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Teachey, D. T.

Publications and source records attributed to Teachey, D. T..

2 recordsLinked to original sources

Harnessing the CD2 axis to broaden and enhance the efficacy of CAR T cell therapies

Patients with T-cell lymphomas and leukemias have overall poor outcomes due to the lack of targeted and effective treatments, particularly in the relapsed and refractory settings. Development of chimeric antigen receptor (CAR) T-cells against T-cell neoplasms is limited by a lack of discriminating T-cell antigens that allow for effective anti-tumor responses while preventing CAR T-cell fratricide. We hypothesized that targeting CD2, a pan-T-cell antigen, using anti-CD2 CAR T-cells engineered without CD2 expression (CART2), would support CAR T-cell manufacturability and preclinical efficacy. Optimized CD2-knockout CART2, generated using CRISPR-Cas9, eradicated primary patient-derived CD2+ hematological neoplasms in vitro and in vivo, secreted effector cytokines, and exhibited adequate proliferative capacity. Nevertheless, CD2 has a key costimulatory function, and its deletion could lead to CAR T-cell dysfunction. Therefore, we tested the role of the CD2:CD58 axis in CAR T-cells, using the anti-CD19 CART models. We demonstrate that CD2 loss attenuates CART19 efficacy by reducing avidity for tumor antigen, co-stimulation, and ultimately in vivo activity. Analogously, we show that tumor CD58 loss reduces CART19 efficacy. To overcome this issue, we developed a novel PD-1:CD2 switch receptor that rescues intracellular CD2 signaling, particularly when PD-L1 is engaged, resulting in improved in vivo outcomes. Collectively, we studied the role of CD2 both as a target for CAR T cell therapy and as a critical costimulatory protein, whose signaling can be rescued using the PD-1:CD2 switch receptor. This receptor can be incorporated into CAR T-cells and provides an effective strategy to overcome CD2-signaling deficiencies.

cancer biology↗

Bootstrap Evaluation of Association Matrices (BEAM) for Integrating Multiple Omics Profiles with Multiple Outcomes

MotivationLarge datasets containing multiple clinical and omics measurements for each subject motivate the development of new statistical methods to integrate these data to advance scientific discovery. ModelWe propose bootstrap evaluation of association matrices (BEAM), which integrates multiple omics profiles with multiple clinical endpoints. BEAM associates a set omic features with clinical endpoints via regression models and then uses bootstrap resampling to determine statistical significance of the set. Unlike existing methods, BEAM uniquely accommodates an arbitrary number of omic profiles and endpoints. ResultsIn simulations, BEAM performed similarly to the theoretically best simple test and outperformed other integrated analysis methods. In an example pediatric leukemia application, BEAM identified several genes with biological relevance established by a CRISPR assay that had been missed by univariate screens and other integrated analysis methods. Thus, BEAM is a powerful, flexible, and robust tool to identify genes for further laboratory and/or clinical research evaluation. AvailabilitySource code, documentation, and a vignette for BEAM are available on GitHub at: https://github.com/annaSeffernick/BEAMR. The R package is available from CRAN at: https://cran.r-project.org/package=BEAMR. ContactStanley.Pounds@stjude.org Supplementary InformationSupplementary data are available at the journals website.

bioinformatics↗