NGR-TNF, a novel vascular-targeting agent, does not induce cytokine recruitment of proangiogenic bone marrow-derived cells
Bibliographic record
Abstract
BACKGROUND: Administration of certain chemotherapy drugs at the maximum tolerated dose, vascular-disrupting agents (VDAs) and irradiation can induce mobilisation and tumour homing of proangiogenic bone marrow-derived cells (BMDCs). Increases in cytokines and chemokines contribute to such mobilisation that eventually promotes tumour (re)growth. NGR-TNF is a vascular-targeting agent in advanced clinical development, coupling the CNGRCG angiogenic vessel-homing peptide with tumour necrosis factor-alpha (TNF). We investigated whether NGR-TNF mobilises host BMDCs and growth factors. METHODS: Blood was obtained from Lewis lung carcinoma and 4T1 tumour-bearing mice at different time points following NGR-TNF, VDA or anti-VEGFR2/flk-1 antibody treatment. Levels of circulating growth factors were assessed by ELISAs. BMDCs were characterised by FACS. Circulating endothelial progenitor cells were defined as CD45(-)/CD13(+)/flk-1(+)/CD117(+)/7AAD(-), Tie2-expressing monocytes as CD45(+)/CD11b(+)/Tie2(+) and myeloid-derived suppressor cells as CD45(+)/CD11b(+)/Gr1(+) cells. RESULTS: NGR-TNF decreases tumour blood vessel density-inducing apoptosis of tumour and tumour endothelial cells. Unlike VDAs, or high-dose NGR-TNF, lower doses of NGR-TNF, comparable to those used in clinical trials, neither mobilise nor recruit to the tumour site proangiogenic BMDCs or induce host growth factors. CONCLUSION: Low-dose NGR-TNF exerts antitumour activity without inducing proangiogenic host responses, conceivably important for preventing/overcoming resistance and designing combination therapeutic strategies.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".