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Record W4392910246 · doi:10.1186/s13550-024-01091-9

89Zr-labeled ImmunoPET targeting the cancer stem cell antigen CD133 using fully-human antibody constructs

2024· article· en· W4392910246 on OpenAlexafffund
Kevin Wyszatko, Nancy Janzen, Luis Rafael Silva, Luke Yongkyu Kwon, Teesha Komal, Manuela Ventura, Chitra Venugopal, Sheila K. Singh, John F. Valliant, Saman Sadeghi

Bibliographic record

VenueEJNMMI Research · 2024
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsDiscovery CentreUniversity Health NetworkUniversity of TorontoMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchGovernment of OntarioOntario Institute for Cancer Research
KeywordsMedicineAntigenAntibodyStem cellCancer researchCancerCancer stem cellImmunologyInternal medicineCell biology

Abstract

fetched live from OpenAlex

Abstract Background Cancer stem cells play an important role in driving tumor growth and treatment resistance, which makes them a promising therapeutic target to prevent cancer recurrence. Emerging cancer stem cell-targeted therapies would benefit from companion diagnostic imaging probes to aid in patient selection and monitoring response to therapy. To this end, zirconium-89-radiolabeled immunoPET probes that target the cancer stem cell-antigen CD133 were developed using fully human antibody and antibody scFv-Fc scaffolds. Results ImmunoPET probes [ 89 Zr]-DFO-RW03 IgG (CA = 0.7 ± 0.1), [ 89 Zr]-DFO-RW03 IgG (CA = 3.0 ± 0.3), and [ 89 Zr]-DFO-RW03 scFv − Fc (CA = 2.9 ± 0.3) were radiolabeled with zirconium-89 (radiochemical yield 42 ± 5%, 97 ± 2%, 86 ± 12%, respectively) and each was isolated in > 97% radiochemical purity with specific activities of 120 ± 30, 270 ± 90, and 200 ± 60 MBq/mg, respectively. In vitro binding assays showed a low-nanomolar binding affinity of 0.6 to 1.1 nM (95% CI) for DFO-RW03 IgG (CA = 0.7 ± 0.1), 0.3 to 1.9 nM (95% CI) for DFO-RW03 IgG (CA = 3.0 ± 0.3), and 1.5 to 3.3 nM (95% CI) for DFO-RW03 scFv − Fc (C/A = 0.3). Biodistribution studies found that [ 89 Zr]-DFO-RW03 scFv − Fc (CA = 2.9 ± 0.3) exhibited the highest tumor uptake (23 ± 4, 21 ± 2, and 23 ± 4%ID/g at 24, 48, and 72 h, respectively) and showed low uptake (< 6%ID/g) in all off-target organs at each timepoint (24, 48, and 72 h). Comparatively, [ 89 Zr]-DFO-RW03 IgG (CA = 0.7 ± 0.1) and [ 89 Zr]-DFO-RW03 IgG (CA = 3.0 ± 0.3) both reached maximum tumor uptake (16 ± 3%ID/g and 16 ± 2%ID/g, respectively) at 96 h p.i. and showed higher liver uptake (10.2 ± 3%ID/g and 15 ± 3%ID/g, respectively) at that timepoint. Region of interest analysis to assess PET images of mice administered [ 89 Zr]-DFO-RW03 scFv − Fc (CA = 2.9 ± 0.3) showed that this probe reached a maximum tumor uptake of 22 ± 1%ID/cc at 96 h, providing a tumor-to-liver ratio that exceeded 1:1 at 48 h p.i. Antibody-antigen mediated tumor uptake was demonstrated through biodistribution and PET imaging studies, where for each probe, co-injection of excess unlabeled RW03 IgG resulted in > 60% reduced tumor uptake. Conclusions Fully human CD133-targeted immunoPET probes [ 89 Zr]-DFO-RW03 IgG and [ 89 Zr]-DFO-RW03 scFv − Fc accumulate in CD133-expressing tumors to enable their delineation through PET imaging. Having identified [ 89 Zr]-DFO-RW03 scFv − Fc (CA = 2.9 ± 0.3) as the most attractive construct for CD133-expressing tumor delineation, the next step is to evaluate this probe using patient-derived tumor models to test its detection limit prior to clinical translation.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.494
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.129
GPT teacher head0.474
Teacher spread0.345 · 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.

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

Citations3
Published2024
Admission routes2
Has abstractyes

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