7:15am Registration Open

8:45 Plenary Keynote Introduction 
Kathryn Lowell, Deputy Secretary, Life Sciences, California Business Transportation & Housing Agency

8:55 Plenary Keynote
Using Molecular Medicine to do Therapeutic Development in the Network Age
Jay M. Tenenbaum, Ph.D., Chairman and Chief Scientist, CollabRx, Inc.
A new paradigm for translational research will be described that combines the integrative and collaborative power of the Internet with personalized molecular analysis to slash the time and cost of therapy development. A key element is the creation of Health Commons, an open web-based ecosystem of researchers, clinicians, patients, pharma/biotechs, and service/technology providers that can be rapidly mobilized to develop targeted therapies for disease subclasses. This ecosystem will stimulate the same radical increase in efficiency for therapy development that ecommerce brought to business in the 1990s, ushering in a new age of collaborative, personalized medicine where every patient can afford custom therapies and discovery is driven by collectively interpreting the outcomes across all patients.

9:40 Grand Opening Refreshment Break in the Exhibit Hall

KEYNOTE PRESENTATIONS

Shared session with Cancer Profiling and Pathways

11:00 Chairperson’s Remarks
Robert L. Strausberg, Ph.D., Deputy Director, J. Craig Venter Institute

11:10 A Single Cell Based Understanding  of Cancer Multiclonality Using Network Architectures Predicts Mechanism, Therapy, and  Clinical Outcomes
Garry P. Nolan, Ph.D., Director, Stanford NHLBI Proteomics Center, Microbiology & Immunology, Stanford University
Intracellular assays of signaling systems has been limited by an inability to correlate functional subsets of cells in complex populations based on active kinase states or other nodal signaling junctions. Such correlations distinguish changes in signaling status that arise in rare cell subsets during functional activation or in disease manifestation. We have demonstrated the ability to simultaneously detect activated kinases and phosphoproteins in simultaneous pathways in subpopulations of complex cell populations by multi-parameter flow cytometric analysis.  We have applied this technology to the study of normal human cell populations as well as several disease states including Acute Myelogenous Leukemia, and Follicular Lymphoma, colon cancer and infiltrating immune cells of cancers among others.  The tremendous amounts of correlated data generated via phospho-flow allows amalgamation that automates signaling network determination using Bayesian analysis (and a unique computational approach using a new electronic architecture for a ‘statistics supercomputer’).  Bringing these together, we have initiated the generation of a comprehensive network topology maps of signaling in primary immune subsets and cancers.   This brings single cell analysis of multiple kinase pathways together with novel computational and electronic approaches to allow diagnostic measures of single cells in relation to drug action, disease course, and predicting patient outcomes.

11:40 Applying Broad Pathway Analysis and Deep Pathway Analysis to Biology and Medicine
Roger Brent, Ph.D., President and Research Director, Molecular Sciences Institute
During the 1980s and 1990s, a combination of classical genetic and other methods enabled the wholesale identification of components of eukaryotic signaling systems. During the early 2000's, increasing application of higher throughput methods added greatly to this embarrassment of riches, but at first failed to provide additional knowledge sufficient to help group these components into functionally separable pathways altered in disease states. More recently, systematic and combined application of DNA sequencing, DNA methylation detection, mRNA and miRNA expression, and proteomic methods has begun to add value to the previous picture by permitting identification of specific functional pathways affected in individual instances of disease states such as cancers. This recent success in broad analysis of affected pathways provides the foundation for this meeting.

Here, I will review the opportunities that the broad understanding of pathways altered in disease states and individual tumors seems to offer for medicine, including drug discovery and drug therapy. I will then review work from the Molecular Sciences Institute that uses genetic, proteomic, and single cell methods to study the quantitative physiology or functioning of a single model cell pathway in great depth. Insights from this approach complement those obtained from the broad approach. These results define "systems level" quantitative phenotypes common to cell signaling systems that give insight into the function of cell signaling systems. They also suggest new concepts that may be useful for understanding disease, and may define potential paths to therapeutic intervention via drug therapy.

12:10pm   A Systems Approach to Breast Cancer Treatment
Joe W. Gray, Ph.D., Director, Life Sciences Division, Lawrence Berkeley National Laboratory

1:10          Walk & Talk Luncheon in the Exhibit Hall

INTEGRATING NEXT GEN TECHNOLOGIES FOR RISK ASSESSMENT
Shared session with Cancer Profiling and Pathways

2:15          Chairperson’s Remarks
David Malkin, M.D., Co-Director of the Cancer Genetics Program, University of Toronto’s Hospital for Sick Children                           

2:20 Genomic Analysis of Cancer with Next Generation DNA Sequencing Technologies
Robert L. Strausberg, Ph.D., Deputy Director, J. Craig Venter Institute
Emerging DNA sequencing technologies are enabling comprehensive views of cancer genes and genomes, thereby providing unprecedented opportunities toward understanding the features of  biological pathways in cancer development and progression. This presentation will highlight the new technologies and specific applications in cancer research. Potential approaches toward improved disease intervention will be discussed.

2:50 Multi-Dimensional Pathways in Cancer
Michael Liebman, Ph.D., Managing Director, Strategic Medicine, Inc
Breast cancer is a complex set of disorders whose characterization requires integration and analysis of data spanning clinical, physiological and molecular dimensions.  We have begun to represent the biological complexity of breast disease in a model that incorporates protein, metabolite and gene regulation dimensions, and which is being analyzed using complex pathway simulation and optimization methods.  This is providing unique insight into the role of SNP’s, mutations, PTM’s, and the underlying physiology in the disease process.

3:20 Target Selection from DNA Copy Number and Expression Analyses of Breast and Ovarian Tumors
Zemin Zhang, Ph.D., Acting Director, Department of Bioinformatics, Genentech, Inc.
Genomic alterations are commonly observed in cancers but it remains a challenge to distinguish driver from passenger genes. We studied the genomic landscape of breast and ovarian cancers using high-resolution 500K SNP arrays and defined minimal regions with statistical significance based on the prevalence of level of copy number alterations. Coupled with expression analyses, the refined regions with amplification led to much increased precision in cancer target identification.

3:50 DNA Copy Number Variation and Cancer Susceptibility: Modifying the Two-Hit Hypothesis
David Malkin, M.D., Co-Director of the Cancer Genetics Program, University of Toronto’s Hospital for Sick Children
Cancer susceptibility is commonly associated with germline alterations of tumor suppressor genes that occur de novo or inherited. The spectrum of cancer susceptibility syndromes and associated susceptibilty genes is extensive. However, phenotypic variability within families or between families in whom specific genetic alterations arise is poorly understood at a genetic or genomic level. This talk will discuss recent observations of genome wide copy number variation in individuals with Li-Fraumeni syndrome that is associated with a wide spectrum of childhood and adult-onset cancers, in the setting of germline p53 mutation. A model of genome wide variation in human cancer, extrapolated from these findings, will be presented and discussed.

4:20          Reception in the Exhibit Hall

5:00          Breakout Discussions in the Exhibit Hall

Taking Account of the Limitations of Biomarker Discovery in Cancer
Moderator: Zoltan Szallasi, M.D., Senior Research Scientist, Children’s Hospital, Boston, USA, Professor, Danish Technical Uiniversity, Lyngby, Denmark

• Are we ready to face the limitations of genome scale screening based biomarker discovery?

• Worst and best case scenarios:  the variables are not independent and this should give us cautious hope if we know how to take advantage of it

• Can we optimize the efficiency of genome scale screening based biomarker discovery in cancer – technical and cohort design related issues

DNA Copy Number Variation and Cancer Susceptibility
Moderator: David Malkin, M.D., Co-Director of the Cancer Genetics Program, University of Toronto’s Hospital for Sick Children

• How can DNA Copy Number variation be used to enhance molecular profiling of cancer susceptibility?

• What genetic mechanisms explain the development of copy number variable regions of DNA?

• What is the relationship of copy number variable regions and specific target cancer genes’ in the carcinogenic process?

Epigenetics and MicroRNA
Moderators: Dalia Cohen, Ph.D., Chief Scientific Officer, Rosetta Genomics Inc. and Enal Razvi, Ph.D., System Biosciences SBI

• Role of epigenetics and microRNAs in cancer and differentiation—exploring the connections

• Impact of epigenetics and microRNA on the expanding stem cells research and regenerative medicine efforts

• How to commercialize upon the microRNA space?

• What about epigenetics?  The current landscape and what value it offers for diagnostics/therapeutics in the future

Expression Profiles for Individual Tumors
Moderator: Craig N. Giroux, Ph.D., Associate Professor, Research, Karmanos Cancer Institute, Wayne State University

• How can we best use static snapshots (gene signatures) of a moving target (dynamic tumor progression) to guide cancer treatment?

• How do we deal with tumor heterogeneity and its confounding effects on our ability to distinguish driver from passenger effects in tumor progression?

• Can we define an individual tumor expression profile (private variation) or must we settle for a sub-type classification (common variation)?

•  How do we score a gene expression signature: statistical metrics or network topology?

6:00          Close of Day

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