Treatments using a patient’s own T cells to target cancer have applied advances in genetic engineering and cancer immunotherapy as the basis for a powerful, targeted cell-based therapy. Tumor-infiltrating lymphocytes and T cells that have been genetically modified to express cancer antigen-specific receptors—such as T cell receptors (TCRs) and chimeric antigen receptors (CARs)—leverage the innate targeted cytotoxicity of T cells against cancer. Despite promising results in clinical trials, these therapies have elicited serious and sometimes fatal responses. These toxicities include killing of healthy tissue expressing lower levels of antigen, as well as an overstimulation of the immune response called cytokine release syndrome. These outcomes reflect the double-edged nature of T cell-based therapies: the same powerful capability that makes them strong therapeutic candidates can become fatal if modified cells are left to their own devices.
T cell therapies would greatly benefit from the development of tools that enable doctors to have bedside control over a cell’s behavior and truly respond to each patient’s needs. The work of this thesis aims to develop genetic circuits that control T cell activity. This platform has been adapted to control when CARs are expressed and at what level. In contrast to the current approach where patients are treated to one therapeutic “state,” these genetic circuits will allow doctors to decide between multiple states defined by CAR expression through the addition of a drug. These circuits contain memory such that long-term administration of the drug is not required to maintain a change. I have designed an ON switch and an OFF switch to control when a CAR is expressed, and an EXPRESSION switch to increase CAR expression. I characterized the performance of these circuits to demonstrate their dynamics over time, as well as their ability to control T cell behavior. I also demonstrate that these circuits contain memory, and that they are tunable. / 2020-07-02T00:00:00Z
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/30728 |
| Date | 03 July 2018 |
| Creators | Chakravarti, Deboki |
| Contributors | Wong, Wilson W. |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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