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Erschienen in:
25.02.2019 | Research Article
Quantitative Imaging of Morphometric and Metabolic Signatures Reveals Heterogeneity in Drug Response of Three-Dimensional Mammary Tumor Spheroids
Erschienen in: Molecular Imaging and Biology | Ausgabe 3/2019
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Purpose
In order to monitor the drug responses of three-dimensional mammary tumor spheroids and to elucidate the role of inter- and intra-spheroid heterogeneity in determining drug sensitivity in the spheroids, an integrated image analysis framework was developed for morphometric and metabolic characterization of the three-dimensional tumor spheroids.
Procedure
Three-dimensional spheroid cultures of primary mammary tumor epithelial cells isolated from freshly excised tumors from a transgenic mouse model of adenocarcinoma (MMTV-PyMT) were imaged by using vital dyes and mitochondrial membrane potential markers. Custom-developed java and python program codes facilitated image processing, numerical computation, and graphical analysis of large datasets generated from the experiments. A panel of cancer drugs (rapamycin, BEZ235, MK2206, and flavopiridol) was tested to determine the degree of drug sensitivity as well as heterogeneity in drug response.
Results
A new quantitative metric (growth/toxicity) was developed based on morphometric parameters that were found to track the growth and apoptotic cell populations. Further, this study identified two parameters, namely, skew and kurtosis—which report the spatial heterogeneity in mitochondrial metabolism within the spheroids. The results of this study show that three-dimensional tumor spheroids selectively respond to cancer drugs depending on the specific metabolic pathways (AKT inhibition pathway in the present study), and there exists significant heterogeneity in the untreated tumor spheroids. Drug sensitivity of the spheroids was found to be associated with significant alterations in mitochondrial heterogeneity within the spheroids.
Conclusions
In conclusion, the quantitative imaging of morphometric and metabolic analysis in large image datasets can serve as an excellent tool box for characterizing tumor heterogeneity in three-dimensional tumor spheroids and potentially, in intact tumors as well.