Abstract A33: HIF2 in pediatric high-grade glioma and its targeting
Bibliographic record
Abstract
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.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".