Senolytic Targeting of Bcl-2 Anti-Apoptotic Family Increases Cell Death in Irradiated Sarcoma Cells
Bibliographic record
Abstract
Radiotherapy (RT) is a key component of cancer treatment. Most of the time, radiation is given after surgery but for soft-tissue sarcomas (STS), pre-surgical radiation is commonly utilized. However, despite improvements in RT accuracy, the rate of local recurrence remains high and is the major cause of death for patients with STS. A better understanding of cell fates in response to RT could provide new therapeutic options to enhance tumour cell killing by RT and facilitate surgical resection. Here, we showed that irradiated STS cell cultures do not die but instead undergo therapy-induced senescence (TIS), which is characterized by proliferation arrest, senescence-associated β-galactosidase activity, secretion of inflammatory cytokines and persistent DNA damage. STS-TIS was also associated with increased levels of the anti-apoptotic Bcl-2 family of proteins which rendered cells targetable using senolytic Bcl-2 inhibitors. As oppose to radiation alone, the addition of senolytic agents Venetoclax (ABT-199) or Navitoclax (ABT-263) after irradiation induced a rapid apoptotic cell death in STS monolayer cultures and in a more complex three-dimensional culture model. Together, these data suggest a new promising therapeutic approach for sarcoma patients who receive neoadjuvant RT. The addition of senolytic agents to radiation treatments may significantly reduce tumour volume prior to surgery and thereby improve the clinical outcome of patients.
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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".