A rapid fluorescent multiplexed‐PCR analysis (FMPA) for founder mutations in the <i>BRCA</i>1 and <i>BRCA</i>2 genes
Bibliographic record
Abstract
Mutations of the BRCA1 and BRCA2 genes account for approximately 80% of hereditary breast/ovarian cancer families, but the size of these two genes makes mutation analysis time-consuming and technically challenging. In some populations such as the Ashkenazi Jewish and the French-Canadian, a small number of recurrent founder mutations account for the majority of mutations in cancer families. We have therefore developed two rapid genetic screening tests, which allow us to detect three frequent frameshift mutations in the Ashkenazi Jewish population and five frameshift mutations in the French-Canadian population. These fluorescent non-radioactive methods permit the simultaneous detection of multiple mutations by generating multiplexed PCR-amplified gene fragments, and by discriminating these on the basis of their size in a denaturing polyacrylamide gel. Using these methods, we were able to correctly identify all mutants in a blinded analysis of 276 DNA samples, including 30 derived from paraffin-embedded tumor samples and 10 from buccal-cell brushes, with no false positive or false negative results. These techniques designed for the direct detection of recurrent mutations in the BRCA1 and BRCA2 genes, have the advantages of being efficient, sensitive, cost-effective, and are applicable to large scale screening for epidemiologic studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".