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Record W2040030637 · doi:10.1158/1538-7445.am10-5172

Abstract 5172: Role of CEACAM1 on cancer cell endothelial adhesion and liver metastasis in vivo

2010· article· en· W2040030637 on OpenAlexaff
Carlos H.F. Chan, Azadeh Arabzadeh, Luisa DeMarte, Jonathan Spicer, Claire Turbide, Pnina Brodt, Nicole Beauchemin, Lorenzo Ferri

Bibliographic record

VenueCancer Research · 2010
Typearticle
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsMetastasisCancerCancer researchPathologyPopulationCell adhesion moleculeAntibodyCancer cellBiologyChemistryImmunologyMedicine

Abstract

fetched live from OpenAlex

Abstract Background: There are emerging data to suggest that tumor over-expression of the carcinoembryonic antigen-related cell adhesion molecules (CEACAM1, 5 and 6) may influence cancer metastasis; however, the mechanisms for this are unclear. CEACAM1 was shown to bind homotypically to itself and heterotypically to CEACAM5 and CEACAM6. Since CEACAM1 is the only known CEACAM family member expressed on the hepatic endothelium, we hypothesized that CEACAM1 promotes cancer metastasis by directly mediating binding between circulating tumor cells and the sinusoidal endothelium. Methods: Using intra-vital microscopy (IVM), a physiologically relevant model system to assess the early stages of liver metastasis in vivo, we have compared the hepatic endothelial adhesion of intra-splenically injected MC38 cells (mouse colon cancer cell line with minimal CEACAM1 expression) between Ceacam1+/+ and Ceacam1−/− mice. A CEACAM1-negative population of MC38 cells (MC38-null) was sorted out by FACS using specific anti-CEACAM1 antibodies. MC38-null cells were then infected with retroviruses produced from ψ2 packaging cells carrying pLXSN-CEACAM1 expression construct and infected cells were selected using neomycin-containing media. A CEACAM1-positive population of MC38 cells (MC38-CC1) was sorted out by FACS using specific anti-CEACAM1 antibodies. The migratory ability of MC38-null and MC38-CC1 cells were compared by IVM in Ceacam1+/+ mice as mentioned above. Liver metastasis assays were performed by means of intra-splenic injection of MC38 cells into the Ceacam1+/+ and Ceacam1−/− mice. Data expressed as mean ± SEM, student t-test determined significance (*p<0.05). Results: By using IVM, we observed a 3-fold decrease in hepatic sinusoidal endothelial adhesion of intra-splenically injected MC38-null cells in Ceacam1−/− mice compared to Ceacam1 +/+ mice (0.8±0.2 vs 2.5±0.3 adhered cells)*. Forced expression of CEACAM1 in MC38 cells however did not significantly increase hepatic endothelial adhesion in the Ceacam1+/+ mice (2.7±0.9 adhered MC38-CC1 cells vs 2.5±0.2 adhered MC38-null cells). Development of liver metastasis after intra-splenic injection of MC38-null cells was significantly reduced in the Ceacam1−/− mice (81% reduction in comparison to Ceacam1+/+ mice)*. Conclusion: Sinusoidal endothelial cell, but not cancer cell, CEACAM1 expression supports liver metastasis by increasing the adherence of circulating tumor cells to hepatic endothelium. Further investigation will be required to dissect the exact mechanism of how endothelial CEACAM1 influence hepatic endothelial adhesion of cancer cells. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 5172.

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 score0.006
Threshold uncertainty score0.020

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.080
GPT teacher head0.437
Teacher spread0.357 · 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
Published2010
Admission routes1
Has abstractyes

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