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Proc Natl Acad Sci. 2016; 113(10):2660. doi:10.1073\u002Fpnas.1522330113.",{"EN":39},"Multicellular pattern formation plays an important role in developmental biology, cancer metastasis and wound healing. While many physical factors have been shown to regulate these multicellular processes, the role of ECM micro-to-meso scale geometry has been poorly understood in 3D collective cancer invasion. We have developed a mechanical-based strategy, Diskoid In Geometrically Micropatterned ECM (DIGME). DIGME allows easy engineering of the shape of 3D tissue organoid, the mesoscale ECM heterogeneity, and the fiber alignment of collagen-based ECM all at the same time. We have employed DIGME to study the 3D invasion of MDA-MB-231 diskoids in engineered collagen matrix. We find that the collective cancer invasion is closely regulated by the micro-to-meso scale geometry of the ECM. We conclude that DIGME provides a simple yet powerful tool to probe 3D dynamics of tissue organoids in physically patterned microenvironments.",{"EN":41},"Probing three-dimensional collective cancer invasion with DIGME",{"VOID":43},"10.1186\u002Fs41236-017-0004-9","PUBLICATION","VERIFIED","Auto Verify","https:\u002F\u002Fcancerconvergence.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs41236-017-0004-9",[49,66],{"id":50,"sortIndex":21,"researcher":20,"roles":51,"affiliations":53,"properties":63},"abb49c1d-49fe-462a-9fa6-4f512f64e02a",[52],"AUTHOR",[54],{"id":20,"sortIndex":21,"affiliation":55,"properties":20},{"id":56,"createTime":57,"updateTime":57,"relativeEntities":58,"slug":20,"properties":59,"entityType":62,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"9b658e40-eb12-4851-97dc-27b1bca0d604","2023-12-27T12:04:33.502+00:00",[],{"title":60},{"VI":61},"Department of Physics, Oregon State University, Corvallis, USA","AFFILIATION",{"title":64},{"VI":65},"Amani A. 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Hyperactivation of phosphatidylinositol-3 kinase promotes escape from hormone dependence in estrogen receptor-positive human breast cancer. J Clin Invest. 2010;120(7):2406–13.\nMilo R, Jorgensen P, Moran U, Weber G, Springer M. BioNumbers—the database of key numbers in molecular and cell biology. Nucleic Acids Res. 2010;38(suppl_1):D750–3.\nMilo R, Phillips R. Cell biology by the numbers: Garland Science; 2015. http:\u002F\u002Fgarlandscience.com\u002Fproduct\u002Fisbn\u002F9780815345374\nMorris MK, Saez-Rodriguez J, Sorger PK, Lauffenburger DA. Logic-based models for the analysis of cell signaling networks. Biochemistry. 2010;49:3216–24.\nMuellner MK, Uras IZ, Gapp BV, Kerzendorfer C, Smida M, Lechtermann H, et al. A chemical-genetic screen reveals a mechanism of resistance to PI3K inhibitors in cancer. Nat Chem Biol. 2011;7(11):787–93.\nMusgrove EA, Sutherland RL. Biological determinants of endocrine resistance in breast cancer. Nat Rev Cancer. 2009;9(9):631–43.\nNahta R, Yuan LXH, Zhang B, Kobayashi R, Esteva FJ. Insulin-like growth factor-I receptor\u002Fhuman epidermal growth factor receptor 2 heterodimerization contributes to trastuzumab resistance of breast cancer cells. Cancer Res. 2005;65(23):11118–28.\nO’Leary B, Finn RS, Turner NC. Treating cancer with selective CDK4\u002F6 inhibitors. Nat Rev Clin Oncol. 2016;13(7):417–30.\nO’Reilly KE, Rojo F, She QB, Solit D, Mills GB, Smith D, et al. mTOR inhibition induces upstream receptor tyrosine kinase signaling and activates Akt. Cancer Res. 2006;66(3):1500–8.\nPereira B, Chin S-F, Rueda OM, H-KM V, Provenzano E, Bardwell HA, et al. The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes. Nat Commun. 2016;7:11479.\nRodrik-Outmezguine VS, Chandarlapaty S, Pagano NC, Poulikakos PI, Scaltriti M, Moskatel E, et al. mTOR kinase inhibition causes feedback-dependent biphasic regulation of AKT signaling. Cancer Discov. 2011;1(3):248–59.\nSaadatpour A, Albert I, Albert R. Attractor analysis of asynchronous Boolean models of signal transduction networks. J Theor Biol. 2010;266(4):641–56.\nSaadatpour A, Albert R, Reluga TC. A reduction method for Boolean network models proven to conserve attractors. SIAM J Appl Dyn Syst. 2013;12(4):1997–2011.\nSarbassov DD, Guertin DA, Ali SM, Sabatini DM. Phosphorylation and Regulation of Akt\u002FPKB by the Rictor-mTOR Complex. Science. 2005;307(5712):1098 LP–1101.\nSarosiek KA, Fraser C, Muthalagu N, Bhola PD, Chang W, McBrayer SK, et al. Developmental regulation of mitochondrial apoptosis by c-Myc governs age- and tissue-specific sensitivity to cancer therapeutics. Cancer Cell. 2017;31(1):142–56.\nSerra V, Scaltriti M, Prudkin L, Eichhorn PJ, Ibrahim YH, Chandarlapaty S, et al. PI3K inhibition results in enhanced HER signaling and acquired ERK dependency in HER2-overexpressing breast cancer. Oncogene. 2011;30(22):2547–57.\nSteinway SN, Zanudo JGT, Ding W, Rountree CB, Feith DJ, Loughran TP, et al. Network modeling of TGFβ signaling in hepatocellular carcinoma epithelial-to-mesenchymal transition reveals joint sonic hedgehog and Wnt pathway activation. Cancer Res. 2014;74(21):5963–77.\nStephens PJ, Tarpey PS, Davies H, Van Loo P, Greenman C, Wedge DC, et al. The landscape of cancer genes and mutational processes in breast cancer. Nature. 2012;486(7403):400–4.\nTian X, Huang B, Zhang X-P, Lu M, Liu F, Onuchic JN, et al. Modeling the response of a tumor-suppressive network to mitogenic and oncogenic signals. Proc Natl Acad Sci. 2017;114(21):5337–42.\nToska E, Osmanbeyoglu HU, Castel P, Chan C, Hendrickson RC, Elkabets M, et al. PI3K pathway regulates ER-dependent transcription in breast cancer through the epigenetic regulator KMT2D. 2017;355(6331):1324–30.\nTurke AB, Song Y, Costa C, Cook R, Arteaga CL, Asara JM, et al. MEK inhibition leads to PI3K\u002FAKT activation by relieving a negative feedback on ERBB receptors. Cancer Res. 2012;72(13):3228–37.\nTurner N, Pearson A, Sharpe R, Lambros M, Geyer F, Lopez-Garcia MA, et al. FGFR1 amplification drives endocrine therapy resistance and is a therapeutic target in breast cancer. Cancer Res. 2010;70(5):2085–94.\nTyson JJ, Baumann WT, Chen C, Verdugo A, Tavassoly I, Wang Y, et al. Dynamic modelling of oestrogen signalling and cell fate in breast cancer cells. Nat Rev Cancer. 2011;11(7):523–32.\nUdyavar AR, Wooten DJ, Hoeksema M, Bansal M, Califano A, Estrada L, et al. Novel hybrid phenotype revealed in small cell lung cancer by a transcription factor network model that can explain tumor heterogeneity. Cancer Res. 2017;77(5):1063–74.\nVasudevan KM, Barbie DA, Davies MA, Rabinovsky R, McNear CJ, Kim JJ, et al. AKT-independent signaling downstream of oncogenic PIK3CA mutations in human cancer. Cancer Cell. 2009;16(1):21–32.\nVeliz-Cuba A, Aguilar B, Hinkelmann F, Laubenbacher R. Steady state analysis of Boolean molecular network models via model reduction and computational algebra. BMC Bioinformatics. 2014;15(1):221.\nVora S, Juric D, Kim N, Mino-Kenudson M, Huynh T, Costa C, et al. CDK 4\u002F6 inhibitors sensitize PIK3CA mutant breast cancer to PI3K inhibitors. Cancer Cell. 2014;26(1):136–49.\nWagle N, Painter C, Anastasio E, Dunphy M, McGillicuddy M, Kim D, et al. The Metastatic Breast Cancer (MBC) project: Accelerating translational research through direct patient engagement. J Clin Oncol. 2017;35(15_suppl):1076.\nWang R-S, Saadatpour A, Albert R. Boolean modeling in systems biology: an overview of methodology and applications. Phys Biol. 2012;9(5):55001.\nWerner HMJ, Mills GB, Ram PT. Cancer systems biology: a peek into the future of patient care? Nat Rev Clin Oncol. 2014;11(3):167–76.\nWill M, Qin ACR, Toy W, Yao Z, Rodrik-Outmezguine V, Schneider C, et al. Rapid induction of apoptosis by PI3K inhibitors is dependent upon their transient inhibition of RAS-ERK signaling. Cancer Discov. 2014;4(3):334–48.\nZañudo JGT, Albert R. An effective network reduction approach to find the dynamical repertoire of discrete dynamic networks. Chaos. 2013;23(2).\nZañudo JGT, Albert R. Cell fate reprogramming by control of intracellular network dynamics. PLoS Comput Biol. 2015;11(4):e1004193.\nZehir A, Benayed R, Shah RH, Syed A, Middha S, Kim HR, et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat Med. 2017;23(6):703–13.\nZhang J, Tian X-J, Zhang H, Teng Y, Li R, Bai F, et al. TGF-β-induced epithelial-to-mesenchymal transition proceeds through stepwise activation of multiple feedback loops. Sci Signal. 2014;7(345):ra91.\nZhang S, Huang W-C, Li P, Guo H, Poh S-B, Brady SW, et al. Combating trastuzumab resistance by targeting SRC, a common node downstream of multiple resistance pathways. Nat Med. 2011;17(4):461–9.\nZhang Y, Kwok-Shing Ng P, Kucherlapati M, Chen F, Liu Y, Tsang YH, et al. A Pan-Cancer Proteogenomic Atlas of PI3K\u002FAKT\u002FmTOR Pathway Alterations. Cancer Cell. 2017;31(6):820–32. e3\nZwang Y, Jonas O, Chen C, Rinne ML, Doench JG, Piccioni F, et al. Synergistic interactions with PI3K inhibition that induce apoptosis. elife. 2017;6:e24523.",{"EN":108},"Mechanistic models of within-cell signal transduction networks can explain how these networks integrate internal and external inputs to give rise to the appropriate cellular response. These models can be fruitfully used in cancer cells, whose aberrant decision-making regarding their survival or death, proliferation or quiescence can be connected to errors in the state of nodes or edges of the signal transduction network. Here we present a comprehensive network, and discrete dynamic model, of signal transduction in ER+ breast cancer based on the literature of ER+, HER2+, and PIK3CA-mutant breast cancers. The network model recapitulates known resistance mechanisms to PI3K inhibitors and suggests other possibilities for resistance. The model also reveals known and novel combinatorial interventions that are more effective than PI3K inhibition alone. The use of a logic-based, discrete dynamic model enables the identification of results that are mainly due to the organization of the signaling network, and those that also depend on the kinetics of individual events. Network-based models such as this will play an increasing role in the rational design of high-order therapeutic combinations.",{"EN":110},"A network modeling approach to elucidate drug resistance mechanisms and predict combinatorial drug treatments in breast cancer",{"VOID":112},"10.1186\u002Fs41236-017-0007-6","https:\u002F\u002Fcancerconvergence.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs41236-017-0007-6",[115,145,179],{"id":116,"sortIndex":117,"researcher":20,"roles":118,"affiliations":119,"properties":142},"fa02c289-6593-44b9-922e-a7e2e099c147",2,[52],[120,133],{"id":121,"sortIndex":68,"affiliation":122,"properties":130},"2706e9c4-3811-46e6-8c23-13c9d4c05342",{"id":123,"createTime":124,"updateTime":124,"relativeEntities":125,"slug":126,"properties":127,"entityType":62,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"88c2ac63-56bf-479e-b908-8c530be3c026","2024-10-14T20:10:34.108+00:00",[],"Department-of-Biology-The-Pennsylvania-State-University-University-Park-United-States",{"title":128},{"EN":129},"Department of Biology, The Pennsylvania State University, University Park, United States",{"title":131},{"VI":132},"Department of Biology, The Pennsylvania State University, University Park, USA",{"id":20,"sortIndex":21,"affiliation":134,"properties":20},{"id":135,"createTime":136,"updateTime":136,"relativeEntities":137,"slug":138,"properties":139,"entityType":62,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"163de981-5a72-4fa2-8ec0-a845a1030d46","2024-04-19T15:50:34.734+00:00",[],"Department-of-Physics-The-Pennsylvania-State-University-University-Park-USA",{"title":140},{"EN":141},"Department of Physics, The Pennsylvania State University, University Park, USA",{"title":143},{"VI":144},"Réka Albert",{"id":146,"sortIndex":21,"researcher":20,"roles":147,"affiliations":148,"properties":176},"d62b3ff5-20ce-4cf5-bcae-4448f31cc7f2",[52],[149,154,166],{"id":20,"sortIndex":21,"affiliation":150,"properties":20},{"id":135,"createTime":136,"updateTime":136,"relativeEntities":151,"slug":138,"properties":152,"entityType":62,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":153},{"EN":141},{"id":155,"sortIndex":68,"affiliation":156,"properties":165},"b7b91b15-1cb5-42cf-9f1d-fbef49cfdcb1",{"id":157,"createTime":158,"updateTime":159,"relativeEntities":160,"slug":161,"properties":162,"entityType":62,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"18c76307-ede9-49da-a817-a81622b42c34","2023-12-06T14:50:45.880+00:00","2025-01-29T08:53:20.956+00:00",[],"Department-of-Medical-Oncology-Dana-Farber-Cancer-Institute-Boston-USA",{"title":163},{"VI":164},"Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, USA",{},{"id":167,"sortIndex":117,"affiliation":168,"properties":175},"bc233f64-2044-46e8-bb1c-31cf562e20e5",{"id":169,"createTime":170,"updateTime":170,"relativeEntities":171,"slug":20,"properties":172,"entityType":62,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"5d689029-a5ba-4afe-ab7c-0ea4e4d6b9d5","2024-01-14T21:11:42.074+00:00",[],{"title":173},{"VI":174},"Broad Institute of Harvard and Massachusetts Institute of Technology, 7 Cambridge Center, Cambridge, USA",{},{"title":177},{"VI":178},"Jorge Gómez Tejeda Zañudo",{"id":180,"sortIndex":68,"researcher":20,"roles":181,"affiliations":182,"properties":202},"e6aae287-b141-4ad1-a8bf-a4733bdcff23",[52],[183,194],{"id":184,"sortIndex":68,"affiliation":185,"properties":193},"6bdfa3ce-f66e-4744-8410-daa465e6f138",{"id":186,"createTime":187,"updateTime":187,"relativeEntities":188,"slug":189,"properties":190,"entityType":62,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"14f9355e-339e-442f-8097-f944fbd1b886","2024-04-16T04:25:56.114+00:00",[],"Department-of-Pathology-Memorial-Sloan-Kettering-Cancer-Center-New-York-USA",{"title":191},{"EN":192},"Department of Pathology, Memorial-Sloan-Kettering Cancer Center, New York, USA",{},{"id":20,"sortIndex":21,"affiliation":195,"properties":20},{"id":196,"createTime":197,"updateTime":197,"relativeEntities":198,"slug":20,"properties":199,"entityType":62,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"1b690449-8828-43f6-b4b0-1ddfe0bc70cd","2024-01-14T21:11:42.086+00:00",[],{"title":200},{"VI":201},"Human Oncology & Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, USA",{"title":203},{"VI":204},"Maurizio Scaltriti",{"url":113,"publisher":206,"properties":215},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":207,"slug":10,"properties":208,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":212,"manageAffiliations":213,"indexDatabases":214,"url":20,"thumbnailPath":20,"statistic":20,"gsStatistic":20,"type":20,"analyzePriority":20},[],{"issn":209,"title":210,"url":211},{"VOID":13},{"EN":15},{"VOID":17},[],[],[],{"volume":216,"pages":217},{"VOID":92},{"VOID":218},"1-25","2017-12-29",{"id":221,"createTime":222,"updateTime":223,"relativeEntities":224,"slug":225,"properties":226,"entityType":44,"verifyStatus":45,"verifyTime":223,"verifyNote":46,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":235,"fullTextUrl":20,"authors":236,"publicationType":79,"publisherRelationship":392,"citationCount":20,"citationInfo":20,"publishDate":407,"publishYear":408,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":97},"b4b08933-2808-4f93-9bed-6268f271a6b1","2024-01-19T04:57:14.582+00:00","2025-01-15T16:53:28.429+00:00",[],"Cancer-dormancy-and-criticality-from-a-game-theory-perspective",{"references":227,"abstract":229,"title":231,"doi":233},{"VOID":228},"Adami, C, Schossau J, Hintze A. Evolutionary game theory using agent-based methods. Phys Life Rev. 2016; 19:1–26.\nAxelrod, R, Axelrod DE, Pienta KJ. Evolution of cooperation among tumor cells. Proc Natl Acad Sci U S A. 2006; 103(36):13474–13479.\nDurrett, R, Levin S. The Importance of Being Discrete (and Spatial). Theor Popul Biol. 1994; 46(3):363–94. https:\u002F\u002Fdoi.org\u002F10.1006\u002Ftpbi.1994.1032. Accessed 25 Feb 2016.\nHan, J, Jun Y, Kim SH, Hoang HH, Jung Y, Kim S, Kim J, Austin RH, Lee S, Park S. Rapid emergence and mechanisms of resistance by u87 glioblastoma cells to doxorubicin in an in vitro tumor microfluidic ecology. Proc Natl Acad Sci U S A. 2016; 113(50):14283–8.\nJensen, HJ, Vol. 10. Self-organized Criticality: Emergent Complex Behavior in Physical and Biological Systems. Cambridge lecture notes in physics. Cambridge: Cambridge University Press; 1998.\nKrotov, D, Dubuis JO, Gregor T, Bialek W. Morphogenesis at criticality. Proc Natl Acad Sci. 2014; 111(10):3683–688. https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.1324186111. Accessed 25 Feb 2016.\nMaynard Smith, J. Evolution and the Theory of Games. Cambridge: Cambridge University Press; 1982.\nRaup, DM. The role of extinction in evolution. Proc Natl Acad Sci. 1994; 91(15):6758–763. https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.91.15.6758. Accessed 25 Feb 2016.\nSornette, D. Critical Phenomena in Natural Sciences: Chaos, Fractals, Selforganization, and Disorder: Concepts and Tools. Springer series in synergetics. Berlin: Springer; 2000.\nWu, A, Liao D, Tlsty TD, Sturm JC, Austin RH. Game theory in the death galaxy: interaction of cancer and stromal cells in tumour microenvironment. Interf Focus. 2014; 4(4):20140028–0140028. https:\u002F\u002Fdoi.org\u002F10.1098\u002Frsfs.2014.0028. Accessed 25 Feb 2016.\nYang, CB. The origin of power-law distributions in self-organized criticality. J Phys A Math Gen. 2004; 37(42):523–9.",{"EN":230},"The physics of cancer dormancy, the time between initial cancer treatment and re-emergence after a protracted period, is a puzzle. Cancer cells interact with host cells via complex, non-linear population dynamics, which can lead to very non-intuitive but perhaps deterministic and understandable progression dynamics of cancer and dormancy. We explore here the dynamics of host-cancer cell populations in the presence of (1) payoffs gradients and (2) perturbations due to cell migration. We determine to what extent the time-dependence of the populations can be quantitively understood in spite of the underlying complexity of the individual agents and model the phenomena of dormancy.",{"EN":232},"Cancer dormancy and criticality from a game theory perspective",{"VOID":234},"10.1186\u002Fs41236-018-0008-0","https:\u002F\u002Fcancerconvergence.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs41236-018-0008-0",[237,255,270,286,302,318,333,346,359,379],{"id":238,"sortIndex":239,"researcher":20,"roles":240,"affiliations":241,"properties":252},"4fa123cc-92ea-48a9-86cd-8bf1e0c7b119",7,[52],[242],{"id":20,"sortIndex":21,"affiliation":243,"properties":20},{"id":244,"createTime":245,"updateTime":246,"relativeEntities":247,"slug":248,"properties":249,"entityType":62,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"17b25672-6082-4c68-8773-b4e6d27780bd","2024-04-18T04:16:51.834+00:00","2025-02-01T17:18:28.188+00:00",[],"Department-of-Electrical-Engineering-Princeton-University-Princeton-USA-",{"title":250},{"EN":251},"Department of Electrical Engineering Princeton University Princeton (USA)",{"title":253},{"VI":254},"James C. Sturm",{"id":256,"sortIndex":21,"researcher":20,"roles":257,"affiliations":258,"properties":267},"8e3149ae-2718-45a1-ac17-9712b4b96c91",[52],[259],{"id":20,"sortIndex":21,"affiliation":260,"properties":20},{"id":261,"createTime":262,"updateTime":262,"relativeEntities":263,"slug":20,"properties":264,"entityType":62,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"49ace0a9-1cf0-419a-9d6a-baa95041d286","2024-01-19T04:57:14.609+00:00",[],{"title":265},{"VI":266},"Banter AI, Palo Alto CA, USA",{"title":268},{"VI":269},"Amy Wu",{"id":271,"sortIndex":272,"researcher":20,"roles":273,"affiliations":274,"properties":283},"6b67fa81-8ceb-4d1c-b6f6-87201959deb0",8,[52],[275],{"id":20,"sortIndex":21,"affiliation":276,"properties":20},{"id":277,"createTime":278,"updateTime":278,"relativeEntities":279,"slug":20,"properties":280,"entityType":62,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"a2fd4618-3c87-4385-aee8-0835bae72a49","2023-12-25T14:58:20.283+00:00",[],{"title":281},{"VI":282},"The Johns Hopkins Hospital, Baltimore, Md. USA",{"title":284},{"VI":285},"Kenneth Pienta",{"id":287,"sortIndex":288,"researcher":20,"roles":289,"affiliations":290,"properties":299},"cf8491b8-79d9-4a63-ac98-f3a2e8935dcd",5,[52],[291],{"id":20,"sortIndex":21,"affiliation":292,"properties":20},{"id":293,"createTime":294,"updateTime":294,"relativeEntities":295,"slug":20,"properties":296,"entityType":62,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"7fbfb7d6-0ef9-4fed-9faf-4cc4e7f2dfb8","2023-12-14T04:17:34.900+00:00",[],{"title":297},{"VI":298},"College of Optoelectronic Engineering, Shenzhen University, Shenzhen, China",{"title":300},{"VI":301},"Junle Qu",{"id":303,"sortIndex":304,"researcher":20,"roles":305,"affiliations":306,"properties":315},"0227eac2-e4f2-42d1-99dd-e157ee7b64c2",6,[52],[307],{"id":20,"sortIndex":21,"affiliation":308,"properties":20},{"id":309,"createTime":310,"updateTime":310,"relativeEntities":311,"slug":20,"properties":312,"entityType":62,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"f49f3b8c-f171-43a9-9538-1acecb6ae48f","2024-01-19T04:57:14.696+00:00",[],{"title":313},{"VI":314},"College of Physics, Chongqing University, Chongqing China, China",{"title":316},{"VI":317},"Liyu Liu",{"id":319,"sortIndex":117,"researcher":20,"roles":320,"affiliations":321,"properties":330},"02d80847-e13e-4e2e-9710-c1e5ac04c3de",[52],[322],{"id":20,"sortIndex":21,"affiliation":323,"properties":20},{"id":324,"createTime":325,"updateTime":325,"relativeEntities":326,"slug":20,"properties":327,"entityType":62,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"1768b4fa-3d1b-4fe2-b75c-d61d1096beaa","2023-12-26T05:56:09.436+00:00",[],{"title":328},{"VI":329},"Department of Physics, Princeton University, Princeton, USA",{"title":331},{"VI":332},"Vlamimir 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Clin Gastroenterol Hepatol 2011a;9(2):161–167.",{"doi":779},"10.1016\u002Fj.cgh.2010.09.017",{"id":781,"createTime":782,"updateTime":783,"relativeEntities":784,"slug":785,"properties":786,"entityType":44,"verifyStatus":45,"verifyTime":783,"verifyNote":46,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":795,"fullTextUrl":20,"authors":796,"publicationType":79,"publisherRelationship":1087,"citationCount":20,"citationInfo":20,"publishDate":95,"publishYear":96,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":97},"2a5580ae-56b6-4f39-b315-f4d7145a75ad","2024-01-22T16:15:20.619+00:00","2025-02-23T09:54:01.246+00:00",[],"Distinguishing-mechanisms-underlying-EMT-tristability",{"references":787,"abstract":789,"title":791,"doi":793},{"VOID":788},"Abshire CF, Carroll JL, Dragoi A-M. FLASH protects ZEB1 from degradation and supports cancer cells’ epithelial-to-mesenchymal transition. 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TGF-β-induced epithelial-to-mesenchymal transition proceeds through stepwise activation of multiple feedback loops. Sci Signal. 2014;7(345):ra91.\nZhao S, Chen C, Chang K, Karnad A, Jagirdar J, Kumar AP, Freeman JW. CD44 Expression Level and Isoform Contributes to Pancreatic Cancer Cell Plasticity, Invasiveness, and Response to Therapy. Clinical Cancer Research. 2016;22(22):5592–604.\nZheng X, Carstens JL, Kim J, Scheible M, Kaye J, Sugimoto H, et al. Epithelial-to-mesenchymal transition is dispensable for metastasis but induces chemoresistance in pancreatic cancer. Nature. 2015;527(7579):525–30.\nZhou JX, Huang S. Understanding gene circuits at cell-fate branch points for rational cell reprogramming. Trends Genet. 2011;27(2):55–62.",{"EN":790},"The Epithelial-Mesenchymal Transition (EMT) endows epithelial-looking cells with enhanced migratory ability during embryonic development and tissue repair. EMT can also be co-opted by cancer cells to acquire metastatic potential and drug-resistance. Recent research has argued that epithelial (E) cells can undergo either a partial EMT to attain a hybrid epithelial\u002Fmesenchymal (E\u002FM) phenotype that typically displays collective migration, or a complete EMT to adopt a mesenchymal (M) phenotype that shows individual migration. The core EMT regulatory network - miR-34\u002FSNAIL\u002FmiR-200\u002FZEB1 - has been identified by various studies, but how this network regulates the transitions among the E, E\u002FM, and M phenotypes remains controversial. Two major mathematical models – ternary chimera switch (TCS) and cascading bistable switches (CBS) - that both focus on the miR-34\u002FSNAIL\u002FmiR-200\u002FZEB1 network, have been proposed to elucidate the EMT dynamics, but a detailed analysis of how well either or both of these two models can capture recent experimental observations about EMT dynamics remains to be done. Here, via an integrated experimental and theoretical approach, we first show that both these two models can be used to understand the two-step transition of EMT - E→E\u002FM→M, the different responses of SNAIL and ZEB1 to exogenous TGF-β and the irreversibility of complete EMT. Next, we present new experimental results that tend to discriminate between these two models. We show that ZEB1 is present at intermediate levels in the hybrid E\u002FM H1975 cells, and that in HMLE cells, overexpression of SNAIL is not sufficient to initiate EMT in the absence of ZEB1 and FOXC2. These experimental results argue in favor of the TCS model proposing that miR-200\u002FZEB1 behaves as a three-way decision-making switch enabling transitions among the E, hybrid E\u002FM and M phenotypes.",{"EN":792},"Distinguishing mechanisms underlying EMT tristability",{"VOID":794},"10.1186\u002Fs41236-017-0005-8","https:\u002F\u002Fcancerconvergence.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs41236-017-0005-8",[797,812,859,871,896,908,923,947,959,1005,1027,1046,1068],{"id":798,"sortIndex":335,"researcher":20,"roles":799,"affiliations":800,"properties":809},"14170949-a200-4040-b553-24c1f89bb33c",[52],[801],{"id":20,"sortIndex":21,"affiliation":802,"properties":20},{"id":803,"createTime":804,"updateTime":804,"relativeEntities":805,"slug":20,"properties":806,"entityType":62,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"e9c2736c-5901-4678-873d-5fd1962bc44e","2023-12-28T03:46:40.356+00:00",[],{"title":807},{"VI":808},"Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, USA",{"title":810},{"VI":811},"Petra Den Hollander",{"id":813,"sortIndex":348,"researcher":20,"roles":814,"affiliations":815,"properties":856},"be5938d2-cbea-42dd-9a98-5bf2f0342dd6",[52],[816,826,836,846],{"id":817,"sortIndex":68,"affiliation":818,"properties":825},"48c62d79-6f69-45de-8de8-e19d3fb2ed3e",{"id":819,"createTime":820,"updateTime":820,"relativeEntities":821,"slug":20,"properties":822,"entityType":62,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"6688e46e-1bff-4dfb-8724-24d86a2424f5","2024-01-14T19:25:40.571+00:00",[],{"title":823},{"VI":824},"Department of Biosciences, Rice University, Houston, USA",{},{"id":827,"sortIndex":335,"affiliation":828,"properties":835},"223ae6df-01fe-4cd3-a4f4-df31c7f14624",{"id":829,"createTime":830,"updateTime":830,"relativeEntities":831,"slug":20,"properties":832,"entityType":62,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"6cd88e2b-8c46-4b6e-b144-0f20aa6e5822","2023-12-13T04:28:40.149+00:00",[],{"title":833},{"VI":834},"Department of Chemistry, Rice University, Houston, USA",{},{"id":837,"sortIndex":117,"affiliation":838,"properties":845},"7386af70-1254-4e13-87de-616d9d5b2c85",{"id":839,"createTime":840,"updateTime":840,"relativeEntities":841,"slug":20,"properties":842,"entityType":62,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"5e8446de-fb73-4aea-98b9-8b42fc1b6e92","2023-12-05T05:57:41.821+00:00",[],{"title":843},{"VI":844},"Department of Physics and Astronomy, Rice University, Houston, USA",{},{"id":20,"sortIndex":21,"affiliation":847,"properties":20},{"id":848,"createTime":849,"updateTime":850,"relativeEntities":851,"slug":852,"properties":853,"entityType":62,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"769f95a0-593e-4fb8-aa2f-59422279c26b","2024-01-11T08:59:11.300+00:00","2024-10-08T08:50:21.759+00:00",[],"Center-for-Theoretical-Biological-Physics-Rice-University-Houston-USA",{"title":854},{"VI":855},"Center for Theoretical Biological Physics, Rice University, Houston, USA",{"title":857},{"VI":858},"José N. 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