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Bayesian inference of tissue-migration histories in metastatic cancer from cell-lineage tracing data

Staklinski, S. J.; Scheben, A.; Brault, L. M.; Hassett, R.; Serio, R. N.; Xing, J.; Nowak, D. G.; Siepel, A.

2025-09-12 cancer biology
10.1101/2025.09.09.668633 bioRxiv
Show abstract

Cell-lineage tracing now enables direct study of tissue migration in metastatic cancer, but current reconstruction algorithms are limited by a reliance on strong parsimony assumptions and pre-estimated cell-lineage phylogenies. Here, we introduce a probabilistic modeling and inference framework, called BEAM (Bayesian Evolutionary Analysis of Metastasis), that provides richer information about complex metastatic histories. Based on the flexible BEAST 2 platform for Bayesian phylogenetics, BEAM infers a full posterior distribution over cell-lineage phylogenies and tissue migration graphs, complete with timing information. We show using simulated data that BEAM reliably outperforms current methods for inference of tissue migration graphs, especially for more complex histories. We then apply BEAM to public data sets for lung and prostate cancer, finding support for distinct modes of migration across clones and reseeding of primary tumors. Overall, BEAM serves as a powerful framework for revealing the modes, timing, and directionality of tissue migration in metastatic cancer.

Published in Cell Genomics · training set

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