Characterization of IGF2R Molecular Expression in Canine Osteosarcoma as Part of a Novel Comparative Oncology Approach
Bibliographic record
Abstract
Progress in prognostic factors, treatments, and outcome for both canine and human osteosarcoma (OS) has been minimal over the last three decades. Surface overexpression of the cation independent mannose-6-phosphate/insulin-like growth factor receptor type 2 (IGF2R) has been proven to occur in human OS cells. Subsequently, radioimmunotherapy (RIT) targeting IGF2R has demonstrated promising preliminary results. The main aims of this study were to investigate the expression of IGF2R in spontaneously occurring canine OS cells using immunohistochemistry (IHC) on archived biopsy samples and to assess its prognostic significance. Thirty-four dogs were included in the study. All cases showed that 80-100% of OS cells stained positive for IGF2R. IGF2R overexpression alone was not shown to have prognostic significance using both visual and quantitative methods of IHC staining intensity. This study has established for the first time the consistent expression of IGF2R in spontaneously occurring canine OS. This comparative oncology approach will allow further investigation into RIT as a novel treatment modality; first in canines and then in humans with OS. In addition, further studies should be performed to assess the true prognostic significance of IGF2R overexpression.
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 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 | 0.000 | 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".