A practical guide to cancer subclonal reconstruction from DNA sequencing

Abstract

Subclonal reconstruction from bulk tumor DNA sequencing has become a pillar of cancer evolution studies, providing insight into the clonality and relative ordering of mutations and mutational processes. We provide an outline of the complex computational approaches used for subclonal reconstruction from single and multiple tumor samples. We identify the underlying assumptions and uncertainties in each step and suggest best practices for analysis and quality assessment. This guide provides a pragmatic resource for the growing user community of subclonal reconstruction methods.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Standard workflow and input data for subclonal reconstruction.
Fig. 2: Subclonal reconstruction using multiple samples.
Fig. 3: CNA reconstructions and uncertainty from whole-genome duplications.

References

  1. 1.

    Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Nowell, P. C. The clonal evolution of tumor cell populations. Science 194, 23–28 (1976).

    CAS  PubMed  Google Scholar 

  3. 3.

    Gundem, G. et al. The evolutionary history of lethal metastatic prostate cancer. Nature 520, 353–357 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Hong, M. K. H. et al. Tracking the origins and drivers of subclonal metastatic expansion in prostate cancer. Nat. Commun. 6, 6605 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Mitchell, T. J. et al. Timing the landmark events in the evolution of clear cell renal cell cancer: TRACERx Renal. Cell 173, 611–623.e17 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Turajlic, S. et al. Tracking cancer evolution reveals constrained routes to metastases: TRACERx Renal. Cell 173, 581–594.e12 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Andor, N. et al. Pan-cancer analysis of the extent and consequences of intratumor heterogeneity. Nat. Med. 22, 105–113 (2016).

    CAS  Google Scholar 

  8. 8.

    Espiritu, S. M. G. et al. The evolutionary landscape of localized prostate cancers drives clinical aggression. Cell 173, 1003–1013.e15 (2018).

    CAS  PubMed  Google Scholar 

  9. 9.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Fittall, M. W. & Van Loo, P. Translating insights into tumor evolution to clinical practice: promises and challenges. Genome Med. 11, 20 (2019).

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Sendorek, D. H. et al. Germline contamination and leakage in whole genome somatic single nucleotide variant detection. BMC Bioinformatics 19, 28 (2018).

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    Alioto, T. S. et al. A comprehensive assessment of somatic mutation detection in cancer using whole-genome sequencing. Nat. Commun. 6, 10001 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Sun, R. et al. Between-region genetic divergence reflects the mode and tempo of tumor evolution. Nat. Genet. 49, 1015–1024 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Salehi, S. et al. ddClone: joint statistical inference of clonal populations from single cell and bulk tumour sequencing data. Genome Biol. 18, 44 (2017).

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Dentro, S. C. et al. Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes. Preprint at bioRxiv https://doi.org/10.1101/312041 (2020).

  16. 16.

    Gerstung, M. et al. The evolutionary history of 2,658 cancers. Nature 578, 122–128 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Abbosh, C. et al. Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nature 545, 446–451 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Noorani, A. et al. Genomic evidence supports a clonal diaspora model for metastases of esophageal adenocarcinoma. Nat. Genet. 52, 74–83 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    McGranahan, N. et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 351, 1463–1469 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Gomez, K. et al. Somatic evolutionary timings of driver mutations. BMC Cancer 18, 85 (2018).

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    Opasic, L., Zhou, D., Werner, B., Dingli, D. & Traulsen, A. How many samples are needed to infer truly clonal mutations from heterogenous tumours? BMC Cancer 19, 403 (2019).

    PubMed  PubMed Central  Google Scholar 

  22. 22.

    Salcedo, A. et al. A community effort to create standards for evaluating tumor subclonal reconstruction. Nat. Biotechnol. 38, 97–107 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Griffith, M. et al. Optimizing cancer genome sequencing and analysis. Cell Syst. 1, 210–223 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Caravagna, G. et al. Subclonal reconstruction of tumors by using machine learning and population genetics. Nat. Genet. 52, 898–907 (2020).

    CAS  PubMed  Google Scholar 

  25. 25.

    Deshwar, A. G. et al. PhyloWGS: reconstructing subclonal composition and evolution from whole-genome sequencing of tumors. Genome Biol. 16, 35 (2015).

    PubMed  PubMed Central  Google Scholar 

  26. 26.

    Laks, E. et al. Clonal decomposition and DNA replication states defined by scaled single-cell genome sequencing. Cell 179, 1207–1221.e22 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Schwarz, R. F. et al. Phylogenetic quantification of intra-tumour heterogeneity. PLOS Comput. Biol. 10, e1003535 (2014).

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    Ha, G. et al. TITAN: inference of copy number architectures in clonal cell populations from tumor whole-genome sequence data. Genome Res. 24, 1881–1893 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    El-Kebir, M. SPhyR: tumor phylogeny estimation from single-cell sequencing data under loss and error. Bioinformatics 34, i671–i679 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Zhang, J. et al. Intratumor heterogeneity in localized lung adenocarcinomas delineated by multiregion sequencing. Science 346, 256–259 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Roth, A. et al. PyClone: statistical inference of clonal population structure in cancer. Nat. Methods 11, 396–398 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Shi, W. et al. Reliability of whole-exome sequencing for assessing intratumor genetic heterogeneity. Cell Rep. 25, 1446–1457 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Yates, L. R. et al. Subclonal diversification of primary breast cancer revealed by multiregion sequencing. Nat. Med. 21, 751–759 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Schuh, A. et al. Monitoring chronic lymphocytic leukemia progression by whole genome sequencing reveals heterogeneous clonal evolution patterns. Blood 120, 4191–4196 (2012).

    CAS  PubMed  Google Scholar 

  35. 35.

    Boutros, P. C. et al. Spatial genomic heterogeneity within localized, multifocal prostate cancer. Nat. Genet. 47, 736–745 (2015).

    CAS  PubMed  Google Scholar 

  36. 36.

    Robbe, P. et al. Clinical whole-genome sequencing from routine formalin-fixed, paraffin-embedded specimens: pilot study for the 100,000 Genomes Project. Genet. Med. 20, 1196–1205 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Chin, S.-F. et al. Shallow whole genome sequencing for robust copy number profiling of formalin-fixed paraffin-embedded breast cancers. Exp. Mol. Pathol. 104, 161–169 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Nik-Zainal, S. et al. The life history of 21 breast cancers. Cell 149, 994–1007 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Deshpande, A., Walradt, T., Hu, Y., Koren, A. & Imielinski, M. Robust foreground detection in somatic copy number data. Preprint at bioRxiv https://doi.org/10.1101/847681 (2019).

  40. 40.

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

    PubMed  Google Scholar 

  41. 41.

    Dentro, S. C., Wedge, D. C. & Van Loo, P. Principles of reconstructing the subclonal architecture of cancers. Cold Spring Harb. Perspect. Med. 7, a026625 (2017).

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Chiang, D. Y. et al. High-resolution mapping of copy-number alterations with massively parallel sequencing. Nat. Methods 6, 99–103 (2009).

    CAS  PubMed  Google Scholar 

  43. 43.

    Olshen, A. B., Venkatraman, E. S., Lucito, R. & Wigler, M. Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 5, 557–572 (2004).

    Google Scholar 

  44. 44.

    Nilsen, G. et al. Copynumber: efficient algorithms for single- and multi-track copy number segmentation. BMC Genomics 13, 591 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Lai, D. & Shah, S. HMMcopy: copy number prediction with correction for GC and mappability bias for HTS data. R Package Version 1 (2012).

  46. 46.

    Fischer, A., Vázquez-García, I., Illingworth, C. J. R. & Mustonen, V. High-definition reconstruction of clonal composition in cancer. Cell Rep. 7, 1740–1752 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    McPherson, A. W. et al. ReMixT: clone-specific genomic structure estimation in cancer. Genome Biol. 18, 140 (2017).

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Oesper, L., Mahmoody, A. & Raphael, B. J. THetA: inferring intra-tumor heterogeneity from high-throughput DNA sequencing data. Genome Biol. 14, R80 (2013).

    PubMed  PubMed Central  Google Scholar 

  49. 49.

    Jiang, Y., Qiu, Y., Minn, A. J. & Zhang, N. R. Assessing intratumor heterogeneity and tracking longitudinal and spatial clonal evolutionary history by next-generation sequencing. Proc. Natl Acad. Sci. USA 113, E5528–E5537 (2016).

    CAS  PubMed  Google Scholar 

  50. 50.

    Müller, C. A. et al. The dynamics of genome replication using deep sequencing. Nucleic Acids Res. 42, e3 (2014).

    PubMed  Google Scholar 

  51. 51.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Steele, C. D. et al. Undifferentiated sarcomas develop through distinct evolutionary pathways. Cancer Cell 35, 441–456.e8 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Almendro, V. et al. Genetic and phenotypic diversity in breast tumor metastases. Cancer Res. 74, 1338–1348 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Farahani, H. et al. Engineered in-vitro cell line mixtures and robust evaluation of computational methods for clonal decomposition and longitudinal dynamics in cancer. Sci. Rep. 7, 13467 (2017).

    PubMed  PubMed Central  Google Scholar 

  55. 55.

    Miller, C. A. et al. SciClone: inferring clonal architecture and tracking the spatial and temporal patterns of tumor evolution. PLOS Comput. Biol. 10, e1003665 (2014).

    PubMed  PubMed Central  Google Scholar 

  56. 56.

    Popic, V. et al. Fast and scalable inference of multi-sample cancer lineages. Genome Biol. 16, 91 (2015).

    PubMed  PubMed Central  Google Scholar 

  57. 57.

    Sottoriva, A. et al. A Big Bang model of human colorectal tumor growth. Nat. Genet. 47, 209–216 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Williams, M. J., Werner, B., Barnes, C. P., Graham, T. A. & Sottoriva, A. Identification of neutral tumor evolution across cancer types. Nat. Genet. 48, 238–244 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Williams, M. J. et al. Quantification of subclonal selection in cancer from bulk sequencing data. Nat. Genet. 50, 895–903 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Strino, F., Parisi, F., Micsinai, M. & Kluger, Y. TrAp: a tree approach for fingerprinting subclonal tumor composition. Nucleic Acids Res. 41, e165 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Marass, F. et al. A phylogenetic latent feature model for clonal deconvolution. Ann. Appl. Stat. 10, 2377–2404 (2016).

    Google Scholar 

  62. 62.

    Jiao, W., Vembu, S., Deshwar, A. G., Stein, L. & Morris, Q. Inferring clonal evolution of tumors from single nucleotide somatic mutations. BMC Bioinformatics 15, 35 (2014).

    PubMed  PubMed Central  Google Scholar 

  63. 63.

    Ewing, A. D. et al. Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection. Nat. Methods 12, 623–630 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Zhou, T., Müller, P., Sengupta, S. & Ji, Y. PairClone: a Bayesian subclone caller based on mutation pairs. J. R. Stat. Soc. Ser. C Appl. Stat. 68, 705–725 (2019).

    Google Scholar 

  65. 65.

    El-Kebir, M., Satas, G. & Raphael, B. J. Inferring parsimonious migration histories for metastatic cancers. Cancer 2, 5 (2018).

    Google Scholar 

  66. 66.

    Zamani Esteki, M. et al. Concurrent whole-genome haplotyping and copy-number profiling of single cells. Am. J. Hum. Genet. 96, 894–912 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Mantere, T., Kersten, S. & Hoischen, A. Long-read sequencing emerging in medical genetics. Front. Genet. 10, 426 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Sedlazeck, F. J. et al. Accurate detection of complex structural variations using single-molecule sequencing. Nat. Methods 15, 461–468 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Dong, X. et al. Accurate identification of single-nucleotide variants in whole-genome-amplified single cells. Nat. Methods 14, 491–493 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Martelotto, L. G. et al. Whole-genome single-cell copy number profiling from formalin-fixed paraffin-embedded samples. Nat. Med. 23, 376–385 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Huddleston, J. et al. Discovery and genotyping of structural variation from long-read haploid genome sequence data. Genome Res. 27, 677–685 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Malikic, S., Jahn, K., Kuipers, J., Sahinalp, S. C. & Beerenwinkel, N. Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data. Nat. Commun. 10, 2750 (2019).

    PubMed  PubMed Central  Google Scholar 

  73. 73.

    Abécassis, J. et al. Assessing reliability of intra-tumor heterogeneity estimates from single sample whole exome sequencing data. PLoS One 14, e0224143 (2019).

    PubMed  PubMed Central  Google Scholar 

  74. 74.

    Liu, L. Y. et al. Quantifying the influence of mutation detection on tumour subclonal reconstruction. Preprint at bioRxiv https://doi.org/10.1101/418780 (2020).

  75. 75.

    Parikh, A. R. et al. Liquid versus tissue biopsy for detecting acquired resistance and tumor heterogeneity in gastrointestinal cancers. Nat. Med. 25, 1415–1421 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Litchfield, D. K. et al. Representative sequencing: unbiased sampling of solid tumor tissue. Cell Rep. 31, 107550 (2019).

    Google Scholar 

  77. 77.

    Eirew, P. et al. Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution. Nature 518, 422–426 (2015).

    CAS  PubMed  Google Scholar 

  78. 78.

    Kim, C. et al. Chemoresistance evolution in triple-negative breast cancer delineated by single-cell sequencing. Cell 173, 879–893.e13 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. 79.

    Gawad, C., Koh, W. & Quake, S. R. Single-cell genome sequencing: current state of the science. Nat. Rev. Genet. 17, 175–188 (2016).

    CAS  PubMed  Google Scholar 

  80. 80.

    Yoshida, K. et al. Tobacco smoking and somatic mutations in human bronchial epithelium. Nature 578, 266–272 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. 81.

    Zahn, H. et al. Scalable whole-genome single-cell library preparation without preamplification. Nat. Methods 14, 167–173 (2017).

    CAS  PubMed  Google Scholar 

  82. 82.

    Chkhaidze, K. et al. Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data. PLOS Comput. Biol. 15, e1007243 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. 83.

    Eaton, J., Wang, J. & Schwartz, R. Deconvolution and phylogeny inference of structural variations in tumor genomic samples. Bioinformatics 34, i357–i365 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. 84.

    Cmero, M. et al. Inferring structural variant cancer cell fraction. Nat. Commun. 11, 730 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. 85.

    Noorbakhsh, J. & Chuang, J. H. Uncertainties in tumor allele frequencies limit power to infer evolutionary pressures. Nat. Genet. 49, 1288–1289 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. 86.

    Tarabichi, M. et al. Neutral tumor evolution? Nat. Genet. 50, 1630–1633 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. 87.

    Heide, T. et al. Reply to ‘Neutral tumor evolution?’. Nat. Genet. 50, 1633–1637 (2018).

    CAS  PubMed  Google Scholar 

  88. 88.

    Williams, M. J., Werner, B., Barnes, C. P., Graham, T. A. & Sottoriva, A. Reply: Uncertainties in tumor allele frequencies limit power to infer evolutionary pressures. Nat. Genet. 49, 1289–1291 (2017).

    CAS  PubMed  Google Scholar 

  89. 89.

    Zare, F., Dow, M., Monteleone, N., Hosny, A. & Nabavi, S. An evaluation of copy number variation detection tools for cancer using whole exome sequencing data. BMC Bioinformatics 18, 286 (2017).

    PubMed  PubMed Central  Google Scholar 

  90. 90.

    Gerlinger, M. et al. Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing. Nat. Genet. 46, 225–233 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. 91.

    Vinci, M. et al. Functional diversity and cooperativity between subclonal populations of pediatric glioblastoma and diffuse intrinsic pontine glioma cells. Nat. Med. 24, 1204–1215 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. 92.

    Kuipers, J., Jahn, K., Raphael, B. J. & Beerenwinkel, N. Single-cell sequencing data reveal widespread recurrence and loss of mutational hits in the life histories of tumors. Genome Res. 27, 1885–1894 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. 93.

    Rieber, N. et al. Coverage bias and sensitivity of variant calling for four whole-genome sequencing technologies. PLoS One 8, e66621 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

A.S. was supported by an NSERC CGS. M.T. and P.V.L. are supported by the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001202), the UK Medical Research Council (FC001202) and the Wellcome Trust (FC001202). M.T. is a postdoctoral fellow supported by the European Union’s Horizon 2020 research and innovation program (Marie Skłodowska-Curie grant agreement no. 747852-SIOMICS). 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. M.N.L. was supported by a Junior Research Fellowship (Trinity College, University of Oxford). P.C.B. was supported by a Terry Fox Research Institute New Investigator Award and a CIHR New Investigator Award. Q.M. is supported by an Associate Investigator Award from the Ontario Institute of Cancer Research and holds a Canada CIFAR AI chair. This work was supported by the NIH/NCI under award numbers P30CA016042 (P.C.B.) and P30-CA008748 (Q.M.), and through support from the ITCR (1U24CA248265-01) to P.C.B.

Author information

Affiliations

Authors

Contributions

M.T., A.S., A.G.D., M.N.L., J.W., D.C.W., Q.D.M., P.V.L. and P.C.B. wrote the text. D.C.W., Q.D.M., P.V.L. and P.C.B. oversaw the completion of this work.

Corresponding author

Correspondence to Paul C. Boutros.

Ethics declarations

Competing interests

P.C.B is a member of the Scientific Advisory Boards of BioSymetrics Inc. and Intersect Diagnostics Inc. M.T., A.S., A.G.D., M.N.L., J.W., D.C.W., Q.D.M., and P.V.L. declare no competing interests.

Additional information

Peer review information Nature Methods thanks the anonymous reviewers for their contribution to the peer review of this work. Lin Tang was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Note

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Tarabichi, M., Salcedo, A., Deshwar, A.G. et al. A practical guide to cancer subclonal reconstruction from DNA sequencing. Nat Methods 18, 144–155 (2021). https://doi.org/10.1038/s41592-020-01013-2

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing