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Record W2607162073 · doi:10.1186/s41181-017-0023-y

Evaluation of agonist and antagonist radioligands for somatostatin receptor imaging of breast cancer using positron emission tomography

2017· article· en· W2607162073 on OpenAlexafffund
Iulia Dude, Zhengxing Zhang, Julie Rousseau, Navjit Hundal-Jabal, Nadine Colpo, Helen Merkens, Kuo‐Shyan Lin, François Bénard

Bibliographic record

VenueEJNMMI Radiopharmacy and Chemistry · 2017
Typearticle
Languageen
FieldMedicine
TopicNeuroendocrine Tumor Research Advances
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
FundersBC Cancer AgencyCanadian Cancer Society
KeywordsBiodistributionSomatostatin receptor 2Somatostatin receptorChemistryAgonistPositron emission tomographyDOTASomatostatinOctreotideNuclear medicineReceptorIn vitroEndocrinologyMedicineBiochemistryChelation

Abstract

fetched live from OpenAlex

The somatostatin receptor subtype 2 (sstr2) is expressed on a majority of luminal breast cancers, however SPECT and scintigraphy imaging with agonistic sstr2 probes has been sub-optimal. High affinity antagonists can access more binding sites on the cell surface, resulting in higher tumor uptake and improved sensitivity. We compared the tumor uptake and biodistribution of the antagonist 68 Ga-NODAGA-JR11 with two agonists 68 Ga-DOTA-Tyr 3 -octreotide ( 68 Ga-DOTATOC) and 68 Ga-DOTA-Tyr 3 -octreotate ( 68 Ga-DOTATATE), in the human, sstr2-positive, luminal breast cancer model: ZR-75-1. Peptides were assayed for binding affinity using a filtration-based competitive assay to sstr2. nat Ga-DOTATOC and nat Ga-DOTATATE had excellent affinity (inhibition constant K i : 0.9 ± 0.1 nM and 1.4 ± 0.3 nM respectively) compared to nat Ga-NODAGA-JR11 (25.9 ± 0.2 nM). The number of binding sites on ZR-75-1 cells was determined in vitro by saturation assays. Agonist 67/nat Ga-DOTATOC bound to 6.64 ± 0.39 × 10 4 sites/cells, which was 1.5-fold higher than 67/nat Ga-NODAGA-JR11 and 2.3-fold higher than 67/nat Ga-DOTATATE. All three 68 Ga-labeled peptides were obtained in good decay-corrected radiochemical yield (61-68%) and were purified by high performance liquid chromatography to ensure high specific activity (137 – 281 MBq/nmol at the end of synthesis). NOD scid gamma mice bearing ZR-75-1 tumors were injected intravenously with the labeled peptides and used for PET/CT imaging and biodistribution at 1 h post-injection. We found that 68 Ga-DOTATOC had the highest tumor uptake (18.4 ± 2.9%ID/g), followed by 68 Ga-DOTATATE (15.2 ± 2.2%ID/g) and 68 Ga-NODAGA-JR11 (12.2 ± 0.8%ID/g). Tumor-to-blood and tumor-to-muscle ratios were also higher for the agonists (>40 and >150 respectively), compared to the antagonist (15.6 ± 2.2 and 45.2 ± 11.6 respectively). The antagonist 68 Ga-NODAGA-JR11 had the lowest tumor uptake and contrast compared to agonists 68 Ga-DOTATOC and 68 Ga-DOTATATE in ZR-75-1 xenografts. The main contributing factor to this result could be the use of an endogenously expressing cell line, which may differ from previously published transfected models in the number of low-affinity, antagonist-specific binding sites. The relative merit of agonists versus antagonists for sstr2 breast cancer imaging warrants further investigation, first in preclinical models with other sstr2-positive breast cancer xenografts, and ultimately in luminal breast cancer patients.

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 score0.064
Threshold uncertainty score0.570

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 head0.030
GPT teacher head0.388
Teacher spread0.358 · 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

Citations29
Published2017
Admission routes2
Has abstractyes

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