ABSTRACT
Numerous studies are exploring the use of cell adoptive therapies to treat hematological malignancies as well as solid tumors. However, there are numerous factors that dampen the immune response, including viruses like human immunodeficiency virus. In this study, we leverage human-derived microphysiological models to reverse-engineer the HIV-immune system interaction and evaluate the potential of memory-like natural killer cells for HIV+ head and neck cancer, one of the most common tumors in patients living with human immunodeficiency virus. Here, we evaluate multiple aspects of the memory-like natural killer cell response in human-derived bioengineered environments, including immune cell extravasation, tumor penetration, tumor killing, T cell dependence, virus suppression, and compatibility with retroviral medication. Overall, these results suggest that memory-like natural killer cells are capable of operating without T cell assistance and could simultaneously destroy head and neck cancer cells as well as reduce viral latency.
Subject(s)
HIV Infections , Head and Neck Neoplasms , Viruses , Humans , HIV , Killer Cells, Natural , Immunotherapy/methodsABSTRACT
New non-destructive tools are needed to reliably assess lymphocyte function for immune profiling and adoptive cell therapy. Optical metabolic imaging (OMI) is a label-free method that measures the autofluorescence intensity and lifetime of metabolic cofactors NAD(P)H and FAD to quantify metabolism at a single-cell level. Here, we investigate whether OMI can resolve metabolic changes between human quiescent versus IL4/CD40 activated B cells and IL12/IL15/IL18 activated memory-like NK cells. We found that quiescent B and NK cells were more oxidized compared to activated cells. Additionally, the NAD(P)H mean fluorescence lifetime decreased and the fraction of unbound NAD(P)H increased in the activated B and NK cells compared to quiescent cells. Machine learning classified B cells and NK cells according to activation state (CD69+) based on OMI parameters with up to 93.4% and 92.6% accuracy, respectively. Leveraging our previously published OMI data from activated and quiescent T cells, we found that the NAD(P)H mean fluorescence lifetime increased in NK cells compared to T cells, and further increased in B cells compared to NK cells. Random forest models based on OMI classified lymphocytes according to subtype (B, NK, T cell) with 97.8% accuracy, and according to activation state (quiescent or activated) and subtype (B, NK, T cell) with 90.0% accuracy. Our results show that autofluorescence lifetime imaging can accurately assess lymphocyte activation and subtype in a label-free, non-destructive manner.
