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A survey of cancer genome signatures identifies genes connected to distinct chromosomal instability phenotypes

The Pharmacogenomics Journal volume 21, pages 390–401 (2021)Cite this article

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Abstract

Certain breast and ovarian cancers are characterised by high levels of chromosomal instability. We established a suite of eleven SNP array-based signatures of various forms of chromosomal instability. To understand what biological mechanisms might underpin these signatures, we generated and assembled genetic-feature data including allele-specific expression, fusion genes, gene expression, methylation, somatic coding mutations and protein expression. For each signature, we extracted a compendium of significantly associated genetic features using machine learning. We established an association between subchromosomal allelic imbalance-based measures and DNA repair genes. Numerical chromosomal instability and chromothripsis were found to have distinct genetic associations but shared a relationship to mitotic processes, while whole-genome doubling was characterised by TP53 mutation, and high AURKA and GINS1 expression. Furthermore, we identified two genetically distinct forms of tandem duplicator phenotypes. These findings identify potentially novel genomic targets for validation and drug development for specific subsets of breast and ovarian cancer.

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Fig. 1: A signature of numerical chromosomal instability.
Fig. 2: Correlation between genomic signatures across tumour cohorts.
Fig. 3: Data types and data subsets used as regressors in three elastic net models.
Fig. 4: Multi-omic genetic signatures associated with SAi.

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Code availability

R code (version 3.0.2) for the estimation of numerical CN and the generation of the SNCNA score is available upon request.

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Authors and Affiliations

  1. Research Department of Haematology, UCL Cancer Institute, University College London, London, UK

    Manar S. Shafat

  2. PILAR Research Network, London, UK

    Manar S. Shafat, Eamaan S. Rufaie & Johnathan Watkins

  3. Department of Natural Sciences, School of Science and Technology, Middlesex University, London, UK

    Eamaan S. Rufaie

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  1. Manar S. Shafat
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Contributions

JW conceived and designed the study. JW collected and managed ‘omic and clinical data. JW performed copy-number analysis. JW generated ASE data. JW, MS and ER performed platinum treatment response prediction analysis. JW and MS performed elastic net regression. JW and MS wrote the paper.

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Correspondence to Johnathan Watkins.

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Shafat, M.S., Rufaie, E.S. & Watkins, J. A survey of cancer genome signatures identifies genes connected to distinct chromosomal instability phenotypes. Pharmacogenomics J 21, 390–401 (2021). https://doi.org/10.1038/s41397-021-00217-9

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