A combined FAK, c-MET, and MST1R three-protein panel risk-stratifies colorectal cancer patients
Bibliographic record
Abstract
Focal adhesion kinase (FAK) is a key tyrosine kinase downstream of c-MET (or hepatocyte growth factor receptor, HGFR) and MST1R (macrophage-stimulating protein receptor or recepteur d'origine Nantais, RON) membrane receptors. The pathway plays an important role in cancer survival and invasion. In this study, we examined the protein expression of FAK, c-MET, and MST1R levels in a well-annotated cohort of 330 colorectal cancer patients. We found FAK to be overexpressed in colorectal adenocarcinomas (p = 0.0002), and FAK levels correlated positively with phospho-FAK levels (R2 = 0.81). In comparison, MST1R levels were not significantly different, and c-MET levels were slightly higher in the normal samples. We then developed a combined 3-protein panel of FAK, c-MET, and MST1R expression signatures that can robustly risk-stratify colorectal cancer across all stages into three clusters that differ in progression-free survival. The colorectal cancer subgroup with high FAK, low c-MET, and low MST1R protein levels showed the worst progression-free survival with particularly early progression of disease (p = 0.0053). Combined FAK, c-MET, and MST1R were independently prognostic for progression-free survival in stage II colorectal cancers in a multivariate model. The 3-protein panel provides a potentially clinically attractive method for risk-stratification and adjuvant therapy guidance, especially in stage II 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) | 0.001 | 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 | 0.000 |
| 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".