In the past, tissue-based diagnosis of human disease has largely occurred under the rubric of the medical specialty of anatomic pathology. Despite the recent advances in medical science, we still rely on the well-trained human eye of a pathologist for tissue diagnosis and classification. Diagnosis is largely made on the basis of morphology and pattern recognition involving multiple variables, including tissue architecture, cellular configurations, pleomorphism, nuclear shape and contour, and staining patterns. For example, cancer cells typically have higher nuclear-to-cytoplasmic ratios, prominent nucleoli, distinctive chromatin patterns, and a high mitotic index. Accurate diagnosis requires years of experience, as benign reactive conditions can also exhibit similar characteristics. Immunohistochemical analysis and use of antibody stains for subclassification of tumors has recently added a much-improved dimension to tissue-based clinical diagnostics. However, although tumors often display the same histologic and immunohistochemical profiles, there is a wide range of patient response to treatments. This disparity suggests that there is a diverse biology of tumors on a molecular level that is not apparent by outward microscopic morphology. For example, diffuse large B-cell lymphoma has a very heterogeneous outcome pattern. Gene microarray studies have been able uncover several distinct gene-expression patterns that correlate with distinct patient outcome patterns not readily apparent by pathologic analysis [4, 5, 11]. It is likely that the differential gene-expression patterns seen in these examples give rise to unique combinations of protein products that cooperate along multiple deranged signaling pathways and ultimately regulate the malignancy in a patient-specific manner. The complex portrait of functional protein expression is predicted to contain important information about the pathologic process taking place in the cells within their tissue microenvironment. This proteomic information ultimately will contain valuable information for diagnostic classification of tumors, for prognosis, and more importantly, for therapeutic targeting .
Once disease has been diagnosed and characterized, the identification of specific derangements within the molecular networks serves as the basis for the formulation of personalized molecularly targeted therapeutic strategies . The ability to characterize information flow through known protein-protein-signaling networks that interconnect the extracellular tissue microenvironment to the intracellular transcrip-tional regulatory processes will be the nexus for patient-specific therapy. Using cancer as a model, the malignant phenotype is the culmination of multiple genetic or epigenetic "hits" [14, 15], which cooperate to change and modulate protein function along multiple protein-signaling pathways regulating cellular physiologic processes including proliferation, differentiation, apoptosis, metabolism, immune recognition, invasion, and metastasis. Many approaches to elucidating altered protein function in human disease have relied on the use of in vitro cultured cell lines originally derived from fresh tissue. However, cultured cells may not accurately represent the molecular events taking place in the actual tissue they were derived from. Protein expression levels and posttranslational modifications affecting protein activity of the cultured cells are influenced by the culture environment, and these properties can be quite different from those of the proteins expressed in the native tissue state. This is because the cultured cells have inevitably lost the contextuality of the in situ tissue elements that regulate gene expression, such as soluble factors, extracellular matrix molecules, and cell-cell communication. Human disease occurs in the context of complex tissue microenvironments  involving host stromata, immune cells, cytokines, and growth factors that may not be adequately reflected in either in vitro studies or nonhuman animal studies. In the context of clinical medicine and patient treatment, individual biologic heterogeneity must be taken into consideration. In fact, it is predictable that each patient can harbor unique attributes that are critical, for example, to an understanding of the tumor-host behavior that can be utilized for effective tailored therapeutic targeting.
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