Understanding Micrometastatic Disease and <i>Anoikis</i> Resistance in Ewing Family of Tumors and Osteosarcoma
Bibliographic record
Abstract
Abstract Learning Objectives After completing this course, the reader will be able to: Explain the importance of resistance to anoikis in the development of metastases.Describe the mechanisms of anoikis resistance in EFTs and osteosarcoma and their potential use in development of new therapies. CME This article is available for continuing medical education credit at CME.TheOncologist.com Detection of micrometastatic tumor cells in the bone marrow or peripheral blood of patients with Ewing family of tumors (EFTs) and osteosarcoma has been shown to correlate with poor outcome. Although one of the aims of chemotherapy is eradication of micrometastatic disease, these cells vary phenotypically from primary tumor cells and appear to be more resistant to chemotherapy. As a barrier to metastasis, cells normally undergo a form of cell death termed anoikis after they lose contact with the extracellular matrix or neighboring cells. Tumor cells that acquire malignant potential have developed mechanisms to resist anoikis and thereby survive after detachment from their primary site and while traveling through the circulation. Investigating mechanisms of resistance to anoikis, therefore, provides a valuable model to investigate regulation of micrometastatic disease. This review focuses on the current understanding of the mechanisms involved in mediating cell survival and resistance to anoikis in EFTs and osteosarcoma and discusses future studies that may help to identify novel therapeutics targeted at micrometastatic disease.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".