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
(Shared Session)
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 and 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 flouted 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 Standard 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
CONCURRENT SESSIONS:
Genomic Biomarkers | Protein Biomarkers | Metabolic Biomarkers | Biomarker Data Analysis
BIOMARKER DATA ANALYSIS
Download the Biomarker Data Analysis PDF
7:00 Registration Open
7:30-8:15 Morning Coffee or Technology Workshops
(Sponsorship Available. Contact Ilana Schwartz at 781-972-5457 or ischwartz@healthtech.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.
GENE EXPRESSION PROFILING OF HEALTH AND DISEASE: BRIDGING STATISTICS AND BIOLOGY
(Shared session between Genomic Biomarkers and Biomarker Data Analysis)
8:30-8:35 Chairperson’s Opening Remarks
8:35-9:00 Biomarkers: Understanding the Disease Process
Michael N. Liebman, Ph.D., Senior Institute Fellow, Windber Research Institute; Managing Director, Strategic Medicine, Inc.
Measurement of gene expression data presents an opportunity to further classify patients and their disease using biological specimens, robust experimental methods and statistical analysis to enhance clinical decision making. It is critical, however, to appropriately evaluate this perspective on patient and/or disease stratification in terms of the complexity of the disease process and clinical need, rather than solely on the concept of a disease state. This presentation will describe both the conceptual framework for understanding the relationship between biomarkers and the disease process and results from its application in breast cancer.
9:00-9:25 NextBio - Searching Large Scale Biological Data for Metabolic Syndrome X Biomarkers
James Flynn, Ph.D., Field Application Scientist, NextBio
NextBio presents a powerful research tool to analyze Metabolic Syndrome X, one of the most complex and pervasive medical conditions that combines several disorders into one. Patients with Metabolic Syndrome X exhibit clinical signs of diabetes, obesity, dyslipidemia, hypertension and heart disease. This fact underscores the complexity involved in the study of this disorder and highlights the value of NextBio, which enables researchers to look at the interplay of various disease pathways. In this talk, we will demonstrate diverse search strategies for the discovery and validation of Metabolic Syndrome X biomarkers and drug toxicities through NextBio. NextBio’s collection of public experimental data will be used to explore mechanisms of potentially applicable compounds, discover tissue specific expression profiles associated with the disease and drill into the interplay of relevant pathways (such as lipid metabolism, glucose metabolism and inflammation).
9:25-9:50 Ingenuity Pathways Analysis: Prioritization of Biomarker Candidates from Omics Data Based on Phenotypic Association
Deborah Riley, Ph.D., Senior Application Scientist, Ingenuity Systems
As gene expression profiling has matured to become a common component of biomarker discovery programs, the challenge has shifted to translating large scale datasets into biomarkers that can be used to diagnose disease and predict patient response to treatment. Prioritization of biomarker candidates requires – at a very practical level - an understanding of candidates’ expression patterns in bodily fluids and target tissues and - at the mechanistic level – identification of biologically plausible paths between candidate markers and the physiological responses, cellular phenotypes, or disease processes of interest. In this session we will present a case study in which the biomarker discovery tool IPA was used to prioritize biomarker candidates and elucidate the molecular mechanisms connecting those markers to disease phenotypes and pathways.
9:50-10:15 Defining Health at the Molecular Level
Martin Grigorov, Ph.D., Head of Bioinformatics, Nestlé Research Center
The challenge for the Life Sciences in the new century resides in promoting health and in preventing disease. In order to meet this challenge, knowledge should be built to define and better understand the function of the molecular markers which define the healthy status of a biological system. The aim of the present study was to generate a map of gene expression patterns along the human healthy adult gastro-intestinal tract in order to use such sets of biomarkers as references when screening for pathological deviations. Nearly 150 marker genes were found to perfectly discriminate the five major GI regions considered. Fourteen had never been described in the GI tract, and six were novel genes. This work offers a perspective on nutrition-specific biomarkers discovery programs. It shows such studies to be complementary to typical drug development programs focusing on disease-specific biomarkers, rather than on the molecular signatures of health.
10:15-11:10 Networking Coffee Break, Poster and Exhibit Viewing
BRIDGING OMIC AND CLINICAL DATA
11:10-11:35 Advances in Bioinformatics for Next Generation Clinical Research and Biomarker Development Studies
James Lyons-Weiler, Ph.D., Director, Bioinformatics Analysis Core, Genomics and Proteomics Core Laboratories, Department of Biomedical Informatics, Department of Pathology, University of Pittsburgh Cancer Institute
Algorithms have been central to most advances in bioinformatics and its role in basic and clinical research. In this presentation, I will (a) provide a glimpse into the potential future of biomarker development via Integrative Translational Research, (b) examine why a revolution is needed in survivorship prediction modeling, including a description of the first phases of that revolution, and (c) examine how and why adaptive study designs may or may not be able to boost biomarker development studies. Early case studies will be provided. Software that implements each of these developments is either now available, or is under construction at the Bioinformatics Analysis Core at the University of Pittsburgh.
11:35-12:00 Integrating Omics and Clinical Data to Better Understand Disease Mechanism and Predict Treatment Outcome
A. Jamie Cuticchia, Ph.D., Director, Duke Bioinformatics, Duke Institute for Genome Sciences & Policy, Duke University Comprehensive Cancer Center
With the continuing maturity of the collection of Omics data and a better understanding of interpretation methodologies, the relationship between these data and clinical phenotypes are becoming a growing component of modern health care. This is more than just gene association studies or personal medicine. It is an opportunity to better understand the underlying mechanisms of disease and predict the outcomes of treatment. Large studies such as the Framingham heart study are indicative of the value of collecting longitudinal clinical data. When such processes are combined with underlying Omics data, the value of such datasets become exponentially valuable.
12:00-1:40 Luncheon Technology Showcases
An Automated and Streamlined Solution to Increase Productivity and Confidence in Microarray Studies
Jean-Francois Olivier, Ph.D., 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
SYSTEMS BIOLOGY APPROACHES TO BIOMARKER DISCOVERY
1:40-1:45 Chairperson’s Opening Remarks
1:45-2:10 A Translational Systems Biology Approach to Panel Biomarker Discoveries
Jake Chen, Ph.D., Assistant Professor, Informatics, Indiana University; Computer Science, Purdue University; Director, Indiana Center for Systems Biology and Personalized Medicine; Founder and Chief Executive Officer, MedeoLinx, LLC
Translational systems biology is an emerging research area that aims to help biomedical researchers derive novel insights on drug targets and biomarkers with the rapid development of computational models and systems biology informatics software tools. Key questions that we need to address for biomarker discoveries are how to incorporate prior knowledge into these models and tools, with which we can improve the interpretation of complex experimental Omics data sets. In our lab and the new Indiana Center for Systems Biology and Personalized Medicine, we have developed several new software tools such as HAPPI (a comprehensive human protein interactome database), C-maps (a disease drug target mining web server), HIP2 (a database of all plasma proteins detected in healthy human with tandem mass spectrometry techniques), and GeneTerrain (a visualization software tool for panel expression biomarker discoveries). We show the concept behind these tools and how they can be integrated for panel biomarker discovery applications.
2:10-2:35 High-Throughput Functional Proteomics Facilitates a Systems Biology Approach to Personalized Medicine
Prahlad T. Ram, Ph.D., Assistant Professor, Department of Systems Biology, The University of Texas, M. D. Anderson Cancer Center
Systems biology is the study of the emergence of functional properties that are present in a biological system but that are not obvious from a study of its individual components. Systems biology is a data-driven process requiring comprehensive databases at the DNA, RNA, and protein level to integrate systems biology with cancer biology. Combining these patient and model-based databases with the ability to interrogate functional networks by a systematic analysis using siRNA libraries and chemical genomics provides an ability to link in silico modeling, computational biology, and interventional approaches to develop robust predictive models applicable to patient management.
2:35-3:00 Identification of Candidate Biomarkers Using Large-Scale Mathematical Models
Ananth Kadambi, Ph.D., Senior Scientist, Entelos, Inc.
Entelos develops large-scale mathematical models of human physiology known as PhysioLab platforms, and applies them to support the pharmaceutical drug discovery and development process. This talk will discuss the application of Entelos PhysioLab platforms to the discovery of candidate biomarkers. First, the development of virtual populations will be discussed; these virtual populations reproduce the diversity in both underlying patient biology and phenotype seen in clinical trial populations. Second, case studies will be shown to describe the application of virtual populations to the identification of candidate biomarkers. These biomarkers can both predict compound efficacy and characterize responder/non-responder sub-populations.
3:00-4:00 Networking Refreshment Break with Poster and Exhibit Viewing
4:00-4:25 System-Wide Peripheral Biomarker Discovery Through an Information-Theoretic Framework
Gil Alterovitz, Ph.D., Research Fellow, Children's Hospital Informatics Program, Harvard/MIT Division of Health Sciences and Technology; Research Affiliate, Massachusetts Institute of Technology, Computer Science and Artificial Intelligence
The identification of reliable peripheral biomarkers for clinical diagnosis, patient prognosis, and biological functional studies would allow for access to biological information currently available only through invasive methods. Here, we introduce an information theoretic framework for biomarker discovery, integrating biofluid and tissue information. This approach was applied to the analysis of 204 tissue/biofluid pairs to determine the quantitative predictive ability of certain biofluids for specific tissues via relative entropy calculation of proteomes mapped onto functional space. A network of significant biofluid-tissue relationships interconnected via functional protein biomarker proxies, namely the biofluidome network, resulted. Over 200 unique candidate biomarker proxies were identified, including novel ones that were validated via gene expression. This work offers a novel method of biomarker discovery, providing an efficient way of selecting proteins to analyze and validate experimentally for eventual diagnostic and prognostic use.
4:25-4:50 Using Causal Network Models for Mechanistic Biomarker Discovery and Development
Keith Elliston, Ph.D., President & Chief Executive Officer, Genstruct, Inc.
The development of personalized medicine requires a detailed understanding of the molecular mechanisms of disease, and of drug action. The implementation of personalized healthcare requires the development of mechanistic diagnostics to enable the matching of a patient’s disease to the right therapeutic regimen. To meet this demand, Genstruct has implemented a Causal Network Modeling™ Platform capable of using any Omic data source to develop mechanistic models of disease and drug action, and to define mechanistic biomarkers. Mechanistic Biomarkers are distinct from conventional, correlative biomarkers by their placement within a molecular mechanism explaining how the Mechanistic Biomarker relates to the pathophysiology in question or to the efficacious response triggered by a successful therapeutic. Similarly, mechanistic toxicity biomarkers can measure undesirable molecular networks affected by drug treatment, as applicable. In all cases, a Mechanistic Biomarker research program starts by building Causal Network Models for disease progression and for drug response to map the molecular networks controlling the biological processes relevant to the disease. A data-driven approach to understanding the causal network topology governing a disease state or underlying an efficacious drug response is the critical starting point for the discovery of Mechanistic Biomarkers.
4:50-5:15 Achieving Confidence in Mechanism for Biomarker Discovery and Accelerated Drug Development
Iya Khalil, Ph.D., Vice President and Co-Founder, Gene Network Sciences
Despite advances in our powers of observation, the ability to determine biological mechanisms from large-scale multi-omic technologies continues to be a major bottleneck in the discovery of biomarkers of disease progression and drug response. This can be overcome by utilizing computational learning methods that identify directly from the data the circuits and connections between drug-affected molecular constituents and physiological observables. The marriage of multi-omics technologies with reverse engineering approaches can provide missing insights needed to improve drug development success rates.
5:15 Close of Day
Wednesday, October 1
7:00 Registration Open
7:30-8:15 Breakfast Presentation 
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).
- Seamless transition from discovery to high quality, high sensitivity assay development will shorten validation time
- Multiplex Assay Capability is in-line with the expected multitude of biomarkers to be validated
- No need to invest into lengthy immunoassay-development prior to validation of the marker itself
- Quick translation from pre-clinical to human trials
INTEGRATING SEMANTIC AND OMIC APPROACHES FOR BIOMARKER DISCOVERY
8:30-8:35 Chairperson’s Opening Remarks
8:35-9:00 MOA Biomarker Discovery for IKKb Inhibition Program: An Interdisciplinary Approach
Zhenhao Qi, Ph.D., Senior Principal Scientist, Translational Sciences, Boehringer Ingelheim Pharmaceuticals, Inc.
There is increasing demand for Mechanism of Action (MOA) biomarker(s) in Phase I clinical trials to demonstrate compound hits the target in vivo, thus allowing better decision making at early phase. In this IKKß inhibition MOA biomarker program, we employed an interdisciplinary approach to combine statistical genome-wide search on in-house established gene expression database and literature/text mining to narrow down to a set of biologically relevant genes. Our statistics-driven in vitro LPS whole blood assay allowed us to find optimal inhibition window for IKKß-selective inhibitor; we were able to identify 2 high potential biomarkers whose responses were statistically significant at their EC50 concentrations in the gene (Taqman) and protein (ELISA) expression experiments with a full dose range of inhibitor, these 2 genes will serve as ex vivo predictive MOA biomarkers in the Phase I trial.
9:00-9:25 Semantic Web Abstractions for Biomedical Informatics: A Better Foundation for Integrating Sensitive Heterogeneous Data Sources
Jonas S. Almeida, Ph.D., Abell-Hanger Distinguished Professor, Department of Bioinformatics and Computational Biology, The University of Texas, M. D. Anderson Cancer Center
The maturation of semantic web technologies (SW) offers a more generic foundation to weave integrated data management systems than the relational and object oriented approaches that precede it. With the deceivingly simple claim of taking a step back to the foundations of Entity-Relationship-Entity models, SW enables evolvable knowledge representation and inference systems that can cope with the systemic approach of modern biomedical research and its translation into personalized medicine. Because the meaning of data changes with the analysis that its representation enables, data management and data analysis can no longer be treated as separate components of knowledge engineering for the Life Sciences. The same applies to security and privacy issues intrinsic to biomarker discovery initiatives. The more generic nature of SW abstractions enables the incorporation of access permission in the data model such that permission to access travels with the data rather then staying at the point of access to the data store.
9:25-9:50 Using Networks to Integrate Omic and Semantic Data: Towards Understanding Protein Function on a Genome Scale
Mark Gerstein, Ph.D., Albert L. Williams Professor of Biomedical Informatics, Molecular Biophysics and Biochemistry, Computer Science, Yale University
My talk will be concerned with topics in proteomics, in particular predicting protein function on a genomic scale. We approach this through the prediction and analysis of biological networks, focusing on protein-protein interaction and transcription-factor-target ones. I will describe how these networks can be determined through integration of many genomic features (including those derived from using the semantic web and text mining) and how they can be analyzed in terms of various simple topological statistics. In particular, I will discuss: (1) Integrating gene expression data with the regulatory network illuminates transient hubs; (2) Integration of the protein interaction network with 3D molecular structures reveals different types of hubs, depending on the number of interfaces involved in interactions (one or many); (3) Analysis of betweenness in biological networks; (4) Analysis of structure of the regulatory network shows that it has a hierarchal layout with the "middle-managers" acting as information bottlenecks; (5) Development of useful web-based tools for the analysis of networks, PubNet and tYNA; (6) Using known semantic web relationships as training sets to improve biological query applications; and (7) Using literature data to predict protein interactions.
9:50-10:50 Networking Coffee Break, 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 Erico, Ph.D., 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.jpg "Next Gen Sciences")
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
(Shared Session)
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 integration 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