Genomic biomarkers to predict outcome in Gleason Score 9-10 disease.
Bibliographic record
Abstract
44 Background: Gleason score (GS) 9-10 prostate cancer (PCa) has classically been considered the most aggressive form of clinically localized disease. However, outcomes remain heterogeneous. Whether specific biomarkers may help guide prognostication within GS 9-10 disease remains unknown. Methods: Microarray-derived gene expression data were obtained from six retrospective radical prostatectomy cohorts (n=1076) and two prospective cohorts with data from the Decipher GRID (n=7000). A total of 957 patients had GS 9-10 disease. Clinical outcomes data (i.e., distant metastasis [DM] and prostate cancer-specific mortality [PCSM]) were available for 1077 patients (201 with GS 9-10 disease). We filtered for genes with high expression levels and differential expression between GS 9-10 and GS ≤8 (via Wilcoxon test with adjustment for false discovery rate [FDR]), and then used using weighted gene co-expression network analysis [WGCNA] to identify distinct modules. Genes with both a low p-value and a high connectivity within each module were included as potential predictors in logistic regression models constructed using elastic net regularization. We also chose genes within the GS 9-10 cohort with q-value <0.01 and <0.3 after FDR correction for outcomes of DM and PCSM, respectively. We used the cross-validated AUC for quantifying the discrimination of each gene set. Results: We identified a set of 12 genes with an AUC of 0.81 for discriminating GS 9-10 vs. GS ≤8. A separate set of 7 genes had an AUC of 0.83 for predicting DM within GS 9-10 patients, compared with an AUC of 0.68 within GS ≤8 patients. A third set of 13 genes had an AUC of 0.93 for predicting PCSM within GS 9-10 patients, but an AUC of only 0.57 for predicting PCSM within GS ≤8. Conclusions: These data suggest that a genomic biomarker signature can strongly discriminate GS 9-10 from GS ≤8. Separate gene sets can also predict DM and PCSM within GS 9-10 patients with high fidelity, but are not as predictive of these outcomes within GS ≤8 patients. These data support the hypothesis that GS 9-10 disease is a biologically distinct yet heterogeneous entity, and biomarker discovery efforts to better guide upfront treatment intensification in this subset are feasible and warranted. (NCT02609269).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".