Feasibility of ctDNA in detecting minimal residual disease and predicting recurrence for colorectal cancer liver metastases
Bibliographic record
Abstract
Introduction: Approximately 50% of patients diagnosed with colorectal cancer develop colorectal cancer liver metastases (CRLM). Although curative intent liver resection provides 5-year survival of 40-50%, up to 70% of patients develop recurrence of CRLM. Detection of minimal residual disease (MRD) is essential for timely, optimized treatment. This study evaluated the feasibility and utility of using circulating tumor DNA (ctDNA) to identify MRD and predict disease recurrence. Methods: Patients with CRLM that underwent liver resection and had known KRAS or PIK3CA mutations were retrospectively identified. Serial blood samples were collected every 3 months following surgery for disease surveillance. ctDNA was isolated from the samples and analyzed with digital PCR (dPCR). Results: KRAS and PIK3CA mutations were identified by dPCR in 29 patients over 115 timepoints. In patients with detectable ctDNA at time of liver resection, 81% (13/16) developed disease recurrence, while 46% (6/13) of the patients with undetectable ctDNA recurred (p=0.064). Presence of ctDNA was detected in 27.6% (8/29) of the initial postoperative samples. Radiologic recurrence was later diagnosed in 100% (8/8) of these patients, while 52% (11/21) who had undetectable ctDNA postoperatively recurred (p=0.026). Detectable ctDNA postoperatively was associated with a shorter disease-free survival (DFS) of 9 months vs 13 months in patients who had undetectable ctDNA (HR 2.95, 95% CI 1.16-7.49; p=0.02). Conclusion: Liquid biopsy using dPCR can identify low levels of ctDNA, enabling early detection of disease recurrence. Additionally, the presence of ctDNA postoperatively was predictive of recurrence. This study corroborates current literature and provides rational for moving toward a clinical trial using ctDNA and dPCR to detect MRD after CRLM resection.
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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 | 0.000 | 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) | 0.000 | 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".