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Record W4380372553 · doi:10.1093/neuonc/noad073.176

HGG-27. THE IMPACT OF MISMATCH REPAIR DEFICIENCY ON GLIOMAS IN CHILDREN, ADOLESCENTS, AND YOUNG ADULTS; A REPORT FROM THE IRRDC AND THE GLIOMA TASK FORCE

2023· article· en· W4380372553 on OpenAlexaffabout
Logine Negm, Liana Nobre, Julie Bennett, Jiil Chung, Martin Komosa, Nick Fernandez, Michelle Ku, Monique Johnson, Vanessa Bianchi, Melyssa Aronson, Cindy Zhang, Lucie Stengs, Mary Jane Lim-Fat, Julia Keith, Derek S. Tsang, Andrew Gao, David G. Muñoz, Lananh Nguyen, Sunit Das, Adrian Levine, Anirban Das, Adam Resnick, David A. Wheeler, Cynthia Hawkins, Uri Tabori

Bibliographic record

VenueNeuro-Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsUniversity Health NetworkUniversity of TorontoMount Sinai HospitalHealth Sciences CentreSunnybrook Health Science CentreStructural Genomics ConsortiumHospital for Sick Children
Fundersnot available
KeywordsMedicineGermlineOncologyGliomaDNA mismatch repairInternal medicineCancer researchGermline mutationCancerMutationGeneticsBiologyGene

Abstract

fetched live from OpenAlex

Abstract Mismatch repair deficiency (MMRD) is a pan-cancer mechanism resulting in universal hypermutation. MMRD-gliomas are resistant to chemoradiation but respond to immunotherapy. MMRD can occur somatically or be inherited as part of Lynch Syndrome (LS) or Constitutional Mismatch Repair Deficiency (CMMRD). However, the prevalence of MMRD in gliomas of children, adolescents, and young adults (CAYA), and impact of germline inheritance is unknown. We utilized functional genomic tools on population-based and validation cohorts from 3 large databases (Toronto, St-Jude and CBTN) to determine the prevalence, subgroup, and impact of germline mutations on MMRD-gliomas. Ongoing data on 998 pediatric-gliomas from Toronto reveals that MMRD is extremely rare in pediatric low-grade gliomas (PLGG) but common in pediatric high-grade gliomas (PHGG) (9%, p<0.0001). Similarly, data from St-Jude and CBTN (n=374) reveals that MMRD exists only in PHGG (8%). In both cohorts, MMRD-PHGG are enriched for RAS/MAPK, IDH, and TP53 mutations while pediatric-type fusions, BRAF-V600E and histone mutations are absent. In AYA-HGG (n=884), the most common groups include glioblastomas without chromosome-7/10 alterations (9%) and IDH1-astrocytomas, while MMRD is not present in IDH-oligodendrogliomas. All but 1 CAYA-MMRD-gliomas harbored germline MMR mutations. Data from the IRRDC on 174 patients reveal that LS is more common than CMMRD in gliomas, with the median age of onset 11 and 29 years in CMMRD and LS, respectively (p<0.001). Furthermore, CMMRD gliomas are commonly caused by germline PMS2 and MSH6 mutations, enriched for secondary polymerase mutations (60%, p<0.001), and exhibit ultra-hypermutation. In contrast, LS-gliomas are caused by MSH2 and MLH1 mutations, and enriched for IDH1 mutations (32%, p<0.025) with a lower mutational burden. Our data reveals high prevalence of MMRD in CAYA-HGG with alarming impact of germline predisposition. Specifically, unrecognized LS patients with AYA MMRD-HGG. These findings support universal screening for MMRD in HGG to identify patients for immunotherapy and surveillance.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.043
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.289
Teacher spread0.277 · 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 teacher head, 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

Citations3
Published2023
Admission routes2
Has abstractyes

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