Inferring models of multiscale copy number evolution for single-tumor phylogenetics

• Published in: Bioinformatics (Oxford, England) Vol. 31; no. 12; pp. i258 - i267
• Main Authors: Chowdhury, Salim Akhter, Gertz, E Michael, Wangsa, Darawalee, Heselmeyer-Haddad, Kerstin, Ried, Thomas, Schäffer, Alejandro A, Schwartz, Russell
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 15.06.2015
• Subjects: Algorithms; Disease Progression; Evolution, Molecular; Female; Gene Dosage; Genomics; Humans; Models, Genetic; Neoplasms - genetics; Phylogeny; Software; Uterine Cervical Neoplasms - genetics
• ISSN: 1367-4803; 1367-4811
• DOI: 10.1093/bioinformatics/btv233

Phylogenetic algorithms have begun to see widespread use in cancer research to reconstruct processes of evolution in tumor progression. Developing reliable phylogenies for tumor data requires quantitative models of cancer evolution that include the unusual genetic mechanisms by which tumors evolve, such as chromosome abnormalities, and allow for heterogeneity between tumor types and individual patients. Previous work on inferring phylogenies of single tumors by copy number evolution assumed models of uniform rates of genomic gain and loss across different genomic sites and scales, a substantial oversimplification necessitated by a lack of algorithms and quantitative parameters for fitting to more realistic tumor evolution models. We propose a framework for inferring models of tumor progression from single-cell gene copy number data, including variable rates for different gain and loss events. We propose a new algorithm for identification of most parsimonious combinations of single gene and single chromosome events. We extend it via dynamic programming to include genome duplications. We implement an expectation maximization (EM)-like method to estimate mutation-specific and tumor-specific event rates concurrently with tree reconstruction. Application of our algorithms to real cervical cancer data identifies key genomic events in disease progression consistent with prior literature. Classification experiments on cervical and tongue cancer datasets lead to improved prediction accuracy for the metastasis of primary cervical cancers and for tongue cancer survival. Our software (FISHtrees) and two datasets are available at ftp://ftp.ncbi.nlm.nih.gov/pub/FISHtrees.