EGFR Family Members’ Regulation of Autophagy Is at a Crossroads of Cell Survival and Death in Cancer
Bibliographic record
Abstract
The epidermal growth factor receptor (EGFR) signaling pathways are altered in many cancers contributing to increased cell survival. These alterations are caused mainly through increased expression or mutation of EGFR family members EGFR, ErbB2, ErbB3, and ErbB4. These receptors have been successfully targeted for cancer therapy. Specifically, a monoclonal antibody against ErbB2, trastuzumab, and a tyrosine kinase inhibitor against EGFR, gefitinib, have improved the survival of breast and lung cancer patients. Unfortunately, cancer patients frequently become resistant to these inhibitors. This has led to investigating how EGFR can contribute to cell survival and how cancer cells can overcome inhibition of its signaling. Indeed, it is coming into focus that EGFR signaling goes beyond a single signal triggering cell proliferation and survival and is a sensor that regulates the cell's response to microenvironmental stresses such as hypoxia. It acts as a switch that modulates the ability of cancer cells to survive. Autophagy is a process of self-digestion that is inhibited by EGFR allowing cancer cells to survive under stresses that would normally cause death and become resistant to chemotherapy. Inhibiting EGFR signaling allows autophagy to contribute to cell death. This gives new opportunities to develop novel therapeutic strategies to treat cancers that rely on EGFR signaling networks and autophagy. In this review, we summarize the current understanding of EGFR family member regulation of autophagy in cancer cells and how new therapeutic strategies could be developed to overcome drug resistance.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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 teacher head, 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".