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      A Big Bang model of human colorectal tumor growth

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          Abstract

          What happens in the early, still undetectable human malignancy is unknown because direct observations are impractical. Here we present and validate a “Big Bang” model, whereby tumors grow predominantly as a single expansion producing numerous intermixed sub-clones that are not subject to stringent selection, and where both public (clonal) and most detectable private (subclonal) alterations arise early during growth. Genomic profiling of 349 individual glands from 15 colorectal tumors revealed the absence of selective sweeps, uniformly high intra-tumor heterogeneity (ITH), and sub-clone mixing in distant regions, as postulated by our model. We also verified the prediction that most detectable ITH originates from early private alterations, and not from later clonal expansions, thus exposing the profile of the primordial tumor. Moreover, some tumors appear born-to-be-bad, with sub-clone mixing indicative of early malignant potential. This new model provides a quantitative framework to interpret tumor growth dynamics and the origins of ITH with significant clinical implications.

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          Most cited references30

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          A genetic model for colorectal tumorigenesis.

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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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              Dynamics of cancer progression.

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                Author and article information

                Journal
                9216904
                2419
                Nat Genet
                Nat. Genet.
                Nature genetics
                1061-4036
                1546-1718
                26 June 2015
                09 February 2015
                March 2015
                19 September 2015
                : 47
                : 3
                : 209-216
                Affiliations
                [1 ] Department of Preventive Medicine, Keck School of Medicine of the University of Southern California, Los Angeles, CA USA
                [2 ] Department of Pathology, Keck School of Medicine of the University of Southern California, Los Angeles, CA USA
                [3 ] Department of Pathology, CHA University, Seongnam-si, Gyeonggi-do, South Korea
                [4 ] Center for Evolution and Cancer, University of California, San Francisco, San Francisco, CA USA
                [5 ] Centre for Tumor Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
                Author notes
                [6]

                Present addresses: Division of Molecular Pathology, Institute of Cancer Research, London, UK (A.S); Department of Medicine, Stanford University, Stanford, CA, USA (Z.M, C.C); Department of Genetics, Stanford University, Stanford, CA, USA (Z.M, C.C)

                Correspondence should be addressed to: Darryl Shibata ( dshibata@ 123456usc.edu ) or Christina Curtis ( cncurtis@ 123456stanford.edu )
                Article
                NIHMS655882
                10.1038/ng.3214
                4575589
                25665006
                f9364354-b936-46f2-b282-aade9e0ec0b5
                History
                Categories
                Article

                Genetics
                Genetics

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