Timing Is Everything: Preclinical Evidence Supporting Simultaneous Rather Than Sequential Chemohormonal Therapy for Prostate Cancer
Bibliographic record
Abstract
PURPOSE: Androgen ablation is the mainstay of systemic therapy for prostate cancer, with cytotoxic therapies reserved for hormone-refractory disease. It is not clear, however, that this is the most appropriate sequence of interventions for this disease. This study addresses the ideal timing of systemic treatments in the Shionogi and LNCaP xenograft models. We explored the hypothesis that stress-induced gene expression changes after chemotherapy can induce a hormone-independent phenotype. EXPERIMENTAL DESIGN: Three groups of mice bearing either Shionogi or LNCaP xenografts were treated with (a) initial castration and delayed paclitaxel, (b) initial paclitaxel and delayed castration, or (c) simultaneous castration plus paclitaxel. End points were time to tumor progression and time to sacrifice. Microarray and reverse transcription-PCR analyses were carried out to assess changes in gene expression induced by paclitaxel. RESULTS: Mice receiving simultaneous therapy showed a significant improvement in median time to progression (TTP: Shionogi, 65 versus 38 days, P = 0.004; LNCaP, 105 versus 70 days, P = 0.032) and time to sacrifice (Shionogi, 83 versus 66 days, P < 0.014) versus best sequential therapy. A marked lack of response to castration was observed after initial paclitaxel therapy. Gene expression and reverse transcription-PCR studies confirmed that several genes known to play a role in androgen independence were up-regulated in response to paclitaxel exposure. CONCLUSIONS: In laboratory models of prostate cancer, simultaneous androgen deprivation plus paclitaxel is more effective than sequential treatments. These findings provide preclinical proof-of-principle for ongoing clinical trials addressing the role and timing of systemic therapies in 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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".