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Record W2307983749 · doi:10.1158/1538-8514.pi3k14-b14

Abstract B14: Targeting amino acid transport to block mTORC1 and cell cycle in prostate cancer

2015· article· en· W2307983749 on OpenAlexaff
Qian Wang, Rae‐Anne Hardie, Andrew J. Hoy, Ladan Fazli, Charles G. Bailey, John E.J. Rasko, Jeff Holst

Bibliographic record

VenueMolecular Cancer Therapeutics · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAmino Acid Enzymes and Metabolism
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGlutaminemTORC1Amino acid transporterCell growthCancer researchProstate cancerCancer cellLNCaPChemistryBiologyCancerAmino acidCell biologyBiochemistrySignal transductionPI3K/AKT/mTOR pathwayTransporter

Abstract

fetched live from OpenAlex

Abstract Background: Amino acids such as leucine and glutamine are important for tumor cell growth, mTORC1 activation, cell cycle and cellular metabolism. As such, amino acid transporters such as LAT1, LAT3 and ASCT2 are commonly upregulated in a variety of cancers1-3. The amino acid transporter ASCT2 (SLC1A5) mediates uptake of glutamine in cancer cells, working together with leucine transporters such as LAT1 and LAT3. We have recently reported that ASCT2 and LAT1 are significantly upregulated in melanoma, and that ASCT2 inhibition significantly decreases glutamine uptake, cell growth, cell cycle and mTORC1 pathway activation1. Furthermore, we have previously shown that both LAT3 and ASCT2 expression are regulated by the androgen receptor in prostate cancer2,3. In this current study we further examine ASCT2 expression levels in prostate cancer, and target ASCT2-mediated glutamine uptake in order to inhibit mTORC1 pathway, metabolism and cell growth. Results: We have used immunohistochemistry on prostate cancer tissue microarrays to show that ASCT2 protein is highly expressed in primary prostate cancer. Interestingly, levels decreased after neoadjuvant hormone therapy, before returning to pretreatment levels in recurrent disease. Blocking ASCT2 function using the amino acid analogue benzylserine led to a significant reduction in glutamine uptake, metabolism (oxygen consumption rate, glutamine oxidation and lipogenesis), cell cycle progression, mTORC1 pathway activation and cell growth. Furthermore, shRNA-mediated ASCT2 knockdown in vitro and in vivo (PC-3 prostate cancer cell xenografts) led to a significant reduction in cell/tumor growth. Conclusions: Amino acid uptake mediated by ASCT2 is essential for multiple cancer cell pathways including mTORC1 signaling and metabolism. As such, ASCT2 targeted therapies are an effective means of inhibiting cellular energy, protein synthesis and cell growth in prostate cancer. References: 1Wang Q, Beaumont KA, Otte NJ, Font J, Bailey CG, van Geldermalsen M, Sharp DM, Tiffen JC, Ryan RM, Jormakka M, Haass NK, Rasko JEJ, Holst J. Targeting glutamine transport to suppress melanoma cell growth. Int J Cancer 135(5):1060-71, 2014 2Wang Q, Tiffen J, Bailey CG, Lehman ML, Ritchie W, Fazli L, Metierre C, Feng Y, Li E, Gleave M, Buchanan G, Nelson CC, Rasko JEJ and Holst J. Targeting amino acid transport in metastatic castration-resistant prostate cancer: Effects on cell cycle, cell growth and tumor development. J Natl Cancer Inst 105(19):1463-73, 2013 3Wang Q, Bailey CG, Ng C, Tiffen J, Thoeng A, Minhas V, Lehman ML, Hendy SC, Buchanan G, Nelson CC, Rasko JEJ and Holst J. Androgen receptor and nutrient signaling pathways coordinate the demand for increased amino acid transport in prostate cancer progression. Cancer Res 71(24):7525-36, 2011. Citation Format: Qian Wang, Rae-Anne Hardie, Andrew Hoy, Ladan Fazli, Charles Bailey, John EJ Rasko, Jeff Holst. Targeting amino acid transport to block mTORC1 and cell cycle in prostate cancer. [abstract]. In: Proceedings of the AACR Special Conference: Targeting the PI3K-mTOR Network in Cancer; Sep 14-17, 2014; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(7 Suppl):Abstract nr B14.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.193
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.014
GPT teacher head0.264
Teacher spread0.251 · 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 teacher head, not a consensus.

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

Citations1
Published2015
Admission routes1
Has abstractyes

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