c-Src Modulates Estrogen-Induced Stress and Apoptosis in Estrogen-Deprived Breast Cancer Cells
Bibliographic record
Abstract
The emergence of anti-estrogen resistance in breast cancer is an important clinical phenomenon affecting long-term survival in this disease. Identifying factors that convey cell survival in this setting may guide improvements in treatment. Estrogen (E2) can induce apoptosis in breast cancer cells that have been selected for survival after E2 deprivation for long periods (MCF-7:5C cells), but the mechanisms underlying E2-induced stress in this setting have not been elucidated. Here, we report that the c-Src kinase functions as a key adapter protein for the estrogen receptor (ER, ESR1) in its activation of stress responses induced by E2 in MCF-7:5C cells. E2 elevated phosphorylation of c-Src, which was blocked by 4-hydroxytamoxifen (4-OHT), suggesting that E2 activated c-Src through the ER. We found that E2 activated the sensors of the unfolded protein response (UPR), IRE1α (ERN1) and PERK kinase (EIF2AK3), the latter of which phosphorylates eukaryotic translation initiation factor-2α (eIF2α). E2 also dramatically increased reactive oxygen species production and upregulated expression of heme oxygenase HO-1 (HMOX1), an indicator of oxidative stress, along with the central energy sensor kinase AMPK (PRKAA2). Pharmacologic or RNA interference-mediated inhibition of c-Src abolished the phosphorylation of eIF2α and AMPK, blocked E2-induced ROS production, and inhibited E2-induced apoptosis. Together, our results establish that c-Src kinase mediates stresses generated by E2 in long-term E2-deprived cells that trigger apoptosis. This work offers a mechanistic rationale for a new approach in the treatment of endocrine-resistant breast cancer.
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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) | 0.001 | 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".