Abstract

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. Recent advances in sequencing technologies suggest that massively parallel sequencing may provide a feasible alternative to DNA microarrays for detecting copy-number alterations. Here, we present: (i) a statistical analysis of the power to detect copy-number alterations of a given size; (ii) SegSeq, an algorithm to identify chromosomal 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 ∼14 million aligned sequence reads from human cell lines has comparable power to detect events as the current generation of DNA microarrays and has over two-fold better precision for localizing breakpoints (typically, to within ∼1 kb).