Transfection of FcγRIIIa (CD16) Alone Can Be Sufficient To Enable Human αβTCR T Lymphocytes To Mediate Antibody-Dependent Cellular Cytotoxicity
Bibliographic record
Abstract
To combine the immune potential of T cells and Ab therapy, we and others have previously shown that T cells transduction with a fusion receptor that binds the Fc portion of human Ig enable them to mediate Ab-dependent cellular cytotoxicity (ADCC). The fusion receptors previously described included the FcγRIIIa (CD16) receptor coupled to different chains intended to translate the signal. In this work, we questioned whether the transfection of CD16 alone into T human lymphocytes and NK cells could be sufficient for CD16 expression and function, or whether the cotransfection of a transducing chain was mandatory. Our results demonstrated that: 1) transfection of CD16 alone into a human NK cell line and primary T cells can be sufficient for CD16 expression and function; 2) cotransfection of CD3ζ or FcεRIγ increased CD16 expression; 3) yet this increased CD16 expression increased the ADCC score only for trastuzumab, not for rituximab or cetuximab; and 4) compared with that of peripheral NK cells, ADCC scores by autologous CD16-transfected T cells ranked differently according to the opsonized target cell. Together, these results showed that neither the use of a fusion receptor nor the cotransfection of a transducing chain is mandatory to transfer the ADCC function to human lymphocytes. Thus, depending on the effector/Ab/target combination considered, transfection of CD16 alone can be sufficient to enable T cells to mediate ADCC. In the context of immunotherapy, such a strategy is by nature safer than the use of a chimeric receptor, and is freely available.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".