We studied the whole-genome point mutation and structural variation patterns of 133 tumors (59 high-grade serous (HGSC), 35 clear cell (CCOC), 29 endometrioid (ENOC), and 10 adult granulosa cell (GCT)) as a substrate for class discovery in ovarian cancer. Ab initio clustering of integrated point mutation and structural variation signatures identified seven subgroups both between and within histotypes. Prevalence of foldback inversions identified a prognostically significant HGSC group associated with inferior survival. This finding was recapitulated in two independent cohorts (n = 576 cases), transcending BRCA1 and BRCA2 mutation and gene expression features of HGSC. CCOC cancers grouped according to APOBEC deamination (26%) and age-related mutational signatures (40%). ENOCs were divided by cases with microsatellite instability (28%), with a distinct mismatch-repair mutation signature. Taken together, our work establishes the potency of the somatic genome, reflective of diverse DNA repair deficiencies, to stratify ovarian cancers into distinct biological strata within the major histotypes.
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We wish to acknowledge generous long-term funding support from the BC Cancer Foundation, supporting the research program of S.P.S. and OvCaRe. The authors graciously thank the Gray Family Ovarian Clear Cell Carcinoma Research Resource, which provided funding critical to this project. Additional funding was provided by a Terry Fox Research Institute New Investigator grant to S.P.S. and a Canadian Cancer Society Research Institute Impact grant to D.G.H. and S.P.S. The CRCHUM Ovarian Cancer Tumour Bank was supported by the Réseau de Recherche sur le Cancer, Fonds de Recherche Québec Santé, affiliated with the Canadian Tumour Repository Network. S.P.S. is a Michael Smith Foundation for Health Research (MSFHR) scholar and holds a Canadian Institutes for Health Research (CIHR) Foundation grant. S.P.S. and D.G.H. hold Canada Research Chairs. Y.K.W. is an MSFHR postdoctoral fellow. Support for the development of software used in this study was provided by the Genome Canada and Genome British Columbia Bioinformatics and Computational Biology program. Finally, the authors wish to acknowledge the funding support to S.P.S. from the Discovery Frontiers: Advancing Big Data Science in Genomics Research program (grant RGPGR/448167-2013, 'The Cancer Genome Collaboratory'), which is jointly funded by the Natural Sciences and Engineering Research Council of Canada, the Canadian Institutes of Health Research, Genome Canada, and the Canada Foundation for Innovation and with in-kind support from the Ontario Research Fund of the Ministry of Research, Innovation, and Science.
S.P.S., D.G.H., and S.A. are founders and shareholders of Contextual Genomics, Inc.
Integrated supplementary information
Supplementary Figure 1 Patterns of somatically acquired genomic variants in GCT, CCOC, ENOC, and HGSC ovarian cancers.
(a) Top, mutation load showing the total number of mutations (y axis; log10 scale) for each tumor (x axis). Samples are sorted in descending order based on the total number of mutations. Second panel, contributions of the six identified mutation signatures per sample. Six mutation signatures were extracted from the trinucleotide substitutions of 133 tumor genomes: S.APOBEC, signature similar to the COSMIC APOBEC signature (COSMIC signature 13); S.POLE, a mimic of COSMIC signature 10 related to altered activity of the error-prone polymerase POLE; S.AGE, the age-related signature (COSMIC signature 1) that has been known to correlate with age at cancer diagnosis; S.BC, closely matched with the pattern of COSMIC signature 8 previously found in breast cancer and medulloblastoma; S.MMR, matched to COSMIC signature 6 associated with defective mismatch repair; S.HRD, associated with COSMIC signature 3 representing deficiency in homologous recombination DNA repair. (b) Proportion of the genome harboring high-level copy number amplifications (AMP; top), dominant loss of heterozygosity (LOH; second panel), and copy number loss (LOSS; bottom) per sample. (c) Total number of rearrangements (top; y axis, log10 scale) and the proportion of rearrangement types (second panel) observed in each sample. Balanced, balanced rearrangements; Deletion, deletion rearrangements; Duplication, tandem duplications; Foldback, foldback inversions; Unbalanced, unbalanced rearrangements; Inversion, inversion rearrangements.
(a) Top, total number of SNVs (y axis; log10 scale) for each tumor (x axis). Second panel, proportion of six base substitution patterns per sample. Samples are sorted in descending order according to the total number of mutations. (b) Residual sum of squares (RSS; top) and explained variance (second panel) for the number of signatures from 2 to 12, each with 200 replicates. (c) Six inferred mutational signature profiles. S.APOBEC, signature similar to the COSMIC APOBEC signature (COSMIC signature 13); S.POLE, a mimic of COSMIC signature 10 related to altered activity of the error-prone polymerase POLE; S.AGE, the age-related signature (COSMIC signature 1) that has been known to correlate with age at cancer diagnosis; S.BC, closely matched with the pattern of COSMIC signature 8 previously found in breast cancer; S.MMR, matched to COSMIC signature 6 associated with defective mismatch repair; S.HRD, associated with COSMIC signature.3 representing deficiency in homologous recombination DNA repair.
Description of the 20 genomic features used in the integrative clustering of patients with ovarian cancer, including 6 mutation signatures (S.APOBEC, S.POLE, S.AGE, S.BC, S.MMR, and S.HRD); 6 rearrangement types and 1 homology length (Foldback.Inversion, Inversion, Tandem.Duplication, Deletion.Rearrangement, Balanced.Rearrangement, Unbalanced.Rearrangement, and Homology>=5bp); 3 copy number aberrations (CN.Amplification, CN.Loss, and CN.LOH); and 4 mutation variant types (Nonsynonymous, Splicesite, Stop.Lost/Gained, and Frameshift).
(a) Hierarchical clustering of 133 patients with ovarian cancer (columns) by integrating genomic features including point mutation, copy number, and structural rearrangement profiles identifies seven major genomic subgroups. Scaled values of genomic features (rows in the top panel) are shown in a heat map with a dendrogram of the hierarchical cluster analysis. The color-coding reflects the scaled value of genomic features obtained by subtracting the value of each feature from its mean and dividing the value by its standard deviation. Mutation status (presence, gray; absence, white; rows for bottom panel) for the significantly mutated genes (MutSigCV q < 0.01) and DNA repair genes across patients is displayed. Histotype is included as an annotation row (red, HGS; blue, COCC; green, ENOC; purple, GCT). (b) Comparison of the estimated cellularities of the subgroups of each histotype, where no significant differences were observed. The cellularity of each sample was estimated using Titan. Student’s t test was performed, and the corresponding P value is annotated on top of the box plots for each histotype. (c) Determining the number of clusters by the ‘elbow’ rule. The plot shows the explained variance (EV; y axis) computed as a function of the number of clusters (x axis) generated from hierarchical clustering. Given the threshold of EV (at 0.45; horizontal dashed line) and its increment threshold of 0.05, the optimal number of clusters (k = 7) was identified (vertical dashed line). (d) Mutation load in the HGSC subgroups. The mutation load for the HGSC samples in the H-HRD subgroup, on average, was higher than in the H-FBI subgroup (Mann–Whitney–Wilcoxon test, P < 0.001). (e) Focal amplifications (red) and deletions (blue) in the CCOC (C-APOBEC and C-AGE) subgroups. (f) Focal amplifications (red) and deletions (blue) in E-MSI and MSS ENOC samples.
(a) Hierarchical clustering of 59 patients with HGSC by integrating genomic variant profiles highlights two major genomic subgroups, H-FBI (n = 22) and H-HRD (n = 37), of HGSC tumors. The plot shows the contribution of genomic features (scaled value; top heat map) with a dendrogram illustrating hierarchical clustering. The mutation status (presence, gray; absence, white) for the significantly mutated genes (MutSigCV q < 0.01) and DNA repair genes across patients is shown in the second panel. Histotype and two subgroups are included as annotation rows (red, HGSC; dark green, H-FBI subgroup; dark orange, H-HRD subgroup). (b) Importance of genomic features segregating the HGSC subgroups of H-FBI (n = 22) and H-HRD (n = 37). Left, genomic features (y axis) are sorted in descending order of the average Gini score (x axis), reflecting the importance of features in stratifying the two subgroups of HGSC tumors. Right, box plot showing the distribution of the top six genomic features contributing to the differences between H-HRD and H-FBI. The y axis shows the value of genomic features. (c,d) Kaplan–Meier plots showing significant differences in overall survival (c) and progression-free survival (d) between the HGSC subgroups H-HRD and H-FBI (log-rank test, P = 0.0083 and 0.0108), in which samples enriched in foldback inversions (H-FBI) had poor survival outcomes.
Supplementary Figure 6 Integration/clustering of genomic features/cases in patients with endometrioisis-associated cancers only.
(a) Hierarchical clustering of 35 patients with CCOC and 29 patients with ENOC by integrating genomic variant profiles highlights six major genomic subgroups. The plot shows the contribution of genomic features (scaled value; top heat map) with a dendrogram illustrating hierarchical clustering. The mutation status (presence, gray; absence, white) for the significantly mutated genes (MutSigCV q < 0.01) and DNA repair genes across patients is displayed. (b) Contribution of genomic subgroup memberships in ENOC and CCOC. The number (n) and proportion (%) of samples from each subgroup are shown. (c) Importance of genomic features segregating the C-APOBEC (n = 9) and C-AGE (n = 15) subgroups of CCOC tumors. Left, genomic features (y axis) are sorted in descending order of the average Gini score (x axis), reflecting the importance of features in stratifying the two subgroups of CCOC tumors. Right, box plot showing the distribution of the top six genomic features contributing to the differences between C-APOBEC and C-AGE. The y axis shows the value of genomic features. (d) Importance of genomic features segregating the E-MSI (n = 8) and MSS (n = 20) subgroups of ENOC tumors. Left, genomic features (y axis) are sorted in descending order of the average Gini score (x axis), reflecting the importance of features in stratifying MSI and E-MSS ENOC tumors. Right, box plot showing the distribution of the top six genomic features contributing to the differences between the E-MSI and MSS subgroups. The y axis shows the value of genomic features. (e) Box plots showing the distribution of immunogenic epitope counts in the MSS and E-MSI ENOC subgroups.
(a,b) Examples of homozygous deletion in PTEN (a) and RB1 (b). In each example, the following are shown: a chromosome ideogram highlighting the region of interest (top); a log-ratio plot overlaying rearrangement events (if present, shown with arcs) on copy number aberration segments (middle); an allelic ratio plot showing the corresponding LOH profile in each region (bottom). ALOH, amplified LOH; HOMD, homozygous deletion; NLOH, neutral LOH; Deletion, deletion rearrangement.
(a) HGSC cases could be stratified into two subgroups based on the proportion of foldback inversions, in which cases with a higher proportion of foldback inversions (with reference to the median) referred to as the High FBI group had statistically significant inferior overall and progression-free survival outcomes (log-rank test, P = 0.0187 and 0.0286) as compared to cases with a low proportion of foldback inversions (Low FBI group). (b) Distribution of the break distance for foldback inversions in our HGSC cohort. (c–f) Two examples of foldback inversions at chromosome 8. In c and e, the genomic locus of the events is illustrated. A red bar marked on an ideogram of the chromosome 8 q arm shows where the events occurred (top). Two foldback inversions are illustrated: SV1, on the forward strand (arrows pointing to the right in orange; second panel) and SV2, on the reverse strand (backward arrows pointing to the left in grey; third panel). Coverage depth and reads (fourth and fifth panels) covering the breakpoints of foldback inversions and the locations of breakpoints on the genomic scale (bottom) are shown. In d and f, two foldback inversions are shown schematically at nucleotide sequence level. Annotated red on the genomic scale shows the breakpoints of the forward strand and the reverse strand sequences. In d, two foldback inversions co-occurred within 1 kb. Left, the reverse strand inverted and was fused to the forward strand by a 4-bp homology sequence, CTTT (highlighted in green). Right, the forward strand inverted and was fused to the reverse strand by a 12-bp homology sequence, TTCACATGTGAA (highlighted in green). In f, two foldback inversions co-occurred within 2 kb. Left, the reverse strand inverted and was fused to the forward strand by a 4-bp homology sequence, GAGC (highlighted in green). Right, the forward strand inverted and was fused to the forward strand by a 12-bp homology sequence, AGAGTATACTCT (highlighted in green).
Supplementary Figure 9 Co-occurrence of foldback inversions and focal high-level amplifications (HLAMPs) in HGSC samples.
Examples of focal HLAMPs colocalized with foldback inversions in CCNE1 and KRAS in our discovery HGSC cohort and in CCNE1 and MYC in the ICGC HGSC cohort. In each example, the following are shown: a chromosome ideogram highlighting the region of interest (top); a log-ratio plot overlaying rearrangement events (shown with arcs) on copy number (CN) aberration segments (middle); an allelic ratio plot showing the corresponding LOH profile in each region (bottom). ALOH, amplified LOH; ASCNA, allele-specific copy number amplification; BCNA, balanced copy number amplification; GAIN, copy number gain; HET, diploid heterozygous; NLOH, neutral LOH.
Supplementary Figure 10 High-level amplification–associated foldback inversions (HLAMP-FBIs) in HGSC cell lines.
(a) The proportion of HLAMP-FBI for the primary (TOV1369) and relapse (OV1369(R2) cell lines) (red dotted lines) superimposed on the distribution of HLAMP-FBI from the H-HRD (blue) and H-FBI (green) subgroups. (b) Examples of HLAMP-associated foldback inversions present in the relapse cell line (OV1369(R2)) but absent in the primary-tumor-derived cell line (TOV1369) from the same patient. In each example, the following are shown: a chromosome ideogram highlighting the region of interest (top); log-ratio plots overlaying rearrangement events (shown with arcs) on copy number (CN) aberration segments in the primary cell line (middle) and relapse cell line (bottom). HOMD, homozygous deletion; DLOH, deletion LOH; HET, diploid heterozygous; GAIN, CN gain; AMP, copy number amplification; HLAMP, high-level amplification.
Supplementary Figures 1–10 and Supplementary Note (PDF 3715 kb)
Clinical information of all samples in the study. (XLSX 67 kb)
Targeted deep sequencing analysis for validating case-specific SNV calls. (XLS 3492 kb)
PCR validation experiment on the selected breakpoints identified from sample DAH208 to determine breakpoint filtering criteria for destruct predictions. (XLSX 50 kb)
Sample, sequencing, and variant summary information. (XLSX 77 kb)
Genomic feature matrix and sample–gene variant table. (XLSX 100 kb)
Significantly mutated genes (SMGs) identified from all ovarian samples inferred by MutSigCV. (XLSX 1782 kb)
Feature ranking by shrinkage discriminant analysis. (XLSX 59 kb)
Feature importance table. (XLSX 18 kb)
Mutations found in the non-BRCA1/2 BROCA genes in the H-HRD and H-FBI subgroups (XLSX 19 kb)
Focal amplifications and deletions. (XLSX 170 kb)
ICGC HGSC cohort stratified by foldback-inversion profile. (XLSX 71 kb)
Comparisons of copy number gains/amplifications associated with rearrangement events. (XLSX 67 kb)
TCGA HGSC cohort stratified by FBI-AMP profile. (XLSX 64 kb)
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Wang, Y., Bashashati, A., Anglesio, M. et al. Genomic consequences of aberrant DNA repair mechanisms stratify ovarian cancer histotypes. Nat Genet 49, 856–865 (2017). https://doi.org/10.1038/ng.3849
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