Targeting the Ezrin Adaptor Protein Sensitizes Metastatic Breast Cancer Cells to Chemotherapy and Reduces Neoadjuvant Therapy–induced Metastasis
Bibliographic record
Abstract
The main cause of cancer-associated deaths is the spread of cancer cells to distant organs. Despite its success in the primary tumor setting, modern chemotherapeutic strategies are rendered ineffective at treating metastatic disease, largely due to the development of resistance. The adaptor protein ezrin has been shown to promote cancer metastasis in multiple preclinical models and is associated with poor prognosis in several cancer types, including breast cancer. Ezrin promotes pro-survival signaling, particularly in disseminated cancer cells, to facilitate metastatic outgrowth. However, the role of ezrin in breast cancer chemoresistance is not fully known. In this study, we show that upregulating or downregulating ezrin expression modifies the sensitivity of breast cancer cells to doxorubicin and docetaxel treatment in vitro and is associated with changes in PI3K/Akt and NFκB pathway activation. In addition, we tested the effects of systemic treatment with a small-molecule ezrin inhibitor, NSC668394, on lung metastatic burden in vivo as a monotherapy, or in combination with anthracycline- or taxane-based chemotherapy treatment. We show that anti-ezrin treatment alone reduces metastatic burden and markedly sensitizes metastases to doxorubicin or docetaxel in neoadjuvant as well as neoadjuvant plus adjuvant treatment models. Taken together, our findings demonstrate the impact of anti-ezrin treatment in modulating response to chemotherapy in breast cancer cells as well as the efficacy of anti-ezrin treatment in combination with chemotherapy at reducing metastatic burden. Significance: This work provides preclinical evidence for combining anti-ezrin treatment with chemotherapy as a novel strategy for effectively targeting metastasis, particularly in a neoadjuvant treatment setting.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".