Targeting senescence as an anticancer therapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cellular senescence is a stress response elicited by different molecular insults. Senescence results in cell cycle exit and is characterised by multiple phenotypic changes such as the production of a bioactive secretome. Senescent cells accumulate during ageing and are present in cancerous and fibrotic lesions. Drugs that selectively kill senescent cells (senolytics) have shown great promise for the treatment of age-related diseases. Senescence plays paradoxical roles in cancer. Induction of senescence limits cancer progression and contributes to therapy success, but lingering senescent cells fuel progression, recurrence, and metastasis. In this review, we describe the intricate relation between senescence and cancer. Moreover, we enumerate how current anticancer therapies induce senescence in tumour cells and how senolytic agents could be deployed to complement anticancer therapies. "One-two punch" therapies aim to first induce senescence in the tumour followed by senolytic treatment to target newly exposed vulnerabilities in senescent tumour cells. "One-two punch" represents an emerging and promising new strategy in cancer treatment. Future challenges of "one-two punch" approaches include how to best monitor senescence in cancer patients to effectively survey their efficacy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it