Modeling Androgen Deprivation Therapy–Induced Prostate Cancer Dormancy and Its Clinical Implications
Bibliographic record
Abstract
Treatment-induced tumor dormancy is a state in cancer progression where residual disease is present but remains asymptomatic. Dormant cancer cells are treatment-resistant and responsible for cancer recurrence and metastasis. Prostate cancer treated with androgen-deprivation therapy (ADT) often enters a dormant state. ADT-induced prostate cancer dormancy remains poorly understood due to the challenge in acquiring clinical dormant prostate cancer cells and the lack of representative models. In this study, we aimed to develop clinically relevant models for studying ADT-induced prostate cancer dormancy. Dormant prostate cancer models were established by castrating mice bearing patient-derived xenografts (PDX) of hormonal naïve or sensitive prostate cancer. Dormancy status and tumor relapse were monitored and evaluated. Paired pre- and postcastration (dormant) PDX tissues were subjected to morphologic and transcriptome profiling analyses. As a result, we established eleven ADT-induced dormant prostate cancer models that closely mimicked the clinical courses of ADT-treated prostate cancer. We identified two ADT-induced dormancy subtypes that differed in morphology, gene expression, and relapse rates. We discovered transcriptomic differences in precastration PDXs that predisposed the dormancy response to ADT. We further developed a dormancy subtype-based, predisposed gene signature that was significantly associated with ADT response in hormonal naïve prostate cancer and clinical outcome in castration-resistant prostate cancer treated with ADT or androgen-receptor pathway inhibitors. IMPLICATIONS: We have established highly clinically relevant PDXs of ADT-induced dormant prostate cancer and identified two dormancy subtypes, leading to the development of a novel predicative gene signature that allows robust risk stratification of patients with prostate cancer to ADT or androgen-receptor pathway inhibitors.
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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".