An international initiative to identify genetic modifiers of cancer risk in BRCA1 and BRCA2 mutation carriers: the Consortium of Investigators of Modifiers of BRCA1 and BRCA2 (CIMBA)
Bibliographic record
Abstract
BRCA1 and BRCA2 mutations exhibit variable penetrance that is likely to be accounted for, in part, by other genetic factors among carriers. However, studies aimed at identifying these factors have been limited in size and statistical power, and have yet to identify any convincingly validated modifiers of the BRCA1 and BRCA2 phenotype. To generate sufficient statistical power to identify modifier genes, the Consortium of Investigators of Modifiers of BRCA1 and BRCA2 (CIMBA) has been established. CIMBA contains about 30 affiliated groups who together have collected DNA and clinical data from approximately 10,000 BRCA1 and 5,000 BRCA2 mutation carriers. Initial efforts by CIMBA to identify modifiers of breast cancer risk for BRCA1 and BRCA2 mutation carriers have focused on validation of common genetic variants previously associated with risk in smaller studies of carriers or unselected breast cancers. Future studies will involve replication of findings from pathway-based and genome-wide association studies in both unselected and familial breast cancer. The identification of genetic modifiers of breast cancer risk for BRCA1 and BRCA2 mutation carriers will lead to an improved understanding of breast cancer and may prove useful for the determination of individualized risk of cancer amongst carriers.
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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 | 0.026 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".