Persistence of senescent prostate cancer cells following prolonged neoadjuvant androgen deprivation therapy
Bibliographic record
Abstract
PURPOSE: Androgen deprivation therapy (ADT) commonly leads to incomplete cell death and the fate of persistent cells involves, in part, a senescent phenotype. Senescence is terminal growth arrest in response to cell stress that is characterized by increased lysosomal-β-galactosidase (GLB1) the origin of senescence associated-β-gal activity (SA-β-gal). In the current study senescence is examined in vivo after ADT use in a neoadjuvant trial. METHODS AND MATERIALS: Tissue microarrays were generated from prostate cancer specimens (n = 126) from a multicenter neoadjuvant ADT trial. Arrays were subjected to multiplexed immunofluorescent staining for GLB1, Ki67, cleaved caspase 3 (CC3) and E-cadherin. Automated quantitative imaging was performed using Vectra™ and expression correlated with clinicopathologic features. RESULTS: Tissue was analyzed from 59 patients treated with neoadjuvant ADT and 67 receiving no therapy preoperatively. Median follow-up was 85.3 mo and median ADT treatment was 5 mo. In PC treated with neoadjuvant ADT, GLB1 expression increased in intermediate Gleason score (GS 6-7; p = 0.001), but not high grade (GS 8-10) cancer. Significantly higher levels of GLB1 were seen in tissues undergoing neoadjuvant ADT longer than 5 months compared to untreated tissues (p = 0.002). In contrast, apoptosis significantly increased earlier (1-4 mo) after ADT treatment (p<0.5). CONCLUSIONS: Increased GLB1 after neoadjuvant ADT occurs primarily among more clinically favorable intermediate grade cancers and enrichment of the phenotype occurs in a temporally prolonged fashion. Senescence may explain the persistence of PCa cells after ADT. Given concerns for the detrimental longer term presence of senescent cells, targeting these cells for removal may improve outcomes.
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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) | 0.001 | 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".