Abstract 2894: Neratinib effects significant changes in human brain endothelial cells, demonstrating that it may have a therapeutic use in cancers with brain metastasis
Bibliographic record
Abstract
Abstract Background: Brain metastases constitute a significant part of intracranial tumors and the majority of brain metastases originate from lung, breast cancers and malignant melanoma. Breast cancer patients who develop brain metastases tend to have poor prognosis with short overall survival. Moreover, human epidermal growth factor receptor 2 (HER2)-positive breast cancer have an increased propensity for brain metastases. Currently, patients with brain metastases have limited therapeutic options. The failure of many cancer therapeutics for the treatment of brain metastasis has been partly attributed to an intact blood-brain barrier (BBB) tightly controlled by endothelial tight junctions (TJ). Neratinib is an orally available tyrosine kinase inhibitor that irreversibly binds to and inhibits EGFR, HER2 and HER4 receptor tyrosine kinases. This study aimed to examine the effect of neratinib on protein expression and phosphorylation status in human brain endothelial cells. Methods: Changes in protein phosphorylation in human brain (TY09 and CMEC D3) and venous endothelial cells (HECV) was assessed following neratinib treatment using protein microarrays (Kinexus, Canada). Z scores and percent changes from control (%CFC) were calculated between neratinib vs. control treatment samples at IC50. Alterations in gene expression were ascertained using AmpliSeq™ technology. Results: Of over 800 proteins evaluated, neratinib caused significant %CFC (>50%) increase in 78 targets (such as EGFR, vimentin) and a %CFC decrease in 56 targets across all endothelial cells (including ROCKI). When comparing changes between brain and vascular cells, there was a significant change in %CFC for 36 proteins, including β-catenin and FYN. Of interest β-catenin is involved in TJ regulation. Differential expression of 21 genes associated with TJ including TJP1, JAM2, MABI1, CLDN7 and CLDN10 was also observed following neratinib treatment of brain or vascular endothelial cells I comparison to vascular cells treated with neratinib. Conclusions: These results show that neratinib may alter both gene expression and phosphorylation status of a number of proteins linked to metastasis in human brain endothelial cells. Interestingly, several of these proteins are known to be involved in TJ regulation or function, suggesting that neratinib may modulate the activity of TJ in the BBB. Studies validating these findings are ongoing and may provide valuable insights into a new mode of action of neratinib for the treatment for cancers with brain metastasis. Citation Format: Tracey A. Martin, Sioned Owen, Dafydd A. Dart, Francesca Avogadri Connors, Alshad S. Lalani, Richard P. Bryce, Wen G. Jiang. Neratinib effects significant changes in human brain endothelial cells, demonstrating that it may have a therapeutic use in cancers with brain metastasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2894.
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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) | 0.003 | 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".