Autophagy Inhibition Augments the Anticancer Effects of Epirubicin Treatment in Anthracycline-Sensitive and -Resistant Triple-Negative Breast Cancer
Bibliographic record
Abstract
PURPOSE: Triple-negative breast cancers (TNBC) are defined by a lack of expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (ERBB2/HER2). Although initially responsive to chemotherapy, most recurrent TNBCs develop resistance, resulting in disease progression. Autophagy is a lysosome-mediated degradation and recycling process that can function as an adaptive survival response during chemotherapy and contribute to chemoresistance. Our goal was to determine whether autophagy inhibition improves treatment efficacy in TNBC cells in tumors either sensitive or refractory to anthracyclines. EXPERIMENTAL DESIGN: We used in vitro and in vivo models of TNBC using cell lines sensitive to epirubicin and other anthracyclines, as well as derivative lines, resistant to the same drugs. We assessed basal autophagy levels and the effects of chemotherapy on autophagy in parental and resistant cells. Applying various approaches to inhibit autophagy alone and in combination with chemotherapy, we assessed the effects on cell viability in vitro and tumor growth rates in vivo. RESULTS: We demonstrated that epirubicin induced autophagic flux in TNBC cells. Epirubicin-resistant lines exhibited at least 1.5-fold increased basal autophagy levels and, when treated with autophagy inhibitors, showed a significant loss in viability, indicating dependence of resistant cells on autophagy for survival. Combination of epirubicin with the autophagy inhibitor hydroxychloroquine resulted in a significant reduction in tumor growth compared with monotherapy with epirubicin. CONCLUSION: Autophagy inhibition enhances therapeutic response in both anthracycline-sensitive and -resistant TNBC and may be an effective new treatment strategy for this disease.
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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".