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Genome doubling shapes the evolution and prognosis of advanced cancers

Nature Genetics volume 50, pages 1189–1195 (2018)Cite this article

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Abstract

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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Fig. 1: Prevalence of genome doubling in advanced cancers.
Fig. 2: Genome correlates of genome doubling.
Fig. 3: Genome doubling and outcome.

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References

  1. Van de Peer, Y., Mizrachi, E. & Marchal, K. The evolutionary significance of polyploidy. Nat. Rev. Genet. 18, 411–424 (2017).

    Article  Google Scholar 

  2. 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).

    Article  CAS  Google Scholar 

  3. Storchova, Z. & Pellman, D. From polyploidy to aneuploidy, genome instability and cancer. Nat. Rev. Mol. Cell Biol. 5, 45–54 (2004).

    Article  CAS  Google Scholar 

  4. Fujiwara, T. et al. Cytokinesis failure generating tetraploids promotes tumorigenesis in p53-null cells. Nature 437, 1043–1047 (2005).

    Article  CAS  Google Scholar 

  5. Carter, S. L. et al. Absolute quantification of somatic DNA alterations in human cancer. Nat. Biotechnol. 30, 413–421 (2012).

    Article  CAS  Google Scholar 

  6. Zack, T. I. et al. Pan-cancer patterns of somatic copy number alteration. Nat. Genet. 45, 1134–1140 (2013).

    Article  CAS  Google Scholar 

  7. Jamal-Hanjani, M. et al. Tracking the evolution of non-small-cell lung cancer. N. Engl. J. Med. 376, 2109–2121 (2017).

    Article  CAS  Google Scholar 

  8. Dewhurst, S. M. et al. Tolerance of whole-genome doubling propagates chromosomal instability and accelerates cancer genome evolution. Cancer Discov. 4, 175–185 (2014).

    Article  CAS  Google Scholar 

  9. 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).

    Article  CAS  Google Scholar 

  10. Zehir, A. et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat. Med. 23, 703–713 (2017).

    Article  CAS  Google Scholar 

  11. 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).

    Article  Google Scholar 

  12. Cancer Genome Atlas Research Network. Integrated genomic characterization of papillary thyroid carcinoma. Cell 159, 676–690 (2014).

  13. Semon, M. & Wolfe, K. H. Consequences of genome duplication. Curr. Opin. Genet. Dev. 17, 505–512 (2007).

    Article  CAS  Google Scholar 

  14. 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).

    Article  CAS  Google Scholar 

  15. Aylon, Y. & Oren, M. p53: guardian of ploidy. Mol. Oncol. 5, 315–323 (2011).

    Article  CAS  Google Scholar 

  16. 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).

    Article  Google Scholar 

  17. Chang, M. T. et al. Identifying recurrent mutations in cancer reveals widespread lineage diversity and mutational specificity. Nat. Biotechnol. 34, 155–163 (2016).

    Article  CAS  Google Scholar 

  18. 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).

    Article  CAS  Google Scholar 

  19. Barthel, F. P. et al. Systematic analysis of telomere length and somatic alterations in 31 cancer types. Nat. Genet. 49, 349–357 (2017).

    Article  CAS  Google Scholar 

  20. Crockford, A. et al. Cyclin D mediates tolerance of genome-doubling in cancers with functional p53. Ann. Oncol. 28, 149–156 (2017).

    CAS  Google Scholar 

  21. Peng, J. et al. Stabilization of MCRS1 by BAP1 prevents chromosome instability in renal cell carcinoma. Cancer Lett. 369, 167–174 (2015).

    Article  CAS  Google Scholar 

  22. 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).

    Article  CAS  Google Scholar 

  23. 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).

    Article  Google Scholar 

  24. Tomasetti, C., Li, L. & Vogelstein, B. Stem cell divisions, somatic mutations, cancer etiology, and cancer prevention. Science 355, 1330–1334 (2017).

    Article  CAS  Google Scholar 

  25. Armenia, J. et al. The long tail of oncogenic drivers in prostate cancer. Nat. Genet. 50, 645–651 (2018).

    Article  CAS  Google Scholar 

  26. Hieronymus, H. et al. Copy number alteration burden predicts prostate cancer relapse. Proc. Natl Acad. Sci. USA 111, 11139–11144 (2014).

    Article  CAS  Google Scholar 

  27. Taylor, B. S. et al. Integrative genomic profiling of human prostate cancer. Cancer Cell 18, 11–22 (2010).

    Article  CAS  Google Scholar 

  28. 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).

    Article  CAS  Google Scholar 

  29. 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).

    Article  Google Scholar 

  30. Zardavas, D., Irrthum, A., Swanton, C. & Piccart, M. Clinical management of breast cancer heterogeneity. Nat. Rev. Clin. Oncol. 12, 381–394 (2015).

    Article  CAS  Google Scholar 

  31. 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).

    Article  Google Scholar 

  32. Toy, W. et al. ESR1 ligand-binding domain mutations in hormone-resistant breast cancer. Nat. Genet. 45, 1439–1445 (2013).

    Article  CAS  Google Scholar 

  33. 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).

    Article  CAS  Google Scholar 

  34. Vivian, J. et al. Toil enables reproducible, open source, big biomedical data analyses. Nat. Biotechnol. 35, 314–316 (2017).

    Article  CAS  Google Scholar 

  35. 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).

    Article  CAS  Google Scholar 

  36. 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).

    Article  CAS  Google Scholar 

  37. Niu, B. et al. MSIsensor: microsatellite instability detection using paired tumor-normal sequence data. Bioinformatics 30, 1015–1016 (2014).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

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.).

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  1. Matthew T. Chang

    Present address: Genentech, San Francisco, CA, USA

Authors and Affiliations

  1. Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Craig M. Bielski, Mark T. A. Donoghue, Chaitanya Bandlamudi, Nikolaus Schultz, David B. Solit, Michael F. Berger & Barry S. Taylor

  2. Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Ahmet Zehir & Michael F. Berger

  3. Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Alexander V. Penson, Walid Chatila, Joshua Armenia, Matthew T. Chang, Philip Jonsson, Nikolaus Schultz & Barry S. Taylor

  4. Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Alexander V. Penson, Matthew T. Chang, Philip Jonsson, Jose Baselga, David B. Solit & Barry S. Taylor

  5. Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Alison M. Schram, Pedram Razavi, Gopa Iyer, Mark E. Robson, Zsofia K. Stadler, Jose Baselga, David B. Solit & David M. Hyman

  6. Department of Medicine, Weill Cornell Medical College, Cornell University, New York, NY, USA

    David B. Solit & David M. Hyman

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  1. Craig M. Bielski
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Contributions

C.M.B. and B.S.T. conceived the study. C.M.B., A.V.P., M.T.A.D., M.T.C., P.J., C.B., M.F.B., and B.S.T. designed and performed the data analysis. A.Z., W.C., J.A., A.M.S., P.R., G.I., M.E.R., Z.K.S., N.S., J.B., D.B.S., and D.M.H. assisted with the prospective genomic and clinical data collection and sample annotation. C.M.B. and B.S.T. wrote the manuscript with input from all authors.

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Correspondence to Barry S. Taylor.

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Integrated supplementary information

Supplementary Fig. 1 WGD inference from targeted capture and deep sequencing.

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.

Supplementary Fig. 2 Modeling WGD in simulated cancer genomes.

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.

Supplementary Fig. 3 Assessing WGD-positive FACETS solutions.

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.

Supplementary Fig. 4 Thyroid cancer type-specific rates of WGD.

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).

Supplementary Fig. 5 WGD and MSI.

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.

Supplementary Fig. 6 WGD and mutational burden.

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.

Supplementary Fig. 7 Timing the chronology of mutations relative to WGD.

Schematic representation of the timing of mutations relative to WGD in affected cases.

Supplementary Fig. 8 WGD and telomere length.

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).

Supplementary Fig. 9 Genomic alterations in TP53-wild-type WGD-positive tumors.

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).

Supplementary Fig. 10 WGD in primary and metastatic cancers.

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.

Supplementary Fig. 11 WGD and Gleason grade in primary prostate cancers.

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).

Supplementary Information

Supplementary Text and Figures

Supplementary Figures 1–11 and Supplementary Tables 1 and 3

Reporting Summary

Supplementary Table 2

Per-patient WGD and outcome information

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Bielski, C.M., Zehir, A., Penson, A.V. et al. Genome doubling shapes the evolution and prognosis of advanced cancers. Nat Genet 50, 1189–1195 (2018). https://doi.org/10.1038/s41588-018-0165-1

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