Molecular Testing for Lymph Node Metastases as a Determinant of Colon Cancer Recurrence: Results from a Retrospective Multicenter Study
Bibliographic record
Abstract
PURPOSE: Recurrence risk assessment to make treatment decisions for early-stage colon cancer patients is a major unmet medical need. The aim of this retrospective multicenter study was to evaluate the clinical utility of guanylyl cyclase C (GCC) mRNA levels in lymph nodes on colon cancer recurrence. METHODS: The proportion of lymph nodes with GCC-positive mRNA (LNR) was evaluated in 463 untreated T3N0 patients, blinded to clinical outcomes. One site's (n = 97) tissue grossing method precluded appropriate lymph node assessment resulting in post hoc exclusion. Cox regression models tested the relationship between GCC and the primary endpoint of time to recurrence. Assay methods, primary analyses, and cut points were all prespecified. RESULTS: Final dataset contained 366 patients, 38 (10%) of whom had recurrence. Presence of four or more GCC-positive lymph nodes was significantly associated with risk of recurrence [hazard ratio (HR) = 2.46, 95% confidence interval (CI), 1.07-5.69, P = 0.035], whereas binary GCC LNR risk class (HR = 1.87, 95% CI, 0.99-3.54, P = 0.054) and mismatch repair (MMR) status (HR = 0.77, 95% CI, 0.36-1.62, P = 0.49) were not. In a secondary analysis using a 3-level GCC LNR risk group classification of high (LNR > 0.20), intermediate (0.10 < LNR ≤ 0.20), and low (LNR ≤ 0.10), high-risk patients had a 2.5 times higher recurrence risk compared with low-risk patients (HR = 2.53, 95% CI, 1.24-5.17, P = 0.011). CONCLUSIONS: GCC status is a promising prognostic factor independent of traditional histopathology risk factors in a contemporary population of patients with stage IIa colon cancer not treated with adjuvant therapy, but GCC determination must be performed with methodology adapted to the tissue procurement and fixation technique.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".