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Record W2083836971 · doi:10.1158/1538-7445.am10-4417

Abstract 4417: Uptake of the new paclitaxel-derivative (ANG1005) by the low-density lipoprotein receptor-related protein1 (LRP1) is related to the aggressive phenotype of glioblastoma cells

2010· article· en· W2083836971 on OpenAlexaff
Yanick Bertrand, Jean-Christophe Currie, Michel Demeule, Anthony Régina, Danica Stanimirovic, Jean‐Paul Castaigne, Richard Béliveau

Bibliographic record

VenueCancer Research · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsAngiochem (Canada)National Research Council CanadaUniversité du Québec à Montréal
Fundersnot available
KeywordsLRP1Cancer researchTaxanePaclitaxelU87MedicineCancerImmunohistochemistryTemozolomideGliomaInternal medicineBreast cancerLipoproteinLDL receptorCholesterol

Abstract

fetched live from OpenAlex

Abstract The most common primary brain tumour is glioblastoma multiforme (GBM). Despite latest cancer researches to improve the outcome of patients with GBM, this highly aggressive tumour remains one of the most difficult tumors to treat. Angiochem's Engineered Peptide Compounds (EPiC) provides a non-invasive and flexible platform for small and large molecules to treat brain diseases. Based on these properties, we have created a portfolio of new drug entities composed of siRNA, peptides and mAbs, The most advanced, ANG1005, is a new taxane derivative currently completing two Phase ½ clinical trials for the treatment of primary and secondary brain tumors. This EPiC drug is comprised of one Angiopep-2 peptide conjugated to three molecules of paclitaxel. Recently, Angiopep-2 has been shown to use the low-density lipoprotein related protein 1 (LRP1) has a gateway across the blood-brain barrier. Interestingly, LRP1 seems to be required in many tumours for the aggressive behaviour and is overexpressed in GBM. Here, we show an increase uptake of Angiopep-2 in implanted brain tumour using in vivo imaging when compared to the contralateral normal brain region. In these tumors, ANG1005 could be detected by LC/MS/MS analysis and immunohistochemistry. We next characterized Angiopep-2 and ANG1005 uptake in human U87 glioblastoma cell line. The transfection of U87 cells with siRNA LRP1 decreased the uptake of both Angiopep-2 and ANG1005 indicating that LRP1 is involved in their internalization in glioblastoma cells. Furthermore, we measured an increase in the uptake of both Angiopep-2 and ANG1005 in glioblastoma (U87) cells under three experimental conditions that mimic the aggressive cancer cell microenvironment: acidic pH, serum deprivation and hypoxia. Interestingly, under these conditions LRP1 expression was also increased. The increase of ANG1005 uptake and LRP1 expression indicates that the aggressive cancer microenvironment phenotype favors the entry of this anticancer drug into glioblastoma cells. Overall these results demonstrate that the EPiC drug ANG1005 can be an effective therapeutic strategy to target brain cancer cells and aggressive tumors such as GBM. 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 4417.

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 score0.006

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 head0.013
GPT teacher head0.303
Teacher spread0.290 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

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