Abstract 1988: Detection of minimal residual disease using cell free plasma DNA to guide maintenance therapy in high grade serous ovarian cancer
Bibliographic record
Abstract
Purpose: Optimal maintenance strategy following re-challenge with platinum-based chemotherapy in patients with relapsed high grade serous ovarian cancer (HGSOC) after PARP inhibitor (PARPi) therapy remains to be established. Use of cell-free DNA (cfDNA) sequencing to detect minimal residual disease (MRD) has emerged as a promising strategy to identify patients at higher risk of relapse who might benefit from more timely, additional treatment. Methods: Nineteen patients with histologically confirmed TP53 mutant HGSOC with platinum sensitive relapse were enrolled into an interventional cfDNA study selecting for patients with confirmed MRD (NCT04510584). Each patient provided a blood sample at the end of standard-of-care platinum treatment (pre or post cycle 6) from which cfDNA was analyzed using targeted sequencing (10,000X coverage) using a 10 gene panel. As a comparator, 30X cell-free whole genome sequencing (cfWGS) was performed for tumour-informed genome-wide mutational sampling benchmarked against 13 platinum-sensitive HGSOC patients and 25 healthy controls. Results: The targeted panel approach detected a somatic TP53 mutation in 1/19 patients above the limit of detection of 1%. In contrast, 30X cfWGS identified evidence of MRD in 8/19 patients (47%) with detection of 319-2,327 known somatic mutations sampled genome-wide (including single-read evidence for 4 somatic TP53 mutations). Genome-wide fragmentomics approaches further identified MRD in 17/19 platinum-resistant patients (89%). Conclusions: cfWGS can be used to examine numerous features capable of identifying patients likely to experience rapid relapse of high grade serous ovarian cancer. Citation Format: Pamela Soberanis Pina, Stephenie D. Prokopec (co-primary author), Eduardo Gonzalez Ochoa, Derek Wong, Tracy Stockley, Amit Oza, Neesha C. Dhani, Robert C. Grant, Nitthusha Singaravelan, Alexander Fortuna, Bernard Lam, Ilinca Lungu, Madhuran Thiagarajah, Faridah Mbabaali, Sharanjit Singh, Danny Xie, Steven Siman, Judy Quintos, Valerie Bowering, Lisa Wang, Stephanie Lheureux (co-corresponding author), Trevor Pugh (co-corresponding author). Detection of minimal residual disease using cell free plasma DNA to guide maintenance therapy in high grade serous ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1988.
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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) | 0.001 | 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".