Abstract 1032: Super-resolution imaging studies of zanidatamab: Providing insights into its bispecific mode of action
Bibliographic record
Abstract
Abstract Zanidatamab (ZW25) is a biparatopic antibody that simultaneously binds two distinct epitopes of the oncogenic cell surface receptor HER2. Zanidatamab is currently being evaluated in global Phase 1, Phase 2 and registration-enabling clinical trials as a potential new treatment for patients with HER2-expressing cancers, including biliary tract, gastroesophageal adenocarcinomas, breast, and other tumor types. The extracellular domain of the HER2 receptor comprises four domains with zanidatamab binding to epitopes on domain 2 and the membrane proximal domain 4. Domain 2 and domain 4 of HER2 are also respectively targeted by the antibodies pertuzumab and trastuzumab, currently approved for clinical use in a subset of HER2 overexpressing cancers. The unique engineering and HER2-engagement of zanidatamab results in multiple mechanisms of action, including dual HER2 signal blockade, increased antibody binding, receptor clustering, and removal of HER2 from the cell surface, and potent effector function. Preclinical work shows that zanidatamab can exhibit differentiated activity over trastuzumab or combination of trastuzumab and pertuzumab but the precise molecular mechanism by which zanidatamab differentiates itself from these approved agents remains unclear. Structural modeling of zanidatamab in complex with HER2 suggests that unlike trastuzumab and pertuzumab, steric features of zanidatamab induce a complex of this antibody with HER2 comprised of alternating chains of the antibody and HER2 molecules, with each copy of zanidatamab bridging two HER2 molecules and vice-versa. In this study we report on the spatial distribution of cell surface HER2 molecules and the effect of antibody-induced receptor reorganization on prototypical cancer cells with high and low expression levels of this receptor. We employ super-resolution single-molecule microscopy to map the receptor distribution below the diffraction limit of traditional optical imaging techniques. The use of Direct Stochastic Optical Reconstruction Microscopy (dSTORM) allows us to quantitatively differentiate properties such as cluster size, frequency and receptor density induced by the antibodies. We demonstrate that the unique geometry of HER2 engagement achieved by zanidatamab results in the induction of strikingly aggregated HER2 receptor cluster we refer to as “capping” on the cell surface. This effect induced by zanidatamab is notably distinct relative to the receptor reorganization observed with trastuzumab or combination of trastuzumab and pertuzumab which typically induce multiple smaller microclusters on the cell surface. We observe differences in the lifetime and persistence of these capped configurations induced by zanidatamab, leading us to believe that these cell surface reorganization events are likely coupled to events such as internalization and receptor down regulation. Citation Format: Surjit Dixit, Libin Abraham, Nina Weiser, Michael R. Gold. Super-resolution imaging studies of zanidatamab: Providing insights into its bispecific mode of action [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1032.
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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) | 0.004 | 0.001 |
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".