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Inferring parsimonious migration histories for metastatic cancers

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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Fig. 1: Phylogenetic analysis of metastatic tumors.
Fig. 2: The migration number µ does not determine the migration pattern.
Fig. 3: Migration history analysis requires evaluation of tradeoffs between migration pattern, migration number and comigration number.
Fig. 4: The MACHINA algorithm for joint clone tree inference and migration history analysis.
Fig. 5: MACHINA accurately infers clone trees and migration histories on simulated data.
Fig. 6: Joint analysis of migrations and comigrations leads to more parsimonious migration histories in metastatic ovarian cancer.
Fig. 7: Joint analysis of mutations and migrations shows a monoclonal single-source migration history for a patient with metastatic breast cancer.

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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.).

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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.

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Correspondence to Benjamin J. Raphael.

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

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El-Kebir, M., Satas, G. & Raphael, B.J. Inferring parsimonious migration histories for metastatic cancers. Nat Genet 50, 718–726 (2018). https://doi.org/10.1038/s41588-018-0106-z

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