IMMUNOHISTOCHEMISTRY AS A TOOL FOR MECHANISTIC STUDIES OF RADIOIMMUNOTHERAPY OF OSTEOSARCOMA
Bibliographic record
Abstract
Osteosarcoma (OS), characterised by the direct formation of immature bone or osteoid tissue by tumor cells, is a primary malignant bone cancer affecting humans and canines. This study explores the potential of Targeted Radionuclide Therapy (TRT), specifically Radioimmunotherapy (RIT), as a novel treatment approach for OS. RIT utilises antibody molecules to deliver radiation to tumor cells, with promising applications in treating metastatic disorders. Notably, the cation-independent mannose-6-phosphate/insulin-like growth factor-2 receptor (IGF2R) has been identified as a viable therapeutic target for RIT in OS due to its high expression in tumor cells. To investigate the impact of RIT on the OS tumor microenvironment (TME), we conducted Immunohistochemistry (IHC) analyses. The results revealed a reduction in IGF2R-positive cells, OS stem cells, and pro-tumorigenic M2 macrophages following RIT. Notably, RIT showed diverse effects on natural killer (NK) cells and M1 macrophages. Specifically, RIT employed two radioisotopes with different decay schemes: the alpha-emitting Actinium-225 (225Ac) and the betaemitting Lutetium-177 (177Lu). The alpha-emitting 225Ac led to a decrease in NK cell numbers; at the same time, the beta-emitting 177Lu increased NK cell populations, potentially indicating a stimulating effect. Likewise, the increase in M1 macrophages numbers in Gracie (canine OS cell line) and the decrease in M1 macrophages numbers in OS33 (human OS cell line) post-RIT suggests the highly dynamic and variable behaviour of TME. This research underscores the potential of RIT in modulating the TME and offers new insights into its efficacy against OS. Understanding the intricate relationship between RIT, the choice of a radioisotope, and TME is essential for refining treatment strategies and harnessing the immune system's capabilities. This study paves the way for more personalised and effective therapeutic approaches, which could benefit patients facing this challenging cancer.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".