MétaCan
Menu
← Back to cohort
Record W4309016321 · doi:10.1093/neuonc/noac209.341

DNAR-09. THE IMPACT OF MISMATCH REPAIR DEFICIENCY ON HIGH GRADE GLIOMAS IN CHILDREN, ADOLESCENTS AND YOUNG ADULTS; A REPORT FROM THE IRRDC AND THE GLIOMA TASKFORCE

2022· article· en· W4309016321 on OpenAlexaff
Logine Negm, Liana Nobre, Julie Bennett, Jiil Chung, Nick Fernandez, Monique Johnson, Melyssa Aronson, Cindy Zhang, Martin Komosa, Vanessa Bianchi, Lucie Stengs, Mary Jane Lim-Fat, Julia Keith, Derek S. Tsang, Andrew Gao, David G. Muñoz, Lananh Nguyen, Sunit Das, Adrian Levine, Anirban Das, Cynthia Hawkins, Uri Tabori

Bibliographic record

VenueNeuro-Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsSt. Michael's HospitalMount Sinai HospitalHealth Sciences CentreUniversity Health NetworkUniversity of TorontoSunnybrook Health Science CentreHospital for Sick Children
Fundersnot available
KeywordsMicrosatellite instabilityDNA mismatch repairLynch syndromeMedicineGliomaGermline mutationGermlineCancerInternal medicineOncologyCancer researchMutationGeneticsBiologyGeneAlleleMicrosatelliteColorectal cancer

Abstract

fetched live from OpenAlex

Abstract Mismatch repair deficiency (MMRD) is a pan-cancer mechanism resulting in universal hypermutation and aggressive cancers that are resistant to chemoradiation yet sensitive to immunotherapy. MMRD mutations can occur somatically or be inherited as a part of Lynch Syndrome or Constitutional Mismatch Repair Deficiency (CMMRD). Although MMRD affects children, adolescents and young adults (CAYA, ages 0-40) with gliomas, its prevalence and impact of germline inheritance is unknown. Given that high microsatellite instability (MSI) is a key characteristic of MMRD, we previously developed a robust low-coverage whole genome-based tool to quantify MSI, which allows for accurate MMRD detection. We are therefore performing a large-scale MMRD screen of CAYA high grade gliomas (HGGs) and utilizing data from the International Replication Repair Deficiency Consortium (IRRDC) to determine the impact of germline mutations in MMRD gliomas. Ongoing data on 346 HGGs from CAYA patients reveals that MMRD is identified in 6% of HGGs and is not present in tumors with pediatric type alterations. Moreover, of MMRD tumors with IDH1 mutations, none harbor 1p/19q co-deletions. Of patients with available information, all are diagnosed with Lynch Syndrome (69%) or CMMRD (31%), with all Lynch Syndrome diagnoses occurring in patients above 18 years of age. Complementary data from the IRRDC on 113 MMRD patients with gliomas reveal that the median age of glioma is 9.7 and 17.5 years in CMMRD and Lynch Syndrome, respectively (p < 0.001). Strikingly, CMMRD gliomas are enriched for secondary polymerase mutations (60%, p < 0.001) and exhibit ultra-hypermutation, while MMRD gliomas with Lynch Syndrome are enriched for IDH1 mutations (32%, p < 0.025) and harbor a lower mutational burden. Our data reveal a high prevalence of MMRD in CAYA HGGs with alarming impact of germline predisposition. These data can support universal screening for MMRD in high grade glioma diagnostics and identify patients for precision therapeutics.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.022
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.011
GPT teacher head0.277
Teacher spread0.266 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueNeuro-Oncology→Same topicGenetic factors in colorectal cancer→French-language works237,207→