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Record W2984561122 · doi:10.1093/neuonc/noz175.298

DRES-10. TEMOZOLOMIDE-ASSOCIATED HYPERMUTATION DETECTED WITH A GENE PANEL SIGNATURE IMPROVES IMMUNE RESPONSE IN GLIOBLASTOMA

2019· article· en· W2984561122 on OpenAlexaff
Paul Daniel, Brian Meehan, Siham Sabri, Jann N. Sarkaria, Janusz Rak, Bassam Abdulkarim

Bibliographic record

VenueNeuro-Oncology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsMcGill University
Fundersnot available
KeywordsSomatic hypermutationTemozolomideMicrosatellite instabilityCancer researchGene signatureBiologyGeneConcordanceMedicineGlioblastomaOncologyGeneticsGene expressionAntibody

Abstract

fetched live from OpenAlex

Abstract Glioblastoma (GBM) is the most common and deadly type of malignant brain cancer in adults. While current standard of care which combines resection, radiation therapy (RT) and Temozolomide (TMZ) effectively eliminates primary disease, recurrence is inevitable, occurs rapidly following treatment and is ultimately lethal due to limited therapeutic opportunities of recurrent GBM. Hypermutation has been reported to occur in a subset of both low and high-grade gliomas and emerges after exposure to TMZ. Mutational inactivation and loss of mismatch repair (MMR) gene expression lead to the accumulation of single nucleotide polymorphisms throughout the genome. To date, the gain of hypermutation and subsequent therapeutic responses are still largely unknown. We hypothesized that hypermutant (HM) and non-hypermutant (NH) tumors represent two recurrent GBM subtypes, which has distinct therapeutic vulnerabilities. In addition, given the lack of concordance between microsatellite instability (MSI) and occurrence of hypermutation in GBM, we sought to derive a limited gene panel which can be used as surrogate biomarker for hypermutation following TMZ to replace whole exome sequencing (WES). Using public datasets, we demonstrated that recurrent GBM can be clustered into two subtypes: HM and NH. We used matched primary and recurrent GBM datasets to derive a gene panel signature, which is uniquely mutated at recurrence in HM GBM and confirmed the specificity of this panel in an independent dataset. Furthermore, we utilized patient derived xenograft (PDX) models to generate pre-clinical models and demonstrated that HM recurrent GBM are more immune responsive while NH recurrent GBM maintained sensitivity to a range of alternate chemotherapies such as cisplatin and RT. Finally, we demonstrated that this signature is represented in exosomes and can be enriched by use of tumor specific antibody capture methods to improve the sensitivity of hypermutation detection in liquid biopsy.

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 score0.004

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)0.0010.000

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 head0.006
GPT teacher head0.230
Teacher spread0.224 · 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
Published2019
Admission routes1
Has abstractyes

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