The overall hypothesis is that genomes record ancestry because genomes are almost perfect copies of copies. The greater the numbers of divisions since a """"""""start"""""""" or common ancestor, on average the greater the number of differences between genomes (a molecular clock hypothesis). Although molecular phylogeny is commonly used to reconstruct the past of species and populations, this approach has not been translated to normal or neoplastic somatic human cells. Molecular phylogeny is uniquely suited to reconstruct human somatic cell ancestry because it requires no prior experimental intervention?genomes surreptitiously record ancestry through replication errors. More conventional cell fate markers used in experimental systems such as mice require the prior introduction of genetic markers, which is impractical for humans. Therefore, molecular phylogeny, which does not require prior experimental manipulations, is one of the few practical approaches to reconstruct the histories of human somatic cells. This Project hypothesizes that tumor genomes become polymorphic after transformation, and these variations record ancestry, or how and how fast it took for a single transformed cell to become the present day tumor population. We will apply this hypothesis to analyze human acute myelogenous leukemia specimens. The research will further develop epigenetic somatic cell molecular clocks based on DNA methylation patterns, which appear to drift fast enough to record somatic cell ancestries. These studies will complement the mouse studies of the other Projects, and will help translate the mouse studies to human diseases. Success of this Project will provide a systematic method to take any human tumor and look back in time to reconstruct its ancestry from variations in its cancer genomes.

Public Health Relevance

The project will translate modern molecular phylogeny approaches to human somatic cells. In this way, using both quantitative and evolutionary principles, it should be possible to read the past of any human cell by measuring somatic variations in its genome. Reading the past of a cancer should improve our understanding of how cancers develop and help predict how a cancer may respond to different therapies

National Institute of Health (NIH)
National Cancer Institute (NCI)
Specialized Center--Cooperative Agreements (U54)
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Special Emphasis Panel (ZCA1-SRLB-9)
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University of Southern California
Los Angeles
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Yan, Huaming; Romero-Lopez, Monica; Frieboes, Hermann B et al. (2017) Multiscale Modeling of Glioblastoma Suggests that the Partial Disruption of Vessel/Cancer Stem Cell Crosstalk Can Promote Tumor Regression Without Increasing Invasiveness. IEEE Trans Biomed Eng 64:538-548
Garvey, Colleen M; Gerhart, Torin A; Mumenthaler, Shannon M (2017) Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques. J Vis Exp :
Tennill, Thomas A; Gross, Mitchell E; Frieboes, Hermann B (2017) Automated analysis of co-localized protein expression in histologic sections of prostate cancer. PLoS One 12:e0178362
Yan, Huaming; Romero-López, Mónica; Benitez, Lesly I et al. (2017) 3D Mathematical Modeling of Glioblastoma Suggests That Transdifferentiated Vascular Endothelial Cells Mediate Resistance to Current Standard-of-Care Therapy. Cancer Res 77:4171-4184
Ng, Chin F; Frieboes, Hermann B (2017) Model of vascular desmoplastic multispecies tumor growth. J Theor Biol 430:245-282
Leonard, Fransisca; Curtis, Louis T; Yesantharao, Pooja et al. (2016) Enhanced performance of macrophage-encapsulated nanoparticle albumin-bound-paclitaxel in hypo-perfused cancer lesions. Nanoscale 8:12544-52
Baugh, Evan H; Simmons-Edler, Riley; Müller, Christian L et al. (2016) Robust classification of protein variation using structural modelling and large-scale data integration. Nucleic Acids Res 44:2501-13
Ghaffarizadeh, Ahmadreza; Friedman, Samuel H; Macklin, Paul (2016) BioFVM: an efficient, parallelized diffusive transport solver for 3-D biological simulations. Bioinformatics 32:1256-8
Juarez, Edwin F; Lau, Roy; Friedman, Samuel H et al. (2016) Quantifying differences in cell line population dynamics using CellPD. BMC Syst Biol 10:92
Park, Seung-Min; Lee, Jae Young; Hong, Soongweon et al. (2016) Dual transcript and protein quantification in a massive single cell array. Lab Chip 16:3682-8

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