Ploidy abnormalities are a hallmark of cancer, but their impact on the evolution and outcomes of cancers is unknown. Here, we identified whole-genome doubling (WGD) in the tumors of nearly 30% of 9,692 prospectively sequenced advanced cancer patients. WGD varied by tumor lineage and molecular subtype, and arose early in carcinogenesis after an antecedent transforming driver mutation. While associated with TP53 mutations, 46% of all WGD arose in TP53-wild-type tumors and in such cases was associated with an E2F-mediated G1 arrest defect, although neither aberration was obligate in WGD tumors. The variability of WGD across cancer types can be explained in part by cancer cell proliferation rates. WGD predicted for increased morbidity across cancer types, including KRAS-mutant colorectal cancers and estrogen receptor-positive breast cancers, independently of established clinical prognostic factors. We conclude that WGD is highly common in cancer and is a macro-evolutionary event associated with poor prognosis across cancer types.
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Van de Peer, Y., Mizrachi, E. & Marchal, K. The evolutionary significance of polyploidy. Nat. Rev. Genet. 18, 411–424 (2017).
Davoli, T. & de Lange, T. The causes and consequences of polyploidy in normal development and cancer. Annu. Rev. Cell Dev. Biol. 27, 585–610 (2011).
Storchova, Z. & Pellman, D. From polyploidy to aneuploidy, genome instability and cancer. Nat. Rev. Mol. Cell Biol. 5, 45–54 (2004).
Fujiwara, T. et al. Cytokinesis failure generating tetraploids promotes tumorigenesis in p53-null cells. Nature 437, 1043–1047 (2005).
Carter, S. L. et al. Absolute quantification of somatic DNA alterations in human cancer. Nat. Biotechnol. 30, 413–421 (2012).
Zack, T. I. et al. Pan-cancer patterns of somatic copy number alteration. Nat. Genet. 45, 1134–1140 (2013).
Jamal-Hanjani, M. et al. Tracking the evolution of non-small-cell lung cancer. N. Engl. J. Med. 376, 2109–2121 (2017).
Dewhurst, S. M. et al. Tolerance of whole-genome doubling propagates chromosomal instability and accelerates cancer genome evolution. Cancer Discov. 4, 175–185 (2014).
Kuznetsova, A. Y. et al. Chromosomal instability, tolerance of mitotic errors and multidrug resistance are promoted by tetraploidization in human cells. Cell Cycle 14, 2810–2820 (2015).
Zehir, A. et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat. Med. 23, 703–713 (2017).
Shen, R. & Seshan, V. E. FACETS: allele-specific copy number and clonal heterogeneity analysis tool for high-throughput DNA sequencing. Nucleic Acids Res. 44, e131 (2016).
Cancer Genome Atlas Research Network. Integrated genomic characterization of papillary thyroid carcinoma. Cell 159, 676–690 (2014).
Semon, M. & Wolfe, K. H. Consequences of genome duplication. Curr. Opin. Genet. Dev. 17, 505–512 (2007).
Thompson, D. A., Desai, M. M. & Murray, A. W. Ploidy controls the success of mutators and nature of mutations during budding yeast evolution. Curr. Biol. 16, 1581–1590 (2006).
Aylon, Y. & Oren, M. p53: guardian of ploidy. Mol. Oncol. 5, 315–323 (2011).
McGranahan, N. et al. Clonal status of actionable driver events and the timing of mutational processes in cancer evolution. Sci. Transl. Med. 7, 283ra54 (2015).
Chang, M. T. et al. Identifying recurrent mutations in cancer reveals widespread lineage diversity and mutational specificity. Nat. Biotechnol. 34, 155–163 (2016).
Davoli, T. & de Lange, T. Telomere-driven tetraploidization occurs in human cells undergoing crisis and promotes transformation of mouse cells. Cancer Cell 21, 765–776 (2012).
Barthel, F. P. et al. Systematic analysis of telomere length and somatic alterations in 31 cancer types. Nat. Genet. 49, 349–357 (2017).
Crockford, A. et al. Cyclin D mediates tolerance of genome-doubling in cancers with functional p53. Ann. Oncol. 28, 149–156 (2017).
Peng, J. et al. Stabilization of MCRS1 by BAP1 prevents chromosome instability in renal cell carcinoma. Cancer Lett. 369, 167–174 (2015).
Pan, H. et al. BAP1 regulates cell cycle progression through E2F1 target genes and mediates transcriptional silencing via H2A monoubiquitination in uveal melanoma cells. Int. J. Biochem. Cell Biol. 60, 176–184 (2015).
Ramaker, R. C. et al. RNA sequencing-based cell proliferation analysis across 19 cancers identifies a subset of proliferation-informative cancers with a common survival signature. Oncotarget 8, 38668–38681 (2017).
Tomasetti, C., Li, L. & Vogelstein, B. Stem cell divisions, somatic mutations, cancer etiology, and cancer prevention. Science 355, 1330–1334 (2017).
Armenia, J. et al. The long tail of oncogenic drivers in prostate cancer. Nat. Genet. 50, 645–651 (2018).
Hieronymus, H. et al. Copy number alteration burden predicts prostate cancer relapse. Proc. Natl Acad. Sci. USA 111, 11139–11144 (2014).
Taylor, B. S. et al. Integrative genomic profiling of human prostate cancer. Cancer Cell 18, 11–22 (2010).
Punt, C. J., Koopman, M. & Vermeulen, L. From tumour heterogeneity to advances in precision treatment of colorectal cancer. Nat. Rev. Clin. Oncol. 14, 235–246 (2017).
Hart, C. D. et al. Challenges in the management of advanced, ER-positive, HER2-negative breast cancer. Nat. Rev. Clin. Oncol. 12, 541–552 (2015).
Zardavas, D., Irrthum, A., Swanton, C. & Piccart, M. Clinical management of breast cancer heterogeneity. Nat. Rev. Clin. Oncol. 12, 381–394 (2015).
Chandarlapaty, S. et al. Prevalence of ESR1 mutations in cell-free DNA and outcomes in metastatic breast cancer: a secondary analysis of the BOLERO-2 clinical trial. JAMA Oncol. 2, 1310–1315 (2016).
Toy, W. et al. ESR1 ligand-binding domain mutations in hormone-resistant breast cancer. Nat. Genet. 45, 1439–1445 (2013).
Cheng, D. T. et al. Memorial Sloan Kettering-integrated mutation profiling of actionable cancer targets (MSK-IMPACT): a hybridization capture-based next-generation sequencing clinical assay for solid tumor molecular oncology. J. Mol. Diagn. 17, 251–264 (2015).
Vivian, J. et al. Toil enables reproducible, open source, big biomedical data analyses. Nat. Biotechnol. 35, 314–316 (2017).
Venet, D., Dumont, J. E. & Detours, V. Most random gene expression signatures are significantly associated with breast cancer outcome. PLoS Comput. Biol. 7, e1002240 (2011).
Peduzzi, P., Concato, J., Kemper, E., Holford, T. R. & Feinstein, A. R. A simulation study of the number of events per variable in logistic regression analysis. J. Clin. Epidemiol. 49, 1373–1379 (1996).
Niu, B. et al. MSIsensor: microsatellite instability detection using paired tumor-normal sequence data. Bioinformatics 30, 1015–1016 (2014).
We thank our patients and their families for participating in this study, and members of the Taylor Laboratory and Marie-Josée and Henry R. Kravis Center for Molecular Oncology for discussions and support. We also thank V. Balachandran and the Avner Pancreatic Cancer Foundation for assistance with ICGC pancreas sequencing and clinical data. This work was supported by National Institutes of Health awards P30 CA008748, U54 OD020355 (to D.B.S. and B.S.T.), R01 CA207244 (to D.M.H. and B.S.T.), and R01 CA204749 (to B.S.T.), and the American Cancer Society, Cycle for Survival, Sontag Foundation, Prostate Cancer Foundation, and Josie Robertson Foundation (to B.S.T.).
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Integrated supplementary information
Total (top), allele-specific (middle), and integer (bottom) DNA copy number segmentation (red) in a single tumor and matched blood normal from a patient with a TP53-mutant uterine leiomyosarcoma profiled by MSK-IMPACT (left) as well as by whole-exome sequencing (right) indicating their concordance and how WGD was inferred across the cohort.
At top is the fraction of the autosomal tumor genome with a MCN greater than or equal to two, as in Fig. 1a. In red is the threshold used to determine genome doubling. In green are 1,000 simulated cancer genomes constructed from randomly sampling 22 autosomes from all samples in the cohort indicated; the majority are weighted to WGD-negative samples. Light and dark blue are the same simulations (as in green) repeated but only from randomly sampling WGD-negative and WGD-positive cases, respectively, indicating the inability to simulate a WGD-positive genome (having greater than or equal to 50% of the genome with MCN of two or greater) from chromosomal aberrations drawn from WGD-negative cases.
a–c, The top row shows the distribution of observed mutant allele fractions for somatic mutations in balanced regions of the genome with total copy number (TCN) of two in WGD-negative samples (a), TCN of two in WGD-positive tumors (b), and TCN of four in WGD-positive tumors (c). Predicted values for two-copy and four-copy solutions are indicated with dashed and solid lines, respectively. d, The distribution of mutant allele fractions for somatic mutations in balanced regions of the subset of WGD-positive tumors with an alternative WGD-negative solution. The peak located at approximately 0.25*purity is consistent with one mutant allele of four total copies under the WGD-positive solution. e,f, A representative FACETS segmentation profile for an individual tumor with a WGD-positive solution (e) and its alternative WGD-negative solution (f). Problematic segments (those with either no copy number assignment or those that imply multiple tumor–normal log ratios associated with diploidy) are highlighted (arrows), indicating the alternative WGD-negative solution fits the segmentation data less well than the WGD-positive fit.
The percentage of different thyroid cancer subtypes (sample sizes indicated in parentheses) that have undergone WGD. Asterisks reflect statistically significant differences (two-sided Fisher’s exact test, P = 0.02, 0.002, 4.2 × 10–5, and 7,9 × 10–5 for PTC versus PDTC, MTC, ATC, and HCTC, respectively).
The microsatellite status of colorectal cancers, endometrial cancers, and stomach adenocarcinomas in this cohort according to their MSIsensor score, as described in the Supplementary Methods. Tumors that underwent WGD are annotated in blue; the dotted line corresponds to the threshold for MSI positivity.
Somatic mutational burden (point mutations and small insertions and deletions) in tumors with and without WGD in each of 20 cancer types with 20 or more WGD-positive specimens (sample sizes indicated in parentheses). All box plots represent the minimum, first quartile, median, third quartile, and maximum values (outliers detected using the standard 1.5*IQR method) within a given cancer type. Asterisks reflect statistically significant differences within cancer types (nominal P < 0.05, two-sided Wilcoxon test; one, two and three asterisks correspond to P values between 0.01 and 0.05, 0.001 and 0.01, and less than 0.001, respectively). Data used here are from whole-exome sequencing from specimens in the TCGA that are of cancer types overlapping with those included in our prospective cohort. TCGA data were used for their increased power to determine mutational burden.
Schematic representation of the timing of mutations relative to WGD in affected cases.
Telomere length (TL ratio is matching tumor over normal samples) as a function of WGD status in 25 cancer types. TL was inferred from either high- or low-pass whole-genome sequencing or from whole-exome sequencing data from the TCGA19. All box plots represent the minimum, first quartile, median, third quartile, and maximum values (outliers detected using the standard 1.5*IQR method) within a given cancer type. Individual samples are dots that are colored based on TERT status (when available; wild type, those harboring a known TERT promoter mutation, or TERT rearrangements).
The most common genomic alterations in the 1,347 TP53-wild-type WGD-positive tumors are shown including key effectors of E2F-mediated G1 arrest, which account for 31.8% of such tumors (15% when including only those covariates identified as significant in our model; Methods). Alteration types are indicated by the legend (bottom).
The rate of WGD in primary and metastatic samples in the indicated cancer types in the prospective cohort is shown (number of primary and metastatic samples indicated in parentheses; error bars are the binomial CIs). Asterisks reflect statistically significant differences as in Fig. 3b.
The rate of WGD in 797 primary prostate cancers as a function of Gleason grade (n = 97, 375, and 325 for Gleason grades 6, 7, and 8+, respectively). Error bars are the binomial CIs. Asterisks reflect statistical significance (P = 7.3 × 10–7, two-sided chi-squared test).
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