A personalised approach to prostate cancer screening based on genotyping of risk founder alleles
Bibliographic record
Abstract
BACKGROUND: To evaluate whether genotyping for 18 prostate cancer founder variants is helpful in identifying high-risk individuals and for determining optimal screening regimens. METHODS: A serum PSA level was measured and a digital rectal examination (DRE) was performed on 2907 unaffected men aged 40-90. Three hundred and twenty-three men with an elevated PSA (≥4 ng ml⁻¹) or an abnormal DRE underwent a prostate biopsy. All men were genotyped for three founder alleles in BRCA1 (5382insC, 4153delA and C61G), for four alleles in CHEK2 (1100delC, IVS2+1G>A, del5395 and I157T), for one allele in NBS1 (657del5), for one allele in HOXB13 (G84E), and for nine low-risk single-nucleotide polymorphisms (SNPs). RESULTS: On the basis of an elevated PSA or an abnormal DRE, prostate cancer was diagnosed in 135 of 2907 men (4.6%). In men with a CHEK2 missense mutation I157T, the cancer detection rate among men with an elevated PSA or an abnormal DRE was much higher (10.2%, P=0.0008). The cancer detection rate rose with the number of SNP risk genotypes observed from 1.2% for men with no variant to 8.6% for men who carried six or more variants (P=0.04). No single variant was helpful on its own in predicting the presence of prostate cancer, however, the combination of all rare mutations and SNPs improved predictive power (area under the curve=0.59; P=0.03). CONCLUSION: These results suggest that testing for germline CHEK2 mutations improves the ability to predict the presence of prostate cancer in screened men, however, the clinical utility of incorporating DNA variants in the screening process is marginal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".