A major challenge to personalized oncology is that driver mutations vary among cancer cells inhabiting the same tumor. Whether this reflects principally disparate patterns of Darwinian evolution in different tumor regions has remained unexplored1,2,3,4,5. We mapped the prevalence of genetically distinct clones over 250 regions in 54 childhood cancers. This showed that primary tumors can simultaneously follow up to four evolutionary trajectories over different anatomic areas. The most common pattern consists of subclones with very few mutations confined to a single tumor region. The second most common is a stable coexistence, over vast areas, of clones characterized by changes in chromosome numbers. This is contrasted by a third, less frequent, pattern where a clone with driver mutations or structural chromosome rearrangements emerges through a clonal sweep to dominate an anatomical region. The fourth and rarest pattern is the local emergence of a myriad of clones with TP53 inactivation. Death from disease was limited to tumors exhibiting the two last, most dynamic patterns.

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This study was supported by grants to D.G. from the Swedish Research Foundation (2016-01022), the Swedish Cancer Society (CAN2015/284), the Swedish Childhood Cancer Foundation (PR2016-024, NCP2015-0035), the Crafoord Foundation, the Royal Physiographic Society and the Medical Faculty of Lund University Sweden. We also acknowledge technical support from the Science for Life Laboratory, the Knut and Alice Wallenberg Foundation, the National Genomics Infrastructure founded by the Swedish Research Council and Uppsala Multidisciplinary Center for Advanced Computational Science for assistance with massively parallel sequencing and access to the UPPMAX computational infrastructure. We also thank the Swegene Centre for Integrative Biology at Lund University for assistance.

Author information

Author notes

  1. These authors contributed equally: Jenny Karlsson, Anders Valind.


  1. Division of Clinical Genetics, Department of Laboratory Medicine, Lund University, Lund, Sweden

    • Jenny Karlsson
    • , Anders Valind
    • , Linda Holmquist Mengelbier
    • , Sofia Bredin
    • , Louise Cornmark
    • , Caroline Jansson
    • , Amina Wali
    • , Tord Jonson
    •  & David Gisselsson
  2. Division of Oncology and Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden

    • Johan Staaf
    •  & David Gisselsson
  3. Science for Life Laboratory, Department of Medical Sciences, Uppsala University, Uppsala, Sweden

    • Björn Viklund
    •  & Anders Isaksson
  4. Division of Pediatric Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden

    • Ingrid Øra
  5. Division of Pediatric Surgery, Department of Clinical Sciences, Lund University, Lund, Sweden

    • Anna Börjesson
    •  & Torbjörn Backman
  6. Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund, Sweden

    • Noémie Braekeveldt
    •  & Daniel Bexell
  7. Division of Pediatric Oncology and the Childhood Cancer Research Unit, Department of Women’s and Children’s Health, Karolinska Institute, Stockholm, Sweden

    • Bengt Sandstedt
    •  & Niklas Pal
  8. Department of Pathology, Laboratory Medicine, Medical Services Skåne, Lund, Sweden

    • Barbara Gürtl Lackner
    • , Daniel Bexell
    •  & David Gisselsson


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J.K., A.V. and D.G. conceived and designed the project. J.K., A.V., L.H.M., J.S., B.V., T.J. and A.I. coordinated and analyzed whole-genome genotyping and sequencing data, while A.V. performed phylogenetic analysis. S.B., I.Ø., A.B., T.B., B.S., N.P., B.G.L., N.B., D.B. and D.G. performed clinical correlation studies and contributed tumor material. L.C. prepared knockout cell lines. C.J. and A.W. performed technical work.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to David Gisselsson.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–19 and Supplementary Tables 1 and 2

  2. Reporting Summary

  3. Supplementary Data 1

    Overview of patients and analytical platforms

  4. Supplementary Data 2

    Segmented allelic imbalances

  5. Supplementary Data 3

    Mutations detected by whole-exome sequencing

  6. Supplementary Data 4

    Variants identified with deep resequencing

  7. Supplementary Data 5

    Analysis performed on each sample

  8. Supplementary Data 6

    Genome profiles from whole-genome genotyping

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