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Inferring parsimonious migration histories for metastatic cancers

Nature Geneticsvolume 50pages718726 (2018) | Download Citation

Abstract

Metastasis is the migration of cancerous cells from a primary tumor to other anatomical sites. Although metastasis was long thought to result from monoclonal seeding, or single cellular migrations, recent phylogenetic analyses of metastatic cancers have reported complex patterns of cellular migrations between sites, including polyclonal migrations and reseeding. However, accurate determination of migration patterns from somatic mutation data is complicated by intratumor heterogeneity and discordance between clonal lineage and cellular migration. We introduce MACHINA, a multi-objective optimization algorithm that jointly infers clonal lineages and parsimonious migration histories of metastatic cancers from DNA sequencing data. MACHINA analysis of data from multiple cancers shows that migration patterns are often not uniquely determined from sequencing data alone and that complicated migration patterns among primary tumors and metastases may be less prevalent than previously reported. MACHINA’s rigorous analysis of migration histories will aid in studies of the drivers of metastasis.

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Acknowledgements

We thank the authors of the studies by McPherson et al.12, Gundem et al.11, Hoadley et al.10 and Sanborn et al.4 for providing analyzed data in their published manuscripts. This work is supported by US National Institutes of Health (NIH) grants R01HG007069 (B.J.R.), R01CA180776 (B.J.R.) and U24CA211000 (B.J.R.) and US National Science Foundation (NSF) CAREER Award CCF-1053753 (B.J.R.).

Author information

Author notes

    • Mohammed El-Kebir

    Present address: Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA

Affiliations

  1. Department of Computer Science, Princeton University, Princeton, NJ, USA

    • Mohammed El-Kebir
    • , Gryte Satas
    •  & Benjamin J. Raphael
  2. Department of Computer Science, Brown University, Providence, RI, USA

    • Gryte Satas

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Contributions

M.E.-K. and B.J.R. conceived the project; M.E.-K., G.S. and B.J.R. developed the theory and algorithms; M.E.-K. implemented the algorithms; M.E.-K. and G.S. performed simulations and analysis of real data; M.E.-K., G.S. and B.J.R. wrote the manuscript.

Competing interests

B.J.R. is a cofounder of, and consultant to, Medley Genomics.

Corresponding author

Correspondence to Benjamin J. Raphael.

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https://doi.org/10.1038/s41588-018-0106-z