Data from Allelic Transcripts Dosage Effect in Morphologically Normal Ovarian Cells from Heterozygous Carriers of a <i>BRCA1</i>/<i>2</i> French Canadian Founder Mutation
Bibliographic record
Abstract
Abstract We hypothesized that the transcriptome of primary cultures of morphologically normal ovarian surface epithelial cells could be altered by the presence of a heterozygous BRCA1 or BRCA2 mutation. We aimed to discover early events associated with ovarian carcinogenesis, which could represent putative targets for preventive strategies of this silent killer tumor. We identified the first molecular signature associated with French Canadian BRCA1 or BRCA2 founder mutations in morphologically normal ovarian epithelial cells. We discovered that wild-type and mutated BRCA2 allelic transcripts were expressed not only in morphologically normal but also in tumor cells from BRCA2-8765delAG carriers. Further analysis of morphologically normal ovarian and tumor cells from BRCA1-4446C>T carriers lead to the same observation. Our data support the idea that one single hit in BRCA1 or BRCA2 is sufficient to alter the transcriptome of phenotypically normal ovarian epithelial cells. The highest level of BRCA2-mutated allele transcript expression was measured in cells originating from the most aggressive ovarian tumor. The penetrance of the mutation and the aggressiveness of the related tumor could depend on a dosage effect of the mutated allele transcript. Cancer Prev Res; 5(5); 765–77. ©2012 AACR.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".