Clinical implications of family history of prostate cancer and genetic risk single nucleotide polymorphism ( <scp>SNP</scp> ) profiles in an active surveillance cohort
Bibliographic record
Abstract
What's known on the subject? and What does the study add? Family history ( FH ) is a major risk factor for the development of prostate cancer. The search for genetic variants has led to genome‐wide association studies ( GWAS ), which have so far reported 47 susceptibility loci that predispose men to prostate cancer. However, the use of genetics or FH status in predicting clinical outcomes after prostate cancer diagnosis remains uncertain. Guidelines currently exist for clinicians and patients summarising evidence relating to the best outcomes of different prostate cancer treatment methods. Genetics and FH could potentially add to this stratification. Our study aimed to ascertain the potential prognostic roles of FH or genetic risk scores in patients managed by active surveillance. Objectives To explore the potential prognostic role of family history ( FH ) of prostate cancer and prostate cancer risk single nucleotide polymorphisms ( SNPs ) in patients undergoing active surveillance ( AS ) for prostate cancer. This is the first study to date, which has investigated the potential prognostic role of SNP profiles in an AS cohort Patients and Methods FH data were collected from patients in the Royal Marsden Hospital AS study. In all, 39 prostate cancer‐risk SNPs identified from published genome wide association studies ( GWAS ) were genotyped using the Sequenom Platform and TaqMan™ assays from available DNA . The cumulative genetic‐risk scores for each patient were then calculated using the weighted effect estimated from previous GWAS (log‐additive model). FH status and the genetic‐risk scores were assessed against adverse outcomes in AS , time to treatment and adverse histology on repeat biopsy, using univariable and multivariable Cox regression models to address time to treatment; and binary logistic regression to address biopsy upgrade. Results Of 471 patients, 55 (13.6%) had adverse histology on repeat biopsies and 145 (30.8%) had deferred treatment. On univariate analysis, there was no significant relationship between FH of prostate cancer in any degree of relation, and adverse histology or time to treatment. For risk score analyses, 386 patients' DNA was studied; and there was also no relationship found between the calculated genetic risk scores and adverse histology or time to treatment ( P = 0.573 and P = 0.965, respectively). The retrospective study design and the few events were the main limitation of the study. Conclusions There is currently insufficient data to support the use of FH status or prostate cancer SNP profile risk scores as prognostic factors in AS and these should not be used to influence management decisions. As more genetic variants are discovered this may change and should be reassessed in multicentre AS cohorts.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".