Abstract B001: Next-generation sequencing in ovarian cancer to identify actionable mutations
Bibliographic record
Abstract
Abstract Objective PARP inhibitors have demonstrated significant benefit in ovarian cancers with evidence of homologous recombination deficiency. Next-generation sequencing (NGS) testing in ovarian cancer is not currently reimbursed across Canada and real-world data is lacking. The aim of this study was to identify actionable genomic alterations beyond somatic BRCA1/2 testing using tumor/plasma NGS testing (including genomic instability scoring (GIS)). Method: This is a retrospective single tertiary center (Jewish General Hospital, Montreal, Canada) study. Tissue somatic BRCA1/2 testing was performed through our local Molecular Pathology facility. NGS testing (including GIS) was performed using the Myriad Genetics MyChoice CDx and/or FoundationOne CDx testing. The GIS score for each assay enabled identification of tumors with homologous recombination deficiency (HRD). Results: Since 2019, 170 JGH HGSOC patients have had somatic BRCA1/2 testing with a mutation rate of 24.7%. Of those patients, 89 had NGS testing identifying 23.6% with pathogenic/likely pathogenic BRCA1/2 mutation and 13.5% with non-BRCA pathogenic/likely pathogenic HR mutations. Interestingly, 5/89 (5.6%) patients had non-HR actionable gene mutations. Furthermore, 64 of the 89 cases had HRD status reported showing 10 patients (15.6%) with positive HRD scores without pathogenic/likely pathogenic HR gene mutations. Six cases received inconclusive results and 17 were not reported (total of 23/89 cases: 25.8%). Together, these data reveal missed opportunities for PARP inhibitor therapy in 13.5% (non-BRCA HR mutations) and in 15.6% (HRD without HR gene mutations) representing a total of 29.1% of patients, as well as an additional potential cases from who did not have an HRD result reported (25.8%). Conclusion: Performing NGS testing with HRD scoring beyond BRCA1/2 testing increases the proportion of patients who would benefit from targeted therapy including PARP inhibitors from 24.7% to over 50%. Citation Format: Olivia Mckeeman, Angela Tatar, Shannon Salvador, Walter Gotlieb, Susie Lau, Melica Brodeur. Next-generation sequencing in ovarian cancer to identify actionable mutations [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Ovarian Cancer Research; 2025 Sep 19-21; Denver, CO. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl):Abstract nr B001.
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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 | 0.001 |
| 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) | 0.004 | 0.001 |
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".