Insights into HER2 signaling from step-by-step optimization of anti-HER2 antibodies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it