Reconstructing metastatic seeding patterns of human cancers

Johannes G. Reiter (), Alvin P. Makohon-Moore, Jeffrey M. Gerold, Ivana Bozic, Krishnendu Chatterjee, Christine A. Iacobuzio-Donahue, Bert Vogelstein and Martin A. Nowak ()
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Johannes G. Reiter: Program for Evolutionary Dynamics, Harvard University
Alvin P. Makohon-Moore: The David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center
Jeffrey M. Gerold: Program for Evolutionary Dynamics, Harvard University
Ivana Bozic: Program for Evolutionary Dynamics, Harvard University
Krishnendu Chatterjee: IST (Institute of Science and Technology) Austria
Christine A. Iacobuzio-Donahue: The David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center
Bert Vogelstein: The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine
Martin A. Nowak: Program for Evolutionary Dynamics, Harvard University

Nature Communications, 2017, vol. 8, issue 1, 1-10

Abstract: Abstract Reconstructing the evolutionary history of metastases is critical for understanding their basic biological principles and has profound clinical implications. Genome-wide sequencing data has enabled modern phylogenomic methods to accurately dissect subclones and their phylogenies from noisy and impure bulk tumour samples at unprecedented depth. However, existing methods are not designed to infer metastatic seeding patterns. Here we develop a tool, called Treeomics, to reconstruct the phylogeny of metastases and map subclones to their anatomic locations. Treeomics infers comprehensive seeding patterns for pancreatic, ovarian, and prostate cancers. Moreover, Treeomics correctly disambiguates true seeding patterns from sequencing artifacts; 7% of variants were misclassified by conventional statistical methods. These artifacts can skew phylogenies by creating illusory tumour heterogeneity among distinct samples. In silico benchmarking on simulated tumour phylogenies across a wide range of sample purities (15–95%) and sequencing depths (25-800 × ) demonstrates the accuracy of Treeomics compared with existing methods.

Date: 2017
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DOI: 10.1038/ncomms14114

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