Mutant Epidermal Growth Factor Receptors as Targets for Cancer Therapy
Bibliographic record
Abstract
The epidermal growth factor (EGF) receptor is overexpressed in many cancers, and is under intensive investigation as a target for cancer therapy. Cancer cells have also been shown to express mutated EGF receptors; these are potentially highly specific targets for cancer therapeutics, as they have not been detected in any normal adult tissues. The most common of these mutant EGF receptors, EGFRvIII, is one in which amino acids 6 - 273 of the extracellular domain are deleted. This specific mutation is common in glioblastoma and in several other types of cancer, and has been shown to promote aggressive growth of tumors in vivo. The loss of part of the extracellular domain results in a receptor that has constitutive tyrosine kinase activity. Current evidence suggests that EGFRvIII has altered signalling properties compared to normal EGF receptor. The mutation in EGFRvIII also creates a new, cancer cell-specific epitope. This epitope is extracellular and therefore represents a very promising target for antibody-directed therapeutics. This review covers our current understanding of the properties of EGFRvIII, and recent developments in the characterization and therapeutic application of EGFRvIII-specific antibodies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".