The diverse genomic landscape of low-risk prostate cancer.
Bibliographic record
Abstract
72 Background: Active surveillance (AS) is becoming standard of care for men with low-risk prostate cancer; however a need exists for better tools to assess which men are optimal candidates for AS. In this study we compare genomic expression profiles of AS candidates against higher-risk radical prostatectomy (RP) patients to characterize the genomics of clinically low-risk prostate cancer. Methods: Biopsies from 473 UCSF patients potentially suitable for AS (stage ≤ cT2N0M0, PSA ≤ 10 ng/ml, Gleason 3+3 or low-volume 3+4 ) were profiled using the Affymetrix HuEx microarray to generate RNA expression data. These cases were compared to 2043 RP cases previously profiled on the same microarray platform. Scores for 21 published prognostic signatures were calculated and pathway associated genes were summarized to provide levels of patient risk and pathway activity. Results: Of the 473 AS biopsies profiled, 408 (86%) passed quality control and were used for analysis. Based on the quartiles of average scores for 21 prognostic signature risk models, 49%, 36%, 11%, and 4%, respectively, were classified into the 1st, 2nd, 3rd, or 4th score quartiles. Considering only the clinically low-risk patients at diagnosis, 356 (87%) were low, 45 (11%) were intermediate and 7 (2%) were high risk. Genomic risk was positively associated with cell cycle related pathways (p < 0.001) and negatively associated with apical junction (p < 0.001), epithelial−mesenchymal transition (p < 0.001), and androgen receptor (p < 0.05) pathways. Clustering of patients based on the expression of 36 pathways revealed two biologic groups corresponding to putative basal and luminal subtypes. Compared to higher risk RP patients, the low risk prostate cancer tumors at diagnosis were enriched for basal-like tumors (20% vs 33%, p < 0.001). Conclusions: Although only 2% of low risk AS candidates have high risk genomic characteristics, very substantial genomic heterogeneity exists in this population, and pathway activation overlaps significantly with higher-risk RP patients. These results suggest that even in potential AS candidates, genomic profiling could eventually be used to better guide management.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".