Sequence Variant Discovery in DNA Repair Genes from Radiosensitive and Radiotolerant Prostate Brachytherapy Patients
Bibliographic record
Abstract
PURPOSE: The presence of intrinsic radiosensitivity within prostate cancer patients may be an important factor contributing to development of radiation toxicity. We investigated whether variants in genes responsible for detecting and repairing DNA damage independently contribute to toxicity following prostate brachytherapy. EXPERIMENTAL DESIGN: Genomic DNA was extracted from blood samples of 41 prostate brachytherapy patients, 21 with high and 20 with low late toxicity scores. For each patient, 242 PCR amplicons were generated containing 173 exons of eight candidate genes: ATM, BRCA1, ERCC2, H2AFX, LIG4, MDC1, MRE11A, and RAD50. These amplicons were sequenced and all sequence variants were subjected to statistical analysis to identify those associated with late radiation toxicity. RESULTS: Across 41 patients, 239 sites differed from the human genome reference sequence; 170 of these corresponded to known polymorphisms. Sixty variants, 14 of them novel, affected protein coding regions and 43 of these were missense mutations. In our patient population, the high toxicity group was enriched for individuals with at least one LIG4 coding variant (P = 0.028). One synonymous variant in MDC1, rs28986317, was associated with increased radiosensitivity (P = 0.048). A missense variant in ATM, rs1800057, associated with increased prostate cancer risk, was found exclusively in two high toxicity patients but did not reach statistical significance for association with radiosensitivity (P = 0.488). CONCLUSIONS: Our data revealed new germ-line sequence variants, indicating that existing sequence databases do not fully represent the full extent of sequence variation. Variants in three DNA repair genes were linked to increased radiosensitivity but require validation in larger populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".