Biology subjects

Benjamin, R.

Publications and source records attributed to Benjamin, R..

2 recordsLinked to original sources

bioRxiv2024

A balanced CAR-T cell metabolism driven by CD28 or 4-1BB co-stimulation correlates with clinical success in lymphoma patients

Chimeric antigen receptor (CAR) T cell therapy has led to unprecedented success in treating relapsed/refractory diffuse large B cell lymphoma (DLBCL). The most common CAR-T cell products currently in the clinic for DLBCL differ in their co-stimulation moiety, containing either CD28 or 4-1BB, which initiate distinct signalling pathways. Previous work has highlighted the importance of T cell metabolism in fuelling anti-cancer function. We have studied the metabolic characteristics induced by CD28 versus 4-1BB co-stimulation in patient CAR-T cells ex vivo. Our data show that in patients, CD28 and 4-1BB drive significantly divergent metabolic profiles. CD28 signalling endows T cells with a preferentially glycolytic metabolism supporting an effector phenotype and increased expansion capacity, while 4-1BB co-stimulation preserves mitochondrial fitness and results in memory-like differentiation. Despite this divergent programming, T cells in patients responding successfully to therapy were metabolically similar, irrespective of co-stimulator, suggesting that efficient fuelling of CAR-T cells in lymphoma requires a balanced metabolism. In contrast, CAR-T cells in non-responders were pushed to their metabolic extremes. One sentence summaryCD28 and 4-1BB signalling drive divergent metabolic profiles in patient CAR-T cells, however patients that respond to therapy have CAR-T cells that maintain metabolic balance.

immunology↗

bioRxiv2023

Accurate prediction of transcriptional activity of single missense variants in HIV Tat with deep learning

Tat is an essential gene for increasing the transcription of all HIV genes, and it affects HIV replication, HIV exit from latency, and AIDS progression. The Tat gene frequently mutates in vivo producing variants with diverse activities, contributing to HIV viral heterogeneity, as well as drug-resistant clones. Thus, identifying the transcriptional activities of Tat variants will help to better understand AIDS pathology and treatment. We recently reported the missense mutation landscape of all single amino acid Tat variants. In these experiments, a fraction of double missense alleles exhibited intragenic epistasis. It is too time-consuming and costly to determine a variants effect for all double mutant alleles with experiments. Therefore, we propose a combined GigaAssay/Deep learning approach. As a first step for determining activity landscapes for complex variants, we evaluated a deep learning framework using previously reported GigaAssay experiments to predict how transcription activity is affected by Tat variants with single missense substitutions. Our approach achieves a 0.94 Pearson correlation coefficient when comparing experimental to predicted activities. This hybrid approach should be extensible to more complex Tat alleles for better understanding the genetic control of HIV genome transcription.

bioinformatics↗