Therapeutic targets for antimetastatic therapy
Bibliographic record
Abstract
Metastases are responsible for most cancer deaths. Despite dramatic advances in cancer therapy, the presence of metastases implies a significantly shortened survival and reduced quality of remaining life. Aside from prevention of cancer altogether, or significant improvements in early detection for most cancers, effective novel therapeutic strategies targeting metastasis should provide the greatest clinical benefit. Metastasis research has shown that many of the initial steps in metastasis are completed with a high degree of efficiency and may have occurred by the time of clinical diagnosis. Therefore, targeting the later stages of metastasis may offer a more promising therapeutic approach for the development of antimetastatic therapies. Appropriate clinical strategies include targeting dormant solitary cells, active preangiogenic metastases, or vascularised metastases. Dormancy of solitary single cells, seen clinically and experimentally, may be an explanation for cancer recurrence. Eradication or inactivation of these dormant cells could provide large benefit for patients. However, little is known about what makes cancer cells dormant and, therefore, a greater knowledge of the mechanisms of dormancy is needed. This review discusses potential biological targets, as defined by the steps in the metastatic process, for antimetastatic therapies and provides examples of clinical strategies for preventing or treating successful metastasis.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".