Reconstructing metastatic seeding patterns of human cancers

• DOI: 10.1038/ncomms14114
• Publication Date: 2017-01-31T11:03:53Z
• Authors: Johannes G. Reiter; Alvin P. Makohon-...; Jeffrey M. Gerold; Ivana Bozic; Krishnendu Chatte...; Christine A. Iaco...; Bert Vogelstein; Martin A. Nowak
• Keywords: Male; Proteomics; 004 Data processing & computer science; Science; Article; 576; Genetic Heterogeneity; 03 medical and health sciences; Humans; Neoplasm Metastasis; Phylogeny; Ovarian Neoplasms; 0303 health sciences; Q; High-Throughput Nucleotide Sequencing; Prostatic Neoplasms; Bayes Theorem; DNA, Neoplasm; 004; 3. Good health; Pancreatic Neoplasms; Benchmarking; 006 Special computer methods; Mutation; Female

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