Genetic and epigenetic profiling of BRCA1/2 in ovarian tumors reveals additive diagnostic yield and evidence of a genomic BRCA1/2 DNA methylation signature
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Bibliographic record
Abstract
Poly-ADP-ribose-polymerase inhibitor (PARPi) treatment is indicated for advanced-stage ovarian tumors with BRCA1/2 deficiency. The "BRCAness" status is thought to be attributed to a tumor phenotype associated with a specific epigenomic DNA methylation profile. Here, we examined the diagnostic impact of combined BRCA1/2 sequence, copy number, and promoter DNA methylation analysis, and evaluated whether genomic DNA methylation patterns can predict the BRCAness in ovarian tumors. DNA sequencing of 172 human tissue samples of advanced-stage ovarian adenocarcinoma identified 36 samples with a clinically significant tier 1/2 sequence variants (point mutations and in/dels) and 9 samples with a CNV causing a loss of function in BRCA1/2. DNA methylation analysis of the promoter of BRCA1/2 identified promoter hypermethylation of BRCA1 in two mutation-negative samples. Computational modeling of genome-wide methylation markers, measured using Infinium EPIC arrays, resulted in a total accuracy of 0.75, sensitivity: 0.83, specificity: 0.64, positive predictive value: 0.76, negative predictive value: 0.74, and area under the receiver's operating curve (AUC): 0.77, in classifying tumors harboring a BRCA1/2 defect from the rest. These findings indicate that the assessment of CNV and promoter DNA methylation in BRCA1/2 increases the cumulative diagnostic yield by 10%, compared with the 20% yield achieved by sequence variant analysis alone. Genomic DNA methylation data can partially predict BRCAness in ovarian tumors; however, further investigation in expanded BRCA1/2 cohorts is needed, and the effect of other double strand DNA repair gene defects in these tumors warrants further investigations.
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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 | 0.000 | 0.001 |
| 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 it