Tumor-Derived Extracellular Vesicles Impair CD171-Specific CD4+ CAR T Cell Efficacy

Chimeric antigen receptor (CAR) T cell efficacy against solid tumors is currently limited by several immune escape mechanisms, which may include tumor-derived extracellular vesicles. Advanced neuroblastoma is an aggressive childhood tumor without curative treatment options for most relapsed patients today. We here evaluated the role of tumor-derived extracellular vesicles on the efficacy of CAR T cells targeting the neuroblastoma-specific antigen, CD171. For this purpose, CAR T cell activation, cytokine production, exhaustion, and tumor cell-directed cytotoxicity upon co-culture was evaluated. Tumor-derived extracellular vesicles isolated from SH-SY5Y neuroblastoma cells neither affected CAR T cell activation nor expression of inhibitory markers. Importantly, exposure of CD4+ CD171-specific CAR T cells to tumor-derived extracellular vesicles significantly impaired tumor cytotoxicity of CAR T cells. This effect was independent of neurotrophic receptor tyrosine kinases 1 or 2 (NTRK1, NTRK2) expression, which is known to impact immune responses against neuroblastoma. Our results demonstrate for the first time the impact of tumor-derived extracellular vesicles and non-cell-mediated tumor-suppressive effects on CD4+ CAR T cell efficacy in a preclinical setting. We conclude that these factors should be considered for any CAR T cell-based therapy to make CAR T cell therapy successful against solid tumors

Streamlining Quantitative Analysis of Long RNA Sequencing Reads

Transcriptome analyses allow for linking RNA expression profiles to cellular pathways and phenotypes. Despite improvements in sequencing methodology, whole transcriptome analyses are still tedious, especially for methodologies producing long reads. Currently, available data analysis software often lacks cost- and time-efficient workflows. Although kit-based workflows and benchtop platforms for RNA sequencing provide software options, e.g., cloud-based tools to analyze basecalled reads, quantitative, and easy-to-use solutions for transcriptome analysis, especially for non-human data, are missing. We therefore developed a user-friendly tool, termed Alignator, for rapid analysis of long RNA reads requiring only FASTQ files and an Ensembl cDNA database reference. After successful mapping, Alignator generates quantitative information for each transcript and provides a table in which sequenced and aligned RNA are stored for further comparative analyses