Track 11 - April 21 – 23, 2015
Applying Computational Biology to Cancer Research & Care
Track 11 explores the important technology and informatics trends and challenges of applying computational biology to cancer research and care. Themes that will be covered in expert-led presentations include collaborations, network models, data access/management/integration strategies, and applications of biological interpretation to aid in research at the bench side or care at the bedside.
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Tuesday, April 21
7:00 am Workshop Registration and Morning Coffee
8:00 – 11:30 Recommended Morning Pre-Conference Workshops*
Biologics, Bioassay and Biospeciment Registration Systems
12:30 – 4:00 pm Recommended Afternoon Pre-Conference Workshops*
How Data-Driven Patient Networks are Transforming Biomedical Research
The Impact of Research Informatics on Laboratory Evolutions
* Separate registration required
2:00 – 6:30 Main Conference Registration
5:00 – 7:00 Welcome Reception in the Exhibit Hall with Poster Viewing
Wednesday, April 22
7:00 am Registration Open and Morning Coffee
9:00 Benjamin Franklin Awards and Laureate Presentation
9:30 Best Practices Awards Program
9:45 Coffee Break in the Exhibit Hall with Poster Viewing
10:50 Chairperson’s Opening Remarks
11:00 FEATURED PRESENTATION: CHALLENGES AND OPPORTUNITIES IN ESTABLISHING IT SUPPORT FOR CONTINUOUS LEARNING IN HEALTHCARE: THE POTENTIAL FOR APPLYING LESSONS LEARNED FROM CLINICAL GENOMIC IT SUPPORT TO BROADER CONTINUOUS LEARNING CHALLENGES
Samuel (Sandy) Aronson, Executive Director, IT, Partners HealthCare Center forPersonalized Genetic Medicine
Continuously updated knowledge bases will be required to enable a true continuous learning healthcare environment. However, modern healthcare pressures make their maintenance difficult. The clinical genomic IT community has been wrestling with this issue for some time. We present lessons learned from supporting clinical genomic IT processes that may be generalizable to broader development of IT support for continuous learning healthcare processes.
11:30 FEATURED PRESENTATION: THE PENETRANCE OF INCIDENTAL FINDINGS IN GENOMIC MEDICINE
Robert C. Green, M.D., MPH, Director, G2P Research Program; Associate Director, Research, Partners Personalized Medicine, Division of Genetics, Department of Medicine, Brigham & Women’s Hospital and Harvard Medical School
Much of the controversy surrounding the implementation of incidental findings in clinical sequencing is due to uncertainty about the penetrance of such findings in persons unselected for clinical features or family history. This uncertainty also influences the question of genomic population screening, i.e., whether actionable sequence variants should be sought and reported in ostensibly healthy individuals. In this talk, new data will be presented estimating the penetrance of actionable incidental findings.
12:00 pm Census of the Apoptosis Pathway
Philip L. Lorenzi, Ph.D., Department of Bioinformatics and Computational Biology & the Proteomics and Metabolomics Core Facility, MD Anderson Cancer Center
We recently compared several different “omic” approaches to constructing the autophagy pathway de novo, including siRNA screening, mass spectrometry-based proteomics, and three different pathway analysis software packages. Unexpectedly, although merging all of the validated data sets yielded 739 autophagy-modulating genes, each individual approach alone yielded sparse coverage of the autophagy pathway. The best individual siRNA screen, for example, yielded only 169 of the 739 (23%) genes. Nevertheless, text mining-based pathway analysis with Pathway Studio in conjunction with manual curation provided the most comprehensive coverage, yielding 417 targets (56% of the pathway). Here, we explored the generalizability of those findings by examining a more well-characterized pathway—apoptosis. We compiled apoptosis-modulating genes from 12 published siRNA screens and two pathway analysis software packages—Ingenuity Pathway Analysis (IPA) and Pathway Studio. The resulting inventory of 6,882 proteins consisted of 215 targets identified by siRNA screening, 3,378 targets by IPA, and 6,381 targets by Pathway Studio. The extensive coverage (93%) of the apoptosis pathway provided by text mining with Pathway Studio can likely be attributed to recent upgrades in the software, including an expanded database and collection of full-text articles. Together with our previous autophagy pathway analysis, the new apoptosis results support the generalizable conclusions that: 1) siRNA screening has a large false negative rate (i.e., fails to identify many true “hits”), and 2) text mining-based pathway analysis using Pathway Studio provides the most comprehensive pathway coverage.
12:30 Session Break
12:40 Luncheon Presentation I: Computational Enablement of the Hippocratic Oath in a Clinical Oncology Setting
David B. Jackson, Ph.D., Chief Innovation Officer, Molecular Health, Gmbh
The clinical response of cancer patients to oncolytic agents is influenced by three major classes of molecular determinant; tumor intrinsic factors (e.g. tumor biomarkers); patient intrinsic factors (e.g. polymorphisms) and patient extrinsic factors (e.g. co-medications). In my talk, I will present a novel computational technology and associated treatment decision support process that was designed to provide this knowledge-driven approach to clinical care in oncology.
1:10 Luncheon Presentation II: A High Performance Application Development Platform for Collaborative Genomics Research
John Shon, Vice President, Bioinformatics & Data Services, Illumina, Inc.
Collaborative research among groups working with genomic data presents major logistical challenges. Transferring huge volumes of data can be prohibitively expensive for projects utilizing WGS data sets. Illumina has addressed this challenge by building a platform that enables collaborators to not only share data in a secure multitenant environment, but to develop and deploy their own applications close to the data.
1:40 Session Break
1:50 Chairperson’s Remarks
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.
2:25 Personalized Medicine: Moving from Correlation to Causality in Breast Cancer
Michael Liebman, Ph.D., Managing Director, Strategic Medicine, Inc.
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.
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.
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.
3:25 Refreshment Break in the Exhibit Hall with Poster Viewing
4:00 CureAccelerator™: How A New Global Platform Will Help Propel Cures for the World’s Unsolved Diseases
Bruce Bloom, D.D.S., J.D., President and Chief Science Officer, Cures Within Reach
More than 7,000 diseases have no fully effective treatment, affecting more than 500 million people worldwide. CureAccelerator™ is the world’s first open-access, online platform dedicated to repurposing research – the quest to create new medical treatments from existing therapies, to drive more cures more quickly to more patients. Learn how this innovative IT tool will enable researchers, funders, the biomedical industry and patient groups to collaborate far more efficiently, to propel the pace of repurposing research.
4:30 Delivering on a Promise: Achieving a Patient-Centric Open Information Ecosystem
Walter Capone, President and CEO, The Multiple Myeloma Research Foundation
Patient-centric, open access research models are widely acknowledged catalysts of scientific and medical progress. Here we present a first-in-kind model for combining an observational clinical study with a participatory, community driven research program for Mutliple Myeloma, and in doing so, providing a powerful example that can lead the way forward in other disease areas.
5:00 Presentation to be Announced
5:30 Best of Show Awards Reception in the Exhibit Hall with Poster Viewing
6:30 Close of Day
Thursday, April 23
7:00 am Registration Open and Morning Coffee
10:00 Coffee Break in the Exhibit Hall and Poster Competition Winners Announced
10:30 Chairperson’s Remarks
10:40 PANEL DISCUSSION: Multiple Sclerosis Conquered by the Data Science Revolution: Patients Win
Ken Buetow, Ph.D., Director, Computational Sciences and Informatics, Arizona State University
Robert McBurney, CEO, Office of the Chief Executive, Accelerated Cure Project
Marcia Kean, Chairman, Strategic Initiatives, Feinstein Kean Healthcare
Funded by a grant from PCORI (Patient-Centered Outcomes Research Institute), the Accelerated Cure Project for MS is collaborating with all the key organizations in the MS community, gathering patient-reported and EHRs from 20,000 patients. It’s a best-practice model for data-enabled research; patient-centricity; and public-private partnerships. The key players from life sciences, data sciences, medicine patient advocacy and communications will describe the winning formulas that are making it successful. Attendees will learn how to design and fund such an initiative; how to collect standards-based data including the horrendous challenges around EHRs; best tools for analytics and data visualization; handling research queries; and overcoming the traditional silos that prevent seamless data exchange and global big data-enable basic, clinical and comparative effectiveness research.
11:40 Sponsored Presentation (Opportunity Available)
12:10 pm Session Break
12:20 Luncheon Presentation I: Accelerating Cancer Informatics at Foundation Medicine using SciDB
Eric Neumann, Ph.D., Neurobiology and Developmental Genetics, Vice President, Knowledge Informatics, Foundation Medicine
Marilyn Matz, CEO, Paradigm4
Alex Poliakov, Solutions Architect, Paradigm4
Much can be learned from the proper analysis of large sets of genomic data. We will describe a few examples of scalable analytics applied to cancer genomics, and how SciDB enables this kind of analytics. Combining statistical analysis with other knowledge discovery tools can help accelerate this transformation of large data sets into biological insights.
12:50 Luncheon Presentation II (Sponsorship Opportunity Available) or Lunch on Your Own
1:20 Dessert Refreshment Break in the Exhibit Hall with Poster Viewing
1:55 Chairperson’s Remarks
Nils Gehlenborg, Ph.D., Research Associate, Center for Biomedical Informatics, Harvard Medical School
2:00 Combing the Hairball: Network Visualization with BioTapestry and BioFabric
William J.R. Longabaugh, MS, Senior Software Engineer, Institute for Systems Biology
Networks models are crucial for understanding complex biological systems, yet traditional node-link diagrams of large networks provide very little visual intuition, and there is a need to develop scalable, unambiguous, and rational network visualization techniques. Our applications, BioTapestry (http://www.BioTapestry.org) and BioFabric (http://www.BioFabric.org), are designed to address this need, and I will discuss how they use novel approaches to avoid the “hairball” trap.
2:30 Visualization of Comparative Genomics Data: Results, Challenges, and Open Questions
Inna Dubchak, Ph.D., Senior Scientist, Lawrence Berkeley National Laboratory
As the rate of generating sequence data continues to increase, visualization tools for interactive exploration and interpretation of comparative data at the level of gene, genome, and ecosystem are of critical importance. We will talk about strengths and limitations of existing methods, and highlight new challenges in the visualization of huge volumes of complex comparative data.
3:00 Interactive and Exploratory Visualization of Epigenome-Wide Data
Hector Corrada Bravo, Ph.D., Assistant Professor, Center for Bioinformatics and Computational Biology, Department of Computer Science, University ofMaryland, College Park
Data visualization is an integral aspect of the analysis of epigenomic experimental results. Commonly, the data visualized in these tools is the output of analyses performed in computing environments like Bioconductor. These two essential aspects of data analysis, algorithmic/statistical analysis and visualization, are usually distinct and disjoint but are most effective when used iteratively. We will introduce epigenomics data visualization tools that provide tight-knit integration with computational and statistical modeling and data analysis: Epiviz (http://epiviz.cbcb.umd.edu), a web-based genome browser application, and the Epivizr Bioconductor package that provides interactive integration with R/Bioconductor sessions. This combination of technologies permits interactive visualization within a state-of-the-art functional genomics analysis platform. The web-based design of our tools facilitates the reproducible dissemination of interactive data analyses in a user-friendly platform. We will illustrate these tools via analyses of the colon cancer epigenome, in particular, the relationship between clonal and population heterogeneity as inferred from DNA methylation sequencing data.
3:30 Visual-Analytic Systems for Integrative Genomic Analysis of Cancer Data
Raghu Machiraju, Ph.D., Professor, Ohio State University
Cancers are highly heterogeneous with different subtypes. Recently, integrative approaches were adopted that combined multiple types of omics data. In this talk, I present visual analytic solutions for the simultaneous and integrative exploration of multiple types genomics data including those from The Cancer Genome Atlas (TCGA) project. Using different combinations of mRNA and microRNA features we suggest potential combined markers for prediction of patient survival.
4:00 Conference Adjourns
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