GC1118, an Anti-EGFR Antibody with a Distinct Binding Epitope and Superior Inhibitory Activity against High-Affinity EGFR Ligands
Bibliographic record
Abstract
The EGFR-targeted monoclonal antibodies are a valid therapeutic strategy for patients with metastatic colorectal cancer (mCRC). However, only a small subset of mCRC patients has therapeutic benefits and there are high demands for EGFR therapeutics with a broader patient pool and more potent efficacy. In this study, we report GC1118 exhibiting a different character in terms of binding epitope, affinity, mode of action, and efficacy from other anti-EGFR antibodies. Structural analysis of the EGFR-GC1118 crystal complex revealed that GC1118 recognizes linear, discrete N-terminal epitopes of domain III of EGFR, critical for EGF binding but not overlapping with those of other EGFR-targeted antibodies. GC1118 exhibited superior inhibitory activity against high-affinity EGFR ligands in terms of EGFR binding, triggering EGFR signaling, and proliferation compared with cetuximab and panitumumab. EGFR signaling driven by low-affinity ligands, on the contrary, was well inhibited by all the antibodies tested. GC1118 demonstrated robust antitumor activity in tumor xenografts with elevated expression of high-affinity ligands in vivo, whereas cetuximab did not. Considering the significant role of high-affinity EGFR ligands in modulating tumor microenvironment and inducing resistance to various cancer therapeutics, our study suggests a potential therapeutic advantage of GC1118 in terms of efficacy and a range of benefited patient pool. Mol Cancer Ther; 15(2); 251-63. ©2015 AACR.
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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 | 0.001 |
| 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".