Breakout Discussions

Tuesday, March 12, 2019 • 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 group to join. Each group has a moderator to ensure focused discussions around key issues within the topic. 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. The list of topics below span Tri-Con’s three Channels: Molecular Dx & Digital Health, Liquid Biopsy & Immuno-Oncology, and Bio-IT World West.

Table 1
Data-Driven Diagnostics

Bryan Cobb, Partner Lead, Diagnostics Information Solutions, Roche Mark Nunes, MD, Division Chief, Medical Genetics, Kaiser Permanente

  • Advanced analytics for integrative diagnostics
  • Advanced analytics and AI for genomics applications
  • The Learning Healthcare System/RWD to enable precision medicine

Table 2
Payers and Regulators: Recent Developments

Wade M. Aubry, MD, Associate Clinical Professor of Medicine and Core Faculty, PRL-IHPS, UCSF, Former BCBS and Medicare Medical Director

Alberto Gutierrez, PhD, Partner, NDA Partners LLC; Former Director, Office of In Vitro Diagnostics and Radiological Health, FDA

Table 3
Whole Genome Sequencing as a Diagnostic Test

Phil Febbo, MD, CMO, Senior Vice President, Clinical Genomics, Illumina

  • What are the logistical challenges to clinical whole genome sequencing?
  • In what settings is there already clinical utility?
  • How can we get to a better annotated whole genome and more utility?

Table 4
Utilizing Whole Slide Imaging and Image Analysis in the Histology Laboratory for Quality Assurance and Improvement

Elizabeth A. Chlipala, BS, HTL(ASCP)QIHC, Laboratory Manager, Premier Laboratory, LLC

  • The importance of standardization and quality process improvement in histotechnology and its significance to the success of implementing a digital pathology solution
  • Utilization of digital pathology technology to improve overall efficiency and quality of the histology preparations
  • Utilization of digital pathology technology to document the accuracy and precision of a histology process

Table 5
Digital Pathology Interoperability Priorities

Raj C. Dash, MD, Professor and Vice Chair, Pathology IT, Duke University Health System; Medical Director, Laboratory Information Systems

  • Describe the needs of your organization as a consumer of or contributor to a larger digital pathology environment
  • Discuss the current level of interoperability among current market offerings in Digital Pathology
  • Help prioritize the efforts of standard setting and interoperability initiatives for Digital Pathology

Table 6
Digital Pathology Applications for Predictive Biomarkers

Ehab A. ElGabry, MD, Senior Director, Pathology & Companion Diagnostics, Pharma Services Medical Director, Ventana Medical Systems, Inc., A Member of the Roche Group

  • Multiplexing
  • Digital training and electronic learning modules
  • Clinical scoring algorithms development

Table 7
Practical Considerations when Implementing Digital Pathology

Douglas J. Hartman, MD, Associate Professor of Pathology and Director, Division of Pathology Informatics, University of Pittsburgh Medical Center, Ventana Medical Systems, Inc., A Member of the Roche Group

  • Presenting digital pathology to different stakeholders in your institution
  • Establishing a return on investment for digital pathology
  • Unique capabilities that can only be offered thru digital pathology

Table 8
Pathology AI: The Promise and the Problems

Michael C. Montalto, PhD, Vice President, Pathology and Clinical Biomarker Laboratories, Translational Medicine, Bristol- Myers Squibb

  • Application of AI to pathology in translational medicine
  • Pathology AI based companion and complementary diagnostics
  • Barriers to adoption of pathology AI in clinical practice

Table 9
The Future of Vertical and Lateral Flow Diagnostic Devices

Shawn Mulvaney, PhD, Section Head, Surface Nanoscience and Sensor Technology Section, Chemistry, US Naval Research Laboratory

  • Overcoming the limitations in sensitivity
  • Making these devices quantitative
  • POC to hospital lab? Just how far up the healthcare ladder will VFI/LFI devices reach?

Table 10
AI and ML in Pathology Practice

Hooman H. Rashidi, MD, FASCP, Professor and Vice Chair, GME, Director of Residency Program; Director, Flow Cytometry & Immunology, Department of Pathology and Laboratory Medicine, University of California, Davis School of Medicine

  • Data types
  • Types of ML platforms
  • Validation of ML platforms in pathology

Table 11
Home-Based Diagnostic Testing

Paul Yager, PhD, Professor, Bioengineering, University of Washington

  • Ongoing changes in US domestic healthcare
  • POC NAAT Testing
  • New opportunities for home testing

Table 12
The Future of Pharmacy-Based Point-of-Care Testing

Donald Klepser, PhD, MBA, Associate Professor and Vice Chair, Pharmacy Practice, University of Nebraska Medical Center

  • Opportunities and challenges in pharmacy-based POCT
  • Training and certification needs
  • Current regulations

Table 13
Overcoming Barriers to More Accurate and Scalable Genetic Variant Classification

Julie M. Eggington, MS, PhD, Co-Founder and CEO, Center for Genomic Interpretation (CGI)

  • Identify the real barriers that may be holding back the industry
  • Discuss current solutions and how they might be improved
  • Imagine the continued evolution of paradigms

Best Practices for Clinical Validation of Bioinformatics Pipeline

Somak Roy, MD, Director, Molecular Informatics, Genetics Services, & MGP fellowship, Molecular and Genomic Pathology, University of Pittsburgh Medical Center

  • Applicability of the guidelines in the context of distributive NGS testing model
  • Can in silico datasets be used for bioinformatics pipeline validation?
  • Guidelines in the context of clinical laboratory accreditation checklist (CAP) for NGS bioinformatics

Table 15
Bioinformatics Quality at Clinical Scale

Elaine P.S. Gee, PhD, Founder & President, BigHead Analytics Group; Principal Algorithm Development Engineer, Sensor R&D, Diabetes R&D, Medtronic

  • Scaling bioinformatics pipelines while maintaining quality
  • Designing validations for distributed compute systems
  • Regulatory technology for clinical bioinformatics

Table 16
Digital Health Platforms Incorporating Diverse Data Sources

NEW: Kuan-Fu Ding, PhD, Chief Science Officer, Sapiens Data Science, Inc.

  • What are the most important data sources?
  • How can in silico algorithm validation be used to accelerate progress?
  • How (and when) will medicine transition to from pattern recognition to quantitative data science for clinical decision support?
  • How will consumers be involved in use of quantitative data science use on their individual health journeys?

Table 17
Around the Globe Strategies for Diagnostics and Clinical Biomarkers

Marielena Mata, PhD, Director and Diagnostic Lead, Companion Diagnostics, Pfizer

Mark Curran, PhD, Vice President, Immunology, Head, Companion Diagnostics, Janssen R&D LLC

  • Preparing for the implementation of the new EU regulatory framework for CDx
  • Challenges with single site PMAs and worldwide implementation
  • Similarities and differences in regulatory requirements across the world. How to plan Dx global strategy and satisfy all requirements

Table 18
Evolutionary Biomarkers: New Ways to Work with Pharma Companies

Alex Vadas, PhD, L.E.K. Consulting

Table 19
Genomic Diagnostics in Solid Tumors: Current Practices and Future Developments

Larissa V. Furtado, MD, Medical Director, Molecular Oncology, ARUP Laboratories, Associate Professor of Pathology, University of Utah School of Medicine

  • Recognize the indications, specimen requirements, applications, and limitations of next generation sequencing (NGS)‐based test for solid tumor testing
  • Demonstrate familiarity with NGS clinical implementation and interpretive principles of NGS‐ based test for solid tumor testing
  • Become familiar with future trends in personalized solid tumor management
  • Table 20
    Hidden Challenges in Building and Analyzing Biological Networks

    Kimberly Glass, PhD, Assistant Professor of Medicine, Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School

    • The role of networks in understanding biological systems and diseases
    • Methods for constructing biological networks
    • The strengths and limitations of different types of biological networks
    • The types of questions that can be answered using biological networks

    Table 21
    Implementing Molecular Tumor Board in The Community Setting

    Timothy Cannon, MD, Gastrointestinal Malignancies, Clinical Director, Inova Schar Cancer Institute Molecular Tumor Board; Assistant Professor, Virginia Commonwealth University

    • How a molecular tumor board changes treatment paradigms
    • Molecular tumor board. Cost effective?
    • Addressing physician concerns that targeted therapy is overhyped and under‐delivers
    • Patient perception of molecular tumor board and targeted therapy in general

    Table 22
    Roadblocks to Functional Precision Medicine

    Christopher Kemp, PhD, Full Member, Human Biology, Fred Hutchinson Cancer Research Center

    • How to obtain off label drugs
    • How to routinely save live cancer biopsies
    • How to design N=1 or drug combination clinical trials

    Table 23
    Frontiers in Wearable Sensors, Ambient Sensing and Big Data Analytics

    Peter G. Jacobs, PhD, Assistant Professor, Department of Biomedical Engineering, Artificial Intelligence for Medical Systems (AIMS) Lab, Oregon Health & Science University

    • What are the new sensors in development/coming soon expected to impact health?
    • What new techniques in ambient sensing techniques are being developed and how are they displacing or augmenting wearables?
    • How are big data sets including electronic health records, public donated data sets, and genomic data sets impacting new healthcare solutions?
    • How is machine learning leveraging the intersection of ubiquitous sensing and big data sets?

    Table 24
    Parallel Analysis of Circulating Biomarkers in Immunotherapy

    Genevieve Boland, MD, PhD, Director, Melanoma Surgery Program, Massachusetts General Hospital; Director, Surgical Oncology Research Laboratories, Massachusetts General Hospital; Assistant Professor, Harvard Medical School; Associate Member, Broad Institute

    • Clinical application of blood-based biomarkers in melanoma
    • Unmet clinical needs in blood-based biomarkers
    • Microvesicle applications in immunotherapy

    Table 25
    Clinical Utility and Impact of Liquid Biopsy

    Rajan Kulkarni, MD, PhD, Assistant Professor, Medicine and Radiation Oncology, David Geffen School of Medicine, UCLA

    • Necessary features of technologies/tests for clinical utility
    • Tumor information that is of clinical relevance
    • Necessity for standardization

    Table 26
    The Importance and Challenge in CTC Culture

    Yong-Jie Lu, MBBS, MD, PhD, Professor, Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University of London

    • Why is CTC culture important?
    • What is the challenge?
    • Does it worth to try it?
    • Alternative ways to avoid it?
    • How can we success with CTC culture? The researcher, technology development and the funding supporter/policy marker.
    • Openness on collaboration for the benefit of all

    Table 28
    Isolation and Analysis of CTCs

    Min Yu, MD, PhD, Assistant Professor, Stem Cell Biology and Regenerative Medicine Member, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California

    • Downstream analysis of circulating tumor cells
    • Technologies for CTC isolation
    • Experimental mouse models for metastasis analysis
    • Using CTCs as liquid biopsy

    Table 29
    Precision Medicine in IO

    George Green, PhD, Head, Pharmacodiagnostics, Bristol-Myers Squibb

    • How can we better identify clinically relevant combination therapies upfront?
    • Are there ways to improve patient selection for IO therapy?
    • What are common challenges with IO therapy in the real-world setting?

    Table 30
    Emerging Technologies for IO Biomarkers

    Benoit Destenaves, PharmD, Director, Precision Medicine Lead, Precision Medicine and Genomics, Innovative Medicines and Early Development, AstraZeneca

    Table 31
    Target Identification from Omic Data

    Zhongming Zhao, PhD, Professor and Director, Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston

    • Target identification and validation using genomics and genetics
    • Using clinical transcriptomics-based generation of disease signatures, and their application in drug discovery
    • Clinical trial-derived data for discovery

    Table 32
    Machine Learning for Data Driven Drug Discovery

    Renee Deehan Kenney, Vice President, Computational Biology, PatientsLikeMe

    Pankaj Agarwal, PhD, FRSB, Senior Fellow & Senior Director, Computational Biology, RD Target Sciences, GSK

    • What are the biggest challenges we are facing in the application of machine-learning to omics data?
    • How are researchers applying prior knowledge to solve this problem?
    • How are researchers applying purely data-driven approaches to select features?

    Table 33
    Practical Machine Learning in Industry

    Patryk Laurent, PhD, Director of Emerging Technology, Office of the CTO, DMGT plc

    • Accessibility of machine learning hardware, software, and expertise
    • How to assess if machine learning will succeed or fail on your problem
    • Workflows for data scientists: what works, what remains a challenge

    Table 34
    Exploring the Power of Quantum Computing for Science and Discovery

    Ahmed Abdeen Hamed, PhD, Applied Computer Scientist, Quantum Computing, Merck & Co.

    • Current scientific needs
    • State of the art quantum computing
    • Applications and computation

    Table 35
    Diversity and Inclusion in Bio-IT

    Tanya Cashorali, CEO, Founder, TCB Analytics

    Lisa Dahm, PhD, Director, Enterprise Data & Analytics, UC Irvine Health Information Services; Associate Director, Center for Biomedical Informatics, Institute for Clinical and Translational Sciences; Director, UC Health Data Warehouse, Center for Data Driven Insight, UC Health, University of California, Irvine

    Chris Dwan, Senior Technologist and Independent Life Sciences Consultant

    Ruchi Munshi, Product Manager, Data Sciences Platform, The Broad Institute

    • Useful practices for people at all levels and roles in an organization
    • Today’s major challenges
    • Measurable goals and aspirations
    • Role models and resources