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Record W2953610759 · doi:10.1158/1538-7445.sabcs18-86

Abstract 86: Pre-existing neurovascular inflammation increases the occurrence of brain metastases

2019· article· en· W2953610759 on OpenAlexaff
Dina Sikpa, Lisa Whittingstall, Jérémie P. Fouquet, Luc Tremblay, Réjean Lebel, Martin Lepage

Bibliographic record

VenueTumor Biology · 2019
Typearticle
Languageen
FieldMedicine
TopicBrain Metastases and Treatment
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsInflammationMedicineCell adhesion moleculeVCAM-1PathologyCancer researchMetastasisCancerImmunologyInternal medicineICAM-1

Abstract

fetched live from OpenAlex

BACKGROUND: Brain metastases (BM) are the most prevalent intracranial neoplasm. Inflammation is central to the development of cancer. While an intra-tumoral inflammatory microenvironment contributes to the acquisition of malignant phenotypes and leads to the release of circulating tumor cells (CTCs), pre-existing inflammation at distant sites facilitates the adhesion of CTCs to the activated vascular endothelium and the consequent formation of metastases. Cell adhesion molecules expressed by activated endothelial cells contribute to metastatic spread outside of the brain and the vascular cell adhesion molecule-1 (VCAM-1) is a key mediator of inflammation. Herein we assessed if cancer cells entry into the brain is aided by VCAM-1 upregulation with inflammation.METHODS: Stereotaxic lipopolysaccharide (LPS, 1 µg) injection into the right hemisphere was used to induce neurovascular inflammation in Balb/c mice. The distribution of VCAM-1 was semi-quantified 24 h post LPS injection using molecular magnetic resonance imaging (MRI) with microparticles of iron oxide (MPIOs) functionalized with VCAM-1 antibody (MPIO-VCAM-1). Mice injected with saline served as control. VCAM-1 is also expressed on vessels associated with metastases, MPIO-VCAM-1 were therefore used to detect metastases. Basal level of metastases in animal brains was measured in a group of mice intracardially injected with cancer cells without LPS intra-cortical injection. To study the impact of pre-existing inflammation on tumor cell entry into the brain, mice were injected with LPS (or saline as above) 24 h prior to intracardiac injection of 4T1 breast cancer cells (105 cells in 100 µL PBS). Metastases imaging was performed 18 days post cancer cells injection. To assess if blocking VCAM-1 would affect metastases implantation, an extra LPS-injected group of mice was injected intravenously with MPIO-VCAM-1 4 hours before 4T1 cells injection. In this group, MRI was performed 3 hours after MPIO-VCAM-1 injection (VCAM-1 imaging), and on day 18 post tumor cell injection (metastases imaging). All MRI experiments were conducted on a small animal 7T scanner (Varian Inc.) with a dedicated mouse head-coil (RAPID MR International) using a T2*-weighted sequence. Following the final imaging session brains were extracted for histological analysis.RESULTS: Both MR and histological data reveal that the metastatic burden significantly increases in the LPS-injected group compared to control conditions. In inflamed animals, blocking VCAM-1 with MPIO-VCAM-1 reduces the metastatic burden back to control values. This suggests that (1) a pre-existing inflammation increases the occurrence of brain metastases and (2) blocking VCAM-1 reduces this effect.CONCLUSION: We demonstrate that inflammation-induced VCAM-1 contributes to tumor cells adhesion in the brain. Therefore, VCAM-1 may represent an attractive therapeutic target to reduce risks of metastasis.Citation Format: Dina Sikpa, Lisa Whittingstall, Jérémie P. Fouquet, Luc Tremblay, Réjean Lebel, Martin Lepage. Pre-existing neurovascular inflammation increases the occurrence of brain metastases [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 86.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.303
Teacher spread0.280 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2019
Admission routes1
Has abstractyes

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