Ex.50.T aptamer impairs tumor–stroma cross-talk in breast cancer by targeting gremlin-1
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The tumor microenvironment profoundly influences tumor complexity, particularly in breast cancer, where cancer-associated fibroblasts play pivotal roles in tumor progression and therapy resistance. Extracellular vesicles are involved in mediating communication within the TME, specifically highlighting their role in promoting the transformation of normal fibroblasts into cancer-associated fibroblasts. Recently, we identified an RNA aptamer, namely ex.50.T, that binds with remarkable affinity to extracellular vesicles shed from triple-negative breast cancer cells. Here, through in vitro assays and computational analyses, we demonstrate that the binding of ex.50.T to extracellular vesicles and parental breast cancer cells is mediated by recognition of gremlin-1 (GREM1), a bone morphogenic protein antagonist implicated in breast cancer aggressiveness and metastasis. Functionally, we uncover the role of ex.50.T as an innovative therapeutic agent in the process of tumor microenvironment re-modeling, impeding GREM1 signaling, blocking triple-negative breast cancer extracellular vesicles internalization in recipient cells, and counteracting the transformation of normal fibroblasts into cancer-associated fibroblasts. Altogether, our findings highlight ex.50.T as a novel therapeutical avenue for breast cancer and potentially other GREM1-dependent malignancies, offering insights into disrupting TME dynamics and enhancing cancer treatment strategies.
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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 | 0.000 | 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) | 0.000 | 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 it