Understanding Heterogeneity and Permeability of Brain Metastases in Murine Models of HER2-Positive Breast Cancer Through Magnetic Resonance Imaging: Implications for Detection and Therapy
Bibliographic record
Abstract
OBJECTIVES: Brain metastases due to breast cancer are increasing, and the prognosis is poor. Lack of effective therapy is attributed to heterogeneity of breast cancers and their resulting metastases, as well as impermeability of the blood-brain barrier (BBB), which hinders delivery of therapeutics to the brain. This work investigates three experimental models of HER2+ breast cancer brain metastasis to better understand the inherent heterogeneity of the disease. We use magnetic resonance imaging (MRI) to quantify brain metastatic growth and explore its relationship with BBB permeability. DESIGN: Brain metastases due to breast cancer cells (SUM190-BR3, JIMT-1-BR3, or MDA-MB-231-BR-HER2) were imaged at 3 T using balanced steady-state free precession and contrast-enhanced T1-weighted spin echo sequences. The histology and immunohistochemistry corresponding to MRI were also analyzed. RESULTS: There were differences in metastatic tumor appearance by MRI, histology, and immunohistochemistry (Ki67, CD31, CD105) across the three models. The mean volume of an MDA-MB-231-BR-HER2 tumor was significantly larger compared to other models (F2,12 = 5.845, P < .05); interestingly, this model also had a significantly higher proportion of Gd-impermeable tumors (F2,12 = 22.18, P < .0001). Ki67 staining indicated that Gd-impermeable tumors had significantly more proliferative nuclei compared to Gd-permeable tumors (t[24] = 2.389, P < .05) in the MDA-MB-231-BR-HER2 model. CD31 and CD105 staining suggested no difference in new vasculature patterns between permeable and impermeable tumors in any model. CONCLUSION: Significant heterogeneity is present in these models of brain metastases from HER2+ breast cancer. Understanding this heterogeneity, especially as it relates to BBB permeability, is important for improvement in brain metastasis detection and treatment delivery.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".