Innate immune agonist, dsRNA, induces apoptosis in ovarian cancer cells and enhances the potency of cytotoxic chemotherapeutics
Bibliographic record
Abstract
ABSTRACT Ovarian cancer is the most lethal gynecological cancer. Here we show that innate immune agonist, dsRNA, directly induces ovarian cancer cell death and identify biomarkers associated with responsiveness to this targeted treatment. Nuclear staining and MTT assays following dsRNA stimulation revealed two subpopulations, sensitive (OVCAR‐3, CAOV‐3; patient samples malignant 1 and 2) and resistant (DOV‐13, SKOV‐3). Microarray analysis identified 75 genes with differential expression that further delineated these two subpopulations. qPCR and immunoblot analyses showed increased dsRNA receptor expression after stimulation as compared to resistant and immortalized ovarian surface epithelial cells ( e.g ., 70‐fold with malignant 2, 43‐fold with OVCAR‐3). Using agonists, antagonists, and shRNA‐mediated knockdown of dsRNA receptors, we show that TLR3, RIG‐I, and mda5 coordinated a caspase 8/9‐ and interferon‐dependent cell death. In resistant cells, dsRNA receptor overexpression restored dsRNA sensitivity. When dsRNA was combined with carboplatin or paclitaxel, cell viability significantly decreased over individual treatments (1.5‐to 7.5‐fold). Isobologram analyses showed synergism in dsRNA combinations (CI=0.4–0.82) vs . an additive effect in carboplatin/paclitaxel treatment (CI= 1.5–2). Our data identify a predictive marker, dsRNA receptor expression, to target dsRNA responsive populations and show that, in dsRNA‐sensitive cells, dsRNA induces apoptosis and enhances the potency of cytotoxic chemotherapeutics.—Van, D. N., Roberts, C. F., Marion, J. D., Lépine, S., Harikumar, K. B., Schreiter, J., Dumur, C. I., Fang, X., Spiegel, S., Bell, J. K. Innate immune agonist, dsRNA, induces apoptosis in ovarian cancer cells and enhances the potency of cytotoxic chemotherapeutics. FASEB J. 26, 3188–3198 (2012). www.fasebj.org
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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".