Synergistic Effects of Neratinib in Combination With Palbociclib or Miransertib in Brain Cancer Cells
Bibliographic record
Abstract
Background: Aberrant expression and activation of epidermal growth factor receptor (EGFR) resulted in approval of several forms of EGFR inhibitors in the treatment of patients with a wide range of epithelial cancers. However, no EGFR inhibitor has yet been approved for the treatment of patients with brain cancer, indicating that targeting EGFR alone may not be sufficient in some patients. Methods: In this study, we investigated the role of all members of the EGFR family, other growth factor receptors, cell-cycle proteins, and downstream cell signaling pathways (e.g., mitogen-activated protein kinase (MAPK), serine/threonine protein kinase (AKT), signal transducer and activator of transcription (STAT3), Src, Abelson murine leukemia viral oncogene homolog (Abl)) on the growth of a panel of human brain cancer cell lines (HBCCLs). We examined the growth response of HBCCLs to treatment with 17 targeted agents compared to two cytotoxic drugs. Results: Of the targeted agents, the irreversible pan-human epidermal growth factor receptor (HER) inhibitors neratinib and afatinib were more effective than erlotinib and lapatinib at inhibiting the growth of all HBCCLs, and the cyclin-dependent kinase (CDK)1/2/5/9 inhibitor dinaciclib was the most potent targeted agent. We found that treatment with Src/Abl/c-kit inhibitor dasatinib, signal transducer and activator of transcription (STAT3) inhibitor stattic, Abl/platelet-derived growth factor receptor (PDGFR)α/vascular endothelial growth factor (VEGFR)2/fibroblast growth factor receptor (FGFR)1 inhibitor ponatinib, and the tropomyosin receptor kinase (TRK)/ROS proto-oncogene 1 receptor tyrosine kinase (ROS)/anaplastic lymphoma kinase (ALK) inhibitor entrectinib, also inhibited the growth of all HBCCLs. Interestingly, these agents were more effective in inhibiting growth of HBCCLs when proliferating at a slower rate. In addition to inhibiting the proliferation of HBCCLs, treatment with neratinib, dinaciclib, dasatinib, stattic and trametinib inhibited the migration of brain tumor cell line A172. Conclusions: Notably, we found that treatment with neratinib in combination with palbociclib (CDK4/6 inhibitor), or miransertib (AKT1/2/3 inhibitor) resulted in synergistic growth inhibition of all HBCCLs. Our results support that repurposing drugs like neratinib in combination with the palbociclib or miransertib may be of therapeutic potential in brain cancer and warrants further investigations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".