Genomic Gain of 16p13.3 in Prostate Cancer Predicts Poor Clinical Outcome after Surgical Intervention
Bibliographic record
Abstract
Abstract Identifying tumors with high metastatic potential is key to improving the clinical management of prostate cancer. Recently, we characterized a chromosome 16p13.3 gain frequently observed in prostate cancer metastases and now demonstrate the prognostic value of this genomic alteration in surgically treated prostate cancer. Dual-color FISH was used to detect 16p13.3 gain on a human tissue microarray representing 304 primary radical prostatectomy (RP) cases with clinical follow-up data. The results were validated in an external dataset. The 16p13.3 gain was detected in 42% (113/267) of the specimens scorable by FISH and was significantly associated with clinicopathologic features of aggressive prostate cancer, including high preoperative PSA (P = 0.03) levels, high Gleason score (GS, P < 0.0001), advanced pathologic tumor stage (P < 0.0001), and positive surgical margins (P = 0.009). The 16p13.3 gain predicted biochemical recurrence (BCR) in the overall cohort (log-rank P = 0.0005), and in subsets of patients with PSA ≤10 or GS ≤7 (log-rank P = 0.02 and P = 0.006, respectively). Moreover, combining the 16p13.3 gain status with standard prognostic markers improved BCR risk stratification and identified a subgroup of patients with high probability of recurrence. The 16p13.3 gain status was also associated with an increased risk of developing distant metastases (log-rank P = 0.03) further substantiating its role in prostate cancer progression. Implications: This study demonstrates the prognostic significance of the 16p13.3 genomic gain in primary prostate tumors, suggesting potential utility in the clinical management of the disease by identifying patients at high risk of recurrence who may benefit from adjuvant therapies. Mol Cancer Res; 16(1); 115–23. ©2017 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".