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Marraffa, A.

Publications and source records attributed to Marraffa, A..

2 recordsLinked to original sources

Adoptive cell therapy using T cell receptors equipped with ICOS yields durable anti-tumor response

Treatment with adoptively transferred T cells is challenged by limited longevity of therapeutic cells within tumors. To enhance the durability of anti-tumor T cell products, we have created T cell receptors (TCRs) with built-in co-stimulatory molecules. We observed that TCRs coupled to ICOS mediated exceptionally long-term responses including delay of tumor recurrence and cures in a mouse tumor model. TCR:ICOS T cells showed enhanced and antigen-specific production of inflammatory cytokines and resistance to exhaustion. Genetic ablation of ICOS-mediated activation of the PI3K-NF{kappa}B pathway neutralized the long-term anti-tumor effects. To translate TCR:ICOS to human T cells, we identified a single amino acid change in the cytosolic tail which was necessary for functional surface expression. Notably, the optimized receptor sustained performance of human T cells upon repeated stimulation across multiple antigen specificities. Collectively, we present a novel and uniformly applicable TCR:ICOS format that supports fitter T cell products for adoptive cell therapy. HighlightsNewly designed co-stimulatory TCR, with extracellular TCR-V and C domains coupled to CD28 transmembrane domain, and ICOS and CD3{varepsilon} intracellular domains (in short TCR:ICOS) provides:[tpltrtarr] durable anti-tumor response and T cell persistence in mouse model [tpltrtarr]inflammatory T cell phenotype and resistance to T cell exhaustion [tpltrtarr]effects via PI3K and NF{kappa}B activation [tpltrtarr]translation to human T cells upon single amino acid mutation in TCR:ICOS tail [tpltrtarr]extension to multiple clinically relevant TCRs while preserving prolonged T cell fitness O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=168 SRC="FIGDIR/small/682056v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1ff752borg.highwire.dtl.DTLVardef@6579d9org.highwire.dtl.DTLVardef@22bdc4org.highwire.dtl.DTLVardef@d8c445_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

Data-Driven Reduced Modeling of Recurrent Neural Networks

Artificial Recurrent Neural Networks (RNNs) are widely used in neuroscience to model the collective activity of neurons during behavioral tasks. The high dimensionality of their parameter and activity spaces, however, often make it challenging to infer and interpret the fundamental features of their dynamics. In this study, we employ recent nonlinear dynamical system techniques to uncover the core dynamics of several RNNs used in contemporary neuroscience. Specifically, using a data-driven approach, we identify Spectral Submanifolds (SSMs), i.e., low-dimensional attracting invariant manifolds tangent to the eigenspaces of fixed points. The internal dynamics of SSMs serve as nonlinear models that reduce the dimensionality of the full RNNs by orders of magnitude. Through low-dimensional, SSM-reduced models, we give mathematically precise definitions of line and ring attractors, which are intuitive concepts commonly used to explain decision-making and working memory. The new level of understanding of RNNs obtained from SSM reduction enables the interpretation of mathematically well-defined and robust structures in neuronal dynamics, leading to novel predictions about the neural computations underlying behavior.

neuroscience↗