A Preclinical Xenograft Model Identifies Castration-Tolerant Cancer-Repopulating Cells in Localized Prostate Tumors
Bibliographic record
Abstract
A lack of clinically relevant experimental models of human prostate cancer hampers evaluation of potential therapeutic agents. Currently, androgen deprivation therapy is the gold standard treatment for advanced prostate cancer, but inevitably, a subpopulation of cancer cells survives and repopulates the tumor. Tumor cells that survive androgen withdrawal are critical therapeutic targets for more effective treatments, but current model systems cannot determine when they arise in disease progression and are unable to recapitulate variable patient response to treatment. A model system was developed in which stromal-supported xenografts from multiple patients with early-stage localized disease can be tested for response to castration. The histopathology of these xenografts mimicked the original tumors, and short-term host castration resulted in reduced proliferation and increased apoptosis in tumor cells. After 4 weeks of castration, residual populations of quiescent, stem-like tumor cells remained. Without subsequent treatment, these residual cells displayed regenerative potential, because testosterone readministration resulted in emergence of rapidly proliferating tumors. Therefore, this model may be useful for revealing potential cellular targets in prostate cancer, which exist before the onset of aggressive incurable disease. Specific eradication of these regenerative tumor cells that survive castration could then confer survival benefits for 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".