A Systems Approach to Renal Cancer Clear Cell Carcinoma

Maciek Sasinowski, CEO, SysGen will be speaking on the above topic at 5th Annual BioPharma Asia Convention 2012, held on 19th to 22nd March 2012 at Marina Bay Sands, Singapore. He will be conducting an on-floor seminar at Theatre 1 at 12:00.

Here is a short abstract of his presentation:

The use of mass spectrometry (MS) and gene expression microarray (MA) technologies for clinical applications has extraordinary potential for accurate, early, and minimally invasive diagnoses of complex diseases, such as cancer. However, collecting, integrating, and analyzing heterogeneous sets of data represent a substantial undertaking, both technical and managerial, that requires resources not available to most investigators. As the field continues to move into a direction that requires effective integration of diverse knowledge bases and technologies, there is an increasing need for a robust bioinformatics framework that allows researchers to meld their distinct expertise and most efficiently apply their contributions toward a comprehensive understanding of biological phenomena.

We developed an integrated, modular environment for combining MA and MS data that provides interaction between the three major components for data classification: signal processing, profile construction, and classification and validation. This platform has allowed our multidisciplinary team of researchers to meaningfully integrate knowledge about genes and proteins toward the systematic understanding of cancer biology, and we present an example integrative study of renal cell carcinoma. During the study, researchers were able to: 1) perform platform-specific analyses of MS and MA data sets to reduce the size of the data sets and identify the statistically/diagnostically significant features in the data, 2) allow a meaningful integration of the resulting data sets, 3) generate an integrated network that describes the relationships between the genes and the proteins and incorporate existing biological knowledge into the network, and 4) visualize, explore, and validate the network and generated hypotheses in statistical and biological contexts. The discovery of potential biomarkers and therapeutic strategies through this multidisciplinary project offers clinically relevant information toward early diagnosis, accurate prognosis, and improved therapy of cancer.

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