Initial insights into the interaction of antibodies radiolabeled with Lutetium-177 and Actinium-225 with tumor microenvironment in experimental human and canine osteosarcoma
Bibliographic record
Abstract
Background Osteosarcoma (OS) is a prevalent primary bone cancer affecting both humans and canines . This study describes initial insights into the interaction of the human monoclonal antibody IF3 to an insulin-like growth factor 2 receptor (IGF2R) radiolabeled with either alpha-emitting Actinium-225 ( 225 Ac) or beta-emitting Lutetium-177 ( 177 Lu) radionuclides with the OS cells and tumor microenvironment (TME) in experimental human and canine OS. Basic procedures SCID mice bearing canine Gracie or human OS-33 OS tumors were treated with 177 Lu- or 225 Ac-labeled IF3 antibody, sacrificed at 24, 72 or 168 h post-treatment and their tumors were analyzed by immunohistochemistry (IHC) for the presence of OS cells, various elements of TME as well as for the double DNA strand breaks with γH2AX and caspase 3 assays. Main findings IHC revealed a reduction in IGF2R-positive OS cells and OS stem cell populations post therapy with 225 Ac- and 177 Lu-labeled IF3 antibody. Notably, radiolabeled IF3 antibody effectively diminished pro-tumorigenic M2 macrophages , highlighting its therapeutic promise. The study also unveiled varied responses of natural killer (NK) cells and M1 macrophages, shedding light on the intricate TME interplay. Time-dependent increase in γ-H2AX staining in canine Gracie and human OS-33 tumors treated with [ 177 Lu]Lu-IF3 and [ 225 Ac]Ac-IF3 was observed at 24 and 72 h post-RIT. Principal conclusions These findings suggest that radiolabeled antibodies offer a hopeful avenue for personalized OS treatment, emphasizing the importance of understanding their impact on the TME and potential synergies with immunotherapy .
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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".