Abstract 279: Combinatorial strategies for glioblastoma using brain tumor-initiating cells: targeting the JAK/STAT and EGFR pathways
Bibliographic record
Abstract
Abstract Glioblastoma multiforme (GBM), characterized by an aggressive clinical course, therapeutic resistance, and striking molecular heterogeneity, remains incurable. Recent evidence, from our group and others, indicates that the JAK2/STAT3 pathway is an important mediator of tumor cell survival, growth, and invasion in GBM. We investigated the efficacy of a novel JAK2 inhibitor, pacritinib, in brain tumor initiating cell (BTIC) lines to evaluate its 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 reported outcomes. Treatment with pacritinib resulted in on-target JAK2/STAT3 inhibition at 1-2μM and dramatically reduced BTIC proliferation, regardless of endogenous MGMT promoter methylation or EGFR, PTEN, and TP53 mutational status. Pacritinib in combination with temozolomide, the current standard of care agent for GBM, prolonged survival over either drug alone in orthotopically xenografted NOD-SCID mice. We tested the hypothesis that combinatorial targeting of the JAK2/STAT3 pathway and other oncogenic drivers would be effective in GBM. A large number of GBMs have EGFR alterations and, despite poor clinical translation to date, EGFR inhibition remains of therapeutic relevance. Interestingly, EGFR inhibition leads to activation of survival-signalling pathways such as STAT3, diminishing the effectiveness of EGFR inhibition. We find that concurrent inhibition of JAK2/STAT3 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 clinically relevant EGFR inhibitors (erlotinib, afatinib, lapatinib and AZD9291). Combinatorial treatment with pacritinib and EGFR inhibitors showed 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. In vivo pharmacokinetic and pharmacodynamic studies demonstrated that pacritinib and the EGFR inhibitors afatinib and AZD9291 penetrate the brain and have on-target activity. Ongoing in vivo studies using orthotopic xenograft BTIC models will determine whether combinatorial inhibition of these two pathways will provide survival benefit. Further studies are aimed at investigating whether combinatorial inhibition of other pro-oncogenic pathways, using the BTIC model both in vitro and in vivo, may be effective strategies in GBM. Citation Format: Hema A. Luchman, Katharine V. Jensen, Ahmed Aman, Samuel Weiss. Combinatorial strategies for glioblastoma using brain tumor-initiating cells: targeting the JAK/STAT and EGFR pathways. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 279.
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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) | 0.003 | 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".