The JAK2/STAT3 inhibitor pacritinib effectively inhibits patient-derived GBM brain tumor initiating cells in vitro and when used in combination with temozolomide increases survival in an orthotopic xenograft model
Bibliographic record
Abstract
PURPOSE: The prognosis for patients diagnosed with glioblastoma multiforme (GBM) remains dismal, with current treatment prolonging survival only modestly. As such, there remains a strong need for novel therapeutic strategies. The janus kinase (JAK)2/signal transducer and activator of transcription (STAT)3 pathway regulates many cellular processes in GBM, including survival, proliferation, invasion, anti-apoptosis, and immune evasion. Here, we evaluated the preclinical efficacy of pacritinib, a novel compound targeting JAK2, using a collection of diverse patient-derived brain tumor initiating cells (BTICs). EXPERIMENTAL DESIGN: The effects of pacritinib on BTIC viability and sphere forming capacity were evaluated in vitro using the alamarBlue and neurosphere assays, respectively. On-target inhibition of JAK2/STAT3 signaling was investigated using western blotting. The efficacy of pacritinib was tested in vivo in pharmacokinetic analyses, liver microsome analyses, and Kaplan-Meier survival studies. RESULTS: In vitro, pacritinib decreased BTIC viability and sphere forming potential at low micromolar doses and demonstrated on-target inhibition of STAT3 signaling. Additionally, pacritinib was found to improve the response to temozolomide (TMZ) in TMZ-resistant BTICs. In vivo, systemic treatment with pacritinib demonstrated blood-brain barrier penetration and led to improved overall median survival in combination with TMZ, in mice orthotopically xenografted with an aggressive recurrent GBM BTIC culture. CONCLUSION: This preclinical study demonstrates the efficacy of pacritinib and supports the feasibility of testing pacritinib for the treatment of GBM, in combination with the standard of care TMZ.
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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".