Navigating the Blood–Brain Barrier: Challenges and Therapeutic Strategies in Breast Cancer Brain Metastases
Bibliographic record
Abstract
Breast cancer (BC) is the most common cancer in women, with metastatic BC being responsible for the highest number of deaths. A frequent site for BC metastasis is the brain. Brain metastasis derived from BC involves the cooperation of multiple genetic, epigenetic, angiogenic, and tumor-stroma interactions. Most of these interactions provide a unique opportunity for development of new therapeutic targets. Potentially targetable signaling pathways are Notch, Wnt, and the epidermal growth factor receptors signaling pathways, all of which are linked to driving BC brain metastasis (BCBM). However, a major challenge in treating brain metastasis remains the blood-brain barrier (BBB). This barrier restricts the access of unwanted molecules, cells, and targeted therapies to the brain parenchyma. Moreover, current therapies to treat brain metastases, such as stereotactic radiosurgery and whole-brain radiotherapy, have limited efficacy. Promising new drugs like phosphatase and kinase modulators, as well as BBB disruptors and immunotherapeutic strategies, have shown the potential to ease the disease in preclinical studies, but remain limited by multiple resistance mechanisms. This review summarizes some of the current understanding of the mechanisms involved in BC brain metastasis and highlights current challenges as well as opportunities in strategic designs of potentially successful future therapies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".