Optimize Utility of Gene and SNP Array Outputs



December 3, 2013
11:00 am to 12:00 pm EST

 

Sponsored by
Biomodels
 

Symposium Course Description:

While the application of sophisticated computational techniques has provided novel ways of evaluating genomic data, its intricacies may obscure the ultimate objective of providing translational scientists and clinicians with easily understandable, accurate and actionable endpoints. Following a brief review of genomic fundamentals, this symposium will compare and contrast different analytical approaches to genomic data and present a novel approach that provides users with clear, reproducible, and meaningful practical outcomes.

Learning Objectives:


  • Understand the fundamentals of genomics: genes, SNPs, and techniques
  • Be able to articulate the differences between candidate gene and GWAS studies
  • Provide some answers as to lack of reproducibility of clinical genomic studies
  • Introduce the concept of how learned gene and SNP networks are derived, analyzed and applied
  • Give specific examples (case studies) of the application of networks to understand mechanism of action, toxicity risk prediction, and responder/non-responder in pre-clinical and clinical trials.

Who Should Attend:


  • Translational (bench to bedside) scientists
  • Individuals interested in optimizing personalized medicine approaches in clinical trials or marketing
  • Clinical study program directors responsible for trial design
  • Third party payers interested in identifying methods which truly personalize medication use by identifying individuals most likely to respond to therapy without toxicity risk.

Program Agenda: 

1. Introduction to genomics (10 minutes)

2. Standard methods for genomic analysis – a critical comparison of candidate gene and GWAS approaches (10 minutes)

3. How learned networks of cooperating SNPs and genes are developed (15 minutes)

4. Examples of how SNP or gene clusters and networks can inform our pre-clinical and clinical decision making (15 minutes)

5. Conclusions (5 minutes)

6. Q+A (10 minutes)


Speaker Information:


Stephen T. Sonis, D.M.D., D.M.Sc.
Founder, Partner, Chief Scientific Officer
Biomodels, LLC

Dr. Sonis is a world-renowned expert in epithelial injury associated with cancer therapy. His development of predictive animal models has enabled the investigation of the biological basis of mucositis and has assisted in the development of potential therapies. The results of his studies on the molecular and cellular pathogenesis of mucositis have established the basis of the mechanistic paradigm for mucosal injury. Dr. Sonis’ interest in the genomic basis for toxicity risk and its pathology has led to innovative genomics-based analytical approaches to clinically actionable outcomes to personalize disease therapy. These studies have resulted in the identification of drug and toxicity specific SNP and gene networks which identify individuals at risk of treatment toxicities and define the genomic parameters which differentiate patients who respond to a specific drug treatment from those who do not.

Dr. Sonis has published extensively on the clinical, biological, and health economic aspects of cancer and complications associated with its treatment. He is the author of over 200 original publications, reviews and chapters, 10 books, and 5 patents. Dr. Sonis has obtained degrees from Tufts University and Harvard University and completed his post-doctoral education (tumor immunology) at Oxford University. He holds appointments at the Harvard School of Dental Medicine (Clinical Professor of Oral Medicine, Department of Oral Medicine, Infection and Immunity), the Dana-Farber Cancer Institute and Brigham and Women’s Hospital where he is Division Chief and Senior Surgeon.

Gil Alterovitz, PhD
Biomodels Senior Genomics Consultant 

Dr. Alterovitz acts as a consultant for analysis of genomic data. His interest in bridging the fields of engineering and medicine led him to develop a Bayesian and information theoretic framework for genomics research. The outcome is the ability to discover hidden relationships in networks which expose global connectivity patterns. This method is widely applicable to areas of personalized genomics and pharmacogenomics. Dr. Alterovitz is currently an Assistant Professor at the Harvard Medical School. He received his Ph.D. in electrical and biomedical engineering and masters in electrical engineering and computer science from the Massachusetts Institute of Technology. He also holds a B.S. in electrical and computer engineering from Carnegie Mellon University.



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