IL-4 drives exhaustion of CD8+ CART cells.

Durable response to chimeric antigen receptor T (CART) cell therapy remains limited in part due to CART cell exhaustion. Here, we investigate the regulation of CART cell exhaustion with three independent approaches including: a genome-wide CRISPR knockout screen using an in vitro model for exhaustion, RNA and ATAC sequencing on baseline and exhausted CART cells, and RNA and ATAC sequencing on pre-infusion CART cell products from responders and non-responders in the ZUMA-1 clinical trial. Each of these approaches identify interleukin (IL)-4 as a regulator of CART cell dysfunction. Further, IL-4-treated CD8+ CART cells develop signs of exhaustion independently of the presence of CD4+ CART cells. Conversely, IL-4 pathway editing or the combination of CART cells with an IL-4 monoclonal antibody improves antitumor efficacy and reduces signs of CART cell exhaustion in mantle cell lymphoma xenograft mouse models. Therefore, we identify both a role for IL-4 in inducing CART exhaustion and translatable approaches to improve CART cell therapy.

Mesenchymal stromal cells with chimaeric antigen receptors for enhanced immunosuppression.

Allogeneic mesenchymal stromal cells (MSCs) are a safe treatment option for many disorders of the immune system. However, clinical trials using MSCs have shown inconsistent therapeutic efficacy, mostly owing to MSCs providing insufficient immunosuppression in target tissues. Here we show that antigen-specific immunosuppression can be enhanced by genetically modifying MSCs with chimaeric antigen receptors (CARs), as we show for E-cadherin-targeted CAR-MSCs for the treatment of graft-versus-host disease in mice. CAR-MSCs led to superior T-cell suppression and localization to E-cadherin+ colonic cells, ameliorating the animals' symptoms and survival rates. On antigen-specific stimulation, CAR-MSCs upregulated the expression of immunosuppressive genes and receptors for T-cell inhibition as well as the production of immunosuppressive cytokines while maintaining their stem cell phenotype and safety profile in the animal models. CAR-MSCs may represent a widely applicable therapeutic technology for enhancing immunosuppression.

CD19 occupancy with tafasitamab increases therapeutic index of CART19 cell therapy and diminishes severity of CRS.

ABSTRACT: In the development of various strategies of anti-CD19 immunotherapy for the treatment of B-cell malignancies, it remains unclear whether CD19 monoclonal antibody therapy impairs subsequent CD19-targeted chimeric antigen receptor T-cell (CART19) therapy. We evaluated the potential interference between the CD19-targeting monoclonal antibody tafasitamab and CART19 treatment in preclinical models. Concomitant treatment with tafasitamab and CART19 showed major CD19 binding competition, which led to CART19 functional impairment. However, when CD19+ cell lines were pretreated with tafasitamab overnight and the unbound antibody was subsequently removed from the culture, CART19 function was not affected. In preclinical in vivo models, tafasitamab pretreatment demonstrated reduced incidence and severity of cytokine release syndrome and exhibited superior antitumor effects and overall survival compared with CART19 alone. This was associated with transient CD19 occupancy with tafasitamab, which in turn resulted in the inhibition of CART19 overactivation, leading to diminished CAR T apoptosis and pyroptosis of tumor cells.

Real-Time Task Assignment Approach Leveraging Reinforcement Learning with Evolution Strategies for Long-Term Latency Minimization in Fog Computing.

The emerging fog computing technology is characterized by an ultralow latency response, which benefits a massive number of time-sensitive services and applications in the Internet of things (IoT) era. To this end, the fog computing infrastructure must minimize latencies for both service delivery and execution phases. While the transmission latency significantly depends on external factors (e.g., channel bandwidth, communication resources, and interferences), the computation latency can be considered as an internal issue that the fog computing infrastructure could actively self-handle. From this view point, we propose a reinforcement learning approach that utilizes the evolution strategies for real-time task assignment among fog servers to minimize the total computation latency during a long-term period. Experimental results demonstrate that the proposed approach reduces the latency by approximately 16.1% compared to the existing methods. Additionally, the proposed learning algorithm has low computational complexity and an effectively parallel operation; therefore, it is especially appropriate to be implemented in modern heterogeneous computing platforms.