Recorded presentation

Giovanni Parmigiani, PhD, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, 550 North Broadway, Suite 1103, Baltimore, MD 21205-2011, (410) 614-3426, gp@jhu.edu

Investigations of transcript levels on a genomic scale using hybridization-based arrays led to formidable advances in our understanding of the biology of many human illnesses. At the same time, these investigations have generated controversy, because of the probabilistic nature of the conclusions, and the surfacing of noticeable discrepancies between the results of studies addressing the same biological question. In this lecture I will use simple exploratory data analysis tools for gauging the degree to which the finding of one study are reproduced by others, and for integrating multiple studies in a single analysis. I will describe these approaches in the context of studies of both lung and breast cancer. The main conclusion of our work to date is that it is possible to identify a substantial, biologically relevant, subset of the human genome within which hybridization results are reproducible. The subset generally varies with the platform used, the tissues studied, and the populations being sampled. Despite important differences, it is also possible to develop simple expression measures that allow comparison across platforms, studies, labs and populations. While these are not perfect,important biological signal is often preserved or enhanced.

Learning Objectives: At the end of this session the participant should be able to

• Define the terms human genome, genomics, functional genomics, structural genomics and bioinformatis
• Describe computational methods required for genomics research.
• List statistical methods useful for understanding public health problems which can be addressed by genomics research.
• Describe methods for identifying effects of heterogeneity on key disease outcomes and also suggest alternative approaches to risk prediction and clinical trial design.
• Utilize exploratory data analysis tools for gauging the degree to which the finding of one study are reproduced by others, and for integrating multiple studies in a single analysis.
• Gain an understanding of the use of these techniques for research involving lung and breast cancer.

Keywords: Genetics,

Presenting author's disclosure statement:
I do not have any significant financial interest/arrangement or affiliation with any organization/institution whose products or services are being discussed in this session.

Recorded presentation