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DeWolf, S.

Publications and source records attributed to DeWolf, S..

2 recordsLinked to original sources

Leucine zipper-based sorting system enables generation of multi-functional CAR T cells

Resistance to chimeric antigen receptor (CAR) T cell therapy develops through multiple mechanisms including antigen-loss escape and tumor-induced immune suppression. Expression of multiple CARs may overcome multi-antigen-loss escape. Similarly, expression of switch receptors that convert inhibitory immune checkpoint signals into positive costimulatory signals may enhance CAR T cell activity in the tumor microenvironment. Engineering multiple features into one cell product, however, is limited by transgene packaging constraints of current vector systems. Here, we describe a leucine zipper-based cell sorting methodology that enables selective single-step immunomagnetic purification of cells co-transduced with two vectors, designed to potentially double the number of incorporated transgenes. This "Zip-sorting" system facilitated generation of T cells simultaneously expressing up to four CARs and co-expressing up to three switch receptors. These multi-CAR multi-Switch receptor arrays enabled T cells to eliminate antigenically heterogeneous syngeneic leukemia populations co-expressing multiple inhibitory ligands. Zip-sorted multi-CAR multi-Switch receptor T cells represent a potent therapeutic strategy to overcome multiple mechanisms of CAR T cell resistance.

synthetic biology↗

TCRi: Information theoretic metrics for single cell RNA and TCR sequencing in cancer

Single-cell T cell repertoire sequencing can pair both T cell receptor (TCR) and gene expression sequence data, providing an enriched view of T cell behavior. This powerful tool can identify and characterize specific clonotypes and phenotypes as well as track their changes in response to therapy, such as immune checkpoint blockade (ICB). We present a novel information theoretic framework called TCRi for characterizing single cell T cell repertoires by formalizing the relationship between clonotype and phenotype in a joint probability distribution. Our strategy allows for the identification of subpopulations of T cells and jointly quantifies their TCR and expression profiles in response to stimuli, in addition the framework tracks the phenotypic changes in individual T cell clones over time. We applied this framework to four datasets of T cells sequenced from cancer patients treated with anti-PD-(L)1 ICB immunotherapies and examined evolution of T cell responses pre- and post-treatment. Quantitative of phenotypic and clonotypic entropy analysis with TCRi demonstrated improvements in characterization of the transcriptional signature of clonotypes. Furthermore, TCRi highlighted the importance of phenotypic flux and specific T-cell phenotypes as determinants of therapeutic response.

bioinformatics↗