Mouse models of breast cancer metastasis

Institute of Biochemistry and Genetics, Department of Clinical-Biological Sciences (DKBW), Center of Biomedicine, University of Basel, Mattenstrasse 28, CH-4058 Basel, Switzerland.
Breast cancer research: BCR (Impact Factor: 5.49). 02/2006; 8(4):212. DOI: 10.1186/bcr1530
Source: PubMed

ABSTRACT Metastatic spread of cancer cells is the main cause of death of breast cancer patients, and elucidation of the molecular mechanisms underlying this process is a major focus in cancer research. The identification of appropriate therapeutic targets and proof-of-concept experimentation involves an increasing number of experimental mouse models, including spontaneous and chemically induced carcinogenesis, tumor transplantation, and transgenic and/or knockout mice. Here we give a progress report on how mouse models have contributed to our understanding of the molecular processes underlying breast cancer metastasis and on how such experimentation can open new avenues to the development of innovative cancer therapy.

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    • "Breast cancer is the most frequently diagnosed type of cancer and one of the leading causes of death in women (Fantozzi and Christofori, 2006). The main cause of death in these patients is, however, not the primary tumor, but its metastases at distant sites (e.g. in bone, lung, liver and brain) (Weigelt et al., 2005). "
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    ABSTRACT: Breast cancer outcome prediction based on gene expression profiles is an important strategy for personalize patient care. To improve performance and consistency of discovered markers of the initial molecular classifiers, network-based outcome prediction methods (NOPs) have been proposed. In spite of the initial claims, recent studies revealed that neither performance nor consistency can be improved using these methods. NOPs typically rely on the construction of meta-genes by averaging the expression of several genes connected in a network that encodes protein interactions or pathway information. In this article, we expose several fundamental issues in NOPs that impede on the prediction power, consistency of discovered markers and obscures biological interpretation. To overcome these issues, we propose FERAL, a network-based classifier that hinges upon the Sparse Group Lasso which performs simultaneous selection of marker genes and training of the prediction model. An important feature of FERAL, and a significant departure from existing NOPs, is that it uses multiple operators to summarize genes into meta-genes. This gives the classifier the opportunity to select the most relevant meta-gene for each gene set. Extensive evaluation revealed that the discovered markers are markedly more stable across independent datasets. Moreover, interpretation of the marker genes detected by FERAL reveals valuable mechanistic insight into the etiology of breast cancer. All code is available for download at: j.deridder@tudelft.nlSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.
    Bioinformatics 06/2015; 31(12):i311-i319. DOI:10.1093/bioinformatics/btv255 · 4.98 Impact Factor
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    • "Oraevsky et al. have developed a laser optoacoustic imaging system (LOIS) for the detection and imaging of the breast tumor tissues using 1064nm laser light [12] [13] . Extensive research is also being conducted with the immune-compromised mice models for understanding tumors and its development, prognosis of the cancer, finding biomarkers, understanding the drug delivery pathway, effect of chemicals/drugs on the cancer growth and much more [14] [15] [16] [17] [18] [19] . "
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    ABSTRACT: Breast cancer is the most frequently diagnosed cancer type and its detection at an early stage can reduce the mortality rate substantially. With the aim to detect breast cancer early, by studying tumor progression in nude mice, a pulsed laser induced photoacoustic spectroscopy set up has been designed and developed. MCF-7 cells xenografts were developed using six to eight weeks old female nude mice and tumor tissues were extracted on different days (10th, 15th and 20th Day) post injection and the corresponding photoacoustic spectra were recorded at 281nm excitation. A total of 144 time domain spectra were recorded from 36 animals belonging to the three time points (10th, 15th and 20th day post injection) and converted into frequency domains by Fast Fourier Transform (FFT) tools of the MATLAB algorithms and analyzed. The frequency patterns of the tumor masses on 10th, 15th and 20th day of tumor development showed a gradual increase in intensity at certain frequencies, 5.93 x103 Hz, 15.9 x103 Hz, 29.69 x103 Hz and 32.5 x103 Hz in the FFT patterns indicating that these frequencies were more sensitive towards tumor development. Further analysis of the data yielded a clear variation in the spectral parameters with progression of the disease suggesting that the technique may be suitable for early detection of the disease. Thus, we expect that the developed setup may be useful in assessing the different phases of tumor development which may have clinical implications. © (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
    SPIE BiOS, International Society for Optics and Photonics, CA; 03/2014
    • "For this reason, we estimate that mice carrying human vascularized BME-rich organoids would be a more suitable model for studying the interactions between disseminated cancer cells and host endothelium. In fact, a murine microenvironment is not always a “congenial soil” for metastatic human cells [37]. For example, in a mouse model of human breast cancer, bone metastases were more frequent when the target organ was of human origin, suggesting a species-specific tropism [38]. "
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    ABSTRACT: Metastasic breast cancer is the leading cause of death by malignancy in women worldwide. Tumor metastasis is a multistep process encompassing local invasion of cancer cells at primary tumor site, intravasation into the blood vessel, survival in systemic circulation, and extravasation across the endothelium to metastasize at a secondary site. However, only a small percentage of circulating cancer cells initiate metastatic colonies. This fact, together with the inaccessibility and structural complexity of target tissues has hampered the study of the later steps in cancer metastasis. In addition, most data are derived from in vivo models where critical steps such as intravasation/extravasation of human cancer cells are mediated by murine endothelial cells. Here, we developed a new mouse model to study the molecular and cellular mechanisms underlying late steps of the metastatic cascade. We have shown that a network of functional human blood vessels can be formed by co-implantation of human endothelial cells and mesenchymal cells, embedded within a reconstituted basement membrane-like matrix and inoculated subcutaneously into immunodeficient mice. The ability of circulating cancer cells to colonize these human vascularized organoids was next assessed in an orthotopic model of human breast cancer by bioluminescent imaging, molecular techniques and immunohistological analysis. We demonstrate that disseminated human breast cancer cells efficiently colonize organoids containing a functional microvessel network composed of human endothelial cells, connected to the mouse circulatory system. Human breast cancer cells could be clearly detected at different stages of the metastatic process: initial arrest in the human microvasculature, extravasation, and growth into avascular micrometastases. This new mouse model may help us to map the extravasation process with unprecedented detail, opening the way for the identification of relevant targets for therapeutic intervention.
    PLoS ONE 08/2013; 8(8):e72957. DOI:10.1371/journal.pone.0072957 · 3.23 Impact Factor
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