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Record W2064590689 · doi:10.1158/1538-7445.am2014-3233

Abstract 3233: Targeting system xc- in breast cancer cells: Development of novel therapeutics

2014· article· en· W2064590689 on OpenAlexaff
Jennifer Fazzari, Hanxin Lin, Katja Linher‐Melville, Gurmit Singh

Bibliographic record

VenueCancer Research · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAmino Acid Enzymes and Metabolism
Canadian institutionsMcMaster University
Fundersnot available
KeywordsGlutathioneCancer cellGlutamate receptorBreast cancerCancerGlutathione synthetasePharmacologyChemistryTransfectionCysteineBiochemistryCancer researchMolecular biologyBiologyGeneGeneticsEnzyme

Abstract

fetched live from OpenAlex

Abstract Cancer-induced bone pain is a major palliative condition suffered by many afflicted with breast cancer. With current anti-cancer therapeutics prolonging the lives of these patients without eradicating the disease itself, quality of life concerns predominate patient care. A potential mechanism implicates glutamate, and specifically the glutamate-cystine antiporter called system xc-. Due to the accelerated metabolism of cancer cells, this transporter (xCT) is upregulated to supply the cell with adequate cysteine for detoxification of reactive oxygen species via glutathione (GSH) synthesis. We conducted a high-throughput screening of 30,000 compounds to identify novel small molecules capable of reducing glutamate release from MDA-MB-231 cells. Glutamate release was quantified using Amplex Red fluorometric assay after 48 hours incubation with test compounds. Compounds providing the greatest effect were: 4-[2-(Dipropylamino)ethyl]-1,2-benzenediol, SKF 38393, Capsazepine and SPB 05855. Follow-up entailed confirmation of screening results (i.e. inhibition of glutamate secretion) in a dose dependent manner (0-200uM). GSH content of treated cells was also analyzed as it's production is linked to xCT activity. Intracellular GSH levels were analyzed in cell lysates using DNTB. In addition, to further explore mode of action of these compounds, we cloned 2.6 kilobase pairs (-2329 to +278 bp) of the human xCT promoter region from genomic DNA isolated from MDA-MB-231 breast cancer cells. Four truncations were also generated by PCR. Full length and truncated clones were transferred into a luciferase reporter gene construct (PGL3-Basic). This acted as a tool to test whether any of these compounds inhibit glutamate release through transcriptional regulation of xCT. MDA-MB-231 cells were transiently co-transfected with the dual luciferase system and subsequently treated with compounds. Glutamate release data confirmed the results of most positive hits but revealed that SPB 05855 did not meet the original criteria, as it had no effect on inhibition of glutamate release when re-tested. Compound cytotoxicity was assessed using crystal violet staining and dose-response data indicated that a concentration of 50 µM was the highest effective dose that did not result in cytotoxicity for all confirmed compounds. GSH results indicate that only SKF 38393 reduces total GSH levels in a dose-dependent manner. Promoter activity revealed that, of the four compounds, only SKF 38393 significantly affected promoter activity resulting in a 30% reduction in luciferase production. These experiments indicate a series of potent compounds that inhibit glutamate release and have the potential for development into novel therapeutics aimed at the treatment of cancer-induced bone pain. Citation Format: Jennifer Fazzari, Hanxin Lin, Katja Linher-Melville, Gurmit Singh. Targeting system xc- in breast cancer cells: Development of novel therapeutics. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3233. doi:10.1158/1538-7445.AM2014-3233

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 score0.002
Threshold uncertainty score0.006

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.050
GPT teacher head0.359
Teacher spread0.309 · 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
Published2014
Admission routes1
Has abstractyes

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