of selection, with four of these also showing selection in the cancer associated sequences.

Conclusions

These results provide preliminary evidence that the same genes may experience different selection pressures within normal and cancer tissues. Application of this technique could identify genes under unique selection pressure in cancer tissues and thereby indicate possible targets for therapeutic intervention.