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Record W3087177675 · doi:10.2217/epi-2020-0186

EZH2 Inhibition: A Promising Strategy to Prevent Cancer Immune Editing

2020· review· en· W3087177675 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEpigenomics · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsUniversity of British ColumbiaUniversité Laval
FundersNational Cancer InstituteCanadian Institutes of Health ResearchTerry Fox Research InstituteMitacsCancer Research UK
KeywordsBiologyImmune systemEZH2CancerComputational biologyCancer researchImmunologyBioinformaticsGeneticsEpigeneticsGene

Abstract

fetched live from OpenAlex

Immunotherapies are revolutionizing the clinical management of a wide range of cancers. However, intrinsic or acquired unresponsiveness to immunotherapies does occur due to the dynamic cancer immunoediting which ultimately leads to immune escape. The evolutionarily conserved histone modifier enhancer of zeste 2 (EZH2) is aberrantly overexpressed in a number of human cancers. Accumulating studies indicate that EZH2 is a main driver of cancer cells' immunoediting and mediate immune escape through downregulating immune recognition and activation, upregulating immune checkpoints and creating an immunosuppressive tumor microenvironment. In this review, we overviewed the roles of EZH2 in cancer immunoediting, the preclinical and clinical studies of current pharmacologic EZH2 inhibitors and the prospects for EZH2 inhibitor and immunotherapy combination for cancer treatment.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.039
GPT teacher head0.339
Teacher spread0.300 · 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