Immune cell function varies tremendously between individuals, posing a major challenge to the development and success of emerging cellular immunotherapies. In the context of T cell therapy for cancer, long-term diseases such as Chronic Lymphocytic Leukemia (CLL) often induce T cell deficiencies resembling cellular exhaustion, complicating the preparation of therapeutic quantities of cells and maintaining efficacy once reintroduced to patients. The ability to rapidly estimate the responsiveness of an individual’s T cells could provide a powerful tool for tailoring treatment conditions and monitoring T cell functionality over the course of therapy.
This dissertation investigates the use of short-term cellular behavior assays as a predictive indicator of long-term T cell function. Specifically, the short-term spreading of T cells on functionalized planar, elastic surfaces was quantified by 11 morphological parameters. These parameters were analyzed to discern the impact of both intrinsic factors, such as disease state, and extrinsic factors, such as substrate stiffness. This study identified morphological features that varied between T cells isolated from healthy donors and those from patients being treated for CLL. Combining multiple features through a machine learning approach such as Decision Tree or Random Forest provided an effective means for identifying whether T cells came from healthy or CLL donors.
To further automate this assay and enhance the classification outcome, an image-based deep learning workflow was developed. The image-based deep learning approach notably outperformed morphometric analysis and showed great promise in classifying both intrinsic disease states and extrinsic environmental stiffness. Furthermore, we applied this imaging-based deep learning method to predict T cell proliferative capacity under different stiffness conditions, enabling rapid and efficient optimization of T cell expansion conditions to better guide cellular immunotherapy. Looking ahead, future efforts will focus on optimizing and generalizing the model to enhance its predictive accuracy and applicability across diverse patient populations.
Additionally, we aim to incorporate multi-channel imaging that captures detailed T cell subset information, enabling the model to better understand the complex interactions between different cellular features and their influence on long-term proliferation. Our ultimate vision is to translate this technology into an automated device that offers a streamlined and efficient assessment of T cell functions. This device could serve as a critical tool in optimizing T cell production and monitoring T cell functions for both autologous and allogeneic cell therapies, significantly improving the effectiveness and personalization of cancer immunotherapy.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/5vk4-n914 |
| Date | January 2024 |
| Creators | Wang, Xin |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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