Mutations in <scp><i>ATM</i></scp>, <scp><i>NBN</i></scp> and <scp><i>BRCA2</i></scp> predispose to aggressive prostate cancer in Poland
Bibliographic record
Abstract
In designing national strategies for genetic testing, it is important to define the full spectrum of pathogenic mutations in prostate cancer (PCa) susceptibility genes. To investigate the frequency of mutations in PCa susceptibility genes in Polish familial PCa cases and to estimate gene-related PCa risks and probability of aggressive disease, we analyzed the coding regions of 14 genes by exome sequencing in 390 men with familial prostate cancer and 308 cancer-free controls. We compared the mutation frequencies between PCa cases and controls. We also compared clinical characteristics of prostate cancers between mutation carriers and noncarriers. Of the 390 PCa cases, 76 men (19.5%) carried a mutation in BRCA1, BRCA2, NBN, ATM, CHEK2, HOXB13, MSH2 or MSH6 genes. No mutations were found in BRIP1, PTEN, TP53, MLH1, PMS2 and SPOP. Significant associations with familial PCa risk were observed for CHEK2, NBN, ATM, and HOXB13. High-grade (Gleason 8-10) tumors were seen in 56% of BRCA2, NBN or ATM carriers, compared to 21% of patients who tested negative for mutations in these genes (OR = 4.7, 95% CI 2.0-10.7, P = .0003). In summary, approximately 20% of familial prostate cancer cases in Poland can be attributed to mutations in eight susceptibility genes. Carriers of mutations in BRCA2, NBN and ATM develop aggressive disease and may benefit from intensified screening and/or chemotherapy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".