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Record W2321199954 · doi:10.1158/1538-7445.am10-1607

Abstract 1607: Effect of folate and GGH modulation on chemosensitivity of colon and breast cancer cells to 5-fluorouracil and methotrexate

2010· article· en· W2321199954 on OpenAlexaff
Sung‐Eun Kim, Peter D. Cole, Kyoung‐Jin Sohn, Robert C. Cho, Ruth Croxford, Barton A. Kamen, Young‐In Kim

Bibliographic record

VenueCancer Research · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Research and Treatments
Canadian institutionsSunnybrook Health Science CentreCentre for Social InnovationUniversity of Toronto
Fundersnot available
KeywordsThymidylate synthaseDihydrofolate reductaseIntracellularAntifolateAntimetaboliteBiochemistryPharmacologyMethotrexateChemistryCytotoxicityCancer researchIn vitroBiologyEnzymeCancerFluorouracilImmunology

Abstract

fetched live from OpenAlex

Abstract Background: γ-Glutamyl hydrolase (GGH) removes terminal glutamates of polyglutamylated folates and antifolates, thereby facilitating hydrolysis and efflux of folates and antifolates from the cell. Therefore, GGH plays an important role in regulating intracellular folates and antifolates for optimal folate-dependent one-carbon transfer reactions and antifolate-induced cytotoxic effects, respectively. We have previously reported that GGH modulation significantly affects chemosensitivity of colon and breast cancer cells to 5-fluorouracil (5FU) and methotrexate (MTX) by changing intracellular retention of a folate cofactor (5,10-methylenetetrahydrofolate) necessary for the cytotoxic effects of 5FU and by changing intracellular retention of MTX, respectively. We investigated whether the GGH modulation-induced changes in chemosensitivity might be counterbalanced by GGH modulation-induced changes in polyglutamylation of other intracellular folate cofactors and total intracellular folate pools. Methods: Human HCT116 colon and MDA-MB-435 breast cancer cells were stably transfected with the sense GGH cDNA or GGH-targeted siRNA, respectively, to generate an in vitro model of GGH overexpression and inhibition. In vitro chemosensitivity to 5FU and MTX under 2.3 μM folic acid (FA) and 50 nM and 100 nM of 5-methyltetrahydrofolate (5MTHF) was determined. Results: GGH overexpression was associated with lower total and long-chain polyglutamylated intracellular folate concentrations, thymidylate synthase (TS) catalytic enzyme activity, and dihydrofolate reductase (DHFR) protein expression than controls, while GGH inhibition showed higher total and long-chain polyglutamylated intracellular folate concentrations, TS activity, and DHFR protein expression than controls (P<0.05). In HCT116 cells, GGH overexpression decreased chemosensitivity to 5FU and MTX at 2.3 uM FA while it enhanced chemosensitivity to 5FU and MTX at 50 nM 5MTHF (P<0.05). At all concentrations, GGH inhibition increased chemosensitivity to 5FU while it decreased chemosensitivity to MTX (P<0.05). In MDA-MB-435 cells, GGH overexpression decreased chemosensitivity to 5FU and MTX at all concentration (P<0.05). GGH inhibition increased chemosensitivity to 5FU at 2.3 uM FA and 100 nM 5MTHF whereas it decreased chemosensitivity to MTX at all concentrations (P<0.05). Conclusions: As proof of principle, we provide functional evidence that GGH overexpression and inhibition can modulate chemosensitivity of colon and breast cancer cells to 5FU and MTX. These GGH modulation-induced changes in chemosensitivity are further influenced by different folate forms and concentrations. Our data suggest that both GGH modulation and folate status affect chemosensitivity of colon and breast cancer cells to 5FU and MTX. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 1607.

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 score0.005

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)0.0020.000

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 head0.021
GPT teacher head0.385
Teacher spread0.364 · 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".

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Citations0
Published2010
Admission routes1
Has abstractyes

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