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Lymph node metastases develop through a wider evolutionary bottleneck than distant metastases

Nature Genetics volume 52, pages 692–700 (2020)Cite this article

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Abstract

Genetic diversity among metastases is poorly understood but contains important information about disease evolution at secondary sites. Here we investigate inter- and intra-lesion heterogeneity for two types of metastases that associate with different clinical outcomes: lymph node and distant organ metastases in human colorectal cancer. We develop a rigorous mathematical framework for quantifying metastatic phylogenetic diversity. Distant metastases are typically monophyletic and genetically similar to each other. Lymph node metastases, in contrast, display high levels of inter-lesion diversity. We validate these findings by analyzing 317 multi-region biopsies from an independent cohort of 20 patients. We further demonstrate higher levels of intra-lesion heterogeneity in lymph node than in distant metastases. Our results show that fewer primary tumor lineages seed distant metastases than lymph node metastases, indicating that the two sites are subject to different levels of selection. Thus, lymph node and distant metastases develop through fundamentally different evolutionary mechanisms.

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Fig. 1: Lymph node but not distant metastases form polyphyletic clades.
Fig. 2: Validation cohort confirms higher inter-metastatic heterogeneity in lymph node than in distant metastases.
Fig. 3: Stochastic model of metastasis diversity.
Fig. 4: Intra-metastatic diversity is higher in lymph node than in distant metastases.
Fig. 5: Confirmation of increased intra-metastatic diversity in lymph node metastases at single-cell resolution.

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Data availability

Raw polyguanine profiling data and phylogenetic trees for the discovery cohort (Naxerova et al.10) can be downloaded from https://datadryad.org (https://doi.org/10.5061/dryad.vv53d). Original whole-exome sequencing data of Kim et al.11 were deposited to the Sequence Read Archive at the NCBI under the project ID of PRJNA271316. Raw polyguanine profiling data for the new validation cohort are available from https://datadryad.org (https://doi.org/10.5061/dryad.9ghx3ffdf).

Code availability

The source code to calculate the RDS as well as to produce various figure panels is available as jupyter notebooks at http://github.com/johannesreiter/rootdiversity. The notebooks are implemented in Python 3.6. All required input data are contained in Supplementary Tables 1–7.

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Acknowledgements

This work was support by grants from the NIH (R37CA225655), AACR (561314) and NHLBI (P01HL142494) to K.N. and from NCI (R00CA22999102) to J.G.R. We thank S. Bian, F. Tang and W. Fu for sharing with us the frequencies of CRC01 subclones across different anatomic sites.

Author information

Author notes

  1. These authors contributed equally: Johannes G. Reiter, Wei-Ting Hung.

Authors and Affiliations

  1. Canary Center for Cancer Early Detection, Department of Radiology, Stanford University School of Medicine, Palo Alto, CA, USA

    Johannes G. Reiter & Shriya Nagpal

  2. Stanford Cancer Institute, Stanford University School of Medicine, Palo Alto, CA, USA

    Johannes G. Reiter

  3. Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA

    Johannes G. Reiter

  4. Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Wei-Ting Hung, I-Hsiu Lee, Peter Giunta, Sebastian Degner, Gang Liu, Emma C. E. Wassenaar & Kamila Naxerova

  5. Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Wei-Ting Hung, I-Hsiu Lee, Peter Giunta, Sebastian Degner, Gang Liu, Emma C. E. Wassenaar & Kamila Naxerova

  6. Center for Applied Mathematics, Cornell University, Ithaca, NY, USA

    Shriya Nagpal

  7. Department of Surgery, St Antonius Hospital, Nieuwegein, the Netherlands

    Emma C. E. Wassenaar

  8. Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    William R. Jeck, Martin S. Taylor, Alexander A. Farahani, Hetal D. Marble & Jochen K. Lennerz

  9. Center for Integrated Diagnostics, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    William R. Jeck, Martin S. Taylor, Alexander A. Farahani, Hetal D. Marble & Jochen K. Lennerz

  10. Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Simon Knott

  11. Division of Cancer and Imaging, University Medical Center Utrecht, Utrecht, the Netherlands

    Onno Kranenburg

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Contributions

J.G.R., K.N., W.-T.H., P.G., G.L., I.L., S.D. and E.C.E.W. analyzed data. J.G.R. and S.N. developed the mathematical framework. W.-T.H., P.G. and G.L. performed experiments. W.R.J., M.S.T., A.A.F., H.D.M. and J.K.L. obtained and reviewed clinical samples and clinical data. S.K. and O.K. contributed to data interpretation. K.N. and J.G.R. designed the study. K.N., J.G.R. and W.-T.H. wrote the manuscript with input from all authors.

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Correspondence to Johannes G. Reiter or Kamila Naxerova.

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Extended data

Extended Data Fig. 1 Distances to tree root (germline).

Mean distances between the root normal sample and samples of primary tumors, lymphatic metastases, and distant metastases, respectively. Distance was measured as the number of internal nodes separating a pair of samples and then normalized by the total number of internal nodes in a given phylogeny. Means are 0.51 for N=16 primary tumors, 0.55 for N=16 lymphatic metastases, and 0.68 for N=16 distant metastases. Box plot elements: center line, median; magenta diamond, mean; box limits, lower and upper quartiles; whiskers, lowest and highest value within 1.5 IQR.

Extended Data Fig. 2 Branch lengths to tree root (germline) for the validation cohort.

Comparison of normalized branch lengths from the normal sample to N=107 primary tumor regions, N=86 lymphatic metastases, and N=34 distant metastases in the validation cohort. Branch lengths were significantly different (p=4.9e-7, Kruskal-Wallis test). Branch lengths for distant metastases were significantly longer than for primary tumor samples (mean 0.9 vs 0.75; p=1.8e-7, Conover’s test) and longer than for lymphatic metastases (mean 0.9 vs 0.77; p=1.9e-6, Conover’s test). Box plot elements: center line, median; magenta diamond, mean; box limits, lower and upper quartiles; whiskers, lowest and highest value within 1.5 IQR.

Extended Data Fig. 3 Spatial classification of primary tumor biopsies.

Spatial classification of primary tumor samples. a, Primary tumor biopsies are classified as luminal or deep by a board-certified pathologist based on established anatomical landmarks. b, Percentages of luminal and deep primary tumor samples in the Naxerova and Reiter/Hung cohorts. c, For each lymphatic and distant metastasis, the closest primary tumor sample is found in the polyguanine marker-based distance matrix. Luminal/deep classifications of closest primary tumor samples are plotted separately for lymphatic and distant metastases. d, As in (c) for the Reiter/Hung cohort. e, as in (c) and (d) for the combined two cohorts. White numbers in panels (b)-(e) denote the number of samples in each group. Two-tailed Fisher’s exact tests were used to calculate the p-values.

Extended Data Fig. 4 Numbers of LN-seeding and DM-seeding clones.

Median RDSLN/RDSDM values for simulations of 10 LN-seeding clones and variable numbers of DM-seeding clones. 100 patients were simulated per parameter combination. The experimentally determined fold change (Naxerova & Reiter/Hung & Kim cohorts) is shown as vertical red line.

Supplementary information

Supplementary Information

Supplementary Figs. 1–32 and Notes

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Supplementary Tables

Supplementary Tables 1–7

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Reiter, J.G., Hung, WT., Lee, IH. et al. Lymph node metastases develop through a wider evolutionary bottleneck than distant metastases. Nat Genet 52, 692–700 (2020). https://doi.org/10.1038/s41588-020-0633-2

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