MétaCan
Menu
← Back to cohort
Record W1986242878 · doi:10.1158/1538-7445.am2011-1763

Abstract 1763: The discovery of novel non-RGD-containing αvβ3 ligands for molecular imaging

2011· article· en· W1986242878 on OpenAlexaff
Choi‐Fong Cho, Giulio A. Amadei, Daniel Breadner, Leonard G. Luyt, John D. Lewis

Bibliographic record

VenueCancer Research · 2011
Typearticle
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsWestern University
Fundersnot available
KeywordsIntegrinPeptideExtracellular matrixTripeptideMetastasisCancer researchMolecular imagingRGD motifChemistryCancerCellMedicineMolecular biologyBiochemistryBiologyIn vivoInternal medicine

Abstract

fetched live from OpenAlex

Abstract Metastasis is the cause of 90% of cancer deaths. The detection of neoplasms before they metastasize using non-invasive molecular imaging approaches can have a significant impact on patient survival. For many years it has been known that αvβ3 integrin, which is over-expressed on angiogenic endothelium and many tumours, associates with the extracellular matrix through an RGD tripeptide motif. RGD-mediated targeting has been utilized for a wide variety of peptide and nanoparticle molecular imaging agents that are in various stages of development and in clinical trials. As RGD peptide decreases cell adhesion, concerns regarding its utilization as a molecular imaging agent have been raised (Kiessling et. al., 2009 Radiology). RGD peptides exert an anti-angiogenic effect through their inhibition of αvβ3 integrin, raising the concern given recent studies (Ebos et. al., 2009 Cancer Cell v15; Paez-Ribes et. al., 2009 Cancer Cell v15) that this anti-angiogenic activity may result in increased tumour invasion and metastasis. We sought, therefore, to discover novel αvβ3 integrin-targeted peptides that do not contain RGD for the development of new molecular imaging agents. Using a recently developed “beads on a bead” approach, we screened a linear octapeptide one bead one compound library using purified αvβ3 integrin. Over one hundred peptides were isolated and sequenced “on bead” using a novel MALDI-TOF/MS technique and a number of non-RGD containing peptides were identified. Two of these peptides had a higher binding affinity for purified αvβ3 integrin than the linear RGD peptide (LCE62, KD = 4.7 nM and LCE64, KD = 16.3 nM) as determined by surface plasmon resonance. In contrast to peptides containing RGD, these peptides did not impact the morphology and adhesion of αvβ3 integrin-expressing MDA-435 cells, nor did they inhibit angiogenesis. The uptake of both fluorescein-labeled LCE62 and LCE64 by αvβ3 integrin-expressing breast cancer cells were significantly higher compared with a control AGD peptide. We have also demonstrated that this uptake was effectively blocked by an excess of free unlabeled peptide. These novel αvβ3 integrin-binding peptides could provide a basis for a new and potentially safer generation of molecular imaging agents for the early diagnosis of cancers. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 1763. doi:10.1158/1538-7445.AM2011-1763

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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.137
GPT teacher head0.433
Teacher spread0.296 · 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
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueCancer Research→Same topicCell Adhesion Molecules Research→French-language works237,207→