Insights into HER2 signaling from step-by-step optimization of anti-HER2 antibodies
Bibliographic record
Abstract
HER2, a ligand-free tyrosine kinase receptor of the HER family, is frequently overexpressed in breast cancer. The anti-HER2 antibody trastuzumab has shown significant clinical benefits in metastatic breast cancer; however, resistance to trastuzumab is common. The development of monoclonal antibodies that have complementary mechanisms of action results in a more comprehensive blockade of ErbB2 signaling, especially HER2/HER3 signaling. Use of such antibodies may have clinical benefits if these antibodies can become widely accepted. Here, we describe a novel anti-HER2 antibody, hHERmAb-F0178C1, which was isolated from a screen of a phage display library. A step-by-step optimization method was employed to maximize the inhibitory effect of this anti-HER2 antibody. Crystallographic analysis was used to determine the three-dimensional structure to 3.5 Å resolution, confirming that the epitope of this antibody is in domain III of HER2. Moreover, this novel anti-HER2 antibody exhibits superior efficacy in blocking HER2/HER3 heterodimerization and signaling, and its use in combination with pertuzumab has a synergistic effect. Characterization of this antibody revealed the important role of a ligand binding site within domain III of HER2. The results of this study clearly indicate the unique potential of hHERmAb-F0178C1, and its complementary inhibition effect on HER2/HER3 signaling warrants its consideration as a promising clinical treatment.
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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) | 0.001 | 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".