Homing to solid cancers: a vascular checkpoint in adoptive cell therapy using CAR T-cells
Bibliographic record
Abstract
The success of adoptive T-cell therapies for the treatment of cancer patients depends on transferred T-lymphocytes finding and infiltrating cancerous tissues. For intravenously transferred T-cells, this means leaving the bloodstream (extravasation) from tumour blood vessels. In inflamed tissues, a key event in extravasation is the capture, rolling and arrest of T-cells inside blood vessels which precedes transmigration across the vessel wall and entry into tissues. This depends on co-ordinated signalling of selectins, integrins and chemokine receptors on T-cells by their respective ligands which are up-regulated on inflamed blood vessels. Clinical data and experimental studies in mice suggest that tumour blood vessels are anergic to inflammatory stimuli and the recruitment of cytotoxic CD8(+)T-lymphocytes is not very efficient. Interestingly, and somewhat counter-intuitively, anti-angiogenic therapy can promote CD8(+)T-cell infiltration of tumours and increase the efficacy of adoptive CD8(+)T-cell therapy. Rather than inhibit tumour angiogenesis, anti-angiogenic therapy 'normalizes' (matures) tumour blood vessels by promoting pericyte recruitment, increasing tumour blood vessel perfusion and sensitizing tumour blood vessels to inflammatory stimuli. A number of different approaches are currently being explored to increase recruitment by manipulating the expression of homing-associated molecules on T-cells and tumour blood vessels. Future studies should address whether these approaches improve the efficacy of adoptive T-cell therapies for solid, vascularized cancers in patients.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 0.000 |
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".