FCCC LOGO Faculty Publications
Kumar S , Chroni A , Tamura K , Sanderford M , Oladeinde O , Aly V , Vu T , Miura S
PathFinder: Bayesian inference of clone migration histories in cancer
Bioinformatics. 2020 Dec 30;36(Supplement_2) :i675-i683
PMID: 33381835    PMCID: PMC7773489    URL: https://www.ncbi.nlm.nih.gov/pubmed/33381835
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SUMMARY: Metastases cause a vast majority of cancer morbidity and mortality. Metastatic clones are formed by dispersal of cancer cells to secondary tissues, and are not medically detected or visible until later stages of cancer development. Clone phylogenies within patients provide a means of tracing the otherwise inaccessible dynamic history of migrations of cancer cells. Here, we present a new Bayesian approach, PathFinder, for reconstructing the routes of cancer cell migrations. PathFinder uses the clone phylogeny, the number of mutational differences among clones, and the information on the presence and absence of observed clones in primary and metastatic tumors. By analyzing simulated datasets, we found that PathFinder performes well in reconstructing clone migrations from the primary tumor to new metastases as well as between metastases. It was more challenging to trace migrations from metastases back to primary tumors. We found that a vast majority of errors can be corrected by sampling more clones per tumor, and by increasing the number of genetic variants assayed per clone. We also identified situations in which phylogenetic approaches alone are not sufficient to reconstruct migration routes.In conclusion, we anticipate that the use of PathFinder will enable a more reliable inference of migration histories and their posterior probabilities, which is required to assess the relative preponderance of seeding of new metastasis by clones from primary tumors and/or existing metastases. AVAILABILITY AND IMPLEMENTATION: PathFinder is available on the web at https://github.com/SayakaMiura/PathFinder.
1367-4811 Kumar, Sudhir Chroni, Antonia Tamura, Koichiro Sanderford, Maxwell Oladeinde, Olumide Aly, Vivian Vu, Tracy Miura, Sayaka R01 LM013385/LM/NLM NIH HHS/United States Journal Article Bioinformatics. 2020 Dec 30;36(Supplement_2):i675-i683. doi: 10.1093/bioinformatics/btaa795.