Overcoming brain-derived therapeutic resistance in HER2+ breast cancer brain metastasis
Bibliographic record
Abstract
Brain metastasis of HER2+ breast cancer occurs in about 50% of all women with metastatic HER2+ breast cancer and confers poor prognosis for patients. Despite effective HER2-targeted treatments of peripheral HER2+ breast cancer with Trastuzumab +/-HER2 inhibitors, limited brain permeability renders these treatments inefficient for HER2+ breast cancer brain metastasis (BCBM). The scarcity of suitable patient-derived in-vivo models for HER2+ BCBM has compromised the study of molecular mechanisms that promote growth and therapeutic resistance in brain metastasis. We have generated and characterized new HER2+ BCBM cells (BCBM94) isolated from a patient HER2+ brain metastasis. Repeated hematogenic xenografting of BCBM94 consistently generated BCBM in mice. The clinically used receptor tyrosine kinase inhibitor (RTKi) Lapatinib blocked phosphorylation of all ErbB1-4 receptors and induced the intrinsic apoptosis pathway in BCBM94. Neuregulin-1 (NRG1), a ligand for ErbB3 and ErbB4 that is abundantly expressed in the brain, was able to rescue Lapatinib-induced apoptosis and clonogenic ability in BCBM94 and in HER2+ BT474. ErbB3 was essential to mediate the NRG1-induced survival pathway that involved PI3K-AKT signalling and the phosphorylation of BAD at serine 136 to prevent apoptosis. High throughput RTKi screening identified the brain penetrable Poziotinib as highly potent compound to reduce cell viability in HER2+ BCBM in the presence of NRG1. Successful in-vivo ablation of BCBM94- and BT474-derived HER2+ brain tumors was achieved upon two weeks of treatment with Poziotinib. MRI revealed BCBM remission upon poziotinib, but not with Lapatinib treatment. In conclusion, we have established a new patient-derived HER2+ BCBM in-vivo model and identified Poziotinib as highly efficacious RTKi with excellent brain penetrability that abrogated HER2+ BCBM brain tumors in our mouse models.
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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 | 0.000 |
| 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".