The role of advanced genetic testing in the management of prostate cancer post radical prostatectomy.
Bibliographic record
Abstract
5089 Background: The genomic classifier (Decipher, GC) is a prospectively validated assay that predicts clinical metastasis post radical prostatectomy (RP) more accurately than standard clinicopathologic factors. While over 70% of high risk patients tested in a previous validation had low GC scores and good prognosis, patients with high GC scores had a cumulative incidence of metastasis over 25% over the study duration. Among men diagnosed with localized prostate cancer, these most at risk patients may derive the greatest benefit from novel therapies. We thus examined differential expression (DE) of druggable genes that may be targeted in this group. Methods: High-density microarray expression profiles of primary FFPE tumor specimens from 764 men treated with RP at the Mayo Clinic (1987-2006) were evaluated. A subset of 323 patients was flagged as high risk of clinical metastasis (mets) by virtue of having GC score ≥ 0.4. Enrichment and identification of DE genes as druggable targets were pursued using DAVID and DrugBank. Results: Median follow-up of patients was 15.1 years. Among the 323 patients with high GC scores, 62% had mets during follow-up.We identified 2,262 genes DE between mets and non-mets, 230 of which are associated with 331 approved pharmaceuticals and 547 experimental agents. These agents included multiple established anti-neoplastic therapies not currently used to treat prostate cancer such as bortezomib, capecitabine, dasatinib, etoposide, gemcitabine, imatinib, irinotecan, pemetrexed and vinblastine. The two most enriched pathways, spliceosome and ubiquitin-mediated proteolysis, have been proposed previously as therapeutic targets in cancer. Conclusions: Advanced genomic testing that includes validated molecular risk scores as well as transcriptome profiling from a single assay may better enable application of directed, multimodal therapy for individual patients with high risk prostate cancer.
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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 | 0.002 |
| 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 | 0.001 | 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".