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Abstract C205: Cytotoxicity and target modulation in pediatric solid tumors by the proteasome inhibitor carfilzomib.

2013· article· en· W2002734325 on OpenAlexaff
Yibing Ruan, David Liu, Aarthi Jayanthan, Tony H. Truong, Jessica Boklan, Aru Narendran

Bibliographic record

VenueMolecular Cancer Therapeutics · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicUbiquitin and proteasome pathways
Canadian institutionsAlberta Children's Hospital
Fundersnot available
KeywordsCarfilzomibTolerabilityProteasome inhibitorCancer researchProteasomeCytotoxicityApoptosisPharmacologyCell cycleCell cultureChemistryMedicineIn vitroBiologyAdverse effectBiochemistry

Abstract

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Abstract Rationale: Currently, even with escalating multimodal treatment regimens, most children with recurrent metastatic solid tumors endure unacceptably high mortality rates. Hence, there is an urgent need to identify novel targets and new therapeutic approaches. Recent studies have shown that proteasome inhibition leads to effective tumor killing in cells that have acquired treatment resistance and metastatic properties. Carfilzomib (CFZ) is a selective and potent proteasome inhibitor that binds and inhibits the 20S proteasome resulting in the accumulation of polyubiquinated proteins and consequently, cycle arrest, growth inhibition and apoptosis. Clinical trials in adult myeloma have shown activity and a high tolerability of CFZ. However, data with respect to the potential of this agent for refractory pediatric solid tumors are not yet available. Methods: Cells from a panel of lines including neuroblastoma (n=6), Ewings sarcoma (n=2), osteosarcoma (n=2), rhabdomyosarcoma (n=4) and ATRT (n=2), were treated with increasing concentrations of CFZ and cell growth inhibition was quantified by Alamar blue assay. Drug pulsing experiments were carried out by adding new drug preparations at defined time intervals. Target modulation and apoptosis analyses were done by Western blotting. Drug combination studies were interpreted using the Chou and Talalay method. Results and Discussion: CFZ showed effective cytotoxicity against all cell lines tested (mean IC50 = 7nM, range = 1-20nM) and activity in a fluorophore tagged cell based proteasome assay. More detailed target modulation studies in NB cells showed a dose dependent initial up-regulation of MCL-1 that subsequently decreased in a manner corresponding with PARP cleavage. Up-regulation of BCL-2 was also noted but required the exposure to higher drug concentrations. This indicated the initial stabilization or activation of survival pathways in response to drug treatment. Drug scheduling showed that the minimum exposure of 4 to 8 hours /day is needed for effective cumulative killing coinciding with MCL-1 regulation. Drug combination studies identified the ability of CFZ to synergistically enhance the activity of a number of chemotherapeutic agents, including etoposide, vincristine, mefloquine and the BCL-2 antagonist ABT-263. However, the extent of synergy differed greatly between cell types indicating the potentially variable relationship of proteasome functions to distinct oncogenic pathways present in different cells. Our studies provide initial in vitro data on the potential of CFZ to treat pediatric solid tumors and support further investigations in to the components of drug scheduling, biological correlates and drug combinations for future early phase clinical trials. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):C205. Citation Format: Yibing Ruan, David Liu, Aarthi Jayanthan, Tony Truong, Jessica Boklan, Aru Narendran. Cytotoxicity and target modulation in pediatric solid tumors by the proteasome inhibitor carfilzomib. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr C205.

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 score0.036
Threshold uncertainty score0.762

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 head0.013
GPT teacher head0.253
Teacher spread0.240 · 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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Citations1
Published2013
Admission routes1
Has abstractyes

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