Browsing by Author "George, Jason T."

Now showing 1 - 5 of 5
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    Computational Modeling of the Crosstalk Between Macrophage Polarization and Tumor Cell Plasticity in the Tumor Microenvironment
    (Frontiers, 2019) Li, Xuefei; Jolly, Mohit Kumar; George, Jason T.; Pienta, Kenneth J.; Levine, Herbert; Bioengineering; Physics and Astronomy
    Tumor microenvironments contain multiple cell types interacting among one another via different signaling pathways. Furthermore, both cancer cells and different immune cells can display phenotypic plasticity in response to these communicating signals, thereby leading to complex spatiotemporal patterns that can impact therapeutic response. Here, we investigate the crosstalk between cancer cells and macrophages in a tumor microenvironment through in silico (computational) co-culture models. In particular, we investigate how macrophages of different polarization (M1 vs. M2) can interact with epithelial-mesenchymal plasticity of cancer cells, and conversely, how cancer cells exhibiting different phenotypes (epithelial vs. mesenchymal) can influence the polarization of macrophages. Based on interactions documented in the literature, an interaction network of cancer cells and macrophages is constructed. The steady states of the network are then analyzed. Various interactions were removed or added into the constructed-network to test the functions of those interactions. Also, parameters in the mathematical models were varied to explore their effects on the steady states of the network. In general, the interactions between cancer cells and macrophages can give rise to multiple stable steady-states for a given set of parameters and each steady state is stable against perturbations. Importantly, we show that the system can often reach one type of stable steady states where cancer cells go extinct. Our results may help inform efficient therapeutic strategies.
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    Contact map dependence of a T-cell receptor binding repertoire
    (American Physical Society, 2022) Chau, Kevin Ng; George, Jason T.; Onuchic, José N.; Lin, Xingcheng; Levine, Herbert; Center for Theoretical Biological Physics
    The T-cell arm of the adaptive immune system provides the host protection against unknown pathogens by discriminating between host and foreign material. This discriminatory capability is achieved by the creation of a repertoire of cells each carrying a T-cell receptor (TCR) specific to non-self-antigens displayed as peptides bound to the major histocompatibility complex (pMHC). The understanding of the dynamics of the adaptive immune system at a repertoire level is complex, due to both the nuanced interaction of a TCR-pMHC pair and to the number of different possible TCR-pMHC pairings, making computationally exact solutions currently unfeasible. To gain some insight into this problem, we study an affinity-based model for TCR-pMHC binding in which a crystal structure is used to generate a distance-based contact map that weights the pairwise amino acid interactions. We find that the TCR-pMHC binding energy distribution strongly depends both on the number of contacts and the repeat structure allowed by the topology of the contact map of choice; this in turn influences T-cell recognition probability during negative selection, with higher variances leading to higher survival probabilities. In addition, we quantify the degree to which neoantigens with mutations in sites with higher contacts are recognized at a higher rate.
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    Interconnected feedback loops among ESRP1, HAS2, and CD44 regulate epithelial-mesenchymal plasticity in cancer
    (AIP Publishing LLC, 2018) Jolly, Mohit Kumar; Preca, Bogdan-Tiberius; Tripathi, Satyendra C.; Jia, Dongya; George, Jason T.; Hanash, Samir M.; Brabletz, Thomas; Stemmler, Marc P.; Maurer, Jochen; Levine, Herbert; Bioengineering; Center for Theoretical Biological Physics
    Aberrant activation of epithelial-mesenchymal transition (EMT) in carcinoma cells contributes to increased migration and invasion, metastasis, drug resistance, and tumor-initiating capacity. EMT is not always a binary process; rather, cells may exhibit a hybrid epithelial/mesenchymal (E/M) phenotype. ZEB1—a key transcription factor driving EMT—can both induce and maintain a mesenchymal phenotype. Recent studies have identified two novel autocrine feedback loops utilizing epithelial splicing regulatory protein 1 (ESRP1), hyaluronic acid synthase 2 (HAS2), and CD44 which maintain high levels of ZEB1. However, how the crosstalk between these feedback loops alters the dynamics of epithelial-hybrid-mesenchymal transition remains elusive. Here, using an integrated theoretical-experimental framework, we identify that these feedback loops can enable cells to stably maintain a hybrid E/M phenotype. Moreover, computational analysis identifies the regulation of ESRP1 as a crucial node, a prediction that is validated by experiments showing that knockdown of ESRP1 in stable hybrid E/M H1975 cells drives EMT. Finally, in multiple breast cancer datasets, high levels of ESRP1, ESRP1/HAS2, and ESRP1/ZEB1 correlate with poor prognosis, supporting the relevance of ZEB1/ESRP1 and ZEB1/HAS2 axes in tumor progression. Together, our results unravel how these interconnected feedback loops act in concert to regulate ZEB1 levels and to drive the dynamics of epithelial-hybrid-mesenchymal transition.
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    RACER-m leverages structural features for sparse T cell specificity prediction
    (AAAS, 2024) Wang, Ailun; Lin, Xingcheng; Chau, Kevin Ng; Onuchic, José N.; Levine, Herbert; George, Jason T.; Center for Theoretical Biological Physics
    Reliable prediction of T cell specificity against antigenic signatures is a formidable task, complicated by the immense diversity of T cell receptor and antigen sequence space and the resulting limited availability of training sets for inferential models. Recent modeling efforts have demonstrated the advantage of incorporating structural information to overcome the need for extensive training sequence data, yet disentangling the heterogeneous TCR-antigen interface to accurately predict MHC-allele-restricted TCR-peptide interactions has remained challenging. Here, we present RACER-m, a coarse-grained structural model leveraging key biophysical information from the diversity of publicly available TCR-antigen crystal structures. Explicit inclusion of structural content substantially reduces the required number of training examples and maintains reliable predictions of TCR-recognition specificity and sensitivity across diverse biological contexts. Our model capably identifies biophysically meaningful point-mutant peptides that affect binding affinity, distinguishing its ability in predicting TCR specificity of point-mutants from alternative sequence-based methods. Its application is broadly applicable to studies involving both closely related and structurally diverse TCR-peptide pairs.
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    Survival outcomes in cancer patients predicted by a partial EMT gene expression scoring metric
    (American Association for Cancer Research, 2017) George, Jason T.; Jolly, Mohit Kumar; Xu, Shengnan; Somarelli, Jason A.; Levine, Herbert; Bioengineering; Physics and Astronomy
    with a posterior probability from 0.80 to 1.00 contained more than 50% neoplasia 99% (84/85) of the time. This study demonstrates a proof-of-concept of how noninvasive optical imaging can be used as a tool to study expression levels of multiple biomarkers and their heterogeneity across a large mucosal surface and how biomarker characteristics