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Role of non-coding sequence variants in cancer

Nature Reviews Genetics volume 17, pages 93–108 (2016)Cite this article

Subjects

Key Points

  • Germline and somatic sequence variants in non-coding regions can play an important role in cancer.

  • Many different modes of action of non-coding variants are known. For example, point mutations and complex genomic rearrangements can disrupt or create transcription factor-binding sites or affect non-coding RNA loci.

  • Oncogenesis involves an interplay between germline and somatic variants.

  • Drivers in non-coding regions can be identified using computational methods that analyse functional effects of variants and recurrence across multiple samples.

  • Functional effects of non-coding variants can be studied by various experimental approaches.

  • The overall role of non-coding variants in tumorigenesis is currently likely underestimated as only a handful of genome-wide studies of tumours have analysed them. However, current and future efforts involving large-scale whole-genome sequencing of tumours are likely to shed more light on the importance of non-coding variants in cancer.

Abstract

Patients with cancer carry somatic sequence variants in their tumour in addition to the germline variants in their inherited genome. Although variants in protein-coding regions have received the most attention, numerous studies have noted the importance of non-coding variants in cancer. Moreover, the overwhelming majority of variants, both somatic and germline, occur in non-coding portions of the genome. We review the current understanding of non-coding variants in cancer, including the great diversity of the mutation types — from single nucleotide variants to large genomic rearrangements — and the wide range of mechanisms by which they affect gene expression to promote tumorigenesis, such as disrupting transcription factor-binding sites or functions of non-coding RNAs. We highlight specific case studies of somatic and germline variants, and discuss how non-coding variants can be interpreted on a large-scale through computational and experimental methods.

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Figure 1: Somatic mutations in various cancer types.
Figure 2: Identification of cis-regulatory elements using functional genomics assays and evolutionary conservation.
Figure 3: Study designs commonly used to identify germline and somatic non-coding sequence variants linked with tumorigenesis.
Figure 4: Effect of sequence variants in non-coding regions in tumorigenesis.
Figure 5: Methods for functional validation of non-coding variants.

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Acknowledgements

F.D. would like to acknowledge grant IG 13562 from AIRC (Associazione Italiana per la Ricerca sul Cancro).

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Authors and Affiliations

  1. Meyer Cancer Center, Weill Cornell Medical College, New York, 10065, New York, USA

    Ekta Khurana & Mark A. Rubin

  2. Institute for Precision Medicine, Weill Cornell Medical College, New York, 10065, New York, USA

    Ekta Khurana, Dimple Chakravarty, Francesca Demichelis & Mark A. Rubin

  3. Institute for Computational Biomedicine, Weill Cornell Medical College, New York, 10021, New York, USA

    Ekta Khurana & Francesca Demichelis

  4. Department of Physiology and Biophysics, Weill Cornell Medical College, New York, 10065, New York, USA

    Ekta Khurana

  5. Bina Technologies, Roche Sequencing, Redwood City, 94065, California, USA

    Yao Fu

  6. Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, New York, 10065, New York, USA

    Dimple Chakravarty & Mark A. Rubin

  7. Centre for Integrative Biology, University of Trento, Trento, 38123, Italy

    Francesca Demichelis

  8. Program in Computational Biology and Bioinformatics, Yale University, New Haven, 06520, Connecticut, USA

    Mark Gerstein

  9. Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, 06520, Connecticut, USA

    Mark Gerstein

  10. Department of Computer Science, Yale University, New Haven, 06520, Connecticut, USA

    Mark Gerstein

Authors
  1. Ekta Khurana
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  2. Yao Fu
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  3. Dimple Chakravarty
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  4. Francesca Demichelis
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  5. Mark A. Rubin
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PowerPoint slides

Glossary

Exome sequencing

Sequencing the protein-coding portion of the genome using target-enrichment and high-throughput sequencing technology.

Driver mutations

Sequence variants that confer growth advantage to tumour cells.

Passenger mutations

Sequence variants that do not contribute to cancer growth.

Germline variants

Heritable variants that are transmitted to offspring. These variants are constitutional (that is, present in all cells of the body).

Genome-wide association studies

(GWASs). Studies that interrogate multiple common genetic variants along the genome in large cohorts of individuals to evaluate whether any variant is associated with a specific trait.

Single nucleotide variants

DNA sequence changes at single nucleotides.

Somatic variants

Variants that are not inherited from a parent and are not transmitted to offspring.

Penetrance

The proportion of individuals carrying an allele (or a genotype) that also express the trait (phenotype) associated with it.

Chromoplexy

(From the Greek pleko, meaning to weave, or to braid). A class of complex somatic DNA rearrangements whereby abundant DNA deletions and intra- and inter-chromosomal translocations that have originated in an interdependent way occur within a single cell cycle.

Chromothripsis

(From the Greek thripsis, meaning shattering into pieces). A clustered chromosomal rearrangement in confined genomic regions that results from a single catastrophic event, usually limited to one chromosome.

Kataegis

(From the Greek kataigis, meaning thunder). A phenomenon that is characterized by large clusters of mutations (hypermutation) in the genome of cancer cells. An APOBEC family enzyme might be responsible for the kataegis process.

Cis-regulatory regions

Regions that regulate the expression of genes on the same DNA molecule. These include promoters, enhancers, silencers, insulators and untranslated regions.

Enhancers

Distal cis-regulatory regions bound by transcription factors that activate genes by helping the recruitment of RNA polymerase to the promoters.

Silencers

Distal cis-regulatory regions bound by transcription factors that repress gene expression by preventing RNA polymerase from binding to the gene promoter.

Insulators

Regions that block the interaction between enhancers and promoters.

DNase I footprinting

A method to detect the exact binding sites of DNA-binding proteins based on the fact that a protein bound to DNA protects it from cleavage by DNase I.

Chromosome conformation capture

(3C). A biochemical method whereby the three-dimensional organization of chromatin in living cells is fixed and analysed.

Expression quantitative trait loci

(eQTLs). Loci in which DNA sequence variants are related with expression levels of mRNAs.

Endo-siRNAs

Endogenously produced small interfering RNAs that regulate gene expression by binding and cleaving mRNA targets or mediating heterochromatin formation.

Negative selection

Selective pressure that results in the removal of deleterious alleles.

Single nucleotide polymorphisms

(SNPs). Single nucleotide variants that show variability in the human population. As used in the context of this Review, they may be common (with high allele frequency) or rare (with low allele frequency).

Oncogene

A gene that is often upregulated in cancer and can lead to or promote cancer growth.

Burden tests

Statistical methods to test the cumulative effect of multiple variants in a genomic region.

Positive selection

Directed selection that forces the allele frequency of advantageous mutations to increase.

Minigene assays

Assays using a plasmid with a minimal gene fragment necessary for the gene to be expressed. It can include exons as well as introns, and it serves as a tool for evaluating splicing patterns.

Precision medicine

Medical care tailored to the individual patient, usually using the patient's genomic sequence.

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Khurana, E., Fu, Y., Chakravarty, D. et al. Role of non-coding sequence variants in cancer. Nat Rev Genet 17, 93–108 (2016). https://doi.org/10.1038/nrg.2015.17

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