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Record W2171839817 · doi:10.1093/neuonc/nou208.33

IDENTIFYING DRUG COMPOUNDS TARGETING TUMOR METABOLISM IN GLIOBLASTOMAS

2014· article· en· W2171839817 on OpenAlexaff
Gelareh Zadeh

Bibliographic record

VenueNeuro-Oncology · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWarburg effectGene knockdownGlycolysisCancer researchHexokinaseAnaerobic glycolysisBiologyReprogrammingIn vivoCell cultureOxidative phosphorylationCell biologyGeneEnzymeBiochemistryGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: Rapidly proliferating tumour cells preferentially use aerobic glycolysis over oxidative phosphorylation (OXPHOS) to support growth and survive unfavorable microenvironment conditions. This metabolic reprogramming is referred to as the “Warburg effect”. We have shown that glycolytic enzyme hexokinase 2 (HK2) is crucial for the Warburg effect in human glioblastoma multiforme (GBM). We also demonstrate that loss of HK2 sensitizes GBM cells in-vitro and in-vivo to chemo and radiation therapy, significantly prolonging survival in intracranial models. We also show that loss of HK2 is most relevant in hypoxia. Given these results and that HK2 is an enzyme with little to no expression in normal brain, it provides an attractive target for targeting in GBMs. However, no direct inhibitor of HK2 that crosses the blood brain barrier exists. We therefore explored whether a system biology approach to identify gene networks regulated by or associated with HK2 that could lead to promising treatment strategies. METHODS: Using HK2 knockdown by siRNA in established GBM cell lines and primary GBM cultures we established gene signatures and networks associated with HK2 expression, identifying 1000 genes with a 2 fold change with p-value <0.01 and false discovery rate of <1%. Using a drug screen of 30 compounds that were predicted to repress HK2 expression and associated metabolic gene signatures we identified the azole class of antifungals as inhibitors of tumour metabolism by reducing proliferation, lactate production, glucose uptake and increasing O2 consumption in GBM cells but not primary normal human astrocytes or normal neural stem cells. RESULTS: We evaluated the in vivo efficacy of these azole compounds in pre-clinical orthotopic xenograft mouse models and transgenic models of GBM. The compounds in combination with standard chemo and radiation therapy significantly restrict tumor growth, alter tumor hypoxia and ROS production. We also show that there is a altered response to DNA damage pathways using these compounds, suggesting novel metabolic mechanisms by which these antifungals sensitize GBMs to treatment. CONCLUSIONS: The azole class of antifungals may represent a new way of targeting tumour metabolism in tumours dependent on HK2. SECONDARY CATEGORY: Tumor Biology.

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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.011
GPT teacher head0.268
Teacher spread0.258 · 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
Published2014
Admission routes1
Has abstractyes

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