EGFR blockade in GBM brain tumor stem cells synergizes with JAK2/STAT3 pathway inhibition to abrogate compensatory mechanisms in vitro and in vivo
Bibliographic record
Abstract
Abstract Background The EGFR pathway is frequently mutated in glioblastoma (GBM). However, to date, EGFR therapies have not demonstrated efficacy in clinical trials. Poor brain penetration of conventional inhibitors, lack of patient stratification for EGFR status, and mechanisms of resistance are likely responsible for the failure of EGFR-targeted therapy. We aimed to address these elements in a large panel of molecularly diverse patient-derived GBM brain tumor stem cells (BTSCs). Methods In vitro growth inhibition and on-target efficacy of afatinib, pacritinib, or a combination were assessed by cell viability, neurosphere formation, cytotoxicity, limiting dilution assays, and western blotting. In vivo efficacy was assessed with mass spectrometry, immunohistochemistry, magnetic resonance imaging, and intracranial xenograft models. Results We show that afatinib and pacritinib decreased BTSC growth and sphere-forming capacity in vitro. Combinations of the 2 drugs were synergistic and abrogated the activation of STAT3 signaling observed upon EGFR inhibition in vitro and in vivo. We further demonstrate that the brain-penetrant EGFR inhibitor, afatinib, improved survival in EGFRvIII mt orthotopic xenograft models. However, upregulation of the oncogenic STAT3 signaling pathway was observed following afatinib treatment. Combined inhibition with 2 clinically relevant drugs, afatinib and pacritinib, synergistically decreased BTSC viability and abrogated this compensatory mechanism of resistance to EGFR inhibition. A significant decrease in tumor burden in vivo was observed with the combinatorial treatment. Conclusions These data demonstrate that brain-penetrant combinatorial therapies targeting the EGFR and STAT3 signaling pathways hold therapeutic promise for GBM.
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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".