ATPS-52A PRE-CLINICAL COMBINATORIAL STRATEGY TO TARGET THE JAK/STAT AND EGFR PATHWAYS IN GBM
Bibliographic record
Abstract
Glioblastoma multiforme (GBM), characterized by an aggressive clinical course, therapeutic resistance and striking molecular heterogeneity, remains incurable. A large number GBMs have EGFR alterations and, despite poor clinical translation to date, EGFR inhibition remains of therapeutic relevance. Recent evidence, from our group and others, indicates that JAK2/STAT3 pathway is an important mediator of tumor cell survival, growth, and invasion in GBM. Interestingly, EGFR inhibition leads to activation of survival-signalling pathways such as STAT3, diminishing effectiveness of EGFR inhibition. We investigated the efficacy of a novel JAK2 inhibitor, pacritinib, in brain tumour initiating cell (BTIC) lines to evaluate potential use in the treatment of GBM patients. In a Phase III study of patients with myelofibrosis, pacritinib demonstrated manageable toxicity and clinically and statistically improved patient spleen volume and patient reported outcomes. Pacritinib results in on-target JAK2/STAT3 inhibition at 1-2 µM and dramatically reduces BTIC proliferation, regardless of endogenous MGMT promoter methylation or EGFR, PTEN, and TP53 mutational status. Pacritinib in combination with temozolomide prolongs survival, over either drug alone, in orthopically xenografted NOD-SCID. We are testing the hypothesis that concurrent inhibition of JAK/STAT and EGFR signalling may be an effective, clinically relevant therapeutic, strategy for GBM. We examined the in vitro actions of pacritinib on BTICs, in combination with three clinically approved EGFR inhibitors, erlotinib, afatinib and lapatinib and are investigating other clinically relevant EGFR inhibitors for GBM. Combinatorial treatment with pacritinib and EGFR inhibitors shows striking responses, with lowered IC50s and BTIC viability. The combinatorial actions of EGFR and STAT3 inhibition were particularly effective in BTIC lines with EGFR activating vIII and missense point mutations. On target activity was demonstrated with reduced phospho-EGFR, phospho-STAT3 and effectors of both pathways. Current studies are aimed at investigating the combined actions of Pacritinib and EGFR inhibitors, over single agents, in orthotopic xenograft animal survival.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".