Engineering T cells with improved TCR-CD3 interaction for optimal tumor killing
Bibliographic record
Abstract
Abstract T cell activation requires extracellular stimulatory signals mainly mediated by T cell receptor complex (TCR/CD3). Obtaining detailed mechanistic knowledge of the TCR-mediated signaling pathway holds significant importance in understanding immune diseases, formulating new immunotherapies, and overcoming immunosuppression in current therapies. Here, we used a novel strategy to overcome ineffective anti-tumor responses by modifying the TCR interaction with CD3 subunits without affecting the binding affinity and/or specificity of the TCR-antigen interaction. Based on NMR and mutational studies, we introduced specific alanine mutations in the Cβ helix 3 and Cβ helix 4 – F strand of the TCR extracellular domain. We demonstrated varied functionality in terms of IL-2 production as well as altered bond lifetime measured by biomembrane force probe assay (BFP). Selected mutations retained or displayed enhanced CD3 binding ability in a CD3 tetramer assay. Based on these results we generated retroviral TCR libraries by random mutagenesis at specific extracellular TCR-CD3 interaction sites, expressed them in a T cell hybridoma system and selected TCRs with novel binding specificity using soluble CD3γɛ- and CD3δɛ-tetramers. We are currently determining the correlation between CD3 tetramer binding, bond lifetime, and functionality (kinase activity, IL-2 production, and tumor rejection) of selected TCR mutants. By integrating binding data, T cell signaling data, and functional outcomes, we expect to be able to manipulate the human immune system via the extracellular TCR-CD3 interaction site to be utilized in novel human T cell therapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".