Cancer–stromal cell interactions in breast cancer brain metastases induce glycocalyx-mediated resistance to HER2-targeting therapies
Bibliographic record
Abstract
Brain metastatic breast cancer is particularly lethal largely due to therapeutic resistance. Almost half of the patients with metastatic HER2-positive breast cancer develop brain metastases, representing a major clinical challenge. We previously described that cancer-associated fibroblasts are an important source of resistance in primary tumors. Here, we report that breast cancer brain metastasis stromal cell interactions in 3D cocultures induce therapeutic resistance to HER2-targeting agents, particularly to the small molecule inhibitor of HER2/EGFR neratinib. We investigated the underlying mechanisms using a synthetic Notch reporter system enabling the sorting of cancer cells that directly interact with stromal cells. We identified mucins and bulky glycoprotein synthesis as top-up-regulated genes and pathways by comparing the gene expression and chromatin profiles of stroma-contact and no-contact cancer cells before and after neratinib treatment. Glycoprotein gene signatures were also enriched in human brain metastases compared to primary tumors. We confirmed increased glycocalyx surrounding cocultures by immunofluorescence and showed that mucinase treatment increased sensitivity to neratinib by enabling a more efficient inhibition of EGFR/HER2 signaling in cancer cells. Overexpression of truncated MUC1 lacking the intracellular domain as a model of increased glycocalyx-induced resistance to neratinib both in cell culture and in experimental brain metastases in immunodeficient mice. Our results highlight the importance of glycoproteins as a resistance mechanism to HER2-targeting therapies in breast cancer brain metastases.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 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".