Peptabody‐EGF: A novel apoptosis inducer targeting ErbB1 receptor overexpressing cancer cells
Bibliographic record
Abstract
The epidermal growth factor receptor (EGFR) plays a central role in cell life by controlling processes such as growth or proliferation. This receptor is commonly overexpressed in a number of epithelial malignancies and its upregulation is often associated with an aggressive phenotype of the tumor. Thus, targeting of EGFR represents a very promising challenge in oncology, and antibodies raised against this receptor have been investigated as potential antitumor agents. Various putative mechanisms of action were proposed for such antibodies, including decreased proliferation, induction of apoptosis, stimulation of the immunological response against targeted cancer cells or combinations thereof. We report here the development of an alternative high affinity molecule that is directed against EGFR. Production of this pentameric protein, named peptabody-EGF, includes expression in a bacterial expression system and subsequent refolding and multimerization of peptabody monomers. The protein complex contains 5 human EGF ligand domains, which confer specific binding towards the extracellular portion of EGFR. Receptor binding of the peptabody-EGF had a strong antiproliferative effect on different cancer cell lines overexpressing EGFR. However, cells expressing constitutive levels of the target receptor were barely affected. Peptabody-EGF treated cancer cells exhibited typical characteristics of apoptosis, which was found to be induced within 30 min after the addition of the peptabody-EGF. In vitro experiments demonstrated a significantly higher binding activity for peptabody-EGF than for the therapeutic monoclonal EGFR antibody Mab-425. Furthermore, the antitumor action provoked by the peptabody-EGF was greatly superior than antibody mediated effects when tested on EGFR overexpressing cancer cell lines. These findings suggest a potential application of this high affinity molecule as a novel tool for anti-EGFR therapy.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".