A TRAMP‐derived orthotopic prostate syngeneic (TOPS) cancer model for investigating anti‐tumor treatments
Bibliographic record
Abstract
BACKGROUND: Patients with advanced prostate cancer have limited curative options, therefore new treatments are needed. Mouse models play a pivotal role in the discovery and development of new treatments. In the present study, a TRAMP-derived Orthotopic Prostate Syngeneic (TOPS) mouse model was developed and found to provide a consistent means of monitoring tumor and metastatic responses to novel treatments. METHODS: The mouse TOPS model was generated using luciferase transduced TRAMP-C2 prostate cancer cells that were orthotopically injected into Bl6 mice by ultrasound guidance. Tumor growth and development was monitored using ultrasound and bioluminescence imaging. RESULTS: Tumors and metastases were consistently established and increases in tumor size correlated with increases in bioluminescence. In addition, when mice with an established tumor were castrated, tumor progression mirrored clinical progression. We further treated the TOPS model with an oncolytic Herpes Simplex virus and showed that we were able to monitor the therapeutic effect of the orthotopic tumor after virus treatment through IVIS imaging system. CONCLUSION: We have developed a powerful animal model to advance the current selection of effective treatments for patients with advanced prostate cancer.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".