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 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 | 0.001 |
| 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".