Abstract NTOC-110: UNEXPECTED ROLES FOR KDM4A: PROTEIN SYNTHESIS AND MTOR INHIBITOR SENSITIVITY
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".