Tumors are complex structures of malignant cells and stromal cells that function as an integrated system that promotes tumor progression. Immune cells and other stromal components serve vital cooperative functions that often support tumor growth and metastasis; stromal content and function are strongly associated with disease progression and clinical outcome in cancer patients. Cellular systems biology considers tissues and tumors, and the cells within them, as integrated and interactive networks that function in concert as a system. Assessment of tumors as a “system” within the system of a patient using the cellular systems biology approach has the potential to improve on the current diagnostic tools for breast cancer by creating high content profiles of an individual patient tumor. The application of cellular systems biology (CSBTM) profiling to early drug discovery using cellular models of disease  and to drug development using the CellCiphrTM Cytotoxicity Profiling panels  can optimize the efficacy and decrease the potential toxicity of compounds taken into pre-clinical trials. However, it has become clear that patient sub-populations can respond differently to drug candidates in clinical trials due to patient variability. Therefore, cellular systems biology can also be a powerful approach to patient stratification for clinical trials and could become an important diagnostic tool. This review describes how the cellular systems biology approach can be applied to patient stratification and diagnostics in breast cancer, focusing on the advantages of quantifying functional biomarkers representing key tumor system processes in intact tissues from patients in order to make highly specific and sensitive predictions towards development of individualized medicine for breast cancer. We discuss the state-of-the-art of multiplexing of functional biomarkers in tissues and the practical utilization of the cellular systems biology approach in creating classifiers for patient stratification and diagnostics.
Keywords: Cellular systems biology, breast cancer, diagnostics, biomarkers, tissue microarrays
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