High Fidelity Patient-Derived Xenografts for Accelerating Prostate Cancer Discovery and Drug Development
Bibliographic record
Abstract
Standardized and reproducible preclinical models that recapitulate the dynamics of prostate cancer are urgently needed. We established a bank of transplantable patient-derived prostate cancer xenografts that capture the biologic and molecular heterogeneity currently confounding prognostication and therapy development. Xenografts preserved the histopathology, genome architecture, and global gene expression of donor tumors. Moreover, their aggressiveness matched patient observations, and their response to androgen withdrawal correlated with tumor subtype. The panel includes the first xenografts generated from needle biopsy tissue obtained at diagnosis. This advance was exploited to generate independent xenografts from different sites of a primary site, enabling functional dissection of tumor heterogeneity. Prolonged exposure of adenocarcinoma xenografts to androgen withdrawal led to castration-resistant prostate cancer, including the first-in-field model of complete transdifferentiation into lethal neuroendocrine prostate cancer. Further analysis of this model supports the hypothesis that neuroendocrine prostate cancer can evolve directly from adenocarcinoma via an adaptive response and yielded a set of genes potentially involved in neuroendocrine transdifferentiation. We predict that these next-generation models will be transformative for advancing mechanistic understanding of disease progression, response to therapy, and personalized oncology.
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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 | 0.002 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".