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A community effort to create standards for evaluating tumor subclonal reconstruction

Nature Biotechnology volume 38, pages 97–107 (2020)Cite this article

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Abstract

Tumor DNA sequencing data can be interpreted by computational methods that analyze genomic heterogeneity to infer evolutionary dynamics. A growing number of studies have used these approaches to link cancer evolution with clinical progression and response to therapy. Although the inference of tumor phylogenies is rapidly becoming standard practice in cancer genome analyses, standards for evaluating them are lacking. To address this need, we systematically assess methods for reconstructing tumor subclonality. First, we elucidate the main algorithmic problems in subclonal reconstruction and develop quantitative metrics for evaluating them. Then we simulate realistic tumor genomes that harbor all known clonal and subclonal mutation types and processes. Finally, we benchmark 580 tumor reconstructions, varying tumor read depth, tumor type and somatic variant detection. Our analysis provides a baseline for the establishment of gold-standard methods to analyze tumor heterogeneity.

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Fig. 1: Features of tumor subclonal reconstruction.
Fig. 2: Quantifying performance of subclonal reconstruction algorithms.
Fig. 3: Simulating subclonal CNAs in tumor BAM files and spiking somatic mutations.
Fig. 4: Simulated realistic tumor genomes.
Fig. 5: Error profiles of subclonal reconstruction algorithms.
Fig. 6: Impact of CNA error profiles on subclonal reconstruction.

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Data availability

Sequences files are available at EGA under study accession no. EGAD00001003971.

Code availability

BAMSurgeon is available at: https://github.com/adamewing/bamsurgeon. The framework for subclonal mutation simulation is available at http://search.cpan.org/~boutroslb/NGS-Tools-BAMSurgeon-v1.0.0/. The PhaseTools BAM phasing toolkit is available at https://github.com/mateidavid/phase-tools. Scripts providing the complete scoring harness are available at: https://github.com/asalcedo31/SMC-Het_Scoring/smc_het_eval.

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Acknowledgements

We thank the members of their laboratories for support, and Sage Bionetworks and the DREAM Challenge organization for their ongoing support of the SMC-Het Challenge. In particular, we thank T. Norman, J.C. Bare, S. Friend and G. Stolovitzky for their patience, technical support and scientific insight. We also thank R. Sun and C. Curtis for kindly sharing code for calculating the intra-tumor heterogeneity metrics and building the support vector machine predictor in multi-region sequencing simulations. This study was conducted with the support of the Ontario Institute for Cancer Research to P.C.B. and J.T.S. through funding provided by the Government of Ontario. This work was supported by Prostate Cancer Canada and is proudly funded by the Movember Foundation (Grant no. RS2014-01 to P.C.B.). This study was conducted with the support of Movember funds through Prostate Cancer Canada and with the additional support of the Ontario Institute for Cancer Research, funded by the Government of Ontario. This project was supported by Genome Canada through a Large-Scale Applied Project contract to P.C.B., S.P. Shah and R.D. Morin. This work was supported by the Discovery Frontiers: Advancing Big Data Science in Genomics Research program, which is jointly funded by the Natural Sciences and Engineering Research Council of Canada, the Canadian Institutes of Health Research (CIHR), Genome Canada and the Canada Foundation for Innovation (CFI). Q.M. is a Canada CIFAR AI chair and is supported by an Associate Investigator award from OICR. This research is part of the University of Toronto’s Medicine by Design initiative, which receives funding from the Canada First Research Excellence Fund (CFREF). J.A.W. was partially supported by an Ontario Graduate Scholarship. This work was supported by The Francis Crick Institute, which receives its core funding from Cancer Research UK (grant no. FC001202), the UK Medical Research Council (grant no. FC001202), and the Wellcome Trust (grant no. FC001202). M.T. is a postdoctoral fellow supported by the European Union’s Horizon 2020 research and innovation program (Marie Sklodowska-Curie Grant Agreement no. 747852-SIOMICS). P.V.L. is a Winton Group Leader in recognition of the Winton Charitable Foundation’s support toward the establishment of The Francis Crick Institute. This project was enabled through access to the MRC eMedLab Medical Bioinformatics infrastructure, supported by the UK Medical Research Council (grant no. MR/L016311/1 to M.T. and P.V.L.). A.S. was partly supported by a CIHR CGS-doctoral award. P.C.B. was supported by a Terry Fox Research Institute New Investigator Award and a CIHR New Investigator Award. D.C.W. is supported by the Li Ka Shing foundation. The Galaxy portions of the evaluation system were supported by National Institutes of Health (NIH) grant nos. U41 HG006620 and R01 AI134384-01 as well as NSF grant no. 1661497. The following NIH grants supported this work: no. R01-CA180778 (to J.M.S.), no. U24-CA143858 (to J.M.S.) and no. P30-CA008748 (to Thompson, subgrant to Q.M.). We thank Google Inc. (in particular N. Deflaux) for their ongoing support of the ICGC-TCGA DREAM Somatic Mutation Calling Challenge. This work was supported by the NIH/NCI under award no. P30CA016042.

Author information

Author notes

  1. These authors contributed equally: Adriana Salcedo, Maxime Tarabichi, Shadrielle Melijah G. Espiritu, Amit G. Deshwar.

  2. These authors jointly supervised this work: Kyle Ellrott, David C. Wedge, Quaid Morris, Peter Van Loo, Paul C. Boutros.

Authors and Affiliations

  1. Ontario Institute for Cancer Research, Toronto, Canada

    Adriana Salcedo, Shadrielle Melijah G. Espiritu, Matei David, Nathan M. Wilson, Lydia Y. Liu, Minjeong Ko, Srinivasan Sivanandan, Christopher M. Lalansingh, Christine P’ng, Catalina V. Anghel, Imaad Umar, Bryan Lo, William Zou, Jared T. Simpson & Quaid Morris

  2. Department of Medical Biophysics, University of Toronto, Toronto, Canada

    Adriana Salcedo & Paul C. Boutros

  3. The Francis Crick Institute, London, UK

    Maxime Tarabichi, Stefan Dentro & Peter Van Loo

  4. Wellcome Trust Sanger Institute, Hinxton, UK

    Maxime Tarabichi & Stefan Dentro

  5. The Edward S. Rogers Senior Department of Electrical & Computer Engineering, Toronto, Canada

    Amit G. Deshwar

  6. Donnelly Centre, University of Toronto, Toronto, Canada

    Jeff A. Wintersinger & Quaid Morris

  7. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA

    Hongjiu Zhang & Yuanfang Guan

  8. Department of Systems Biology, Columbia University, New York, NY, USA

    Kaiyi Zhu, Tai-Hsien Ou Yang & Dimitris Anastassiou

  9. Center for Cancer Systems Therapeutics, Columbia University, New York, NY, USA

    Kaiyi Zhu, Tai-Hsien Ou Yang & Dimitris Anastassiou

  10. Department of Electrical Engineering, Columbia University, New York, NY, USA

    Kaiyi Zhu, Tai-Hsien Ou Yang & Dimitris Anastassiou

  11. Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, USA

    John M. Chilton & Kyle Ellrott

  12. Oregon Health & Sciences University, Portland, OR, USA

    Alex Buchanan & Kyle Ellrott

  13. Department of Biomolecular Engineering, Center for Biomolecular Sciences and Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA

    Joshua M. Stuart

  14. Herbert Irving Comprehensive Cancer Center, Columbia University, New York, USA

    Dimitris Anastassiou

  15. Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA

    Yuanfang Guan

  16. Department of Electronic Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA

    Yuanfang Guan

  17. Mater Research Institute, University of Queensland, Woolloongabba, Queensland, Australia

    Adam D. Ewing

  18. Big Data Institute, University of Oxford, Oxford, UK

    David C. Wedge

  19. Oxford NIHR Biomedical Research Centre, Oxford, UK

    David C. Wedge

  20. Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Quaid Morris

  21. Vector Institute for Artificial Intelligence, Toronto, Canada

    Quaid Morris

  22. Department of Human Genetics, University of Leuven, Leuven, Belgium

    Peter Van Loo

  23. Department of Pharmacology and Toxicology, University of Toronto, Toronto, Canada

    Paul C. Boutros

  24. Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA

    Paul C. Boutros

  25. Department of Urology, University of California, Los Angeles, Los Angeles, CA, USA

    Paul C. Boutros

  26. Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA, USA

    Paul C. Boutros

  27. Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, USA

    Paul C. Boutros

  28. Insight Centre for data analytics, NUIG, Dublin, Ireland

    Alokkumar Jha

  29. Bioinfo,HaploX Biotechnology, Shenzhen, China

    Tanxiao Huang

  30. University of Cologne, Cologne, Germany

    Tsun-Po Yang & Martin Peifer

  31. Indiana University, Bloomington, IN, USA

    Cenk Sahinalp

  32. Simon Fraser University, Burnaby, Canada

    Salem Malikic

  33. Memorial Sloan Kettering Cancer Center, Columbia University, Wellcome Sanger Institute, University of Cambridge, Cambridge, UK

    Ignacio Vázquez-García

  34. Wellcome Sanger Institute, University of Helsinki, Helsinki, Finland

    Ville Mustonen

  35. Levine Cancer Institute, Atrium Health, Charlotte, NC, USA

    Hsih-Te Yang

  36. Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan

    Ken-Ray Lee

  37. The University of Chicago, Chicago, IL, USA

    Yuan Ji

  38. NorthShore University HealthSystem, Evanston, IL, USA

    Subhajit Sengupta

  39. Bordeaux University, Bordeaux, France

    Justine Rudewicz & Quentin Schaeverbeke

  40. Bordeaux University/CNRS, Bordeaux, France

    Macha Nikolski

  41. School of Computing Science, University of Glasgow, Glasgow, Scotland

    Ke Yuan

  42. CRUK Cambridge Institute, University of Cambridge, Cambridge, UK

    Florian Markowetz, Geoff Macintyre & Belal Chaudhary

  43. University of Melbourne, Melbourne, Australia

    Marek Cmero

  44. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Ignaty Leshchiner, Dimitri Livitz & Gad Getz

  45. Computational Medicine Center, Thomas Jefferson University, Philadelphia, PA, USA

    Phillipe Loher

  46. The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Kaixian Yu & Wenyi Wang

  47. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Hongtu Zhu

Authors
  1. Adriana Salcedo
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  2. Maxime Tarabichi
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  3. Shadrielle Melijah G. Espiritu
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  4. Amit G. Deshwar
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  5. Matei David
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  6. Nathan M. Wilson
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  7. Stefan Dentro
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  8. Jeff A. Wintersinger
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  9. Lydia Y. Liu
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  10. Minjeong Ko
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  11. Srinivasan Sivanandan
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  12. Hongjiu Zhang
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  13. Kaiyi Zhu
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  14. Tai-Hsien Ou Yang
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  15. John M. Chilton
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  17. Christopher M. Lalansingh
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  18. Christine P’ng
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  19. Catalina V. Anghel
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  20. Imaad Umar
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  21. Bryan Lo
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  22. William Zou
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  23. Jared T. Simpson
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  26. Yuanfang Guan
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  27. Adam D. Ewing
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  28. Kyle Ellrott
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  29. David C. Wedge
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  30. Quaid Morris
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  32. Paul C. Boutros
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Consortia

DREAM SMC-Het Participants

  • Alokkumar Jha
  • , Tanxiao Huang
  • , Tsun-Po Yang
  • , Martin Peifer
  • , Cenk Sahinalp
  • , Salem Malikic
  • , Ignacio Vázquez-García
  • , Ville Mustonen
  • , Hsih-Te Yang
  • , Ken-Ray Lee
  • , Yuan Ji
  • , Subhajit Sengupta
  • , Justine Rudewicz
  • , Macha Nikolski
  • , Quentin Schaeverbeke
  • , Ke Yuan
  • , Florian Markowetz
  • , Geoff Macintyre
  • , Marek Cmero
  • , Belal Chaudhary
  • , Ignaty Leshchiner
  • , Dimitri Livitz
  • , Gad Getz
  • , Phillipe Loher
  • , Kaixian Yu
  • , Wenyi Wang
  •  & Hongtu Zhu

Contributions

All authors edited and approved the final manuscript. A.S. wrote the first draft of the paper, designed experiments, performed statistical analyses, performed bioinformatics analyses and performed data visualization. M.T. wrote the first draft of the paper, designed experiments, generated tools and reagents, performed statistical analyses, performed bioinformatics analyses and performed data visualization. S.M.G.E. wrote the first draft of the paper, generated tools and reagents, performed bioinformatics analyses and performed data visualization. A.G.D. wrote the first draft of the paper, designed experiments, generated tools and reagents and performed bioinformatics analyses. M.D., S.D., L.Y.L., S.S., H.Z. J.M.C., A.B., C.M.L., I.U. and B.L. generated tools and reagents. K.Z. and T.-H.O.Y. generated tools and reagents and performed bioinformatics analyses. A.D.E. generated tools and reagents and supervised research. N.M.W. performed bioinformatics analyses and performed data visualization. J.A.W., M.K., H.Z. and C.V.A. performed bioinformatics analyses. C.P. performed data visualization. J.T.S., J.M.S., D.A. and Y.G. supervised research. K.E. wrote the first draft of the paper and supervised research. D.C.W. designed experiments and supervised research. Q.M. wrote the first draft of the paper, designed experiments, generated tools and reagents and supervised research. P.V.L. wrote the first draft of the paper, designed experiments and supervised research. P.C.B. wrote the first draft of the paper, designed experiments and supervised research.

Corresponding author

Correspondence to Paul C. Boutros.

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Supplementary information

Supplementary Materials

Supplementary Figs. 1–6, Tables 2–5 and Notes 1–3.

Reporting Summary

Supplementary Table 1

Benchmark scores Unnormalized benchmark scores for all tumors and all subchallenges varying depth, mutation caller, CNA input, and subclonal reconstruction algorithms. The number of SNVs detected (SNVs), false positive (FP), false negative (FN), true positive (TP) and true negative (TN) rates for SNV detection are included as well the estimated cF.

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Salcedo, A., Tarabichi, M., Espiritu, S.M.G. et al. A community effort to create standards for evaluating tumor subclonal reconstruction. Nat Biotechnol 38, 97–107 (2020). https://doi.org/10.1038/s41587-019-0364-z

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