Abstract

Most adult carcinomas develop from noninvasive precursor lesions, a progression that is supported by genetic analysis. However, the evolutionary and genetic relationships among co-existing lesions are unclear. Here we analysed the somatic variants of pancreatic cancers and precursor lesions sampled from distinct regions of the same pancreas. After inferring evolutionary relationships, we found that the ancestral cell had initiated and clonally expanded to form one or more lesions, and that subsequent driver gene mutations eventually led to invasive pancreatic cancer. We estimate that this multi-step progression generally spans many years. These new data reframe the step-wise progression model of pancreatic cancer by illustrating that independent, high-grade pancreatic precursor lesions observed in a single pancreas often represent a single neoplasm that has colonized the ductal system, accumulating spatial and genetic divergence over time.

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Data availability

Sequence data have been deposited at the European Genome-phenome Archive (EGA; https://www.ebi.ac.uk/ega/), which is hosted by the European Bioinformatics Institute (EBI) and the Centre for Genomic Regulation (CRG), under accession number EGAS00001002778. Source data are provided for Fig. 3b and Extended Data Figs. 1, 7, 8. All other relevant data are included within the manuscript or are available upon request from the corresponding author.

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

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Acknowledgements

Supported by the V Foundation for Cancer Research, NIH grants F31 CA180682, 2T32 CA160001-06 and 5T32 CA067751-13, an Erwin Schrödinger fellowship (Austrian Science Fund FWF J-3996), SPORE grant P50 CA062924, the Michael Rolfe Foundation, The Lustgarten Foundation for Cancer Research, the Sol Goldman Center for Pancreatic Cancer Research, The Virginia and D.K. Ludwig Fund for Cancer Research and D. Troper and S. Wojcicki.

Reviewer information

Nature thanks A. V. Biankin, S. J. Chanock and F. Markowetz for their contribution to the peer review of this work.

Author information

Author notes

  1. These authors contributed equally: Alvin P. Makohon-Moore, Karen Matsukuma, Ming Zhang, Johannes G. Reiter, Jeffrey M. Gerold

Affiliations

  1. The David M. Rubenstein Center for Pancreatic Cancer Research, Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    • Alvin P. Makohon-Moore
    • , Lisa Sikkema
    • , Marc A. Attiyeh
    •  & Christine A. Iacobuzio-Donahue
  2. Department of Pathology, University of California, Davis, Sacramento, CA, USA

    • Karen Matsukuma
  3. The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA

    • Ming Zhang
    • , Ralph H. Hruban
    •  & Bert Vogelstein
  4. Canary Center for Cancer Early Detection, Department of Radiology, Stanford University School of Medicine, Palo Alto, CA, USA

    • Johannes G. Reiter
  5. Program for Evolutionary Dynamics, Harvard University, Cambridge, MA, USA

    • Johannes G. Reiter
    • , Jeffrey M. Gerold
    •  & Martin A. Nowak
  6. The Ludwig Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA

    • Yuchen Jiao
    • , Nickolas Papadopoulos
    • , Kenneth W. Kinzler
    •  & Bert Vogelstein
  7. VU University Amsterdam, Master’s Oncology Program, VU University Medical Center, Amsterdam, The Netherlands

    • Lisa Sikkema
  8. Department of Cancer Genome Informatics, Graduate School of Medicine, Osaka University, Osaka, Japan

    • Shinichi Yachida
  9. Department of Art as Applied to Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA

    • Corinne Sandone
  10. Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA

    • Ralph H. Hruban
  11. Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA

    • Ralph H. Hruban
  12. Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    • David S. Klimstra
    •  & Christine A. Iacobuzio-Donahue
  13. Department of Mathematics, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA

    • Martin A. Nowak
  14. Howard Hughes Medical Institute, The Johns Hopkins Kimmel Cancer Center, Baltimore, MD, USA

    • Bert Vogelstein

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Contributions

A.P.M.-M., K.M., Y.J., N.P., K.K., B.V., and C.I.-D. designed the study; K.M., S.Y., R.H.H., and C.I.-D. selected the samples; S.Y., K.M., R.H.H., D.S.K., and C.I.-D. reviewed pathology; S.Y., K.M. and M.Z. prepared the DNA samples; A.P.M.-M., K.M., M.Z., Y.J., N.P., K.K., B.V., and C.I.-D. performed sequencing, alignment and mutation calling; A.P.M.-M., J.G.R., J.M.G., and M.A. derived the phylogenies; A.P.M.-M., J.G.R., J.M.G., M.A., and L.S. analysed the structural variants; J.G.R., J.M.G., and M.A.N. performed mathematical modelling; C.S. illustrated the spatial evolution of the lesions; and all authors wrote the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Christine A. Iacobuzio-Donahue.

Extended data figures and tables

  1. Extended Data Fig. 1 Mutation counts and features of samples.

    a, Number of somatic mutations detected per sample with clinical features of each patient. The y-axis shows mutation counts and the x-axis shows patient ID. FS Del, frameshift deletion; FS Ins, frameshift insertion; IF Del, in-frame deletion; IF Ins, in-frame insertion; TSS, transcription start site. b, c, Box and whisker plots comparing number of somatic SNVs and CNAs between PanINs and PDACs. Yellow, PanINs; green, PDACs. Horizontal line indicates median, whiskers indicate minimum and maximum, and box indicates quartiles. n = 12 independent PanIN lesions, n = 8 PDACs. b, Number of SNVs and indels in PanINs and PDACs. c, Number of CNAs in PanINs and PDACs (hisens results only). Source data

  2. Extended Data Fig. 2 Allelic CNAs across all patient samples.

    CNAs were inferred using the FACETS algorithm (Supplementary Table 3, FACETS.purity variants shown). Blue, putative losses; red, putative gains.

  3. Extended Data Fig. 3 Phylogenetics of PanINs and the matched primary tumour for patients PIN102 and PIN105.

    See Supplementary Table 1 for sample identities. Gene names in orange text are SNVs or indels, in blue are copy-number losses, and in red are copy-number gains affecting putative driver genes. The sequencing data for each driver gene variant was manually reviewed to verify phylogenetic position. For each phylogeny, the number of acquired mutations is in black. The branch lengths are proportional to the number of SNVs and indels. The dashed line indicates the branch from the germline to the PDAC and PanIN-A. For the Bayesian heat maps, samples are indicated on each row while variants are represented by each column. The colour of each tile indicates the probability that the variant is present or absent in the corresponding sample. Dark blue, >99.9% probability of being present; dark red, >99.9% probability of being absent. Light blue and red indicate lower probabilities; white tiles indicate approximately 50% probability. a, Phylogenetic tree and Bayesian heat map with each variant for PIN102. b, Phylogenetic tree and Bayesian heat map with each variant for PIN105.

  4. Extended Data Fig. 4 Phylogenetics of PanINs and matched primary tumours for patients PIN101, PIN103, PIN104, and PIN108.

    See Supplementary Table 1 for sample identities. Gene names in orange text are SNVs or indels, in blue are copy-number losses, and in red are copy-number gains affecting putative driver genes. The sequencing data for each driver gene variant were manually reviewed to verify phylogenetic position. For each phylogenetic tree, the numbers of acquired mutations are in black. The branch lengths are proportional to the number of SNVs or indels. The dashed lines indicate branches that have been extended to accommodate gene annotation and variant numbers. For the Bayesian heatmaps, samples are indicated on each row and variants are represented by each column. The colour of each tile indicates the probability that the variant is present or absent in the corresponding sample. Dark blue, >99.9% probability of being present; dark red, >99.9% probability of being absent. Light blue and red indicate lower probabilities; white tiles indicate approximately 50% probability. a, PIN101. In manual review of the sequencing data, a read supporting the presence of the KRAS(G12D) variant was detected in both the PDAC and PanIN-A samples and was thus moved to the trunk of the phylogeny despite the overall low coverage of KRAS in PanIN-A. b, PIN103. c, PIN104. The node leading from the first MRCA to the second MRCA has a confidence value of >99%. d, PIN108. The node leading from the first MRCA to the second MRCA has a confidence value of >99%.

  5. Extended Data Fig. 5 Phylogenetics of PanINs and matched primary tumours for patients PIN106 and PIN107.

    See Supplementary Table 1 for sample identities. Gene names in orange text are SNVs or indels, in blue are copy-number losses, and in red are copy-number gains affecting putative driver genes. The sequencing data for each driver gene variant were manually reviewed to verify phylogenetic position. For each phylogeny, the numbers of acquired mutations are in black. The branch lengths are proportional to the number of SNVs or indels. The dashed lines indicate branches that have been extended to accommodate gene annotation and variant numbers. For each Bayesian heat map, samples are indicated on each row while variants are represented by each column. The colour of each tile indicates the probability that the variant is present or absent in the corresponding sample. Dark blue, >99.9% probability of being present; dark red, >99.9% probability of being absent. Light blue and red indicate lower probabilities; white tiles indicate approximately 50% probability. a, PIN106. The node leading from the first MRCA to the second MRCA has a confidence value of >99% and the node leading from the second MRCA to the third MRCA has a confidence value of 82%. b, PIN107.

  6. Extended Data Fig. 6 Average signature abundance across samples.

    Signature numbers 1–30 from Alexandrov et al.43 are shown on the x-axis with signature abundance averaged across phylogenetic branches shown on the y-axis. Each histogram is coloured by signature identity.

  7. Extended Data Fig. 7 The proportion of mutational signatures from Alexandrov et al.43 estimated in PIN101–PIN104.

    Signatures are shown on the x-axis, with the proportion of each signature shown on the y-axis. Each bar is coloured by signature identity. The text on the top of each panel denotes the corresponding phylogenetic branch and the number of mutations acquired along it in parentheses. Error bars depict 90% CIs in the signature proportion estimated by 100 iterations of bootstrap resampling. Source data

  8. Extended Data Fig. 8 The proportion of mutational signatures from Alexandrov et al.43 estimated in PIN105–PIN108.

    Signatures are shown on the x-axis, with the proportion of each signature shown on the y-axis. Each bar is coloured by signature identity. The text on the top of each panel denotes the corresponding phylogenetic branch and the number of mutations acquired along it in parentheses. Error bars depict 90% CIs in the signature proportion estimated by 100 iterations of bootstrap resampling. Source data

Supplementary information

  1. Reporting Summary

  2. Supplementary Table 1

    Clinical features of each patient.

  3. Supplementary Table 2

    Somatic SNVs/INDELs detected among PanIN and PDAC lesions.

  4. Supplementary Table 3

    Putative somatic CNAs among PanIN and PDAC lesions.

  5. Supplementary Table 4

    Structural variants in each patient.

  6. Supplementary Table 5

    Putative driver gene SNVs/INDELs.

  7. Supplementary Table 6

    Jaccard similarity coefficients between all pairs of samples within each case.

Source data

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DOI

https://doi.org/10.1038/s41586-018-0481-8

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