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Record W3128912337 · doi:10.1111/febs.15750

Clinical advances in targeting epigenetics for cancer therapy

2021· review· en· W3128912337 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

VenueFEBS Journal · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Degradation and Inhibitors
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
FundersCanadian Institutes of Health ResearchOntario Institute for Cancer ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsEpigeneticsCancer therapyEpigenetic therapyCancerMedicineComputational biologyBiologyBioinformaticsDNA methylationInternal medicineGenetics

Abstract

fetched live from OpenAlex

The appropriate coordination between epigenetic regulators is essential for spatial and temporal regulation of gene expression and maintenance of cell identity. Cancer is a disease driven by both genetic and epigenetic alterations. The widespread dysregulation and reversible nature of epigenetic alterations confer cancer cells with vulnerabilities for therapeutic interventions. Over the past decades, remarkable progress has been made in developing drugs that target epigenetic regulators, with many drugs under evaluation in clinical trials. Here, we summarize the epigenetic drugs currently in clinical investigations and highlight the potentials and challenges in their implication to treat cancer. We also discuss the preclinical and clinical results of combination therapies with epigenetic drugs and other therapies such as targeted and immune-based therapies.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score0.702

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.073
GPT teacher head0.446
Teacher spread0.373 · 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