Molecular Medicine Tri-Conference

Tuesday, February 13, 2018 • 5:00 - 6:00 pm • Exhibit Hall

These interactive discussion groups are open to all attendees, speakers, sponsors, & exhibitors. Participants choose a specific breakout discussion topic to join. Each topic has a moderator to ensure focused discussions around key issues. This format allows participants to meet potential collaborators, share examples from their work, vet ideas with peers, and be part of a group problem-solving endeavor. The discussions provide an informal exchange of ideas and are not meant to be a corporate or specific product discussion.

Table 1: Next Generation Sequencing (NGS) in Precision Medicine

Prasun Mishra, PhD, Founder & CEO, Agility Pharmaceuticals

  • Whole Genome Sequencing (WGS) and Whole Exome Sequencing (WES)-based genome diagnostics
  • NGS-driven precision medicine approaches for hereditary cancers as an example
  • Utilizing cell-free DNA (cfDNA) for longitudinal patient monitoring
  • NGS applications in drug discovery and development

Table 2: Isothermal Molecular Diagnostics as Alternatives to PCR

Robert Meagher, PhD, Research Scientist, Sandia National Laboratories

  • LAMP, NASBA, RPA, TMA, and more: sorting through the alphabet soup of techniques
  • Tradeoffs of sensitivity, specificity, and simplicity
  • Beyond resource-poor: designing assays and devices for use in awful conditions
  • Integrating assays with mobile technology
  • Intellectual property and licensing considerations

Table 3: Lessons Learned: What I Wish I Had Known Before I Started Validation

Jason N. Rosenbaum, MD, Assistant Professor, Pathology and Laboratory Medicine, University of Pennsylvania

Jennifer J.D. Morrissette, PhD, FACMG, Scientific Director, Clinical Cancer Cytogenetics; Clinical Director, Center for Personalized Diagnostics, Department of Pathology, University of Pennsylvania

Martin Siaw, PhD, MB(ASCP), Technical Consultant, Siaw Consulting

  • Selection of validation samples
  • Comparison to orthogonal assays
  • New CAP/AMP Guidelines
  • RNAseq in the clinical laboratory

Table 4: Challenges of Moving Liquid Biopsy Technologies/Assays Forward to Clinical Utility

Lynn R. Sorbara, PhD, Program Director, Cancer Biomarker Research Group, National Cancer Institute

Anthony Dickherber, PhD, Program Director, Center for Strategic Scientific Initiatives, National Cancer Institute

  • How can we tie together the clinical significance to detection of circulating nucleic acid markers and CTCs?
  • What are the major stumbling blocks?
  • IP issues? MTAs? CDAs?
  • Access to more or better tools?
  • Sample or patient accrual? Storage of biospecimens?
  • What are ways that we can enhance collaboration and exchange of information and data?

Table 5: CTC Capture, Interrogation, and Culture

Richard J. Cote, MD, FRCPath, FCAP, Professor, Joseph R. Coulter Jr. Chair, Pathology & Laboratory Medicine; Professor, Biochemistry and Molecular Biology; Chief of Pathology, Jackson Memorial Hospital

  • Live CTC capture
  • Conditions for expansion and propagation of CTC
  • Biomimetic Systems for CTC propagation
  • Novel interrogation of CTC

Table 6: Tumor Tissue Extracellular Vesicles

Jan Lötvall, PhD, Professor, Krefting Research Centre, University of Gothenburg, Sweden, Chief Scientist, Codiak BioSciences, Cambridge, MA

  • Extracellular vesicle diversity
  • Isolation of extracellular vesicle subpopulations
  • Clinical validation of extracellular vesicle biomarkers
  • Multiplexing extracellular vesicle data

Table 7: Liquid Biopsy for Brain Tumors

Brian V. Nahed, MD, MSc, Associate Professor, Neurosurgery, Harvard Medical School; Associate Program Director, MGH

  • Brain tumor diagnosis, monitoring, and treatment
  • Blood tests / biomarkers
  • Mutational analysis of brain tumors and blood

Table 8: Gaps and Opportunities in Biomarker Development for IO Therapies

Arnold B. Gelb, MD, MS, FASCP, FCAP, Clinical Advisor, Exploratory Biomarkers

  • Need for longitudinal monitoring of immune response
  • Deeper understanding of PDx mechanisms of treatment resistance
  • Identifying biomarkers for combination drug development

Table 9: Tumor Mutation Burden as an IO Biomarker

Terri McClanahan, PhD, Executive Director, Profiling & Expression, Translational Medicine, Merck Research Laboratories

  • What is the likelihood of immunotherapy working in tumors with low tumor mutational burden (Pancreatic, GBM)?
  • If not checkpoint inhibitors, what are the best immunotherapy-related therapeutic strategies in these tumor types?
  • What is the clinical utility of circulating tumor DNA (ctDNA) vs tumor DNA to measure tumor mutational burden?
  • What are the thoughts on a future landscape with a dual biomarker (one for each axis, Inflammation/Neoantigens)?

Table 10: Machine Learning Techniques and Big Data to Enable Precision Medicine

Nicholas J. Schork, PhD, Professor, Quantitative Medicine, The Translational Genomics Research Institute

  • How can machine learning be leveraged in very early, pre-clinical drug development initiatives, e.g., in drug screening studies, that might enable precision medicine?
  • What changes to current clinical trials infrastructure would need to be made to accommodate emerging big data and machine learning techniques?
  • What machine learning and big data-oriented strategies might complement, or even replace, traditional late phase (e.g., phase IV) clinical trials infrastructure?

Table 11: Breaking Down Barriers to Implementing Telepathology

Clayton A. Wiley, MD, PhD, Professor, Pathology; Director, Neuropathology; PERF Endowed Chair, Neuropathology, Presbyterian Hospital, University of Pittsburgh Medical Center

  • Defining the barriers
  • Aligning the incentives
  • Defining the value

Table 12: The Challenge of Implementing Proteomics in Clinical Set Up

Christoph Borchers, PhD, Professor, Director, University of Victoria Genome British Columbia Proteomics Centre

  • How to improve the training for clinical proteomics?
  • Is there demand for proteomics kits? If yes, for which application?
  • Is there a demand for ring studies in clinical proteomics? If yes, who could organize a study?

Table 13: The Impact of NGS on Clinical and Public Health Infectious Disease Laboratories

Duncan MacCannell, PhD, Chief Science Officer, OAMD, NCEZID, Centers for Disease Control and Prevention

  • Changing technical and workforce needs
  • CLIA compliance
  • The impact of CIDT on public health surveillance

Table 14: Infectious Disease Diagnostics in Urgent Care and Emergency Settings

Omai Garner, PhD, D(ABMM), Assistant Professor, Pathology and Laboratory Medicine, UCLA

  • Discuss the unique challenges faced in emergency rooms and urgent care settings
  • Examine diagnostic needs and challenges in developing technologies for these settings
  • Share experiences and lessons learned

Table 15: Is Your IP Ready for the New AI-Driven Era in Genomics and Personalized Medicine?

Erica Pascal, Founder, Ingensity IP

Travis Wohlers, Assistant General Counsel, Luminex Corporation

Amy McCourt, Patent Attorney, Illumina

  • Can you protect your diagnostic assays and tools with patents and how do recent changes in the law impact this strategy?
  • Does the use of big data and AI create new opportunities and challenges for protectingyour technology?
  • Balancing trade secret and patent protection to create a robust protection strategy in sync with your business goals

Table 16: Precision Medicine in The Acute Care Setting

Rourke M. Yeakley, MD, MHA, Head of Innovation, Droice Labs

  • Overcoming current barriers
  • Accessing data in a timely manner
  • Utilizing artificial intelligence for decision making

Table 17: How Do I Know if This Genetic Test is Any Good?

Jeanette McCarthy, MPH, PhD, Founder, Precision Medicine Advisors

  • Re-defining clinical validity
  • Requirements for FDA clearance of genetic tests
  • Expanding definition of utility

Table 18: Preclinical Strategies for Combination Therapies

Mithun Khattar, PhD, Immuno-Oncology Lead, Cancer Pharmacology, Takeda Pharmaceuticals

  • Key factors to consider when selecting combination strategy
  • Immunomodulatory effects of small molecules
  • Translating preclinical models to the clinic-the expected and unexpected

Table 19: How to Integrate Right Technology, Translational Model and Data Science To Accelerate Drug Discovery In Immuno-Oncology

Litao Zhang, PhD, Vice President, Leads Discovery and Optimization, Bristol-Myers Squibb

  • 3D bioprinting technology, co-culture systems, CRISPR and appropriate translational models
  • Challenges and solutions to close the gaps in the IO drug discovery

Table 20: Challenges and Obstacles to Using AI in Pharma R&D

Morten Sogaard, Vice President, Genome Sciences & Technologies, Pfizer Worldwide R&D

  • AI and machine learning at every stage of the pipeline: What projects are possible?
  • Goals of AI: will this drive innovation and productivity?
  • What problems does AI solve and what infrastructure, planning, and resources are needed to succeed?

Table 21: Creating FAIR (Findable, Accessible, Interoperable, Reusable) Data

Lara Mangravite, PhD, President, Sage Bionetworks

  • Importance of FAIR data in biomedical research
  • How to minimize the effort required as a data generator to ensure that data is FAIR
  • Standards and systems for implementing FAIR data practices

Table 22: Creating and Maintaining Hybrid Cloud Environments

Lucila Ohno-Machado, MD, PhD, Associate Dean, Informatics and Technology, University of California, San Diego Health

  • Clinical and human subjects research data on the cloud
  • Security and safety: Dealing with sensitive data
  • Cloud computing and data analysis

Table 23: Compute and Storage Hype vs. Reality

Aaron Gardner, Senior Scientific Consultant, BioTeam, Inc.

  • Death of the hard disk drive?
  • AMD back in the fight?
  • NVMEOF && 3D XPoint
  • Serverless computing
  • Converged infrastructure

Table 24: Integrating RWE into Data Sets for Actionable Insight

Eunice Jung, Associate Director, Healthcare Data Standards, Value Based Medicine, Biogen

  • What are the biggest challenges in terms of infrastructure, data standardization, and systems that need to be addressed to streamline RWE data integration?
  • Once data is integrated, how do we develop algorithms and analysis for meaningful insight?
  • What will be the biggest impact of RWE projects and where do we go next?

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