Blood-based biomarkers for detecting aggressive prostate cancer at time of biopsy
Bibliographic record
Abstract
4637 Background: To date, we have applied our unique methodology (the Sentinel Principle) to identify blood-based gene expressed biomarkers for several diseases including osteoarthritis, bladder cancer and psychiatric disorders. It is well known that new biomarkers for prostate cancer detection are needed, particularly aggressive forms of prostate cancer. Our objective was to identify gene expression signatures and to characterize a set of biomarkers from whole blood to identify patients with aggressive forms of prostate cancer at the time of prostate biopsy. Methods: We conducted a two staged study. The first stage was for gene discovery using microarrays (Affymetrix U133Plus2.0 GeneChips) among 47 patients (all Caucasian) who underwent a prostate biopsy for prostate cancer. We included 16 patients (cases) diagnosed with aggressive forms of prostate cancer defined as having a histologic grade of Gleason Score 7 or more and 31 patients with no evidence of cancer at biopsy (controls). The second stage was a validation study (108 patients) using real-time RT-PCR methods. Logistic regression was used to assess the ability of linear combinations of specific transcripts to distinguish cancers from controls. Results: In the first stage, we identified 1661 probes that were significantly different in blood gene expression profiles between cases and controls (p<0.05). In the second stage, real-time RT-PCR assays validated four genes among 33 cases and 75 controls: three up-regulated genes: A 1.4 fold, p<0.001, B 1.2 fold, p=0.03, C 1.3 fold, p=0.08; and one down-regulated gene D 0.70, p=0.008. Linear combination of these 4 genes discriminated the cases from controls with a sensitivity of 90% and specificity of 53% (AUC: 0.80, 95% C.I.: 0.713∼0.871). The performance of PSA (4 ng/ml cut-off) in the same 108 sample set was 91% sensitivity and 13% specificity (AUC: 0.67, 95% CI: 0.75∼0.98). The performance of combining the four blood biomarkers with PSA yielded an AUC of 0.86 (95% C.I.: 0.77∼0.91) with a sensitivity 90% and specificity 70%. Conclusions: When used with PSA, these four blood-based biomarkers improved the overall accuracy to identify patients with prostate cancer at biopsy. These results would have important implications for new biomarkers discovery for prostate cancer detection. [Table: see text]
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".