An inherited NBN mutation is associated with poor prognosis prostate cancer
Bibliographic record
Abstract
BACKGROUND: To establish the contribution of eight founder alleles in three DNA damage repair genes (BRCA1, CHEK2 and NBS1) to prostate cancer in Poland, and to measure the impact of these variants on survival among patients. METHODS: Three thousand seven hundred fifty men with prostate cancer and 3956 cancer-free controls were genotyped for three founder alleles in BRCA1 (5382insC, 4153delA, C61G), four alleles in CHEK2 (1100delC, IVS2+1G>A, del5395, I157T), and one allele in NBS1 (657del5). RESULTS: The NBS1 mutation was detected in 53 of 3750 unselected cases compared with 23 of 3956 (0.6%) controls (odds ratio (OR)=2.5; P=0.0003). A CHEK2 mutation was seen in 383 (10.2%) unselected cases and in 228 (5.8%) controls (OR=1.9; P<0.0001). Mutation of BRCA1 (three mutations combined) was not associated with the risk of prostate cancer (OR=0.9; P=0.8). In a subgroup analysis, the 4153delA mutation was associated with early-onset (age ≤ 60 years) prostate cancer (OR=20.3, P=0.004). The mean follow-up was 54 months. Mortality was significantly worse for carriers of a NBS1 mutation than for non-carriers (HR=1.85; P=0.008). The 5-year survival for men with an NBS1 mutation was 49%, compared with 72% for mutation-negative cases. CONCLUSION: A mutation in NBS1 predisposes to aggressive prostate cancer. These data are relevant to the prospect of adapting personalised medicine to prostate cancer prevention and treatment.
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How this classification was reachedexpand
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 | 0.001 |
| 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 teacher head, 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".