Comprehensive Analysis of Missense Variations in the BRCT Domain of BRCA1 by Structural and Functional Assays
Bibliographic record
Abstract
Genetic screening of the breast and ovarian cancer susceptibility gene BRCA1 has uncovered a large number of variants of uncertain clinical significance. Here, we use biochemical and cell-based transcriptional assays to assess the structural and functional defects associated with a large set of 117 distinct BRCA1 missense variants within the essential BRCT domain of the BRCA1 protein that have been documented in individuals with a family history of breast or ovarian cancer. In the first method, we used limited proteolysis to assess the protein folding stability of each of the mutants compared with the wild-type. In the second method, we used a phosphopeptide pull-down assay to assess the ability of each of the variants to specifically interact with a peptide containing a pSer-X-X-Phe motif, a known functional target of the BRCA1 BRCT domain. Finally, we used transcriptional assays to assess the ability of each BRCT variant to act as a transcriptional activation domain in human cells. Through a correlation of the assay results with available family history and clinical data, we define limits to predict the disease risk associated with each variant. Forty-two of the variants show little effect on function and are likely to represent variants with little or no clinical significance; 50 display a clear functional effect and are likely to represent pathogenic variants; and the remaining 25 variants display intermediate activities. The excellent agreement between the structure/function effects of these mutations and available clinical data supports the notion that functional and structure information can be useful in the development of models to assess cancer risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 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".