Serum Sex Steroids as Prognostic Biomarkers in Patients Receiving Androgen Deprivation Therapy for Recurrent Prostate Cancer: A <i>Post Hoc</i> Analysis of the PR.7 Trial
Bibliographic record
Abstract
Abstract Purpose: Phenotypic biomarkers are a high priority for patients receiving androgen deprivation therapy (ADT) for prostate cancer given the increasing number of treatment options. This study evaluates serum sex steroids as prognostic biomarkers in men receiving ADT for recurrent prostate cancer. Experimental Design: Retrospective cohort study of Canadian patients in the PR.7 trial (accrual 1999–2005) who received continuous ADT for biochemical recurrence postradiotherapy. Patients were excluded with follow-up <2 years or who received estrogens or corticosteroids. Kaplan–Meier and multivariable Cox regression analyses adjusted for baseline prognostic factors assessed time to castration-resistant prostate cancer (CRPC), prostate cancer survival, and overall survival according to tertile of sex steroid measured by mass spectrometry. Results: Post-ADT initiation, we measured samples in 219 patients as well as two subsequent annual samples in a subset of 101 patients. Testosterone levels correlated with androstenedione (AD) and DHT, while DHT, AD, androsterone (AST), dehydroepiandrosterone (DHEA), and androstenediol (A5diol) were highly correlated to each other and negatively associated with age. Higher tertiles of estrone (E1) and estradiol (E2) were significantly associated with sooner time to CRPC. In patients with longitudinal samples, increases in serum DHEA and AST were significantly associated with sooner time to CRPC. Limitations include the number of events for some groups. Conclusions: Our data suggest the patient hormonal milieu has long-term prognostic value in men receiving ADT for recurrent prostate cancer, including increased levels of E1 and E2 and rising DHEA and AST levels, which predict a shorter time to CRPC. Clin Cancer Res; 24(21); 5305–12. ©2018 AACR.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".