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FRIDAY, FEBRUARY 27

WHOLE GENOME EXPRESSION PROFILING

8:30am Chairperson’s Remarks
Goli Samimi, Ph.D., MPH, Cancer Prevention Fellow, Cell and Cancer Biology Branch, National Cancer Institute

8:35 Whole Genome Expression Profiling of Ovarian Cancer: Heading Toward Individualized Care
Goli Samimi, Ph.D., MPH, Cancer Prevention Fellow, Cell and Cancer Biology Branch, National Cancer Institute
Whole genome expression profiling has the potential to stratify patients by identifying previously unrecognized tumor subsets. We applied this technology to over 300 ovarian cancers activated pathways which contain new and novel therapeutic targets. Although presently, all epithelial ovarian cancers are treated essentially the same with surgery and chemotherapy, integrating these new genomic findings can point to a more tailored approach for this disease. Patients will be stratified according to prognosis, tumor grade and histology and ultimately specific pathways.

9:05 Personalized Cancer Therapy in the Light of Associative Learning: A Systematic Approach to Remove Technical and Analytical Difficulties from its Path
Zoltan Szallasi, M.D., Senior Research Scientist, Children’s Hospital, Boston, USA, Professor, Danish Technical Uiniversity, Lyngby, Denmark 
Genome-scale analysis by microarrays or array CGH are expected to yield information that would enable clinical oncologists to select the most efficient therapy for a given cancer patient. However, the success of such an endeavor is highly dependent on several factors, including the general noise structure and noise level of high-throughput measurements, the strength of association between biomarkers and gene modules and clinical outcome and the rather unfavorable ratio between the number of alternative hypotheses (e.g. quantified genes) and available clinical samples. We will address several important issues deeply rooted in the high-throughput nature of genome scale profiling and highly relevant for the meaningful analysis of clinical microarray data: systematic bias and normal tissue contamination in clinical cancer microarray data, and the difficult task of extracting robust, convergent and clinically useful information from multiple cancer data sets. We will also provide evidence from a clinical cohort that an appropriately selected, biologically motivated robust gene expression signature can determine which of two widely used chemotherapeutic agents will be more effective for a given ovarian cancer patient.

9:35 An Approach to Understanding the Functional Consequences of Susceptibility Alleles Discovered In Genome Wide Association Scans  
Matthew Freedman M.D., Assistant Professor, Harvard Medical School, Associate Physician, Dana-Farber Cancer Institute, Associate Member, Broad Institute of Harvard and MIT 
Genome wide association studies have delivered on the promise of finding risk variants for a large number of clinical traits. Interestingly, the majority of variants discovered to date are located in non-protein coding regions of the genome presenting a considerable challenge to understanding the mechanism by which risk is increased. The talk will outline a multi-disciplinary approach to tackling this question.

10:20 Coffee Break

11:00 Genome-Wide Epigenetic Profiling To Identify Oncology Biomarkers For Diagnostic and Theranostic Applications
Prof. Wim Van Criekinge, Vice President, Biomarker Research and Pharmacogenomics, OncoMethylome Sciences; and Professor, University Ghent, Belgium
A multi-faceted technological approach has been developed based on a proprietary Methylation-Specific PCR (MSP) platform to identify DNA methylation-based oncology biomarkers for early disease detection and theranostic applications. OncoMethylome utilizes epigenetic sensitization, aka pharmacological unmasking and next -generation sequencing methods together with a pathway-based real-time MSP array approach to exhaustively mine the epigenome and identify relevant biomarkers. This approach combines a sensitive and specific discovery phase with a smooth transition to analytically validated assays for clinical trial testing. Applying these approaches in high throughput mode on samples ranging from model systems like cell-line panels and xenografts to primary patient material revealed novel epigenetic insights in cancer progression, efficiently translated in biomarkers for early detection and prediction to response to therapy.

11:30 Panel Discussion

12:00pm Luncheon Workshop (Sponsorship Available) or Lunch on your own

PATHWAY ANALYSIS – Mapping Pathways & Identifying the Intervention Points 

1:00 Chairperson’s Remarks
Stephen J. Chanock, M.D., Director, Core Genotyping Facility Section Head, Translational Genomics Lab, National Cancer Institute

1:05 Genotype Wide Association Studies in Cancer
Stephen J. Chanock, M.D., Director, Core Genotyping Facility Section Head, Translational Genomics Lab, National Cancer Institute

1:35 Expression Profiles for Individual Tumors: Systems Level Modeling and Pathway Analysis 
Craig Giroux, Ph.D., Director of Systems and Computational Biology, Karmanos Cancer Institute, Wayne State University
Despite technical advances in the molecular profiling of biological states at the cellular level, the heterogeneity associated with individual tumors has confounded identification of a genetic signature that is diagnostic for the diseased state of cancer versus the healthy state of a normal cell. As a complex and unstable system, the cellular state of a tumor is intrinsically difficult to precisely define at the level of its individual genetic components, but is more readily visualized as a dynamic network with signature patterns of modular activity. Using graphical network based methods, we demonstrate that the gene expression activity patterns of individual tumor types can be mapped to the global cellular interaction network, thus revealing the presumptive state specific pathways and determinative processes underlying clinically distinct cancer subtypes. We are applying this systems level approach to the analysis of individual breast tumors.

2:05 Roadmap Toward Personalized Medicine
Craig Webb, Ph.D., Director, Program of Translational Medicine, Van Andel Research Institute 
There has been a great deal of excitement and optimism surrounding the era of individualized molecular-based medicine across therapeutic areas, including oncology. In reality, the efficient discovery and practical implementation of biomarkers for optimal therapeutic selection requires a multi-disciplinary approach that includes clinical, laboratory and informatics expertise, as well as knowledge of the major drivers and hurdles to success. This presentation will outline our current efforts to utilize molecular information derived from patient tumors in conjunction with systems biology and knowledge of biomarker-drug interaction to predict treatments with improved therapeutic index.

2:35 Cancer Pathways Analysis Through Inhibitor Profiling
Fei Hua, Ph.D., Senior Scientist, Systems Biology, Pfizer Inc. 
The PI3K/AKT pathway regulates many basic cellular functions including growth, proliferation and apoptosis, and therefore it is heavily targeted as cancer therapy. To better understand this pathway and inhibitors targeting this pathway, we used different inhibitors to perturb this pathway at various points. Effects on protein levels and phosphorylation status were profiled for a single cell line. Current understanding of the pathway and the inhibitors is insufficient to explain many of the experimental observations.

3:05 Close of Conference

Overview | Day 1 | Day 2 | Day 3 | Download Brochure | Breakout Discussions

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