Cellular senescence is a central response to cytotoxic chemotherapy in high-grade serous ovarian cancer
Bibliographic record
Abstract
Abstract High-grade serous ovarian cancer (HGSOC) commonly responds to initial therapy, but this response is rarely durable. Understanding cell fate decisions taken by HGSOC cells in response to treatment could guide new therapeutic opportunities. Here we find that primary HGSOC cultures undergo therapy-induced senescence (TIS) in response to DNA damage induced by chemotherapy. HGSOC-TIS displays most senescence hallmarks including persistent DNA damage, senescence-associated inflammatory secretome, and selective sensitivity to senolytic Bcl-2 family inhibitors, suggesting avenues for preferential synergistic clearance of these cells. Comparison of pre- and post-chemotherapy HGSOC patient tissue samples revealed changes in senescence biomarkers suggestive of post-treatment “in patient” TIS, and a stronger TIS response in post-chemotherapy tissues correlated with better 5-year survival rates for patients. Together, these data suggest that the induction of cellular senescence in HGSOC cells accounts at least in part for beneficial cellular responses to treatment in patients providing a new therapeutic target. One Sentence Summary Cellular senescence is a central beneficial response to chemotherapy in high-grade serous ovarian cancer both in vitro and in patient.
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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) | 0.002 | 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".