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. 2018 Sep;50(9):1262-1270.
doi: 10.1038/s41588-018-0179-8. Epub 2018 Aug 13.

Copy Number Signatures and Mutational Processes in Ovarian Carcinoma

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Free PMC article

Copy Number Signatures and Mutational Processes in Ovarian Carcinoma

Geoff Macintyre et al. Nat Genet. .
Free PMC article

Abstract

The genomic complexity of profound copy number aberrations has prevented effective molecular stratification of ovarian cancers. Here, to decode this complexity, we derived copy number signatures from shallow whole-genome sequencing of 117 high-grade serous ovarian cancer (HGSOC) cases, which were validated on 527 independent cases. We show that HGSOC comprises a continuum of genomes shaped by multiple mutational processes that result in known patterns of genomic aberration. Copy number signature exposures at diagnosis predict both overall survival and the probability of platinum-resistant relapse. Measurement of signature exposures provides a rational framework to choose combination treatments that target multiple mutational processes.

Conflict of interest statement

Competing Financial Interests Statement

The following authors the authors have a competing interest as defined by Nature Research:

C.G. Personal interest: Roche, AstraZeneca, Tesaro, Clovis, Foundation One, Nucana. Research funding: AstraZeneca, Novartis, Aprea, Nucana, Tesaro. Named co-inventor on five patents (issued: PCT/US2012/040805; pending: PCT/GB2013/053202, 1409479.1, 1409476.7 and 1409478.3)

H.G. Employment: AstraZeneca

I.McN. Personal interest: Clovis Oncology.

J.D.B. Cofounder and shareholder of Inivata Ltd (a cancer genomics company that commercializes ctDNA analysis)

All other authors declare that they have no competing financial or non-financial interests as defined by Nature Research.

Figures

Figure 1
Figure 1. Copy-number signature identification from shallow whole genome sequence data and validation in independent cohorts
a. Step 1: Absolute copy-numbers are derived from sWGS data; Step 2: genome-wide distributions of six fundamental copy-number features are computed; Step 3: Gaussian or Poisson mixture models (depending on data type) are fitted to each distribution and the optimal number of components is determined (ranging from 3–10) ; Step 4: the data are represented as a matrix with 36 mixture component counts per tumor. Step 5: Non-negative matrix factorization is applied to the components-by-tumor matrix to derive the tumor-by-signature matrix and the signature-by-components matrix. b. Heat maps show component weights for copy number signatures in two independent cohorts of HGSOC samples profiled using WGS and SNP array. Correlation coefficients are provided in Supplementary Table 2.
Figure 2
Figure 2. Linking copy-number signatures with mutational processes
a Component weights for copy number signature 1. Barplots (upper panel) are grouped by copy number feature and show weights for each of the 36 components. The middle panel shows the mixture model distributions which are shaded by the component weight - solid colours have a high weight and transparent have low weight (contrasting colours are randomly assigned). Lower panel shows genome-wide distribution (histogram or density) of each copy number feature, across the BriTROC-1 cohort, with coloured plots indicating important distributions (> 0.1 component weight). (Note: similar plots for other CN signatures are shown in Figure 3 and Supplementary Figure 5). b Associations between CN signature exposures and other features. Purple indicates positive correlation and orange negative correlation (see also Supplementary Figure 6). Numbers at the right of the panel indicate cases included in each analysis. Only significant correlations are shown (P<0.05). c Associations between CN signature exposures and SNV signatures. Purple indicates positive correlation and orange negative correlation (see also Supplementary Figure 6). The number at the right of the panel indicates cases included in the analysis. d and e Difference in CN signature exposures between cases with mutations in specific genes (d) and mutated/wildtype reactome pathways (e). The absolute difference in mean signature exposures was calculated for cases with and without mutations. Colors in filled circles indicate extent of difference. Only differences with FDR P<0.05 (Mann-Whitney test) are shown (see also Supplementary Figure 7). Numbers at the right of the panel indicate cases with mutations (SNVs, amplifications or deletions) in each gene/pathway.
Figure 3
Figure 3. The seven copy-number signatures in HGSOC
Description of the defining component weights, key associations and proposed mechanisms for the seven copy number signatures. *only the top three mutated genes for each of the pathways associated with CN signatures 4, 6 and 7 are shown (the list of all significant genes is provided in Supplementary Tables 7 and 8).
Figure 4
Figure 4. CN signature exposures of four BriTROC-1 patients with germline BRCA2 mutations and somatic loss of heterozygosity
Stacked bar plots show copy-number signature exposures for four BriTROC-1 cases with pathogenic germline BRCA2 mutations and confirmed somatic loss of heterozygosity (LOH) at the BRCA2 locus.
Figure 5
Figure 5. Association of survival with copy-number signatures
Upper panel: Stacked barplots show CN signature exposures for each patient. Patients were ranked by risk of death estimated by a multivariate Cox proportional hazards model stratified by age and cohort, with CN signature exposures as covariates. Middle panel: The matrix indicates group for each patient assigned by unsupervised clustering of CN signature 1, 2, 3 and 7 exposures (see also Supplementary Figure 10). Lower panel: Linear fit of signature exposures ordered by risk predicted by the Cox proportional hazards model.

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