MS04 - ONCO-1
Ohio Staters Traditions Room (#2120) in The Ohio Union

Dynamics of cellular heterogeneity: consequences of diverse regulatory mechanisms

Tuesday, July 18 at 04:00pm

SMB2023 SMB2023 Follow Tuesday during the "MS04" time block.
Room assignment: Ohio Staters Traditions Room (#2120) in The Ohio Union.
Note: this minisymposia has multiple sessions. The other session is MS03-ONCO-1 (click here).

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Mohit Kumar Jolly, Paras Jain


The advent of new experimental technologies and computational tools over past two decades have helped us to capture heterogeneity seen at the single-cell level. Further, the role of this heterogeneity in enabling a higher fitness of cancer cell population has been demonstrated. Moving ahead, a key focus is to understand the regulatory and stochastic origins of cellular heterogeneity so as to modulate it to achieve desired outcomes, such as better therapeutic outcomes. Here, we bring together a diverse set of experts who employ experimental and mathematical tools to understand the origins and consequences of heterogeneity witnessed in a cell population. Further, the symposium will also fuel the need to integrate information at multiple levels of intra-cellular/extra-cellular regulations to improve our understanding of functional responses at the single-cell/population levels.

Antara Biswas

Rutgers Cancer Institute of New Jersey (Department of Pathology & Laboratory Medicine)
"Transcriptional heterogeneity and cell state plasticity in urothelial bladder carcinoma."
Intra-tumor heterogeneity contributes towards treatment failure and poor survival in urothelial bladder carcinoma (UBC) patients, but underlying drivers are poorly understood. Analysis of single cell transcriptomic data from UBC patients suggests that intra-tumor transcriptomic heterogeneity is, partly due to, admixtures of tumor cells in epithelial and mesenchymal-like transcriptional states, which covary with other cancer hallmarks. Transition between these cell states likely occurs within and between tumor subclones, adding a layer of phenotypic plasticity and dynamic heterogeneity beyond genetic variations. We model spontaneous and reversible transition between partially heritable epithelial- and mesenchymal-like transcriptional states in UBC cell lines and characterize their population dynamics during in vitro evolution. Nutrient limitation, as in large tumors, and radiation treatment perturb the cell-state dynamics, initially selecting for a transiently resistant phenotype and then reconstituting heterogeneity and growth potential, facilitating adaptive evolution. Our data suggests that transcriptional state dynamics contributes towards phenotypic plasticity and non-genetic intra-tumor heterogeneity, modulating the trajectory of disease progression and adaptive treatment response in UBC.
Additional authors: Sarthak Sahoo 2; Gregory R Riedlinger 1; Saum Ghodoussipour 1; Mohit K. Jolly 2; Subhajyoti De 1. 1 Rutgers Cancer Institute of New Jersey, Rutgers the State university of New Jersey, New Brunswick, NJ, USA; 2 Indian Institute of Science, Bangalore, India.

Samuel Oliver

Swansea University (Department of Mathematics)
"Cancer as a matter of fat: The role of adipose tissue in tumour progression"
Ovarian cancer has the highest mortality rate of all gynaecological cancers, possessing a 5-year survival rate of less than 50% [1]. Numerous factors are responsible for the poor prognosis, including asymptomatic cases and accelerated chemoresistance. Malignant neoplasms achieve metastasis by interacting with stromal cells in the tumour microenvironment, enhancing proliferation and enabling key phenotypic changes. To quantify these links, we developed an agent-based 3D mathematical model using a PhysiCell framework [2] to simulate tumour growth and its dependence on the microenvironment. In-silico experiments were used to understand the adipose-tumour interactions for SKOV-3 and OVCAR-3 cell lines, with higher levels of fat in the tumour microenvironment being found to cause more aggressive cases of the disease with higher cell viability, EMT, and chemoresistance to paclitaxel treatment. These results, along with a rising abundance of obesity in the global population, underline the need for intensive research into adipose-tumour cell interactions to develop better treatments that hamper cancer progression by tackling the cells of the tumour microenvironment including adipocytes. Mathematical models such as the one used here are key in giving patient specific results by quantifying the impacts of changes in the microenvironment and treatment protocol. [1] G. C. Jayson, E. C. Kohn, H. C. Kitchener, and J. A. Ledermann, “Ovarian cancer,” The Lancet, vol. 384, no. 9951, pp. 1376–1388, 2014. [2] A. Ghaffarizadeh, R. Heiland, S.H. Friedman, S.M. Mumenthaler, and P. Macklin. PhysiCell: an Open Source Physics-Based Cell Simulator for 3-D Multicellular Systems, PLoS Comput. Biol. 14(2): e1005991, 2018.
Additional authors: D. Gonzalez, Department of Biomedical Sciences, Swansea University; G. Powathil, Department of Mathematics, Swansea University

Simone Bruno

Massachusetts Institute of Technology (Mechanical Engineering)
"Stochastic analysis of chromatin modification circuits that control epigenetic cell memory"
Epigenetic cell memory is a property of multi-cellular organisms that allows different cells to maintain different phenotypes, encoded by distinct gene expression patterns, despite a common genome. Covalent modifications to chromatin are thought to have a role in dictating the long-term stability of these mutually exclusive gene expression states. However, the underlying mechanisms are not well understood. Here, we analyze a chemical reaction model of the chromatin modification circuit within each gene of the mammalian chromosome and demonstrate how the time scale separation between key constituent processes is implicated in long-term maintenance of gene expression states. To achieve this goal, we use the mathematical framework of singularly perturbed continuous-time Markov chains, where the small parameter quantifies the degree of time-scale separation. Unique to our system, is the fact that the limiting behavior as the small parameter decreases is non-ergodic. We, therefore, developed new tools for the analysis of the behavior of stationary distributions as a function of the small parameter. Furthermore, in order to determine the behavior of these distributions and of mean first passage times as biological parameters are varied, we developed comparison theorems. These theorems, beyond being applicable to our system, provide a general stochastic ordering result that can be applied to chemical reaction networks in general.

Paras Jain

Indian Institute of Science (Centre for BioSystems Science and Engineering)
"Epigenetic memory acquired during long-term EMT induction governs the recovery to the epithelial state"
Epithelial–mesenchymal transition (EMT) and its reverse mesenchymal–epithelial transition (MET) are critical during embryonic development, wound healing and cancer metastasis. While phenotypic changes during short-term EMT induction are reversible, long-term EMT induction has been often associated with irreversibility. Here, we show that phenotypic changes seen in MCF10A cells upon long-term EMT induction by TGFβ need not be irreversible but have relatively longer time scales of reversibility than those seen in short-term induction. Next, using a phenomenological mathematical model to account for the chromatin-mediated epigenetic silencing of the miR-200 family by ZEB family, we highlight how the epigenetic memory gained during long-term EMT induction can slow the recovery to the epithelial state post-TGFβ withdrawal. Our results suggest that epigenetic modifiers can govern the extent and time scale of EMT reversibility and advise caution against labelling phenotypic changes seen in long-term EMT induction as ‘irreversible’.
Additional authors: Sophia Corbo, Department of Biology, Widener University, Chester, PA 19013, USA; Kulsoom Mohammad, Department of Biology, Widener University, Chester, PA 19013, USA; Sarthak Sahoo, Centre for BioSystems Science and Engineering, Indian Institute of Science, Bengaluru 560012, India; Santhalakshmi Ranganathan, Department of Biology, Baylor University, Waco, TX 76798, USA; Jason T. George, Department of Biomedical Engineering, Texas A&M University, College Station, TX 77843, USA; Herbert Levine, Center for Theoretical Biological Physics and Departments of Physics and Bioengineering, Northeastern University, Boston, MA 02115, USA; Joseph Taube, Department of Biology, Baylor University, Waco, TX 76798, USA; Michael Toneff, Department of Biology, Widener University, Chester, PA 19013, USA; Mohit Kumar Jolly, Centre for BioSystems Science and Engineering, Indian Institute of Science, Bengaluru 560012, India;

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Annual Meeting for the Society for Mathematical Biology, 2023.