MétaCan
Menu
Back to cohort
Record W2318577602 · doi:10.1158/1538-7445.am2011-4249

Abstract 4249: NGR-TNF, a novel direct-acting vascular targeting agent, does not induce “cytokine-rebound” recruitment of pro-angiogenic bone-marrow derived cells

2011· article· en· W2318577602 on OpenAlexaff
Paola Di Matteo, Christina Hackl, Christopher Jedeszko, Barbara Valentinis, Claudio Bordignon, Catia Traversari, Robert S. Kerbel, Gian‐Paolo Rizzardi

Bibliographic record

VenueCancer Research · 2011
Typearticle
Languageen
FieldMedicine
TopicPeptidase Inhibition and Analysis
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsBone marrowCancer researchAngiogenesisLewis lung carcinomaHoming (biology)Tumor necrosis factor alphaCytokineImmunologyMedicineBiologyInternal medicineCancerMetastasis

Abstract

fetched live from OpenAlex

Abstract NGR-TNF, presently in advanced clinical development, is a direct-acting vascular targeting agent coupling the CNGRCG peptide (NGR), homing to angiogenic blood vessels, and tumour necrosis factor alpha (TNF). In order to further elucidate the mechanism of action, we investigated whether NGR-TNF mobilizes bone-marrow derived cells (BMDCs) and growth factors. Herein, treatment with either certain chemotherapy drugs at maximum tolerated dose (e.g. paclitaxel, cyclophosphamide, 5-FU), vascular-disrupting agents (VDAs; e.g. fosbretabulin, Oxi4503), or local irradiation can rapidly induce mobilization and subsequent tumor homing of proangiogenic BMDCs, such as circulating endothelial progenitor cells (CEPs), CD11b/Tie-2-expressing monocytes (TEMs), CD11b+Gr1+ neutrophils and myeloid-derived suppressor cells (MDSCs), and CD11b+F4/80+ macrophages. Increase in several growth factors and chemokines, such as SDF-1, G-CSF as well as osteopontin, contribute to the mobilization of BMDCs that colonize the tumour site and induce angiogenesis and rapid tumor regrowth. Lewis lung carcinoma (LLC) cells were subcutaneously implanted into immunocompetent C57BL/6J mice or into GFP+ bone marrow-chimeric mice, obtained by transplanting into lethally irradiated C57BL/6J mice bone marrow cells from UBI-GFP/BL6 donors. Blood was obtained 4 or 24 hours after treatment with either NGR-TNF, VDA, an anti-VEGFR2/flk-1 antibody or saline, by either cardiac puncture or retro-orbital sinus bleeding. Using flow cytometry (LSRII), CEPs were defined as CD45-/CD13+/flk-1+/CD117+/7AAD-, TEMs as CD45+/CD11b+/Tie2+/flk-1+ and MDSCs as CD45+/CD11b+/Gr1+ cells. Levels of circulating growth factors were assessed by ELISAs. When administrated at low doses (optimal biological dose), comparable to doses being used in clinical trials, NGR-TNF causes a decrease of tumor blood vessels density and induces apoptosis of tumor cells and tumor endothelial cells in vivo. It is worth noting that, unlike the above-mentioned treatments, NGR-TNF fails to mobilize or recruit to the tumor site different subtypes of pro-angiogenic BMDCs nor induces growth factors. Therefore, low doses of NGR-TNF exert an antitumor activity without inducing reactive a pro-angiogenic host response, which is likely an important feature for preventing/overcoming resistance and for designing combination therapeutic strategies. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4249. doi:10.1158/1538-7445.AM2011-4249

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.223
GPT teacher head0.398
Teacher spread0.175 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueCancer ResearchSame topicPeptidase Inhibition and AnalysisFrench-language works237,207