Intravital imaging reveals systemic ezrin inhibition impedes cancer cell migration and lymph node metastasis in breast cancer
Bibliographic record
Abstract
BACKGROUND: Limited understanding of the cancer biology of metastatic sites is a major factor contributing to poor outcomes in cancer patients. The regional lymph nodes are the most common site of metastasis in most solid cancers and their involvement is a strong predictor of relapse in breast cancer (BC). We have previously shown that ezrin, a cytoskeletal-membrane linker protein, is associated with lymphovascular invasion and promotes metastatic progression in BC. However, the efficacy of pharmacological inhibition of ezrin in blocking cancer cell migration and metastasis remains unexplored in BC. METHODS: We quantified ezrin expression in a BC tissue microarray (n = 347) to assess its correlation with risk of relapse. Next, we developed a quantitative intravital microscopy (qIVM) approach, using a syngeneic lymphatic reporter mouse tumor model, to investigate the effect of systemic ezrin inhibition on cancer cell migration and metastasis. RESULTS: We show that ezrin is expressed at significantly higher levels in lymph node metastases compared to matched primary tumors, and that a high tumor ezrin level is associated with increased risk of relapse in BC patients with regional disease. Using qIVM, we observe a subset of cancer cells that retain their invasive and migratory phenotype at the tumor-draining lymph node. We further show that systemic inhibition of ezrin, using a small molecule compound (NSC668394), impedes the migration of cancer cells in vivo. Furthermore, systemic ezrin inhibition leads to reductions in metastatic burden at the distal axillary lymph node and lungs. CONCLUSIONS: Our findings demonstrate that the tumor ezrin level act as an independent biomarker in predicting relapse and provide a rationale for therapeutic targeting of ezrin to reduce the metastatic capacity of cancer cells in high-risk BC patients with elevated ezrin expression.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".