Coopting T cell proximal signaling molecules enables Boolean logic-gated CAR T cell control
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ABSTRACT:
PROVIDER: PRJNA892932 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:While CAR T cells have altered the treatment landscape for B cell malignancies, the risk of on-target, off-tumor toxicity has hampered their development for solid tumors because most target antigens are shared with normal cells. Researchers have attempted to apply Boolean logic gating to CAR T cells to prevent on-target, off-tumor toxicity; however, a truly safe and effective logic-gated CAR has remained elusive. Here, we describe a novel approach to CAR engineering in which we replace traditional ITAM-containing CD3ζ domains with intracellular proximal T cell signaling molecules. We demonstrate that certain proximal signaling CARs, such as a ZAP-70 CAR, can activate T cells and eradicate tumors in vivo while bypassing upstream signaling proteins such as CD3ζ. The primary role of ZAP-70 is to phosphorylate LAT and SLP-76, which form a scaffold for the propagation of T cell signaling. We leveraged the cooperative role of LAT and SLP-76 to engineer Logic-gated Intracellular NetworK (LINK) CAR, a rapid and reversible Boolean-logic AND-gated CAR T cell platform that outperforms other systems in both efficacy and the prevention of on-target, off-tumor toxicity. LINK CAR will dramatically expand the number and types of molecules that can be targeted with CAR T cells, enabling the deployment of these powerful therapeutics for solid tumors and diverse diseases such as autoimmunity9 and fibrosis10. In addition, this work demonstrates that the internal signaling machinery of cells can be repurposed into surface receptors, a finding that could have broad implications for new avenues of cellular engineering.
2022-12-13 | GSE216286 | GEO
Project description:Numerous gene expression datasets from diverse mouse tissue samples have been already deposited in the public domain. There have been several attempts to do large scale meta-analyses of all of these datasets. Most of these analyses summarize pairwise gene expression relationships using correlation, or identify differentially expressed genes in two conditions. We propose here a new large scale meta-analysis of all of the publicly available mouse datasets to identify Boolean logical relationships between genes. Boolean logic is a branch of mathematics that deals with two possible values. In the context of gene expression datasets we use qualitative high and low expression values. A strong logical relationship between genes emerges if at least one of the quadrants is sparsely populated.
2018-08-29 | GSE119085 | GEO
Project description:Numerous gene expression datasets from diverse fly tissue samples have been already deposited in the public domain. There have been several attempts to do large scale meta-analyses of all of these datasets. Most of these analyses summarize pairwise gene expression relationships using correlation, or identify differentially expressed genes in two conditions. We propose here a new large scale meta-analysis of all of the publicly available fly datasets to identify Boolean logical relationships between genes. Boolean logic is a branch of mathematics that deals with two possible values. In the context of gene expression datasets we use qualitative high and low expression values. A strong logical relationship between genes emerges if at least one of the quadrants is sparsely populated.
2018-08-29 | GSE119084 | GEO
Project description:Numerous gene expression datasets from diverse plant tissue samples have been already deposited in the public domain. There have been several attempts to do large scale meta-analyses of all of these datasets. Most of these analyses summarize pairwise gene expression relationships using correlation, or identify differentially expressed genes in two conditions. We propose here a new large scale meta-analysis of all of the publicly available plant datasets to identify Boolean logical relationships between genes. Boolean logic is a branch of mathematics that deals with two possible values. In the context of gene expression datasets we use qualitative high and low expression values. A strong logical relationship between genes emerges if at least one of the quadrants is sparsely populated.
2018-08-29 | GSE119083 | GEO
Project description:Numerous gene expression datasets from diverse human tissue samples have been already deposited in the public domain. There have been several attempts to do large scale meta-analyses of all of these datasets. Most of these analyses summarize pairwise gene expression relationships using correlation, or identify differentially expressed genes in two conditions. We propose here a new large scale meta-analysis of all of the publicly available human datasets to identify Boolean logical relationships between genes. Boolean logic is a branch of mathematics that deals with two possible values. In the context of gene expression datasets we use qualitative high and low expression values. A strong logical relationship between genes emerges if at least one of the quadrants is sparsely populated.
2018-08-29 | GSE119087 | GEO
Project description:Modeling signaling-dependent pluripotent cell states with boolean logic can predict cell fate transitions
| PRJNA349131 | ENA
Project description:The goal of this study is to test A2B530,an autologous logic-gated Tmod CAR T-cell product in subjects with solid tumors including colorectal cancer (CRC), pancreatic cancer (PANC), non-small cell lung cancer (NSCLC), and other solid tumors that express CEA and have lost HLA-A*02 expression.
The main questions this study aims to answer are:
* Phase 1: What is the maximum or recommended dose of A2B530 that is safe for patients
* Phase 2: Does the recommended dose of A2B530 kill the solid tumor cells and protect the patient’s healthy cells
Participants will be required to perform study procedures and assessments, and will also receive the following study treatments:
* Enrollment and Apheresis in BASECAMP-1 (NCT04981119)
* Preconditioning Lymphodepletion (PCLD) Regimen
* A2B530 Tmod CAR T cells at the assigned dose
| 9005 | ecrin-mdr-crc
Project description:The goal of this study is to test A2B694, an autologous logic-gated Tmod CAR T-cell product in subjects with solid tumors including colorectal cancer (CRC), pancreatic cancer (PANC), non-small cell lung cancer (NSCLC), ovarian cancer (OVCA), mesothelioma (MESO), and other solid tumors that express MSLN and have lost HLA-A*02 expression.
The main questions this study aims to answer are:
Phase 1: What is the recommended dose of A2B694 that is safe for patients
Phase 2: Does the recommended dose of A2B694 kill the solid tumor cells and protect the patient’s healthy cells
Participants will be required to perform study procedures and assessments, and will also receive the following study treatments:
Enrollment and Apheresis in BASECAMP-1 (NCT04981119)
Preconditioning Lymphodepletion (PCLD) Regimen
A2B694 Tmod CAR T cells at the assigned dose
| 106941 | ecrin-mdr-crc
Project description:We present a potential new mode of natural computing in which simple, heat-driven fluid flows perform Boolean logic operations. The system comprises a two-dimensional single-phase fluid that is heated from below and cooled from above, with two obstacles placed on the horizontal mid-plane. The obstacles remove all vertical momentum that flows into them. The horizontal momentum extraction of the obstacles is controlled in a binary fashion, and constitutes the 2-bit input. The output of the system is a thresholded measure of the energy extracted by the obstacles. Due to the existence of multiple attractors in the phase space of this system, the input-output relationships are equivalent to those of the OR, XOR or NAND gates, depending on the threshold and obstacle separation. The ability to reproduce these logical operations suggests that convective flows might have the potential to perform more general computations, despite the fact that they do not involve electronics, chemistry or multiple fluid phases.
| S-EPMC6735482 | biostudies-literature
Project description:Understanding the structure and interplay of cellular signalling pathways is one of the great challenges in molecular biology. Boolean Networks can infer signalling networks from observations of protein activation. In situations where it is difficult to assess protein activation directly, Nested Effect Models are an alternative. They derive the network structure indirectly from downstream effects of pathway perturbations. To date, Nested Effect Models cannot resolve signalling details like the formation of signalling complexes or the activation of proteins by multiple alternative input signals. Here we introduce Boolean Nested Effect Models (B-NEM). B-NEMs combine the use of downstream effects with the higher resolution of signalling pathway structures in Boolean Networks. We show that B-NEMs accurately reconstruct signal flows in simulated data. Using B-NEM we then resolve BCR signalling via PI3K and TAK1 kinases in BL2 lymphoma cell lines. 84 BL2 cell-line samples were hybridized to HGU133+2 Affymetrix GeneChips.
2015-12-10 | E-GEOD-68761 | biostudies-arrayexpress