Copy number alterations of <i>c‐MYC</i> and <i>PTEN</i> are prognostic factors for relapse after prostate cancer radiotherapy
Bibliographic record
Abstract
Despite the use of PSA, Gleason score, and T-category as prognosticators in intermediate-risk prostate cancer, 20-40% of patients will fail local therapy. In order to optimize treatment approaches for intermediate-risk patients, additional genetic prognosticators are needed. Previous reports using array comparative genomic hybridization (aCGH) in radical prostatectomy cohorts suggested a combination of allelic loss of the PTEN gene on 10q and allelic gain of the c-MYC gene on 8q were associated with metastatic disease. We tested whether copy number alterations (CNAs) in PTEN (allelic loss) and c-MYC (allelic gain) were associated with biochemical relapse following modern-era, image-guided radiotherapy (mean dose 76.4 Gy). We used aCGH analyses validated by fluorescence in-situ hybridization (FISH) of DNA was derived from frozen, pre-treatment biopsies in 126 intermediate-risk prostate cancer patients. Patients whose tumors had CNAs in both PTEN and c-MYC had significantly increased genetic instability (percent genome alteration; PGA) compared to tumors with normal PTEN and c-MYC status (p < 0.0001). We demonstrate that c-MYC gain alone, or combined c-MYC gain and PTEN loss, were increasingly prognostic for relapse on multivariable analyses (hazard ratios (HR) of 2.58/p = 0.005 and 3.21/p = 0.0004; respectively). Triaging patients by the use of CNAs within pre-treatment biopsies may allow for better use of systemic therapies to target sub-clinical metastases or locally recurrent disease and improve clinical outcomes.
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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 | 0.000 | 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) | 0.000 | 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".