Literature DB >> 26933088

Mathematical Modeling Reveals That Changes to Local Cell Density Dynamically Modulate Baseline Variations in Cell Growth and Drug Response.

James M Greene1, Doron Levy2, Sylvia P Herrada3, Michael M Gottesman3, Orit Lavi4.   

Abstract

Cell-to-cell variations contribute to drug resistance with consequent therapy failure in cancer. Experimental techniques have been developed to monitor tumor heterogeneity, but estimates of cell-to-cell variation typically fail to account for the expected spatiotemporal variations during the cell growth process. To fully capture the extent of such dynamic variations, we developed a mechanistic mathematical model supported by in vitro experiments with an ovarian cancer cell line. We introduce the notion of dynamic baseline cell-to-cell variation, showing how the emerging spatiotemporal heterogeneity of one cell population can be attributed to differences in local cell density and cell cycle. Manipulation of the geometric arrangement and spatial density of cancer cells revealed that given a fixed global cell density, significant differences in growth, proliferation, and paclitaxel-induced apoptosis rates were observed based solely on cell movement and local conditions. We conclude that any statistical estimate of changes in the level of heterogeneity should be integrated with the dynamics and spatial effects of the baseline system. This approach incorporates experimental and theoretical methods to systematically analyze biologic phenomena and merits consideration as an underlying reference model for cell biology studies that investigate dynamic processes affecting cancer cell behavior. Cancer Res; 76(10); 2882-90. ©2016 AACR. ©2016 American Association for Cancer Research.

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Year:  2016        PMID: 26933088      PMCID: PMC6217846          DOI: 10.1158/0008-5472.CAN-15-3232

Source DB:  PubMed          Journal:  Cancer Res        ISSN: 0008-5472            Impact factor:   12.701


  20 in total

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Authors:  Daniela Morale; Vincenzo Capasso; Karl Oelschläger
Journal:  J Math Biol       Date:  2004-07-05       Impact factor: 2.259

2.  Dynamic proteomics of individual cancer cells in response to a drug.

Authors:  A A Cohen; N Geva-Zatorsky; E Eden; M Frenkel-Morgenstern; I Issaeva; A Sigal; R Milo; C Cohen-Saidon; Y Liron; Z Kam; L Cohen; T Danon; N Perzov; U Alon
Journal:  Science       Date:  2008-11-20       Impact factor: 47.728

3.  Asynchrony and commitment to die during apoptosis.

Authors:  C A Messam; R N Pittman
Journal:  Exp Cell Res       Date:  1998-02-01       Impact factor: 3.905

4.  Lineage correlations of single cell division time as a probe of cell-cycle dynamics.

Authors:  Oded Sandler; Sivan Pearl Mizrahi; Noga Weiss; Oded Agam; Itamar Simon; Nathalie Q Balaban
Journal:  Nature       Date:  2015-03-11       Impact factor: 49.962

5.  Mathematical model of adult stem cell regeneration with cross-talk between genetic and epigenetic regulation.

Authors:  Jinzhi Lei; Simon A Levin; Qing Nie
Journal:  Proc Natl Acad Sci U S A       Date:  2014-02-05       Impact factor: 11.205

6.  Modeling intrinsic heterogeneity and growth of cancer cells.

Authors:  James M Greene; Doron Levy; King Leung Fung; Paloma S Souza; Michael M Gottesman; Orit Lavi
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Review 7.  Tumor heterogeneity: causes and consequences.

Authors:  Andriy Marusyk; Kornelia Polyak
Journal:  Biochim Biophys Acta       Date:  2009-11-18

8.  Cell growth and size homeostasis in proliferating animal cells.

Authors:  Amit Tzur; Ran Kafri; Valerie S LeBleu; Galit Lahav; Marc W Kirschner
Journal:  Science       Date:  2009-07-10       Impact factor: 47.728

9.  A dual-fluorescence high-throughput cell line system for probing multidrug resistance.

Authors:  Kyle R Brimacombe; Matthew D Hall; Douglas S Auld; James Inglese; Christopher P Austin; Michael M Gottesman; King-Leung Fung
Journal:  Assay Drug Dev Technol       Date:  2009-06       Impact factor: 1.738

Review 10.  Paclitaxel (taxol)

Authors:  E K Rowinsky; R C Donehower
Journal:  N Engl J Med       Date:  1995-04-13       Impact factor: 91.245

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3.  Modeling Cancer Cell Growth Dynamics In vitro in Response to Antimitotic Drug Treatment.

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4.  Multiscale image analysis reveals structural heterogeneity of the cell microenvironment in homotypic spheroids.

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5.  Interplay of Darwinian Selection, Lamarckian Induction and Microvesicle Transfer on Drug Resistance in Cancer.

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6.  Leveraging Mathematical Modeling to Quantify Pharmacokinetic and Pharmacodynamic Pathways: Equivalent Dose Metric.

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7.  Cooperative adaptation to therapy (CAT) confers resistance in heterogeneous non-small cell lung cancer.

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8.  Cysteine allows ovarian cancer cells to adapt to hypoxia and to escape from carboplatin cytotoxicity.

Authors:  Sofia C Nunes; Cristiano Ramos; Filipa Lopes-Coelho; Catarina O Sequeira; Fernanda Silva; Sofia Gouveia-Fernandes; Armanda Rodrigues; António Guimarães; Margarida Silveira; Sofia Abreu; Vítor E Santo; Catarina Brito; Ana Félix; Sofia A Pereira; Jacinta Serpa
Journal:  Sci Rep       Date:  2018-06-22       Impact factor: 4.379

Review 9.  Advances in tumor-endothelial cells co-culture and interaction on microfluidics.

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  9 in total

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