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Record W2740022358 · doi:10.1158/1538-7445.am2017-4346

Abstract 4346: S-Adenosyl methionine (SAM) blocks breast cancer growth, invasion and metastasis <i>in vitro</i> and <i>in vivo</i>

2017· article· en· W2740022358 on OpenAlexaff
Niaz Mahmood, David Cheishvili, Ani Arakelian, William J. Muller, Moshe Szyf, Shafaat A. Rabbani

Bibliographic record

VenueCancer Research · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsMcGill University
Fundersnot available
KeywordsIn vivoMetastasisCancer researchCancerBreast cancerCell growthMammary tumorBiologyIn vitroPathologyMedicineBiochemistry

Abstract

fetched live from OpenAlex

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 donar S-adenosyl methionine (SAM) for its ability 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 showed the ability of SAM to decrease the expression 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 pro-metastatic 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. Citation Format: Niaz Mahmood, David Cheishvili, Ani Arakelian, William J. Muller, Moshe Szyf, Shafaat A. Rabbani. S-Adenosyl methionine (SAM) blocks breast cancer growth, invasion and metastasis in vitro and in vivo [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 4346. doi:10.1158/1538-7445.AM2017-4346

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.053
GPT teacher head0.374
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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