Therapy-induced senescence in prostate cancer: mechanisms, therapeutic strategies, and clinical implications
Bibliographic record
Abstract
• Prostate cancer constitutes a leading cause of cancer-related mortality in men. • Cellular senescence, a form of cell cycle arrest with secretory phenotype, is a product of anticancer therapies. • Therapy-induced senescence has tumor-suppressive and tumor-promoting effects in prostate cancer. • Senolytics and senomorphics can mitigate the adverse effects of senescence in prostate cancer. • The effectiveness of senescence-targeting therapies in prostate cancer patients requires further investigation. Prostate cancer (PCa) remains a major cause of cancer-related mortality in men, particularly in its advanced and metastatic stages. While various systemic therapies have improved clinical outcomes, therapy resistance and disease progression remain significant challenges. One critical, yet underappreciated, mechanism influencing treatment response is therapy-induced senescence (TIS), a stable form of cell cycle arrest triggered by anticancer treatments. In PCa, TIS can be elicited by chemotherapy, radiotherapy, hormonal therapies, and targeted agents, and is characterized by a complex interplay of tumor-suppressive and tumor-promoting effects, largely mediated through the senescence-associated secretory phenotype (SASP). This review explores the molecular mechanisms of senescence, the diverse therapeutic strategies that induce it, and the dual roles it plays in PCa progression and treatment resistance. We further discuss emerging approaches that combine senescence-inducing therapies with senescence-targeting strategies, such as senolytics and senomorphics, to mitigate the adverse consequences of persistent senescent PCa cells. Finally, we highlight ongoing clinical trials, translational barriers, and future directions in integrating senotherapy into the clinical management of PCa.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".