Targeting of EGFR by a combination of antibodies mediates unconventional EGFR trafficking and degradation
Bibliographic record
Abstract
Antibody combinations targeting cell surface receptors are a new modality of cancer therapy. The trafficking and signalling mechanisms regulated by such therapeutics are not fully understood but could underlie differential tumour responses. We explored EGFR trafficking upon treatment with the antibody combination Sym004 which has shown promise clinically. Sym004 promoted EGFR endocytosis distinctly from EGF: it was asynchronous, not accompanied by canonical signalling events and involved EGFR clustering within detergent-insoluble plasma mebrane-associated tubules. Sym004 induced lysosomal degradation independently of EGFR ubiquitylation but dependent upon Hrs/Tsg101 that are required for the formation of intraluminal vesicles (ILVs) within late endosomes. We propose Sym004 cross-links EGFR physically triggering EGFR endocytosis and incorporation onto ILVs and so Sym004 sensitivity correlates with EGFR numbers available for binding, rather than specific signalling events. Consistently Sym004 efficacy and potentiation of cisplatin responses correlated with EGFR surface expression in head and neck cancer cells. These findings will have implications in understanding the mode of action of this new class of cancer therapeutics.
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 | 0.000 | 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 | 0.000 |
| 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".