Human double negative T cells target lung cancer via ligand-dependent mechanisms that can be enhanced by IL-15
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: double negative T (DNT) cells as a new cellular therapy for the treatment of lung cancer and underlying mechanisms. METHODS: DNTs were enriched and expanded ex vivo from healthy donors and phenotyped by flow cytometry. Functionally, their cytotoxicity was determined against primary and established non-small-cell lung cancer (NSCLC) cell lines in vitro or through in vivo adoptive transfer into xenograft models. Mechanistic analysis was performed using blocking antibodies against various cell surface and soluble markers. Furthermore, the role of IL-15 on DNT function was determined. RESULTS: We demonstrated that ex vivo expanded DNTs can effectively lyse various human NSCLC cells in vitro and inhibit tumor growth in xenograft models. Expanded DNTs have a cytotoxic phenotype, as they express NKp30, NKG2D, DNAM-1, membrane TRAIL (mTRAIL), perforin and granzyme B, and secrete IFNγ and soluble TRAIL (sTRAIL). DNT-mediated cytotoxicity was dependent on a combination of tumor-expressed ligands for NKG2D, DNAM-1, NKp30 and/or receptors for TRAIL, which differ among different NSCLC cell lines. Furthermore, stimulation of DNTs with IL-15 increased expression of effector molecules on DNTs, their TRAIL production and cytotoxicity against NSCLC in vitro and in vivo. CONCLUSION: Healthy donor-derived DNTs can target NSCLC in vitro and in vivo. DNTs recognize tumors via innate receptors which can be up-regulated by IL-15. DNTs have the potential to be used as a novel adoptive cell therapy for lung cancer either alone or in combination with IL-15.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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 it