Human NK cells activated by EBV<sup>+</sup>lymphoblastoid cells overcome anti-apoptotic mechanisms of drug resistance in haematological cancer cells
Bibliographic record
Abstract
Natural killer (NK) cells recognize and eliminate transformed or infected cells that have downregulated MHC class-I and express specific activating ligands. Recent evidence indicates that allogeneic NK cells are useful to eliminate haematological cancer cells independently of MHC-I expression. However, it is unclear if transformed cells expressing mutations that confer anti-apoptotic properties and chemoresistance will be susceptible to NK cells. Allogeneic primary human NK cells were activated using different protocols and prospectively tested for their ability to eliminate diverse mutant haematological and apoptotic-resistant cancer cell lines as well as patient-derived B-cell chronic lymphocytic leukemia cells with chemotherapy multiresistance. Here, we show that human NK cells from healthy donors activated in vitro with Epstein Barr virus positive (EBV+)-lymphoblastoid cells display an enhanced cytotoxic and proliferative potential in comparison to other protocols of activation such a K562 cells plus interleukin (IL)2. This enhancement enables them to kill more efficiently a variety of haematological cancer cell lines, including a panel of transfectants that mimic natural mutations leading to oncogenic transformation and chemoresistance (e.g., overexpression of Bcl-2, Bcl-XL and Mcl-1 or downregulation of p53, Bak/Bax or caspase activity). The effect was also observed against blasts from B-cell chronic lymphocytic leukemia patients showing multi-resistance to chemotherapy. Our findings demonstrate that particular in vitro activated NK cells may overcome anti-apoptotic mechanisms and oncogenic alterations frequently occurring in transformed cells, pointing toward the use of EBV+-lymphoblastoid cells as a desirable strategy to activate NK cells in vitro for the purpose of treating haematological neoplasia with poor prognosis.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".