Mutant Epidermal Growth Factor Receptors as Targets for Cancer Therapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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 it