Abstract 2952: Targeting CDK9 reactivates epigenetically silenced genes in cancer
Bibliographic record
Abstract
Abstract In cancer, the epigenome is aberrantly reprogrammed leading to a wide range of heritable changes in gene expression, such as silencing of tumor suppressor genes (TSG). Altered epigenetic marks in cancer involve DNA methylation and histone post-translational modifications, and these come about as a result of aging and acquisition of genetic and epigenetic changes in readers/writers/editors of the epigenome. Given the reversible nature of epigenetic modifications, one goal of epigenetic therapy of cancer is to induce TSG reactivation, leading to cancer cell differentiation and cancer cell death. To identify novel targets that can reactivate epigenetically silenced genes, we developed a phenotypic-based system, YB5. YB5 is a colon cancer cell line generated by stably transfecting SW48 cells with a vector containing GFP driven by a methylated and silenced CMV promoter. GFP re-expression can be achieved by known epigenetic drugs that lead to demethylation or induce active chromatin marks in the CMV promoter. We screened a natural compound library for GFP activation in YB5 and identified a novel drug class that shares an aminothiazole core structure, and has epigenetic effects that are equivalent to DNA methyltransferase inhibitor (DNMTi). Target deconvolution identified CDK9 as the target of these drugs, which reactivate gene expression without affecting DNA methylation. It is well established that CDK9, the catalytic subunit of p-TEFb, is a transcriptional activator. CDK9 in complex with its regulatory subunit, Cyclin T1 or T2, is recruited by multiple mechanisms to promote RNAPII promoter-proximal pause release by phosphorylating negative elongation factors (DSIF and NELF). In addition, phosphorylation of the C-terminal domain (CTD) of RNAPII on Serine-2 allows recruitment of RNA processing factors, which work on the nascent RNA as it emerges from RNAPII. Our new data show that long-term CDK9 inhibition can reactivate epigenetically silenced genes with minimal downregulation effects, effects which are the opposite of the canonical role of CDK9-mediated transcriptional elongation. Mechanistically, we showed that CDK9 inhibition dephosphorylates the SWI/SNF protein SMARCA4 and represses HP1α expression, both of which contribute to gene reactivation. Based on gene activation, we developed the highly selective and potent CDK9 inhibitor MC180295 (IC50 =5nM) that has broad anti-cancer activity in-vitro and is effective in in-vivo cancer models. Additionally, CDK9 inhibition sensitizes with the immune checkpoint inhibitor α-PD-1 in vivo, making it an excellent target for epigenetic therapy of cancer. This is the first study that links CDK9 to maintaining gene silencing at epigenetically repressed loci in mammals. Excitingly, this is also the first example of kinase inhibitors as potential drugs that reverse epigenetic silencing. Citation Format: Hanghang Zhang, Somnath Pandey, Meghan Travers, Jittasak Khowsathit, George Morton, Hongxing Sum, Carlos A. Barrero, Carmen Merali, Yasuyuki Okamoto, Takahiro Sato, Judit Garriga, Natarajan V. Bhanu, Johayra Simithy, Bela Patel, Jozef Madzo, Noël Raynal, Benjamin A. Garcia, Marlene A. Jacobson, Salim Merali, Yi Zhang, Wayne Childers, Magid Abou-Gharbia, John Karanicolas, Stephen B. Baylin, Cynthia A. Zahnow, Jaroslav Jelinek, Xavier Graña, Jean-Pierre J. Issa. Targeting CDK9 reactivates epigenetically silenced genes in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2952.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".