Abstract 4349: Adverse & anticancer activities of 5-azaCdR & DNA methyltransferase (DNMT) isoform specific inhibitors: therapeutic implications
Bibliographic record
Abstract
Abstract Activation of methylated and silenced promoters of genes that suppress tumorigenesis has been the rationale behind the clinical use of DNMT family inhibitor 5aza for treating cancer. However, the adverse effects of general DNA methylation inhibition on tumors through activation of tumor promoting and prometastatic genes as well adverse effects on normal cells have not been comprehensively assessed, nor is it known which particular DNMT is responsible for regulating these adverse effects. A related critical question is whether such potential toxicity could be reduced by targeting a specific isoform of DNMT. We, therefore, performed a comprehensive molecular characterization of the effects of the pan DNMT inhibitor 5aza and isoform-specific inhibition of DNMT1, DNMT3a and DNMT3b by shRNA on cell growth and invasiveness, the methylome and transcriptome of the breast, liver and lung cancer cells as well as their normal primary cell counterparts. Our results suggest that 5aza causes general DNA methylation inhibition both as far as genomic features as well as the genes that are affected. This results in activation of genes involved in promoting cancer and metastasis in both cancer and normal cells. 5aza also induces rampant activation of retroviral elements and ectopic transcription initiation in gene bodies and intergenic regions. Second, isoform-specific exhibit a more limited and specific profile of activity on promoter methylation and gene expression. Third, the adverse profile of 5aza corresponds to the effects of specific inhibition of DNMT3a suggesting that the adverse effects of 5aza are partly mediated through inhibition of DNMT3a. Fourth, specific inhibition of DNMT1 results in an enriched inhibition of DNA methylation in promoters and inhibition of growth without triggering activation of tumor suppressor genes and a more favorable molecular footprint on cancer cells. Our data provide a comprehensive assessment of the impact of DNA methylation inhibition on normal and cancer cells and points to the potential adverse effects of such an approach. However, since the different DNMTs have a different molecular footprint, these adverse effects might be inhibited using isoform-specific DNMT inhibitors. Our data needs to be considered for further clinical development of DNMT inhibitors. Note: This abstract was not presented at the meeting. Citation Format: David Cheishvili, Moshe Szyf. Adverse & anticancer activities of 5-azaCdR & DNA methyltransferase (DNMT) isoform specific inhibitors: therapeutic implications [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4349. doi:10.1158/1538-7445.AM2017-4349
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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) | 0.005 | 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".