Accelerated growth and local progression of radiorecurrent prostate cancer in an orthotopic bioluminescent mouse model
Bibliographic record
Abstract
Globally, prostate cancer is the second most common malignancy in males, with over 400 thousand men dying from the disease each year. A common treatment modality for localized prostate cancer is radiotherapy. However, up to half of high-risk patients can relapse with radiorecurrent prostate cancer, the aggressive clinical progression of which remains severely understudied. To address this, we have established an orthotopic mouse model for study that recapitulates the aggressive clinical progression of radiorecurrent prostate cancer. Radiorecurrent DU145 cells which survived conventional fraction (CF) irradiation were orthotopically injected into the prostates of athymic nude mice and monitored with bioluminescent imaging. CF tumours exhibited higher take rates and grew more rapidly than treatment-naïve parental tumours (PAR). Pathohistological analysis revealed extensive seminal vesicle invasion and necrosis in CF tumours, recapitulating the aggressive progression towards locally advanced disease exhibited by radiorecurrent tumours clinically. RNA sequencing of CF and PAR tumours identified ROBO1, CAV1, and CDH1 as candidate targets of radiorecurrent progression associated with biochemical relapse clinically. Together, this study presents a clinically relevant orthotopic model of radiorecurrent prostate cancer progression that will enable discovery of targets for therapeutic intervention to improve outcomes in prostate cancer 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".