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MONDAY, SEPTEMBER 17

3:00-4:00 Conference Registration 

4:00-4:10 Welcoming Remarks from Conference Director
Julia Boguslavsky, Cambridge Healthtech Institute 

SYSTEMS BIOLOGY: BRIDGING THE SILOS IN
BIOMARKER DISCOVERY AND VALIDATION

4:10-4:40 Past, Present and Future of Systems Biology in Biomarker Discovery
Stephen Naylor, Ph.D., Chairman and CEO, Predictive Physiology and Medicine Inc. 
Systems Biology entered the 21st Century with a roar. It offered great promise in unraveling the complexities of human biology and physiology. We all embraced its potential for providing powerful new biomarker panels with unprecedented specificity and sensitivity. The reality has been more sobering, but the potential still remains. We will review the past to learn for the future and discuss the current status of Systems Biology in Biomarker Discovery.

4:40-5:10 To be Announced  

5:10-6:30 Opening Reception in the Exhibit Hall

TUESDAY, SEPTEMBER 18

7:30-8:15 Breakfast Workshop 
Biological Reference Materials for Proteomics Studies
Peter Schulz-Knappe, Ph.D., Chief Scientific Officer, Proteome Sciences plc
Reference materials are routinely used in clinical chemistry to ascertain comparability of analytical tests between labs and over long time periods. The utility of proteomics is severely limited by lack of precision of qualitative and quantitative proteome analysis and poor reproducibility, even within the same lab. We produce biological reference materials for proteomics analysis by tagging plasma, serum, csf and other biological sources with isobaric mass labels, the Tandem Mass Tags (TMT®). These references are spiked into individual study samples to allow for relative quantification of the entire sample proteome with highly improved precision and CV. The use of customised references in clinical trials provides for maximum coverage of the specific proteome of disease populations.

PROTEIN BIOMARKER DISCOVERY

8:30-8:35 Chairperson’s Opening Remarks

8:35-9:05 Carrier Protein Bound Peptidome for Biomarker Discovery: An Untapped Source of Diagnostic Information
Emanuel F. Petricoin, Ph.D., Co-Director, Center for Applied Proteomics and Molecular Medicine; and Professor of Life Sciences, George Mason University
Despite the urgent need for biomarkers that can improve cancer clinical outcome through early detection, risk stratification, and therapy optimization, relatively few new cancer biomarkers have been advanced to routine clinical use. Unfortunately, discovery of cancer specific markers is much harder than was initially anticipated. The three major impediments are (A) molecular heterogeneity between histologically identical appearing tumors; (B) prevalence of non-cancer diseases that reduce biomarker specificity for cancer; and (C) low biomarker concentrations, especially in early stage disease, thus reducing sensitivity. Gene and protein array data has revealed that each malignancy may have a different molecular portrait. Thus, a single given biomarker may work for only a subset of tumors within a group being tested. Recently, the use of high-throughput mass spectrometry based profiling of body fluids has proven to be an intriguing clinical diagnostic method. We have discovered that most, if not all, of this information exists complexed with circulating high abundance carrier proteins that act as molecular “mops” that harvest the LMW fragments and peptides. This information archive contains a vast and unexplored source of potential biomarkers. We are developing new technologies, created at the intersection of proteomics and nanotechnology, to identify and characterize the LMW proteomic biomarker content and will present case studies and a vision of combining this resource with diagnostic imaging to increase accuracy and positive predictive value.

9:05-9:35 The Development of an Integrated Analysis of Plasma Using Glycoproteomic, Peptidomic and Immunochemical Study
William Hancock, Ph.D., Bradstreet Chair, Barnett Institute, and Department of Chemistry and Chemical Biology, Northeastern University
Psoriasis and Rheumatoid Arthritis are autoimmune diseases characterized by inflammation, hyperproliferation of keratinocytes, and angiogenesis. To identify candidate protein or peptide biomarkers of pathology and potentially predictive biomarkers of the response to therapeutic intervention, 20 plasma samples from patients and 20 matched plasma samples from healthy donors were analyzed using two mass spectrometry-based methods. The first method evaluated changes in levels of glycoproteins, known to play an important role in autoimmune disorders, and comprehensively surveyed the plasma proteome. In this method, removal of abundant plasma proteins and multi-lectin affinity chromatography enabled enrichment of plasma glycoproteins and their targeted analysis. The second method, peptidomics, allowed evaluation of potential changes in proteolytic activity of certain classes of proteases via analysis of low molecular weight component of plasma. The experimentation identified a number of cytoskeletal, extracellular matrix and Ca2+ metabolism-related proteins and their proteolytic fragments present at different levels in plasma of psoriasis patients and healthy donors; several of them were verified by ELISA. In addition, the activity of metalloproteases that targets cytoskeletal proteins have been shown to be elevated in plasma of patients as compared to healthy donors. These findings suggest an involvement of the calcium homeostasis, proteolytic system, and cytoskeletal proteins in the disease process, and may further the understanding of the autoimmune pathogenesis of the disease and its modulation by current and emerging therapeutics. This data set serves as a good model for the development of a comprehensive platform for the analysis of plasma and the integration of ELISA measurements with proteomics. 

9:35-10:05 Identifying Secreted Proteins at the Source: a New Method for Biomarker and Diagnostics Discovery
Eustache Paramithiotis, Ph.D, Director, Molecular and Cellular Biology, Caprion Pharmaceuticals
Discovery of low abundance protein markers in the blood has been achieved by isolating those proteins before they are released at the source. Probing the secretory pathway of prostate tumors, compared to normal prostates, with mass spectrometry has resulted in the identification of differentially expressed proteins that can then be confirmed in the blood using more sensitive antibody assays. This approach has found 60 differentially expressed secreted proteins from prostate tumors, including many novel markers. 

10:05-10:35 Proteomic and Non-Proteomic Based Discovery of Protein Biomarkers for Traumatic Brain Injury and Stroke
Kevin Wang, Ph.D., Associate Director, Center for Traumatic Brain Injury Studies (CTBIS); Director, Center for Neuroproteomics and Biomarkers Research (CNBR); and Associate Professor of Psychiatry and Neuroscience, Department of Psychiatry, McKnight Brain Institute, University of Florida
Discovery of protein biomarkers for traumatic brain injury (TBI) and stroke is an intensely pursued area. Our Center utilizes animal model of TBI (controlled cortical impact, CCI) and stroke (middle cerebral artery occlusion, MCAO) for the discovery phase of biomarker research. Injured and controlled samples were subjected to differential neuroproteomic platform and targeted approach to identify potential biomarkers. Selected markers are then confirmed with cerebrospinal fluid and blood from the animal models. Marker candidates are then further validated with clinical samples. 

10:35-11:30 Coffee Break with Poster and Exhibit Viewing

11:30-12:00 Proteomic Analysis of Protein-Protein Interaction for Identification of Membrane Markers of Disease
Jinzhi Chen, Ph.D., Head, Genetics/Genomics, Proteomics/Biomarker Discovery, Roche Palo Alto
We use co-immunoprecipitation process, followed by a mass-spectrometry-based comparative proteomic analysis of CD81-associated proteins in membrane, to identify novel markers of pathology. Gene expression data were used to select tissue-specific membrane proteins and the strategy was suitable to select targets for antibody-based drug delivery applications. Proteomics data of disease-related and control cell lines will be discussed.

12:00-12:30 Redefining Biomarkers for Diabetes and Diabetic Complications: A Systems Biology Approach
Mark Chance, Ph.D., Director, Center for Proteomics, Case Western Reserve University
Diabetes mellitus is estimated to affect approximately 20 million people in the US and more than 150 million people worldwide. There are numerous end organ complications of diabetes, the onset of which can be delayed by early diagnosis and treatment. Recently, studies have been conducted to develop accurate urine based diagnostic testing as conventional assays for diabetes and its complications lack specificity, sensitivity and accuracy. Utilizing gene expression and 2D DIGE platforms, we have extensively investigated the gene and protein changes in a diabetic rat model to better understand the pathophysiological changes that occur in the bladder as a result of diabetes mellitus. Coupling these data to label free expression proteomic results from diabetic urine has provided a panel of putative biomarkers detectable in urine that are sensitive and specific to diabetic complications of the urinary system. In addition, we have found improvements in detection increasing both the sensitivity and specificity of assays for existing biomarkers of diabetic complications such as microalbuminuria.

12:30-2:00 Luncheon Technology Solutions Showcases
(Sponsorships Available. Contact Nicolas Shostak, Manager, Business Development at 781-972-5479 or nshostak@healthtech.com)

2:00-2:30 Searching for Potential Biomarkers of Cisplatin Resistance in Human Ovarian Cancer
Mu Wang, Ph.D., Assistant Professor, Biochemistry and Molecular Biology and Adjunct Assistant Professor, Informatics, Indiana University School of Medicine; and Director, Protein Analysis Research Center, The Indiana Centers for Applied Protein Sciences (INCAPS)
Ovarian cancer is the leading cause of death among all gynecological cancers each year. Platinum-based drugs are primarily used as chemotherapeutic drugs in treatment of ovarian cancer. Most patients with the disease are initially responsive to the treatment, but eventually relapse and become refractory to additional treatment. To date, the mechanisms of drug-resistance remain poorly understood. To discover the underlying mechanisms in which drug-resistance is developed in ovarian cancer cells, we conducted a global quantitative proteomic analysis of drug-sensitive and drug-resistant ovarian cancer cells. The results provide important information to help us better understand the underlying mechanisms of drug-resistance in ovarian cancer.

2:30-3:00 To be Announced

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

BIOMARKER DATA ANALYSIS

4:00-4:30 Comparing Fingerprint-based and Biomarker-based Classifiers
Brian Luke, Ph.D., Senior Scientist, Advanced Biomedical Computing Center, National Cancer Institute
There are two different definitions of biomarkers that, when used in an appropriate classifier, are used to determine whether or not an individual has a particular disease. The first is a panel of markers that are used to construct fingerprints to determine the health category of an individual. Examples of published algorithms include a decision tree (DT), an artificial neural network (ANN), and the medoid classification algorithm (MCA) as used by the laboratories of Perticoin and Liotta. The second is a biomolecule whose full range of concentrations is strongly controlled by the individual’s biochemical state. It has been argued that if a classifier can be constructed from a training set that is able to accurately classify a blinded testing set, then that classifier must be based on some underlying biological principle. This talk will show that the concept that we call coverage strongly affects fingerprint-based classifiers when the available data are divided into training and testing sets. We show that the application of DT or MCA classifiers on datasets that contain random peak intensities are able to accurately classify properly selected training and testing sets even though there is no biological relevance. In contrast, a biomarker-based classifier is unable to construct an accurate classifier using the same random datasets. The inference of biological information from an accurate classifier is only true for a biomarker-based classifier since fingerprint-based classifiers are able to accurately classify a dataset by chance.

4:30-5:00 Discovering Multivariate Biomarkers and Informative Sets of Genes
Darius Dziuda, Ph.D., Assistant Professor, Mathematics, Central Connecticut State University
The presentation will outline and discuss current issues in biomarker discovery, especially multivariate approach to identification of genomic biomarkers for medical diagnosis, prognosis, and drug discovery. In the second part of this talk, a novel method associating a small multivariate biomarker with a larger Informative Set of Genes will be presented. The Informative Set of Genes is important for elucidation of biological processes underlying class differentiation.

5:00-5:30 Chemosensitivity Biomarker Extrapolation from NCI-60 Cancer Cell Lines to Clinical Tumors for Predicting Patients’ Chemotherapeutic Responses
Jae K. Lee, Ph.D., Associate Professor, Biostatistics, University of Virginia School of Medicine; and Director, UVA Bioinformatics Support Core
The U.S. National Cancer Institute has used a panel of 60 diverse human cancer cell lines (the NCI-60) to screen >100,000 chemical compounds for anticancer activity. However, not all important cancer types are included on the panel nor are drug responses on the panel predictive of clinical efficacy in patients. We thus asked whether it would be possible to identify common chemosensitivity biomarkers from that rich database to predict drug activity in cell types not included in the NCI-60 panel or, even further, clinical responses in patients with tumors. We address that challenge by developing a novel algorithm “Co-eXpression ExtrapolatioN” (COXEN), which can effectively identify concordant genomic biomarkers between two independent expression profiling data sets, here extrapolating the genomic expression patterns of NCI-60 chemosensitivity biomakers with those of clinical tumors. Applying our COXEN approach in a prospective fashion, we predicted anticancer drug activities on completely independent bladder cancer, which is not included in the NCI-60 panel, and on breast cancer patients treated with commonly used chemotherapeutics with >80% accuracy. We also used COXEN for in silico screening of 45,545 compounds and identified a novel agent with superior growth inhibition activity against human bladder cancer.

5:30-6:00 Decipher and Select the Genomic Biomarkers with the DAVID Bioinformatics Resources
Richard A. Lempicki, Ph.D., LIB Lab Head, Senior Scientist, Clinical Services Program, Laboratory of Immunopathogenesis and Bioinformatics, SAIC-Frederick, Inc.
In the post-genomic era, one of the challenges is to systematically and comprehensively interpret large amounts of “omic” data results, such as biomarker discovery based on microarray studies. Using the biological knowledge accumulated in the past decades and the aid of computing algorithms, it is possible to assemble potential biological pictures associated with these studies toward the more specific biomarker discovery. The DAVID (The Database for Annotation, Visualization and Integrated Discovery) Bioinformatics Resources provides a set of powerful, novel tools that researchers can use to explore their large gene lists in-depth from many different biological angles in order to extract associated biological meanings to the greatest extent possible. For example, the DAVID Gene Functional Classification Tool is able to quickly group large gene lists into functional gene groups. Therefore, it increases the likelihood that the investigator will identify more specific biomarkers most pertinent to the biological phenomena under study. To present the usefulness of the DAVID Bioinformatics Resources in biomarker discovery, a real life clinical biomarker study conducted in my laboratory will be presented. In this study, with the help of the DAVID Bioinformatics Resources, a set of interferon-stimulated genes, as clinical biomarkers, were successfully selected from Affy microarray studies on the HCV/HIV co-infection patients. The marker genes are able to predict the output of anti-HCV therapy for the HCV/HIV co-infected patients. 

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