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Record W3131121535 · doi:10.1016/j.celrep.2021.108769

Chromatin dysregulation associated with NSD1 mutation in head and neck squamous cell carcinoma

2021· article· en· W3131121535 on OpenAlexaff
Nargess Farhangdoost, Cynthia Horth, Bo Hu, Eric Bareke, Xiao Chen, Yinglu Li, Mariel Coradin, Benjamin A. Garcia, Chao Lü, Jacek Majewski

Bibliographic record

VenueCell Reports · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsMcGill UniversityMcGill Genome Centre
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of General Medical SciencesNational Cancer InstituteNational Institutes of Health
KeywordsChromatinBiologyEpigenomicsCarcinogenesisHistoneGeneticsHistone methyltransferaseGeneMutationDNA methylationPolycomb-group proteinsCancer researchGene expressionRepressor

Abstract

fetched live from OpenAlex

Chromatin dysregulation has emerged as an important mechanism of oncogenesis. To develop targeted treatments, it is important to understand the transcriptomic consequences of mutations in chromatin modifier genes. Recently, mutations in the histone methyltransferase gene nuclear receptor binding SET domain protein 1 (NSD1) have been identified in a subset of common and deadly head and neck squamous cell carcinomas (HNSCCs). Here, we use genome-wide approaches and genome editing to dissect the downstream effects of loss of NSD1 in HNSCC. We demonstrate that NSD1 mutations are responsible for loss of intergenic H3K36me2 domains, followed by loss of DNA methylation and gain of H3K27me3 in the affected genomic regions. In addition, those regions are enriched in cis-regulatory elements, and subsequent loss of H3K27ac correlates with reduced expression of their target genes. Our analysis identifies genes and pathways affected by the loss of NSD1 and paves the way to further understanding the interplay among chromatin modifications in cancer.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.007
GPT teacher head0.222
Teacher spread0.215 · 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 designBench or experimental
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

Citations69
Published2021
Admission routes1
Has abstractyes

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