The Osteosarcoma Microenvironment: A Complex but Targetable Ecosystem
Bibliographic record
Abstract
Osteosarcomas are the most frequent primary bone sarcomas, affecting mainly children, adolescents, and young adults, and with a second peak of incidence in elderly individuals. The current therapeutic management, a combined regimen of poly-chemotherapy and surgery, still remains largely insufficient, as patient survival has not improved in recent decades. Osteosarcomas are very heterogeneous tumors, both at the intra- and inter-tumor level, with no identified driver mutation. Consequently, efforts to improve treatments using targeted therapies have faced this lack of specific osteosarcoma targets. Nevertheless, these tumors are inextricably linked to their local microenvironment, composed of bone, stromal, vascular and immune cells and the osteosarcoma microenvironment is now considered to be essential and supportive for growth and dissemination. This review describes the different actors of the osteosarcoma microenvironment and gives an overview of the past, current, and future strategies of therapy targeting this complex ecosystem, with a focus on the role of extracellular vesicles and on the emergence of multi-kinase inhibitors.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".