A decade of RAD51C/D: Germline pathogenic variants and their phenotypic landscape
Bibliographic record
Abstract
Defects in DNA repair genes have been extensively associated to cancer susceptibility. Germline pathogenic variants (GPV) in genes involved in homologous recombination repair pathway predispose to cancers arising mainly in breast and ovary, but also other tissues. The RAD51 paralogs RAD51C and RAD51D were included in this group 10 years ago, when germline variants were associated to non-BRCA1/2 familial ovarian cancer. However, whether GPVs in these genes are associated with other cancers remains unknown. Here, we have reviewed the landscape of RAD51C and RAD51D germline variants in cancer reported in the literature during the last decade, curating a total of 341 variants and the phenotypes found in families with RAD51C/D variant carriers. A comprehensive catalogue has been generated pinpointing to the existence of recurrent variants in both genes. Investigation of pedigrees found fourteen other cancer types reported more than five times in families with carriers of RAD51C/D pathogenic variants. Among those, colorectal (3.72% and 4.43%) (RAD51C/D respectively), pancreatic (1.19% and 0.86%), lung (1.27% and 2.58%), prostate (1.56% and 1.48%), and leukemia (1.56% and 1.11%) cancer were the most prevalent types. This work highlights how both genes might confer susceptibility to a broader spectrum of cancer types than ovary and breast.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".