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      Extrachromosomal oncogene amplification drives tumor evolution and genetic heterogeneity

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          Abstract

          Human cells have twenty-three pairs of chromosomes but in cancer, genes can be amplified in chromosomes or in circular extrachromosomal DNA (ECDNA), whose frequency and functional significance are not understood 14 . We performed whole genome sequencing, structural modeling and cytogenetic analyses of 17 different cancer types, including 2572 metaphases, and developed ECdetect to conduct unbiased integrated ECDNA detection and analysis. ECDNA was found in nearly half of human cancers varying by tumor type, but almost never in normal cells. Driver oncogenes were amplified most commonly on ECDNA, elevating transcript level. Mathematical modeling predicted that ECDNA amplification elevates oncogene copy number and increases intratumoral heterogeneity more effectively than chromosomal amplification, which we validated by quantitative analyses of cancer samples. These results suggest that ECDNA contributes to accelerated evolution in cancer.

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          Most cited references12

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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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            Evolutionary dynamics of carcinogenesis and why targeted therapy does not work.

            All malignant cancers, whether inherited or sporadic, are fundamentally governed by Darwinian dynamics. The process of carcinogenesis requires genetic instability and highly selective local microenvironments, the combination of which promotes somatic evolution. These microenvironmental forces, specifically hypoxia, acidosis and reactive oxygen species, are not only highly selective, but are also able to induce genetic instability. As a result, malignant cancers are dynamically evolving clades of cells living in distinct microhabitats that almost certainly ensure the emergence of therapy-resistant populations. Cytotoxic cancer therapies also impose intense evolutionary selection pressures on the surviving cells and thus increase the evolutionary rate. Importantly, the principles of Darwinian dynamics also embody fundamental principles that can illuminate strategies for the successful management of cancer.
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              Accumulation of driver and passenger mutations during tumor progression.

              Major efforts to sequence cancer genomes are now occurring throughout the world. Though the emerging data from these studies are illuminating, their reconciliation with epidemiologic and clinical observations poses a major challenge. In the current study, we provide a mathematical model that begins to address this challenge. We model tumors as a discrete time branching process that starts with a single driver mutation and proceeds as each new driver mutation leads to a slightly increased rate of clonal expansion. Using the model, we observe tremendous variation in the rate of tumor development-providing an understanding of the heterogeneity in tumor sizes and development times that have been observed by epidemiologists and clinicians. Furthermore, the model provides a simple formula for the number of driver mutations as a function of the total number of mutations in the tumor. Finally, when applied to recent experimental data, the model allows us to calculate the actual selective advantage provided by typical somatic mutations in human tumors in situ. This selective advantage is surprisingly small--0.004 ± 0.0004--and has major implications for experimental cancer research.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                12 January 2017
                08 February 2017
                02 March 2017
                08 August 2017
                : 543
                : 7643
                : 122-125
                Affiliations
                [1 ]Ludwig Institute for Cancer Research, University of California at San Diego, La Jolla, CA, USA
                [2 ]Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA, USA
                [3 ]Tumor Initiation and Maintenance Program, NCI-Designated Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
                [4 ]Department of Medical and Molecular Pharmacology, David Geffen UCLA School of Medicine, Los Angeles, CA, USA
                [5 ]Neuropsychiatric Institute–Semel Institute for Neuroscience and Human Behavior and Department of Psychiatry and Biobehavioral Sciences, David Geffen UCLA School of Medicine, Los Angeles, CA, USA
                [6 ]The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
                [7 ]Moores Cancer Center, University of California at San Diego, La Jolla, CA, USA
                [8 ]Department of Pathology, University of California San Francisco, San Francisco, CA, USA
                [9 ]Department of Pathology and Laboratory Medicine, David Geffen UCLA School of Medicine, Los Angeles, CA, USA
                [10 ]Gene Expression Laboratory, Salk Institute for Biological Studies, La Jolla, CA, USA
                [11 ]Department of Pathology, University of California at San Diego, La Jolla, CA, USA
                Author notes
                [§ ]Correspondence should be addressed to: pmischel@ 123456ucsd.edu and for computational methods and tools, to vbafna@ 123456cs.ucsd.edu
                [*]

                These authors contributed equally to this work

                [†]

                This work is based on equal contributions from these co-senior authors

                Article
                NIHMS839083
                10.1038/nature21356
                5334176
                28178237
                9706eac1-a5ef-4f03-978b-f18e1f100235

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