Computational models to explore the complexity of the epithelial to mesenchymal transition in cancer.
cancer
computational modeling
epithelial to mesenchymal transition
Journal
Wiley interdisciplinary reviews. Systems biology and medicine
ISSN: 1939-005X
Titre abrégé: Wiley Interdiscip Rev Syst Biol Med
Pays: United States
ID NLM: 101516550
Informations de publication
Date de publication:
11 2020
11 2020
Historique:
received:
20
11
2019
revised:
07
02
2020
accepted:
02
03
2020
pubmed:
26
3
2020
medline:
21
10
2021
entrez:
26
3
2020
Statut:
ppublish
Résumé
Epithelial to mesenchymal transition (EMT) is a complex biological process that plays a key role in cancer progression and metastasis formation. Its activation results in epithelial cells losing adhesion and polarity and becoming capable of migrating from their site of origin. At this step the disease is generally considered incurable. As EMT execution involves several individual molecular components, connected by nontrivial relations, in vitro techniques are often inadequate to capture its complexity. Computational models can be used to complement experiments and provide additional knowledge difficult to build up in a wetlab. Indeed in silico analysis gives the user total control on the system, allowing to identify the contribution of each independent element. In the following, two kinds of approaches to the computational study of EMT will be presented. The first relies on signal transduction networks description and details how changes in gene expression could influence this process, both focusing on specific aspects of the EMT and providing a general frame for this phenomenon easily comparable with experimental data. The second integrates single cell and population level descriptions in a multiscale model that can be considered a more accurate representation of the EMT. The advantages and disadvantages of each approach will be highlighted, together with the importance of coupling computational and experimental results. Finally, the main challenges that need to be addressed to improve our knowledge of the role of EMT in the neoplastic disease and the scientific and translational value of computational models in this respect will be presented. This article is categorized under: Analytical and Computational Methods > Computational Methods.
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
e1488Informations de copyright
© 2020 Wiley Periodicals, Inc.
Références
Andasari, V., Roper, R. T., Swat, M. H., & Chaplain, M. A. J. (2012). Integrating intracellular dynamics using CompuCell3D and Bionetsolver: Applications to multiscale modelling of cancer cell growth and invasion. PLoS One, 7(3), e33726. https://doi.org/10.1371/journal.pone.0033726
Antony, J., Thiery, J. P., & Huang, R. U.-J. (2019). Epithelial-to-mesenchymal transition: Lessons from development, insights into cancer and the potential of EMT-subtype based therapeutic intervention. Physical Biology, 16, 041004. https://doi.org/10.1088/1478-3975/ab157a
Basu, S., Cheriyamundath, S., & Ben-Ze'ev, A. (2018). Cell-cell adhesion: Linking Wnt/β-catenin signaling with partial EMT and stemness traits in tumorigenesis. F1000Research, 7:F1000 Faculty Rev-1488. https://doi.org/10.12688/f1000research.15782.1
Benetatos, L., Hatzimichael, E., Londin, E., Vartholomatos, G., Loher, P., Rigoutsos, I., & Briasoulis, E. (2013). The microRNAs within the DLK1-DIO3 genomic region: Involvement in disease pathogenesis. Cellular and Molecular Life Sciences, 70(5), 795-814. https://doi.org/10.1007/s00018-012-1080-8
Bierie, B., Pierce, S. E., Kroeger, C., Stover, D. G., Pattabiraman, D. R., Thiru, P., … Weinberg, R. A. (2017). Integrin-β4 identifies cancer stem cell-enriched populations of partially mesenchymal carcinoma cells. Proceedings of the National Academy of Sciences of the United States of America, 114(12), E2337-E2346. https://doi.org/10.1073/pnas.1618298114
Boaredo, M., Jolly, M. K., Goldman, A., Pietila, M., Mani, S. A., Sengupta, S., … Onuchic, J. N. (2015). Notch-jagged signalling can give rise to clusters of cells exhibiting a hybrid epithelial/ mesenchymal phenotype. Journal of the Royal Society Interface, 13, 20151106. https://doi.org/10.1098/rsif.2015.1106
Bocci, F., Jolly, M. K., Tripathi, S. C., Aguilar, M., Hanash, S. M., Levine, H., & Onuchic, J. N. (2017). Numb prevents a complete epithelial-mesenchymal transition by modulating Notch signalling. Journal of the Royal Society Interface, 14, 20170512. https://doi.org/10.1098/rsif.2017.0512
Bronsert, P., Enderle-Ammour, K., Bader, M., Timme, S., Kuehs, M., Csanadi, A., … Wellner, U. F. (2014). Cancer cell invasion and EMT marker expression: A three-dimensional study of the human cancer-host interface. Journal of Pathology, 234, 410-422. https://doi.org/10.1002/path.4416
Burger, G. A., Danen, E. H. J., & Beltman, J. B. (2017). Deciphering epithelial-mesenchymal transition regulatory networks in cancer through computational approaches. Frontiers in Oncology, 7, 162. https://doi.org/10.3389/fonc.2017.00162
Celia-Terrassa, T., Bastian, C., Liu, D., Ell, B., Aiello, N. M., Wei, Y., … Kang, Y. (2018). Hysteresis control of epithelial-mesenchymal transition dynamics conveys a distinct program with enhanced metastatic ability. Nature Communications, 9, 5005. https://doi.org/10.1038/s41467-018-07538-7
Chanrion, M., Kuperstein, I., Barrière, C., El Marjou, F., Cohen, D., Vignjevic, D., … Robine, S. (2014). Concomitant Notch activation and p53 deletion trigger epithelial-to-mesenchymal transition and metastasis in mouse gut. Nature Communications, 5, 5005. https://doi.org/10.1038/ncomms6005
Cheng, W.-Y., Kandel, J. J., Yamashiro, D. J., Canoll, P., & Anastassiou, D. (2012). A multi-cancer mesenchymal transition gene expression signature is associated with prolonged time to recurrence in glioblastoma. PLoS One, 7(4), e34705. https://doi.org/10.1371/journal.pone.0034705
Choudhary, K. S., Rohatgi, N., Halldorsson, S., Briem, E., Gudjonsson, T., Gudmundsson, S., & Rolfsson, O. (2016). EGFR signal-network reconstruction demonstrates metabolic crosstalk in EMT. PLOS Computational Biology, 12(6), e1004924. https://doi.org/10.1371/journal.pcbi.1004924
Conacci-Sorrell, M., Simcha, I., Ben-Yedidia, T., Biechman, J., Savagner, P., & Ben-Ze'ev, A. (2003). Autoregulation of E-cadherin expression by cadherin-cadherin interactions: The roles of beta-catenin signaling, Slug, and MAPK. The Journal of Cell Biology, 163(4), 847-857. https://doi.org/10.1083/jcb.200308162
Cortesi, M., Pasini, A., Furini, S., & Giordano, E. (2019). Identification via numerical computation of transcriptional determinants of a cell phenotype decision making. Frontiers in Genetics, 10, 575. https://doi.org/10.3389/fgene.2019.00575
De Domenico, S., & Vergara, D. (2017). The many-faced program of epithelial-mesenchymal transition: A system biology-based view. Frontiers in Oncology, 7, 274. https://doi.org/10.3389/fonc.2017.00274
Dhawan, A., Tonekaboni, S. A. M., Taube, J. H., Hu, S., Sphyris, N., Mani, S. A., & Kohandel, M. (2016). Mathematical modelling of phenotypic plasticity and conversion to a stem-cell state under hypoxia. Scientific Reports, 6, 18074. https://doi.org/10.1038/srep18074
Dongre, A., & Weinberg, R. A. (2019). New insights into the mechanisms of epithelial-mesenchymal transition and implications for cancer. Nature Reviews. Molecular Cell Biology, 20, 69-84. https://doi.org/10.1038/s41580-018-0080-4
Drago-Garcìa, D., Espinal-Enrìquez, J., & Hernàndez-Lemus, E. (2017). Network analysis of EMT and MET micro-RNA regulation in breast cancer. Scientific Reports, 7, 13534. https://doi.org/10.1038/s41598-017-13903-1
Ellsworth, R. E., Blackburn, H. L., Shriver, C. D., Soon-Shiong, P., & Ellsworth, D. L. (2017). Molecular heterogeneity in breast cancer: State of the science and implications for patient care. Seminars in Cell and Developmental Biology., 64, 65-72. https://doi.org/10.1016/j.semcdb.2016.08.025
Engwer, C., Hillen, T., Knappitsch, M., & Surulescu, C. (2015). Glioma follow white matter tracts: A multiscale DTI-based model. Journal of Mathematical Biology, 71(3), 551-582. https://doi.org/10.1007/s00285-014-0822-7
Fabisiewicz, A., & Grzybowska, E. (2017). CTC clusters in cancer progression and metastasis. Medical Oncology, 34(1), 12. https://doi.org/10.1007/s12032-016-0875-0
Ferrell, J. E., Jr. (2002). Self-perpetuating states in signal transduction: Positive feedback, double-negative feedback and bistability. Current Opinion in Cell Biology, 14(2), 140-148. https://doi.org/10.1016/s0955-0674(02)00314-9
Flöttmann, M., Scharp, T., & Klipp, E. (2012). A stochastic model of epigenetic dynamics in somatic cell reprogramming. Frontiers in Physiology, 3, 216. https://doi.org/10.3389/fphys.2012.00216
Ford, C. E., Punnia-Moorthy, G., Henry, C. E., Llamosas, E., Nixdorf, S., Olivier, J., … Heinzelmann-Schwarz, V. (2014). The non-canonical Wnt ligand, Wnt5a, is upregulated and associated with epithelial to mesenchymal transition in epithelial ovarian cancer. Gynecologic Oncology, 134(2), 338-345. https://doi.org/10.1016/j.ygyno.2014.06.004
Gasior, K., Hauck, M., Wilson, A., & Bhattacharya, S. A. (2017). Theoretical model of the Wnt signaling pathway in the epithelial mesenchymal transition. Theoretical Biology & Medical Modelling, 14, 19. https://doi.org/10.1186/s12976-017-0064-7
Ginnebaugh, K. R., Ahmad, A., & Sarkar, F. H. (2014). The therapeutic potential of targeting the epithelial-mesenchymal transition in cancer. Expert Opinion on Therapeutic Targets, 18(7), 731-745. https://doi.org/10.1517/14728222.2014.909807
Gregory, P. A., Bracken, C. P., Smith, E., Bert, A. G., Wright, J. A., Rosian, S., … Goodall, G. J. (2011). An autocrine TGF-beta/ZEB/miR-200 signaling network regulates establishment and maintenance of epithelial-mesenchymal transition. Molecular Biology of the Cell, 22(10), 1686-1698. https://doi.org/10.1091/mbc.E11-02-0103
Grigore, A. D., Kumar Jolly, M., Jia, D., Farach-Carson, M. C., & Levine, H. (2016). Tumor budding: The name is EMT. Partial EMT. Journal of Clinical Medicine, 5(5), 51. https://doi.org/10.3390/jcm5050051
Grosse-Wilde, A., Foquier d'Herouel, A., McIntosh, E., Ertaylan, G., Skupin, A., Kuestner, R. E., … Huang, S. (2015). Stemness of the hybrid epithelial/mesenchymal state in breast cancer and its association with poor survival. PLoS One, 10(5), e0126522. https://doi.org/10.1371/journal.pone.0126522
Hong, J., Liu, Z., Zhu, H., Zhang, X., Liang, Y., Yao, S., … Cheng, C. (2014). The tumor suppressive role of NUMB isoform 1 in esophageal squamous cell carcinoma. Oncotarget, 5(14), 5602-5614. https://doi.org/10.18632/oncotarget.2136
Hong, T., Watanabe, K., Ha Ta, C., Villareal-Ponce, A., Nie, Q., & Dai, X. (2015). An Ovol2-Zeb1 mutual inhibitory circuit governs bidirectional and multi-step transition between epithelial and mesenchymal states, 11(11), e1004569. https://doi.org/10.1371/journal.pcbi.1004569
Huang, R. Y.-J., Wong, M. K., Tan, T. Z., Kuay, K. T., Ng, A. H. C., Chung, V. Y., … Thiery, J. P. (2013). An EMT spectrum defines an anoikis-resistant and spheroidogenic intermediate mesenchymal state that is sensitive to E-cadherin restoration by a Src-kinase inhibitor, saracatinib (AZD0530). Cell Death and Disease, 4(11), e915. https://doi.org/10.1038/cddis.2013.442
Jia, D., Jolly, M. K., Boareto, M., Parsana, P., Mooney, S. M., Pienta, K. J., … Ben-Jacob, E. (2015). OVOL guides the epithelial-hybrid-mesenchymal transition, 6(17). https://doi.org/10.18632/oncotarget.3623
Jolly, M. K., Boareto, M., Huang, B., Jia, D., Lu, M., Ben-Jacob, E., … Levine, H. (2015). Implications of the hybrid epithelial/mesenchymal phenotype in metastasis. Frontiers in Oncology, 5, 155. https://doi.org/10.3389/fonc.2015.00155
Jolly, M. K., & Levine, H. (2017). Computational systems biology of epithelial-hybrid-mesenchymal transitions. Current Opinion in Systems Biology, 3, 1-6. https://doi.org/10.1016/j.coisb.2017.02.004
Jolly, M. K., Tripathi, S. C., Jia, D., Mooney, S. M., Celiktas, M., Hanash, S. M., … Levine, H. (2016). Stability of the hybrid epithelial/mesenchymal phenotype. Oncotarget, 7(19), 27067-27084. https://doi.org/10.18632/oncotarget.8166
Jolly, M. K., Tripathi, S. C., Somarelli, J. A., Hanash, S. M., & Levine, H. (2017). Epithelial/mesenchymal plasticity: How have quantitative mathematical models helped improve our understanding? Molecular Oncology, 11(7), 739-754. https://doi.org/10.1002/1878-0261.12084
Kalluri, R. (2009). EMT: When epithelial cells decide to become mesenchymal-like cells. The Journal of Clinical Investigation, 119(6), 1417-1419. https://doi.org/10.1172/JCI39675
Kalluri, R., & Weinberg, R. A. (2009). The basics of epithelial-mesenchymal transition. The Journal of Clinical Investigation, 119(6), 1420-1428. https://doi.org/10.1172/JCI39104
Kauffman, S. A. (1969). Homeostasis and differentiation in random genetic control networks. Nature, 224, 177-178. https://doi.org/10.1038/224177a0
Koplev, S., Lin, K., Dohlman, A. B., & Ma'ayan, A. (2018). Integration of pan-cancer transcriptomics with RPPA proteomics reveals mechanisms of epithelial-mesenchymal transition. PLOS Computational Biology, 14(1), e1005911. https://doi.org/10.1371/journal.pcbi.1005911
Kumar, S., Das, A., & Sen, S. (2014). Extracellular matrix density promotes EMT by weakening cell-cell adhesions. Molecular BioSystems, 10, 838-850. https://doi.org/10.1039/c3mb70431a
Lau, K. M., & McGlade, C. J. (2011). Numb is a negative regulator of HGF dependent cell scattering and Rac1 activation. Experimental Cell Research, 317(4), 539-551. https://doi.org/10.1016/j.yexcr.2010.12.005
Lehner, B., Kunz, P., Saehr, H., & Fellenberg, J. (2014). Epigenetic silencing of genes and microRNAs within the imprinted Dlk1-Dio3 region at human chromosome 14.32 in giant cell tumor of bone. BMC Cancer, 14, 495. https://doi.org/10.1186/1471-2407-14-495
Li, C., Hong, T., & Nie, Q. (2016). Quantifying the landscape and kinetic paths for epithelial-mesenchymal transition from a core circuit. Physical Chemistry Chemical Physics, 27, 17949-17956. https://doi.org/10.1039/c6cp03174a
Li, Q., Tang, H., Hu, F., & Qin, C. (2019). Silencing of FOXO6 inhibits the proliferation, invasion, and glycolysis in colorectal cancer cells. Journal of Cellular Biochemistry, 120(3), 3853-3860. https://doi.org/10.1002/jcb.27667
Liu F., Heiner M. & Gilbert D. (2018) Fuzzy petri nets for modelling of uncertain biological systems. Briefings in Bioinformatics, 1-13. doi:https://doi.org/10.1093/bib/bby118
Lu, M., Jolly, M. K., Levine, H., Onuchic, J. N., & Ben-Jacob, E. (2013). MicroRNA-based regulation of epithelial-hybrid-mesenchymal fate determination. Proceedings of the National Academy of Sciences of the United States of America, 110(45), 18144-18149. https://doi.org/10.1073/pnas.1318192110
Lu, P., Takai, K., Weaver, V. M., & Werb, Z. (2011). Extracellular matrix degradation and remodeling in development and disease. Cold Spring Harbor Perspective in Biology, 11(2), 3, a005058. https://doi.org/10.1101/cshperspect.a005058
Mak, P. M., Tong, P., Diao, L., Cardnell, R. J., Gibbons, D. L., William, W. N., … Averett Byers, L. (2016). A patient-derived, pan-cancer EMT signature identifies global molecular alterations and immune target enrichment following epithelial to mesenchymal transition. Clinical Cancer Research, 22(3), 609-620. https://doi.org/10.1158/1078-0432.CCR-15-0876
Mendez, M. J., Hoffman, M. J., Cherry, E. M., Lemmon, C. A., & Weinberg, S. H. (2019). Predicting TGF-β-induced epithelial-mesenchymal transition using data assimilation. Biophysical Journal, 116(3), 129a-130a. https://doi.org/10.1016/j.bpj.2018.11.720
Metzcar, J., Wang, Y., Heiland, R., & Macklin, P. (2019). A review of cell-based computational modeling in cancer biology. JCO Clinical Cancer Informatics, 3, 1-13. https://doi.org/10.1200/CCI.18.00069
Mishra, V., Subramaniam, M., Kari, V., Pitel, K. S., Baumgart, S. J., Naylor, R. M., … Johnsen, S. A. (2017). Kruppel-like transcription factor KLF10 suppresses TGFβ-induced epithelial-to-mesenchymal transition via a negative feedback mechanism. Cancer Research, 77(9), 2387-2400. https://doi.org/10.1158/0008-5472.CAN-16-2589
Mouneimne, G., Hansen, S. D., Selfors, L. M., Petrak, L., Hickey, M. M., Gallegos, L. L., … Brugge, J. S. (2012). Differential remodeling of Actin cytoskeleton architecture by profilin isoforms leads to distinct effects on cell migration and invasion. Cancer Cell, 22(5), 615-630. https://doi.org/10.1016/j.ccr.2012.09.027
Nieto, M. A. (2009). Epithelial-mesenchymal transitions in development and disease: Old views and new perspectives. The International Journal of Developmental Biology, 53, 1541-1547. https://doi.org/10.1387/ijdb.072410mn
Nieto, M. A., Huang, R. Y., Jackson, R. A., & Thiery, J. P. (2016). EMT: 2016. Cell, 166(1), 21-45. https://doi.org/10.1016/j.cell.2016.06.028
Nobile, M. S., Votta, G., Palorini, R., Spolaor, S., De Vitto, H., Cazzaniga, P., … Besozzi, D. (2019). Fuzzy modeling and global optimization to predict novel therapeutic targets in cancer cells. Bioinformatics, btz868. https://doi.org/10.1093/bioinformatics/btz868
Park, S., Gonzales, D. G., Guirao, B., Boucher, J. D., Cockburn, K., Marsh, E. D., … Greco, V. (2017). Tissue-scale coordination of cellular behaviour promotes epidermal wound repair in live mice. Nature Cell Biology, 19, 155-163. https://doi.org/10.1038/ncb3472
Pastushenko, I., & Blanpain, C. (2019). EMT transition states during tumor progression and metastasis. Trends in Cell Biology, 29(3), 212-226. https://doi.org/10.1016/j.tcb.2018.12.001
Ramis-Conde, I., Drasdo, D., Anderson, A. R. A., & Chaplain, M. A. J. (2008). Modeling the influence of the E-cadherin-b-catenin pathway in cancer cell invasion: A multiscale approach. Biophysical Journal, 95, 155-165. https://doi.org/10.1529/biophysj.107.114678
Reher, D., Klink, B., Deutsch, A., & Voss-Bohme, A. (2017). Cell adhesion heterogeneity reinforces tumour cell dissemination: Novel insights from a mathematical model. Biology Direct, 12, 18. https://doi.org/10.1186/s13062-017-0188-z
Sanker, J., Selvakumar, G., & Suguna, L. (2019). Epithelial and mesenchymal transition pathway (EMT) and hepatocellular carcinoma: A mini review. Clinical Oncology, 4, 1617.
Sato, K., Watanabe, T., Wang, S., Kakeno, M., Matsuzawa, K., Matsui, T., … Kaibuchi, K. (2011). Numb controls E-cadherin endocytosis through p120 catenin with aPKC. Molecular Biology of the Cell, 22(17), 2983-3275. https://doi.org/10.1091/mbc.E11-03-0274
Steinway, S. N., Tejeda Zanudo, J. G., Michel, P. J., Feith, D. J., Loughran, T. P., & Albert, R. (2015). Combinatorial interventions inhibit TGFβ-driven epithelial-to-mesenchymal transition and support hybrid cellular phenotypes. NPJ Systems Biology and Applications, 1, 15014. https://doi.org/10.1038/npjsba.2015.14
Thiery, J. P. (2002). Epithelial-mesenchymal transitions in tumour progression. Nature Reviews. Cancer, 2(6), 442-454. https://doi.org/10.1038/nrc822
Thiery, J. P., Acloque, H., Huang, R. Y. J., & Nieto, M. A. (2009). Epithelial-mesenchymal transitions in development and disease. Cell, 139(5), 871-890. https://doi.org/10.1016/j.cell.2009.11.007
Tian, X. J., Zhang, H., & Xing, J. (2013). Coupled reversible and irreversible bistable switches underlying TGFβ-induced epithelial to mesenchymal transition. Biophysical Journal, 105, 1079-1089. https://doi.org/10.1016/j.bpj.2013.07.011
Turner, C., & Kohandel, M. (2010). Investigating the link between epithelial-mesenchymal transition and the cancer stem cell phenotype: A mathematical approach. Journal of Theoretical Biology, 265(3), 329-335. https://doi.org/10.1016/j.jtbi.2010.05.024
Valdmanis, P. N., Roy-Chaudhuri, B., Kim, H. K., Sayles, L. C., Zheng, Y., Chuang, C. H., … Kay, M. A. (2015). Upregulation of the microRNA cluster at the Dlk1-Dio3 locus in lung adenocarcinoma. Oncogene, 34(1), 94-103. https://doi.org/10.1038/onc.2013.523
Vargas, D. A., Bates, O., & Zaman, M. H. (2013). Computational model to probe cellular mechanics during epithelial-mesenchymal transition. Cells, Tissues, Organs, 197, 435-444. https://doi.org/10.1159/000348415
Voon, D., Huang, R. Y., Jackson, R. A., & Thiery, J. P. (2017). The EMT spectrum and therapeutic opportunities. Molecular Oncology, 11(7), 878-891. https://doi.org/10.1002/1878-0261.12082
Wang, P., Song, C., Zhang, H., Wu, Z., Tian, X. J., & Xing, J. (2014). Epigenetic state network approach for describing cell phenotypic transitions. Interface Focus, 4(3), 20130068. https://doi.org/10.1098/rsfs.2013.0068
Wang, R.-S., Saadatpour, A., & Albert, R. (2012). Boolean modeling in systems biology: An overview of methodology and applications. Physical Biology, 9(5), 055001. https://doi.org/10.1088/1478-3975/9/5/055001
Wang, X., Sun, Z., Zimmermann, M. T., Bugrim, A., & Kocher, J.-P. (2019). Predict drug sensitivity of cancer cells with pathway activity inference. BMC Medical Genomics, 12, 15. https://doi.org/10.1186/s12920-018-0449-4
Wang, Z., Li, Y., Kong, D., & Sarkan, F. H. (2011). The role of Notch signaling pathway in epithelial-mesenchymal transition (EMT) during development and tumor aggressiveness. Current Drug Targets, 11(6), 745-751. https://doi.org/10.2174/138945010791170860
Wang, Z., Sandiford, S., Wu, C., & Li, S. S. (2009). Numb regulates cell-cell adhesion and polarity in response to tyrosine kinase signalling. The EMBO Journal, 28(16), 2360-2373. https://doi.org/10.1038/emboj.2009.190
Wynn, M. L., Consul, N., Merajver, S. D., & Schnell, S. (2012). Logic-based models in systems biology: A predictive and parameter-free network analysis method. Integrative Biology, 4(11). https://doi.org/10.1039/c2ib20193c
Yamaguchi, H., & Condeelis, J. (2007). Regulation of the actin cytoskeleton in cancer cell migration and invasion. Biochimica et Biophysica Acta, 1773(5), 642-652. https://doi.org/10.1016/j.bbamcr.2006.07.001
Yu, M., Bardia, A., Wittner, B. S., Stott, S. L., Smas, M. E., Ting, D. T., … Maheswaran, S. (2013). Circulating breast tumor cells exhibit dynamic changes in epithelial and mesenchymal composition. Science, 339(6119), 580-584. https://doi.org/10.1126/science.1228522
Zhang, J., Goliwas, K. F., Wang, W., Taufalele, P. V., Bordeleau, F., & Reinhart-King, C. A. (2019). Energetic regulation of coordinated leader-follower dynamics during collective invasion of breast cancer cells. Proceedings of the National Academy of Sciences of the United States of America, 116(16), 7867-7872. https://doi.org/10.1073/pnas.1809964116
Zhang, J., Shao, X., Sun, H., Liu, K., Ding, Z., Chen, J., … Li, H. (2016). NUMB negatively regulates the epithelial-mesenchymal transition of triple-negative breast cancer by antagonizing Notch signaling. Oncotarget, 7(38), 61036-61053. https://doi.org/10.18632/oncotarget.11062
Zhang, J., Tian, X. J., & Xing, J. (2016). Signal transduction pathways of EMT induced by TGF-β, SHH, and WNT and their crosstalks. Journal of Clinical Medicine, 5(4), E41. https://doi.org/10.3390/jcm5040041
Zhang, Y., Li, F., Song, Y., Sheng, X., Ren, F., Xiong, K., … Yu, Z. (2016). Numb and Numbl act to determine mammary myoepithelial cell fate, maintain epithelial identity, and support lactogenesis. The FASEB Journal, 30(10), 3474-3488. https://doi.org/10.1096/fj.201600387R