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

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          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.

          Author Summary

          Cancer is an evolutionary system whose growth and development is attributed to aberrations in well-known genes and to cancer-type specific genomic imbalances. Here, we present methods for reconstructing the evolution of individual tumors based on cell-to-cell variations between copy numbers of targeted regions of the genome. The methods are designed to work with fluorescence in situ hybridization (FISH), a technique that allows one to profile copy number changes in potentially thousands of single cells per study. Our work advances the prior art by developing theory and practical algorithms for building evolutionary trees of single tumors that can model gain or loss of genetic regions at the scale of single genes, whole chromosomes, or the entire genome, all common events in tumor evolution. We apply these methods on simulated and real tumor data to demonstrate substantial improvements in tree-building accuracy and in our ability to accurately classify tumors from their inferred evolutionary models. The newly developed algorithms have been released through our publicly available software, FISHtrees.

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          Most cited references50

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          A genetic model for colorectal tumorigenesis.

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            The clonal evolution of tumor cell populations.

            P C Nowell (1976)
            It is proposed that most neoplasms arise from a single cell of origin, and tumor progression results from acquired genetic variability within the original clone allowing sequential selection of more aggressive sublines. Tumor cell populations are apparently more genetically unstable than normal cells, perhaps from activation of specific gene loci in the neoplasm, continued presence of carcinogen, or even nutritional deficiencies within the tumor. The acquired genetic insta0ility and associated selection process, most readily recognized cytogenetically, results in advanced human malignancies being highly individual karyotypically and biologically. Hence, each patient's cancer may require individual specific therapy, and even this may be thwarted by emergence of a genetically variant subline resistant to the treatment. More research should be directed toward understanding and controlling the evolutionary process in tumors before it reaches the late stage usually seen in clinical cancer.
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              Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor.

              Clear cell renal cell carcinoma (ccRCC) is the most common kidney cancer and has very few mutations that are shared between different patients. To better understand the intratumoral genetics underlying mutations of ccRCC, we carried out single-cell exome sequencing on a ccRCC tumor and its adjacent kidney tissue. Our data indicate that this tumor was unlikely to have resulted from mutations in VHL and PBRM1. Quantitative population genetic analysis indicates that the tumor did not contain any significant clonal subpopulations and also showed that mutations that had different allele frequencies within the population also had different mutation spectrums. Analyses of these data allowed us to delineate a detailed intratumoral genetic landscape at a single-cell level. Our pilot study demonstrates that ccRCC may be more genetically complex than previously thought and provides information that can lead to new ways to investigate individual tumors, with the aim of developing more effective cellular targeted therapies. Copyright © 2012 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                July 2014
                31 July 2014
                : 10
                : 7
                Affiliations
                [1 ]Joint Carnegie Mellon/University of Pittsburgh Ph.D. Program in Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
                [2 ]Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
                [3 ]Intelligent Oncotherapeutics, Pittsburgh, Pennsylvania, United States of America
                [4 ]Genetics Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland, United States of America
                [5 ]Computational Biology Branch, NCBI, NIH, Bethesda, Maryland, United States of America
                [6 ]Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
                University of California San Diego, United States of America
                Author notes

                Dr Shackney is an employee of Intelligent Oncotherapeutics. All other authors declare that no competing interests exist.

                Conceived and designed the experiments: SAC SES KHH TR AAS RS. Performed the experiments: SAC AAS RS. Analyzed the data: SAC SES KHH TR AAS RS. Wrote the paper: SAC SES KHH TR AAS RS. Designed the software used in the analysis: SAC AAS RS.

                Article
                PCOMPBIOL-D-14-00205
                10.1371/journal.pcbi.1003740
                4117424
                25078894
                99d992b3-003a-4ee1-b950-fbe57c453180

                This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

                Page count
                Pages: 19
                Funding
                This research was supported in part by the Intramural Research Program of the U.S. National Institutes of Health, National Cancer Institute, and National Library of Medicine, and by U.S. National Institutes of Health grants 1R01CA140214 (RS and SAC) and 1R01AI076318 (RS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Computational Biology
                Evolutionary Modeling
                Evolutionary Biology
                Evolutionary Processes
                Genetics
                Cancer Genetics
                Computer and Information Sciences
                Physical Sciences
                Mathematics
                Applied Mathematics
                Algorithms

                Quantitative & Systems biology
                Quantitative & Systems biology

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