Contributions of recurrent BRCA1 mutations to ovarian cancer cases in the French Canadian population of Quebec, and characterization of BRCA1-associated genomic anomalies
Bibliographic record
Abstract
Establishing the frequency of BRCA1 mutation carriers and molecular pathways involved in ovarian cancer (OC) are important as it has been proposed that these patients might benefit from emerging chemotherapies, such as Poly (ADP-ribose) polymerase 1 (PARP) inhibitors. Here we report the frequency of BRCA1 mutations in OC cases found previously to recur in French Canadians (FC), a population exhibiting founder effects. We also report on genomic anomalies found in OC from mutation carriers. Mutation screening using PCR-based methods and/or a multiplex bead-array-based technology (Luminex) was largely limited to high-grade serous (HGSC) OCs. Approximately 5.7% (32/562) of mutation positive cases were identified in an analysis limited to the two most common mutations found in the FC population. We identified 10.7% of carriers in a more comprehensive mutation screen involving 11 alleles of a defined subcohort of 121 of the 562 cases that were ascertained over a three-year period at one Montreal-based hospital. The analysis of mutation positive tumors by single nucleotide polymorphism (SNP) BeadArray (Illumina) technology showed a high frequency of chromosomal anomalies involving copy number variants (CNV), loss of heterozygosity (LOH), and complex intrachromosomal rearrangements which overlap with genomic landscapes seen in HGSCs from the FC population observed previously by our group and others, as well as genomic patterns unique to mutation carriers. These results have confirmed the recurrence of specific mutations previously found in FC cancer families, further defined the frequency of BRCA1 mutation carriers in EOC cases, and defined genomic patterns in mutation carrier cases. These findings rationalize genetic testing of OC patients in this demographically defined population with the goal of improving health management of patients and reducing OC risk for their mutation carrier family members.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".