Abstract 4187: Targeting the cytoskeleton protein ezrin sensitizes metastatic breast cancer cells to anthracycline based chemotherapy
Bibliographic record
Abstract
Abstract The main cause of cancer-associated deaths is the spread of cancer cells to distant organ sites. Despite recent advances in treating primary tumors, modern chemotherapeutic strategies are relatively ineffective at treating metastasis, with clinical trials showing minimal improvements in overall survival for patients with metastatic disease. This is in large part due to chemotherapy resistance which remains a major clinical challenge limiting therapeutic responses for metastatic cancer patients. The cytoskeleton crosslinker 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 (BC). Ezrin also promotes pro-survival signaling, particularly in disseminated cancer cells, to facilitate metastatic outgrowth. However, whether ezrin plays a role in chemoresistance in BC is not yet known. In this study, we sought to determine whether ezrin can predict response to chemotherapy in BC patients and whether pharmacologic inhibition of ezrin alters the sensitivity of metastatic BC cells to anthracycline-based chemotherapy in preclinical models of metastasis. Ezrin protein expression was assessed in a BC patient cohort by tissue microarray immunohistochemistry (IHC) using the automated quantitative platform HaloTM. Among patients treated with systemic chemotherapy across all prognostic groups, high ezrin levels were associated with reduced disease-free, distant metastasis-free, as well as overall survival, compared to patients with lower ezrin levels. Next, we sought to determine whether targeting ezrin using a small molecule inhibitor (NSC668394) could enhance the efficacy of systemic doxorubicin treatment in vivo. Using an experimental lung metastasis model, we showed that the addition of NSC668394 sensitized metastatic BC cells to doxorubicin treatment, compared to either agent alone. We also tested the efficacy of these agents in targeting microscopic metastasis using neoadjuvant and adjuvant treatment models. Our results show that in both treatment modalities, NSC668394 or doxorubicin treatment alone was not able to reduce metastasis, however the addition of the ezrin inhibitor markedly sensitized metastases to doxorubicin and reduced overall lung metastatic burden. Taken together, our data suggest that ezrin may be a novel predictive marker of treatment response in BC patients and provide rationale for potential targeting of ezrin in patients with metastatic disease as an adjunct to chemotherapy. (Supported by OMPRN, CRS and BCAK). Citation Format: Victoria Hoskin, Abdi Ghaffari, Xiaolong Yang, Yolanda Madarnas, Sandip SenGupta, Sonal Varma, Peter A. Greer, Bruce E. Elliott. Targeting the cytoskeleton protein ezrin sensitizes metastatic breast cancer cells to anthracycline based chemotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4187.
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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) | 0.002 | 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".