A CAR enhancer increases the activity and persistence of CAR T cells.

Although chimeric antigen receptor (CAR) T cell therapies have demonstrated promising clinical outcomes, durable remissions remain limited. To extend the efficacy of CAR T cells, we develop a CAR enhancer (CAR-E), comprising a CAR T cell antigen fused to an immunomodulatory molecule. Here we demonstrate this strategy using B cell maturation antigen (BCMA) CAR T cells for the treatment of multiple myeloma, with a CAR-E consisting of the BCMA fused to a low-affinity interleukin 2 (IL-2). This selectively induces IL-2 signaling in CAR T cells upon antigen-CAR binding, enhancing T cell activation and antitumor activity while reducing IL-2-associated toxicities. We show that the BCMA CAR-E selectively binds CAR T cells and increases CAR T cell proliferation, clearance of tumor cells and development of memory CAR T cells. The memory cells retain the ability to re-expand upon restimulation, effectively controlling tumor growth upon rechallenge. Mechanistic studies reveal the involvement of both CAR and IL-2 receptor endodomains in the CAR-E mechanism of action. The CAR-E approach avoids the need for specific engineering and enables CAR T cell therapy with lower cell doses.

Reviewing Challenges of Predicting Protein Melting Temperature Change Upon Mutation Through the Full Analysis of a Highly Detailed Dataset with High-Resolution Structures.

Predicting the effects of mutations on protein stability is a key problem in fundamental and applied biology, still unsolved even for the relatively simple case of small, soluble, globular, monomeric, two-state-folder proteins. Many articles discuss the limitations of prediction methods and of the datasets used to train them, which result in low reliability for actual applications despite globally capturing trends. Here, we review these and other issues by analyzing one of the most detailed, carefully curated datasets of melting temperature change (ΔTm) upon mutation for proteins with high-resolution structures. After examining the composition of this dataset to discuss imbalances and biases, we inspect several of its entries assisted by an online app for data navigation and structure display and aided by a neural network that predicts ΔTm with accuracy close to that of programs available to this end. We pose that the ΔTm predictions of our network, and also likely those of other programs, account only for a baseline-like general effect of each type of amino acid substitution which then requires substantial corrections to reproduce the actual stability changes. The corrections are very different for each specific case and arise from fine structural details which are not well represented in the dataset and which, despite appearing reasonable upon visual inspection of the structures, are hard to encode and parametrize. Based on these observations, additional analyses, and a review of recent literature, we propose recommendations for developers of stability prediction methods and for efforts aimed at improving the datasets used for training. We leave our interactive interface for analysis available online at http://lucianoabriata.altervista.org/papersdata/proteinstability2021/s1626navigation.html so that users can further explore the dataset and baseline predictions, possibly serving as a tool useful in the context of structural biology and protein biotechnology research and as material for education in protein biophysics.