FAST Congress

 

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WEDNESDAY, SEPTEMBER 15

7:00 am Registration Open

8:00-8:30 Morning Coffee

 

Integrating “Omic” and Clinical
Data in Biomarker Development

8:25-8:30 Chairperson’s Opening Remarks
Mark Chance, Ph.D., Professor and Director, Center for Proteomics and Bioinformatics, Case Western Reserve University

8:30-9:00 Systems Biology Analysis of Glioblastoma Gene Expression Data Reveal Proteomic Biomarkers of Survival

Mark Chance, Ph.D., Professor and Director, Center for Proteomics and Bioinformatics, Case Western Reserve University

Data from The Cancer Genome Atlas (TCGA) is now available to mine the relationships between gene mutations, gene expression and patient survival. In general, gene expression data from tumors or normal tissues is rapidly accumulating driving development of methods to adequately mine the data for novel disease targets as well as to define diagnostic variables to predict outcome or response to therapy. Systems biology includes attempts to understand tumors in terms of their functional connections and requires integration of multiple data types to generate and score relevant models of disease. We have developed novel algorithms, including CRANE (Combinatorially dysRegulAted subNEtworks), to mine and integrate gene expression data in the context of protein-protein interaction networks that, when trained on TCGA datasets, can predict survival times for the independently derived cohorts from Rembrandt. Overall, we are unifying genomics, proteomics, and clinical data to provide discrete, testable sub-network models of tumors, represented as state-functions of the sub-networks, that provides both a set of biomarkers for diagnosis or prognosis as well as a set of functionally interacting proteins potentially at the heart of disease dysregulation. These methods are generally applicable to many diseases and can integrate many data types.

9:00-9:30 Biomarker Development to Improve Decision Support for the Treatment of Organ Failures: How Far Are We Today?

Raymond T. Ng, Ph.D., CIO, PROOF Centre of Excellence; Professor, Computer Science, University of British Columbia; and Erich A. Gombocz, Ph.D., Vice President & CSO, IO Informatics, Inc.

This talk will focus on what is involved in creating actionable knowledge to improve decision support in the clinics – be it to determine the risk of heart failures, to pre-symptomatically select the necessary aggressive immunosuppression therapy for a transplant patient at a high rejection risk or to estimate effectiveness of alternate treatments. Integration of experimental data and clinical observations alone is in most cases insufficient to provide confident answers. Using examples, this 2-part talk will explore, what avenues have been taken in developing biomarker panels and incorporating mechanistic and functional biology knowledge to generate meaningful semantic systems-biology based network models which can be applied to understand complex biological processes. This presentation will provide an overview of what has been achieved so far and outline what is needed to utilize such approaches in widespread patient-centric personalized medicine.

9:30-10:00 Utilizing Data Mining Of Healthcare Systems To Improve Quality and Clinical Decision-Making

Bharat Rao, Ph.D., Head, Knowledge Solutions Group, Image and Knowledge Management (IKM CKS) Division, Siemens Healthcare

This talk will address Research, Patient Quality and IT groups on how to use data mining methods to collectively mine structured and unstructured data of a patient record with the automatic integration of medical domain knowledge in order to support quality decision-making. The talk will cover mining structured and unstructured data for clinical and financial decisions, and integrating patient data with medical knowledge to determine what diagnosis and care would be optimal for the patient.

10:00-10:30 Using Public ‘Omics Data to Develop Predictive Biomarkers for Diagnostic Development in the Absence of Relevant Clinical Outcomes Data

Renee Deehan Kenney, Ph.D., Senior Director, Scientific Research and Operations, Discovery, Genstruct, Inc.

A fundamental challenge in diagnostics is the development of companion biomarkers prior to the availability of relevant clinical outcomes data. We developed a methodology to leverage vast quantities of public ‘omics data to indentify candidate predictive biomarkers for diagnostic development. We used this methodology to generate a gene expression-based predictive biomarker for c-Met targeted therapy use in lung and colon cancer. A signature for c-Met signaling containing 222 genes was employed to stratify patients by c-Met activation levels, which significantly correlated with patient smoking status in multiple lung cancer data sets. Data from patients with high and low levels of c-Met activation were used to develop a 20 gene expression-based classifier that performed well against independent test sets (AUROC values of 91, 96 and 99% for three lung cancer data sets and 98% for one colon cancer data set).This method can be applied to other targets, pathways and diseases and may facilitate companion diagnostic development by enabling the selection of appropriate patient populations prior to treatment and clinical trial participation.

10:30-11:30 Networking Coffee Break with Poster and Exhibit Viewing

 

Roundtable Discussions

11:30-12:30 pm  Biomarker Data Analysis
Moderator:  Renee Deehan Kenney, Ph.D., Senior Director, Scientific Research and Operations, Discovery, Genstruct, Inc.

11:30-12:30 pm  Protein Biomarkers
Moderator:  Emanuel Petricoin, Ph.D., Co-Director, Center for Applied Proteomics and Molecular Medicine; Professor, Life Sciences, George Mason University



12:30-2:00 Lunch on Your Own

 

PLENARY KEYNOTES

2:00-2:30 Fulfilling the Promise of a Sequenced Human Genome

Eric D. Green, M.D., Ph.D., Director, National Human Genome Research Institute, National Institutes of Health

The Human Genome Project’s completion of the human genome sequence in 2003 was a scientific achievement of historic proportions. It also
signified a critical transition, as this new foundation of genetic information started to be used in powerful ways by researchers and clinicians to tackle increasingly complex problems in biomedicine. Current efforts in genomics research are focused on using genomic data and technologies to acquire a deeper understanding of biology and to uncover the genetic basis of human disease. Together, these pursuits are moving us down an exciting path towards genomic medicine and fulfilling the promise of a sequenced human genome.

2:30-3:00 Personalized Medicine, Companion Diagnostics and Regulatory Considerations

Živana Težak, Ph.D., Associate Director for Science and Technology, Personalized Medicine/OIVD/CDRH, U.S. Food and Drug Administration

The U.S. FDA evaluates many of the products that will ultimately allow personalized medicine to be successfully implemented. This talk will focus on regulatory and scientific issues in personalized medicine, in particular on the diagnostic part, including companion and novel diagnostic devices. There are a number of approaches for clinical study designs used to evaluate companion diagnostic assays that may include specific diagnostic and therapeutic considerations. FDA faces evolving regulatory challenges for in vitro diagnostic assays, including further development of the regulatory structure for companion diagnostics and clarity on co-development issues. The talk will describe some of FDA efforts to integrate the various medical product regulatory authorities in order to improve clarity and efficiency in regulating personalized medicine products.

3:00-4:00 Networking Refreshment Break with Poster and Exhibit Viewing

 

Biomarker Data Analysis

Chairperson's Opening Remarks
Mark Chance, Ph.D., Professor and Director, Center for Proteomics and Bioinformatics, Case Western Reserve University


4:00-4:30 Getting Out of the Quagmire of Associative Learning Based Biomarker Discovery in Cancer

Zoltan Szallasi, M.D., Senior Scientist, Informatics Program, Children’s Hospital Boston, Harvard Medical School

Analyzing high-throughput, such as microarray based, clinical data sets carries a significant risk of overfitting when chance associations or systematic bias produce false leads in biomarker discovery. We are presenting here several alternative successful strategies that produced robust, verifiable, agent specific predictors of chemotherapy response in clinical cohorts. An RNA interference based strategy identified taxane specific predictive modules in breast cancer. Integrative genomic analysis has identified a robust mechanism predicting response to anthracyclines. We are also presenting a parallelized cell line selection based strategy to determine whether functional mechanisms leading to acquired chemo-therapy resistance converge. These alternative strategies will likely play a key role in the identification of potentially sensitive subpopulations of newly developed cancer therapeutic agents.

4:30 Close of Day