Detection and targeting insulin growth factor receptor type 2 (IGF2R) in osteosarcoma PDX in mouse models and in canine osteosarcoma tumors
Bibliographic record
Abstract
Osteosarcoma (OS) represents 3.4% of all childhood cancers with overall survival of 70% not improving in 30 years. The consistent surface overexpression of insulin-like growth factor-2 receptor (IGF2R) has been reported in commercial and patient-derived xenograft (PDX) OS cell lines. We aimed to assess efficacy and safety of treating PDX and commercial OS tumors in mice with radiolabeled antibody to IGF2R and to investigate IGF2R expression on canine OS tumors. IGF2R expression on human commercial lines 143B and SaOS2 and PDX lines OS-17, OS-33 and OS-31 was evaluated by FACS. The biodistribution and microSPECT/CT imaging with 111Indium-2G11 mAb was performed in 143B and OS-17 tumor-bearing SCID mice and followed by radioimmunotherapy (RIT) with 177Lutetium-2G11 and safety evaluation. IGF2R expression in randomly selected canine OS tumors was measured by immunohistochemistry. All OS cell lines expressed IGF2R. Biodistribution and microSPECT/CT revealed selective uptake of 2G11 mAb in 143B and OS-17 xenografts. RIT significantly slowed down the growth of OS-17 and 143B tumors without local and systemic toxicity. Canine OS tumors expressed IGF2R. This study demonstrates the feasibility of targeting IGF2R on OS in PDX and spontaneous canine tumors and sets the stage for further development of RIT of OS using comparative oncology.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".