Vessel co‐option in primary human tumors and metastases: an obstacle to effective anti‐angiogenic treatment?
Bibliographic record
Abstract
Angiogenesis has been regarded as essential for tumor growth and progression. Studies of many human tumors, however, suggest that their microcirculation may be provided by nonsprouting vessels and that a variety of tumors can grow and metastasize without angiogenesis. Vessel co-option, where tumor cells migrate along the preexisting vessels of the host organ, is regarded as an alternative tumor blood supply. Vessel co-option may occur in many malignancies, but so far mostly reported in highly vascularized tissues such as brain, lung, and liver. In primary and metastatic lung cancer and liver metastasis from different primary origins, as much as 10-30% of the tumors are reported to use this alternative blood supply. In addition, vessel co-option is introduced as a potential explanation of antiangiogenic drug resistance, although the impact of vessel co-option in this clinical setting is still to be further explored. In this review we discuss tumor vessel co-option with specific examples of vessel co-option in primary and secondary tumors and a consideration of the clinical implications of this alternative tumor blood supply.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".