On-target JAK2/STAT3 inhibition slows disease progression in orthotopic xenografts of human glioblastoma brain tumor stem cells
Bibliographic record
Abstract
BACKGROUND: Glioblastoma multiforme (GBM) is characterized by an aggressive clinical course, therapeutic resistance, and striking molecular heterogeneity. GBM-derived brain tumor stem cells (BTSCs) closely model this molecular heterogeneity and likely have a key role in tumor recurrence and therapeutic resistance. Emerging evidence indicates that Janus kinase (JAK)2/signal transducer and activator of transcription (STAT)3 is an important mediator of tumor cell survival, growth, and invasion in a large group of GBM. Here, we used a large set of molecularly heterogeneous BTSCs to evaluate the translational potential of JAK2/STAT3 therapeutics. METHODS: BTSCs were cultured from GBM patients and MGMT promoter methylation, and the mutation statuses of EGFR, PTEN, and TP53 were determined. Endogenous JAK2/STAT3 activity was assessed in human GBM tissue, BTSCs, and orthotopic xenografts by immunohistochemistry and Western blotting. STAT3 short hairpin (sh)RNA, cucurbitacin-I, and WP1066 were used to inhibit JAK2/STAT3 activity in vitro and in vivo. RESULTS: The JAK2/STAT3 pathway was demonstrated to be highly activated in human GBM, molecularly heterogeneous BTSCs derived from these tumors, and BTSC xenografts. STAT3 shRNA knockdown or cucurbitacin-I and WP1066 administration resulted in on-target JAK2/STAT3 inhibition and dramatically reduced BTSC survival regardless of endogenous MGMT promoter methylation or EGFR, PTEN, and TP53 mutational status. BTSC orthotopic xenografts maintained the high levels of activated JAK2/STAT3 seen in their parent human tumors. Intraperitoneal WP1066 reduced intratumoral JAK2/STAT3 activity and prolonged animal survival. CONCLUSION: Our study demonstrates the in vitro and in vivo efficacy of on-target JAK2/STAT3 inhibition in heterogeneous BTSC lines that closely emulate the genomic and tumorigenic characteristics of human 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".