MétaCan
Menu
← Back to cohort

Abstract NTOC-110: UNEXPECTED ROLES FOR KDM4A: PROTEIN SYNTHESIS AND MTOR INHIBITOR SENSITIVITY

2017· article· en· W2623526312 on OpenAlexaff
Capucine Van Rechem, Joshua C. Black, Patricia Greninger, Yang Zhao, Myriam Boukhali, Carlos Donado, Martin J. Aryee, Paul d. Burrowes, Brendon Ladd, S. Gräslund, Wilhelm Haas, David C. Christiani, Cyril H. Benes, Johnathan R. Whetstine

Bibliographic record

VenueClinical Cancer Research · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsStructural Genomics Consortium
Fundersnot available
KeywordsPI3K/AKT/mTOR pathwayBiologyProtein biosynthesisTranslation (biology)Translational regulationCancer researchCell biologyBiochemistryMessenger RNASignal transductionGene

Abstract

fetched live from OpenAlex

Abstract Single nucleotide polymorphisms (SNPs) occur within chromatin-modulating factors; however, little is known about how these variants within the coding sequence impact cancer progression or treatment. Therefore, there is a need to establish their biochemical and/or molecular contribution, their use in sub-classifying patients and their impact on therapeutic response. We demonstrate that coding SNP-A482 within the lysine tri-demethylase KDM4A/JMJD2A associates with differential outcome in cancer patients and promotes KDM4A protein turnover. Interestingly, homozygous SNP-482 cells have increased mTOR inhibitor sensitivity. mTOR inhibitors significantly reduce SNP-A482 protein levels, which parallels the increased drug sensitivity observed with KDM4A depletion. Furthermore, we demonstrate that KDM4A interacts with the translation initiation complex and impacts the distribution of translation initiation factors within polysome fractions. Upon KDM4A depletion, protein synthesis was reduced and there was enhanced protein synthesis suppression with mTOR inhibitors, which paralleled an increased sensitivity to these drugs. Lastly, we demonstrate that JIB-04, a JmjC demethylases inhibitor, suppresses translation initiation and enhances mTOR inhibitor sensitivity. These data highlight an unexpected role for KDM4A in regulating protein synthesis, in modulating mTOR inhibitor sensitivity and suggest potential novel therapeutic applications for this class of enzyme. Citation Format: Capucine Van Rechem, Joshua C. Black, Patricia Greninger, Yang Zhao, Myriam Boukhali, Carlos Donado, Martin J. Aryee, Paul d. Burrowes, Brendon Ladd, Susanne Gräslund, Wilhelm Haas, David C. Christiani, Cyril H. Benes and Johnathan R. Whetstine. UNEXPECTED ROLES FOR KDM4A: PROTEIN SYNTHESIS AND MTOR INHIBITOR SENSITIVITY [abstract]. In: Proceedings of the 11th Biennial Ovarian Cancer Research Symposium; Sep 12-13, 2016; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(11 Suppl):Abstract nr NTOC-110.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.125
GPT teacher head0.465
Teacher spread0.340 · 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.

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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueClinical Cancer Research→Same topicCancer Genomics and Diagnostics→French-language works237,207→