Dose-Dependent Increases in Circulating TGF-α and Other EGFR Ligands Act As Pharmacodynamic Markers for Optimal Biological Dosing of Cetuximab and Are Tumor Independent
Bibliographic record
Abstract
PURPOSE: The objective of this study was to characterize treatment-induced circulating ligand changes during therapy with epidermal growth factor receptor (EGFR) inhibitors and evaluate their potential as surrogate indicators of the optimal biological dose. EXPERIMENTAL DESIGN: Conditioned medium from human tumor cell lines, ascites fluid from tumor xenografts, and plasma samples from normal mice, as well as colorectal cancer patients, were assessed for ligand elevations using ELISA, following treatment with cetuximab (Erbitux), an anti-mouse EGFR neutralizing antibody, or a small-molecule EGFR tyrosine kinase inhibitor. RESULTS: A rapid elevation in human transforming growth factor alpha (TGF-alpha) was observed in all cell lines after treatment with cetuximab, but not with small-molecule inhibitors. The elevation showed a dose-response effect and plateau that corresponded to the maximal decrease in A431 proliferation in vitro and HT29 tumor growth in vivo. The TGF-alpha increase was exacerbated by ongoing ligand production and cleavage from the plasma membrane but did not involve transcriptional up-regulation of TGF-alpha or the matrix metalloproteinase tumor necrosis factor-alpha-converting enzyme/ADAM17. Elevations in plasma TGF-alpha, amphiregulin, and epiregulin were also detected in normal mice treated with an anti-mouse EGFR monoclonal antibody, illustrating a host tissue-dependent component of this effect in vivo. Finally, circulating TGF-alpha increased in the plasma of six patients with EGFR-negative colorectal tumors during cetuximab treatment. CONCLUSIONS: Treatment-induced increases in circulating ligands, particularly TGF-alpha, should be serially assessed in clinical trials of anti-EGFR therapeutic antibodies as potential biomarkers to aid in determination of the optimal biological dose.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".