Induction of Autophagy Is an Early Response to Gefitinib and a Potential Therapeutic Target in Breast Cancer
Bibliographic record
Abstract
Gefitinib (Iressa(®), ZD1839) is a small molecule inhibitor of the epidermal growth factor receptor (EGFR) tyrosine kinase. We report on an early cellular response to gefitinib that involves induction of functional autophagic flux in phenotypically diverse breast cancer cells that were sensitive (BT474 and SKBR3) or insensitive (MCF7-GFPLC3 and JIMT-1) to gefitinib. Our data show that elevation of autophagy in gefitinib-treated breast cancer cells correlated with downregulation of AKT and ERK1/2 signaling early in the course of treatment. Inhibition of autophagosome formation by BECLIN-1 or ATG7 siRNA in combination with gefitinib reduced the abundance of autophagic organelles and sensitized SKBR3 but not MCF7-GFPLC3 cells to cell death. However, inhibition of the late stage of gefitinib-induced autophagy with hydroxychloroquine (HCQ) or bafilomycin A1 significantly increased (p<0.05) cell death in gefitinib-sensitive SKBR3 and BT474 cells, as well as in gefitinib-insensitive JIMT-1 and MCF7-GFPLC3 cells, relative to the effects observed with the respective single agents. Treatment with the combination of gefitinib and HCQ was more effective (p<0.05) in delaying tumor growth than either monotherapy (p>0.05), when compared to vehicle-treated controls. Our results also show that elevated autophagosome content following short-term treatment with gefitinib is a reversible response that ceases upon removal of the drug. In aggregate, these data demonstrate that elevated autophagic flux is an early response to gefitinib and that targeting EGFR and autophagy should be considered when developing new therapeutic strategies for EGFR expressing breast 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 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".