Signaling by the Epidermal Growth Factor Receptor regulates DNA repair
Bibliographic record
Abstract
The Epidermal Growth Factor (EGF) Receptor (EGFR) is a receptor tyrosine kinase that when deregulated can drive tumor growth and can also contribute to drug resistance. Upon binding its ligand EGF, EGFR triggers the activation of many signaling pathways including phosphatidylinpositol‐3‐kinase (PI3K)/Akt, Ras‐Erk, signal transducer and activator of transcription (STAT), and phospholipase C γ1 (PLCγ1). EGFR may also control DNA repair mechanisms, although this phenomenon this remains poorly understood. Control of DNA repair by EGFR may be particularly relevant in the context of action of and resistance to anti‐cancer drugs that cause DNA damage (e.g. cisplatin). We examined how acute activation (10–30 min) of EGFR by ligand stimulation regulates DNA damage and repair responses induced by chronic (16 h) cisplatin treatment. To do so, we examined various markers of DNA damage and repair such as γH2AX and 53BP1. We observed that as little as 10 min of EGF stimulation is sufficient to elicit remodelling of DNA damage and repair markers such as γH2AX in chronic cisplatin‐treated cells. This indicates that acute EGFR activation triggers signaling pathway(s) that control the DNA damage response and/or DNA repair. Using these methods, we dissected the contribution of various EGFR signaling pathways and membrane traffic phenomena to this EGFR‐dependent control of DNA repair. This work may reveal new ways to enhance the efficacy of existing chemotherapies such as cisplatin for cancer treatment. Support or Funding Information This work was supported by a Project Grant and a New Investigator Salary Award from the Canadian Institutes of Health Research (CIHR) to C.N.A This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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) | 0.000 | 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) | 0.003 | 0.001 |
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".