Literature DB >> 24041215

A novel noninvasive method for evaluating experimental lung metastasis in mice.

Arnulfo Mendoza1, Radhika Gharpure, John Dennis, Joshua D Webster, Jeremy Smedley, Chand Khanna.   

Abstract

Metastasis remains the most significant problem in the field of cancer. The biologic complexity that characterizes metastasis requires relevant in vivo models. When using murine models for pulmonary metastasis, longitudinal studies are valuable for following the progression of metastatic burden. Currently, the progression of pulmonary metastatic burden in experimental mice over time is monitored through advanced imaging approaches or the clinical assessment of morbidity. Because clinical signs of morbidity are often vague and unpredictable, an inexpensive and reproducible method to detect advanced metastatic burden-before the development of mortality-is needed. We have developed a noninvasive technique for assessing pulmonary metastatic burden in laboratory mice. The pulmonary assessment of advanced metastasis (PAAM) test is performed by restraining an awake mouse and gently applying pressure with the index finger under the xiphoid process. This pressure reduces the diaphragmatic component to respiration. Mice with advanced lung metastases show transient signs of respiratory distress within 3 s of the application of this pressure. Using PAAM in 4 distinct models (including sarcoma and mammary carcinoma histologies) of experimental (tail vein) pulmonary metastasis (n = 114 mice), among 3 independent evaluators yielded 94% positive and negative predictive values, which were validated by histologic assessment of postmortem lung tissue. PAAM is a simple, reproducible, and efficient method to assist in the detection of advanced pulmonary metastasis in mice and contributes to their humane care during longitudinal studies.

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Year:  2013        PMID: 24041215      PMCID: PMC3784665     

Source DB:  PubMed          Journal:  J Am Assoc Lab Anim Sci        ISSN: 1559-6109            Impact factor:   1.232


  11 in total

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10.  Investigation into diagnostic agreement using automated computer-assisted histopathology pattern recognition image analysis.

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5.  Metabolomics uncovers a link between inositol metabolism and osteosarcoma metastasis.

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6.  Characterization of the metastatic phenotype of a panel of established osteosarcoma cells.

Authors:  Ling Ren; Arnulfo Mendoza; Jack Zhu; Joseph W Briggs; Charles Halsey; Ellen S Hong; Sandra S Burkett; James Morrow; Michael M Lizardo; Tanasa Osborne; Samuel Q Li; Hue H Luu; Paul Meltzer; Chand Khanna
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