Antiestrogens affect both pathways of killer cell-mediated oncolysis.
Bibliographic record
Abstract
BACKGROUND: Our previous studies indicate that antiestrogenic drugs tamoxifen (TX) and toremifene (TO) augment immune oncolysis induced by various killer cells. The underlying mechanism(s), however, have not been fully elucidated. MATERIALS AND METHODS: Ovarian carcinoma cells freshly isolated from cancer patients and the human erythroleukemia cell line, K562 were used as targets for killer cells and/or the anti-Fas monoclonal antibody, CH-11 in 51Cr release assays. In a number of experiments, extracellular Ca++ was chelated by EGTA/MgCl2 to distinguish Ca(++)-dependent perforin/granzyme pathway from Fas/FasL pathway. Fas expression was studied by flow cytometry. RESULTS: Ovarian carcinoma cells were sensitized by antiestrogens towards enhanced cytolysis mediated by autologous cytotoxic lymphocytes. Antiestrogens also significantly augmented the killing of ovarian carcinoma cells triggered by anti-Fas monoclonal antibody. Flow cytometry analyses showed an upregulation of Fas (CD 95/Apo-1) upon TX or TO treatment in a number of cases. By contrast, antiestrogen treatment did not induce Fas expression in the Fas-negative K562 cells; yet, natural killer cell-mediated cytotoxicity against K562 was augmented by antiestrogens and maximal lysis was achieved when both target and effector cells were treated. The presence of Ca++ chelator (EGTA/MgCl2) in the assay abrogated killing of K562 and its antiestrogen--mediated augmentation. This indicates the involvement of the perforin/granzyme pathway. CONCLUSION: Antiestrogens can influence both Fas/FasL and perforin/granzyme pathways of killer cell--mediated oncolysis.
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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".