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Abstract Variability in T cell performance presents a major challenge to adoptive cellular immunotherapy (ACT). This includes expansion of a small starting population into therapeutically effective numbers, which can fail due to differences between individuals and disease states. Intriguingly, modulating the mechanical stiffness of materials used to activate T cells can rescue subsequent expansion. However, the magnitude of this effect and the optimal stiffnesses differ between individuals, complicating the use of mechanosensing to improve cell production. The ability to predict this long‐term, substrate‐dependent expansion from a short‐term assay would accelerate the deployment of immunotherapy. Here, it is demonstrated that short‐term cell spreading predicts subsequent, mechanosensitive expansion. As an initial task, cell spreading is used to identify whether a sample of cells came from a healthy donor or a Chronic Lymphocytic Leukemia (CLL) patient. Notably, a deep learning (DL) model outperforms morphometric approaches to this classification task. This system also successfully predicts the long‐term expansion potential of cells as a function of both source and mechanical stiffness of the activating substrate. By predicting long‐term T cell function from small, diagnostic samples, this approach will improve the reliability and efficacy of cell production and immunotherapy.
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Multi-Factor Clustering Incorporating Cell Motility Predicts T Cell Expansion Potential
Expansion of an initial population of T cells is essential for cellular immunotherapy. In Chronic Lymphocytic Leukemia (CLL), expansion is often complicated by lack of T cell proliferation, as these cells frequently show signs of exhaustion. This report seeks to identify specific biomarkers or measures of cell function that capture the proliferative potential of a starting population of cells. Mixed CD4+/CD8+ T cells from healthy donors and individuals previously treated for CLL were characterized on the basis of proliferative potential and in vitro cellular functions. Single-factor analysis found little correlation between the number of populations doublings reached during expansion and either Rai stage (a clinical measure of CLL spread) or PD-1 expression. However, inclusion of in vitro IL-2 secretion and the propensity of cells to align onto micropatterned features of activating proteins as factors identified three distinct groups of donors. Notably, these group assignments provided an elegant separation of donors with regards to proliferative potential. Furthermore, these groups exhibited different motility characteristics, suggesting a mechanism that underlies changes in proliferative potential. This study describes a new set of functional readouts that augment surface marker panels to better predict expansion outcomes and clinical prognosis.
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- Award ID(s):
- 1743420
- PAR ID:
- 10322367
- Date Published:
- Journal Name:
- Frontiers in Cell and Developmental Biology
- Volume:
- 9
- ISSN:
- 2296-634X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fcell.2021.648925
