Homing to solid cancers: a vascular checkpoint in adoptive cell therapy using CAR T-cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it