The last two decades have seen an increased emphasis on system-level, integrated science as clinical researchers have recognized that the characterization of single genes and proteins has provided only limited insight and benefits toward early diagnoses, improved subtyping and prognoses, and treatment of complex, multifactorial diseases. This integrative approach is critical for our ability to elucidate the network of structural, regulatory, and dynamic interactions, thereby providing a comprehensive understanding of the physiology and pathophysiology that ultimately leads to effective intervention strategies.
The wide array of analysis tools and platforms available for analysis of clinical and life science data provide an unprecedented potential for discovery and benefits to health care. In particular, the application of multi-analyte molecular signatures derived from technologies such as protein mass spectrometry and gene expression microarrays has extraordinary potential for diagnostic, prognostic, and treatment strategies for complex diseases such as clear cell renal cell carcinoma (ccRCC). However, rapid advances of these experimental techniques are yielding vast amounts of heterogeneous data that are becoming progressively more difficult to analyze. Therefore, medical researchers must be provided with increasingly more powerful analysis tools to extract knowledge and develop inferences regarding the roles of genomics, proteomics, and metabolomics in disease process.
For example, the presence of a particular cancer may be signaled by a difference in the protein patterns or gene expression patterns in the body fluids, tissues and sera. Advanced data mining algorithms as well as data management and visualization tools have to be integrated to allow researchers to exploit the progress of experimental techniques and to turn the raw data into discovery and benefits. New data architectures must be developed to address issues such as the rapid integration of analysis of biological data files with epidemiologic profiles of human clinical responses. The search for specific proteins associated with disease is a major challenge. Often the discovery of what seems to be a unique protein biomarker that signals the presence of disease cannot be validated in a clinical setting. Recent evidence also suggests that single biomarkers may not be effective in improving detection, diagnosis and prognosis. This evidence supports approaches that include multiple biomarkers to enhance and improve the reliability of results. Rather than focusing on a single marker, approaches that evaluate multi-marker panels maximize the amount of information that can be extracted from the data.
INCOGEN scientists and their collaborators have been involved in the development of statistical pattern recognition and adaptive learning techniques to identify characteristic patterns in gene and protein expression profiles that represent healthy or diseased states. The model systems explored included: clear cell renal cell carcinoma, pediatric Hodgkin's disease, acute myeloid leukemia, and breast cancer. These cancers are remarkably heterogeneous with numerous subtypes and associated prognostic implications. Within each cancer group, treatment may be either intensified or decreased, based on prognosis, in order to improve outcome as well as to avoid side effects of therapy. Accuracy of diagnosis and early stage detection of the disease are life-critical in the fight against cancer.
Laboratory tests have been used to stratify risk and guide medical decision support for decades. In recent years, novel biomarkers and comprehensive biomarker panels have brought these tests into the health care delivery process and are changing the face of medicine. Comprehensive testing provides insight into the individual patient’s pathophysiology, allowing clinicians and other health care professionals to tailor the most appropriate and effective interventions, that may range from lifestyle, to dietary supplementation, to targeted pharmaceutical treatments.