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Record W4210304148 · doi:10.1038/s41467-022-28253-4

Splicing is an alternate oncogenic pathway activation mechanism in glioma

2022· article· en· W4210304148 on OpenAlexafffund
Robert Siddaway, Scott Milos, Arun Kumaran Anguraj Vadivel, Tara Dobson, Jyothishmathi Swaminathan, Scott Ryall, Sanja Pajovic, Palak Patel, Javad Nazarian, Oren J. Becher, Michael Brudno, Arun Ramani, Vidya Gopalakrishnan, Cynthia Hawkins

Bibliographic record

VenueNature Communications · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsUniversity of TorontoUniversity Health NetworkSickKids FoundationHospital for Sick Children
FundersCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchGovernment of CanadaGilbert Family Foundation
KeywordsNeurofibromin 1GliomaRNA splicingDownregulation and upregulationCancer researchMAPK/ERK pathwayAlternative splicingBiologySuppressorCancerMechanism (biology)MutationSignal transductionGene isoformBioinformaticsMedicineGeneticsNeurofibromatosisGene

Abstract

fetched live from OpenAlex

High-grade diffuse glioma (HGG) is the leading cause of brain tumour death. While the genetic drivers of HGG have been well described, targeting these has thus far had little impact on survival suggesting other mechanisms are at play. Here we interrogate the alternative splicing landscape of pediatric and adult HGG through multi-omic analyses, uncovering an increased splicing burden compared with normal brain. The rate of recurrent alternative splicing in cancer drivers exceeds their mutation rate, a pattern that is recapitulated in pan-cancer analyses, and is associated with worse prognosis in HGG. We investigate potential oncogenicity by interrogating cancer pathways affected by alternative splicing in HGG; spliced cancer drivers include members of the RAS/MAPK pathway. RAS suppressor neurofibromin 1 is differentially spliced to a less active isoform in >80% of HGG downstream from REST upregulation, activating the RAS/MAPK pathway and reducing glioblastoma patient survival. Overall, our results identify non-mutagenic mechanisms by which cancers activate oncogenic pathways which need to accounted for in personalized medicine approaches.

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 score0.063
Threshold uncertainty score0.376

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 science0.0010.001
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 head0.025
GPT teacher head0.323
Teacher spread0.298 · 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

Citations59
Published2022
Admission routes2
Has abstractyes

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