RT Journal Article SR Electronic T1 Reconstructing phylogenies of metastatic cancers JF bioRxiv FD Cold Spring Harbor Laboratory SP 048157 DO 10.1101/048157 A1 Johannes G. Reiter A1 Alvin P. Makohon-Moore A1 Jeffrey M. Gerold A1 Ivana Bozic A1 Krishnendu Chatterjee A1 Christine A. Iacobuzio-Donahue A1 Bert Vogelstein A1 Martin A. Nowak YR 2016 UL http://biorxiv.org/content/early/2016/04/11/048157.abstract AB Reconstructing the evolutionary history of metastases is critical for understanding their basic biological principles and has profound clinical implications1–3. Genome-wide sequencing data has enabled modern phylogenomic methods to accurately dissect subclones and their phylogenies from noisy and impure bulk tumor samples at unprecedented depth4–7. However, existing methods are not designed to infer metastatic seeding patterns. We have developed a tool, called Treeomics, that utilizes Bayesian inference and Integer Linear Programming to reconstruct the phylogeny of metastases. Treeomics allowed us to infer comprehensive seeding patterns for pancreatic8, ovarian9, and prostate cancers10,11. Moreover, Treeomics correctly disambiguated true seeding patterns from sequencing artifacts; 7% of variants were misclassified by conventional statistical methods. These artifacts can skew phylogenies by creating illusory tumor heterogeneity among distinct samples. Last, we performed in silico benchmarking on simulated tumor phylogenies across a wide range of sample purities (30-90%) and sequencing depths (50-800x) to demonstrate the high accuracy of Treeomics compared to existing methods.