Dr. Yi Jiang, Georgia State University

Abstract: A major reason for cancer treatment failure and disease progression is the heterogeneous composition of tumor cells at the genetic, epigenetic, and phenotypic levels. While tremendous efforts have tried to characterize the makeups of single cells, much less is known about interactions between heterogeneous cancer cells and between cancer cells and the microenvironment in the context of cancer invasion. Clinical studies show that cancer invasion predominantly occurs via collective invasion packs, which invade more aggressively and result in worse outcomes. Using non-small cell lung cancer spheroids, we show that the invasion packs consist of leaders and followers.  In vitro and in silico experiments show that leaders and followers engage in mutualistic social interactions during collective invasion. Many fundamental questions remain: What is the division of labor within the heterogeneous invasion pack? How does the leader phenotype emerge? Are phenotypes plastic?  How do the invasion packs interact with the stroma? Can the social interaction network be exploited to devise novel treatment strategies? I will present the recent experimental and modeling efforts that try to address these questions. I will try to convince you that analyzing this social interaction network can potentially reveal the ‘weak-links’, which when perturbed can disrupt collective invasion and potentially prevent malignant progression of cancer.

Bio: Prof. Yi Jiang received her PhD in Physics at University of Notre Dame under the direction of James Glazier. She joined the Theoretical Division of Los Alamos National Laboratory, first as a postdoc fellow, then as a research scientist. She is currently Frady Whipple Professor at Department of Mathematics and Statistics at Georgia State University. She serves as Associate Editor for Frontier Physiology, PLoS One, and Mathematical Biosciences and Engineering. Her research interests reside between physics, math, biology, and biomedicine, in recent years focusing on modeling of cancer progression, retinal diseases, liver fibrosis, cell migration and cell-ECM interactions.

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