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Record W4414792352 · doi:10.1093/neuonc/noaf193.562

P17.46.B CLINICAL FEATURES AND MOLECULAR PROFILING USING NEXT GENERATION SEQUENCING AMONG GLIOMA PATIENTS: RETROSPECTIVE REVIEW FROM A QUATERNARY BRAIN TUMOUR CENTER IN CANADA

2025· article· en· W4414792352 on OpenAlexaffabout
Christianne Mojica, Karina Gutierrez, Sarah Cook, Thiago Pimentel Muniz, Julie Bennett, José‐Mario Capo‐Chichi, Andrew Gao, Xin Wang, W. P. Mason

Bibliographic record

VenueNeuro-Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsOligodendrogliomaGliomaAstrocytomaDNA sequencingMolecular diagnosticsMethylationEpendymomaGlioblastoma

Abstract

fetched live from OpenAlex

Abstract BACKGROUND Molecular characterization of tumours is an integral part of diagnosis, treatment decision-making, and prognostication among glioma patients. Next-generation sequencing (NGS) is increasingly being utilized to identify the molecular drivers of these tumours, potentially uncovering targetable alterations that ultimately guide therapy. This is the first study to describe the clinical features and NGS assay results among glioma patients in a brain tumour center in Canada. MATERIAL AND METHODS Consecutive adult glioma patients from 2022 to 2024 were identified. Demographic profile, clinical details, and tumour characteristics were collected through chart review. NGS testing was done using the Oncomine Comprehensive Assay v3. Molecular alterations were assigned based on the ESMO Scale for Clinical Actionability of molecular Targets (ESCAT). RESULTS A total of 187 patients (male 61%, female 39%) were reviewed with a mean age of 45 years at diagnosis. ECOG score was 1 for 114 (61%) patients. Seizure (43%) was the most common presenting symptom and the frontal lobe (43%) was the most common primary tumour location. Glioblastoma (42%) was the leading diagnosis, followed by astrocytoma (19%) and oligodendroglioma (17%). Majority of tumours had a WHO Grade of 4 (53%). MGMT promoter methylation was found in 68 (36%) of patients. Of the 187 patients, 158 (85%) had NGS testing completed. 150 of 158 patients had at least 1 alteration identified. Most common tier 1 alterations were mutations in TERT promoter (57, 36%), CDKN2A/B loss (32, 20%), and IDH1/2 (25, 16%). Most common tier 2 alterations were in TP53 (59, 37%), CDK4/6 amplification (21, 13%), and PIK3CA (18, 11%). Most common tier 3 alterations were in ATRX (9, 6%), MSH6 (7, 4%), and EGFR (6, 4%). Concurrent radiotherapy and temozolomide were given to 109 patients at diagnosis. Ten patients received targeted therapy (BRAF and MEK inhibitor=1, MEK inhibitor=4, FGFR inhibitor=1, IDH1/2 inhibitor=4). With a median follow-up of 22 months, disease progression was seen in 82 (44%) patients on follow-up. At the time of review, 116 (62%) patients were still alive. CONCLUSION We present real-world data on the clinical features and NGS assay results among glioma patients from Princess Margaret Cancer Centre. In the current era of precision oncology, NGS is integral in personalizing treatment approaches by identifying molecular targets to both proven therapies and novel agents under study. Significant associations between these molecular markers, the clinicodemographic features, and outcomes within our population will be presented.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.009
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.041
GPT teacher head0.330
Teacher spread0.289 · 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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