Targeting late-stage non-small cell lung cancer with a combination of DNT cellular therapy and PD-1 checkpoint blockade
Bibliographic record
Abstract
BACKGROUND: Though immune checkpoint blockade (ICB) against PD-1 has shown success in the treatment of lung cancer, not all patients respond. We have previously shown that adoptive transfer of double negative T (DNT) cells expanded from healthy donors can target leukemia but their role in treating established lung cancer is not clear. Here we explore the role of human DNT cells in targeting late-stage established lung cancer either alone or in combination with Nivolumab (anti-PD-1 antibody) and describe underlying mechanisms. METHODS: DNT cells from resected lung cancer tissue of patients were analyzed by flow cytometry to determine their infiltration and PD-1 expression. Expansion capacity and anti-tumor function of lung cancer patient and healthy donor DNT cells were compared. Late-stage lung cancer xenograft models were developed to determine the anti-tumor effect of DNT cells alone or in combination with anti-PD-1 antibody, and the level of tumor-infiltrating DNT cells was quantified by histology and characterized by flow cytometry. RESULTS: Patient-derived tumor infiltrating lymphocytes contained a lower frequency of DNT cells with a higher expression of PD-1 relative to normal lung tissue. Ex vivo expanded patient- and healthy donor-derived DNT cells showed similar levels of cytotoxicity against lung cancer cells in vitro. Healthy donor-derived DNT cells significantly inhibited the growth of late-stage lung cancer xenografts, which was further augmented by anti-PD-1 through increased DNT cell tumor infiltration. CONCLUSION: This study supports the use of DNT cells for adoptive cellular therapy against lung cancer either alone or in combination with anti-PD-1.
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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 | 0.000 | 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".