Article series: Computational tools

Expanding the computational toolbox for mining cancer genomes

Journal name:
Nature Reviews Genetics
Volume:
15,
Pages:
556–570
Year published:
DOI:
doi:10.1038/nrg3767
Published online

Abstract

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.

At a glance

Figures

  1. Sample procurement, sequencing and analysis roadmap.
    Figure 1: Sample procurement, sequencing and analysis roadmap.

    a | Most cancer genomic investigations sequence the genome of a tumour sample from a primary or metastatic lesion, starting with a nonspecific 'global' sample pooled from a biopsy specimen or resection. As the spatial distribution of any resident subclones is not known a priori, it will become increasingly common to sequence specific regions from a tumour section separately. In the limit, single-cell sequencing can also be carried out on nuclei sorted by flow cytometry to assess cellular diversity. b | Tumour and adjacent healthy tissue samples are sequenced using high-throughput methods, such as whole-genome sequencing (WGS), exome sequencing and RNA sequencing (RNA-seq). After alignment, a range of detection tools identifies both small alterations (such as single-nucleotide variants (SNVs), and insertions and deletions (indels)) and large alterations (such as copy-number aberrations (CNAs), structural variants (SVs) and gene fusions), which are then annotated and analysed individually (Level I) — for example, for likely functional implications — and collectively (Level II) — for example, to identify relevant gene pathways and networks. CHASM, CancerSpecific High-throughput Annotation of Somatic Mutations; CREST, clipping reveals structure; Dendrix, De Novo Driver Exclusivity; GASV, geometric analysis of structural variants; GATK, Genome Analysis Toolkit; Genome STRiP, Genome STRucture In Populations; MEMo, Mutual Exclusivity Modules in cancer; SIFT, sorting intolerant from tolerant; SNP, single-nucleotide polymorphism; TieDIE, Tied Diffusion Through Interacting Events; TIGRA, targeted iterative graph routing assembler; VEP, Variant Effect Predictor.

  2. Biological factors relevant to assessing significantly mutated genes in cancer.
    Figure 2: Biological factors relevant to assessing significantly mutated genes in cancer.

    Genomic analyses establish mutation frequencies of genes and help to characterize background mutation rates (BMRs). Specific mutation hot spots have been found in the various cancer types. Other factors such as gene length, expression level and replication timing have also been shown to affect the BMR of a gene. As gene expression level and replication timing are correlated, both are shown on the x axis. State-of-the-art tools, such as MuSiC and MutSig, give proper consideration to these and many other factors — for example, transition versus transversion frequency — in determining the significantly mutated genes (SMGs) that contribute substantially to cancer initiation and progression.

  3. Significantly mutated genes, pathways and networks.
    Figure 3: Significantly mutated genes, pathways and networks.

    Given the mutational status of genes across several patients, one can distinguish driver mutations from passenger mutations using several strategies. Single-gene tests determine whether the observed number of samples having a mutation in the gene is significantly greater than that expected under an appropriate null model. Pathway or gene-set approaches examine whether multiple genes in pre-defined sets — as obtained, for example, from curated databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO) and the Molecular Signatures Database (MSigDB) — have more mutations than expected. These tests are biased to the prior knowledge of gene sets in these databases, but the numbers of tests are fairly small, and the risks associated with type I error therefore tend to be manageable. Conversely, network approaches rely only on knowledge of known protein–protein or protein–DNA interactions — such as those in the iRefIndex, high-quality interactomes (HINT), BioGRID and search tool for the retrieval of interacting genes/proteins (STRING) databases — in examining combinations of mutations on whole-genome interaction networks, for example, using the heat diffusion process. As these approaches are unbiased, it is possible to infer novel combinations of genes that are relevant to cancer, but larger numbers of hypothesis tests imply that greater care must be taken for multiple-testing correction. Indel, insertion and deletion; SNV, single-nucleotide variant; SV, structural variant.

  4. A conceptual example of clonal evolution model and clonality analyses.
    Figure 4: A conceptual example of clonal evolution model and clonality analyses.

    a | The founding clone (yellow) persists during the course of the disease. Another clone (green) that is present at time point 1 faces extinction before time point 2, but new subclones (blue and orange) emerge during disease progression. b | The SciClone algorithm detects the presence of 3 mutation clusters at time point 3.

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Affiliations

  1. The Genome Institute, Washington University in St. Louis, 4444 Forest Park Ave., St. Louis, Missouri 63108, USA.

    • Li Ding,
    • Michael C. Wendl &
    • Joshua F. McMichael
  2. Department of Medicine, Washington University in St. Louis, 660 S. Euclid Ave., St. Louis, Missouri 63110, USA.

    • Li Ding
  3. Department of Genetics, Washington University in St. Louis, 660 S. Euclid Ave., St. Louis, Missouri 63110, USA.

    • Li Ding &
    • Michael C. Wendl
  4. Siteman Cancer Center, Washington University in St. Louis, 4921 Parkview Place, St. Louis, Missouri 63110, USA.

    • Li Ding
  5. Department of Mathematics, Washington University in St. Louis, 1 Brookings Drive, St. Louis, Missouri 63130, USA.

    • Michael C. Wendl
  6. Department of Computer Science and Center for Computational Molecular Biology, Brown University, 115 Waterman Street, Providence, Rhode Island 02912, USA.

    • Benjamin J. Raphael

Competing interests statement

The authors declare no competing interests.

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Author details

  • Li Ding

    Li Ding has concentrated her research on understanding somatic and germline genetic changes that are relevant to cancer initiation and progression, as well as to drug response. Her recent efforts include the discovery of 127 cancer genes across more than 3,000 tumours from 12 major cancer types. She is the principle investigator for the National Human Genome Research Institute (NHGRI) sponsored Genome Sequencing Informatics (GS-IT) Center at Washington University in St. Louis, Missouri, USA; an assistant director at the Genome Institute and an assistant professor of Medicine and Genetics at Washington University in St. Louis.

  • Michael C. Wendl

    Michael C. Wendl focuses on applying mathematics and computational methods to pressing problems in the biomedical sciences. He developed much of DNA sequencing theory and co-wrote the PHRED trace analyser used for processing Sanger sequencing data, including those in the Human Genome Project. He now concentrates on problems in cancer genomics, including detection of somatic mutations, pathway analyses and modelling of clonal evolution.

  • Joshua F. McMichael

    Joshua F. McMichael creates user interfaces and data visualizations for bioinformatics, and specializes in cancer genomics. He worked on the genome modelling system for high-throughput sequencing data analyses and has produced many of the visualizations for cancer genomic discoveries, including clonal evolution in acute myeloid leukaemia. He currently works as a software developer at the Genome Institute at Washington University in St. Louis, Missouri, USA.

  • Benjamin J. Raphael

    Benjamin J. Raphael develops novel combinatorial and statistical algorithms for the interpretation of genomes. His recent work focuses on structural variation in human and cancer genomes, as well as on network and pathway analyses of somatic mutations in cancer. He is an associate professor in the Department of Computer Science and Director of the Center for Computational Molecular Biology at Brown University, Providence, Rhode Island, USA.

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