Functionally Null <i>RAD51D</i> Missense Mutation Associates Strongly with Ovarian Carcinoma
Bibliographic record
Abstract
Abstract RAD51D is a key player in DNA repair by homologous recombination (HR), and RAD51D truncating variant carriers have an increased risk for ovarian cancer. However, the contribution of nontruncating RAD51D variants to cancer predisposition remains uncertain. Using deep sequencing and case–control genotyping studies, we show that in French Canadians, the missense RAD51D variant c.620C>T;p.S207L is highly prevalent and is associated with a significantly increased risk for ovarian high-grade serous carcinoma (HGSC; 3.8% cases vs. 0.2% controls). The frequency of the p.S207L variant did not significantly differ from that of controls in breast, endometrial, pancreas, or colorectal adenocarcinomas. Functionally, we show that this mutation impairs HR by disrupting the RAD51D–XRCC2 interaction and confers PARP inhibitor sensitivity. These results highlight the importance of a functional RAD51D–XRCC2 interaction to promote HR and prevent the development of HGSC. This study identifies c.620C>T;p.S207L as the first bona fide pathogenic RAD51D missense cancer susceptibility allele and supports the use of targeted PARP-inhibitor therapies in ovarian cancer patients carrying deleterious missense RAD51D variants. Cancer Res; 77(16); 4517–29. ©2017 AACR.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".