|Publication Type||Web Article|
|Year of Publication||2008|
|Authors||Derek Y. Chiang, Gad Getz, DJXZSCCRCNMMEBL|
|Keywords||copy-number alterations, next-generation sequencing, segmentation algorithm, tumor genome characterization|
Cancer results from somatic alterations in key genes, including point mutations, copy number alterations and structural rearrangements. A powerful way to discover cancer-causing genes is to identify genomic regions that show recurrent copy-number alterations (gains and losses) in tumor genomes. The analysis of copy-number alterations has traditionally been performed by using DNA microarrays, but recent advances in sequencing technologies suggest that random shotgun sequencing may provide a powerful alternative. Here, we present (i) a statistical analysis of the power to detect copy-number alterations of a given size; (ii) an algorithm to identify breakpoints, using massively parallel sequence data; and (iii) analysis of experimental data from three matched pairs of tumor and normal cell lines. We show that a collection of ~10 million shotgun sequence reads has comparable power to detect events as the current generation of DNA microarrays and has considerably better power to localize breakpoints (typically, to within ~1 kb). Our experimental analysis confirms these analytical predictions. As sequencing costs continue to drop, massively parallel sequencing will be increasingly feasible and will likely become the preferred approach for analyzing cancer genomes.