Abstract 1388: Pharmacologic targeting of DNA methylation blocks breast cancer growth and metastasis
Bibliographic record
Abstract
Abstract Abnormal DNA methylation is a hallmark of cancer which orchestrates changes in gene transcription seen in cancer. Both hypermethylation-mediated inactivation of tumor suppressor genes and hypomethylation-mediated activation of pro-metastatic genes are common attributes of cancer cells which make the methylome an attractive anti-cancer therapeutic target. Interestingly, in contrast to genetic changes, DNA methylation-mediated epigenetic changes are potentially reversible by either dietary supplements or therapeutic strategies. Consequently, a wide variety of epigenetic drugs primarily targeting DNA hypermethylation has been shown to be effective in preclinical and clinical settings. Pioneering works done by us have shown that treatment of various human cell lines (breast, prostate, osteosarcoma) with a methylating agent S-adenosyl methionine (SAM) can block tumor growth and metastasis in vivo. At the molecular level, SAM treatment induces hypermethylation of promoters of key pro-metastatic genes; and thereby inhibits their expression. However, the anti-cancer effect of SAM has never been examined. Based on the heterogeneity of tumor cells which are at different stages of tumor invasiveness, we hypothesized that treatment with demethylating (Decitabine) and methylating agents (SAM) would collectively lead to the activation of tumor suppressor genes and suppression of pro-metastatic genes to block cancer growth and metastasis. In the current study, we first investigated the effects of Decitabine and SAM alone and in combination to prevent breast cancer development, growth, and metastasis using the MDA-MB-231 xenograft model of breast cancer. Our data showed that treatment with Decitabine and SAM resulted in a significant delay in the progression of mammary tumors in experimental animals compared to controls, effects which were significantly more pronounced when Decitabine and SAM were administered in a combination setting. Gene expression analysis of the cancer cells revealed that SAM-treatment repressed the expression of several key genes involved in cancer progression. In addition, immunohistochemical analysis of primary tumors revealed that the combination treatment (Decitabine, SAM) reduced the number of Ki67 positive cells as well reduced the expression of angiogenesis marker CD-31. Further studies examining the effects of combined therapy on genome-wide gene expression changes as well as any potential side effects on animal behavior and toxicity will be presented and discussed. Results from this study will provide compelling evidence and rationale for the initiation of clinical trials with SAM alone as a monotherapy and in combination with currently approved epigenetic drugs (Decitabine) to reduce breast cancer-associated morbidity and mortality. Citation Format: Niaz Mahmood, Ani Arakelian, William J. Muller, Moshe Szyf, Shafaat A. Rabbani. Pharmacologic targeting of DNA methylation blocks breast cancer growth and metastasis [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 1388.
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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".