Evaluating the Utility of ctDNA in Detecting Residual Cancer and Predicting Recurrence in Patients with Serous Ovarian Cancer
Bibliographic record
Abstract
Ovarian cancer has a high case fatality rate, but patients who have no visible residual disease after surgery have a relatively good prognosis. The presence of any cancer cells left in the peritoneal cavity after treatment may precipitate a cancer recurrence. In many cases, these cells are occult and are not visible to the surgeon. Analysis of circulating tumour DNA in the blood (ctDNA) may offer a sensitive method to predict the presence of occult (non-visible) residual disease after surgery and may help predict disease recurrence. We assessed 48 women diagnosed with serous ovarian cancer (47 high-grade and 1 low-grade) for visible residual disease and for ctDNA. 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. We sequenced DNA samples for 59 breast and ovarian cancer driver genes. The plasma sample was collected after surgery and before initiating chemotherapy. We compared survival in women with no residual disease, with and without a positive plasma ctDNA test. We found tumour-specific variants (TSVs) in cancer cells from 47 patients, and these variants were sought in ctDNA in their post-surgery plasma. Fifteen (31.9%) of the 47 patients had visible residual disease; of these, all 15 had detectable ctDNA. Thirty-one patients (65.9%) had no visible residual disease; of these, 24 (77.4%) patients had detectable ctDNA. Of the patients with no visible residual disease, those patients with detectable ctDNA had higher mortality (20 of 27 died) than those without detectable ctDNA (3 of 7 died) (HR 2.32; 95% CI: 0.67–8.05), although this difference was not statistically significant (p = 0.18). ctDNA in post-surgical serum samples may predict the presence of microscopic residual disease and may be a 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 teacher head, 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".