Targeting the EGFR Pathway for Cancer Therapy
Why this work is in the frame
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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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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it