Leaders in Pharmaceutical Business Intelligence (LPBI) Group

LIVE  April 22, 1:50PM – BIG DATA, DIGITAL TOOLS AND BIOINFORMATICS ACROSS MULTIPLE RESEARCH INITIATIVES @Cambridge HealthTech Institute’s 14th Annual Meeting BioIT World – Conference & Expo ’15, April 21 – 23, 2015 @Seaport World Trade Center, Boston, MA

Dr. Aviva Lev-Ari will be in attendance on April 21, 22, 23

 

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BIG DATA, DIGITAL TOOLS AND BIOINFORMATICS ACROSS MULTIPLE RESEARCH INITIATIVES

April 22, 1:50 Chairperson’s Remarks
Michael Liebman, Ph.D., Managing Director, IPQ Analytics, LLC

Disease and Disease Processes

Data

Information

Knowledge

Medical Utility – Slow to get

150,000Biomarkers in Research 100 approved only 20 lead to treatment

Research too focus and the Big picture is never been reached.

1:55 Metabolic Biomarkers in Duchenne Muscular Dystrophy

Simina Boca, Ph.D., Assistant Professor, Innovation Center for Biomedical Informatics, Georgetown University Medical Center

Duchenne Muscular Dystrophy (DMD) is a devastating degenerative X-linked disorder which affects approximately 1 in 5,000 newborn males and results in muscle degeneration, eventual loss of ambulation around the age of 9, and a life expectance of around 25 years of age. We considered serum metabolomic profiling of 51 DMD patients and 22 age-matched healthy volunteers in order to find novel serum circulating metabolites for DMD, with the ultimate goal of discovering molecular surrogate markers associated with disease progression, which can be used in future clinical trials. The DMD patients had a minimum age of 4, a maximum age of 28.7, and a median age of 11.4 years, while the healthy controls had a minimum age of 6, a maximum age of 17.8, and a median age of 13.7 years. 22 of the 51 DMD patients were non-ambulatory at the time of serum collection. As expected, age and ambulation status were strongly correlated in the DMD group, where patients with ages between 4 and 17.8 years, with a median of 6.8 years, were ambulatory, while patients between 11.4 and 28.7 years, with a median of 18 years, had lost ambulation. Liquid chromatography – mass spectrometry (LC-MS) techniques were used to process the serum of the study participants, with the XCMS analysis tool detecting a total of 246 peaks in negative mode and 1676 peaks in positive mode. Metabolite values were further log2 transformed, then normalized using internal standards for both modes. A two-class comparison using a two-sample t-test identified 46 peaks associated with disease status at a false discovery rate (FDR) threshold of 0.05, employing a Benjamini-Hochberg correction. A similar comparison was performed for the DMD cases, comparing ambulatory and non-ambulatory individuals, leading to 154 significant peaks at an FDR threshold of 0.05. After the analyses are finalized, significant peaks will be annotated, in order to match the m/z values to metabolite identities. One particular challenge in interpreting these results is eliminating metabolites which are not associated with disease mechanism from further consideration, such as those associated with drugs or dietary supplements used by certain patients. A bioinformatics platform for metabolic data interpretation has been developed and tested to identify DMD-associated biomarkers and will be made available on GitHub once validation is complete. This platform will be presented along with another use case from a breast cancer metabolomics study.
Contributors/Authors: Simina M. Boca1,2, Maki Nishida1, Michael Harris1, Shruti Rao1, Amrita K. Cheema2,3, Kirandeep Gill2, Haeri Seol4, Eric Hoffman4, Erik Henricson5, Craig McDonald5, Yetrib Hathout4 and Subha Madhavan1,2 1Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, D.C.; 2Department of Oncology, Georgetown University Medical Center, Washington, DC; 3Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, D.C.; 4Children’s National Medical Center and the George Washington University, Washington, D.C., 5 Department of Physical Medicine and Rehabilitation, University of California, Davis School of Medicine, Davis, CA.

LIVE

Disease Progration – DMD – Duchenne – Rare Disease

Metabolite in Blood (Serum), urine Tissue (Tumor)

Metabolomics – assessment of small molecule

2:25 Personalized Medicine: Moving from Correlation to Causality in Breast Cancer

Michael Liebman, Ph.D., Managing Director, IPQ Analytics, LLC

Sabrina Molinaro, Ph.D., Institute for Clinical Physiology, National Research Council, Italy

We have developed a fundamental model of the disease process for breast cancer, from pre-disease through early detection, treatment and outcome, and apply a multi-scalar approach across the risk assessment-enhanced diagnosis-therapeutic decision axis and will present the modeling methodologies.

LIVE

Disease is a Process not a State: Direction (high dimensionality, Progression (Stage), Velocity (rate of progression)

Risk to get a disease : Environmental exposure, genetic make up,

life style events, cumulative history of other diseases

Risk Factor for Breast Cancer – Calculator:

• life style: smoke, alcohol, weigh
• not having children

Correlation vs Causality

NCI – Gail Model:

• One Biopsy with Histological Atypical — PRIMARY FACTOR
• Relative had BR

Invasive Breast Cancer – NCCR

Pathologists agree in Early and in Late NOT IN THE MIDDLE

Breast Pathology:

CO-Concurrence Matrix

Difference in Pre and post menapausal in aggressiveness – How subtypes progress in different time

CAUSALITY: Stages of development, complexity of the disease,Biomarker, Meta analysis – Data accuracy not there

2:55 Streamline R&D and Catalyze Drug Repositioning by Identifying Expert Networks and Expertise

Xavier Pornain, Vice President, Sales & Alliances, Sinequa

Finding networks of experts with similar or complementary expertise on a given subject helps avoid costly redundant research, shed light on a complex research problem from different angles, foster cooperation, facilitate drug repositioning, and accelerate time to market. This session will delve into the benefits pharmaceutical companies are seeing by employing Search & Analytics technology to: “link” researchers and teams with one another, create internal “journals of science” to share internal results and snippets, access “breaking science”, with alerts and spotting trends across all scientific information. We show solutions for dealing with scientific vocabulary, detecting “synonyms” as well as “similar” and “complementary” notions, e.g. brand names for drugs, scientific names for the active ingredients, and even descriptions of molecules using a standard description language. In addition, we analyze vast quantities (200 to 500 million) of highly technical documents and data (billions of records), such as internal and external publications, patent filings, lab reports, clinical test reports, trade databases, etc.

LIVE

Data reside as Web data, Proprietary data, Cloud data

Big data IT Architecture

Project that Profiled search by Scientists

• R&D Search
• R&D KOLs

3:10 Cloud-Based Solutions for Population-Scale, Whole Human Genome and Exome Analysis

George Asimenos, Ph.D., Director, Science & Clinical Solutions, DNAnexus

Thanks to advances in sequencing technology, the size and scope of DNA sequencing projects is rapidly moving towards an era of thousands of whole genomes and tens of thousands of exomes per year. Learn how certain field-leading institutes are using a cloud-based bioinformatics platform to manage their big data deluge across multiple initiatives.

LIVE

DNAnexus, based in Mountainview, CA

• Client: Kinghorn Center for Clinical Genomic — Australia

Large-Scale Whole Genomic Clinical Diagnosis

• Clients: Regeneron, NY and Geisinger, PA (3Million Pt)  – USE DNAnexus CLOUD architecture & Management of Cloud Resources

Challenges for Cloud Computing – DNAnexus

• Security and compliance

1. Input
2. ANALYSIS
3. Distribution

• Control Data & Collaborator Access
• BioInformatics Expertise: Download a subset of a BAM fie without transcoding

4:00 Using Games as Data Analytical Tools

Melanie Stegman, Ph.D., Owner, Molecular Jig Games, LLC.; Director, Science Game Center

Immune Defense is a video game, but it is also a molecular level simulation of the immune system. Individual data points tell us very specific details about cells, and a large database of these details should tell us a more complete story. But do we have enough data yet to tell the story of one cell, facing one bacterium? It has been a challenge gathering the knowledge to create this small story. Part of Immune Defense game development is the creation of a “game level editor.” We can make new molecules, give them new binding partners, assign their affinities for each partner, increase or decrease their relative concentrations and give our enzymes activity… We have created a “medium data” analysis chamber–that is, not Big Data, but more data than one person can hold in their head. We are planning to build up our level editor as a tool for biochemists to analyze their data with much more perspective than ever before. We will also have a tool for scientists, students, public and game developers to use to create realistic scenarios for various purposes, from science fairs to testing to video game development. Play Immune Defense at http://www.MolecularJig.com/demo.

4:30 A Rigorous Methodology for Non-Randomized & Observational Study in Healthcare Testing

Gil Weigand, Ph.D., Director, Strategic Projects, Oak Ridge National Laboratory

Healthcare R&D or innovation trials have for more than a decade experienced an acceleration of the application of non-randomized study (NRS), including observational or pragmatic methods. Driven by a demand for rapid translation and patient centeredness using a randomized controlled trial—todays acknowledged “gold standard” for testing in healthcare—may not be practical or desirable when there is a need for flexibility, responsiveness, or timeliness. The challenge for researchers and clinicians using NRS testing is getting sufficient rigor in the scientific evaluation to assure data and study veracity, particularly as complexity and heterogeneity increase in innovation trials. IDAMS-HC achieves the state-of-the-art available today with regard to rigor, technology, and science-based in evaluation of NRS and it supersedes today’s ad hoc methodologies. Moreover it increases external validity. In this presentation, we present an advanced rigorous science-based evaluation methodology for evaluation in healthcare testing. The methodology extends today’s general practice, rapid cycle evaluation, by introducing in silico methods of big data and modeling & simulation and tightly integrating the methods within a knowledge discovery infrastructure. An ACO intervention trial provides initial experience with IDAMS-HC.
Contributors/Authors: Gil Weigand, PhD, Director, Strategic Projects, Computer and Computational Sciences, Oak Ridge National Laboratory (ORNL); Mallikarjun Shankar, PhD, Senior Research Scientist, Computer and Computational Sciences, ORNL; C. Edward McBride, III, MD, MBA, VP, Clinical Services, Summit Medical Group (SMG); Kimberley Kauffman, VP, Value-Based Care, SMG; and Suzanne Kieltyka, Manager, Health Education, SMG

5:00 Service-Oriented Bioinformatics – the CDC Influenza Sequence Data Management System
John M. Greene, Ph.D., CSM, Senior Director, Bioinformatics, Bioinformatics Solutions and Support, SRA International, Inc.
Next-Generation Sequencing technologies have opened enormous opportunities for improvements in the surveillance of infectious diseases such as influenza. However, effective use of such sequencing information depends on a robust system to store, manage, analyze, and interpret sequence data. The Influenza Sequence Data Management System (ISDMS) at the Centers for Disease Control and Prevention (CDC)’s Influenza Division in Atlanta fills this role using a service-based approach developed by SRA International that we refer to as ‘service-oriented bioinformatics’. Services are small programs that are coordinated by an enterprise service bus, in this case Apache ServiceMix, based on the service-oriented architecture (SOA) model. Services can be written in different languages and act as modular components of the system, providing individual functionality, such as searching, annotation display, and location standardization. These services underpin data loading, data annotation, and data display, and services can be combined to implement new features and reused to speed development.