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Record W2499073646 · doi:10.1158/1538-7445.am2016-279

Abstract 279: Combinatorial strategies for glioblastoma using brain tumor-initiating cells: targeting the JAK/STAT and EGFR pathways

2016· article· en· W2499073646 on OpenAlexaff
H. Artee Luchman, Katharine V. Jensen, Ahmed Aman, Samuel Weiss

Bibliographic record

VenueCancer Research · 2016
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsOntario Institute for Cancer ResearchUniversity of Calgary
Fundersnot available
KeywordsErlotinibTemozolomideCancer researchMedicineEGFR inhibitorsPTENLapatinibSTAT3CancerEpidermal growth factor receptorGliomaSignal transductionPI3K/AKT/mTOR pathwayBiologyInternal medicineBreast cancer

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.094
GPT teacher head0.392
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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