Browsing by Author "George, Jason Thomas"

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    A mechanism-based computational model to capture the interconnections among epithelial-mesenchymal transition, cancer stem cells and Notch-Jagged signaling
    (Oncotarget, 2018) Bocci, Federico; Jolly, Mohit Kumar; George, Jason Thomas; Levine, Herbert; Onuchic, José Nelson; Bioengineering; Biosciences; Chemistry; Physics and Astronomy; Center for Theoretical Biological Physics
    Epithelial-mesenchymal transition (EMT) and cancer stem cell (CSCs) formation are two fundamental and well-studied processes contributing to cancer metastasis and tumor relapse. Cells can undergo a partial EMT to attain a hybrid epithelial/mesenchymal (E/M) phenotype or a complete EMT to attain a mesenchymal one. Similarly, cells can reversibly gain or lose 'stemness'. This plasticity in cell states is modulated by signaling pathways such as Notch. However, the interconnections among the cell states enabled by EMT, CSCs and Notch signaling remain elusive. Here, we devise a computational model to investigate the coupling among the core decision-making circuits for EMT, CSCs and Notch. Our model predicts that hybrid E/M cells are most likely to associate with stem-like traits and enhanced Notch-Jagged signaling – a pathway implicated in therapeutic resistance. Further, we show that the position of the 'stemness window' on the 'EMT axis' is varied by altering the coupling strength between EMT and CSC circuits, and/or modulating Notch signaling. Finally, we analyze the gene expression profile of CSCs from several cancer types and observe a heterogeneous distribution along the 'EMT axis', suggesting that different subsets of CSCs may exist with varying phenotypes along the epithelial-mesenchymal axis. We further investigate therapeutic perturbations such as treatment with metformin, a drug associated with decreased cancer incidence and increased lifespan of patients. Our mechanism-based model explains how metformin can both inhibit EMT and blunt the aggressive potential of CSCs simultaneously, by driving the cells out of a hybrid E/M stem-like state with enhanced Notch-Jagged signaling.
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    NRF2 activates a partial epithelial-mesenchymal transition and is maximally present in a hybrid epithelial/mesenchymal phenotype
    (Oxford University Press, 2019) Bocci, Federico; Tripathi, Satyendra C.; Vilchez Mercedes, Samuel A.; George, Jason Thomas; Casabar, Julian P.; Wong, Pak Kin; Hanash, Samir M.; Levine, Herbert; Onuchic, José Nelson; Jolly, Mohit Kumar
    The epithelial-mesenchymal transition (EMT) is a key process implicated in cancer metastasis and therapy resistance. Recent studies have emphasized that cells can undergo partial EMT to attain a hybrid epithelial/mesenchymal (E/M) phenotype – a cornerstone of tumour aggressiveness and poor prognosis. These cells can have enhanced tumour-initiation potential as compared to purely epithelial or mesenchymal ones and can integrate the properties of cell-cell adhesion and motility that facilitates collective cell migration leading to clusters of circulating tumour cells (CTCs) – the prevalent mode of metastasis. Thus, identifying the molecular players that can enable cells to maintain a hybrid E/M phenotype is crucial to curb the metastatic load. Using an integrated computational-experimental approach, we show that the transcription factor NRF2 can prevent a complete EMT and instead stabilize a hybrid E/M phenotype. Knockdown of NRF2 in hybrid E/M non-small cell lung cancer cells H1975 and bladder cancer cells RT4 destabilized a hybrid E/M phenotype and compromised the ability to collectively migrate to close a wound in vitro. Notably, while NRF2 knockout simultaneously downregulated E-cadherin and ZEB-1, overexpression of NRF2 enriched for a hybrid E/M phenotype by simultaneously upregulating both E-cadherin and ZEB-1 in individual RT4 cells. Further, we predict that NRF2 is maximally expressed in hybrid E/M phenotype(s) and demonstrate that this biphasic dynamic arises from the interconnections among NRF2 and the EMT regulatory circuit. Finally, clinical records from multiple datasets suggest a correlation between a hybrid E/M phenotype, high levels of NRF2 and its targets and poor survival, further strengthening the emerging notion that hybrid E/M phenotype(s) may occupy the ‘metastatic sweet spot’.
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    Stochastic Dynamics of Cancer-Immune System co-Evolution
    (2019-06-19) George, Jason Thomas; Levine, Herbert
    Immunotherapy has revolutionized cancer treatment by delivering durable remission outcomes to many cancer patients in recent years. T-cell immunotherapy relies on enhancing or replacing immune cells, which can recognize and eliminate a growing malignancy in much the same way as infected cells are cleared during an infection. While promising, this strategy does not eliminate cancer in all patients. The fundamental dynamics of the cancer-immune interaction are quite complex owing in part to a large number of unique T-cell clones and significant intra-tumor heterogeneity. To-date, most of the understanding and principles underlying immunotherapy have been driven empirically. It is this complexity, together with the future benefit of improved clinical outcomes, that makes studying the cancer-immune interaction an ideal applied mathematics problem. I sought to create several foundational mathematical models of the interplay between a continuously adaptive immune system and an evolving cancer population that may evade immune recognition. By applying stochastic process theory to this problem, I generated a framework for addressing various questions related to cancer detection, recognition, and evasion. I first studied the effects of thymic negative selection on T-cell recognition of tumor-associated antigens, which are detectable peptide fragments that closely resemble self-peptide. I quantified the detection of near-self peptide relative to a completely random peptide, predicting that thymic selection minimally affects their recognition. I then studied the temporal dynamics of a population of cancer cells which may evolve mechanisms of immune evasion. My foundational model predicts variations in immunotherapeutic efficacy as a function of immune-relevant parameters and tracks the population-level behavior of an evolving threat under adaptive immune recognition. I end by proposing a framework for threats like cancer which optimize their evasion rate in order to maximally evade the immune system. Taken together, this dissertation provides several statistical tools that can be applied to better understand the fundamental dynamics underlying tumor-immune co-evolution and immunotherapy.