Journal Article

Expanding the computational toolbox for mining cancer genomes

Raphael, Benjamin J., Wendl, Michael C., Ding, Li, McMichael, Joshua F.

2014

Overview

Key Points High-throughput sequencing of cancer genomes, exomes and transcriptomes has enabled the identification of many novel somatic aberrations, and has provided new insights into cancer biology and new therapeutic targets. Computational and statistical tools are necessary for interpreting the large and complex data sets that result from high-throughput sequencing approaches. Mature software for detecting single-nucleotide variants, insertions and deletions, copy-number aberrations, structural variants and gene fusions in cancer genomes are now available. Additional challenges remain in increasing the sensitivity and specificity of these algorithms. Computational techniques are essential for assigning priority to somatic aberrations that are likely to be functional for further experimental validation. Two common approaches are to predict functional impact of individual mutations using prior biological knowledge and to identify recurrently mutated genes, pathways and networks across many samples. Algorithms to infer the clonal structure and evolutionary history of a tumour from ultra-deep sequencing data have recently been introduced. Applications of these techniques have shown that minority mutations in primary tumours may increase to majority in relapse or metastasis. Sequencing of cancer genomes has shown a wide range of specialized mutational processes, including kataegis, chromothripsis and chromoplexy that result in rapid genomic changes and punctuated tumour evolution. The field of cancer genomics has been transformed by recent advances in sequencing and the development of new computational methods. This Review outlines the available cancer genomics software and describes recent insights gained from the application of these tools. High-throughput DNA sequencing has revolutionized the study of cancer genomics with numerous discoveries that are relevant to cancer diagnosis and treatment. The latest sequencing and analysis methods have successfully identified somatic alterations, including single-nucleotide variants, insertions and deletions, copy-number aberrations, structural variants and gene fusions. Additional computational techniques have proved useful for defining the mutations, genes and molecular networks that drive diverse cancer phenotypes and that determine clonal architectures in tumour samples. Collectively, these tools have advanced the study of genomic, transcriptomic and epigenomic alterations in cancer, and their association to clinical properties. Here, we review cancer genomics software and the insights that have been gained from their application.

• Publisher: Nature Publishing Group UK,Nature Publishing Group
• Subject: 631/1647/2217 / 631/208/514/1948 / 631/208/69 / 692/699/67 / Agriculture / Animal Genetics and Genomics / Animals / Biomedicine / Cancer / Cancer Research / Computer science / Data mining / Data Mining - methods / DNA methylation / Gene Function / Genetic aspects / Genomes / Genomics / Genomics - methods / High-Throughput Nucleotide Sequencing / Human Genetics / Humans / Medical diagnosis / Medical research / Metastasis / Methods / Mutation / Neoplasms - genetics / Neoplasms - metabolism / review-article / Signal Transduction / Software