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Automated quantitative image analysis for ex vivo metastasis assays reveals differing lung composition requirements for metastasis suppression by KISS1

  • Technical Notes in Metastasis Research
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Abstract

Imaging is broadly used in biomedical research, but signal variation complicates automated analysis. Using the Pulmonary Metastasis Assay (PuMA) to study metastatic colonization by the metastasis suppressor KISS1, we cultured GFP-expressing melanoma cells in living mouse lung ex vivo for 3 weeks. Epifluorescence images of cells were used to measure growth, creating large datasets which were time consuming and challenging to quantify manually due to scattering of light from outside the focal plane. To address these challenges, we developed an automated workflow to standardize the measurement of disseminated cancer cell growth by applying statistical quality control to remove unanalyzable images followed and a filtering algorithm to quantify only in-focus cells. Using this tool, we demonstrate that expression of the metastasis suppressor KISS1 does not suppress growth of melanoma cells in the PuMA, in contrast to the robust suppression of lung metastasis observed in vivo. This result may suggest that a factor required for metastasis suppression is present in vivo but absent in the PuMA, or that KISS1 suppresses lung metastasis at a step in the metastatic cascade not tested by the PuMA. Together, these data provide a new tool for quantification of metastasis assays and further insight into the mechanism of KISS1 mediated metastasis suppression in the lung.

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Acknowledgements

The authors acknowledge Drs. Chand Khanna, DVM, PhD, Michael Lizardo, PhD and Arnulfo Mendoza, DVM for their training in the PuMA. The authors also acknowledge support from the Biostatistics and Informatics Shared Resource of the KU Cancer Center.

Funding

The authors received support from the following organizations: Melanoma Research Foundation (EDY), National Cancer Institute 1F30CA216998 (EDY), CA134981 (DRW) and CA168524 (DRW), the National Foundation for Cancer Research (DRW) and the Hall Family Professorship in Molecular Medicine (DRW).

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Authors and Affiliations

  1. Department of Cancer Biology, University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS, 66160, USA

    Eric D. Young, Ashley F. Tsue, John T. McGuire & Danny R. Welch

  2. Department of Civil and Environmental Engineering, Virginia Tech, 750 Drillfield Dr., Blacksburg, VA, 24061, USA

    Kyle Strom

  3. Department of Biostatistics, University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS, 66160, USA

    Joseph L. Usset

  4. 40Digits, 1740 Main St., Kansas City, MO, 64108, USA

    Seth MacPherson

  5. The University of Kansas Cancer Center, 3901 Rainbow Blvd., Kansas City, KS, 66160, USA

    Danny R. Welch

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  1. Eric D. Young
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  2. Kyle Strom
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  3. Ashley F. Tsue
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  4. Joseph L. Usset
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  6. John T. McGuire
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Contributions

Conceived and designed experiments: EDY, KS, DRW. Analyzed data: EDY, AFT, JLU, SM, JTM. Wrote first draft of the manuscript: EDY. Contributed to manuscript writing: EDY, KS, AFT, JLU, DRW. Agree with manuscript results and conclusions: EDY, KS, AFT, JLU, SM, JTM, DRW. Developed the structure and arguments for the paper: EDY, KS, JLU, DRW. Made critical revisions and approved final version of the manuscript: EDY, KS, AFT, JLU, JTM, DRW.

Corresponding author

Correspondence to Danny R. Welch.

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Young, E.D., Strom, K., Tsue, A.F. et al. Automated quantitative image analysis for ex vivo metastasis assays reveals differing lung composition requirements for metastasis suppression by KISS1. Clin Exp Metastasis 35, 77–86 (2018). https://doi.org/10.1007/s10585-018-9882-1

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  • DOI: https://doi.org/10.1007/s10585-018-9882-1

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