Evaluation of a genomic classifier in primary tumor and lymph node metastases in pre- and post-radical prostatectomy tissue specimens from patients with lymph node positive prostate cancer.
Bibliographic record
Abstract
e16087 Background: Genomic Classifiers (GC) are in common use to predict outcomes for men with prostate cancer (PCA). The Decipher test is a validated genomic classifier (GC) that predicts early metastasis after radical prostatectomy (RP). We sought to evaluate whether the GC scores of pre-treatment diagnostic needle biopsy (Bx) and RP can actually predict the metastatic lesion in patients with lymph node positive (LN+) PCA. Methods: Twenty-five LN+ PCA pts who underwent RP and extended LN dissection from a single institution from 2001-2009 were identified and analyzed for genomic aberrations in Bx, prostate and LN samples. Tissue specimens for Bx, RP and LN were available for 13, 22 and 19 pts, respectively. GC results were calculated for a total of 62 patient specimens that passed quality control. Concordance of risk groups was performed using validated cut-points for low ( < 0.45), intermediate (0.45-0.6) and high ( > 0.6) GC score. PCA markers (ERG+, ETS+ or non-ETS/SPINK1+) were used to determine subtype and clonal relationship between Bx, RP and LN. Results: Pre-operatively, 91% of pts were NCCN high and very high-risk groups. Post-operatively, 77% of patients had Gleason ≥ 9 disease and 86% were pT3 or greater stage. Median GC in RP was 0.76 (IQR: 0.63 - 0.80) and 0.85 (IQR: 0.61-0.91) in LN. Median GC in Bx was 0.64 (IQR: 0.48-0.73) and the Bx with the highest Gleason grade and percent tumor cells had 86% concordance with both RP and LN GC risk. Forty-one percent were ERG+, 17% ETS+ and 39% non-ETS/SPINK1+ in prostate specimens. The clonal subtype in RP and LN tumors was the same in 83% of pts. Conclusions: In our cohort, most pts with LN metastases at RP were classified as high GC risk in Bx, RP and LN specimens. There was a high Bx to RP and LN genomic concordance of 86% in Bx specimens with highest Gleason grade and percent tumor cells. Predicting the presence of LN metastasis could be useful for accurate pre-treatment staging and optimization of radical, neo- and adjuvant therapy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".