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Record W4380355976 · doi:10.1093/neuonc/noad073.175

HGG-26. GENOMIC AND IMMUNE ANALYSIS OF PRIMARY REPLICATION-REPAIR DEFICIENT (RRD) GLIOMAS REVEALS THREE SUBGROUPS WITH DISTINCT DRIVERS AND RESPONSE TO IMMUNOTHERAPY: AN IRRDC REPORT

2023· article· en· W4380355976 on OpenAlexaff
Nicholas R. Fernandez, Anirban Das, Adrian Levine, Logine Negm, Liana Nobre, Vanessa Bianchi, Lucie Stengs, Jiil Chung, Nuno M. Nunes, Melissa Edwards, Éric Bouffet, Cynthia Hawkins, Uri Tabori

Bibliographic record

VenueNeuro-Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsBiologyMicrosatellite instabilityGliomaGenome instabilityATRXCancer researchGeneticsSomatic hypermutationMutationTranscriptomeChromothripsisDNA mismatch repairGeneAlleleMicrosatelliteDNA repairGene expressionAntibody

Abstract

fetched live from OpenAlex

Abstract BACKGROUND Replication-repair deficiency (RRD) caused by germline/somatic defects in mismatch repair (MMRD) and/or polymerase-proofreading genes (PPD) drives 5-10% of gliomas in children, adolescents, and young adults (CAYA). Although RRD-gliomas harbour high mutation-burden (TMB), the basis of their heterogenous biology, clinical behavior, and response to immune-checkpoint inhibitors (ICI) is unknown. METHODS We analyzed the genome (whole exome, low-coverage genome), methylome, transcriptome (bulk, single-nuclei), and the immune-microenvironment of RRD-gliomas in a large cohort of IRRDC patients and correlated these with clinical outcomes and response to ICI. RESULTS Gliomas in 202 CAYA-patients uniformly harbored hypermutation and genomic microsatellite-instability. Median TMB was 297-mutations/megabase, with frequent mutations in TP53 (90%), ATRX (85%), RAS/MAPK (80%) and IDH1/2 (15%). MMRD (but not PPD) mutational signatures contributed to the enrichment of driver mutations in POLE, IDH1, and TP53 while common pediatric mutations (K27M, G34R/V and BRAF;p.V600E) not driven by MMRD signatures were absent. Paired analyses suggested acquisition of novel variants driven by the mutational signatures that also impacted the immune microenvironment. Multi-omic analyses classified RRD-gliomas into three subgroups: RRD1 (MMRD+PPD; 60%), RRD2 (MMRD-only; 23%), and RRD3 (MMRD+IDH1/2; 16%). All RRD1-gliomas were glioblastomas with earlier age of onset, enrichment in CMMRD, arose at diverse locations including the posterior-fossa, classified in proximity to methylation-RTK1-subclass, exhibited balanced copy number profiles, harbored the highest TMB (median: 409-mutations/megabase), and immunogenic PPD signatures. Conversely, RRD3-gliomas were frequent in Lynch syndrome, presented at an older age, with predominant localization in the forebrain, more complex genomic instability, clustered in proximity to IDH1-gliomas, and harbored lower TMB (median: 33-mutations/megabase). RRD1-gliomas revealed the highest immune infiltrates with significantly improved median post-ICI survival of 52-months versus <12-months for RRD2/3 (p<0.0001). CONCLUSIONS The distinct genomic subgroups of RRD-gliomas can explain their diverse clinical outcomes, highlighting the need for developing subgroup-specific, immune-directed treatment approaches for these patients.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.023
GPT teacher head0.302
Teacher spread0.279 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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