A novel biomarker signature to predict aggressive disease in African-American men with prostate cancer.
Bibliographic record
Abstract
24 Background: Numerous studies have reported a significantly higher incidence of PCa and/or adverse pathological features associated with African-American men compared to European-American men. Less however is known about the genomic disparities that exist between these two groups. In this report we compared the race-specific expression of biomarkers linked to PCa pathogenesis in a matched cohort of AA and EA men. Methods: PCa data from AA and EA patients were analyzed from four medical centers. Cases were matched based on CAPRA-S within each institution for a total sample size of 300 (121-AA; 179-EA). The distribution of mRNA expression levels of 20 validated biomarkers associated with PCa initiation and progression was compared by race using a false-discovery-rate adjusted Mann-Whitney U, and logistic regression models. Conditional logistic regression models were used to evaluate the interaction between race and biomarker expression for predicting pathologic T3 PCa. Results: Of 20 biomarkers interrogated, 6 showed statistically significant differential expression in AA compared with EA men in one or more statistical models. These include TMPRSS2-ERG (p<0.001), AMACR (p<0.001), SPINK1 (p=0.005), AR (p=0.018), SRD5A2 (p=0.005), and GSTP1 (p=0.021). Dysregulation of MYCBP (p=0.043) increases risk of pT3 disease in AA but decreases the risk in EA men, while the reverse is true for AMACR (p=0.013), TMPRSS2-ERG (p=0.026), FOXP1 (p=0.016), and GSTP1 (p=0.032). Loss-of-function mutation for tumor suppressors PTEN (p=0.046), TP53 (p=0.042), and RB1 (p=0.027), and dysregulation of AR (p=0.015), EZH2 (p=0.043), NKX3-1 (p=0.024), SRD5A2 (p=0.032), and SPOP (p=0.032) increased risk of pT3 disease for both AA and EA men. Conclusions: We have identified a subset of PCa biomarkers that predict risk of clinico-pathologic outcomes in a race-dependent manner. These biomarkers may in part explain the biological contribution to racial disparity in PCa outcomes between EA and AA men.
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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 | 0.001 |
| 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) | 0.002 | 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".