MétaCan
Menu
Back to cohort
Record W3009065698 · doi:10.1093/neuonc/noaa046

Advancing biology-based therapeutic approaches for atypical teratoid rhabdoid tumors

2020· article· en· W3009065698 on OpenAlexafffund
Lindsey M. Hoffman, Elizabeth A. Richardson, Ben Ho, Ashley Margol, Alyssa Reddy, Lucie Lafay‐Cousin, Susan Chi, Irene Slavc, Alexander R. Judkins, Martin Hasselblatt, Franck Bourdeaut, Michael C. Frühwald, Rajeev Vibhakar, Éric Bouffet, Annie Huang

Bibliographic record

VenueNeuro-Oncology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChromatin Remodeling and Cancer
Canadian institutionsInstitute for Clinical Evaluative SciencesUniversity of CalgaryAlberta Children's HospitalUniversity of TorontoSickKids FoundationHospital for Sick Children
FundersCanadian Cancer Society Research Institute
KeywordsAtypical teratoid rhabdoid tumorSMARCB1EpigenomicsTranscriptomeEpigeneticsCarcinogenesisBiologyDNA methylationCancerBioinformaticsCancer researchMedicineGeneticsGeneGene expression

Abstract

fetched live from OpenAlex

Atypical teratoid rhabdoid tumor (ATRT) is a rare, highly malignant central nervous system cancer arising in infants and younger children, historically considered to be homogeneous, monogenic, and incurable. Recent use of intensified therapies has modestly improved survival for ATRT; however, a majority of patients will still succumb to their disease. While ATRTs almost universally exhibit loss of SMARCB1 (BAF47/INI1/SNF5), recent whole genome, transcriptome, and epigenomic analyses of large cohorts reveal previously underappreciated molecular heterogeneity. These discoveries provide novel insights into how SMARCB1 loss drives oncogenesis and confer specific therapeutic vulnerabilities, raising exciting prospects for molecularly stratified treatment for patients with ATRT.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.512
Threshold uncertainty score0.736

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 head0.040
GPT teacher head0.298
Teacher spread0.258 · 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

Citations40
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueNeuro-OncologySame topicChromatin Remodeling and CancerFrench-language works237,207