Simultaneous Targeting of Two Distinct Epitopes on MET Effectively Inhibits MET- and HGF-Driven Tumor Growth by Multiple Mechanisms
Bibliographic record
Abstract
Abstract Increased MET activity is linked with poor prognosis and outcome in several human cancers currently lacking targeted therapies. Here, we report on the characterization of Sym015, an antibody mixture composed of two humanized IgG1 antibodies against nonoverlapping epitopes of MET. Sym015 was selected by high-throughput screening searching for antibody mixtures with superior growth-inhibitory activity against MET-dependent cell lines. Synergistic inhibitory activity of the antibodies comprising Sym015 was observed in several cancer cell lines harboring amplified MET locus and was confirmed in vivo. Sym015 was found to exert its activity via multiple mechanisms. It disrupted interaction of MET with the HGF ligand and prompted activity-independent internalization and degradation of the receptor. In addition, Sym015 induced high levels of CDC and ADCC in vitro. The importance of these effector functions was confirmed in vivo using an Fc-effector function–attenuated version of Sym015. The enhanced effect of the two antibodies in Sym015 on both MET degradation and CDC and ADCC is predicted to render Sym015 superior to single antibodies targeting MET. Our results demonstrate strong potential for use of Sym015 as a therapeutic antibody mixture for treatment of MET-driven tumors. Sym015 is currently being tested in a phase I dose escalation clinical trial (NCT02648724). Mol Cancer Ther; 16(12); 2780–91. ©2017 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) | 0.001 | 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 | 0.000 |
| 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".