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Record W2561007053 · doi:10.1158/1538-7445.am2015-1707

Abstract 1707: PACE4-dependent cellular uptake and retention of the Multi-Leucine peptide inhibitor into cancer cells

2015· article· en· W2561007053 on OpenAlexaff
Frédéric Couture, Kévin Ly, Christine Levesque, Anna Kwiatkowska, Roxane Desjardins, Brigitte Guérin, Robert Day

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicUbiquitin and proteasome pathways
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsDU145Prostate cancerLNCaPCancer researchCancerCancer cellGene knockdownPeptideProstateBiologyChemistryBiochemistryInternal medicineMedicineApoptosis

Abstract

fetched live from OpenAlex

Abstract The proprotein convertase PACE4 is strongly overexpressed in prostate cancer and plays an important role in tumor progression by promoting cell proliferation and tumor angiogenesis. Because the high expression levels and the necessity of this enzyme for cancer progression, we have developed a peptide-based inhibitor known as the Multi-Leucine (ML) peptide, which is a PACE4-specific inhibitor. Just like PACE4-downregulation through stable shRNA transfections, this compound displays anti-proliferative properties when applied on cancer cells. We showed its target-specific uptake into prostate cancer xenografts by positron emission tomography, which allowed clear tumor visualization. The ML-peptide has potent effects to block the further progression of prostate cancer cells, however, the uptake mechanism of this peptide is not clear and whether it is PACE4-dependent. We therefore tested the uptake and efflux rates of the ML-peptide peptide in prostate cancer cells and correlated these with PACE4 expression levels in wild type and stable PACE4-knockdown cell lines. In addition to prostate cancer cell lines LNCaP, DU145 and PC3, we tested HepG2, Huh7 and HT1080 cells, which also express PACE4. To measure peptide uptake, the ML peptide was modified through the addition a 1,4,7-triazacyclononane-1,4,7-triacetic acid (NOTA) moiety to strongly chelate radiometal such as 64Cu. Our study shows that uptake of the ML-peptide is correlated with expression levels, but more importantly can be blocked in PACE4-knockdown cell lines, demonstrating a PACE4-dependant uptake mechanism. We also correlated uptake with the anti-proliferative effects of the ML-peptide and showed that entry into these cell lines predicted the effects of the ML-peptide on the growth of these cells. These results confirm the notion that ML-peptide entry within cell is an important requirement to exert growth inhibition properties, and further provide a mechanism for that entry. This study demonstrates further support for the use of the ML-peptide or its analogs as potential drugs in prostate cancer, as well as any other PACE4-dependent tumors. These indications of peptide uptake within tumor could allows PACE4-status to be determined by positron emission tomography and may be of great theranostics uses in the context of pharmacological intervention with PACE4 inhibitors. Citation Format: Frédéric Couture, Kevin Ly, Christine Levesque, Anna Kwiatkowska, Roxane Desjardins, Brigitte Guérin, Robert Day. PACE4-dependent cellular uptake and retention of the Multi-Leucine peptide inhibitor into cancer cells. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1707. doi:10.1158/1538-7445.AM2015-1707

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)0.0020.001

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 head0.092
GPT teacher head0.360
Teacher spread0.268 · 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
Published2015
Admission routes1
Has abstractyes

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