Novel Human Antibodies to Insulin Growth Factor 2 Receptor (IGF2R) for Radioimmunoimaging and Therapy of Canine and Human Osteosarcoma
Bibliographic record
Abstract
Etiological and genetic drivers of osteosarcoma (OS) are not well studied and vary from one tumor to another; making it challenging to pursue conventional targeted therapy. Recent studies have shown that cation independent mannose-6-phosphate/insulin-like growth factor-2 receptor (IGF2R) is consistently overexpressed in almost all of standard and patient-derived OS cell lines, making it an ideal therapeutic target for development of antibody-based drugs. Monoclonal antibodies, targeting IGF2R, can be conjugated with alpha- or beta-emitter radionuclides to deliver cytocidal doses of radiation to target IGF2R expression in OS. This approach known as radioimmunotherapy (RIT) can therefore be developed as a novel treatment for OS. In addition, OS is one of the common cancers in companion dogs and very closely resembles human OS in clinical presentation and molecular aberrations. In this study, we have developed human antibodies that cross-react with similar affinities to IGF2R proteins of human, canine and murine origin. We used naïve and synthetic antibody Fab-format phage display libraries to develop antibodies to a conserved region on IGF2R. The generated antibodies were radiolabeled and characterized in vitro and in vivo using human and canine OS patient-derived tumors in SCID mouse models. We demonstrate specific binding to IGF2R and tumor uptake in these models, as well as binding to tumor tissue of canine OS patients, making these antibodies suitable for further development of RIT for OS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".