Format

Send to

Choose Destination
PLoS Comput Biol. 2014 Jul 31;10(7):e1003740. doi: 10.1371/journal.pcbi.1003740. eCollection 2014 Jul.

Algorithms to model single gene, single chromosome, and whole genome copy number changes jointly in tumor phylogenetics.

Author information

1
Joint Carnegie Mellon/University of Pittsburgh Ph.D. Program in Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America; Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
2
Intelligent Oncotherapeutics, Pittsburgh, Pennsylvania, United States of America.
3
Genetics Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland, United States of America.
4
Computational Biology Branch, NCBI, NIH, Bethesda, Maryland, United States of America.
5
Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America; Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.

Abstract

We present methods to construct phylogenetic models of tumor progression at the cellular level that include copy number changes at the scale of single genes, entire chromosomes, and the whole genome. The methods are designed for data collected by fluorescence in situ hybridization (FISH), an experimental technique especially well suited to characterizing intratumor heterogeneity using counts of probes to genetic regions frequently gained or lost in tumor development. Here, we develop new provably optimal methods for computing an edit distance between the copy number states of two cells given evolution by copy number changes of single probes, all probes on a chromosome, or all probes in the genome. We then apply this theory to develop a practical heuristic algorithm, implemented in publicly available software, for inferring tumor phylogenies on data from potentially hundreds of single cells by this evolutionary model. We demonstrate and validate the methods on simulated data and published FISH data from cervical cancers and breast cancers. Our computational experiments show that the new model and algorithm lead to more parsimonious trees than prior methods for single-tumor phylogenetics and to improved performance on various classification tasks, such as distinguishing primary tumors from metastases obtained from the same patient population.

PMID:
25078894
PMCID:
PMC4117424
DOI:
10.1371/journal.pcbi.1003740
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Public Library of Science Icon for PubMed Central
Loading ...
Support Center