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      OncoNEM: inferring tumor evolution from single-cell sequencing data

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          Abstract

          Single-cell sequencing promises a high-resolution view of genetic heterogeneity and clonal evolution in cancer. However, methods to infer tumor evolution from single-cell sequencing data lag behind methods developed for bulk-sequencing data. Here, we present OncoNEM, a probabilistic method for inferring intra-tumor evolutionary lineage trees from somatic single nucleotide variants of single cells. OncoNEM identifies homogeneous cellular subpopulations and infers their genotypes as well as a tree describing their evolutionary relationships. In simulation studies, we assess OncoNEM’s robustness and benchmark its performance against competing methods. Finally, we show its applicability in case studies of muscle-invasive bladder cancer and essential thrombocythemia.

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          The online version of this article (doi:10.1186/s13059-016-0929-9) contains supplementary material, which is available to authorized users.

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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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            R: A Language and environmental for statistical computing

            R Development Core Team, RDC Team, RC Team … (2013)
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              Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor.

              Xun Xu, Yong Hou, Xuyang Yin … (2012)
              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
                Florian Markowetz: florian.markowetz@cruk.cam.ac.uk
                Journal
                Journal ID (nlm-ta): Genome Biol
                Journal ID (iso-abbrev): Genome Biol
                Title: Genome Biology
                Publisher: BioMed Central (London )
                ISSN (Print): 1474-7596
                ISSN (Electronic): 1474-760X
                Publication date (Electronic): 15 April 2016
                Publication date PMC-release: 15 April 2016
                Publication date Collection: 2016
                Volume: 17
                Electronic Location Identifier: 69
                Affiliations
                Cancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge, UK
                Article
                Publisher ID: 929
                DOI: 10.1186/s13059-016-0929-9
                PMC ID: 4832472
                PubMed ID: 27083415
                SO-VID: e77cf3e6-b9c2-437d-8068-c060232831cc
                Copyright © © Ross and Markowetz. 2016
                License:

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                Date received : 29 March 2016
                Date accepted : 30 March 2016
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000289, Cancer Research UK;
                Award ID: C14303/A17197
                Award Recipient :
                Categories
                Subject: Method
                Custom metadata
                issue-copyright-statement © The Author(s) 2016

                ScienceOpen disciplines: Genetics
                Keywords: tumor evolution,cancer evolution,tumor heterogeneity,single-cell sequencing,phylogenetic tree
                Data availability:
                ScienceOpen disciplines: Genetics
                Keywords: tumor evolution, cancer evolution, tumor heterogeneity, single-cell sequencing, phylogenetic tree

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