Tuesday, May 5
7:00 am Conference Registration and Morning Coffee
8:00 Chairperson’s Opening Remarks
Nicholas C. Dracopoli, Ph.D., Vice President, Janssen R&D, Johnson & Johnson
8:10 Big Data and Small Trials: Translating Biological Data into Clinical Biomarkers
Nicholas C. Dracopoli, Ph.D., Vice President, Janssen R&D, Johnson & Johnson
All of the companion diagnostic tests approved by the FDA for use in oncology are for “driver mutations” in genes involved in signal transduction pathways. These tests are for single analytes predicting the functional status of the drug target or pathway. There are no approved companion diagnostics for drugs that work through alternative mechanisms such as chemotherapy or immunomodulation. This presentation will discuss why so few biomarkers have been developed and why we have mostly failed to develop molecular profiles that predict drug response.
8:35 Applying Data Science in Translational Clinical Research
James Cai, Ph.D., Head, Data Science, Roche
The intelligent use of Big Data has transformed many industries. It also presents numerous opportunities for pharmaceutical companies as we collect more genomic Big Data directly from patients. In this talk I will outline a Data Science model that emphasizes mixed-capability teams and impact on science and business decisions. I will discuss how quantitative analytical skills, agile programming, novel technologies and business acumen all contribute to this model. I will illustrate with examples where Data Science was applied to clinical research resulting in new scientific insights and better business decisions.
9:00 Using Clinical and Real World Data for Biomarker Discovery in Precision Medicine
Joan Sopczynski, Ph.D., Senior Manager, Predictive Informatics, Business Insights, R&D Business Technology, Pfizer
Real world and clinical trial databases consist of large patient data sets that can be explored for biological insights. Examples will be presented describing the analysis of this patient data for biomarker identification and disease knowledge. Methods used to analyze the data, including the application of machine learning techniques, will be described highlighting their ability to identify biomarkers distinguishing patient populations.
9:25 Biology Driven Strategies to Discover Novel Biomarkers and Predictive Markers from Genomic Sequencing Data
Ashley Van Zeeland, Ph.D., CEO, Cypher Genomics
The adoption of genomic sequencing beyond targeted panels in clinical development has been impeded by the difficulty of discovering a signal from the noise in small clinical studies. This presentation will describe, and present results from, a comprehensive, biology-based approach that can identify novel biomarkers from whole exome or whole genome sequencing data from the small sample sizes that are typical of drug development and lead to the development of novel companion diagnostic products.
9:55 Q&A with the Speakers
10:10 Coffee Break in the Exhibit Hall with Poster Viewing
10:40 Building and Leveraging “Clinical and EHR Data Stack” to Optimize Clinical Development and Patient Outcomes
Usman Iqbal, M.D., Senior Medical Affairs Leader, AstraZeneca
Big Data analysis of clinical outcomes, genetic profiles and tissue morphology is a big driver of personalized medicine. However, different elements of Big Data have different applications and considerations in personalized medicine. This presentation will focus on the development and application of the Real World Evidence category of Big Data and how innovative platforms linking clinical and EHRs platform can strategically optimize patient segmentation, clinical development and health outcomes.
11:05 Translational Biomarker Identification for Patient Stratification and Disease Indication Selection through Big Data
Bin Li, Ph.D., Associate Director, Computational Biology, Takeda
We implemented a tranSmart-based translational infrastructure, with globally normalized molecular profiling data (~1600 GEO studies) and manually curated patient information (~200 GEO studies). Also, a PLSR-based modeling framework was designed and implemented, using a special splitting strategy and canonical pathways to capture robust information for biomarker discovery. Combining these efforts, we were able to build drug-sensitivity predictive models for SOC drugs, and to successfully re-predict these drugs’ FDA-approved cancer indications.
11:30 pm Enjoy Lunch on Your Own
1:30 Chairperson’s Remarks
Rong Chen, Ph.D., Director, Clinical Genome Informatics, Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai
1:35 The Opportunities and Challenges of Biomarker-Driven, Targeted Therapies
Mark S. Boguski, M.D., Ph.D., Associate Professor, Center for Biomedical Informatics, Harvard Medical School
2:00 Using Big Data to Interpret Personal Genomes for Disease Variant Discovery, Precision Medicine and Novel Therapeutics
Rong Chen, Ph.D., Director, Clinical Genome Informatics, Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai
Hundreds of thousands of individuals have been sequenced and released into public repositories, providing an exciting resource for the discovery of disease variants and therapeutic targets. This presentation will describe how we built a knowledge base through 100,000 exomes, millions of literatures, protein structures, and post-translational modification sites, and developed applications to interpret sequencing cohorts and biobank data for disease variant discovery, diagnosis and novel therapeutics. I will discuss how we interpret exomes and RNA-Seq data for personalized cancer therapeutic reports.
2:25 Data Science Platforms for Molecularly-Targeted Therapy and Personalized Medicine Research
Subha Madhavan, Ph.D., Associate Professor and Director, Innovation Center for Biomedical Informatics; Director, Clinical Research Informatics, Lombardi Comprehensive Cancer Center, Georgetown University
G-DOC Plus is an enhanced web platform that uses cloud computing and other advanced computational tools to handle NGS and medical images so that they can be analyzed in the full context of other omics and clinical information to drive personalized medicine research. G-DOC Plus tools have been leveraged in the cancer and non-cancer realms for hypothesis generation in precision medicine and translational research.
2:50 The Developmental Neuropsychiatry Program at BCH: Using Big Data for Personalized Medicine and Biomarker Discovery
Catherine Brownstein, Ph.D., Instructor, Pediatrics, Harvard Medical School and Boston Children’s Hospital
Boston Children’s Hospital is developing the infrastructure needed for large-scale psychiatric research and treatment discovery with the creation of the Developmental Neuropsychiatry Program (DNP). This new center is in a unique position to improve psychiatric care through precision genetic medicine. The DNP is working towards faster and more accurate diagnosis through assessment of a patient’s phenotype, MRI results and neurophysiology, in combination with next-generation sequencing and gene discovery. The goal is targeted prescribing and increased possibilities for investigational therapies. The DNP is investigating therapeutics to prevent schizophrenia by working with the youngest patients, creating neuronal cell cultures to identify cell autonomous effects, and constructing models of neural networks. The DNP also has cutting-edge research in assessing response to medication and outcomes in the context of their genotype and phenotype. The Clinical Pharmacogenomics Program is working with the Psychiatry Department to study the relationship between genotype and reported adverse drug reactions. Another research study is enrolling patients to identify behavioral and brain biomarkers linked to psychotic illness. Finally, groundbreaking research out of two Harvard laboratories has created a novel all-optical electrophysiology platform to rapidly screen drugs for functional effects in human neurons.
3:20 Refreshment Break in the Exhibit Hall with Poster Viewing
4:10 Catalyzing Delivery of Novel and Targeted Therapeutics from Human Genetics to Patients
Janna Hutz, Ph.D., Director and Head, Quantitative Genetics and Bioinformatics, Eisai
4:35 Realizing Personalized Medicine through Application of a Next-Generation Cyber Capability to See the Emergent Whole in Big Data
Kenneth Buetow, Ph.D., Director, Computational Sciences and Informatics Program for Complex Adaptive Systems; Professor, School of Life Sciences, Arizona State University
Data Science has the capacity to provide the needed “tools” to tackle the unique challenges generated by personalized medicine. Arizona State University’s (ASU) Complex Adaptive Systems team is building a first generation Data Science research platform – the Next Generation Cyber Capability (NGCC). The ASU NGCC – composed of hardware, software and people – transforms “Big Data” to information and creates the evidence necessary to enable personalized medicine. The NGCC permits data “points” to be evaluated in concert using Big Data analytic frameworks thereby identifying an emergent, coherent “whole.” Biologic network analysis represents one such promising integrative approach. These networks account for the individual heterogeneity in underlying etiology as well as the interaction of diverse events necessary to generate complex phenotypes.
5:00-6:00 Welcome Reception in the Exhibit Hall with Poster Viewing
Wednesday, May 6
8:00 am Morning Coffee
8:25 Chairperson’s Remarks
Shrikant Mane, Ph.D., Director, Yale Center for Genome Analysis
8:30 Big Data Experiment – What Does Whole Genome Sequencing Tell Us?
Chris Huang, Ph.D., Principal Scientist, Systems Pharmacology & Biomarkers, Immunology TA, Janssen R&D
8:55 Hypothesis-Free Versus Hypothesis-Driven Approaches to Predictive Modeling of Genomic Data
Viswanath Devanarayan, Ph.D., Global Head & Senior Research Fellow, Exploratory Statistics & Bioinformatics, AbbVie, Inc.
Modeling of genomics Big Data to predict phenotypes of interest typically involved a variety of machine/statistical-learning methods. Hypothesis-free approach entails taking a totally unbiased approach by building the optimal predictive signatures from the whole genome data. Hypothesis-driven approaches tend to focus the predictive modeling efforts to only a subset of the whole genome that is previously known to be implicated to the phenotype, for example, from disease and target based pathway analysis. We will compare the results and relative benefits from these two approaches using traditional microarray as well as RNA-Seq data from neuroblastoma patients.
9:20 Application of Next-Generation Sequencing to Identify Actionable Mutations and Cancer Driver Genes
Zhongming Zhao, Ph.D., Professor, Biomedical Informatics, Cancer Biology and Psychiatry, Vanderbilt University School of Medicine
Next-generation sequencing (NGS) technologies have enabled investigators to sequence thousands of tumor genomes. Correspondingly, personal genomics and personalized medicine is emerging as a new research field. In this talk, I will first present the identification of actionable mutations through our melanoma whole genome sequencing and characterization of mutational changes associated with drug resistance in EGFR-mutant lung cancer cell lines. Then, I will introduce our recently developed bioinformatics methods for identification of cancer genes from NGS data.
9:50 Coffee Break in the Exhibit Hall with Poster Viewing
10:45 Systems Genetics Approaches in Drug Discovery
Peter S. Gargalovic, Ph.D., Principal Scientist, Cardiovascular Drug Discovery Biology, Bristol-Myers Squibb
Evolution of high-throughput analytical methodologies and lowering of the cost is now allowing an unprecedented access to genomic and phenotypic information across large clinical and pre-clinical population studies. Today R&D is faced with a challenge to implement effective ways to leverage the available population data and provide guidance for drug discovery and development. Systems genetics is emerging as a novel “cutting edge” approach to interrogate complex disease population data. This presentation will highlight the concepts and show examples of its application to drug discovery.
11:10 Recent Advancement in Mendelian Genomics and Data Management at Yale Center for Genome Analysis
Shrikant Mane, Ph.D., Director, Yale Center for Genome Analysis
The Yale Center for Genome Analysis (YCGA) is one of the leaders in the identification of disease-causing DNA variants. In the last four years, the use of next-gen sequencing has led to the publication of >150 articles in peer-reviewed journals including >25 in high-profile journals such as Science, Nature, Cell, New England Journal of Medicine and Nature Genetics, reporting new variants in various disorders, including hypertension, autism, several types of cancers, Gaucher disease, skin disorders and cortical malfunctions, all using exome analysis. In 2010, YCGA became part of the NHGRI supported Yale Center for Mendelian Genomics (YCMG), which is one of three centers in the U.S. that together form the Centers for Mendelian Genomics. The primary goal of this consortium is to use NGS and computational approaches to discover the genes and variants that underlie Mendelian conditions on a collaborative basis and at no cost to the investigator. The presentation will focus on recent discoveries made at YCGA, its computer infrastructure and the current challenges and solutions developed for data analysis and management.
11:35 Q&A with the Speakers
12:00 pm Close of Conference