Allelic loss of the loci containing the androgen synthesis gene, <i>StAR</i>, is prognostic for relapse in intermediate‐risk prostate cancer
Bibliographic record
Abstract
BACKGROUND: Androgen deprivation therapy (ADT) and novel agents targeting the androgen synthesis axis (e.g., abiraterone acetate) are adjuvant therapies that are currently, or may in the future be, combined with radiotherapy to reduce the chance of disease relapse. Little is known about allelic loss or gain pertaining to genes associated with the androgen synthesis axis and whether this is prognostic in patients who receive localized radiotherapy. In this hypothesis generating study, we conducted an array comparative genomic hybridization (aCGH) analysis of 33 androgen synthesis genes to identify potential prognostic factors for radiotherapy outcome. METHODS: aCGH analysis of tumor DNA prospectively derived from frozen needle biopsies of 126 men with intermediate-risk disease who underwent image-guided radiotherapy (IGRT) to a mean dose of 76.4 Gy was conducted. Statistical analyses were conducted for allelic loss or gain in genes as potential prognostic factors relative to prostate specific antigen, Gleason-score, and T-category. RESULTS: We observed that allelic losses of loci containing the genes StAR and HSD17B2 were associated with increased genetic instability (as determined by percentage genome alteration). On multivariate analyses these loci were prognostic for biochemical disease-free relapse (StAR: HR = 2.84, 95% CI: 1.44-5.61, P = 0.00269; HSD17B2: HR = 1.97, 95% CI: 1.06-3.64, P = 0.031). The results were validated in a surgical cohort of 131 intermediate-risk patients. CONCLUSIONS: Allelic losses of the loci containing StAR and HSD17B2 have significant prognostic value for intermediate-risk prostate cancer. With this hypothesis generating information future studies should test StAR and HSD17B2 losses as biomarkers of androgen response in combined modality protocols.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".