Nanostructure-Initiator Mass Spectrometry (NIMS) Based Tissue Imaging for the Detection of Metabolite Markers of Drug-Resistant Breast Tumors
Final Report Abstract
One of the major problems of effective drug treatment in tumor therapy is the development of drug resistances. For example, emerging resistances to doxorubicin, one of the most frequently used standard drugs in cancer therapy, or to lapatinib, which is used for the treatment of HER2-positive breast tumors, are very common. Typically, a certain type of tumor is treated in a standardized way, not taking drug resistances into account. In such cases, therapeutic approaches often result in ineffective treatment. The existence of biomarkers for drug resistant tumors would help to preselect the proper medication and greatly facilitate a more tailored and effective therapy. Another factor complicating the treatment of tumors is the large heterogeneity of tumorous tissue. Standard ‘grind-and-find’ extraction approaches only yield averaged information for the whole piece of tissue, the effects of minor cellular subpopulations may be missed out. This means that for example a biomarker for a small subpopulation of drug resistant tumor cells may not be detected within a large piece of tissue. Imaging approaches have the capability to detect biological features in situ and yield a precise molecular picture of the analyzed tissue. The goal of this project was the identification of metabolites as biomarkers for drug resistant breast tumors and the imaging of these biomarkers within heterogeneous tumor tissue. To this end the metabolism of 19 doxorubicin sensitive and resistant breast cancer cell lines was analyzed by LC-MS. However, it turned out that this approach was too simplified. Each cell line has a multitude of unique metabolic, genotypic, and phenotypic features and even when comparing the most doxorubicin sensitive and resistant cell lines, the metabolic differences between these cells never correlated precisely with one single characteristic feature. Therefore, the experimental focus was shifted to a more suitable model system for the detection of biomarkers for lapatinib resistance. BT474 is a HER2-overexpressing breast cancer cell line which shows a relatively high sensitivity to treatment with the small molecule drug lapatinib. A collaborating lab had used this cell line to generate a lapatinib resistant BT474 cell line. As these two cell lines share the same metabolic background and their only difference is the variation in sensitivity to lapatinib, they represent the ideal model system for the identification of a biomarker for lapatinib resistance. The metabolism of both cell lines was analyzed with LC-MS and GC-MS. First data analysis revealed significantly different levels for several metabolites, especially small organic and amino acids and larger lipids. The detailed statistical analysis is ongoing. In parallel, the application of Nanostructure-Initiator Mass Spectrometry (NIMS) for tissue imaging of metabolites was developed further. For example, by imaging sequential sections of a mouse mammary tumor and subsequent computational reconstruction a 3D tissue image was obtained. Statistical analysis revealed distinct metabolic clusters within the tissue. Combination with standard tissue staining techniques showed that these clusters can either represent different types of tissues or different molecular characteristics within the same tissue, clearly demonstrating tissue heterogeneity. In summary, first potential marker metabolites for lapatinib resistance have been identified and will be further validated. Subsequently it will be attempted to detect these molecules in (lapatinib resistant) tumors using NIMS tissue imaging.
Publications
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(2011). Multivariate analysis of a 3D mass spectral image for examining tissue heterogeneity. Integr Biol 3, 460-467
Reindl, W., Bowen, B.P., Balamotis, M.A., Green, J.E., and Northen, T.R.
