A senescence-like state is beneficial for ovarian cancer treatment
Bibliographic record
Abstract
Abstract High-grade serous ovarian carcinoma (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 tissue-derived primary HGSOC epithelial cultures reflecting the original disease primarily undergo therapy-induced senescence in response to DNA damage and first-line carboplatin/paclitaxel chemotherapy. Unlike previous observations using cell lines, primary HGSOC cell TIS displays a stable senescence proliferation arrest and p16INK4A expression, and is accompanied by persistent DNA damage, an inflammatory secretome, and senolytic sensitivity, suggesting new avenues for selective pharmacological manipulation of these cells. Whether cell senescence induced by cancer therapy is beneficial or detrimental to clinical outcomes remains unknown. Single cell comparison of pre- and post-chemotherapy patient HGSOC tissue samples revealed changes in physio-pathological senescence biomarkers supporting a post-treatment senescence-like state. Importantly, patients with stronger senescence signatures post-chemotherapy displayed better 5-year survival suggesting that senescence accounts, at least in part, for beneficial cellular responses to treatment. Given that ovarian cancer epithelial cells almost universally retain the capacity to undergo senescence and that senescence appears beneficial in this context, HGSOC senescence-centric therapeutic avenues should be further explored. Author Summary Whether cancer therapy-induced cell fate decisions like senescence are good or bad for patient treatment outcome is unknown. We find that ovarian cancer cells almost universally retain senescence competence and that senescence in treated cancer tissues correlate with good clinical outcomes. This reveals that senescence is a relevant drug target in ovarian cancer.
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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".