Evaluating the utility of ctDNA in detecting residual cancer and predicting recurrence in patients with serous ovarian cancer.
Bibliographic record
Abstract
e17588 Background: Ovarian cancer remains the most fatal gynecological malignancy. Patients who have no visible residual disease after surgical resection have a relatively good prognosis. Among these patients, those who later succumb to their cancer are believed to harbour non-detectable cancer cells in the peritoneal cavity after treatment which later lead to recurrence. Analyzing circulating tumour DNA (ctDNA) in the blood may offer a sensitive method to detect occult (non-visible) residual disease after surgery and to predict disease recurrence. We proposed to determine the proportion of ovarian cancer patients with and without visible residual disease documented after surgery who had detectable ctDNA from their primary tumour in their blood and to evaluate if the presence of ctDNA is associated with survival. Methods: We included biological samples and clinical information from 48 women diagnosed with high-grade serous ovarian cancer. Plasma, formalin-fixed paraffin-embedded (FFPE) tumour tissue, and white blood cells were used to extract circulating free DNA (cfDNA), tumour DNA and germline DNA, respectively. The plasma sample was collected after surgery and before initiating chemotherapy. We sequenced DNA samples for a panel of 59 breast and ovarian cancer driver genes. DNA variants in matched germline and tumour DNAs were compared to determine tumour specific variants (TSVs) and cfDNA was searched for TSVs to identify the presence of ctDNA in the plasma. The Kaplan-Meier method was used to estimate overall and recurrence-free survival according to the presence or absence of ctDNA. Results: We found TSVs in 47 patients that were used for detecting ctDNA in their post-surgery plasma. Fifteen (31.9%) of the 47 patients had visible residual disease; of these, all 15 had detectable ctDNA. Among the 31 (68.1%) pateints with no visible residual disease, 24 (77.4%) patients had detectable ctDNA. Of those with no visible residual disease, those patients with detectable ctDNA in post-surgery samples had a higher mortality risk compared to those without detectable ctDNA (HR 2.32; 95% CI: 0.67-8.05), although this difference was not statistically significant (p = 0.18). Conclusions: These findings suggest potential clinical utility in ctDNA to improve upon surgical classification of the residual disease status and a potential predictor of recurrence among women with ovarian cancer. Larger studies are necessary to validate these findings.
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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) | 0.001 | 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".