Mesenchyme to epithelial transition protein expression, gene copy number and clinical outcome in a large non-small cell lung cancer surgical cohort
Bibliographic record
Abstract
Background: In non-small cell lung cancer (NSCLC), mesenchyme to epithelial transition (MET) protein abundance increases with disease stage and is implicated in resistance to tyrosine kinase inhibitors. To better clarify the impact of MET overexpression on tumor behavior, we investigated a large cohort of patients who underwent curative surgical resection to determine whether MET gene amplification or protein abundance was prognostic. Methods: Tissue microarrays (TMAs) were constructed using triplicate 1 mm cores of FFPE primary NSCLC specimens. TMAs underwent immunohistochemical (IHC) staining with the SP44 clone (Ventana) and cores were considered positive if >50% of tumor exhibited 2+ staining. The highest of triplicate values was used. MET gene amplification was detected using either SISH using Ventana’s MET DNP probe or FISH using the D7S486/CEP 7 Abbott Probe. DNA was subjected to mutational profiling using Sequenom’s LungCarta panel. Results: Data from two institutions comprising 763 patients (516; 68%) male were generated, including 360 stage I, 226 stage II, 160 stage III and 18 resected stage IV. High MET protein expression was detected in 25% (193/763), and was significantly more common in adenocarcinomas than squamous cell carcinoma (P<0.01). MET gene copy number (GCN) correlated with high MET protein expression by IHC (P=0.01). Increased MET protein expression was associated with EGFR and KRAS mutations (P<0.01 for both). Once polysomy was excluded, true MET gene amplification was detected in only 8/763 (1%) of samples. In multivariate analysis, neither MET protein abundance nor GCN were correlated to overall patient survival. Conclusions: MET expression by IHC and GCN amplification was not prognostic in this large Caucasian surgical series. MET’s primary role remains as a therapeutic target.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".