Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Quantitative evidence for early metastatic seeding in colorectal cancer

Abstract

Both the timing and molecular determinants of metastasis are unknown, hindering treatment and prevention efforts. Here we characterize the evolutionary dynamics of this lethal process by analyzing exome-sequencing data from 118 biopsies from 23 patients with colorectal cancer with metastases to the liver or brain. The data show that the genomic divergence between the primary tumor and metastasis is low and that canonical driver genes were acquired early. Analysis within a spatial tumor growth model and statistical inference framework indicates that early disseminated cells commonly (81%, 17 out of 21 evaluable patients) seed metastases while the carcinoma is clinically undetectable (typically, less than 0.01 cm3). We validated the association between early drivers and metastasis in an independent cohort of 2,751 colorectal cancers, demonstrating their utility as biomarkers of metastasis. This conceptual and analytical framework provides quantitative in vivo evidence that systemic spread can occur early in colorectal cancer and illuminates strategies for patient stratification and therapeutic targeting of the canonical drivers of tumorigenesis.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Study overview.
Fig. 2: The mutational landscape and patterns of genetic divergence in paired primary CRCs and metastases.
Fig. 3: Within- and between-lesion heterogeneity in paired primary CRCs and metastases.
Fig. 4: Correlation between the Lp, Lm and H and primary carcinoma size at the time of dissemination.
Fig. 5: Patient-specific inference of the timing of metastasis in CRC.
Fig. 6: Enrichment of early driver gene modules in mCRC and clinical implications of early dissemination.

Similar content being viewed by others

Data availability

Data have been deposited at the European Genotype Phenotype Archive (EGA) under accession number EGAS00001003573. Data from previously published studies are available from the DDBJ (accession number JGAS00000000060)21 and the SRA (accession numbers SRP052609, SRP074289 and SRP041725)29,30,31.

Code availability

Code used for genomic data analysis and simulation studies are available at https://github.com/cancersysbio/mCRCs and https://github.com/cancersysbio/SCIMET.

References

  1. Vanharanta, S. & Massagué, J. Origins of metastatic traits. Cancer Cell 24, 410–421 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Turajlic, S. & Swanton, C. Metastasis as an evolutionary process. Science 352, 169–175 (2016).

    Article  CAS  PubMed  Google Scholar 

  3. Lambert, A. W., Pattabiraman, D. R. & Weinberg, R. A. Emerging biological principles of metastasis. Cell 168, 670–691 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Jones, S. et al. Comparative lesion sequencing provides insights into tumor evolution. Proc. Natl Acad. Sci. USA 105, 4283–4288 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Campbell, P. J. et al. The patterns and dynamics of genomic instability in metastatic pancreatic cancer. Nature 467, 1109–1113 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Yachida, S. et al. Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature 467, 1114–1117 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Yates, L. R. et al. Genomic evolution of breast cancer metastasis and relapse. Cancer Cell 32, 169–184 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. van de Vijver, M. J. et al. A gene-expression signature as a predictor of survival in breast cancer. N. Engl. J. Med 347, 1999–2009 (2002).

    Article  PubMed  Google Scholar 

  9. Ramaswamy, S., Ross, K. N., Lander, E. S. & Golub, T. R. A molecular signature of metastasis in primary solid tumors. Nat. Genet. 33, 49–54 (2003).

    Article  CAS  PubMed  Google Scholar 

  10. Sänger, N. et al. Disseminated tumor cells in the bone marrow of patients with ductal carcinoma in situ. Int J. Cancer 129, 2522–2526 (2011).

    Article  PubMed  Google Scholar 

  11. Husemann, Y. et al. Systemic spread is an early step in breast cancer. Cancer Cell 13, 58–68 (2008).

    Article  PubMed  Google Scholar 

  12. Rhim, A. D. et al. EMT and dissemination precede pancreatic tumor formation. Cell 148, 349–361 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Hosseini, H. et al. Early dissemination seeds metastasis in breast cancer. Nature 540, 552–558 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Siegel, R. L. et al. Colorectal cancer statistics, 2017. CA Cancer J. Clin. 67, 177–193 (2017).

    Google Scholar 

  15. Andres, A. et al. Surgical management of patients with colorectal cancer and simultaneous liver and lung metastases. Br. J. Surg. 102, 691–699 (2015).

    Article  CAS  PubMed  Google Scholar 

  16. Vatandoust, S., Price, T. J. & Karapetis, C. S. Colorectal cancer: metastases to a single organ. World J. Gastroenterol. 21, 11767–11776 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Christensen, T. D., Spindler, K. L., Palshof, J. A. & Nielsen, D. L. Systematic review: brain metastases from colorectal cancer—incidence and patient characteristics. BMC Cancer 16, 260 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Fearon, E. R. & Vogelstein, B. A genetic model for colorectal tumorigenesis. Cell 61, 759–767 (1990).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ryser, M. D., Min, B. H., Siegmund, K. D. & Shibata, D. Spatial mutation patterns as markers of early colorectal tumor cell mobility. Proc. Natl Acad. Sci. USA 115, 5774–5779 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Uchi, R. et al. Integrated multiregional analysis proposing a new model of colorectal cancer evolution. PLoS Genet. 12, e1005778 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Suzuki, Y. et al. Multiregion ultra-deep sequencing reveals early intermixing and variable levels of intratumoral heterogeneity in colorectal cancer. Mol. Oncol. 11, 124–139 (2017).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Bozic, I., Gerold, J. M. & Nowak, M. A. Quantifying clonal and subclonal passenger mutations in cancer evolution. PLoS Comput. Biol. 12, e1004731 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Hong, W. S., Shpak, M. & Townsend, J. P. Inferring the origin of metastases from cancer phylogenies. Cancer Res. 75, 4021–4025 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Naxerova, K. & Jain, R. K. Using tumour phylogenetics to identify the roots of metastasis in humans. Nat. Rev. Clin. Oncol. 12, 258–272 (2015).

    Article  CAS  PubMed  Google Scholar 

  27. Zhao, Z. M. et al. Early and multiple origins of metastatic lineages within primary tumors. Proc. Natl Acad. Sci. USA 113, 2140–2145 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Schwartz, R. & Schaffer, A. A. The evolution of tumour phylogenetics: principles and practice. Nat. Rev. Genet. 18, 213–229 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Kim, T. M. et al. Subclonal genomic architectures of primary and metastatic colorectal cancer based on intratumoral genetic heterogeneity. Clin. Cancer Res. 21, 4461–4472 (2015).

    Article  CAS  PubMed  Google Scholar 

  30. Leung, M. L. et al. Single-cell DNA sequencing reveals a late-dissemination model in metastatic colorectal cancer. Genome Res. 27, 1287–1299 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Lim, B. et al. Genome-wide mutation profiles of colorectal tumors and associated liver metastases at the exome and transcriptome levels. Oncotarget 6, 22179–22190 (2015).

    PubMed  PubMed Central  Google Scholar 

  32. Yaeger, R. et al. Clinical sequencing defines the genomic landscape of metastatic colorectal cancer. Cancer Cell 33, 125–136 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. The AACR Project GENIE Consortium AACR Project GENIE: powering precision medicine through an international consortium. Cancer Discov. 7, 818–831 (2017).

  34. Lee, S. Y. et al. Comparative genomic analysis of primary and synchronous metastatic colorectal cancers. PLoS One 9, e90459 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Xie, T. et al. Patterns of somatic alterations between matched primary and metastatic colorectal tumors characterized by whole-genome sequencing. Genomics 104, 234–241 (2014).

    Article  CAS  PubMed  Google Scholar 

  36. Brannon, A. R. et al. Comparative sequencing analysis reveals high genomic concordance between matched primary and metastatic colorectal cancer lesions. Genome Biol. 15, 454 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Tan, I. B. et al. High-depth sequencing of over 750 genes supports linear progression of primary tumors and metastases in most patients with liver-limited metastatic colorectal cancer. Genome Biol. 16, 32 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Gonzalez-Perez, A. et al. IntOGen-mutations identifies cancer drivers across tumor types. Nat. Methods 10, 1081–1082 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. The Cancer Genome Atlas Network Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337 (2012).

  40. Martincorena, I. et al. Universal patterns of selection in cancer and somatic tissues. Cell 171, 1029–1041 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Mamlouk, S. et al. DNA copy number changes define spatial patterns of heterogeneity in colorectal cancer. Nat. Commun. 8, 14093 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Yano, J. M. et al. Indigenous bacteria from the gut microbiota regulate host serotonin biosynthesis. Cell 161, 264–276 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Hayakawa, Y. et al. Nerve growth factor promotes gastric tumorigenesis through aberrant cholinergic signaling. Cancer Cell 31, 21–34 (2017).

    Article  CAS  PubMed  Google Scholar 

  44. Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analysis of population-structure. Evolution 38, 1358–1370 (1984).

    CAS  PubMed  Google Scholar 

  45. Fitch, W. M. Toward defining course of evolution: minimum change for a specific tree topology. Syst. Zool. 20, 406 (1971).

    Article  Google Scholar 

  46. Naxerova, K. et al. Origins of lymphatic and distant metastases in human colorectal cancer. Science 357, 55–60 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Beaumont, M. A., Zhang, W. & Balding, D. J. Approximate Bayesian computation in population genetics. Genetics 162, 2025–2035 (2002).

    PubMed  PubMed Central  Google Scholar 

  48. Marjoram, P. & Tavaré, S. Modern computational approaches for analysing molecular genetic variation data. Nat. Rev. Genet. 7, 759–770 (2006).

    Article  CAS  PubMed  Google Scholar 

  49. Sottoriva, A., Spiteri, I., Shibata, D., Curtis, C. & Tavare, S. Single-molecule genomic data delineate patient-specific tumor profiles and cancer stem cell organization. Cancer Res. 73, 41–49 (2013).

    Article  CAS  PubMed  Google Scholar 

  50. Fumagalli, A. et al. Genetic dissection of colorectal cancer progression by orthotopic transplantation of engineered cancer organoids. Proc. Natl Acad. Sci. USA 114, E2357–E2364 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Boutin, A. T. et al. Oncogenic Kras drives invasion and maintains metastases in colorectal cancer. Genes Dev. 31, 370–382 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Wang, Z. et al. Mutational analysis of the tyrosine phosphatome in colorectal cancers. Science 304, 1164–1166 (2004).

    Article  CAS  PubMed  Google Scholar 

  53. Zhang, X. et al. Identification of STAT3 as a substrate of receptor protein tyrosine phosphatase T. Proc. Natl Acad. Sci. USA 104, 4060–4064 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Lui, V. W. et al. Frequent mutation of receptor protein tyrosine phosphatases provides a mechanism for STAT3 hyperactivation in head and neck cancer. Proc. Natl Acad. Sci. USA 111, 1114–1119 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Rogers, Z. N. et al. Mapping the in vivo fitness landscape of lung adenocarcinoma tumor suppression in mice. Nat. Genet. 50, 483–486 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Cohen, J. D. et al. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science 359, 926–930 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Tie, J. et al. Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer. Sci. Transl. Med. 8, 346ra92 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Casadaban, L. et al. Adjuvant chemotherapy is associated with improved survival in patients with stage II colon cancer. Cancer 122, 3277–3287 (2016).

    Article  CAS  PubMed  Google Scholar 

  60. Berghoff, A. S. et al. Differential role of angiogenesis and tumour cell proliferation in brain metastases according to primary tumour type: analysis of 639 cases. Neuropathol. Appl. Neurobiol. 41, e41–e55 (2015).

    Article  CAS  PubMed  Google Scholar 

  61. Berghoff, A. S. et al. Invasion patterns in brain metastases of solid cancers. Neuro-oncol. 15, 1664–1672 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Costello, M. et al. Discovery and characterization of artifactual mutations in deep coverage targeted capture sequencing data due to oxidative DNA damage during sample preparation. Nucleic Acids Res. 41, e67 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  68. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Ha, G. et al. Integrative analysis of genome-wide loss of heterozygosity and mono-allelic expression at nucleotide resolution reveals disrupted pathways in triple-negative breast cancer. Genome Res. 22, 1995–2007 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Li, B. & Li, J. Z. A general framework for analyzing tumor subclonality using SNP array and DNA sequencing data. Genome Biol. 15, 473 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Felsenstein, J. Phylogeny inference package. Cladistics 5, 164–166 (1989).

    Google Scholar 

  72. Siegmund, K. D., Marjoram, P., Woo, Y. J., Tavaré, S. & Shibata, D. Inferring clonal expansion and cancer stem cell dynamics from DNA methylation patterns in colorectal cancers. Proc. Natl Acad. Sci. USA 106, 4828–4833 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Lloyd, M. C. et al. Darwinian dynamics of intratumoral heterogeneity: not solely random mutations but also variable environmental selection forces. Cancer Res. 76, 3136–3144 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Sarapata, E. A. & de Pillis, L. G. A comparison and catalog of intrinsic tumor growth models. Bull. Math. Biol. 76, 2010–2024 (2014).

    Article  CAS  PubMed  Google Scholar 

  75. Finlay, I. G., Meek, D., Brunton, F. & McArdle, C. S. Growth rate of hepatic metastases in colorectal carcinoma. Br. J. Surg. 75, 641–644 (1988).

    Article  CAS  PubMed  Google Scholar 

  76. Kather, J. N. et al. Identification of a characteristic vascular belt zone in human colorectal cancer. PLoS One 12, e0171378 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Bozic, I. et al. Accumulation of driver and passenger mutations during tumor progression. Proc. Natl Acad. Sci. USA 107, 18545–18550 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Tavaré, S., Balding, D. J., Griffiths, R. C. & Donnelly, P. Inferring coalescence times from DNA sequence data. Genetics 145, 505–518 (1997).

    PubMed  PubMed Central  Google Scholar 

  80. Zhao, J., Siegmund, K. D., Shibata, D. & Marjoram, P. Ancestral inference in tumors: how much can we know? J. Theor. Biol. 359, 136–145 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Csilléry, K., François, O. & Blum, M. G. abc: an R package for approximate Bayesian computation (ABC). Methods Ecol. Evol. 3, 475–479 (2012).

    Article  Google Scholar 

Download references

Acknowledgements

C. Curtis is supported by the National Institutes of Health through the NIH Director’s Pioneer Award (DP1-CA238296) and NCI Cancer Target Discovery and Development Network (CA217851). This work was funded in part by grants from the American Cancer Society (IRG–58-007-54), the Emerson Collective Cancer Research Fund and a gift from the Wunderglo Foundation to C. Curtis. Z.H. is supported by an Innovative Genomics Initiative (IGI) Postdoctoral Fellowship. The project was supported in part by Cancer Center Support Grants from the National Cancer Institute to the Stanford Cancer Institute (P30CA124435) and the University of Southern California Norris Comprehensive Cancer Center (P30CA014089). We thank J. Caswell-Jin and A. Harpak for critical feedback on the manuscript. This study is dedicated to the memory of G. Borges, a tireless cancer warrior.

Author information

Authors and Affiliations

Authors

Contributions

Z.H. implemented the computational and mathematical models, performed simulation studies and statistical analyses. J.D. implemented the genomic data analysis pipeline, analyzed and visualized genomic data and provided statistical advice. Z.M. processed clinical samples and generated the genomic data. Z.H., R.S. and J.A.S. analyzed the genomic data. Z.H., J.D., R.S., J.A.S. and C. Curtis interpreted the data. J.S.S. contributed to simulation studies. C.J.S., A.S.B. and P.B. performed pathology review. A.S.B. and M.P. performed immunohistochemistry experiments. M.P., P.B., F.L., C. Cremolini, A.F. and H.-J.L. contributed clinical samples and expertise. Z.H. and C. Curtis wrote the manuscript, which was reviewed by all authors. C. Curtis conceived and supervised the study.

Corresponding author

Correspondence to Christina Curtis.

Ethics declarations

Competing interests

A.S.B. has received research support from Daiichi Sankyo and honoraria for lectures, consultation or advisory board participation from Roche Bristol-Myers Squibb, Merck and Daiichi Sankyo as well as travel support from Roche, Amgen and AbbVie. M.P. has received honoraria for lectures, consultation or advisory board participation from the following for-profit companies: Bayer, Bristol-Myers Squibb, Novartis, Gerson Lehrman Group, CMC Contrast, GlaxoSmithKline, Mundipharma, Roche, Astra Zeneca, AbbVie, Lilly, Medahead, Daiichi Sankyo and Merck Sharp & Dome. P.B. has received travel support, honoraria for lectures, consultation or advisory board participation from the following for-profit companies: Biocartis, Novartis, Pfizer, Roche and Roche Diagnostics. C. Curtis is a scientific advisor to GRAIL and reports stock options as well as consulting for GRAIL and Genentech.

Additional information

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

Supplementary information

Supplementary Information

Supplementary Figures 1–26, Supplementary Tables 1 and 5–7, and Supplementary Note

Reporting Summary

Supplementary Table 2

Somatic SNVs, indels, allele frequencies, cancer cell fraction and LOH status for individual patients and tumor regions

Supplementary Table 3

Colorectal cancer and pan-cancer driver gene lists

Supplementary Table 4

Gene ontology enrichment analyses

Supplementary Table 8

Enrichment of driver gene modules in the MSK-Impact and MSKImpact plus GENIE cohorts

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hu, Z., Ding, J., Ma, Z. et al. Quantitative evidence for early metastatic seeding in colorectal cancer. Nat Genet 51, 1113–1122 (2019). https://doi.org/10.1038/s41588-019-0423-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-019-0423-x

This article is cited by

Search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer