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Record W2076838198 · doi:10.1038/bjc.2012.209

Clusterin inhibition using OGX-011 synergistically enhances antitumour activity of sorafenib in a human renal cell carcinoma model

2012· article· en· W2076838198 on OpenAlexaff
Y. Kususda, Hideaki Miyake, Martin Gleave, M. Fujisawa

Bibliographic record

VenueBritish Journal of Cancer · 2012
Typearticle
Languageen
FieldMedicine
TopicClusterin in disease pathology
Canadian institutionsVancouver General Hospital
Fundersnot available
KeywordsSorafenibClusterinApoptosisIn vivoDownregulation and upregulationRenal cell carcinomaCancer researchMedicineInternal medicinePharmacologyEndocrinologyBiologyHepatocellular carcinomaBiochemistry

Abstract

fetched live from OpenAlex

BACKGROUND: The objective of this study was to investigate whether the therapeutic activity of sorafenib could be enhanced by combining with OGX-011, an antisense oligodeoxynucleotide (ODN) targeting clusterin, in renal cell carcinoma (RCC). METHODS: We investigated the effects of combined treatment with OGX-011 and sorafenib on a human RCC ACHN model both in vitro and in vivo. RESULTS: Although clusterin expression was increased by sorafenib, additional treatment of ACHN with OGX-011 significantly blocked the upregulation of clusterin induced by sorafenib. Despite the lack of a significant effect on the growth of ACHN, OGX-011 synergistically enhanced the sensitivity to sorafenib, reducing the IC(50) by >50%. Apoptotic changes were intensively detected in ACHN after combined treatment with OGX-011 and a sublethal dose of sorafenib, but not either agent alone. Furthermore, this combined treatment resulted in the marked downregulation of phosphorylated Akt and p44/42 mitogen-activated protein kinase in ACHN compared with treatment with either agent alone. In vivo systemic administration of OGX-011 plus sorafenib significantly decreased the ACHN tumour volume compared with control ODN plus sorafenib. CONCLUSION: Combined use with OGX-011 may be useful in enhancing the cytotoxic effect of sorafenib on RCC by inducing apoptosis and inactivating major signal transduction pathways.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.037
GPT teacher head0.334
Teacher spread0.297 · 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".

Quick stats

Citations42
Published2012
Admission routes1
Has abstractyes

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