Li Ding, Michael C. Wendl, Daniel C. Koboldt, Elaine R. Mardis; Analysis of next-generation genomic data in cancer: accomplishments and challenges. Hum Mol Genet 2010; 19 (R2): R188-R196. doi: 10.1093/hmg/ddq391
The application of next-generation sequencing technology has produced a transformation in cancer genomics, generating large data sets that can be analyzed in different ways to answer a multitude of questions about the genomic alterations associated with the disease. Analytical approaches can discover focused mutations such as substitutions and small insertion/deletions, large structural alterations and copy number events. As our capacity to produce such data for multiple cancers of the same type is improving, so are the demands to analyze multiple tumor genomes simultaneously growing. For example, pathway-based analyses that provide the full mutational impact on cellular protein networks and correlation analyses aimed at revealing causal relationships between genomic alterations and clinical presentations are both enabled. As the repertoire of data grows to include mRNA-seq, non-coding RNA-seq and methylation for multiple genomes, our challenge will be to intelligently integrate data types and genomes to produce a coherent picture of the genetic basis of cancer.