Toward a Drug Development Path That Targets Metastatic Progression in Osteosarcoma
Bibliographic record
Abstract
Despite successful primary tumor treatment, the development of pulmonary metastasis continues to be the most common cause of mortality in patients with osteosarcoma. A conventional drug development path requiring drugs to induce regression of established lesions has not led to improvements for patients with osteosarcoma in more than 30 years. On the basis of our growing understanding of metastasis biology, it is now reasonable and essential that we focus on developing therapeutics that target metastatic progression. To advance this agenda, a meeting of key opinion leaders and experts in the metastasis and osteosarcoma communities was convened in Bethesda, Maryland. The goal of this meeting was to provide a "Perspective" that would establish a preclinical translational path that could support the early evaluation of potential therapeutic agents that uniquely target the metastatic phenotype. Although focused on osteosarcoma, the need for this perspective is shared among many cancer types. The consensus achieved from the meeting included the following: the biology of metastatic progression is associated with metastasis-specific targets/processes that may not influence grossly detectable lesions; targeting of metastasis-specific processes is feasible; rigorous preclinical data are needed to support translation of metastasis-specific agents into human trials where regression of measurable disease is not an expected outcome; preclinical data should include an understanding of mechanism of action, validation of pharmacodynamic markers of effective exposure and response, the use of several murine models of effectiveness, and where feasible the inclusion of the dog with naturally occurring osteosarcoma to define the activity of new drugs in the micrometastatic disease setting.
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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".