Plasma Transforming Growth Factor α and Amphiregulin Protein Levels in NCIC Clinical Trials Group BR.21
Bibliographic record
Abstract
PURPOSE: To evaluate the prognostic and predictive significance of plasma levels of the epidermal growth factor receptor (EGFR) ligands, transforming growth factor α (TGF-α) and amphiregulin, in patients with non-small-cell lung cancer (NSCLC) enrolled in NCIC Clinical Trials Group BR.21 comparing erlotinib with placebo. PATIENTS AND METHODS: TGF-α and amphiregulin were assessed retrospectively by enzyme-linked immunosorbent assay from available prospectively collected baseline plasma samples in 565 of 731 BR.21 patients. Cutoff points were determined for both amphiregulin (low, <10 pg/mL; high, ≥10 pg/mL) and TGF-α (low, ≤12 pg/mL; high, >12 pg/mL) using a graphical method. Cox regression models were used to correlate biomarker data and baseline characteristics with outcomes including overall (OS) and progression-free survival (PFS). RESULTS: High TGF-α and amphiregulin were associated with poorer performance status (P=.06 and P<.0001, respectively) and no prior platinum therapy (P=.06 and P=.02, respectively). High amphiregulin was also associated with anemia (P=.001), increased lactate dehydrogenase (P=.03), ever-smokers (P=.04), and non-Asian ethnicity (P=.001). Patients on the placebo arm with high amphiregulin had poorer OS than patients with low amphiregulin (hazard ratio [HR]=1.88; 95% CI, 1.34 to 2.64; P=.0002), which remained significant in multivariate analysis. Amphiregulin levels did not predict for benefit from erlotinib (interaction P=.87). Conversely, TGF-α levels did not have prognostic significance, but high TGF-α predicted lack of benefit from erlotinib compared with low TGF-α (TGF-α low, OS HR=0.66; 95% CI, 0.54 to 0.81; P<.0001; high, OS HR=1.32; 95% CI, 0.73 to 2.39; P=.36; interaction P=.04). CONCLUSION: High baseline amphiregulin is a poor prognostic factor, whereas high baseline TGF-α predicts for lack of benefit from erlotinib in advanced NSCLC.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".