ArticleMethods in molecular biology (Clifton, N.J.)2022
3D and 4D Tumorigenesis Model for the Quantitative Analysis of Cancer Cell Behavior and Screening for Anticancer Drugs.
Article in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 5 citations in OpenAlex.
- Peptide-drug conjugates (PDCs): a novel trend of research and development on targeted therapy, hype or hope?Acta pharmaceutica Sinica. B · 2023Review
- Nasopharyngeal carcinoma ecology theory: cancer as multidimensional spatiotemporal "unity of ecology and evolution" pathological ecosystem.Theranostics · 2023Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Cancer cells from cell lines and tumor biopsy tissue undergo aggregation and aggregate coalescence when dispersed in a 3D Matrigel™ matrix. Coalescence is a dynamic process mediated by a subset of cells within the population of cancer cells. In contrast, non-tumorigenic cells from normal cell lines and normal tissues do not aggregate or coalesce, nor do they possess the motile cell types that orchestrate coalescence of cancer cells. Therefore, coalescence is a cancer cell-specific phenotype that may drive tumor growth in vivo, especially in cases of field cancerization. Here, we describe a simple 3D tumorigenesis model that takes advantage of the coalescence capabilities of cancer cells and uses this feature as the basis for a screen for treatments that inhibit tumorigenesis. The screen is especially useful in testing monoclonal antibodies that target cell-cell interactions, cell-matrix interactions, cell adhesion molecules, cell surface receptors, and general cell surface markers. The model can also be used for 2D imaging in a 96-well plate for rapid screening and is adaptable for 3D high-resolution assessment. In the latter case, we show how the 3D model can be optically sectioned with differential interference contrast (DIC) optics, then reconstructed in 4D and quantitatively analyzed by computer-assisted methods, or, alternatively, imaged with confocal microscopy for 4D quantitative analysis of cancer cell interactions with normal cells within the tumor microenvironment. We demonstrate reconstructions and quantitative analyses using the advanced image analysis software J3D-DIAS 4.2, in order to illustrate the types of detailed phenotypic characterizations that have proven useful. Other software packages may be able to perform similar types of analyses.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.