Patterns of genomic loss of heterozygosity predict homologous recombination repair defects in epithelial ovarian cancer
Bibliographic record
Abstract
BACKGROUND: Defects in BRCA1, BRCA2, and other members of the homologous recombination pathway have potential therapeutic relevance when used to support agents that introduce or exploit double-stranded DNA breaks. This study examines the association between homologous recombination defects and genomic patterns of loss of heterozygosity (LOH). METHODS: Ovarian tumours from two independent data sets were characterised for defects in BRCA1, BRCA2, and RAD51C, and LOH profiles were generated. Publically available data were downloaded for a third independent data set. The same analyses were performed on 57 cancer cell lines. RESULTS: Loss of heterozygosity regions of intermediate size were observed more frequently in tumours with defective BRCA1 or BRCA2 (P=10(-11)). The homologous recombination deficiency (HRD) score was defined as the number of these regions observed in a tumour sample. The association between HRD score and BRCA deficiency was validated in two independent ovarian cancer data sets (P=10(-5) and 10(-29)), and identified breast and pancreatic cell lines with BRCA defects. CONCLUSION: The HRD score appears capable of detecting homologous recombination defects regardless of aetiology or mechanism. This score could facilitate the use of PARP inhibitors and platinum in breast, ovarian, and other cancers.
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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 | 0.001 | 0.003 |
| 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) | 0.002 | 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".