Occult Tumor Burden Predicts Disease Recurrence in Lymph Node–Negative Colorectal Cancer
Bibliographic record
Abstract
PURPOSE: Lymph node involvement by histopathology informs colorectal cancer prognosis, whereas recurrence in 25% of node-negative patients suggests the presence of occult metastasis. GUCY2C (guanylyl cyclase C) is a marker of colorectal cancer cells that identifies occult nodal metastases associated with recurrence risk. Here, we defined the association of occult tumor burden, quantified by GUCY2C reverse transcriptase-PCR (RT-PCR), with outcomes in colorectal cancer. EXPERIMENTAL DESIGN: Lymph nodes (range: 2-159) from 291 prospectively enrolled node-negative colorectal cancer patients were analyzed by histopathology and GUCY2C quantitative RT-PCR. Participants were followed for a median of 24 months (range: 2-63). Time to recurrence and disease-free survival served as primary and secondary outcomes, respectively. Association of outcomes with prognostic markers, including molecular tumor burden, was estimated by recursive partitioning and Cox models. RESULTS: In this cohort, 176 (60%) patients exhibited low tumor burden (Mol(Low)), and all but four remained free of disease [recurrence rate 2.3% (95% CI, 0.1-4.5%)]. Also, 90 (31%) patients exhibited intermediate tumor burden (Mol(Int)) and 30 [33.3% (23.7-44.1)] developed recurrent disease. Furthermore, 25 (9%) patients exhibited high tumor burden (Mol(High)) and 17 [68.0% (46.5-85.1)] developed recurrent disease (P < 0.001). Occult tumor burden was an independent marker of prognosis. Mol(Int) and Mol(High) patients exhibited a graded risk of earlier time to recurrence [Mol(Int), adjusted HR 25.52 (11.08-143.18); P < 0.001; Mol(High), 65.38 (39.01-676.94); P < 0.001] and reduced disease-free survival [Mol(Int), 9.77 (6.26-87.26); P < 0.001; Mol(High), 22.97 (21.59-316.16); P < 0.001]. CONCLUSION: Molecular tumor burden in lymph nodes is independently associated with time to recurrence and disease-free survival in patients with node-negative colorectal cancer.
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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 | 0.002 |
| 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".