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Record W3047584058 · doi:10.1158/1538-7445.pedca19-a33

Abstract A33: HIF2 in pediatric high-grade glioma and its targeting

2020· article· en· W3047584058 on OpenAlexaboutno aff
Quentin Fuchs, Anne Florence Blandin, Isabelle Lelong Rebel, Marina Pierrevelcin, Monique Dontenwill, Natacha Entz‐Werlé

Bibliographic record

VenueCancer Research · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsnot available
Fundersnot available
KeywordsEpigeneticsBiomarkerCancer researchBiologyHistonePediatric cancerMedicineBioinformaticsCancerOncologyGeneGenetics

Abstract

fetched live from OpenAlex

Abstract Pediatric high-grade gliomas (pHGGs) represent a very dismal disease that is needing new innovative compound for treatment. Despite the past discovery of histone H3 driver mutations, we are not able for instance to stop this induced process on epigenetic remodulation. Therefore, there is since a decade proactive works aimed to discover new targetable proteins in these tumors. In our recent previous works in pHGGs, we highlighted HIF2alpha as a biomarker of worse prognosis and outcome and a key in treatment resistance. Therefore, this new project was designed to determine in several patient-derived cell lines (5 PDCLs) the presence of HIF2alpha, its role and its induction in normoxic and hypoxic microenvironment concomitantly by immunofluorescence and Western blot assessments and RNA sequencing analyses. Complementary ChipSeq was also performed using HIF2 antibodies to determine its role as a transmission factor. Specific promoters were involved. After the confirmation of its frequent presence in multiple PDCLs initiated from thalamic pHGGs and DIPG, we used allosteric inhibitors to target HIF2alpha. Surprisingly, this protein was expressed constantly in hypoxic and normoxic conditions and specifically in PDCLs bearing stem cell features. Specific expression associations were established between stemness markers and HIF2alpha. To go further, we tested specific HIF2A inhibitors, which were having an impact on cell proliferation and on the decrease of the target itself. In conclusion, HIF2 seem to be a major biomarker in pHGGs that might be targeted, and it is a useful new opportunity for pHGGs treatments. Citation Format: Quentin Fuchs, Anne Florence Blandin, Isabelle Lelong Rebel, Marina Pierrevelcin, Monique Dontenwill, Natacha Entz-Werlé. HIF2 in pediatric high-grade glioma and its targeting [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr A33.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.048
GPT teacher head0.354
Teacher spread0.306 · 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 designObservational
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
Published2020
Admission routes1
Has abstractyes

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