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Record W2314631958 · doi:10.1158/1538-7445.am2013-3186

Abstract 3186: Characterizing the antitumor effects of inhibiting translation initiation in glioblastoma multiforme.

2013· article· en· W2314631958 on OpenAlexaff
Arjuna Rajakumar, Joséphine Nalbantoglu

Bibliographic record

VenueCancer Research · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsMcGill University
Fundersnot available
KeywordsEIF4ECancer researchCarcinogenesisPTENTranslation (biology)BiologyOncogeneEukaryotic translationCancerGeneCell cycleMessenger RNAPI3K/AKT/mTOR pathwayGeneticsApoptosis

Abstract

fetched live from OpenAlex

Abstract Oncogene addiction is the process by which a tumor cell becomes increasingly dependent on the expression of a particular gene for the maintenance of its tumorigenicity. Alleviating this dependence via genetic or pharmacological means may profoundly inhibit tumor growth. However many cancers are not dependent on a single gene for survival. Glioblastoma Multiforme (GBM) presents itself with many genetic aberrations contributing to its aggressive phenotype. These include loss of PTEN, amplification of CDK4 and EGFR among others, making targeting a single genetic node ineffective where other oncogenic networks may buffer any therapeutic benefit. Over the last 10 years it has been shown that targeting protein translation initiation results in the simultaneous targeting of many oncogenic pathways. Translation initiation is highly implicated in tumorigenesis with over 10 initiation factors acting as either proto-oncogenes or tumor suppressors, the most well described being the eIF4E. The overexpression of eIF4E in many cancers results in messenger RNA discrimination giving rise to an increased translation of a subset of oncogenic mRNAs with highly structured 5’UTRs. However the precise role translation initiation plays in maintaining tumorigenicity in GBM and the subsequent anti-neoplastic properties of its inhibition are not known. Here, we characterize the anti-tumor effects of targeting protein translation in three glioblastoma cell lines U87MG, U251N and the highly aggressive U87ΔEGFR, using a small molecule inhibitor of eIF4E, 4EGI-1. We show that 4EGI-1 severely impairs cell survival over a 72 hour time interval, in a dose-dependent manner. This is marked by an arrest of cell proliferation starting at 24 hours, induction of apoptosis at 48 hours with increased annexin V staining and a decrease in cell motility. We found these effects coincide with a decrease in protein expression of several oncogenes with highly structured 5’UTRs after treatment with 4EGI-1 including cell proliferation proteins c-Myc and Cyclin D1 which are completely lost by 48 hours and regulators of apoptosis such as Bcl-xL, Mcl-1 and Survivin which decrease and are lost by 72 hours, while not affecting overall transcription of these genes. Our results demonstrate the sensitivity of gliomas towards inhibition of translation initiation. This further highlights translation regulation control as a strong force in cancer therapy, particularly in glioblastoma where options are limiting. Citation Format: Arjuna K. Rajakumar, Josephine Nalbantoglu. Characterizing the antitumor effects of inhibiting translation initiation in glioblastoma multiforme. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3186. doi:10.1158/1538-7445.AM2013-3186

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 score0.003

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)0.0010.000

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 head0.033
GPT teacher head0.338
Teacher spread0.305 · 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
Published2013
Admission routes1
Has abstractyes

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