グループ討論

Tuesday, March 12, 2019 • 5:00 - 6:00 pm • Exhibit Hall

特定のトピックに焦点を絞り込み、その分野の専門家が進行役となって重要課題を集中的に議論するグループ討論が3月12日 (火) に展示会ホールで開催されます。学会の出席者は、いずれか1つのグループに参加し、打ち解けた雰囲気のなかでアイデアを出し合ったり、他の参加者と交流を深めたりすることができます。

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

Sean 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

Table 14
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

Bradley A. Perkins, MD, Co-founder & CEO, 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

Shirin Khambata Ford, PhD, Global Head, Biomarkers and Diagnostics, Oncology Global Medical Affairs

  • 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

Jean-Claude Marshall, PhD, Head, Clinical Biomarkers, Early Clinical Development, Pfizer

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

Professor 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.

Table 27
Deploying Bioengineered 3D Tumor Models into Clinical Practice and the Pharmaceutical Pipeline

Aleksander Skardal, PhD, Assistant Professor, Regenerative Medicine, Wake Forest Institute for Regenerative Medicine, Wake Forest School of Medicine

  • Where is the clinical enterprise and/or pharma in terms of seeing value in these model systems?
  • How do we convince clinicians and more traditional scientists that these new technologies are important and effective?
  • 3D models are typically better representations of in vivo biology; Why the slow adoption? (besides decreased throughput)
  • Complexity (more cell types or multiple organoids per model system) versus scale-up/high throughput
  • Simple tumor organoids (aka spheroids) are amenable to medium-high throughput screening. What about multi-organ system (aka body-on-a-chip)? Where in the pharmaceutical pipeline do more advanced systems that are less amenable to high throughput fall? Post-preclinical/pre-human Phase I? Serve as go/no go points?

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

Zhen Su, MD, Senior Vice President & CMO, EMD Serono

Theresa LaVallee, PhD, Vice President, Translational Medicine and Regulatory Affairs, Parker Institute for Cancer Immunotherapy

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

Majid Ghoddusi, DVM, PhD, Head, Pathology, Translational Science, Juno Therapeutics, A Celgene Company

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


Table 31
Target Identification from Omic Data

Deepak K. Rajpal, Senior Scientific Director, Computational Biology, Target Sciences, GSK

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


 

* 不測の事態により、事前の予告なしにプログラムが変更される場合があります。

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