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Abstract B14: Wnt signaling circuits in glioblastoma multiforme

2016· article· en· W2404785887 on OpenAlexaff
Nishani Rajakulendran, Hayden Selvadurai, Katherine Rowland, Nicole Park, Nizar N. Batada, Peter B. Dirks, Stéphane Angers

Bibliographic record

VenueMolecular Cancer Research · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topic14-3-3 protein interactions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWnt signaling pathwayAutocrine signallingBiologyCancer researchDKK1FrizzledGliomaLRP5TemozolomideReceptorSignal transductionCell biologyGenetics

Abstract

fetched live from OpenAlex

Abstract Glioblastoma multiforme (GBM) is the most common malignant tumour in the central nervous system with a prevalence of 2-3 cases per 100 000 people. Although the standard treatment of surgery, chemotherapy and radiotherapy improve survival, the median survival continues to remain at only 15 months with a 5-year survival rate of under 10%. Glioma neural stem-like (GNS) cells have been identified in GBM and have the capability of regenerating the tumour. Treatment strategies that target the majority of the tumour may be incapable of also targeting GNS cells and thus characterization of GNS cells may provide insight into additional treatment options. The Wnt signalling pathway has been linked to several cancers including GBM. Wnt signalling involves the secretion of Wnt ligand proteins that bind to specific Frizzled (FZD) receptor complexes on the cell surface of Wnt-responding cells to activate intracellular signalling cascades. The transcriptional and epigenetic regulation in GNS cells is the focus of several recent studies. The transcription factor ASCL1 was identified to be overexpressed and to lead to Wnt signalling activation by repressing Dickkopf (DKK1, Wnt inhibitor). Using microarray data we analyzed the expression of FZD receptors and Wnt target genes in over 50 primary GNS cell lines cultured in serum free conditions in order to maintain the GIC population and identified a subgroup of glioma lines with activated Wnt signalling. To determine the requirement of autocrine Wnt signalling for GNS cell renewal we inhibited Wnt secretion, using the porcupine inhibitor LGK974, and measured self-renewal using a limited dilution assay. There was a significant reduction in GNS cell frequency with LGK974 (1uM) treatment in four out of eight lines tested (G432NS, G472NS, G511NS and G523NS). Furthermore, a secondary sphere assay with G523NS cells also showed a significant reduction in GNS cell frequency. When G511NS and G523NS cells were treated with LGK974 (1uM) over a two week period, there was a significant increase in the percentage of GFAP (astrocytic marker) expressing cells whereas a two week treatment with Bio (1uM, Wnt activator), significantly increased the percentage of Tuj1 (neuronal marker) expressing cells. This finding suggest that these cells require a specific amount of Wnt signalling for self-renewal and Wnt inhibition or activation may lead to differentiation. RNAseq analysis comparing the four LGK974 responsive lines with the four LGK974 unresponsive lines identified ASCL1 to be highly expressed in the responsive lines. Gene set enrichment analysis identified four gene sets significantly enriched in the responsive group including the Glioblastoma Proneural gene set whereas genes from the Glioblastoma Mesenchymal gene set were significantly enriched in the unresponsive group. We have identified a subset of GNS cells that are dependent on Wnt secretion for self-renewal and RNAseq analysis suggests that GBMs that fall under the Proneural subtype may be sensitive to Wnt inhibition. Citation Format: Nishani Rajakulendran, Hayden Selvadurai, Katherine Rowland, Nicole Park, Nizar Batada, Peter Dirks, Stephane Angers. Wnt signaling circuits in glioblastoma multiforme. [abstract]. In: Proceedings of the AACR Special Conference: Developmental Biology and Cancer; Nov 30-Dec 3, 2015; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(4_Suppl):Abstract nr B14.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.017
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.382
Teacher spread0.340 · 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 teacher head, 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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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