Abstract 1556: Preclinical development of antibody drug conjugates targetting EGFRvIII
Bibliographic record
Abstract
Abstract EGFRvIII is a naturally occurring variant of epidermal growth factor receptor (EGFR) that results from an in-frame deletion that removes 267 amino acids from the extracellular domain. EGFRvIII is a tumor-specific receptor that is amplified and present in 25-64% of glioblastoma multiforme and in 20-36% of breast cancers. Numerous studies show that normal tissues are devoid of EGFRvIII, thus making this target ideal for development as an antibody drug conjugate (ADC) for the treatment of cancer,including glioblastoma. At the National Research Council (NRC) of Canada, we have selected mouse monoclonal mAbs that are selective for the extracellular domain of EGFRvIII and which have been shown to be internalized into EGFRvIII expressing cells for ADC development . A panel of EGFRvIII internalizing antibodies was directly conjugated to maytansine, the microtubule disrupting agent through a noncleavable linker. EGFRvIII -DM1 ADCs were shown to efficiently induce growth inhibition in vitro, in EGFRvIII expressing U87MG and DKMG glioblastoma cells. When tested in vivo, EGFRvIII -DM1 ADCs were shown to have potent anti-tumor activity in the U87MG-EGFRIII expressing tumor xenograft model. Citation Format: Maria Luz Jaramillo, Anne Marcil, Alma Robert, Normand Jolicoeur, Cunle Wu, Yves Fortin, Yuneivy Cepero Donates, Mauro Acchione, Jacqueline Slinn, Binbing Ling, Maria Moreno. Preclinical development of antibody drug conjugates targetting EGFRvIII [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1556.
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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".