Genomic Characterization of Prostatic Ductal Adenocarcinoma Identifies a High Prevalence of DNA Repair Gene Mutations
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: Ductal prostate cancer (dPC) is a rare variant of prostatic adenocarcinoma associated with poor outcomes. Although its histopathologic features are well characterized, the underlying molecular hallmarks of this aggressive subtype are not well described. We sought to provide a comprehensive overview of the spectrum of mutations associated with dPC. METHODS: Three case series across multiple institutions were assembled. All patients had a diagnosis of dPC, and histopathologic classification was confirmed by an expert genitourinary pathologist. Case series 1 included men who were prospectively enrolled in a tumor sequencing study at the University of Washington (n = 22). Case series 2 and 3 included archival samples from men treated at Johns Hopkins Hospital (n = 21) and University of Calgary (n = 8), respectively. Tumor tissue was sequenced on a targeted next-generation sequencing assay, UW-OncoPlex, according to previously published methods. The frequency of pathogenic/likely pathogenic mutations are reported. RESULTS: Overall, 25 patients (49%) had at least one DNA damage repair gene alteration, including seven (14%) with a mismatch repair gene mutation and 16 (31%) with a homologous repair mutation. Germline autosomal dominant mutations were confirmed or suspected in 10 patients (20%). Activating mutations in the PI3K pathway (n = 19; 37%), WNT pathway (n = 16; 31%), and MAPK pathway (n = 8; 16%) were common. CONCLUSION: This study strongly suggests that dPCs are enriched for actionable mutations, with approximately 50% of patients demonstrating DNA damage repair pathway alteration(s). Patients with dPC should be offered next-generation sequencing to guide standard-of-care treatment (eg, immune checkpoint inhibitors) or triaged toward an appropriate clinical trial (eg, poly [ADP-ribose] polymerase inhibitors).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 it