Статті в журналах: "Heterogeneity and plasticity"

1

Margadant, Coert. "Endothelial heterogeneity and plasticity." Angiogenesis 24, no. 2 (May 2021): 197–98. http://dx.doi.org/10.1007/s10456-021-09794-6.

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2

Friedmann-Morvinski, Dinorah. "Glioblastoma Heterogeneity and Cancer Cell Plasticity." Critical Reviews in Oncogenesis 19, no. 5 (2014): 327–36. http://dx.doi.org/10.1615/critrevoncog.2014011777.

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3

Meacham, Corbin E., and Sean J. Morrison. "Tumour heterogeneity and cancer cell plasticity." Nature 501, no. 7467 (September 2013): 328–37. http://dx.doi.org/10.1038/nature12624.

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4

Morel, F., and N. Huyen. "Plasticity and damage heterogeneity in fatigue." Theoretical and Applied Fracture Mechanics 49, no. 1 (February 2008): 98–127. http://dx.doi.org/10.1016/j.tafmec.2007.10.006.

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5

Pabst, Reinhard. "Plasticity and heterogeneity of lymphoid organs." Immunology Letters 112, no. 1 (September 2007): 1–8. http://dx.doi.org/10.1016/j.imlet.2007.06.009.

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6

Marjanovic, Nemanja D., Robert A. Weinberg, and Christine L. Chaffer. "Cell Plasticity and Heterogeneity in Cancer." Clinical Chemistry 59, no. 1 (January 1, 2013): 168–79. http://dx.doi.org/10.1373/clinchem.2012.184655.

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BACKGROUND Heterogeneity within a given cancer arises from diverse cell types recruited to the tumor and from genetic and/or epigenetic differences amongst the cancer cells themselves. These factors conspire to create a disease with various phenotypes. There are 2 established models of cancer development and progression to metastatic disease. These are the clonal evolution and cancer stem cell models. CONTENT The clonal evolution theory suggests that successive mutations accumulating in a given cell generate clonal outgrowths that thrive in response to microenvironmental selection pressures, dictating the phenotype of the tumor. The alternative cancer stem cell (CSC) model suggests that cancer cells with similar genetic BACKGROUNDs can be hierarchically organized according to their tumorigenic potential. Accordingly, CSCs reside at the apex of the hierarchy and are thought to possess the majority of a cancer's tumor-initiating and metastatic ability. A defining feature of this model is its apparent unidirectional nature, whereby CSCs undergo symmetric division to replenish the CSC pool and irreversible asymmetric division to generate daughter cells (non-CSCs) with low tumorigenic potential. However, evolving evidence supports a new model of tumorigenicity, in which considerable plasticity exists between the non-CSC and CSC compartments, such that non-CSCs can reacquire a CSC phenotype. These findings suggest that some tumors may adhere to a plastic CSC model, in which bidirectional conversions are common and essential components of tumorigenicity. SUMMARY Accumulating evidence surrounding the plasticity of cancer cells, in particular, suggests that aggressive CSCs can be created de novo within a tumor. Given the current focus on therapeutic targeting of CSCs, we discuss the implications of non-CSC-to-CSC conversions on the development of future therapies.

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7

Khan, Arshad, Vipul Kumar Singh, Robert L. Hunter, and Chinnaswamy Jagannath. "Macrophage heterogeneity and plasticity in tuberculosis." Journal of Leukocyte Biology 106, no. 2 (April 2, 2019): 275–82. http://dx.doi.org/10.1002/jlb.mr0318-095rr.

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8

Lüönd, Fabiana, Stefanie Tiede, and Gerhard Christofori. "Breast cancer as an example of tumour heterogeneity and tumour cell plasticity during malignant progression." British Journal of Cancer 125, no. 2 (April 6, 2021): 164–75. http://dx.doi.org/10.1038/s41416-021-01328-7.

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AbstractHeterogeneity within a tumour increases its ability to adapt to constantly changing constraints, but adversely affects a patient’s prognosis, therapy response and clinical outcome. Intratumoural heterogeneity results from a combination of extrinsic factors from the tumour microenvironment and intrinsic parameters from the cancer cells themselves, including their genetic, epigenetic and transcriptomic traits, their ability to proliferate, migrate and invade, and their stemness and plasticity attributes. Cell plasticity constitutes the ability of cancer cells to rapidly reprogramme their gene expression repertoire, to change their behaviour and identities, and to adapt to microenvironmental cues. These features also directly contribute to tumour heterogeneity and are critical for malignant tumour progression. In this article, we use breast cancer as an example of the origins of tumour heterogeneity (in particular, the mutational spectrum and clonal evolution of progressing tumours) and of tumour cell plasticity (in particular, that shown by tumour cells undergoing epithelial-to-mesenchymal transition), as well as considering interclonal cooperativity and cell plasticity as sources of cancer cell heterogeneity. We review current knowledge on the functional contribution of cell plasticity and tumour heterogeneity to malignant tumour progression, metastasis formation and therapy resistance.

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9

Sun, Wenfei, Salvatore Modica, Hua Dong, and Christian Wolfrum. "Plasticity and heterogeneity of thermogenic adipose tissue." Nature Metabolism 3, no. 6 (June 2021): 751–61. http://dx.doi.org/10.1038/s42255-021-00417-4.

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10

Lee, Sunju, Inho Choi, and Young-Kwon Hong. "Heterogeneity and Plasticity of Lymphatic Endothelial Cells." Seminars in Thrombosis and Hemostasis 36, no. 03 (April 2010): 352–61. http://dx.doi.org/10.1055/s-0030-1253457.

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11

Zhu, Jinfang. "T Helper Cell Differentiation, Heterogeneity, and Plasticity." Cold Spring Harbor Perspectives in Biology 10, no. 10 (August 28, 2017): a030338. http://dx.doi.org/10.1101/cshperspect.a030338.

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12

Sell, S. "Heterogeneity and plasticity of hepatocyte lineage cells." Hepatology 33, no. 3 (March 2001): 738–50. http://dx.doi.org/10.1053/jhep.2001.21900.

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13

Zhu, Jinfang, and William E. Paul. "Heterogeneity and plasticity of T helper cells." Cell Research 20, no. 1 (December 15, 2009): 4–12. http://dx.doi.org/10.1038/cr.2009.138.

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14

Tang, Dean G. "Understanding cancer stem cell heterogeneity and plasticity." Cell Research 22, no. 3 (January 17, 2012): 457–72. http://dx.doi.org/10.1038/cr.2012.13.

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15

Will, Bruno E., John C. Dalrymple-Alford, and Georges Di Scala. "The heterogeneity and plasticity of cerebral structures." Behavioral and Brain Sciences 10, no. 1 (March 1987): 131–32. http://dx.doi.org/10.1017/s0140525x00056685.

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16

Donati, Giacomo, and Fiona M. Watt. "Stem Cell Heterogeneity and Plasticity in Epithelia." Cell Stem Cell 16, no. 5 (May 2015): 465–76. http://dx.doi.org/10.1016/j.stem.2015.04.014.

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17

Schepeler, T., M. E. Page, and K. B. Jensen. "Heterogeneity and plasticity of epidermal stem cells." Development 141, no. 13 (June 24, 2014): 2559–67. http://dx.doi.org/10.1242/dev.104588.

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18

Carding, Simon R., та Paul J. Egan. "γδ T cells: functional plasticity and heterogeneity". Nature Reviews Immunology 2, № 5 (травень 2002): 336–45. http://dx.doi.org/10.1038/nri797.

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19

Kalirad, Ata, and Ralf J. Sommer. "Spatial and temporal heterogeneity alter the cost of plasticity in Pristionchus pacificus." PLOS Computational Biology 20, no. 1 (January 30, 2024): e1011823. http://dx.doi.org/10.1371/journal.pcbi.1011823.

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Phenotypic plasticity, the ability of a single genotype to produce distinct phenotypes under different environmental conditions, has become a leading concept in ecology and evolutionary biology, with the most extreme examples being the formation of alternative phenotypes (polyphenisms). However, several aspects associated with phenotypic plasticity remain controversial, such as the existence of associated costs. While already predicted by some of the pioneers of plasticity research, i.e. Schmalhausen and Bradshaw, experimental and theoretical approaches have provided limited support for the costs of plasticity. In experimental studies, one common restriction is the measurement of all relevant parameters over long time periods. Similarly, theoretical studies rarely use modelling approaches that incorporate specific experimentally-derived fitness parameters. Therefore, the existence of the costs of plasticity remains disputed. Here, we provide an integrative approach to understand the cost of adaptive plasticity and its ecological ramifications, by combining laboratory data from the nematode plasticity model system Pristionchus pacificus with a stage-structured population model. Taking advantage of measurements of two isogenic strains grown on two distinct diets, we illustrate how spatial and temporal heterogeneity with regard to the distribution of resources on a metapopulation can alter the outcome of the competition and alleviate the realized cost of plasticity.

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20

Biswas, Antara, and Subhajyoti De. "Drivers of dynamic intratumor heterogeneity and phenotypic plasticity." American Journal of Physiology-Cell Physiology 320, no. 5 (May 1, 2021): C750—C760. http://dx.doi.org/10.1152/ajpcell.00575.2020.

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Cancer is a clonal disease, i.e., all tumor cells within a malignant lesion trace their lineage back to a precursor somatic cell that acquired oncogenic mutations during development and aging. And yet, those tumor cells tend to have genetic and nongenetic variations among themselves—which is denoted as intratumor heterogeneity. Although some of these variations are inconsequential, others tend to contribute to cell state transition and phenotypic heterogeneity, providing a substrate for somatic evolution. Tumor cell phenotypes can dynamically change under the influence of genetic mutations, epigenetic modifications, and microenvironmental contexts. Although epigenetic and microenvironmental changes are adaptive, genetic mutations are usually considered permanent. Emerging reports suggest that certain classes of genetic alterations show extensive reversibility in tumors in clinically relevant timescales, contributing as major drivers of dynamic intratumor heterogeneity and phenotypic plasticity. Dynamic heterogeneity and phenotypic plasticity can confer resistance to treatment, promote metastasis, and enhance evolvability in cancer. Here, we first highlight recent efforts to characterize intratumor heterogeneity at genetic, epigenetic, and microenvironmental levels. We then discuss phenotypic plasticity and cell state transition by tumor cells, under the influence of genetic and nongenetic determinants and their clinical significance in classification of tumors and therapeutic decision-making.

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21

Syga, Simon, Harish P. Jain, Marcus Krellner, Haralampos Hatzikirou, and Andreas Deutsch. "Evolution of phenotypic plasticity leads to tumor heterogeneity with implications for therapy." PLOS Computational Biology 20, no. 8 (August 9, 2024): e1012003. http://dx.doi.org/10.1371/journal.pcbi.1012003.

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Cancer is a significant global health issue, with treatment challenges arising from intratumor heterogeneity. This heterogeneity stems mainly from somatic evolution, causing genetic diversity within the tumor, and phenotypic plasticity of tumor cells leading to reversible phenotypic changes. However, the interplay of both factors has not been rigorously investigated. Here, we examine the complex relationship between somatic evolution and phenotypic plasticity, explicitly focusing on the interplay between cell migration and proliferation. This type of phenotypic plasticity is essential in glioblastoma, the most aggressive form of brain tumor. We propose that somatic evolution alters the regulation of phenotypic plasticity in tumor cells, specifically the reaction to changes in the microenvironment. We study this hypothesis using a novel, spatially explicit model that tracks individual cells’ phenotypic and genetic states. We assume cells change between migratory and proliferative states controlled by inherited and mutation-driven genotypes and the cells’ microenvironment. We observe that cells at the tumor edge evolve to favor migration over proliferation and vice versa in the tumor bulk. Notably, different genetic configurations can result in this pattern of phenotypic heterogeneity. We analytically predict the outcome of the evolutionary process, showing that it depends on the tumor microenvironment. Synthetic tumors display varying levels of genetic and phenotypic heterogeneity, which we show are predictors of tumor recurrence time after treatment. Interestingly, higher phenotypic heterogeneity predicts poor treatment outcomes, unlike genetic heterogeneity. Our research offers a novel explanation for heterogeneous patterns of tumor recurrence in glioblastoma patients.

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22

Markouli, Mariam, Dimitrios Strepkos, Kostas A. Papavassiliou, Athanasios G. Papavassiliou, and Christina Piperi. "Bivalent Genes Targeting of Glioma Heterogeneity and Plasticity." International Journal of Molecular Sciences 22, no. 2 (January 7, 2021): 540. http://dx.doi.org/10.3390/ijms22020540.

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Gliomas account for most primary Central Nervous System (CNS) neoplasms, characterized by high aggressiveness and low survival rates. Despite the immense research efforts, there is a small improvement in glioma survival rates, mostly attributed to their heterogeneity and complex pathophysiology. Recent data indicate the delicate interplay of genetic and epigenetic mechanisms in regulating gene expression and cell differentiation, pointing towards the pivotal role of bivalent genes. Bivalency refers to a property of chromatin to acquire more than one histone marks during the cell cycle and rapidly transition gene expression from an active to a suppressed transcriptional state. Although first identified in embryonal stem cells, bivalent genes have now been associated with tumorigenesis and cancer progression. Emerging evidence indicates the implication of bivalent gene regulation in glioma heterogeneity and plasticity, mainly involving Homeobox genes, Wingless-Type MMTV Integration Site Family Members, Hedgehog protein, and Solute Carrier Family members. These genes control a wide variety of cellular functions, including cellular differentiation during early organism development, regulation of cell growth, invasion, migration, angiogenesis, therapy resistance, and apoptosis. In this review, we discuss the implication of bivalent genes in glioma pathogenesis and their potential therapeutic targeting options.

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23

Mathur, Radhika, and Joseph F. Costello. "Epigenomic contributions to tumor cell heterogeneity and plasticity." Nature Genetics 53, no. 10 (October 2021): 1403–4. http://dx.doi.org/10.1038/s41588-021-00932-w.

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24

Markouli, Mariam, Dimitrios Strepkos, Kostas A. Papavassiliou, Athanasios G. Papavassiliou, and Christina Piperi. "Bivalent Genes Targeting of Glioma Heterogeneity and Plasticity." International Journal of Molecular Sciences 22, no. 2 (January 7, 2021): 540. http://dx.doi.org/10.3390/ijms22020540.

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Gliomas account for most primary Central Nervous System (CNS) neoplasms, characterized by high aggressiveness and low survival rates. Despite the immense research efforts, there is a small improvement in glioma survival rates, mostly attributed to their heterogeneity and complex pathophysiology. Recent data indicate the delicate interplay of genetic and epigenetic mechanisms in regulating gene expression and cell differentiation, pointing towards the pivotal role of bivalent genes. Bivalency refers to a property of chromatin to acquire more than one histone marks during the cell cycle and rapidly transition gene expression from an active to a suppressed transcriptional state. Although first identified in embryonal stem cells, bivalent genes have now been associated with tumorigenesis and cancer progression. Emerging evidence indicates the implication of bivalent gene regulation in glioma heterogeneity and plasticity, mainly involving Homeobox genes, Wingless-Type MMTV Integration Site Family Members, Hedgehog protein, and Solute Carrier Family members. These genes control a wide variety of cellular functions, including cellular differentiation during early organism development, regulation of cell growth, invasion, migration, angiogenesis, therapy resistance, and apoptosis. In this review, we discuss the implication of bivalent genes in glioma pathogenesis and their potential therapeutic targeting options.

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25

Xiao, Keyan, Dan Yu, Jinwang Wang, and Wen Xiong. "Clonal Plasticity ofVallisneria spiralisin Response to Substrate Heterogeneity." Journal of Freshwater Ecology 21, no. 1 (March 2006): 31–38. http://dx.doi.org/10.1080/02705060.2006.9664093.

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26

Tsamados, M. "Plasticity and dynamical heterogeneity in driven glassy materials." European Physical Journal E 32, no. 2 (June 2010): 165–81. http://dx.doi.org/10.1140/epje/i2010-10609-0.

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27

Taylor, Annabel, Lina Dobnikar, Mikhail Spivakov, and Helle JÃƒÂ¸rgensen. "207 Vascular smooth muscle cell heterogeneity and plasticity." Heart 103, Suppl 5 (June 2017): A138.2—A138. http://dx.doi.org/10.1136/heartjnl-2017-311726.205.

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28

Ardavín, Carlos. "Dendritic cell heterogeneity: Developmental plasticity and functional diversity." Seminars in Immunology 17, no. 4 (August 2005): 251–52. http://dx.doi.org/10.1016/j.smim.2005.05.014.

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29

Holm, Annegret, Tina Heumann, and Hellmut G. Augustin. "Microvascular Mural Cell Organotypic Heterogeneity and Functional Plasticity." Trends in Cell Biology 28, no. 4 (April 2018): 302–16. http://dx.doi.org/10.1016/j.tcb.2017.12.002.

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30

Sell, Stewart. "The hepatocyte: heterogeneity and plasticity of liver cells." International Journal of Biochemistry & Cell Biology 35, no. 3 (March 2003): 267–71. http://dx.doi.org/10.1016/s1357-2725(02)00182-6.

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31

Sarode, Poonam, Siavash Mansouri, Annika Karger, Martina Barbara Schaefer, Friedrich Grimminger, Werner Seeger, and Rajkumar Savai. "Epithelial cell plasticity defines heterogeneity in lung cancer." Cellular Signalling 65 (January 2020): 109463. http://dx.doi.org/10.1016/j.cellsig.2019.109463.

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32

Orkin, Stuart H., and Leonard I. Zon. "Hematopoiesis and stem cells: plasticity versus developmental heterogeneity." Nature Immunology 3, no. 4 (April 2002): 323–28. http://dx.doi.org/10.1038/ni0402-323.

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33

Deb, Barnali, and Prashant Kumar. "Tumor Heterogeneity and Phenotypic Plasticity in Bladder Carcinoma." Journal of the Indian Institute of Science 100, no. 3 (July 2020): 567–78. http://dx.doi.org/10.1007/s41745-020-00183-4.

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34

Dinh, Huy, Athena Golfinos, Wei Wang, Aisha Mergaert, and Paul Lambert. "Defining myeloid plasticity and heterogeneity in immunotherapy response." Journal of Immunology 208, no. 1_Supplement (May 1, 2022): 179.08. http://dx.doi.org/10.4049/jimmunol.208.supp.179.08.

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Abstract Like many cancer types, Head and neck cancers (HNC) have a <20% response rate to immune checkpoint inhibitor (ICI) therapy, making it imperative to learn what causes high treatment resistance. In addition, dissecting the tumor immune heterogeneity and plasticity is crucial to understand and overcome these challenges. Recent scRNA-seq studies have extensively characterized lymphocyte cells in HNC. However, myeloid cells are not well characterized, despite composing many in tumors and exhibiting pro- and anti-tumoral phenotypes. Here we show a comprehensive cross-species characterization of myeloid cells in HNC humans and mouse models and how they influence ICI response. To this end, we generated scRNA-Seq of immune cells in tumors from two HNC mouse models with different responses to ICI. Our comparative analysis identified critical changes in the myeloid compartment in responder and non-responder mice receiving ICI (combined anti-PD1 + anti-CTLA4) therapies. Examples are macrophage and dendritic cell subsets expressing CXCL9 and PDL1, two predictors of ICI response reported in HNC clinical trials and recently published work. Our bioinformatics approaches also defined ligand-receptor cell-cell interactions between myeloid and T cells that influence downstream gene expression networks as potential underlying mechanisms and actionable targets for ICI response. Interestingly, we observed a high concordance between mouse and human myeloid cells from ours and publicly available scRNA-Seq data. Our data and analysis provide the first translational mouse-to-human atlas of myeloid cells in HNC, providing a way to inform biomarkers and immunotherapeutic targets for potential ICI therapy success in HNC and other cancer types. This research was supported by the University of Wisconsin Head and Neck SPORE grant CEP award (P50DE026787 to H.Q.D)

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35

Sacco, Jessica L., and Esther W. Gomez. "Epithelial–Mesenchymal Plasticity and Epigenetic Heterogeneity in Cancer." Cancers 16, no. 19 (September 27, 2024): 3289. http://dx.doi.org/10.3390/cancers16193289.

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The tumor microenvironment comprises various cell types and experiences dynamic alterations in physical and mechanical properties as cancer progresses. Intratumoral heterogeneity is associated with poor prognosis and poses therapeutic challenges, and recent studies have begun to identify the cellular mechanisms that contribute to phenotypic diversity within tumors. This review will describe epithelial–mesenchymal (E/M) plasticity and its contribution to phenotypic heterogeneity in tumors as well as how epigenetic factors, such as histone modifications, histone modifying enzymes, DNA methylation, and chromatin remodeling, regulate and maintain E/M phenotypes. This review will also report how mechanical properties vary across tumors and regulate epigenetic modifications and E/M plasticity. Finally, it highlights how intratumoral heterogeneity impacts therapeutic efficacy and provides potential therapeutic targets to improve cancer treatments.

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36

Tashireva, Liubov A., Olga E. Savelieva, Evgeniya S. Grigoryeva, Yuri V. Nikitin, Evgeny V. Denisov, Sergey V. Vtorushin, Marina V. Zavyalova, Nadezhda V. Cherdyntseva, and Vladimir M. Perelmuter. "Heterogeneous Manifestations of Epithelial–Mesenchymal Plasticity of Circulating Tumor Cells in Breast Cancer Patients." International Journal of Molecular Sciences 22, no. 5 (March 2, 2021): 2504. http://dx.doi.org/10.3390/ijms22052504.

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To date, there is indisputable evidence of significant CTC heterogeneity in carcinomas, in particular breast cancer. The heterogeneity of CTCs is manifested in the key characteristics of tumor cells related to metastatic progression – stemness and epithelial–mesenchymal (EMT) plasticity. It is still not clear what markers can characterize the phenomenon of EMT plasticity in the range from epithelial to mesenchymal phenotypes. In this article we examine the manifestations of EMT plasticity in the CTCs in breast cancer. The prospective study included 39 patients with invasive carcinoma of no special type. CTC phenotypes were determined by flow cytometry before any type of treatment. EMT features of CTC were assessed using antibodies against CD45, CD326 (EpCam), CD325 (N-cadherin), CK7, Snail, and Vimentin. Circulating tumor cells in breast cancer are characterized by pronounced heterogeneity of EMT manifestations. The results of the study indicate that the majority of heterogeneous CTC phenotypes (22 out of 24 detectable) exhibit epithelial–mesenchymal plasticity. The variability of EMT manifestations does not prevent intravasation. Co-expression of EpCAM and CK7, regardless of the variant of co-expression of Snail, N-cadherin, and Vimentin, are associated with a low number of CTCs. Intrapersonal heterogeneity is manifested by the detection of several CTC phenotypes in each patient. Interpersonal heterogeneity is manifested by various combinations of CTC phenotypes in patients (from 1 to 17 phenotypes).

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37

Power, John M., and Pankaj Sah. "Dendritic spine heterogeneity and calcium dynamics in basolateral amygdala principal neurons." Journal of Neurophysiology 112, no. 7 (October 1, 2014): 1616–27. http://dx.doi.org/10.1152/jn.00770.2013.

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Glutamatergic synapses on pyramidal neurons are formed on dendritic spines where glutamate activates ionotropic receptors, and calcium influx via N-methyl-d-aspartate receptors leads to a localized rise in spine calcium that is critical for the induction of synaptic plasticity. In the basolateral amygdala, activation of metabotropic receptors is also required for synaptic plasticity and amygdala-dependent learning. Here, using acute brain slices from rats, we show that, in basolateral amygdala principal neurons, high-frequency synaptic stimulation activates metabotropic glutamate receptors and raises spine calcium by releasing calcium from inositol trisphosphate-sensitive calcium stores. This spine calcium release is unevenly distributed, being present in proximal spines, but largely absent in more distal spines. Activation of metabotropic receptors also generated calcium waves that differentially invaded spines as they propagated toward the soma. Dendritic wave invasion was dependent on diffusional coupling between the spine and parent dendrite which was determined by spine neck length, with waves preferentially invading spines with short necks. Spine calcium is a critical trigger for the induction of synaptic plasticity, and our findings suggest that calcium release from inositol trisphosphate-sensitive calcium stores may modulate homosynaptic plasticity through store-release in the spine head, and heterosynaptic plasticity of unstimulated inputs via dendritic calcium wave invasion of the spine head.

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38

Shimizu, Hiroyuki, Masamichi Maruoka, Naofumi Ichikawa, Akhil Ranjan Baruah, Naohiro Uwatoko, Yoshio Sano, and Kazumitsu Onishi. "Genetic control of phenotypic plasticity in Asian cultivated and wild rice in response to nutrient and density changes." Genome 53, no. 3 (March 2010): 211–23. http://dx.doi.org/10.1139/g09-099.

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Phenotypic plasticity is an adaptive mechanism adopted by plants in response to environmental heterogeneity. Cultivated and wild species adapt in contrasting environments; however, it is not well understood how genetic changes responsible for phenotypic plasticity were involved in crop evolution. We investigated the genetic control of phenotypic plasticity in Asian cultivated ( Oryza sativa ) and wild rice ( O. rufipogon ) under 5 environmental conditions (2 nutrient and 3 density levels). Quantitative trait locus (QTL) analysis was conducted for traits affecting plant architecture and biomass production. By analysing the phenotypic means, QTLs of large effects were detected as a cluster on chromosome 7 under all the environmental conditions investigated; this might have contributed to transitions of plant architecture during domestication, as reported previously. Multiple QTLs of plasticity were also found within this QTL cluster, demonstrating that allele-specific environmental sensitivity might control plasticity. Furthermore, QTLs controlling plasticity without affecting phenotypic means were also identified. The mode of action and direction of allele effects of plasticity QTLs varied depending on the traits and environmental signals. These findings confirmed that cultivated and wild rice show distinctive genetic differentiation for phenotypic plasticity, which might have contributed to adaptation under contrasting environmental heterogeneity during the domestication of rice.

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39

Malhotra, Pulkit, and Ruman Rahman. "Tumour Heterogeneity and Disease Infiltration as Paradigms of Glioblastoma Treatment Resistance." Onco 4, no. 4 (October 18, 2024): 349–58. http://dx.doi.org/10.3390/onco4040024.

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Isocitrate dehydrogenase wild-type glioblastoma, a Grade 4 malignant brain neoplasm, remains resistant to multimodal treatment, with a median survival of 16 months from diagnosis with no geographical bias. Despite increasing appreciation of intra-tumour genotypic variation and stem cell plasticity, such knowledge has yet to translate to efficacious molecular targeted therapies in this post-genomic era. Critically, the manifestation of molecular heterogeneity and stem cell biological process within clinically relevant infiltrative disease is little understood. Here, we review the interactions between neural plasticity, intra-tumour heterogeneity and residual infiltrative disease, and we draw upon antibiotic resistance as an insightful analogy to further explain tumour heterogeneity.

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40

Ballermann, Barbara J. "Endothelial Cell Identity, Heterogeneity and Plasticity in the Kidney." Journal of the American Society of Nephrology 31, no. 1 (December 9, 2019): 1–2. http://dx.doi.org/10.1681/asn.2019111179.

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41

Wang, J. G., D. Q. Zhao, M. X. Pan, C. H. Shek, and W. H. Wang. "Mechanical heterogeneity and mechanism of plasticity in metallic glasses." Applied Physics Letters 94, no. 3 (January 19, 2009): 031904. http://dx.doi.org/10.1063/1.3073985.

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42

Ofori-Acquah, Solomon F., and Troy Stevens. "Heterogeneity of Endothelial Sheet Migration: Role in Angiogenic Plasticity." Blood 106, no. 11 (November 16, 2005): 3692. http://dx.doi.org/10.1182/blood.v106.11.3692.3692.

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Abstract The vascular system is a complex network of conduit and microvascular vessels exposed to different microenvironments that imprints unique phenotypic traits on individual endothelial cell populations. Endothelial cells in fully differentiated blood vessels in adult tissues have a quiescent phenotype characterized by an increased resistance to proliferate, migrate or undergo apoptosis. The intrinsic capacity of endothelial cells to switch from a quiescent to angiogenic phenotype however plays an important role in wound healing, several vascular proliferative disorders and tumor angiogenesis. Endothelial cells revert to an angiogenic phenotype as isolated cells in culture nonetheless they are contact inhibited at confluence reflecting their in vivo phenotype in the endothelium. In this study, we used time-lapse video microscopy to study early events in endothelial sheet migration in confluent monolayers of primary endothelial cells derived from conduit (PAECs) and microvascular (PMVECs) blood vessels. Recordings were made in a live cell chamber and were restricted to six hours to minimize the effect of proliferation on sheet migration. PMVECs at the wound edge were significantly highly spread and squamous in appearance compared to PAECs, which had a distinctly more cuboidal morphology. Majority (>95%) of PMVECs at the wound edge produced extensive lamellipodia based on morphology and dynamics that measured 25 μm ± 4 μm. By contrast, PAECs formed significantly smaller lamelipodia which extended by 8 μm ± 4 μm. On average PMVEC sheets migrated at a speed of 12.5 μm per hour covering a total distance of 75 μm ± 15 μm (n=6). Sheet migration rate in PMVECs was 3-fold faster than in PAECs (3.8 μm per hour), which covered a total distance of 23 μm ± 10 μm in the same time period. To unravel the molecular basis for this functional diversity, gene micro array analysis was performed. We identified unique transcriptional profiles for cell-cell adhesion molecules, integrins and disintegrin-metalloproteases each with a distinct role in collective cell migration. In particular, integrin alpha 7, which is a major regulator of lamellipodia formation was found to be 20-fold more abundant in PMVECs than in PAECs. This study provides molecular and functional evidence for heterogeneity of endothelial sheet migration. This central finding highlights variability in angiogenic plasticity in fully differentiated endothelial cells, which may have important ramifications for anti-angiogenesis therapy.

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43

Shih, Chu-chih, David DiGiusto, Adam Mamelak, Thomas LeBon, and Stephen J. Forman. "Hematopoietic Potential of Neural Stem Cells: Plasticity Versus Heterogeneity." Leukemia & Lymphoma 43, no. 12 (January 2002): 2263–68. http://dx.doi.org/10.1080/1042819021000040215.

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44

Blinova, Varvara G., and Dmitry D. Zhdanov. "Many Faces of Regulatory T Cells: Heterogeneity or Plasticity?" Cells 13, no. 11 (June 1, 2024): 959. http://dx.doi.org/10.3390/cells13110959.

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Regulatory T cells (Tregs) are essential for maintaining the immune balance in normal and pathological conditions. In autoimmune diseases and transplantation, they restrain the loss of self-tolerance and promote engraftment, whereas in cancer, an increase in Treg numbers is mostly associated with tumor growth and poor prognosis. Numerous markers and their combinations have been used to identify Treg subsets, demonstrating the phenotypic diversity of Tregs. The complexity of Treg identification can be hampered by the unstable expression of some markers, the decrease in the expression of a specific marker over time or the emergence of a new marker. It remains unclear whether such phenotypic shifts are due to new conditions or whether the observed changes are due to initially different populations. In the first case, cellular plasticity is observed, whereas in the second, cellular heterogeneity is observed. The difference between these terms in relation to Tregs is rather blurred. Considering the promising perspectives of Tregs in regenerative cell-based therapy, the existing confusing data on Treg phenotypes require further investigation and analysis. In our review, we introduce criteria that allow us to distinguish between the heterogeneity and plasticity of Tregs normally and pathologically, taking a closer look at their diversity and drawing the line between two terms.

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45

Granados, Karol, Juliane Poelchen, Daniel Novak, and Jochen Utikal. "Cellular Reprogramming—A Model for Melanoma Cellular Plasticity." International Journal of Molecular Sciences 21, no. 21 (November 5, 2020): 8274. http://dx.doi.org/10.3390/ijms21218274.

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Cellular plasticity of cancer cells is often associated with phenotypic heterogeneity and drug resistance and thus remains a major challenge for the treatment of melanoma and other types of cancer. Melanoma cells have the capacity to switch their phenotype during tumor progression, from a proliferative and differentiated phenotype to a more invasive and dedifferentiated phenotype. However, the molecular mechanisms driving this phenotype switch are not yet fully understood. Considering that cellular heterogeneity within the tumor contributes to the high plasticity typically observed in melanoma, it is crucial to generate suitable models to investigate this phenomenon in detail. Here, we discuss the use of complete and partial reprogramming into induced pluripotent cancer (iPC) cells as a tool to obtain new insights into melanoma cellular plasticity. We consider this a relevant topic due to the high plasticity of melanoma cells and its association with a strong resistance to standard anticancer treatments.

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46

Kraft, Agnieszka, Josephine Yates, Florian Barkmann, and Valentina Boeva. "Abstract IA024: Unraveling potential genetic drivers of intratumor transcriptional heterogeneity and phenotypic plasticity in malignant cells." Cancer Research 84, no. 3_Supplement_2 (February 1, 2024): IA024. http://dx.doi.org/10.1158/1538-7445.canevol23-ia024.

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Abstract The concepts of transcriptional intratumor heterogeneity and phenotypic plasticity have gained significant attention in relation to the development of tumor resistance to cancer treatments [1]. In several cancer types, recent studies have linked certain somatic genomic aberrations to the presence of specific transcriptional states in malignant cells [2,3]. However, our general understanding of drivers of epigenetic and transcriptional plasticity stays somewhat fragmented. In theory, this gap could be filled through the generation and analysis of single-cell multi-omics datasets of human tumors coupled with clinical information. In practice, such datasets are pretty scarce, and existing data certainly do not allow for a global analysis of the genetic drivers of cell plasticity and heterogeneity across cancer types. On the other hand, the scientific community has already generated rich bulk transcriptomics datasets coupled with genetic and clinical information (e.g., TCGA) that could be used to answer the question about the relationship between genetic and epigenetic landscapes if proportions of cells in different malignant states could be extracted via a computational deconvolution of the transcriptomic signal. Unfortunately, available methods for accurate bulk data deconvolution require reference single-cell transcriptomics datasets, which limits the applicability of such approaches to study large pan-cancer datasets. We provide a solution to this problem by creating and benchmarking an algorithmic approach that allows extracting the transcriptional heterogeneity information from bulk tumor profiles without the need for a single-cell reference when a matched DNA-sequencing experiment is available. The latter is true for thousands of tumor samples, including all TCGA datasets. We present results of a pan-cancer analysis linking genetic aberrations with proportions of malignant cells in specific transcriptional states that shed light on the potential genetic drivers underlying malignant cell heterogeneity. References 1. Boeva, V. et al. Heterogeneity of neuroblastoma cell identity defined by transcriptional circuitries. Nat. Genet. 49, 1408–1413 (2017). 2. Neftel, C. et al. An Integrative Model of Cellular States, Plasticity, and Genetics for Glioblastoma. Cell 178, 835-849.e21 (2019). 3. Moorman, A. R. et al. Progressive plasticity during colorectal cancer metastasis. BioRxiv Prepr. Serv. Biol. 2023.08.18.553925 (2023) doi:10.1101/2023.08.18.553925. Citation Format: Agnieszka Kraft, Josephine Yates, Florian Barkmann, Valentina Boeva. Unraveling potential genetic drivers of intratumor transcriptional heterogeneity and phenotypic plasticity in malignant cells [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr IA024.

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47

Mathis, Robert Austin, Ethan S. Sokol, and Piyush B. Gupta. "Cancer cells exhibit clonal diversity in phenotypic plasticity." Open Biology 7, no. 2 (February 2017): 160283. http://dx.doi.org/10.1098/rsob.160283.

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Phenotypic heterogeneity in cancers is associated with invasive progression and drug resistance. This heterogeneity arises in part from the ability of cancer cells to switch between phenotypic states, but the dynamics of this cellular plasticity remain poorly understood. Here we apply DNA barcodes to quantify and track phenotypic plasticity across hundreds of clones in a population of cancer cells exhibiting epithelial or mesenchymal differentiation phenotypes. We find that the epithelial-to-mesenchymal cell ratio is highly variable across the different clones in cancer cell populations, but remains stable for many generations within the progeny of any single clone—with a heritability of 0.89. To estimate the effects of combination therapies on phenotypically heterogeneous tumours, we generated quantitative simulations incorporating empirical data from our barcoding experiments. These analyses indicated that combination therapies which alternate between epithelial- and mesenchymal-specific treatments eventually select for clones with increased phenotypic plasticity. However, this selection could be minimized by increasing the frequency of alternation between treatments, identifying designs that may minimize selection for increased phenotypic plasticity. These findings establish new insights into phenotypic plasticity in cancer, and suggest design principles for optimizing the effectiveness of combination therapies for phenotypically heterogeneous tumours.

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48

Tu, Shi-Ming, Miao Zhang, Christopher G. Wood, and Louis L. Pisters. "Stem Cell Theory of Cancer: Origin of Tumor Heterogeneity and Plasticity." Cancers 13, no. 16 (August 9, 2021): 4006. http://dx.doi.org/10.3390/cancers13164006.

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In many respects, heterogeneity is one of the most striking revelations and common manifestations of a stem cell origin of cancer. We observe heterogeneity in myriad mixed tumors including testicular, lung, and breast cancers. We recognize heterogeneity in diverse tumor subtypes in prostate and kidney cancers. From this perspective, we illustrate that one of the main stem-ness characteristics, i.e., the ability to differentiate into diverse and multiple lineages, is central to tumor heterogeneity. We postulate that cancer subtypes can be meaningless and useless without a proper theory about cancer’s stem cell versus genetic origin and nature. We propose a unified theory of cancer in which the same genetic abnormalities, epigenetic defects, and microenvironmental aberrations cause different effects and lead to different outcomes in a progenitor stem cell versus a mature progeny cell. We need to recognize that an all-encompassing genetic theory of cancer may be incomplete and obsolete. A stem cell theory of cancer provides greater universality, interconnectivity, and utility. Although genetic defects are pivotal, cellular context is paramount. When it concerns tumor heterogeneity, perhaps we need to revisit the conventional wisdom of precision medicine and revise our current practice of targeted therapy in cancer care.

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49

Tsagrakis, Ioannis, and Elias C. Aifantis. "Recent Developments in Gradient Plasticity—Part I: Formulation and Size Effects." Journal of Engineering Materials and Technology 124, no. 3 (June 10, 2002): 352–57. http://dx.doi.org/10.1115/1.1479695.

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The purpose of this two-part article, is first to give an update of recent developments of gradient plasticity as this was advanced by Aifantis and co-workers in the early eighties to address dislocation patterning and shear band problems, and then to elaborate on two specific issues of current interest: size effects and plastic heterogeneity. In Part I, a brief review of gradient dislocation dynamics as providing a direct motivation for the simplest form of gradient plasticity is given. Then, a more general phenomenological formulation of gradient plasticity is given and used to interpret size effects. In Part II, wavelet analysis is used as a potential tool to describe plastic heterogeneity at very fine scales for which experimental results are not available, as well as for providing another means to interpret size effects through the derivation of scale-dependent constitutive equations.

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50

Öhlund, Daniel, Ela Elyada, and David Tuveson. "Fibroblast heterogeneity in the cancer wound." Journal of Experimental Medicine 211, no. 8 (July 28, 2014): 1503–23. http://dx.doi.org/10.1084/jem.20140692.

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Fibroblasts regulate the structure and function of healthy tissues, participate transiently in tissue repair after acute inflammation, and assume an aberrant stimulatory role during chronic inflammatory states including cancer. Such cancer-associated fibroblasts (CAFs) modulate the tumor microenvironment and influence the behavior of neoplastic cells in either a tumor-promoting or tumor-inhibiting manner. These pleiotropic functions highlight the inherent plasticity of fibroblasts and may provide new avenues to understand and therapeutically intervene in malignancies. We discuss the emerging themes of CAF biology in the context of tumorigenesis and therapy.

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