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Computational analysis of cancer genome sequencing data

Nature Reviews Genetics volume 23, pages 298–314 (2022)Cite this article

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Abstract

Distilling biologically meaningful information from cancer genome sequencing data requires comprehensive identification of somatic alterations using rigorous computational methods. As the amount and complexity of sequencing data have increased, so has the number of tools for analysing them. Here, we describe the main steps involved in the bioinformatic analysis of cancer genomes, review key algorithmic developments and highlight popular tools and emerging technologies. These tools include those that identify point mutations, copy number alterations, structural variations and mutational signatures in cancer genomes. We also discuss issues in experimental design, the strengths and limitations of sequencing modalities and methodological challenges for the future.

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Fig. 1: Workflow for cancer genome analysis.
Fig. 2: Identifying variants and artefacts in sequencing reads.
Fig. 3: Mutational signature analysis of cancer genomes.
Fig. 4: Impact of different copy number alterations on read depth and BAF profiles.
Fig. 5: Examples of detecting somatic structural variants from patterns of paired-end reads.

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Acknowledgements

This work was supported by grants from EMBL (to I.C-C.) and the Harvard Ludwig Center (to P.J.P.) and an award from the Cancer Research UK Grand Challenge and the Mark Foundation for Cancer Research to the SPECIFICANCER team.

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Authors and Affiliations

  1. European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK

    Isidro Cortés-Ciriano

  2. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Doga C. Gulhan, Jake June-Koo Lee, Giorgio E. M. Melloni & Peter J. Park

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  1. Isidro Cortés-Ciriano
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  2. Doga C. Gulhan
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  3. Jake June-Koo Lee
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  4. Giorgio E. M. Melloni
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All authors contributed to all aspects of the article.

Corresponding author

Correspondence to Peter J. Park.

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Competing interests

D.C.G. and P.J.P. have filed a patent application on SigMA. I.C-C., J.J.-K.L. and G.E.M.M. declare no competing interests.

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1000 Genomes Project: https://www.internationalgenome.org/home

Catalogue of Somatic Mutations In Cancer: https://cancer.sanger.ac.uk/cosmic

cBioPortal: http://www.cbioportal.org/

ClinVar: https://www.ncbi.nlm.nih.gov/clinvar/

COSMIC Mutational Signatures: https://cancer.sanger.ac.uk/signatures

OncoKB: https://www.oncokb.org/

Pan-Cancer Analysis of Whole Genomes (PCAWG) project: https://dcc.icgc.org/pcawg

The Cancer Genome Atlas (TCGA): https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga

Glossary

‘Driver’ mutations

Somatic alterations of the DNA sequence that confer a selective advantage to cells harbouring it.

Mutational signatures

Distinct patterns of mutational spectra, often associated with specific mutational processes.

Mapping quality

A measure of confidence that a sequencing read originated from the aligned position.

Single-nucleotide variants

(SNVs). Changes in the sequence of the DNA involving one base pair.

Variant allele fraction

(VAF). The number of reads supporting a candidate mutation divided by the read depth at that position.

Read depth

Number of sequenced reads at a genomic position.

Tumour purity

The fraction of cancer cells in the sequenced sample.

Tumour ploidy

The amount of DNA that cancer cells contain, usually estimated for the major clone in a tumour sample.

Panel of normals

A set of ‘normal’ samples that are used as a control to remove germline variants in a population as part of somatic variant calling.

GC content

Percentage of bases in a genomic region that are either guanine (G) or cytosine (C).

Strand bias

In the context of variant calling, the presence of the variant allele in either forward or reverse reads with a frequency higher than expected for binomial sampling.

Read ‘pile-up’

Text-based format that represents the base calls in sequencing reads aligned to a reference genome.

Quantile–quantile plots

A graphical method used to compare two probability distributions by plotting the quantiles of one distribution against the same quantiles of a second distribution.

‘Clock-like’ signatures

Mutational signatures that correspond to those mutations that accumulate in normal somatic cells at a steady rate.

Non-negative least squares

(NNLS). A method for finding the optimal non-negative coefficients for a set of predefined vectors such that their weighted sum is as close as possible to another given vector. In signature analysis, it is used to calculate the contribution of each signature to the mutational spectrum of a sample.

Enhancer hijacking

Juxtaposition of an active enhancer element from a distant locus into the proximity of another gene, usually caused by a genomic rearrangement, leading to gene activation.

Enhancer amplification

Increased copy number of regulatory regions (enhancers) that leads to the overexpression of target genes.

Loss of heterozygosity

(LOH). Loss of one allele in biallelic regions, which often results from a somatic deletion.

B-allele frequency

(BAF). The fraction of sequencing reads supporting one allele at a heterozygous single-nucleotide polymorphism (SNP) with respect to the total read depth at that position.

Split reads

Reads containing two contiguous DNA sequences mapping to non-adjacent regions in the reference genome.

Discordant read pairs

Pairs of sequencing reads that do not map to the reference genome with the expected forward–reverse orientation or insert size, suggesting the presence of structural variation.

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Cortés-Ciriano, I., Gulhan, D.C., Lee, J.JK. et al. Computational analysis of cancer genome sequencing data. Nat Rev Genet 23, 298–314 (2022). https://doi.org/10.1038/s41576-021-00431-y

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