Inflammatory cytokine production in tumor cells upon chemotherapy drug exposure or upon selection for drug resistance
Bibliographic record
Abstract
Tumor Necrosis Factor alpha (TNF-α) has been shown to be released by tumor cells in response to docetaxel, and lipopolysaccharides (LPS), the latter through activation of toll-like receptor 4 (TLR4). However, it is unclear whether the former involves TLR4 receptor activation through direct binding of the drug to TLR4 at the cell surface. The current study was intended to better understand drug-induced TNF-α production in tumor cells, whether from short-term drug exposure or in cells selected for drug resistance. ELISAs were employed to measure cytokine release from breast and ovarian tumor cells in response to several structurally distinct chemotherapy agents and/or TLR4 agonists or antagonists. Drug uptake and drug sensitivity studies were also performed. We observed that several drugs induced TNF-αrelease from multiple tumor cell lines. Docetaxel-induced cytokine production was distinct from that of LPS in both MyD88-positive (MCF-7) and MyD88-deficient (A2780) cells. The acquisition of docetaxel resistance was accompanied by increased constitutive production of TNF-αand CXCL1, which waned at higher levels of resistance. In docetaxel-resistant MCF-7 and A2780 cell lines, the production of TNF-α could not be significantly augmented by docetaxel without the inhibition of P-gp, a transporter protein that promotes drug efflux from tumor cells. Pretreatment of tumor cells with LPS sensitized MyD88-positive cells (but not MyD88-deficient) to docetaxel cytotoxicity in both drug-naive and drug-resistant cells. Our findings suggest that taxane-induced inflammatory cytokine production from tumor cells depends on the duration of exposure, requires cellular drug-accumulation, and is distinct from the LPS response seen in breast tumor cells. Also, stimulation of the LPS-induced pathway may be an attractive target for treatment of drug-resistant disease.
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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) | 0.000 | 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) | 0.000 | 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 teacher head, 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".