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Algorithms to Model Single Gene, Single Chromosome, and Whole Genome Copy Number Changes Jointly in Tumor Phylogenetics

Overview of attention for article published in PLoS Computational Biology, July 2014
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Title
Algorithms to Model Single Gene, Single Chromosome, and Whole Genome Copy Number Changes Jointly in Tumor Phylogenetics
Published in
PLoS Computational Biology, July 2014
DOI 10.1371/journal.pcbi.1003740
Pubmed ID
Authors

Salim Akhter Chowdhury, Stanley E. Shackney, Kerstin Heselmeyer-Haddad, Thomas Ried, Alejandro A. Schäffer, Russell Schwartz

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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 4%
Portugal 1 2%
Unknown 46 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 33%
Researcher 12 24%
Student > Master 6 12%
Student > Postgraduate 3 6%
Student > Bachelor 2 4%
Other 3 6%
Unknown 7 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 31%
Computer Science 10 20%
Biochemistry, Genetics and Molecular Biology 8 16%
Medicine and Dentistry 5 10%
Immunology and Microbiology 1 2%
Other 3 6%
Unknown 7 14%