The frequency of druggable targets in localized prostate cancer: Initial analysis from the Decipher GRID.
Bibliographic record
Abstract
98 Background: Prostate cancers patient management has been enhanced with commercially available genomic prognostic tests such as the Decipher prostate cancer classifier that are useful for making treatment decision-making. In addition to being the most validated predictor of metastasis in prostate cancer, Decipher is also a genome-wide assay that measures the expression of many druggable targets. Methods: Decipher GRID (Genomic Resource Information Database), was queried to assess the expression patterns of 14 genes from 5 biological pathways (Table) in 1,850 patients from previously published Decipher validation studies. The frequency of high (or low) expression of each gene was ascertained using a standard and more conservative thresholds based on the median absolute deviation (MAD) metric. For the standard threshold, genes whose high expression is of clinical relevance, patients with gene expression above the median + 1.48*MAD were annotated as high expression and for genes whose low expression is of clinical relevance, patients with gene expression below the median - 1.48*MAD were annotated as low expression. For the conservative threshold, median +/- 2*1.48*MAD was used. Results: See Table. Conclusions: Since every patient receiving the Decipher test also has a genome-wide expression profile, the Decipher GRID will allow researchers to evaluate on a systematic population-level the expression of genes that may be targeted with existing therapies. Such information may be useful for selection of optimal systemic therapy and inclusion into clinical trials of novel targeted agents. This rich genomic resource is being made available on a research use only basis to prostate cancer researchers and to clinicians seeking to uncover individualized genomic insights for patients to advance precision medicine. [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 | 0.003 |
| 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 | 0.001 |
| 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".