Genetic and epigenetic profiling of BRCA1/2 in ovarian tumors reveals additive diagnostic yield and evidence of a genomic BRCA1/2 DNA methylation signature
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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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 | 0.001 |
| 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 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".