Monday, September 29
3:00-4:00 Conference Registration
4:00-4:10 Welcoming Remarks from Conference Director
Julia Boguslavsky, Cambridge Healthtech Institute
Establishing Biomarker Utility
4:10-4:15 Chairperson’s Opening Remarks
4:15-4:40 Five Characteristics of a Biomarker to be Useful for Personalizing Medicine
Felix Frueh, Ph.D., Vice President, Research & Development, Personalized Medicine, Medco Health Solutions, Inc.
4:40-5:05 Biomarkers for What? Diagnostic, Prognostic or Predictive?
Sudhir Srivastava, Ph.D., Chief, Cancer Biomarkers Research Group, NIH National Cancer Institute
Biomarkers have been touted as a next frontier in the realm of personalized medicine. However, one has to be specific and clear about its intended use: Diagnostic, Prognostic, or Predictive? Each type has a different purpose. Each has to meet certain criteria to be fit for the purpose. Therefore, when discussing biomarkers, one must clearly state its targeted goal and population.
5:05-5:30 Building a Biomarker Information Pipeline and Enabling Translational and Personalized Medicine: Leveraging Industry Standards to Bring Omics Closer to Medicine
Martin D. Leach, Ph.D., Executive Director, Basic Research & Biomarker IT, Merck & Co., Inc.
5:30-6:30 Opening Reception in the Exhibit Hall
Tuesday, September 30
Genomic Biomarkers | Protein Biomarkers | Metabolic Biomarkers | Biomarker Data Analysis
7:00 Registration Open
7:30-8:15 Morning Coffee or Technology Workshops
(Sponsorship Available. Contact Ilana Schwartz at 781-972-5457 or email@example.com.)
7:30 Breakfast Workshop
Biological Variation Based Data Interpretation. Why Can This be of Value?
Gordon F. Kapke, Ph.D., Senior Director of Biomarker Services, Covance Central Laboratory Services
With the emphasis on biomarkers to improve drug development the question arises as how to interpret the data. The traditional clinical laboratory methodology for interpreting data involves the identification of the expected values (the normal range) and from this range defining the probably of disease or no disease (sensitivity and specificity). The challenge in drug development is in monitoring the patient overtime while identifying if important changes have occurred in the biomarker values that indicate inappropriate toxicity or demonstrate appropriate efficacy. The use of the reference interval as a means of identifying toxicity or efficacy will be challenged and an alternative approached based embracing biological variation will be proposed.
Metabolomics to Assess Drug Response
8:30-8:35 Chairperson’s Opening Remarks
8:35-9:00 Using Metabonomics and DNA Microarrays to Find Biomarkers of Liver Injury
Richard Beger, Ph.D., Branch Chief, Center for Metabolomics, Division of Systems Toxicology, National Center for Toxicological Research, U.S. Food and Drug Administration
Drug-induced liver injury has often been associated with the generation of reactive metabolites, which are primarily detoxified via glutathione conjugation. Since S-adenosylmethionine (SAMe) is the primary source of the sulfur atom in glutathione, SAMe was shown to be significantly reduced following toxic dosing in day 1 and day 2 urine samples. N-methylnicotinate, which is a byproduct of the conversion of S-adenosylmethionine (SAMe) to S-adenosylhomocysteine (SAH), was also shown to be significantly decreased in day 1 and day 2 urine samples following toxic dosing. In order to further validate the results from the metabonimc studies, the Gene Expression Omnibus (GEO) database was used to obtain microarray data from the rat liver treated by liver toxicants. Some genes involved in trans-sulfuration pathway, including glycine-N-methyltransferase and betaine-homocystein methyltransferase (GNMT and Bhmt, respectively), were found to be significantly decreased in the toxic compounds compared with the control groups. The metabolic and transcriptomic results show that N-methylnicotinate is a potential non-invasive preclinical biomarker of toxicity that is related to SAMe and glutathione depletion for detoxification of reactive drug metabolites.
9:00-9:25 Metabolomics/Proteomics: Tools for Biomarker Discovery within Safety Assessment
Johan Lindberg, Ph.D., Principal Scientist, Molecular Toxicology, Safety Assessment, AstraZeneca
Metabolite and protein profiling are methods used for discovery of new safety biomarkers and toxicity problem solving. Examples from the area of liver toxicology and fibrodysplasia will be used to describe the biomarker discovery process. Key components are analytical platforms, bio-statistics, biological contextualization and early biomarker qualification. In addition a novel data reduction tool, Tracmass, for LC-MS data reduction will be described.
9:25-9:50 Metabolomics: A Global Biochemical Approach to the Study of Human Disease and Drug Effects
Rima Kaddurah-Daouk, Ph.D., Associate Professor, Department of Psychiatry and Behavioral Sciences, Duke University Medical Center
We have developed and used sophisticated metabolomics analytical platforms and informatics tools to define initial metabolic signatures for several central nervous system (CNS) diseases and for drugs used to treat these diseases. We will share our experience and early findings from the study of schizophrenia and depression and steps we have taken towards defining biomarkers for disease and response to therapy. We will also highlight the “National Metabolomics Network for Drug Response Phenotypes” a consortium funded by NIH and that brings metabolomics and pharmacogenomic approaches towards providing insights into the underlying basis for individual variation to drug response. Such knowledge could enhance significantly our effort for developing biomarkers that can predict drug response outcomes.
9:50-10:15 Tracer Substrate Derived Metabolite Profiles in Combination with Principal Component Analysis To Assess Drug Response and Toxicity
Laszlo G. Boros, M.D., Associate Professor of Pediatrics, University of California Los Angeles; Co-Director, Stable Isotope Research Laboratory, Harbor-UCLA Medical Center; Chief Scientific Advisor, SiDMAP, LLC
Altered structural and intermediary metabolite synthesis as revealed by stable isotope labeled tracer substrates enhances flux studies for the identification of biomarker products for the purpose of assessing drug response in vitro and in vivo. First order kinetics of labeling from a tracer substrate to its numerous products in healthy individuals establishes the strong dependence of product formation in many biochemical reactions for a single substrate, which, in turn, can be used to evaluate drug response using Principal Component Analysis in the large data sets obtained by pathway specific positional 13C labeling. Valproic acid treatment, for example, decreases liver and brain cell glycogen and RNA turnover, while the severe decrease in 13C glucose labeling to cholesterol serves as an early and easy to obtain plasma marker to assess valproic acid action on glucose homeostasis, limited acetate formation and sterol synthesis. The severe inhibition of glycogen and RNA turnover, as well as fatty acid and cholesterol syntheses as shown by Principal Component Analysis, which are critical fluxes in liver cells, by a single valproate dose reveals the severe impairment of liver function with permanent cell damage and liver toxicity to follow if valproate treatment continues in a susceptible individual.
10:15-11:10 Networking Coffee Break with Poster and Exhibit Viewing
11:10-11:35 Clinical Biomarkers from Biofluid Metabolomics
David Wishart, Ph.D., Professor, Departments of Computing Science and Biological Sciences, University of Alberta
A continuing challenge for metabolomics is having a consolidated resource of “normal” metabolite concentration ranges for most metabolites in most biofluids. Indeed, without a list of normal values, it is very hard to know what is abnormal. In this presentation I will provide an update of our computational and experimental efforts to assemble complete metabolite concentration profiles for most of the clinically relevant biofluids, including cerebrospinal fluid, serum, urine and saliva. I will also briefly survey the known set of diseases and conditions that can be unequivocally characterized by single metabolite biomarkers as well as those conditions that must be characterized by multiple metabolite biomarkers. I will also discuss the need and provide examples of how to develop more sophisticated approaches to using metabolites as disease biomarkers.
11:35-12:00 Global Metabolomics: Navigating from Methods to Biology
William Wikoff, Ph.D., Research Associate, Center for Mass Spectrometry, Scripps Research Institute
Human diseases manifest in complex downstream effects, affecting multiple biochemical pathways. We use a non-targeted, mass-spectrometry approach to metabolomics to investigate disease. Plasma samples from patients with inborn errors of metabolism were investigated for validation, and identified additional biomarkers. Investigating the microbiome interaction with the host revealed a surprisingly large effect on plasma biochemistry, and a drug-like response. The neurochemical effect of SIV-induced encephalitis was examined in rhesus macaques, addressing problems in central nervous system biochemistry and neurodegenerative diseases.
12:00-1:40 Luncheon Technology Showcases
An Automated and Streamlined Solution to Increase Productivity and Confidence in Microarray Studies
Speaker to be Announced, Affymetrix
This case study highlights the increase in productivity and confidence in microarray results for whole genome expression analysis using a microplate-based high throughput platform. The platform includes automated target preparation, an array processing instrument and complementary reagents that minimize hands-on time. Applications in drug discovery and development will be discussed.
Cancer DSA™ - Disease Focused Microarrays: A Platform for Biomarker Discovery and Validation, Optimised for Use with FFPE Tissue
Austin Tanney, Ph.D., Scientific Liaison Manager, Almac Diagnostics
WideScreen™ Assays for the Multiplex Detection and Quantitation of Cell Signaling Proteins and Serum Biomarkers
Laura Juckem, Ph.D., R&D Senior Scientist, Multiplex Assays, EMD Chemicals, Novagen
Bead-based immunoassays allow multiplex analysis of 5-20 protein analytes from a single sample. This session will discuss select WideScreen™ Assays and their use in the characterization of cell signaling events and in the analysis of serum biomarkers. These assays include:
·Multiplex assays to analyze the expression and tyrosine phosphorylation status of several key receptor tyrosine kinases, enabling the profiling of protein kinase inhibitors in a cellular context.
·An assay that uses EpiTag™ Technology (Epitome Biosystems) for the simultaneous, multiplex quantitation of total and phosphorylated ERK pathway proteins in a single well.
·Highly validated and functionally defined serum biomarker panels developed in partnership with Rules Based Medicine.
Diagnostic Potential of Metabolite Biomarkers
1:40-1:45 Chairperson’s Opening Remarks
1:45-2:10 Mass Spectrometry-Derived Metabolic Biomarkers and Signatures in
Vladimir Tolstikov, Ph.D., Metabolomics Core Manager, The University of California Davis Genome Center
This presentation focuses on identification and validation of metabolic biomarkers for diagnostic development. It covers the workflow, methods and instrumentation applied at UC Davis Metabolomics Core. We utilize Mass Spectrometry based data acquisition/analysis coupled with a variety of separation techniques, in particular GC and LC chromatography. Biomarker identification will be described with the use of MarkerView (Applied Biosystems) software, as well as with the alternative genetic algorithms method developed for metabolomics data mining in my laboratory. Very high resolution ESI Mass Spectrometry, with the assistance of MassWorks sCLIPS software (Cerno Bioscience) used for unidentified metabolites elemental composition assignment will be reported. Case studies on Renal Cell Carcinoma (RCC) diagnostic test development and Polycystic Kidney Disease (PKD) diagnostic test development will also be presented.
2:10-2:35 Noninvasive Diagnosis of Cancers: A Metabolomics Approach Combining 1H NMR Spectroscopy and a Robust Classification Strategy
Ray Somorjai, Ph.D., Head, Biomedical Informatics, Institute for Biodiagnostics, National Research Council Canada
Non-invasively acquired biomedical data tend to be sample-poor (sample sizes of 10 - 100), and initially feature-rich (number of features 1,000 - 10,000). Consequently, they need special considerations before they can be used with confidence as diagnostic/prognostic aids in the clinic. Our statistical classification strategy (SCS) was developed specifically to process and analyze such data. The SCS is a multi-stage interactive approach, consisting of visualization, data preprocessing, best discriminatory feature selection, robust classifier development, and possible aggregation of several classifiers. It recognizes the fact that diagnostic aid methodologies must be data-driven. Special attention will be given to the difficulties/pitfalls of biomedical data classification. Of the currently used non-invasive methods (NMR, IR, fluorescence, mass spectroscopy and microarrays), I’ll focus on proton NMR spectroscopy and discuss several examples of successfully diagnosing a number of commonly occurring cancers.
2:35-3:00 NMR and MS-Based Metabolite Biomarker Discovery for Disease Diagnostics
G.A. Nagana Gowda, Ph.D., Research Scientist, Department of Chemistry, Purdue University
The realization that a large number of small molecular metabolites can be relatively easily measured under dynamic conditions has led to the exploration of tissue and human body fluids to discover new biomarkers for early diagnosis or prediction of life threatening diseases. High resolution analytical methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy are the main focus of analysis in the fast developing area of metabolomics. While MS is highly sensitive, NMR spectroscopy provides more reproducible and quantifiable data for high throughput analysis. The combination of complementary NMR and MS data has an added benefit of better classification of disease samples. Examples of this approach in several human and animal studies will be highlighted. Currently, the relatively low sensitivity of NMR is one of the bottlenecks for the detection of low concentrated early disease biomarker candidates. To circumvent such problems, we have explored isotope labeled chemical derivatization methods. These methods, which are based on a class selection of metabolites, enhance sensitivity as well as improve spectral resolution. Advancements in this and other developments will be discussed with emphasis on the studies of human diseases.
3:00-4:00 Networking Refreshment Break with Poster and Exhibit Viewing
4:00-4:25 Applications of Stable Isotopomer Analysis in Cancer Metabolomics
Andrew N. Lane, Ph.D., J.G. Brown Chair of Structural Biology, Associate Director (NMR development) CREAM, J.G. Brown Cancer Center and Department of Chemistry, University of Louisville
We have been developing NMR and MS approaches to determining accurate positional and mass isotopomer distributions in a wide variety of metabolites from media and cells exposed to 13C-labeled precursors. The simultaneous acquisition of concentrations and isotopomer information for critical reporter metabolites greatly increases the biochemical information content, and with the aid of simple modeling, provides mechanistic insights into the response of cancer cells and tissues to environmental stress including disease states and response to therapeutic agents. Important biochemical pathways that are presently probed include glycolysis, pentose phosphate pathways, Krebs cycle, anaplerotic reactions, nucleotide biosynthesis, redox stress pathways, protein biosynthesis and phospholipid turnover. The pathway information is especially helpful for generating new hypotheses as well as testing current ideas based on other Omics such as gene expression or protein data. The principles will be illustrated with applications in lung and breast cancer.
4:25-4:50 NMR-Based Metabolomics: Translational Application in Cancer and Anti-Cancer Treatment
Natalie J. Serkova, Ph.D., Associate Professor of Anesthesiology and Radiology, Director, Biomedical MRI/PET/CT Cancer Center Core, University of Colorado Health Sciences Center
Cancer cells possess a highly unique metabolic phenotype which is characterized by high glucose uptake, increased glycolytic activity, decreased mitochondrial activity, low bioenergetic and increased phospholipid turnover. In addition to these general metabolic markers of malignancy, specific endogenous metabolites are implicated in particular tumors, such as N-acetyl aspartate in neuroblastoma, myo-inositol in glioma, citrate in prostate cancer, based on tissue-specific biochemistry. All these metabolic hallmarks can be readily assessed to monitor responsiveness and resistance developments to novel targeted drugs, where specific inhibition of cell proliferation (cytostatic effect) occurs rather than direct induction of cell death (cytotoxicity). Using modern analytical technologies in combination with statistical approaches, “metabolomics”, a global metabolic profile on patient samples can be established and validated for responders and non-responders, providing additional metabolic end-points. This review describes existing nuclear magnetic resonance (NMR)-based approaches for global metabolic profiling in tissue biopsies, body fluids and, finally, non-invasive assessment of metabolic biomarkers using non-invasive radiological techniques. Most recent studies on metabolic response to novel targeted drugs (tyrosine kinase inhibitors, metabolic modulators) are analyzed.
4:50-5:15 Metabolic Fingerprinting of Breast Cancer Development
Vladimir Shulaev, Ph.D., Associate Professor, Virginia Polytechnic Institute & State University, Bioinformatics Institute
We use metabolomics approach to study the progression of malignancy in human breast epithelial cells. Weinberg cell model is used to categorize the metabolic changes associated with malignant transformation. We performed the liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS)-based metabolomics analysis followed by multivariate statistical analysis to identify robust molecular signatures that can provide accurate classification of normal and malignant cells.
5:15 Close of Day
Wednesday, October 1
7:00 Registration Open
7:30-8:15 Breakfast Workshop
An Integrated Quantitiative Mass Spectrometric Workflow for the Discovery and Validation of Protein Biomarkers Using Tandem Mass Tags (TMT®)
Dr. Rainer Voegeli, Commercial Director, Proteome Sciences plc.
The establishment of highly specific and sensitive validation assays is a key task to transfer candidate biomarkers from discovery into validation studies. In response to the limited availability of ELISA’s, single and multiple reaction monitoring (SRM, MRM) is emerging as a highly selective and sensitive approach for the quantitation of proteins. Here we present an integrated workflow for discovery, assay development and validation studies which utilises chemical labelling by Tandem Mass Tags (TMT). In discovery mode up to six samples are labelled with different colours of TMT, mixed and subsequently analysed by Tandem Mass Spectrometry in a single assay run. From individual samples generally >1,000 peptides and proteins are analysed. Peptides and proteins are selected as candidates for validation if they appear differentially regulated, and meet appropriate biostatistical properties across an adequately powered discovery study. Assay development is then performed using peptides unique to the candidate biomarkers. By combining MRM with peptide labelling by Tandem Mass Tags TMTzero (label mass: 224 Da) and sample labelling by TMTsixplex (label mass: 229 Da), peptide pairs are generated which exhibit perfect co-elution and show a mass difference of 5 Da per added Tag. Quantitation against a reference labelled with TMTzero is so made possible whilst maintaining the selectivity/specificity of MRM combined with the superior sensitivity of Triple Quadrupole instrumentation. This approach allows the very fast transfer of discovery candidates into high quality assays and alleviates the need for immunoassay development or the synthesis of heavy-isotope labelled peptides (as routinely used in MRM’s).
1. Seamless transition from discovery to high quality, high sensitivity assay development will shorten validation time
2. Multiplex Assay Capability is in-line with the expected multitude of biomarkers to be validated
3. No need to invest into lengthy immunoassay-development prior to validation of the marker itself
4. Quick translation from pre-clinical to human trials
Technology Advances for Metabolic Profiling
8:30-8:35 Chairperson’s Opening Remarks
8:35-9:00 NMR-Based In-Situ Metabolic Profiling of the Life Cycle of Malaria Parasite P. Falciparum with High Temporal Resolution
Istvan Pelczer, Ph.D., Lecturer, Senior NMR Spectroscopist, Department of Chemistry, Frick Lab, Princeton University
Metabolic mixture analysis can be most efficient when using unbiased and quantitative analytical methods, such as NMR spectroscopy, and sophisticated statistical techniques. In addition, keeping the sample in its native condition can be an essential benefit. We have been studying the 48 hour life cycle of synchronized malaria parasites, P. falciparum, in red blood cell (RBC) cultures. The current research to be discussed in this talk has been focusing on in-situ media analysis of the cell culture following temporal changes with high resolution. Metabolic changes in the media present a sensitive and very informative reflection of the metabolism over the various stages of the parasitic infection and development. We have been able to characterize this process with high accuracy using the combination of NMR spectroscopy and statistical analysis, follow the high temporal resolution kinetics of metabolic changes in the media, and screen the effects of known antimalarial drugs and anticipated drug candidates.
9:00-9:25 Mass Spectral Metabonomics Beyond Elemental Formula: Chemical Database Querying by Matching Experimental with Computational Fragmentation Spectra
David F. Grant, Ph.D., Associate Professor of Toxicology; Co-Head, Mass Spectrometry Facility, Department of Pharmaceutical Sciences, University of Connecticut
Despite recent advances in NMR and mass spectrometry, the structural identification of organic compounds in complex biofluids remains a significant analytical challenge. For mass spectroscopy applications, chemical identification is generally limited to determination of elemental formula. Here we test the hypothesis that unknown chemical structures can be determined by matching their experimental collision induced dissociation (CID) fragmentation spectra with computational fragmentation spectra of compounds retrieved from chemical databases. The monoisotopic molecular weights (MIMW + 10 ppm) of 102 “test” compounds were used to download 102 “bins” from the PubChem database. Each bin contained the corresponding test compound and, on average, 272 other candidate compounds, including 158 compounds having the same elemental formula as the test compound. Commercially available software was used to generate fragmentation spectra for all compounds in each of the 102 bins. Experimental CID spectra for each of the 102 test compounds were then compared to the computational spectra in order to rank candidate compounds based on number of fragment MIMW matches. This method returned the test compound as the highest ranking (or tied with the highest ranking) compound for 65 of the 102 bins. The test compound was ranked within the top 20 candidate compounds for 87 bins. In addition, the correct elemental formula was ranked first for 98 of 102 bins. Thus, matching experimental with computational fragmentation spectra is a valid method for rapidly discriminating among compounds having the same elemental formula and provides a novel approach for querying chemical databases for structural information.
9:25-9:50 Addressing the Analytical Problems of Global Systems Biology Using Ultra High Resolution LC and Exact Mass MS(MS)
Robert Plumb, Ph.D., Senior Applications Manager, Pharmaceutical Business Operations, Waters Corporation
The analytical challenges that face global systems biology in an animal study or a human population environment are many fold; These include the detection of all of the analytes in the samples, the accurate measurement of their relative concentrations, data reduction, the identification of effected biological pathways, all this must be achieved in a short time frame to allow rapid the processing of thousands of samples. The detection of endogenous metabolites requires a high resolution, high sensitivity instrumentation such as chromatography and mass spectrometry system. This is especially true in biological samples such as plasma, urine and bile, where the matrix is particularly complicated. Reversed-phase liquid chromatography coupled to electrospray mass spectrometry is particularly suited to this task as it is compatible with the biological matrix; more recently the use of sub 2um particles operated at higher temperatures has been demonstrated to give increased LC performance. In this presentation we will illustrate the benefits of this technique for the metabonomics, and show how the use of higher operating temperatures and longer columns can generate very high resolution chromatograms with peak capacities of 1000 in just 1 hour with peak widths of less than 1 second. Reference will be made to the effect of these narrow peak widths on MS data capture rates and mass accuracy. We will demonstrate how this approach has been used to analyze samples from a toxicology study to elucidate the effects of the gut microflora play in controlling drug metabolism and metabolic response to a toxicological insult. We will also show how this approach has been used to process samples from a human metabonomics study with a very short analysis time to elucidate the effected biological pathways via the identification of putative biomarkers by exact mass hybrid quadrupole TOF MS(MS).
9:50-10:50 Networking Coffee Break with Poster and Exhibit Viewing
Development Considerations for Single-Analyte Markers, Panels, and Profiles
10:50-11:15 Optimal Biomarker Approach: Data Analysis Considerations of Individual, Panel or Profile
Stephen Naylor, Ph.D., Chief Executive Officer & Chairman, PPM, Inc.
The advent of relatively high throughput and broad analyte coverage analysis in “omic” measurements has reignited a debate about what constitutes the optimal biomarker solution. Is it a single analyte per biological event, or a panel (3-10 analytes) or even a profile (>20 analytes)? This will be discussed in the context of statistical and data analysis as well as data mining characteristics.
11:15-11:40 Panel Discussion
11:40-1:10 Luncheon Technology Workshop
Enabling Drug Development Through Multiplexed Assays
Monica Errico, Ph.D., Application Scientist, Scientific Services, Meso Scale Discovery
Meso Scale Discovery (MSD) has an electrochemiluminescence platform that is fast (1-3 minutes per plate independent of plate density), robust (non-fluidics instrument), radioactive free, sensitive (detection limits near 10 attomoles) and has a wide dynamic range (5 logs) with multiplexing capabilities. The performance (sensitivity, reproducibility, and ease of use) of multiplexing cytokines, cell signaling pathways, and multiplexed toxicology biomarkers assays will be presented. Development of multiplex panels for complex matrices is challenging because of varying levels of biomarkers, interfering substances in the sample, and interactions between proteins measured. The platform allows for simple assay development that can greatly reduce the amount of time to develop novel assays. The combined properties of the system provide both a cost and time savings with a highly quantitative assay format while improving productivity.
11:40-1:10 Luncheon Technology Workshop
Multiplexed Biomarker Assay Development
Michael Pisano, Ph.D., President and CEO, NextGen Sciences
Biomarkerexpress™ is a suite of mass spectrometry-based biomarker services that utilize proprietary methods to significantly decrease timelines and increase the success rates traditionally associated with biomarker development. The services include discovery of protein biomarkers, development of protein biomarker assays, testing biological samples utilizing the assays to determine levels of protein biomarkers.
Case Study 1: Discovery and Assay Development for Putative Biomarkers of Lung Cancer Progression
Case Study 2: Assay Development for a Panel of 30 Biomarkers for Alzheimer’s Disease
Bridging Silos: Integrating Omic Data
1:10-1:15 Chairperson’s Opening Remarks
1:15-1:40 Integration of Metabolic and Transcriptomic Profiling for Understanding of Diabetes and Obesity Mechanisms
Christopher B. Newgard, Ph.D., Director, Sarah W. Stedman Nutrition and Metabolism Center, Duke University Medical Center
Type 2 diabetes is a disease that occurs as a result of metabolic dysfunction in multiple tissues, including most prominently liver, skeletal muscle, and the pancreatic islets of Langerhans. An understanding of the transcriptional and metabolic networks that control normal functions in these tissues, and identification of the network elements that are perturbed during development of type 2 diabetes, are essential steps in the development of new therapies for the disease. The value of targeted mass spectrometry-based profiling of key clusters of intermediary metabolites for identifying specific network perturbations will be highlighted, as will recent examples of integration of metabolomic and transcriptomic profiling for identifying heretofore unrecognized regulatory pathways.
1:40-2:05 Integrating Gene and Protein Expression Biomarkers in a Systems Biology Approach to Colon Cancer
Mark R. Chance, Ph.D., Director, Case Center for Proteomics; Director, Center for Synchrotron Biosciences; Professor, Department of Physiology & Biophysics, Case Western Reserve University
Protein interaction networks are at the heart of functional control of human disease. Network and pathway modeling driven by Omics based approaches are increasingly important to our understanding of disease progression and drug responses. However, deriving and validating network models are complex research problems requiring integra tion of multiple types of high-throughput data. We have recently employed a systems biology approach to find small networks of proteins discriminative of late stage human colorectal cancer (CRC). Expression proteomics studies were initially used to identify proteins differentially regulated when comparing normal and late stage tumor tissues obtained from adequately sized cohorts of human patients. Proteins identified by these experiments were used to seed a search for protein-protein interaction networks selective for biological relevance to the human colon. We chose four significant networks returned by this search and illustrated using measures of mutual information, calculated using gene expression data, that certain protein “signatures” within each network are highly discriminative of late stage cancer versus control. These signatures would not have been discovered using only proteomic data, or by merely clustering the gene expression data. Expanding these signatures by a single hop generated four sub-networks, which were analyzed for biological relevance to human CRC. A number of the proteins in these sub-networks have been shown to be critically involved in the progression of CRC. Others have been recently identified as potential markers of CRC, and still others merit follow-on experimental validation for biological significance in this disease. This general approach can be applied to network modeling for a number of diseases.
2:05-2:30 A Systems Biology Approach to Biomarker Discovery
Karin Rodland, Ph.D., Science Lead for NIH Programs, Pacific Northwest National Laboratory
Efforts to identify biomarkers for early diagnosis or prognosis of cancer and other disease have often focused on a singular molecular species, with preference given to mRNA, microRNA, proteins, autoantibodies or metabolites based on available technologies and model systems. Each one of these measurements provides a snapshot of cell function, but a dynamic understanding of disease processes really requires the integration of all these modalities to the extent possible. Particularly in the context of using biomarkers to guide therapeutic interventions, it is necessary to understand the relationship between changes in expression, and changes in function. One aspect of systems biology is the integration of heterogeneous datasets to define relationships that predict function. This talk will describe the application of this approach to models of chronic obstructive pulmonary disease.
2:30-2:55 Connecting the Biomarker Dots in Cancer and Neurodegenerative Diseases
Ira L. Goldknopf, Ph.D., Director, Proteomics, Power3 Medical Products, Inc.
The application of fundamental principles to Omic integration to address unmet clinical needs will be illustrated with examples from cancer and neurodegenerative diseases. The integrations relate analytical with clinical validation across different analytical processes and platforms; clinical diagnostics with assessment of severity, disease progression, and efficacy; and data analysis integrating proteomic and genomic biomarkers, post-translational modifications, and protein isoforms. The clinical applications cover testing of blood serum for early detection of breast cancer as well as for early differential diagnosis and monitoring of the neurodegenerative diseases. The attainment of biological significance in terms of monitoring mechanisms of disease through blood testing as well as practical clinical diagnostic applications of such testing will also be discussed.
3:00 Close of Conference