Detection of Molecular Residual Disease by Circulating Tumor DNA in Early-Stage Node-Negative Rectal Cancers (CCTG CO.28) Using a Tumor-Informed Assay
Bibliographic record
Abstract
PURPOSE There is increasing interest in organ preservation for early-stage rectal cancer. Circulating tumor DNA (ctDNA) for detection of molecular residual disease may aid in clinical decision making for these approaches. METHODS The Canadian Cancer Trials Group (CCTG) CO.28 NEO trial was a phase II study of 58 patients with early-stage, clinically node-negative rectal cancers exploring the impact of 3 months of neoadjuvant infusional fluorouracil, leucovorin, and oxaliplatin or capecitabine and oxaliplatin chemotherapy followed by transanal excision surgery (TES) in patients with treatment-responsive tumors. Patients without a clinical response were recommended total mesorectal excision (TME). A total of 52 patients had blood available for retrospective ctDNA analysis with the tumor-informed ctDNA assay FoundationOneTracker. Blood samples were taken prechemotherapy, postchemotherapy but before TES and/or TME, on surveillance (months 12, 24, and 46), and at progression. RESULTS ctDNA was detected in 24.5% of patients before chemotherapy and detection increased with clinical T stage ( P = .022). The ctDNA detection rate decreased to 6.7% of patients postchemotherapy but pre-TES ( P = .024). Two thirds of patients with ctDNA detected after neoadjuvant chemotherapy (66.7%) had been recommended TME based on lack of clinical response or TES pathology. Of patients (n = 3) with a sample taken at the time of progression, 33.3% (n = 1) had detectable ctDNA; however, no surveillance samples had ctDNA detected for any patient who had a clinical recurrence. CONCLUSION Early-stage node-negative rectal cancers shed ctDNA that can be detected in a subset of patients, particularly those with a higher clinical stage. This offers a potential future decision aid to augment endoscopy and MRI, particularly in cases of clinical uncertainty. More data in larger studies with denser surveillance sampling are needed to support ctDNA integration into clinical decision making for organ preservation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".