Targeting the EGFR Pathway for Cancer Therapy
Bibliographic record
Abstract
Clinical studies have shown that HER-2/Neu is over-expressed in up to one-third of patients with a variety of cancers, including B-cell acute lymphoblastic leukemia (B-ALL), breast cancer and lung cancer, and that these patients are frequently resistant to conventional chemo-therapies. Additionally, in most patients with multiple myeloma, the malignant cells over-express a number of epidermal growth factor receptors (EGFR)s and their ligands, HB-EGF and amphiregulin, thus this growth-factor family may be an important aspect in the patho-biology of this disease. These and other, related findings have provided the rationale for the targeting of the components of the EGFR signaling pathways for cancer therapy. Below we discuss various aspects of EGFR-targeted therapies mainly in hematologic malignancies, lung cancer and breast cancer. Beside novel therapeutic approaches, we also discuss specific side effects associated with the therapeutic inhibition of components of the EGFR-pathways. Alongside small inhibitors, such as Lapatinib (Tykerb, GW572016), Gefitinib (Iressa, ZD1839), and Erlotinib (Tarceva, OSI-774), a significant part of the review is also dedicated to therapeutic antibodies (e.g.: Trastuzumab/Herceptin, Pertuzumab/Omnitarg/rhuMab-2C4, Cetuximab/Erbitux/IMC-C225, Panitumumab/Abenix/ABX-EGF, and also ZD6474). In addition, we summarize, both current therapy development driven by antibody-based targeting of the EGFR-dependent signaling pathways, and furthermore, we provide a background on the history and the development of therapeutic antibodies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".