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Record W4388589072 · doi:10.1093/neuonc/noad179.1050

TMET-06. METABOLOGENOMIC CHARACTERIZATION UNCOVERS HETEROGENEITY AMONGST IDH MUTANT

2023· article· en· W4388589072 on OpenAlexaff
Andrew Ajisebutu, Farshad Nassiri, Yasin Mamatjan, Vikas Patil, Justin Z. Wang, Mathew Voisin, Sheila Mansouri, Gelareh Zadeh

Bibliographic record

VenueNeuro-Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsToronto Western HospitalPrincess Margaret Cancer CentreUniversity of TorontoThompson Rivers UniversityUniversity Health Network
Fundersnot available
KeywordsIsocitrate dehydrogenaseBiologyEpigeneticsGliomaIDH1DNA methylationMutantCancer researchGeneticsGeneEnzymeGene expressionBiochemistry

Abstract

fetched live from OpenAlex

Abstract INTRODUCTION Mutations in isocitrate dehydrogenase (IDH) enzymes are recognised to drive the molecular footprint of diffuse gliomas through the accumulation of oncometabolite R-2-hydroxyglutarate which drives the widespread restructuring of the DNA methylome; however, beyond R-2-hydroxyglutarate , a comprehensive metabologenomic characterization of IDH-mutant gliomas has yet to be performed. We aimed to characterize the global metabolic profile of IDH-mut and IDH-wt gliomas, identify clinically relevant subgroups based on distinct metabolic differences, and utilize matched epigenetic and transcriptional data to characterize these distinct groups. Method: Glioma samples from a cohort of 154 patients underwent multiplatform molecular analysis, including metabolomic studies, genome-wide DNA methylation profiling and bulk RNA sequencing. A integrative analysis was performed, including hierarchical clustering and principal component analysis of identified metabolites, differentially methylated probes, copy number alteration and bulk mRNA data. Survival analysis as well as multivariable hazard ratios for clinical variables were calculated by fitting Cox Proportional Hazards Models. RESULTS We discovered a group of IDH-mutant gliomas whose metabolic profile highly resembled IDH wildtype tumors. Notably, these IDH-mutant gliomas with dysregulated metabolism were distinguished from their IDH-mutant counterparts by significantly shorter overall survival (median OS 118.9 months vs 173.6 months, p=0.048). The IDH-mutant tumors with dysregulated metabolism harbored distinct epigenetic alterations that converged to drive proliferative and stem-like transcriptional profiles. The prognostic relevance of dysregulated metabolism complements, but was not wholly explained by canonically recognized prognostic classifications in IDH-mutant gliomas including 1p/19q codeletion, glioma CpG Island Hypermethylator (GCIMP) status and CDKN2A homozygous deletion. CONCLUSION Utilizing a cross-platform analysis we have uncovered a novel subtyping of IDH-mutant gliomas with dysregulated cellular metabolism with similar survival to IDH-wildtype tumors. The metabolic profile provides unique information on glioma phenotypes, which may facilitate a more comprehensive understanding of glioma biology, and provide a window to target novel dependencies.

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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.028
GPT teacher head0.298
Teacher spread0.269 · 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
Published2023
Admission routes1
Has abstractyes

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