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Record W4361841447 · doi:10.1158/0008-5472.22425820

Supplementary Materials from Epigenetic Control of <i>Cdkn2a.Arf</i> Protects Tumor-Infiltrating Lymphocytes from Metabolic Exhaustion

2023· preprint· en· W4361841447 on OpenAlexfundno aff
Brian Koss, Bradley D. Shields, Erin M. Taylor, Aaron J. Storey, Stephanie D. Byrum, Allen Gies, Charity L. Washam, Samrat Roy Choudhury, Jeong Hyun Ahn, Hidetaka Uryu, Jason B. Williams, Kimberly J. Krager, Tung-Chin Chiang, Samuel G. Mackintosh, Rick D. Edmondson, Nükhet Aykin‐Burns, Thomas F. Gajewski, Gang Greg Wang, Alan J. Tackett

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsnot available
FundersBC Cancer AgencyGraduate School, University of MarylandBroad Institute
KeywordsEZH2EpigeneticsHistoneCancer researchReprogrammingHistone H4BiologyChemistryCell biologyCellGeneticsGene

Abstract

fetched live from OpenAlex

Figure S1-7. Figure S1: Assessing the effect of EZH2i on in vitro T cell cytotoxic function. Figure S2: Systems approach uncovers drivers of T cell exhaustion resulting from EZH2-inhibtion. Figure S3: Loss of EZH2 function leads to metabolic exhaustion of T cells. Figure S4: Histone epigenetic landscape post EZH2 inhibition. Figure S5: EZH2 inhibition leads to histone epigenetic reprogramming. Figure S6: Arf contributes metabolic exhaustion independent of p53. Figure S7: Exogenous expression of EZH2Y641F in lymphocytes. Extended methods, table of key reagents and resources

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.725
Threshold uncertainty score0.393

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.003
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.7250.333

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 head0.017
GPT teacher head0.253
Teacher spread0.236 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2023
Admission routes1
Has abstractyes

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