Label-free in vitro assays predict the potency of anti-disialoganglioside chimeric antigen receptor T-cell products.

BACKGROUND AIMS: Chimeric antigen receptor (CAR) T cells have demonstrated remarkable efficacy against hematological malignancies; however, they have not experienced the same success against solid tumors such as glioblastoma (GBM). There is a growing need for high-throughput functional screening platforms to measure CAR T-cell potency against solid tumor cells. METHODS: We used real-time, label-free cellular impedance sensing to evaluate the potency of anti-disialoganglioside (GD2) targeting CAR T-cell products against GD2+ patient-derived GBM stem cells over a period of 2 days and 7 days in vitro. We compared CAR T products using two different modes of gene transfer: retroviral transduction and virus-free CRISPR-editing. Endpoint flow cytometry, cytokine analysis and metabolomics data were acquired and integrated to create a predictive model of CAR T-cell potency. RESULTS: Results indicated faster cytolysis by virus-free CRISPR-edited CAR T cells compared with retrovirally transduced CAR T cells, accompanied by increased inflammatory cytokine release, CD8+ CAR T-cell presence in co-culture conditions and CAR T-cell infiltration into three-dimensional GBM spheroids. Computational modeling identified increased tumor necrosis factor α concentrations with decreased glutamine, lactate and formate as being most predictive of short-term (2 days) and long-term (7 days) CAR T cell potency against GBM stem cells. CONCLUSIONS: These studies establish impedance sensing as a high-throughput, label-free assay for preclinical potency testing of CAR T cells against solid tumors.

Predicting T-cell quality during manufacturing through an artificial intelligence-based integrative multiomics analytical platform.

Large-scale, reproducible manufacturing of therapeutic cells with consistently high quality is vital for translation to clinically effective and widely accessible cell therapies. However, the biological and logistical complexity of manufacturing a living product, including challenges associated with their inherent variability and uncertainties of process parameters, currently make it difficult to achieve predictable cell-product quality. Using a degradable microscaffold-based T-cell process, we developed an artificial intelligence (AI)-driven experimental-computational platform to identify a set of critical process parameters and critical quality attributes from heterogeneous, high-dimensional, time-dependent multiomics data, measurable during early stages of manufacturing and predictive of end-of-manufacturing product quality. Sequential, design-of-experiment-based studies, coupled with an agnostic machine-learning framework, were used to extract feature combinations from early in-culture media assessment that were highly predictive of the end-product CD4/CD8 ratio and total live CD4+ and CD8+ naïve and central memory T cells (CD63L+CCR7+). Our results demonstrate a broadly applicable platform tool to predict end-product quality and composition from early time point in-process measurements during therapeutic cell manufacturing.

Early systemic immune biomarkers predict bone regeneration after trauma.

Severe traumatic injuries are a widespread and challenging clinical problem, and yet the factors that drive successful healing and restoration of function are still not well understood. One recently identified risk factor for poor healing outcomes is a dysregulated immune response following injury. In a preclinical model of orthopedic trauma, we demonstrate that distinct systemic immune profiles are correlated with impaired bone regeneration. Most notably, elevated blood levels of myeloid-derived suppressor cells (MDSCs) and the immunosuppressive cytokine interleukin-10 (IL-10) are negatively correlated with functional bone regeneration as early as 1 wk posttreatment. Nonlinear multivariate regression also implicated these two factors as the most influential in predictive computational models. These results support a significant relationship between early systemic immune responses to trauma and subsequent local bone regeneration and indicate that elevated circulating levels of MDSCs and IL-10 may be predictive of poor functional healing outcomes and represent novel targets for immunotherapeutic intervention.