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Record W2886128131 · doi:10.1158/1538-7445.am2018-684

Abstract 684: Inhibition of translation by aglaiastatins: Mechanism of action

2018· article· en· W2886128131 on OpenAlexaff
Rayelle Itoua Maïga, Regina Cencic, Jennifer Chu, Lauren E. Brown, Daniel D. Waller, Mònica Gómez Palou, Michaël Sébag, John A. Porco, Jerry Pelletier

Bibliographic record

VenueCancer Research · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhytochemical compounds biological activities
Canadian institutionsOccupational Cancer Research CentreMcGill University Health CentreMcGill University
Fundersnot available
KeywordseIF4API3K/AKT/mTOR pathwayBiologyTranslation (biology)Eukaryotic translationMAPK/ERK pathwayMechanism of actionEukaryotic initiation factorRNAProtein subunitKinaseBiochemistryCancer researchChemistryCell biologyMolecular biologySignal transductionIn vitroMessenger RNAGene

Abstract

fetched live from OpenAlex

Abstract Background: Secondary metabolites from plants of the Aglaia genus consist of several classes of compounds, including the cyclopenta[b]benzopyrans, benzo[b]oxepines and cyclopenta[b]benzofurans (rocaglates). The best characterized of these is silvestrol; a rocaglate, which has been shown to target eukaryotic initiation factor 4A (eIF4A), the RNA helicase subunit of the eukaryotic initiation factor 4F (eIF4F) complex. The formation of this complex is regulated by the PI3K/mTOR and Ras-MAPK pathways. Hence, being at the nexus of important oncogenic pathways, eIF4F represents an attractive target for cancer therapy. Silvestrol and its analogs have demonstrated potent activity in human tumor cell lines and xenograft models. Some of the most responsive mRNAs are those encoding oncogenic proteins such as Myc and Mcl-1. This places this group of compounds as promising therapeutic agents against Myc-driven cancers. Purpose of the study: The purpose of this study is to characterize a sub-group of Aglaia secondary metabolites known as aglaiastatins, which are characterized by the presence of a pyrimidone subunit fused to the cyclopenta[b]benzofuran structure, resulting in a pentacyclic skeleton. Method: Using in vitro and in vivo assays, we assessed the potency of representative aglaiastatins towards inhibition of protein synthesis and cytotoxicity of tumor cells. Results: We showed that aglaiastatins induce a specific inhibition of cap-dependent translation by interfering with eIF4A's RNA binding activity, similar to rocaglates. This strong correlation in the mechanism of action was further demonstrated in an eIF4AF163L rocaglate-resistant cell line. Aglaiastatins also demonstrated single agent potency in vitro against a diverse panel of human lymphoma cell lines as well as primary patient samples. In vivo, the compound of interest was found to have a chemosensitization capability, by reversing chemoresistance to doxorubicin in a pre-clinical murine lymphoma model. Conclusion: Our results indicate that the aglaiastatins also target eIF4A, similarly to rocaglates. Their activity against rocaglate-resistant cells indicates a high similarity in drug target binding pattern. Moreover, the targeting of cap-dependent translation through the RNA helicase allows for the potent activity of the drug against several lymphoma lines, regardless of their mutational landscape, thus providing a potential therapeutic opportunity against difficult-to-treat hematological malignancies. Citation Format: Rayelle Itoua Maïga, Regina Cencic, Jennifer Chu, Lauren E. Brown, Daniel Dirck Waller, Mònica Gómez Palou, Michael Sebag, John A. Porco, Jerry Pelletier. Inhibition of translation by aglaiastatins: Mechanism of action [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 684.

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 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 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 score0.006
Threshold uncertainty score0.226

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 head0.118
GPT teacher head0.410
Teacher spread0.292 · 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.

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
Published2018
Admission routes1
Has abstractyes

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