A Classification Model for <i>BRCA2</i> DNA Binding Domain Missense Variants Based on Homology-Directed Repair Activity
Bibliographic record
Abstract
The relevance of many BRCA2 variants of uncertain significance (VUS) to breast cancer has not been determined due to limited genetic information from families carrying these alterations. Here, we classified six new variants as pathogenic or nonpathogenic by analysis of genetic information from families carrying 64 individual BRCA2 DNA binding domain (DBD) missense mutations using a multifactorial likelihood model of cancer causality. Next, we evaluated the use of a homology-directed DNA break repair (HDR) functional assay as a method for inferring the clinical relevance of VUS in the DBD of BRCA2 using 18 established nonpathogenic missense variants and all 13 established pathogenic missense mutations from the BRCA2 DBD. Compared with the known status of these variants based on the multifactorial likelihood model, the sensitivity of the HDR assay for pathogenic mutations was estimated at 100% [95% confidence interval (CI): 75.3%-100%] and specificity was estimated at 100% (95% CI: 81.5%-100%). A statistical classifier for predicting the probability of pathogenicity of BRCA2 DBD variants was developed using these functional results. When applied to 33 additional VUS, the classifier identified eight with 99% or more probability of nonpathogenicity and 18 with 99% or more probability of pathogenicity. Thus, in the absence of genetic evidence, a cell-based HDR assay can provide a probability of pathogenicity for all VUS in the BRCA2 DBD, suggesting that the assay can be used in combination with other information to determine the cancer relevance of BRCA2 VUS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".