Campana, D. | Search bioRxiv

bioRxiv2026

A CHIMERIC RECEPTOR ENABLING ANTIBODY-GUIDED RETARGETING OF CAR T- CELLS

Chimeric antigen receptor (CAR)-T cell therapies achieve remarkable responses in hematologic malignancies but are limited by target heterogeneity and antigen escape; therapeutic antibodies offer flexible targeting yet cannot leverage T-cell effector function. Integrating CAR-T cell potency with antibody targeting adaptability could expand the clinical reach of cellular immunotherapy. We engineered a CAR platform (termed CARFcR) incorporating an Fc receptor (CD16V158) into the CAR extracellular domain, and enabling both CAR-mediated targeting and antibody-dependent recognition. CARFcR-T cells were activated through either pathway, exhibiting robust cytokine secretion, degranulation, and proliferation. Anti-CD19 CARFcR-T cells mediated cytotoxicity comparable to conventional CAR-T cells and eradicated CD19+ leukemia in xenograft models. Importantly, they also exerted antibody-dependent killing of CD19-negative tumor cells when guided by clinically approved antibodies, such as anti-HER2 trastuzumab and anti-CD20 rituximab. Dual engagement further enhanced cytotoxicity and enabled elimination of CD19-negative escape variants. This modular CARFcR architecture was successfully applied to additional CAR targets, including BCMA, CD123, and CD33, maintaining CAR function while allowing redirection with antibodies or CD16-binding bispecific engagers. Overall, CARFcR represents a versatile and clinically adaptable platform that integrates the strengths of CAR-T cells and antibody therapeutics to expand tumor targeting and overcome antigen escape.

bioengineering↗

bioRxiv2018

Reverse-engineering flow-cytometry gating strategies for phenotypic labelling and high-performance cell sorting

MotivationRecent flow and mass cytometers generate 1,000,000 single cell datasets of dimensions 20 to 40. Many tools facilitate the discovery of new cell populations associated with diseases or physiology. These discoveries require the identification of new gating strategies, but gating strategies become exponentially harder to optimize when dimensionality increases. To facilitate this step we developed Hypergate, an algorithm which given a cell population of interest identifies a gating strategy optimized for high yield and purity.\n\nResultsHypergate achieves higher yield and purity than human experts, Support Vector Machines and Random-Forests on public datasets. We use it to revisit some established gating strategies for the identification of Innate lymphoid cells, which identifies concise and efficient strategies that allow gating these cells with fewer parameters but higher yield and purity than the current standards. For phenotypic description, Hypergates outputs are consistent with fields knowledge and sparser than those from a competing method.\n\nAvailability and ImplementationHypergate is implemented in R and available at http://github.com/ebecht/hypergate under an Open Source Initiative-compliant licence.

bioinformatics↗