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Interplay between whole-genome doubling and the accumulation of deleterious alterations in cancer evolution

Abstract

Whole-genome doubling (WGD) is a prevalent event in cancer, involving a doubling of the entire chromosome complement. However, despite its prevalence and prognostic relevance, the evolutionary selection pressures for WGD in cancer have not been investigated. Here, we combine evolutionary simulations with an analysis of cancer sequencing data to explore WGD during cancer evolution. Simulations suggest that WGD can be selected to mitigate the irreversible, ratchet-like, accumulation of deleterious somatic alterations, provided that they occur at a sufficiently high rate. Consistent with this, we observe an enrichment for WGD in tumor types with extensive loss of heterozygosity, including lung squamous cell carcinoma and triple-negative breast cancers, and we find evidence for negative selection against homozygous loss of essential genes before, but not after, WGD. Finally, we demonstrate that loss of heterozygosity and temporal dissection of mutations can be exploited to identify novel tumor suppressor genes and to obtain a deeper characterization of known cancer genes.

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Fig. 1: Prevalence of WGD and LOH in NSCLC.
Fig. 2: WGD buffers the deleterious effect of passenger alterations.
Fig. 3: Timing mutations relative to WGD.
Fig. 4: Purifying selection before but not after WGD.
Fig. 5: Exploiting LOH to identify cancer genes.

Data availability

HCT-116 sequence data used during the study have been deposited at the National Center for Biotechnology Information Sequence Read Archive under accession code PRJNA595067.

Code availability

R code to reproduce the figures is available at https://github.com/ucbtsl1/lopez_etal_2019_wgd-cancer.

References

  1. 1.

    Bielski, C. M. et al. Genome doubling shapes the evolution and prognosis of advanced cancers. Nat. Genet. 50, 1189–1195 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

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

    CAS  PubMed  Google Scholar 

  5. 5.

    Huxley, J. Evolution: The Modern Synthesis (George Allen & Unwin, 1942).

  6. 6.

    Madlung, A. Polyploidy and its effect on evolutionary success: old questions revisited with new tools. Heredity (Edinb.) 110, 99–104 (2013).

    CAS  Google Scholar 

  7. 7.

    Muller, H. J. The relation of recombination to mutational advance. Mutat. Res. 106, 2–9 (1964).

    CAS  PubMed  Google Scholar 

  8. 8.

    Loewe, L. & Lamatsch, D. K. Quantifying the threat of extinction from Muller’s ratchet in the diploid Amazon molly (Poecilia formosa). BMC Evol. Biol. 8, 88 (2008).

    PubMed  PubMed Central  Google Scholar 

  9. 9.

    Loewe, L. & Cutter, A. D. On the potential for extinction by Muller’s ratchet in Caenorhabditis elegans. BMC Evol. Biol. 8, 125 (2008).

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Andersson, D. I. & Hughes, D. Muller’s ratchet decreases fitness of a DNA-based microbe. Proc. Natl Acad. Sci. USA 93, 906–907 (1996).

    CAS  PubMed  Google Scholar 

  11. 11.

    Maciver, S. K. Asexual amoebae escape Muller’s ratchet through polyploidy. Trends Parasitol. 32, 855–862 (2016).

    PubMed  Google Scholar 

  12. 12.

    Engelstadter, J. Muller’s ratchet and the degeneration of Y chromosomes: a simulation study. Genetics 180, 957–967 (2008).

    PubMed  PubMed Central  Google Scholar 

  13. 13.

    Loewe, L. Quantifying the genomic decay paradox due to Muller’s ratchet in human mitochondrial DNA. Genet. Res. 87, 133–159 (2006).

    CAS  PubMed  Google Scholar 

  14. 14.

    McFarland, C. D., Korolev, K. S., Kryukov, G. V., Sunyaev, S. R. & Mirny, L. A. Impact of deleterious passenger mutations on cancer progression. Proc. Natl Acad. Sci. USA 110, 2910–2915 (2013).

    CAS  PubMed  Google Scholar 

  15. 15.

    McFarland, C. D., Mirny, L. A. & Korolev, K. S. Tug-of-war between driver and passenger mutations in cancer and other adaptive processes. Proc. Natl Acad. Sci. USA 111, 15138–15143 (2014).

    CAS  PubMed  Google Scholar 

  16. 16.

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

    CAS  PubMed  Google Scholar 

  17. 17.

    Campbell, J. D. et al. Distinct patterns of somatic genome alterations in lung adenocarcinomas and squamous cell carcinomas. Nat. Genet. 48, 607–616 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Shlien, A. et al. Combined hereditary and somatic mutations of replication error repair genes result in rapid onset of ultra-hypermutated cancers. Nat. Genet. 47, 257–262 (2015).

    CAS  PubMed  Google Scholar 

  19. 19.

    Martincorena, I. et al. Universal patterns of selection in cancer and somatic tissues. Cell 171, 1029–1041.e21 (2017).

    CAS  Article  Google Scholar 

  20. 20.

    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).

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    Nik-Zainal, S. et al. Mutational processes molding the genomes of 21 breast cancers. Cell 149, 979–993 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Blomen, V. A. et al. Gene essentiality and synthetic lethality in haploid human cells. Science 350, 1092–1096 (2015).

    CAS  PubMed  Google Scholar 

  23. 23.

    Bailey, M. H. et al. Comprehensive characterization of cancer driver genes and mutations. Cell 173, 371–385.e18 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Berger, A. H. et al. High-throughput phenotyping of lung cancer somatic mutations. Cancer Cell 30, 214–228 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Bertrand, D. et al. ConsensusDriver improves upon individual algorithms for predicting driver alterations in different cancer types and individual patients. Cancer Res. 78, 290–301 (2018).

    CAS  PubMed  Google Scholar 

  26. 26.

    Lawrence, M. S. et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505, 495–501 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Meyers, R. M. et al. Computational correction of copy number effect improves specificity of CRISPR–Cas9 essentiality screens in cancer cells. Nat. Genet. 49, 1779–1784 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Hart, T. et al. High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell 163, 1515–1526 (2015).

    CAS  PubMed  Google Scholar 

  29. 29.

    Wang, T. et al. Identification and characterization of essential genes in the human genome. Science 350, 1096–1101 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Davoli, T. et al. Cumulative haploinsufficiency and triplosensitivity drive aneuploidy patterns and shape the cancer genome. Cell 155, 948–962 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Tamborero, D., Gonzalez-Perez, A. & Lopez-Bigas, N. OncodriveCLUST: exploiting the positional clustering of somatic mutations to identify cancer genes. Bioinformatics 29, 2238–2244 (2013).

    CAS  PubMed  Google Scholar 

  32. 32.

    Zapata, L. et al. Negative selection in tumor genome evolution acts on essential cellular functions and the immunopeptidome. Genome Biol. 19, 67 (2018).

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Knudson, A. G. Jr Mutation and cancer: statistical study of retinoblastoma. Proc. Natl Acad. Sci. USA 68, 820–823 (1971).

    PubMed  Google Scholar 

  34. 34.

    Hazawa, M. et al. ZNF750 is a lineage-specific tumour suppressor in squamous cell carcinoma. Oncogene 36, 2243–2254 (2017).

    CAS  PubMed  Google Scholar 

  35. 35.

    Chen, H. Y. & Chen, R. H. Cullin 3 ubiquitin ligases in cancer biology: functions and therapeutic implications. Front. Oncol. 6, 113 (2016).

    PubMed  PubMed Central  Google Scholar 

  36. 36.

    Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Vorontsov, I. E. et al. Negative selection maintains transcription factor binding motifs in human cancer. BMC Genomics 17, 395 (2016).

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Van den Eynden, J., Basu, S. & Larsson, E. Somatic mutation patterns in hemizygous genomic regions unveil purifying selection during tumor evolution. PLoS Genet. 12, e1006506 (2016).

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Hurst, L. D. & Batada, N. N. Depletion of somatic mutations in splicing-associated sequences in cancer genomes. Genome Biol. 18, 213 (2017).

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Rosenthal, R. et al. Neoantigen-directed immune escape in lung cancer evolution. Nature 567, 479–485 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Van den Eynden, J., Jiménez-Sánchez, A., Miller, M. L. & Larsson, E. Lack of detectable neoantigen depletion in the untreated cancer genome. Nat. Genet. 51, 1741–1748 (2019).

    CAS  PubMed  Google Scholar 

  42. 42.

    Laughney, A. M., Elizalde, S., Genovese, G. & Bakhoum, S. F. Dynamics of tumor heterogeneity derived from clonal karyotypic evolution. Cell Rep. 12, 809–820 (2015).

    CAS  PubMed  Google Scholar 

  43. 43.

    Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Van Loo, P. et al. Allele-specific copy number analysis of tumors. Proc. Natl Acad. Sci. USA 107, 16910–16915 (2010).

    CAS  PubMed  Google Scholar 

  47. 47.

    Ellrott, K. et al. Scalable open science approach for mutation calling of tumor exomes using multiple genomic pipelines. Cell Syst. 6, 271–281.e7 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

S.L. receives funding from Rosetrees. P.V.L. is a Winton Group Leader in recognition of the Winton Charitable Foundation’s support towards the establishment of the Francis Crick Institute. C.S. is Royal Society Napier Research Professor. This work was supported by the Francis Crick Institute, which receives its core funding from Cancer Research UK (CRUK; FC001169 and FC001202), the UK Medical Research Council (FC001169 and FC001202) and the Wellcome Trust (FC001169 and FC001202). This work was supported by the CRUK City of London Centre Award (C7893/A26233). C.S. is funded by CRUK (TRACERx, PEACE and the CRUK Cancer Immunotherapy Catalyst Network), the CRUK Lung Cancer Centre of Excellence, the Rosetrees Trust, the NovoNordisk Foundation (ID16584) and the Breast Cancer Research Foundation. This research is supported by a Stand Up To Cancer (SU2C)–LUNGevity Foundation–American Lung Association Lung Cancer Interception Dream Team Translational Research Grant (grant number SU2C-AACR-DT23-17). SU2C is a program of the Entertainment Industry Foundation. Research grants are administered by the American Association for Cancer Research—the scientific partner of SU2C. N.M. is a Sir Henry Dale Fellow, jointly funded by the Wellcome Trust and Royal Society (grant number 211179/Z/18/Z), and also receives funding from the CRUK Lung Cancer Centre of Excellence, Rosetrees and the NIHR BRC at the University College London Hospitals. The research leading to these results has received funding from the European Research Council (ERC) under the European Union’s Seventh Framework Programme (FP7/2007-2013) Consolidator Grant (FP7-THESEUS-617844), European Commission ITN (FP7-PloidyNet 607722), an ERC Advanced Grant (PROTEUS) from the ERC under the European Union’s Horizon 2020 research and innovation program (grant agreement 835297) and Chromavision from the European Union’s Horizon 2020 research and innovation program (grant agreement 665233). The results published here are in part based on data generated by the TCGA pilot project established by the NCI and the National Human Genome Research Institute. The data were retrieved through database of Genotypes and Phenotypes (dbGaP) authorization (accession number phs000178.v9.p8). Information about TCGA and the investigators and institutions that constitute the TCGA research network can be found at http://cancergenome.nih.gov/. We also thank C. McFarland for kindly sharing code for simulating deleterious alterations in cancer evolution.

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N.M. and C.S. conceptualized and supervised the study. S.L. and N.M. prepared the manuscript. S.L., C.S. and N.M. edited and reviewed the manuscript. S.H. and S.L. performed the simulations. S.L. and E.L.L. performed the formal analysis. S.L. visualized and presented the data. S.L., E.L.L., A.H., M.D., T.P.M., T.B.K.W., N.J.B., G.A.W. and N.M. curated the data and interpreted the results. S.M.D., A.R., K.H., P.V.L., M.J.-H., C.S. and N.M. provided resources.

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Correspondence to Charles Swanton or Nicholas McGranahan.

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Competing interests

C.S. receives grant support from Pfizer, AstraZeneca, BMS, Roche-Ventana and Boehringer-Ingelheim. C.S. has consulted for Pfizer, Novartis, GlaxoSmithKline, MSD, BMS, Celgene, AstraZeneca, Illumina, Genentech, Roche-Ventana, GRAIL, Medicxi and the Sarah Cannon Research Institute. C.S. is a shareholder of Apogen Biotechnologies, Epic Bioscience and GRAIL, and has stock options in and is co-founder of Achilles Therapeutics. N.M. has received consultancy fees from Achilles Therapeutics.

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López, S., Lim, E.L., Horswell, S. et al. Interplay between whole-genome doubling and the accumulation of deleterious alterations in cancer evolution. Nat Genet 52, 283–293 (2020). https://doi.org/10.1038/s41588-020-0584-7

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