An anti-HER2 biparatopic antibody that induces unique HER2 clustering and complement-dependent cytotoxicity
Bibliographic record
Abstract
Human epidermal growth factor receptor 2 (HER2) is a receptor tyrosine kinase that plays an oncogenic role in breast, gastric and other solid tumors. However, anti-HER2 therapies are only currently approved for the treatment of breast and gastric/gastric esophageal junction cancers and treatment resistance remains a problem. Here, we engineer an anti-HER2 IgG1 bispecific, biparatopic antibody (Ab), zanidatamab, with unique and enhanced functionalities compared to both trastuzumab and the combination of trastuzumab plus pertuzumab (tras + pert). Zanidatamab binds adjacent HER2 molecules in trans and initiates distinct HER2 reorganization, as shown by polarized cell surface HER2 caps and large HER2 clusters, not observed with trastuzumab or tras + pert. Moreover, zanidatamab, but not trastuzumab nor tras + pert, elicit potent complement-dependent cytotoxicity (CDC) against high HER2-expressing tumor cells in vitro. Zanidatamab also mediates HER2 internalization and downregulation, inhibition of both cell signaling and tumor growth, antibody-dependent cellular cytotoxicity (ADCC) and phagocytosis (ADCP), and also shows superior in vivo antitumor activity compared to tras + pert in a HER2-expressing xenograft model. Collectively, we show that zanidatamab has multiple and distinct mechanisms of action derived from the structural effects of biparatopic HER2 engagement.
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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".