Dr. Ken Buetow: IS & Personalized Medicine Lecture-Part 1

Full Lecture

Creating an Evidence Engine to Support Personalized Medicine

Personalized medicine is transforming biomedical research and healthcare service delivery. Disease definition, diagnosis, treatment, and prevention are being fundamentally altered by the capacity to routinely perform comprehensive molecular characterization. Nowhere is this change happening faster than in the field of cancer. Increasingly sophisticated technology provides the capacity to describe, in multiple molecular dimensions, the tumor and the individual in which it has developed. These technologies identify the millions of variants present in normal individuals and thousands of alterations that occur during the course of the disease process.

The generation of this unprecedented amount of data presents us with the challenge contextualizing that data and converting into actionable information. Currently, the context is drawn from fragmented research literature generated by "siloed" reductionist basic science investigations, t incomplete outcomes of clinical research designed for regulatory approval, t out-of-date recommendations made by bodies of experts, and day-to-day clinical experience of the practitioner. The integration and interpretation of this complex multidimensional information into the evidence necessary to support clinical care exceeds the raw human cognitive capacity.

Information systems have the capacity to provide the needed "tool" to tackle this challenge -- to generate the necessary evidence to support the delivery of personalized medicine. Arizona State University's (ASU) Complex Adaptive Systems team is building such an Evidence Engine in its Next Generation Cyber Capability (NGCC). The ASU NGCC -- composed of networks, hardware, software, and people transforms "Big Data" to information and creates the evidence necessary to enable personalized medicine.

Dr. Buetow currently serves as Director of Computational Sciences and Informatics within Arizona State University's (ASU) Complex Adaptive Systems Initiative (CASI). CASI applies systems approaches that leverage ASU's interdisciplinary research strengths to address complex global challenges. The Computational Sciences and Informatics program is developing and applying information technology to connect and enhance trans-disciplinary knowledge both within ASU and across the broader knowledge-generating ecosystem to address problems in biomedicine, the environment, and national security.

Dr. Buetow previously served as the Director of the Center for Biomedical Informatics and Information Technology within the National Institutes of Health's National Cancer Institute (NCI). In that capacity he initiated and oversaw the NCI's efforts to connect the global cancer community through community-developed, standards-based, interoperable informatics capabilities that enable secure exchange and use of biomedical data. Buetow designed and built one of the largest biomedical computing efforts in the world. He was responsible for coordinating biomedical informatics and information technology. The NCI center he led focused on speeding scientific discovery and facilitated translational research by coordinating, developing and deploying biomedical informatics systems, infrastructure, tools and data in support of NCI research initiatives.

The primary focus of the Buetow laboratory is the application of computational technologies to solve major biomedical challenges, particularly the role of genetics in complex human diseases such as cancer. It undertakes this mission through a systems approach in which genetic analytic approaches are applied to multiple high-throughput molecular characterizations integrated through informatics. The Buetow laboratory approaches diseases such as cancer as a complex adaptive system.

The Buetow laboratory has a long history of developing and applying bioinformatics methods to find genetic components underlying complex traits. The laboratory was instrumental in the earliest studies developing and applying linkage disequilibrium methods as genetic mapping tools. The laboratory also developed methods and pipelines to generate and apply genome-wide genetic maps. In early work with genome-wide gene sequence data, the laboratory developed approaches to efficiently and accurately computationally identify variants. More recently, the analytic approaches have been extended to systematically identify insertion/deletion variation, translocations, and rearrangements. In application to transcriptome data, these methods facilitate identification of splicing and alternative transcripts.

More information available at: