Identification of a Subset of Stage I Colorectal Cancer Patients With High Recurrence Risk
Bibliographic record
Abstract
BACKGROUND: A challenge in early-stage colorectal cancer (CRC) is identifying biomarkers that predict an increased risk for recurrence. A potential clinically adaptable biomarker is focal adhesion kinase (FAK), a tyrosine kinase that promotes invasion and metastasis. METHODS: An initial, single-institution, 298-patient cohort with all stages of CRC and long-term follow-up was assessed for FAK with tissue microarrays using immunohistochemistry. FAK expression was scored and dichotomized into high and low. Subsequently, a validation cohort of 517 early-stage CRCs from a separate institution was evaluated. All statistical tests were 2-sided. RESULTS: FAK overexpression did not correlate with any known histologic feature and was an early event in CRC, increasing from normal colon to stage I, and stage I to II, but not different at higher stages. High FAK was associated with decreased 10-year recurrence-free survival (RFS) among stage I patients (70.2% for high FAK vs 94.1% for low, P = .02), but not among higher stages in the initial cohort. The same finding was seen in the validation cohort (73.1% for high FAK vs 93.1% for low, P = .004). Multivariable survival analysis for stage I patients showed only two statistically significant factors predicting RFS: FAK (hazard ratio = 5.27, 95% confidence interval = 1.81 to 15.33, P = .002) and perineural invasion (hazard ratio = 7.38, 95% confidence interval = 1.01 to 53.96, P = .049). FAK was the only statistically significant factor in multivariable analysis across RFS, overall, and disease-specific survivals. CONCLUSIONS: High FAK expression identified a subset of stage I CRC patients with high incidence of recurrence and reduced survival, suggesting that FAK has important prognostic value. These patients would immediately benefit from more rigorous surveillance protocols for recurrent disease.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".