Abstract A09: Development of epigenetic-based strategies for blocking breast cancer growth, invasion, and metastasis in vitro and in vivo
Bibliographic record
Abstract
Abstract DNA hypomethylation has been implicated in the coordinated targeting of various signaling pathways involved in tumor growth and metastasis. In the current study, through various in vitro and in vivo assays, we have examined the plausibility of using universal methyl donor S-adenosyl methionine (SAM) to block breast cancer development, growth, and metastasis in our xenograft and transgenic models of breast cancer. Treatment of highly invasive human triple-negative breast cancer (TNBC) cell lines MDA-MB-231 and Hs578T with SAM resulted in a significant dose-dependent decrease in cell proliferation, invasion, migration, colony formation, and increased apoptosis in vitro. Affymetrix gene expression array and real-time PCR (qPCR) validated the ability of SAM to decrease the expression of several genes implicated in cancer progression in MDA-MB-231 cells. For the in vivo studies, MDA-MB-231 cells expressing green fluorescent protein (MDA-MB-231-GFP) were inoculated into female CD-1 nude mice via mammary fat pad. From day three post tumor cell inoculation, animals were treated with SAM (0.8-1.6 mg/day) or vehicle alone as control via daily oral gavage, and tumor volume was determined at weekly intervals for 10 weeks. SAM treatment caused a significant dose-dependent decrease in tumor volume and GFP-positive metastasis to lungs, liver, and spleen in experimental animals compared to vehicle-treated controls. Analysis of RNA from primary tumors by qPCR showed the ability of SAM to cause a marked decrease in the expression of several prometastatic and EMT pathway genes. Pyrosequencing of tumoral DNA from control and experimental animals showed that SAM treatment causes a significant increase in the percentage of CpG methylation at the promoter region of several cancer-related genes which were seen to be downregulated in the qPCR assay. We next determined the effect of SAM in MMTV-PyMT transgenic mouse model of breast cancer where SAM treatment resulted in a significant delay in the development of mammary tumors and decreased tumor growth in experimental animals as compared to vehicle-treated controls. SAM was found to bioavailable in the serum of experimental animals as determined by mass spectrometry and no notable adverse side effects were seen, including any change in animal behavior. Results from these studies provide compelling evidence for the therapeutic potential of SAM in breast cancer to provide the rationale for initiating clinical trials with SAM in patients with breast and other common cancers as monotherapy or in combination setting with current therapeutic agents to reduce cancer-associated morbidity and mortality. Note: This abstract was not presented at the conference. Citation Format: Niaz Mahmood, David Cheishvili, Ani Arakelian, William J. Muller, Imrana Tanveer, Haseeb Khan, Moshe Szyf, Shafaat A. Rabbani. Development of epigenetic-based strategies for blocking breast cancer growth, invasion, and metastasis in vitro and in vivo [abstract]. In: Proceedings of the AACR International Conference held in cooperation with the Latin American Cooperative Oncology Group (LACOG) on Translational Cancer Medicine; May 4-6, 2017; São Paulo, Brazil. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(1_Suppl):Abstract nr A09.
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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) | 0.002 | 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".