Data from Natural Immunity Enhances the Activity of a DR5 Agonistic Antibody and Carboplatin in the Treatment of Ovarian Cancer
Bibliographic record
Abstract
Abstract The tumor necrosis factor–related apoptosis-inducing ligand (TRAIL) induces apoptosis specifically in cancer cells with little effect on normal cells. We have previously shown that TRAIL signaling is altered in most ovarian cancer patients and that resistance to TRAIL contributes to ovarian cancer progression. In this study, we investigated whether resistance to TRAIL may be overcome by a monoclonal TRAILR2 (DR5) agonistic antibody (AD5-10). We found that the joint presence of AD5-10 with TRAIL and natural killer (NK) cells expressing TRAIL resensitizes ovarian cancer cells to apoptosis in vitro and in vivo, respectively. The combination of AD5-10 with carboplatin exerts a more than additive effect in vitro, which may at least partially be explained by the fact that carboplatin triggers DR5 expression on ovarian cancer cells. Moreover, AD5-10 restores the sensitivity of platin-resistant ovarian cancer to carboplatin in vivo. In addition, we found that TRAIL expression and NK cells are abundant in the tumor microenvironment and that depletion of NK cells abolishes the antitumor activity of AD5-10. This indicates that NK-mediated immunosurveillance against ovarian cancer might be mediated by TRAIL and that apoptosis induced by AD5-10 requires the presence of NK cells. In conclusion, this study indicates a key role and strong antitumorigenic effect of DR5 and highlights a novel link between NK-mediated immunosurveillance and activation of DR5-mediated apoptosis in ovarian cancer. Mol Cancer Ther; 9(4); 1007–18. ©2010 AACR.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".