Epidermal growth factor receptor (EGFR) involvement in epithelial‐derived cancers and its current antibody‐based immunotherapies
Bibliographic record
Abstract
The epidermal growth factor receptor (EGFR) is a transmembrane glycoprotein that is part of the family of tyrosine kinase receptors. The binding of EGFR to its cognate ligands leads to its autophosphorylation and subsequent activation of the signal transduction pathways involved in regulating cellular proliferation, differentiation, and survival. Accordingly, this receptor carries out both redundant and restricted functions in the germline development of mammals and in the maintenance of various adult tissues. Correspondingly, the loss of EGFR regulation results in many human diseases, with the most notable cancer. This receptor is overexpressed and/or mutated in multiple epithelial-derived tumors, and associated with poor prognosis and survival in cancer patients. Here, we discuss in detail the role of EGFR in specific epithelial-derived cancer pathologies; these include lung cancer, colorectal cancer, and squamous cell carcinomas. The development of multiple anticancer agents against EGFR diminished the progression and metastasis of tumors. Some of the most versatile therapeutic anti-EGFR agents include the monoclonal antibodies (mAbs), demonstrating success in clinical settings when used in combination with cytotoxic treatments, such as chemotherapy and/or radiation. We thus discuss the development and application of two of the most notable therapeutic mAbs, cetuximab, and panitumumab, currently utilized in various EGFR-related epithelial cancers.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".